Futuristic

In this episode of Futuristic, Cam and Steve discuss OpenAI’s groundbreaking o1 model and Advanced Voice Mode. They voice concerns over social media’s impact on youth, touching on legislative measures and the Instagram Kids launch. Cam talks about how he’s used ChatGPT on his weight loss journey. The episode also looks back at the 1973 launch of the first personal computer, MCM 70, the relevance of Moore’s Law and Marvin Minsky, using of AI to digitise handwritten notes and Sam Altman’s vision for The Intelligence Age.

02:36 The Impact of Social Media on Youth
15:46 Using GPT to lose weight
21:07 OpenAI’s New Model: O1
34:31 Advanced Voice Mode in ChatGPT
41:03 The Future of AirPods: Integrating AI
43:54 OpenAI Leadership Changes and Implications
46:43 The Evolution of Silicon Valley
56:45 The Bystander Effect and Social Media Legislation
01:03:43 Sam Altman’s Vision for the Intelligence Age
01:16:15 Technology Time Warp: The First Personal Computer

FUT 30

[00:00:00] SS: Freestyler, futuristic microphone, straight from the top of our domes. We got the Cam Reilly, a Rocker microphone and a Sammotron, s s s s s sammotron.

[00:00:17] CR: Uh, welcome back to Futuristic, uh, episode 30. I think this is, it’s been a while, Steve. Our last show was August 3rd. We’re recording this on August the, uh, September the 27th. So almost two months since we’ve done a show and partially because you had a lot of stuff going on, partially because I had a lot of stuff going on and partially because there was a lot going on in the.

[00:00:47] CR: AI world up until about a week ago, and then everything happened. So, you know, look, there’s a lot of, I follow a lot of, uh, AI based YouTube shows and podcasts. And a lot of time it’s like watching cable news. There’s just. filler stories happening and I’m like, really, this isn’t really, there’s a lot of gossip, there’s a lot of rumour, there’s a lot of minor news and I’m like, nah, I’m not going to waste your time, my time, our audience’s time with minor stuff.

[00:01:21] CR: I’m really here to talk about the big stuff and so if there’s no big stuff, I’m saying, we don’t do a show. There’s nothing really worth talking about, but of course in the last week or two, the big stuff has hit. Stuff that had been rumoured for a long time. Some of it finally landed and it’s definitely worth talking about.

[00:01:40] CR: What do you think, Sammartino?

[00:01:43] SS: You made a really good point on filler stuff in media. And so much of our social feeds are like that as well. And even podcasts that I like to listen to, I’m like, yeah, it was a whatever episode, cause they got to do one every week. You know, it’s all the game is frequency. We used to say that columns need to be filled and now it’s, you know, pixels need to be put across screens, but I like that we’re pretty straight up on it, but definitely a lot happening now.

[00:02:09] SS: And it feels like. AI has kind of crossed another bridge where it’s starting to piece together the islands and the archipelago of intellectual riches that AI is becoming.

[00:02:25] CR: Whoa, there’s some wordsmithing from Steve Sammartino. It’s beautiful. There’s a, all those years of getting up on stage, spitting your rhymes. Uh, let’s, before we get into the news, Steve, let’s, uh, talk about what’s been going on in your personal life from a futuristic perspective. What do you want to, how do you want to start off the show?

[00:02:47] CR: What insights do you want to share with

[00:02:50] SS: I’ve got, got a few insights. My little boy’s been a little bit ill, um, which has kind of kept me out for four weeks. When he was ill, he was using his smartphone a lot because it was one of the only things where he could have a reprieve because he was quiet. Um, you know, without going into too much detail, couldn’t move around much at all.

[00:03:07] SS: Um, and. Yeah, talking to a lot of the doctors and, uh, and various psychologists and people recently, I think we don’t take social media’s impact on youth seriously enough. Now, I know that in Australia and in the U. S. they’re looking at passing legislation. I actually still don’t think It, despite them looking to pass legislation, know the potential serious impact this could have.

[00:03:36] SS: I feel like we might be heading to a quasi lost generation of sorts. The Australian government has looked to ban social media for children under the age of, I think it was touted as 14 and potentially 16. It’s got to be 18 because I’m convinced That this is potentially more dangerous than underage drinking or underage driving.

[00:04:02] SS: Uh, we know that children’s brains and certainly boys brains don’t develop till they’re about 27. The impact it has on the frontal cortex and the lack of ability to delineate what’s worth watching, the fact that the algorithms put them down wormholes of terrible information, uh, misogyny, uh, rude, Uh, get some even quick viral loops.

[00:04:25] SS: One of the other things that we see a lot is that the speed of content and the truncation of content length is having a really significant impact on I won’t be surprised if in 10 years we say, wow, that has really had a developmental issue on the youth’s brain and their ability to think and their ability to Maintain an attention span, which is required for a lot of things to function as an adult human.

[00:04:53] SS: Uh, it’s not just a joke and I’ll make something short and make sure you have a hook. Yeah, that’s all good to get views. Um, but a little bit like junk food, like alcohol or driving dangerously, I would be flummoxed. Um, if we don’t come back and find out that that’s really bad. I did one LinkedIn post which had so much resonance, um, which I, I said, and I’ll, I’ll read it pretty quickly for the audience here.

[00:05:20] SS: The words that I used was, I’m just going to say it. Social media could be more dangerous for our youth than underage drinking or driving. Both of which are age gated at 18. Kids spend five hours plus a day, which is a stat based on Australia, it’s probably more in some areas, on tech purposely designed to create addiction.

[00:05:37] SS: And I said that I’m, I’m going to write a manifesto on how easy this is to fix, which I’m going to do this week. And I’ve said that we just need the political and social will to do it, to care about our kids more than we do about corporate profits. And the reason that that’s really important is that it had like six reposts, more than 10, 000 views, hundreds of comments with.

[00:05:55] SS: Anecdotal stories of so many, uh, kids and families having major issues with this. And, you know, it’s touched me personally in my house. And we had kept my boy away from social media for a long time. And it really just opened my eyes. The conversations that I’m having are that this is really causing a whole lot of social issues.

[00:06:21] SS: So ironic, social

[00:06:23] CR: Yes. And I, and I see it at two different age levels, right? Cause my older boys, uh, are nearly 24 and Fox is 10. And Fox doesn’t have a device of his own, but he borrows our devices to do things with. He used to have a device, uh, for, for a couple of years, he had devices, but then he broke his last iPad and we said, that’s it, you’re not, not getting another one for a while anyway.

[00:06:51] CR: And so, but he borrows our things, but we, we try to limit. He’s a, like one of the things that I limit a lot is YouTube, because he will just go down, it’ll take him down rabbit

[00:07:05] SS: Bad rabbit

[00:07:06] CR: he’s sitting there watching something that is, I deem is good for your brain content, I’ll let him watch it, but if I leave the room for half an hour and I come back and he’s watching shorts, I’m like, dude, this is just bad for your brain.

[00:07:20] CR: Get off it right now. You know, this is rubbish. And we don’t let him on, you know, TikTok or, or anything like that. My older boys, Hunter and Taylor, you know, they, you know, they, they sort of grew up in the early stages of this stuff. Um, and of course they’re both, they, they both TikTok world now. Hunter’s a creator, Taylor’s a manager of creators and You know, we have constant debates about short form content and the value or the danger of it.

[00:07:53] CR: They tend to agree by the way, that there’s a lot of bad stuff associated with this. Um, but you know, I see them like they don’t read. They struggle with long form content, paying attention to it. Um, and they’ve both had a certain amount of success in their early twenties. Um, but, which is great. You’re like, they’re doing okay in many ways, but you know, they, they, they suffer, Hunter suffers a lot from.

[00:08:24] CR: Loneliness. Um, and it’s cause he’s in his room all the time making content for strangers, for 2 million followers, but he doesn’t have any real friends.

[00:08:33] SS: That’s funny because my boy, um, is really struggling for connection. He’s, he’s been quite isolated through his illness and the thing that he’s struggling for his own offer with the people in his house, and yet he looks for it from strangers. There’s, there’s some really interesting element there and the struggling with the short form content.

[00:08:50] SS: You know, we know that it has like a dopa cycle and you’re getting into a dopamine hit. I wonder how similar it is to the physicality of junk food and, and fatty and sugary foods, where it gives you a short term hit, but you know that the broccoli and the protein, long form food, let’s call it, and long form content.

[00:09:08] SS: I do wonder again, now we’re expert in this, whether or not there’s some sort of physical analogy where those things, even though one is food related and one’s content related, whether or not they have the same chemical impact. Um, on your physical development.

[00:09:23] CR: I don’t think, I think the science is already in on that. I don’t think there’s any debate about that. I mean, the brain has a, um, limited number of hormones and neurochemicals that it uses to motivate behaviour and the social media companies have, Paid lots of money to consultants to, and gaming development companies.

[00:09:45] CR: It’s all shit, not just social media companies, to develop the technology in a way that creates those addictive responses. So you want more and more of it. It’s been, as you said earlier, deliberately designed to hack. Our brains to bring us back, to keep us on the devices. No doubt about that. And I can remember early days of this sort of stuff, like 2007, 2008, when Twitter was fairly new.

[00:10:16] SS: Yep. I

[00:10:16] CR: I can remember spending a lot of time on Twitter, having conversations, having debates, and when I wasn’t on my phone, when I was in my car, driving somewhere, feeling the need to have Twitter open to see what the last comment was, or the last part of this conversational debate, and feeling the need.

[00:10:36] CR: Increasingly icky about that. Like the same, the same feeling I get if I’m playing an Xbox game, you know, you get addicted to an Xbox game or something, and then you realize you’ve been playing it for two hours and you’re in a frustrating level and you’re trying to get through the level. I remember this most recently with Red Dead Redemption, which is a terrific, Red Dead Redemption 2, which is a terrific game, really beautiful, well designed game.

[00:11:05] CR: But I remember getting to a point where, um, I just felt kinda queasy after playing it for an hour, and I’d be like, you know what? Fuck this. I’m off. And you know, I got the same with Twitter 10, 15 years ago, whenever it was, where I just was like, you know what, this makes me feel bad. I, and I had to go cold turkey off of it. Like, you know what, I don’t want to, I don’t want this in my life. I don’t want to be part of this. It was a sort of a, a cycle. And I, and I’ve said this, Chrissy and I have had these debates for years and years, the pros and cons of these new technologies. You know, when they’re new as adults, we can get.

[00:11:46] CR: Swept up in them, but then as adults, if you, if you aren’t malfunctioning, I think you have the ability to go, you know what? This isn’t good for me. It’s like eating Tim Tams. Not good for me. I’m gonna step away from this and do something healthier. Kids! You know, as you said before, we know boys brains don’t fully mature until 25, something like that.

[00:12:08] CR: Particularly younger kids don’t have the ability, their brains don’t have the, literally don’t have the ability, to make wise decisions, why we don’t let them drink, why we don’t let them drive. We’ll send them off to war, but outside of that,

[00:12:21] SS: Look, look, I’ve always said Cameron. A toy is his gun, and all he’ll ever know is to fight forever, fight forever. Little bit of the noise works, circa 1987, welcome to the world. I’ve always said that,

[00:12:33] CR: And we’ll, we’ll teach them religion when they don’t have the ability to discern between what’s true and what’s bullshit. Nah, look, I’m, I, I’ve seen the gnashing of teeth over the social media ban, and I’m actually all for it. I, I, I, I don’t have any issue with a ban on this stuff for kids. I don’t think it’s good for their brains.

[00:12:53] CR: I don’t think it’s good for any of us, quite frankly. Um.

[00:12:57] SS: it’s actually really, really easy to do. And this is, and, and, two things I want to say. The first one is, What did I despise? And as you know, this is the futuristic, we talk about the future. And people often say to me, I thought you would be all for technology. Aren’t you a futurist? Isn’t that about technology?

[00:13:16] SS: And my answer is, well, we need to be better than that and think better. The future is about creating human flourishing. Which may involve new technology and may involve some of the things which are the oldest things we’ve had. Like, uh, slow food is a classic example. In the 60s and the 70s was irradiated food and packaged goods and just add water and that was the future.

[00:13:40] SS: And then we went, actually, I like slow made coffee and grow our vegetables. Like we learned that with food, right? And, uh, you know, meditation, thousands of years, like, A flourishing future isn’t just about technology, often it will, and technology, as, uh, we hear from Kevin Kelly, is 51 percent good most times, but what we need is the wisdom to delineate where technology use is good, and where we need to say, actually, now we’ve learned this, we need to, Uh, moderate that, or legislate, or put regulation around it, like we did with cars, like we did with factories, like we did with so many other things, and for some reason, society have this view that you’re a Luddite if you don’t embrace all new technology, no, no, no, I’m far more thoughtful than that, and I’m certainly not a Luddite, and in fact, not being a Luddite is finding civilize the technology, and that’s one of the things that we need to get better at.

[00:14:35] CR: Look, I’m, I’m very cautious about, um, government affairs in personal lives and censorship and having to legislate behaviour. Uh, you know, I, I think it’s necessary in certain Places for a safe and civil society, but you want to be really careful about how much of it you allow and where the line is. It’s something that needs constant vigilance.

[00:15:03] CR: I would rather see parents be responsible for their children’s access to this sort of technology. The parents be the gatekeepers and decide what’s best for them. what is good or what is bad for the children. But if parents aren’t doing that because they themselves are addicted to this sort of stuff and they’re not able to turn it off, then perhaps there is a role for the future.

[00:15:27] CR: Because if the parents aren’t protecting the children, it’s a bit like, uh, I don’t know, paedophile priests. If the parents are allowing the paedophile priests

[00:15:37] SS: Wow,

[00:15:38] CR: close to

[00:15:38] SS: you just Cameron Reilly,

[00:15:40] CR: then the

[00:15:41] SS: Reilly will go

[00:15:42] CR: to step in and do something about it. Well. On my front, Steve, um, since we last spoke, in the last two months, I’ve lost 12 or 13 kilos.

[00:15:53] CR: And I have to say that you are partly the inspiration and my mentor in this. We had this conversation on the show a while back. I said, how do you stay skinny? You said, I don’t eat until the afternoon. So. I started doing that. I started just not eating until after lunch, but the other side of it is Kung Fu.

[00:16:16] CR: I’ve got a grading coming up in a couple of weeks and I decided that I wanted to get down to a lower weight for my grading, uh, for my fitness and my health, but also ChatGPT. Now, as you know, cause we’ve talked about this on the show, I started using ChatGPT as my calorie. tracker about a year ago because I wanted to be more conscious of how much I was eating for my health and to lose weight and it wasn’t working.

[00:16:41] CR: I wasn’t losing weight or even with the amount of Kung Fu that I do, which is insane. Like I’m doing eight to 10 hours of Kung Fu a week. I still wasn’t losing weight. So, A couple of months ago, I jumped into GPT and I said, look, I’ve been tracking my calories for the last 10 months, nine months, whatever it is.

[00:17:01] CR: This is the amount of calories as my input. This is the amount of Kung Fu that I’m doing. I’m not losing weight. What should I do? And it said, well, try cutting your calories back a little bit extra. I was like, Okay, I said I want to lose about a kilo a week. Can I do that? Yeah, you can do that. You have to cut it down by about 1, 100 calories a day.

[00:17:25] CR: So I was eating about 2, 800 calories a day on average. I cut it back. Um, to about 1700 and weight just started dropping off me. So I’ve lost 12 kilos. I’ve lost eight centimetres off my waist, jumped into my swimming trunks for the first time in six or seven months on the weekend. And they fell off. I went into the, I went into a clothing store the other day.

[00:17:53] CR: I’ve dropped down from an XL to an L. in two months. Um, and you know, again, partly because of you, partly because of Kung Fu, largely because of ChatGPT’s given me the ability to track that. Like I used to use MyFitnessPal and some of these calorie tracking apps, and you have to look up everything that you’re reading and, you know, plug it in.

[00:18:16] CR: And it used to take, longer to plug in what I was eating than it actually took to eat what I was eating. And there’s a certain level of commitment that after a while just gets difficult. ChatGPT, I just go, yeah, I had a plate of this. I had some chicken and some vegetables and a bowl of lentil soup and a slice of bread with butter and honey and a coffee and whatever.

[00:18:38] CR: And it just works it out. Now is it completely accurate? Probably not, but it gives me a rough indication. Yeah, so when I said, hey, I need to cut back a bit, it goes, okay, well try cutting back the yogurt and frozen berries and honey that you’re having for dessert every night and replace X with Y. And, you know, not eating until after lunch helped as well.

[00:19:00] CR: And so, although now Tim Ferriss is for our body, I’m having two boiled eggs first thing in the morning when I get up, he reckons protein first thing in the morning, um, puts your body into a different mode. So when you have carbs or sugars later on in the day, it doesn’t, uh, start off your insulin response if you have protein anyway, so that’s been.

[00:19:21] CR: How I’ve used technology most in the last couple of months is just to lose weight. And it’s been fascinating because I’ve been trying to lose weight for 15 years. I haven’t been this light for 20 years. And it,

[00:19:36] SS: it’s easy. Like, I mean, the basic thing is you can never exercise away a bad diet. That’s number one. Often when you’re hungry, you can’t, you just can’t do it. You can no amount of exercise, right? Diet is everything. Because I think 80 percent of your calories are just keeping your body alive.

[00:19:54] CR: Yeah. Just your base

[00:19:55] SS: yeah, just basic,

[00:19:56] CR: through the day, right?

[00:19:57] SS: metabolism. A couple of other things, just simple hacks, is often you think you’re hungry when you’re really thirsty, so have a big chug, a big thing of water, and you won’t be hungry for an hour. So, that’s a good one. And the other one is the density of your food. So, food density. So, for me, high fat, high protein works.

[00:20:15] SS: And I think it works for most people, but most people just don’t have the discipline. If something’s, you know, Carbohydrates are low density foods, like breads and all those other high density. So, you know, broccolis, even bananas, they’re carb based foods, but they’re fine. Um, and, and basically, yeah, the less processed it is, the better it is.

[00:20:32] SS: But I just have a high protein. I can lose a half a kilo a day if I have a bad week. Pretty easy. Because I just not really, I’ve never really counted calories. But I just don’t eat until the afternoon and I just have protein only. Won’t really have any carbs. I just avoid bread, pasta, um, rice. And I eat as much fat as I want.

[00:20:52] CR: Yeah.

[00:20:53] SS: And

[00:20:54] CR: There you go.

[00:20:54] SS: that. Anyway.

[00:20:55] CR: you’re a good looking skinny dude. Let’s get, let’s move,

[00:21:00] SS: with skinny.

[00:21:01] CR: speaking of Kung Fu, I’ve got a hard time, I’ve got to get out of here to get to training soon. So let’s, uh, get into tech news. Steve, uh, obviously I think the biggest thing that’s happened in AI news in the last, This month has been OpenAI finally releasing the model formerly known as Strawberry, uh, which they have called O1. Now um, O1, Strawberry, we started hearing about Strawberry in November last year when Sam Altman was fired from the company. And there were these rumours that Ilya saw something, and there were rumours coming out about something that was codenamed Strawberry or Q Star, and we’ve been hearing rumours about it ever since, and it was this A new model that they had developed that could do thinking and reasoning, but they were very tight lipped about it until it finally dropped a week or so ago, O1 Preview and O1 Mini.

[00:22:12] CR: It’s called Preview because it’s not the full model, there are strict limits which they have increased, but I think you get 50. Queries to O1 preview a week at the moment and 50 a day for O1 mini. And then you get cut off and you have to wait a week. But these are the models for people that haven’t heard about it or haven’t played with it yet.

[00:22:37] CR: It does advanced thinking and reasoning before it answers. And look, it’s not perfect. And in fact, on the launch day, Sam tweeted, ah, it seems impressive when you first use it, but over time, not so much, but they’re also calling it. PhD level intelligence and

[00:22:58] SS: I’ve been using that on stage. They stole my bit. You’ve all got a PhD in every single subject now,

[00:23:05] CR: they, they did a put out a bunch of launch videos for, with, um, physicists and chemists and biologists and coders talking about, uh, how impressive they had found they’d been early testers of the model and what, you know, I’ve seen some other videos.

[00:23:23] CR: There’s a guy who gave it, uh, as a physicist on YouTube who gave it a. Question out of a PhD level physics textbook, which the textbook said it should take a week and a half to complete and O1 did it in 122 seconds and got the

[00:23:45] SS: Now we’re all, now we’re all goodwill hunting. Physicists and mathematics experts just on a chalkboard. I’m just going to, I’m going to get ChatGPT in my pocket and just become a janitor at MIT or Harvard and just try to smash up on the chalkboard some answers. Just bam.

[00:24:02] CR: like apples? Do you like apples,

[00:24:04] SS: like apples?

[00:24:05] SS: That’s what they should call the next one. Do you like apples? That should be called, Do you like apples? I

[00:24:12] CR: Uh, and the fascinating thing, uh, for me playing with O1 is you can watch its reasoning, particularly O1 Preview. You can give it a question and then watch it go through the reasoning steps, how it’s thinking about it. And the, the physicist I mentioned who did the textbook example. He showed the reasoning.

[00:24:34] CR: It started to try and solve this problem, and it spent like a minute going down one path, and then it went, no, no, this is the wrong approach. So I went back to the beginning and started again. So, uh, now they haven’t explained how this works, how they’ve done it. I think the QSTAR methodology came out of a university originally, and I haven’t bothered to drill into how it works, but

[00:24:57] SS: was just about to ask you to take me through the QSTAR methodology. You can’t drop that on me and not take me through it, Cameron.

[00:25:03] CR: man. Nah, nah. I mean, I haven’t had the time to drill down on that yet. I do plan on trying to get my head around it though, but it’s a huge step forward in the ability of LLMs and people like Sam Altman are now saying they have reached the conclusion that there is no upper limit of what we can do with LLMs.

[00:25:25] CR: That LLMs are now capable of way more than just, uh, fancy autocomplete, as people were saying two years ago. It’s,

[00:25:35] SS: That is just a bugbear of mine and Hinton and others, some of the others say that. It’s like,

[00:25:41] CR: oh, Hinton

[00:25:42] SS: what all intelligence is. An advanced level of autocomplete. You work out what comes next. That’s like, it’s just so mind numbingly frustrating that people do not understand. Sorry.

[00:25:54] CR: Cory Doctorow, not Hinton. Geoffrey Hinton’s,

[00:25:56] SS: Oh, sorry. Not Hinton.

[00:25:57] SS: Doctorow. My bad. My bad. Doctorow. Stand corrected. Which I love Doctorow too, by the way, which shows you can still love someone and disagree with some of their bits, right?

[00:26:06] CR: should be able to. I’m exactly in the same camp. I love Ayn Rand. I love Ayn Rand’s novels. I’ve read her letters. I love Ayn Rand. I think she was completely misled and wrong on lots of things. But then, you know, I appreciate a lot of the stuff that she

[00:26:20] SS: Yeah, exactly.

[00:26:23] CR: Um, okay, so let me read a little bit from OpenAI’s launch document, uh, for O1.

[00:26:30] CR: How it works. We train these models to spend more time thinking through problems before they respond much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes. In our tests, the next model update performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology.

[00:26:52] CR: Note PhD students, not PhD graduates, PhD students. Big difference between somebody who’s had a PhD for 20 years and somebody who’s trying to get a PhD, right?

[00:27:09] SS: Yep, okay.

[00:27:11] CR: We also found that it excels in math and coding. In a qualifying exam for the International Mathematics Olympiad, GPT 4. 0 correctly solved only 13 percent of problems, while the reasoning model scored 83%. Their coding abilities were evaluated in contests and reached the 89th percentile in Codeforces competitions.

[00:27:34] CR: Now, Um, you know, there’s been some benchmarking of this done by other people, they disagree or agree to varying degrees, but everyone seems to be blown away by what O1 can do. My personal experience with it from a coding perspective is some days I’m super impressed, other days I’m not so impressed, which was also true of, say, Claude, 3.

[00:28:02] CR: 5 Sonnet, which I was using before O1 came out. Extensively. But, uh, there’s a lot of, if you go into Reddit and look at the OpenAI subreddits, et cetera, people tend to be fairly impressed with what O1 is able to do. And again, it’s only sort of a week old, but, uh, people are pretty impressed. September 12th, it came out.

[00:28:26] CR: So, uh, yeah, two weeks, maybe. Mm

[00:28:29] SS: Yeah, I mean, the coding thing is just so often now, I’ll just get it to create like a little script for what I need. Even this morning, I’ve been trying to get my substack back and start publishing again on a weekly basis. And I’ve got some old MailChimp emails, uh, that people have been subscribing to there and not my substack.

[00:28:49] SS: And I couldn’t get things uploaded, so I just grabbed the data and just said, Write me a little, you know, write me a little CSV, get me some scripts, extract these emails from this, double checks, some things that you would have to muck around a hundred times within the substack or the MailChimp format to find its formatting.

[00:29:05] SS: I just grabbed raw data and then asked it to format in what I needed and I also asked it What is the best format for Substack? And it said, this is the best format. So can you make it? Yes. And it just starts writing the Python script and then it, and then it executes against it, just doing that scripting in there for you really is a magical feeling.

[00:29:23] SS: Cause it’s not like you have to download it onto your client, just getting it to do it in Python. In the interface is really kind of special. It reminds me of the early days of the first web browsers where it does it in the browser, where it writes the script, executes against it and gives you the thing that you need.

[00:29:39] SS: I don’t need it downloaded on my client. I don’t want to have that script sitting there and then have to put data in it and get it to execute against it. It’s a really empowering feeling. And that’s like something I think we should be getting people to use in companies. And one of my clients, I’m like, well, take information from your different data, like dump it in there and, and just get it to execute against and find the links that you need.

[00:30:00] SS: It’s really good at that. And the answer that I always get back is, oh, we’re too worried about the, uh, The safety and security concerns. I’m like, listen, no one’s really worried about your company. You’re going to go in there and steal your little piece of sales information. Seriously dudes, just, just be that, just be that company that waits 10 years.

[00:30:17] SS: That’ll work out. That always works out. What a great

[00:30:19] CR: that always works out. That’s never a bad idea. Yeah, I’ve used it. I’ve had a lot of, um, server issues. Um, I’ve increasingly over the last six to 12 months, my server that I host all my shows on has been struggling and it got to a point where it was going down two or three times a day for five to 10 minutes at a time.

[00:30:37] CR: My IT guy, the company that hosts the server that’s supposed to do support has been completely useless. Um, not able to give me any sort of guidance or fix anything. It’s in the too hard basket for them. And I tried to use GPT in the past and, and didn’t get very far. I jumped into O1 and I said, here’s the situation.

[00:30:59] CR: It said, download your log and give me a look at your log. So I download the log file. Which, if you’ve ever seen a log file, it’s full of unintelligible bullshit. Uploaded that to it, it goes, okay, I can see you’re getting hit by bots, there’s certain processes that are running cron jobs that are taking up a lot of Bandwidth, a lot of, uh, CPU time.

[00:31:25] CR: It got me to run some reports. It read the reports. It gave me the fixes and then told me what to do. All right, install this plugin and set it up like this. Go into CloudFare, set up these rate limiting options. Do this. I spent a day following its instructions and so far, so good. Fixed all of my, um, server performance issues.

[00:31:45] CR: So it was like having a. First tier, um, sysadmin to walk me through the, it couldn’t do it for me. And that’ll be the next stage is when it’s integrated into our OS’s. Yeah. But it could tell me what I needed to do, guide me through it, answer my questions, hold my hand in a friendly way, and enable me to solve problems that I would, no way could I solve them myself.

[00:32:14] CR: It’s like, again, it’s, I said this many times. It’s like having a superpower. Yeah.

[00:32:18] SS: Yeah. The way that it’s sewing together, the pieces of the puzzle, and I used in the first inferences, bridges between those islands of what it can do. Uh, the fact that it, you can ask it, how do I solve this problem? And then it comes up with the idea and then it helps you execute against the idea.

[00:32:35] SS: That’s super powerful, but it makes me feel that, you know, And I’ve, I’ve been reading a lot about the emergence of agents and I’ve been using agent G-P-T-A-A little bit and I still demo it on stage in keynotes to show how it’s objective driven rather than task driven. I mean, I think that’s gonna be the next phase at some point in 2025.

[00:32:55] SS: ’cause so many people are talking about AI agents and it seems like. It has all the pieces of the puzzle to make agency where someone actually manages a situation and a problem for you, just, just for the listeners to understand. Instead of doing discrete tasks, it accumulates all of the tasks to get from point A to point Z, let’s say.

[00:33:16] SS: And we’ll do all of those that, that achieves the objective. And that move towards agency based AI is going to be, I think, the really big part. And that’ll be big for a couple of reasons. It enables individuals to have an agent infiltrate their OS and have a look at all the various data points and find those.

[00:33:34] SS: You won’t even actually have to upload necessarily, oh, upload the file like you’ve just described. You’ll say, here, are you in there? It’ll go, oh, have a look around. And then it finds it. Think about how many calculations it can do per second, you know, trillions of calculations. It’ll be able to find it and find the pieces of the puzzle that it needs and then execute against it.

[00:33:53] SS: And you, Do that in a corporate sense. It’s really the administrative burden of modern life. You would have to feel like 90 percent of that is just going to go away. Like do my tax, go through everything I’ve spent, understand where it was spent, the places, what my benefits are, where I go, what can I claim back?

[00:34:12] SS: I mean, that’s a really complex task that you feel that agents would be able to do.

[00:34:17] CR: Yeah, particularly when they have access to your bank accounts, your emails, your calendar. They know all about, you know, your Xero, when it’s integrated into Xero, you know, all that kind of stuff. So the other thing that happened, Steve, this week is that OpenAI released what they call Advanced Voice Mode for nearly all Plus subscribers. This has been coming for months.

[00:34:47] CR: People have been complaining endlessly that they hadn’t launched it as fast as they suggested they were going to months and months ago. I think they’re still yet to roll it out to the EU for some unknown reason. But, uh, there’s a whole bunch of new voices. Now the voices have supposedly got a lot more capability of expression and personality in them.

[00:35:11] CR: People have been doing all sorts of demos of getting them to sing or to whisper or to do accents, but I just tried and I can’t get my, uh, ChatGPT to do any of those things successfully. So. I don’t know if I don’t have that new functionality, if it hasn’t rolled out into this geography yet and we’re just getting the stock standard voices.

[00:35:34] CR: So Kim in the editing booth here, folks, um, I did play around with chat GBT on my desktop. When we were recording this and tried to get it to do the advanced voice mode stuff. And there was really no difference between it and the normal voice mode, except there were new voices. However. After we finished the show, I jumped onto my iPhone version and oh yeah, it worked. It was far more expressive and I could interrupt it in the middle of it talking. Which is actually kind of useful.

[00:36:10] CR: I don’t know if you’ve ever been using the voice mode and then you pause to think about something and it assumes you’re finished and it starts replying. This new version does the same thing, but you can just stop it and say, hold on, I wasn’t finished and it will pause and you can finish your conversation. The answers are far quicker than they were before.

[00:36:30] CR: It used to be. You would talk to it, then you’d wait. Five seconds and then it would reply. Now it’s almost instantaneous. And I played around with the usual stuff. I asked him to whisper. Something to me. And it did. Um, my son, Taylor said he tried to get it to do an evil laugh and a did Ivan played around with that? And I like a lot of that stuff is kind of gimmicky right now, but over time, I’m sure that we will find it. Just more human, like to have an AI that has a full range of emotion and expression. So, if you haven’t played around with advanced voice mode on your phone or your iPad yet, uh, Definitely give it a go. I stay away from the desktop version for now. Stick with the, um, uh, the, the phone.

[00:37:20] CR: Uh, versions of it. Uh, have you had a play around with any of the advanced voice stuff?

[00:37:27] SS: I’ve had a play around with the advanced voice stuff, uh, not any of the singing or that, the hacks or tricks. I didn’t notice a huge difference. In the way it interacts, it’s pauses and it’s intonation. Um, because it was launched, I played with it more than I have previously. Normally I would only use it really, uh, to talk to it when I’m on my mobile, but when I’m on my desktop, I would always type.

[00:37:53] SS: But the one thing that I did notice is that when you get conversational with an AI, it creates almost like a collective thinking process. You know how you and I, when we chat on here, we’ll, we’ll propose different ideas that put us on different trajectories. When you’re typing, I think it slows down what you’re doing.

[00:38:13] SS: You become more specific and put more pieces into the puzzle that you want to be considered. But when you’re speaking, that has this live thought process where there’s an interchange. And I actually think it helps you go on different paths just by having that. And also the pauses of the AI, to me, just.

[00:38:34] SS: It says, and I do wonder if it is actually thinking harder and looking at more of the data to give its answer or whether it’s just been designed that way to give it a human feel. I don’t know the answer to that. Uh, but it does, I feel like, put you down on a different trajectory that you otherwise might be on because of the way that thoughts happen when they’re expressed verbally versus written.

[00:38:58] CR: Yeah. I don’t know, but I think you’re right. I, even though I’ve had the basic voice for quite a while now, I tend to I’m going to use typing more than I use voice. I use voice in the car. Sometimes I use it in the kitchen if I’m cooking and I want to ask it something, but I will, I will default to using text more than voice for some reason.

[00:39:23] CR: Now, this advanced voice stuff is supposed to And when they did the demo of it, uh, whenever it was, months and months ago, it was supposed to be much smoother in the conversation, not as many pauses. You could interrupt it in the middle of a conversation just by talking. Uh, it, it had, it was multimodal, so it could see what was going on in the front and back cameras of your phone.

[00:39:51] CR: And you could integrate those into the conversation. I haven’t noticed any of that in this new rollout. Really, all I’ve got is new voices. There’s, uh, nothing really that different that I can tell about how it, uh, is performing. So, I don’t know if they’re doing it in increments. Releasing the voices first and then there’s additional technology and different geographies as they go.

[00:40:20] CR: Maybe it’s just a bandwidth thing, they’re rolling it out slowly and incrementally, I don’t know.

[00:40:26] SS: Yeah, I didn’t notice a huge amount, but it does seem, and I’ve always kind of thought that I’ve always said that language is our killer app. We’ve just got talked about that a lot. And also the ability to speak is just so quick. And I think it’s the required element. Again, we talked about screen and addiction and, uh, earlier on, but that idea that you can talk to something and have an intelligence, uh, for me is really interesting.

[00:40:53] SS: Uh, and I, I think that this speaking thing could potentially be an interesting play for. Apple. And the reason I think it could be interesting for Apple is that the AirPods are such an extraordinary product. And the revenue, I can’t remember what the revenue was, but it was, would, would make it, I think, in a top, just the revenue of Apple AirPods alone would make it a top 20 company in the world.

[00:41:20] SS: Just that. You talk about extraordinary product launches. I can see you’re wearing yours. I wear mine all the time. Imagine if you had an intelligence in your ear. You don’t have to touch anything, talk to anything. You have to. Pull your phone out, uh, even potential for little cameras to be on the side of the earpod, sort of, you know, looking forward, um, pointing it with my hands from my ears for everyone who’s just listening.

[00:41:44] SS: It would be an in an incredible form of AI if Apple could get that right, uh, with the voice and integrate that into the AirPods. That would be An absolute product necessity. Certainly delineate it and differentiate it from all the other white AirPod copycats out on the market for, you know, 20 in a Mars bar.

[00:42:07] SS: Feels like that could, that could be extraordinary.

[00:42:10] CR: yeah. Speaking of Apple, my son Taylor got the new iPhone 16 Pro Max the other day. And, uh, after he’d had it for a few days and he’d travelled with it, he’d gone down to Melbourne to meet Katy Perry. And he came back, I picked him up from the airport. I said, uh, what do you think of your phone? He goes, don’t get it, man.

[00:42:32] CR: It’s like, there’s nothing. His battery life was a bit better. He said, instead of it being a. 30%, it’s at 50%, but, uh, apart from that, he’s not, I’m not impressed with the level, but you know, he got it because he knows Apple intelligence will be rolling out and he wants to have that when they finally start to deploy it.

[00:42:51] CR: But right now he said, it’s just, he’s gone from a 13 He goes, yeah, it’s barely noticeable.

[00:42:58] SS: Oh yeah. Yeah. They’re definitely in their bumper bar era. Uh, 100%. Well, sales of AirPods were 14. 5 billion in revenue in 2022, according to the SEC. I mean, that’s,

[00:43:12] CR: and I live in our Airpods. Our Airpods are working most of the day, you know.

[00:43:19] SS: Yeah, so I, I think that that’s a really interesting potential overlap, uh, whether or not Apple, and I think that they should, because they’ve got every resource in the world, they should be doing whatever they can to make an open AI like Uh, AI Engine, and then if they plug that into both their phones, then I think even in their, uh, earpods, that’s kind of, I think, where we would go, and that would enable us to have everything that we need, you know, without the screen necessarily, I think.

[00:43:50] CR: Yeah. Well, I’m sure those sorts of things will happen. Meanwhile, more OpenAI news. Mira Murari, the Chief Technology Officer of OpenAI, who’s been pretty much the face of them from a technological front since Ilya. Well, actually, for the last year since the attempted coup against Sam, just announced yesterday that she’s leaving the company as well.

[00:44:19] CR: So, of the, uh, sort of original team that was there a year ago, um, Sam, Greg, Ilya, and Mira, Now there’s only Sam left and he’s not a technology guy. He’s a marketing, venture capital CEO guy, which I’m not

[00:44:40] SS: I didn’t know, I thought it was a, I thought it was a developer as well. Like

[00:44:44] CR: No, Sam is not a developer.

[00:44:47] SS: like Jobsy, ask the average man on the street and they, they think, they think that Elon Musk has got the, the, the Sigweld out, and he’s out just welding up a couple of rockets, and Steve Jobs had the soldering iron just putting a couple of microchips in the back of a smartphone, that’s what people think, ask the person on the street, he’s a genius, he invented everything,

[00:45:06] CR: he is a coder though. He is an engineer and a coder. He’s not

[00:45:10] SS: uh, Elon is.

[00:45:11] CR: Altman. Yeah.

[00:45:12] SS: yeah, Steve Jobs wasn’t, but I just always loved that Bill Burr when he said Steve Jobs genius, he told other people what to invent.

[00:45:21] CR: That is the genius though. Yeah. That guy, you want to, have a look at what Apple did in the 10 years Steve wasn’t running the

[00:45:31] SS: know. No, he definitely did great

[00:45:33] CR: it did before and after.

[00:45:35] SS: Yeah,

[00:45:36] CR: You can’t, you can’t

[00:45:37] SS: no, no. They’re cool. Well, I’m not dizzying him. I’m just trying to, I’m just trying to, I’m just, I didn’t know that Sam was a marketing business guy. I actually thought he was a developer, but anyway, that’s okay. You learn something every day, don’t you?

[00:45:49] SS: Can

[00:45:50] CR: Sam’s just an entrepreneur. And, and, but look, again, I’m not taking anything away from that. You know, the suggestion seems to be that, I mean, Ilya obviously left because of the political situation, but he’s since announced his own startup. Probably Myra is going to like, she must be getting, You know, unlimited amounts of money thrown at her to go and do something.

[00:46:14] CR: So it’s going to be interesting. It is this Cambrian explosion already happening in the AI space. Now we’re going to have the startups and even we already have that within Anthropic. And Anthropic is run by the people that put out Claude, is run by ex OpenAI people. And then the ex OpenAI people are all ex Google DeepMind people in a lot of cases.

[00:46:35] CR: So Google spawned OpenAI, OpenAI spawned Anthropic, and now you’ll have people leaving from there to go and do stuff as there’s just unlimited money.

[00:46:43] SS: was it, was it the Fairchild 8? Was that what they were called?

[00:46:47] CR: Yeah, I think so. Yeah. That started the entire Silicon Valley

[00:46:52] SS: so Silicon Valley emerged out of Fairchild, was it Fairchild Semiconductor? Which was one of the first microchip plants, which just so happened to be in a particular area near some orange groves, uh, a valley, uh, in California.

[00:47:06] SS: Uh, which then spawned, uh, Intel, and a whole lot of other technology firms, and Hewlett Packard, and all of these guys emerged out of that.

[00:47:17] CR: the, the traitorous eight. We’re the guys that left Shockley Semiconductor in 1957 and founded Fairchild Semiconductor,

[00:47:30] SS: Right.

[00:47:31] CR: right?

[00:47:32] SS: then there was those that left Fairchild and founded Intel. And it is really interesting. And this hearkens back to one of the things that I say to people, I say, how do you get such a good view of the future? And, and, and, and I know that you do the same Cam, you more than anyone, is to be a student of history while the technology changes, the patterns don’t.

[00:47:54] SS: Because humans haven’t had a software upgrade in 200, 000 years. Although, it looks like we’re about to have one with some BMIs coming in real soon. So we’re about to have our first upgrade when we merge with the machines, according to Kurzweil. So

[00:48:07] CR: Mm.

[00:48:08] SS: Uh, yeah, but it is interesting that we’re seeing this same pattern again with the AI Cambrian explosion.

[00:48:13] SS: And we saw that, I think, we definitely saw that with the semiconductor explosion. And we also saw that with, um, a lot of the big tech companies as well, where they all spawned out in the early 2000s and a lot of startups have since come from there.

[00:48:29] CR: Do you remember FLIR, by the way? F L A I R? FLIR

[00:48:34] SS: No, I don’t. You better tell me.

[00:48:36] CR: was the Fairchild Laboratory for Artificial Intelligence Research started in 1980 at Fairchild.

[00:48:46] SS: Wow. That’s

[00:48:48] CR: And you could probably draw a line from Fairchild to OpenAI, right?

[00:48:54] CR: so, uh, anyway, expect to see more announcements in the not too distant future from what, from Mira Murati about what she’s doing. Um, of course, the other big news that’s just happened in the last 24 hours as well, coincidentally, when she announced she was leaving, is that OpenAI, uh, really, really, really, really According to rumours, are removing their non profit control and giving Sam Altman, Altman equity.

[00:49:20] CR: Don’t know if there’s any connection between those two stories, but what we all knew was gonna happen at some point.

[00:49:26] SS: feel like there, there, there, there could be a connection there. One of the things that clearly has happened, OpenAI obviously started, uh, it was meant to be open artificial intelligence for all non profit, and then it just became incredibly profit centric. Uh, When there’s a lot of money involved, it’s very, very hard for something to remain non profit.

[00:49:46] SS: It’s a miracle that Wikipedia has remained, um, as is still, I think, a pretty important, uh, website. It’s the only one that I can really think of. Uh, but that non profit thing was inevitable. When you have that much venture money chasing it, remember, uh, venture capitalists only ever get married with divorce in mind and, and, you know, the prenup is big and the numbers are big when they make that split.

[00:50:09] SS: And I’ll tell you what, a non profit just, just does not Fit that at all.

[00:50:15] CR: Yeah. And you know, you’re right. You really got to give Jimmy Wales credit for what he’s done. The establishment of Wikipedia and how he’s maintained that. I know a lot of people, including Jason Calacanis, have been telling him for years that he should take advertising. He should take money and should do this and that.

[00:50:33] CR: I think Calacanis tried to set up his own competitor to Wikileaks, uh, Wikimedia, Wikipedia, 10 years ago or so, which didn’t get very far. But, uh, Jimmy Wales set up a good board of trustees that have maintained the

[00:50:48] SS: rare. I can’t think of any kind of commercial media format that is as powerful. And I think it’s still a top 10 website in the world as powerful. And there’s no doubt that if it was commercial, it would have been poisoned. No doubt.

[00:51:05] CR: And you got to wonder how long it has left now that, uh, you know, AI is where people are going to go to get their questions answered. Um,

[00:51:14] SS: good point. I find now just in hindsight that you’ve mentioned that, that now a lot of things that I would go to Wikipedia for I now go to ChatGPT for, because that’s actually what you’re after, is a truncated answer to a particular topic, which it can give you, but you can also guide which parts of that you want and how long you want it to be in the format.

[00:51:33] SS: Whereas Wikipedia is in a fixed format, let’s say, um, I guess you could take Wikipedia pages and summarize it. Uh, but that’s actually, I’d be interested to see if Wikipedia’s traffic has declined since then.

[00:51:50] CR: Yeah. But you know, you could also, uh, try perplexity if you haven’t. Are you using perplexity as

[00:51:57] SS: I have used it. I have used it, but not

[00:52:01] CR: I go to. I go to Perplexity if it’s something that I would Google because its answers are much more truncated than OpenAI’s, GHRGPT’s, but it, um, standard provides you with sources and links is how it’s been designed.

[00:52:18] CR: So you can ask ChatGPT for sources, but it won’t always provide them straight up. Perplexity provides it straight up every time. So you can go and check the validity of the information. Uh, Steve, moving right along. Do you want to talk about Instagram kids?

[00:52:35] SS: Yeah, look, we opened up on the idea that around the world, America and Australia are moving pretty rapidly to get some form of legislation. I just think it’s kind of disgraceful that Instagram kids Miracle. It gets launched on the eve of legislation. And what it says to me is that we have a very nefarious corporation that understands the dangers of exposing kids and teenagers to a product.

[00:53:09] SS: And at the 11th hour, they’ve. Buy Miracle started to put it in some of the features that we’ve been asking for a long time. It feels like a corporation getting on the front foot to further delay and obfuscate potential legislation when they know that they have a potentially very damaging product. Um, you know, some of the features that go into Instagram Kids.

[00:53:29] SS: Under 18’s web account set to private by default. Wow, what a miracle. And 16’s only allowed to say, change the permission, uh, change the setting with parental permission. Teens only able to receive messages. From people they’re already connected with, as opposed to all the sexual predators and, uh, scammers that they’re getting to now.

[00:53:49] SS: Content restrictions, blocking of offensive words and messages. To the maximum set possible. Notifications turned off between 10 PM and 7 AM because yeah, we know it’s bad for kids while they should be sleeping. A reminder to leave 60, uh, to leave Instagram after 60 minutes of use each day. Ironic we’ve, uh, given my boy a 60 minute screen time of all screens limit, uh, to 60 minutes while he’s trying to get better.

[00:54:15] SS: Uh, I mean, for me, this really tells us that we’ve had a flaccid set of legislators Who really should have acted a lot quicker and a very deceptive set of corporations, which are driven by what corporations are always driven by, which is to maximize profit and to obfuscate the difference between something that’s possible and profitable.

[00:54:40] SS: And, and if anything, it shouldn’t make us far more suspicious that it is even worse. than what we think, based on the fact that they’ve flicked a switch and put these forward. And anyone who went out there and clapped Meta Corporation for doing this, really need to have a good hard look at themselves.

[00:54:58] CR: But you can trust Zuck now, Steve. He’s got his, he’s grown his hair out

[00:55:03] SS: Well, listen, I think the fact that he’s, he’s got, he’s got the chain, the gold chain of the curly hair and, and you know, he’s a surfer, apparently he’s a cool surfer. And look, we just

[00:55:16] CR: between a surfer and a rapper.

[00:55:18] SS: And a wrapper, and then we just put Jensen

[00:55:20] CR: And there’s nobody we trust more right now than hip hop stars. I think if

[00:55:27] SS: They’re right up

[00:55:28] CR: us anything,

[00:55:29] SS: can we just, can we just divert to a little bit of P. Diddy? Look, P. Diddy wasn’t really on my radar, but it sounds like he’s really done some iniquitous work there. I mean, what’s going on here?

[00:55:44] CR: uh, yeah, let’s not go down that rabbit hole. Anywho, yeah, Instagram kids, like you’re right, these tech companies, um, A, can’t be trusted, B, are going to try and find ways to manipulate governments and, um, To, you know, get around any legislative changes that are made. Um, that’s what we expect corporations to do, right?

[00:56:10] CR: Is to try and manipulate things to their advantage, to enable them to keep. generating profits. And, uh, yeah, but again, I think it’s the primary responsibility in this area is and should be on parents to be the first line of defense for their kids. Uh, but then if parents aren’t doing the job, then, uh, I, I do think governments have a responsibility to protect, uh, the people

[00:56:42] SS: look, you’re right about the parents. You’re right about that. Parents, what is that social phenomenon where if someone’s being injured, people all stand around and no one does anything about it? It’s got, there’s a certain psychological phenomenon that has a name for it. I can’t remember what that is.

[00:56:58] SS: You can think of it while I explain why I think this is really important that legislators

[00:57:02] CR: Samaritan, you’re talking about the Samaritan legislation, Good Samaritan?

[00:57:07] SS: No, I’m not talking about good Samaritan legislation. I’m talking about the idea that when someone’s getting injured or something and no one acts and everyone just stands around and watches it, even though they know that they should be doing something, there’s

[00:57:19] CR: That was it.

[00:57:20] SS: for it,

[00:57:21] CR: That’s what the Seinfeld episode was based on, the Samaritan laws that were introduced. Yeah, if you stand by and watch someone get hurt, and you’re not a Good Samaritan, you don’t step in and help them, it’s based

[00:57:33] SS: But there’s a psychological phenomenon where there’s a number, a large number of people not helping. Uh, you can go to ChatGPT and ask that while I explain why this is important. So, so,

[00:57:45] CR: can ask ChatGPT, hold on, let’s ask ChatGPT. live

[00:57:50] CR: What’s the phenomenon called where a lot of people will stand around and watch somebody getting hurt and do nothing about it? Um, the phenomenon you’re describing is called the bystander effect. It happens when individuals are less likely to help someone in need when there are others around.

[00:58:07] SS: I thought it had a far more fancy name like that, like the Dunning Kruger or something, you know, where two psychologists named it after themselves, which if I ever invent anything, I’m going to call it the Sammartino. Although we’ve already got one. We’ve already got the Sammartino

[00:58:19] CR: Yeah, I coined that on your behalf. Yeah.

[00:58:22] SS: So look, I think what happens is you get this, um, bystander effect where it’s a real challenge socially. And Scott Galloway has said some really important things on this. He, I think is really courageous. Again, I don’t agree with everything he says, but on this one he said there’s a challenge because if anyone is on these social forums, the deep desire for connection for other teenagers to be on it and not be the one left out has this collective bias where it becomes increasingly difficult for parents to isolate their children because some are on it.

[00:58:55] SS: If you make it a law that none of them are on it, it makes it easier for the parents to implement that because they can use the law as a way to Show that this isn’t just us. This is what society agrees upon. These are the values of society, and there’s reasons behind this, as it is with alcohol, as it is with cars.

[00:59:15] SS: And I think all of the research shows that a large majority of parents wish their children weren’t on social media up to the age of 18. And this is where the law becomes our proxy to enable us to collectively implement something which is difficult because if you even get 10 or 20 percent of the cool kids or whoever on it, others feel isolated and left off and you get this social paradigm where they all go down into it.

[00:59:38] SS: So I think that this is where legislation is important because it’s not the government handing down a bunch of Uh, laws which reduce free speech or our ability to connect. This is what the government should do, which is implement against the collective value systems that we have so that we can have a more civilized society.

[00:59:57] SS: Rather than them deciding That they want that for their benefit. We, we collectively, and the research shows it, don’t want our kids exposed to this. The help of a government implementing laws that we would want would make it easier for us to develop a society that benefits our kids by reducing their exposure to a negative tech.

[01:00:16] SS: That’s my thought.

[01:00:18] CR: Yeah, but the, the, the, I agree. The challenge though, and um, I talked about this in the Psychopath book is we don’t have, let’s take Australia as an example, we don’t have a commonly agreed upon set of values. We don’t have a ministry of ethics or a ministry of values.

[01:00:39] SS: No, you never do. And it’s always a moving, it’s always a moving beast as well. Values

[01:00:44] CR: how do we know what the common values are of a society at any given point in time?

[01:00:49] SS: Well, this is what research is for, right? The way that we know is that we do, we conduct robust research where we can assign an entire population to this. And those research methods have been long established for over a hundred years where we can understand what the large majority feel and we need to govern to the majority.

[01:01:11] SS: I mean, that, that’s not particularly hard. Now, values as a term is difficult because you and I might agree on some things and not others, but on a particular singular issue that we can isolate like this, exposure to social media, it’s actually not that difficult. The only difficult thing I think in this legislation is what is social media?

[01:01:32] SS: The definition of that, I think is probably the most difficult thing. And I would take YouTube as the example. Is YouTube social media? I mean, that, that, that’s an, that’s an interesting question. There is social media is something where you can publish. Maybe if you can’t publish, if you can publish, then it is social media.

[01:01:50] SS: I mean, that, that’s actually the hardest part of this equation. It’s determining what it is.

[01:01:55] CR: You know, I would love to take it up a level and say that we need a Department of Ethics, and then it should be responsible for looking at Murdoch Media. Uh, you know, I mean, we already have Some bodies that are supposedly there to restrain media companies from stirring up the shit or doing falsehoods, but, uh, it’s not very effective.

[01:02:20] CR: I mean, there is, again, there’s a line between censorship. and holding media organizations, be they traditional one to many media or social media, to certain values and standards that we as a society believe are important to uphold. Not that it’s the government just implementing things because the people in government think that this is good or this is not good.

[01:02:47] CR: That we as a society, the majority of a society believe that in order to live together, there are always going to be people that are bitching on the sidelines about who want ultimate freedom of speech and ultimate freedom of behaviour. But as we were saying during the COVID times, I don’t have the freedom to walk up to your table at a restaurant, drop my pants and take a dump on your meal.

[01:03:10] CR: So are. Restrictions. If we’re all going to get along and live together in a society, there have to be some certain standards that we all agree to. And it’s the same when it comes to freedom of speech as it does in freedom of behaviour. You can’t do 150 kilometres an hour down past a school. I mean, for good reason.

[01:03:33] CR: And I think the same applies to what we allow publishing and what we should allow with social media. So anyway. Instagram for kids. What’s next, Steve? I wanted to talk about one other, I know it’s all open AI today, but Sam Altman published a piece on his blog a couple of days ago. It’s called The Intelligence Age.

[01:03:54] CR: I’m just going to quickly flick through it for people and then you and I can discuss. He says, In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents. This phenomenon is not new, but it will be newly accelerated. People have become dramatically more capable over time.

[01:04:12] CR: We can already accomplish things now that our predecessors would have believed to be impossible. I like to tell the story that my, my mother grew up in a house with no electricity until I think she was four years old when they got electricity. Now she has AI on her mobile phone. Like it’s an incredible change in 75 years.

[01:04:34] CR: This phenomenon, oh no, we are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us. In an important sense, society itself is a form of advanced intelligence. Our grandparents and the generations that came before them built and achieved great things.

[01:04:55] CR: They contributed to the scaffolding of human progress that we all benefit from. AI will give people tools to solve hard problems and help us add new struts to that scaffolding that we couldn’t have figured out. on our own. The story of progress will continue and our children will be able to do things we can’t.

[01:05:16] CR: Um, and then later on he says, how did we get to the doorstep of the next leap in prosperity? In three words, deep learning worked. And this, I think, is the crux. In 15 words, deep learning worked, got predictably better with scale, and we dedicated increasing resources to it. That’s really it. Humanity discovered an algorithm that could really, truly learn any distribution of data, or, really, the underlying rules that produce any distribution of data, to a shocking degree of precision.

[01:05:53] CR: The more compute and data available, the better it gets at helping people solve hard problems. I find that no matter how much time I spend thinking about this, I can never really internalize how consequential it is. There are lots of details we still have to figure out, but it’s a mistake to get distracted by any particular challenge.

[01:06:14] CR: Deep learning works and we will solve the remaining problems. We can say a lot of things about what may happen next, but the main one is that AI is going to get better with scale, and that will lead to meaningful improvements to the lives of people around the world. You know, it reminds me of conversations I was having with my friend Dennis Bastas.

[01:06:37] CR: 20, 25 years ago, Dennis is now billionaire owner of one of Australia’s largest pharmaceutical companies. But back then he was the CEO of a startup and we used to hang out a lot together in Melbourne. We used to talk about consciousness and, and, and, uh, artificial intelligence and how we thought it would play out.

[01:07:00] CR: And we, we agreed back then that. Consciousness and intelligence were probably just very, very high levels of computation in that any sufficiently advanced system of computation would exhibit something that looked like consciousness and or intelligence and it would be increasingly difficult to discern between what is really conscious and what is not, what is really intelligent and what is not.

[01:07:30] CR: Sam is basically saying that deep learning. works. Throw enough compute and enough data at a system and it becomes intelligent.

[01:07:40] SS: It’s funny. It’s kind of, when you describe it that way, this is where my mind went. Not to say that this is right, but it’s almost like consciousness is the space between thoughts. Just a general awareness of being like you have like a bunch of thoughts and then you kind of there, I’m here, I can think of, I’m not thinking of anything in particular, but I have this general awareness and it’s kind of almost, yeah, it’s almost this, it’s almost a bit ironic that We were talking about some of the recent evolutions in the large language models, obviously open AI and chat JBT of those thoughts and deep thought and thinking between spaces, it’s like it’s progressing towards that.

[01:08:20] SS: The way that you described it then kind of reminds you of the, the human neural network and how it has this idea of consciousness and it, I often think. About a lot when I’m gardening, Cameron, about a worm, I’ll dig up a worm. I’m like, does this worm know that it’s here? Like, does it, where does consciousness kick in, in terms of creatures, whether it’s plants or grass or worms or bees or birds or other mammals?

[01:08:46] SS: When do they, you know, what level does it kick in?

[01:08:52] SS: Because we already have computation, which far exceeds many organic species in their computations per second and the size of their neural networks in their brains versus what we have now in large language models.

[01:09:04] CR: Yeah. I mean, my thinking on this back then was led by Marvin Minsky’s book, The Society of Mind, which I read At some point in the 90s, um, Marvin Minsky, for people who don’t know, was one of the guys who was at the beginning of artificial intelligence research and cognitive science. Um, he was, um, at MIT, co founded MIT’s AI Laboratory.

[01:09:36] CR: You know, he was, uh, one of Kurzweil’s, um, Uh, teachers and mentors, uh, K. Eric Drexler, who wrote the first book on nanotechnology, came out of Minsky’s lab. Um, Danny Hillis and the, uh, you know, what’s his clock, the immortal clock or whatever it is that he set up. A lot of the greatest, uh, current thinkers around AI and cognitive science, um, came out of, Marvin Minsky’s lab.

[01:10:05] CR: And in his book, Society of Mind, he basically just talked about how he believed that the concept of self was just a society, it was a society of thoughts. Basically, the brain is just a whole bunch of single unit processes happening, billions of single unit processes that somehow become aware of each other.

[01:10:29] CR: And it’s what he calls the interaction between those. Agents is what he referred to them as, was what we call mind. It was a society of. agents, billions of agents operating in conjunction, which became a society like millions of people living together, doing their own thing, become a society. There’s like a, a meta level of behaviour that emerges, emergent behaviour that comes out of enough people doing their own thing that exhibits a behaviour of a society that the brain, the mind is really no different.

[01:11:05] CR: So, you know, these ideas have gone right back to. You know, at least his book in the 80s that he, you know, 86, he came out with Society of Mind, one of the foundational books on artificial intelligence. So anyway, I thought it was really interesting and, you know, I remember I was talking about this a long time ago that, um, in an early interview that I saw that, um, Jensen Huang at NVIDIA did with Ilya Sutskever.

[01:11:30] CR: Um, he said what was the most surprising thing about ChatGPT to Ilya and he said it was that it worked. Like, we had this idea that if you threw enough data and enough compute at a large language model that it would, that it might show intelligence and it did. You know, that was, that was still surprising to him, um, a year and a half ago.

[01:11:53] SS: Yeah. It’s interesting. I mean, again, in Kurzweil’s book, which I’m savouring the words, almost don’t want to read it because I’m enjoying it so much because once I’ve read it, it’ll be read. The singularity is near. I just love the idea that he talks about the connectionist versus symbolic AIs. And that, that was the first type of AI they developed in 52, but they just didn’t have the computation to make it possible, but they, they thought that it would be the right approach because it had a sense of biomimicry about it, which is just, if you have enough, enough, uh, computation and enough input, which is really all there is, it’s computation and input, that’s all really there is, input out, it’s like something to mix it up and, and, and the data inputs really.

[01:12:34] CR: but he talks about, um, how this, like what became the LLM model, which is where there is no hard architecture between symbols and concepts that you just feed data. No, everyone thought that was, uh, the wrong approach and wouldn’t work. And, and so for 30, 30 years, We just gave up on that.

[01:12:55] SS: yeah, for a really long time. I mean, the simple example is, uh, words that have the exact same sound and spelling, but two different meanings, like boot and boot. If you feed it enough information, boot, car boot, put it in the boot, um, the boot was full, it’ll eventually work out that that boot in the right context is to do with cars versus it’s climbing up a mountain with the boot, you’re chewy on your boot, all that, and it can work it out just by the words that are near it.

[01:13:23] SS: Yeah,

[01:13:24] CR: Yeah, so, like, but those three words I think are incredibly profound. As Sam said in the article, deep learning worked. Like that’s, it’s like one of the pivotal statements. It will be one of the pivotal turning points, tipping points of not just human society and human history, but the history of intelligence on this planet.

[01:13:54] CR: And potentially. In the entire universe, because we don’t know how much intelligence there is in the universe, we can’t assume that there’s anything outside of this.

[01:14:04] SS: Wow.

[01:14:04] CR: Deep learning worked. To me, like, that is as, it’s going to be as profound a discovery as the taming of fire. Or The Wheel, or Electricity, or General Relativity, or Quantum Mechanics.

[01:14:24] CR: It is one of the most prof and it’s just slid by and I don’t think, most, I mean, most people still don’t know how electricity

[01:14:32] SS: I too, I too am absolutely astounded by the capability and I think that anyone who isn’t maybe hasn’t really played with it enough. Really?

[01:14:42] CR: Oh, absolutely. Yeah, yeah, yeah. Anyone who’s dismissive. I had lunch, one of my listeners from Boston, shout out to Doug if he’s listening, was

[01:14:50] SS: Aggie boy!

[01:14:52] CR: He was here in Brisbane a couple of weeks ago. I had lunch with him and he, he runs a biotechnology company, which is really interesting and we should get him on to talk about the work that they’re doing in solving

[01:15:02] SS: that’d be cool.

[01:15:04] CR: But I asked him about how much they’re using AI and he was still kind of dismissive about AI and I said, have you played around with it much? He goes, no, not really. And I was like, man, I pulled out my phone and did my usual parlour trick, which is like, let’s ask him a question about, Oncology or biotechnology or cancer research and have it have a voice conversation.

[01:15:24] CR: This is a few weeks ago before advanced voice came in. Uh, I just think I said, have you played with the voice? He goes, no. And I was like, seriously,

[01:15:33] SS: Get

[01:15:33] CR: just have a listen to this. Talk to you, man. Like anyway. Yeah. People who aren’t blown away either haven’t played with it or don’t understand it. But as I keep saying to people, the most profound thing is didn’t know this would work.

[01:15:51] CR: We just had a, we, I mean, the people who developed it

[01:15:55] SS: Well, I mean, when you and I were developing the LLMs back in 2007, we couldn’t believe what we stumbled upon. Could we,

[01:16:02] CR: Using the, the broad we of humans,

[01:16:05] SS: way. Look, I’m part of the human species. I’m one of the developers. If humanity is the

[01:16:10] CR: we. Yeah. Well, yeah. All right. Let’s move on. Let’s move on. Technology Time Warp, Steve, what have you got for us

[01:16:18] SS: I just wanted to put in a titbit. Every now and again, I’ll just say today in technology, and I did find out that the first ever personal computer was launched in 1973 today, and it was the Microcomputer Machines of Canada, the MCM 70, which had the first 8008 processor in it, but it failed, and I just, no one really knows about the Microcomputer Machines of Canada Corporation.

[01:16:45] SS: Big companies, Chevron, Mutual Life Assurance, NASA, And the US Army were its first customers and it was launched at a, uh, programmers, microcomputers, programmers conference in Toronto in 1973. So interesting how often it’s kind of 10 years before it gets in the zeitgeist and a, and a product then becomes available to general users, late seventies, early eighties.

[01:17:13] SS: I just thought that was an interesting little titbit as a technology time warp.

[01:17:18] CR: The company, Microcomputer Machines, was founded by Canadian inventor, Merz Kutt. Merzlau Kutt, born 1933, still alive.

[01:17:30] SS: Oh,

[01:17:32] CR: Former professor of mathematics at Queen’s University. Um, worked at Phillips, IBM, and Honeywell. Wow. There was a professor of mathematics at Queen’s during the 1960s, served as president of the Canadian Information Processing Society, was the director of the university’s computing center, and then in 1968 he was interacting with punched card based input systems and thought Maybe there’s an idea to put a keyboard on one of these things,

[01:18:10] SS: it’s one of the crazy things about. All of the early computers, none of them had screens, and most of them just had tapes. And that’s, that’s the interesting thing, you would do little printouts of what you get, a little punch card analysis. Uh, which is an interesting idea, a screen, and then from a screen to a voice.

[01:18:31] SS: I mean, it actually is really interesting on how things have this anthropological Natural flow. First, it’s something physical, whether it’s a cave wall drawing or whether it’s a printout or a punch card, then it’s a screen, then it’s a voice. It’s really interesting in the way that that flows. And it almost shows that in the first instance, it’s hands, hands driven, it’s typing, punch cards, physicality, and then it’s screen, it’s visual, it’s eyes.

[01:18:58] SS: And our eyes are working before our language developed. And then it’s language. And there’s almost this anthropological pattern that technological develops have followed and they’ve done it with AI again. Now we’re at voice. Actually, that’s ironic. I didn’t plan this. It sounds like, sounds like I planned that, that this technology time warp, that it would fit into all the other things, but I didn’t, it just turned out that way, just like large language models.

[01:19:23] SS: I can’t believe it actually worked.

[01:19:26] CR: And now we have vision, like visual inputs into the AI as well. Um, not just a screen as an output, but you can, you know, I, I mean, I do this increasingly with chat. PT is take a screenshot of whatever it is I’m working on a report or a spreadsheet or an error message and just drop it in and say, what do you make of this, you know, screenshots.

[01:19:50] CR: We can’t do live video input yet, but that’s what we’ll have. Oh, you do it with

[01:19:54] SS: my scrappy notes. I’m writing a piece on dead internet theory and you would be astounded at the accuracy that it can decipher my scribbles, which I don’t think a lot of humans would be able to make sense of what that word is. And I, and I’m guessing that the reason that it can do that is that it looks at the words around it and guesses what the word should be, even if it’s not spelled right.

[01:20:18] CR: He just gave me a great idea. I’m going to scan all my old paper diaries

[01:20:23] SS: I do that. I do it all the time.

[01:20:26] CR: and say,

[01:20:26] SS: when I’m doing a briefing, when I’m doing a briefing with the client on the phone for an event or some consulting work, I’ll write it all down verbally. I take a, I take a photo of it, dump it into ChatGPT, turn it into a Word doc, bam, I’ve got it all done, and then I’ll file it.

[01:20:42] CR: Wow. Good thinking. Just gave me another project to waste my weekend on. Uh, by the way, that Uh, computer. We talked about the M cm 70 based of course on Intel’s 8,008 or 80 0 8 processor, one of the earliest eight bit microprocessors, which had 16 kilobytes of memory

[01:21:07] SS: it was.

[01:21:08] CR: introduced in April, 1972.

[01:21:13] SS: And my first computer was the 16K RAM Terus 80, which had the same microchip in it, which was way back in about 1981. So, The recursion then was a lot slower, wasn’t it? I mean, you were still selling the older, slower ones for quite a long period of time, whereas now I imagine, you know, things don’t even last 12 months before they’re off the shelf.

[01:21:33] SS: Uh, but that, that was interesting.

[01:21:37] CR: just the leap from the 8008 through to what Apple are putting in their iPhone 16s, the processing power and the chips, all through to the NVIDIA, the NVIDIA chips that are running AI now, like that is just, it’s just a mind boggling demonstration of Moore’s Law. I was talking to my mum about AI the other night, I was explaining Moore’s Law.

[01:22:04] CR: And how over the last 20 years, I, there’s been so many people saying, Oh, Moore’s Law is coming to an end. We can’t get the gates down small enough now for an electron to pass through them. And then everyone’s like, no, well, now we just put them together. You know, what NVIDIA is doing where they’re just slamming these things together.

[01:22:23] CR: Uh, Moore’s Law has not, uh, died. It continues to speed up our ability

[01:22:28] SS: Yeah. I mean, I always liked Kurzweil’s and I think he did it in How to Build a Mind, where he talked about Moore’s law being, or the transistor being the fourth iteration of what is truncated ways of transmitting information, where he starts with punch cards and then he goes from punch cards to vacuum tubes, vacuum tubes to, um, microprocessors, microprocessors to transistors, and that, you know, Eventually you’ll invent a new technology or a new way to weave information together to hold and process more information.

[01:22:58] SS: It’s not people confuse Moore’s law with the overriding idea of the law of accelerating returns where technology finds a way to fit more information into less. Um, you could probably go all the way back to cave wall drawings, you know, bits of

[01:23:12] CR: Yeah.

[01:23:13] SS: right?

[01:23:14] CR: think Kurzweil does, right? I think he goes back that far. Yeah. All right. Uh, well, how are we doing for time? An hour and a half, Steve. Geez. And we’re not

[01:23:25] SS: Jeez. Well, what happens when you have

[01:23:27] CR: a line under it.

[01:23:29] SS: we tidy this up,

[01:23:32] CR: Yeah. Like, um, I think we’ve, we’ve talked about social media gating anyway. I think that’s pretty much it.

[01:23:39] CR: Um, yeah. Big couple of weeks. Let’s see how long it is before we do another one of these, Steve. But, uh, good to chat and

[01:23:48] SS: Oh, so enjoyable. Look, just, just, you know, exploring things with you, uh, and hopefully, yeah, and hopefully Lewis has enjoyed some of the explorations we went on, but so good. Mate, appreciate the chat.

[01:24:03] CR: Yeah. Thanks for taking time, man. And, uh, anyone out there, you got any ideas you’d like to, like us to cover in future episodes, uh, let us know, reach out. You know where to find us. Thanks, Steve.

[01:24:15] SS: Thanks, Cam.

In this episode of Futuristic, we chat about balancing screen time with reading physical books, Mistral Large 2, another bloody AI pendant (The Friend), Chinese text to video app Kling, and the use of crypto financing in the U.S. Presidential election. Our guest Martin Murray, helps us understand the potential role of AI and humanoid robots in agriculture. Finally we discuss the role of open-source AI, Mark Zuckerberg’s push against Apple’s closed ecosystem and how this affects industries beyond tech.

00:59 Steve’s Desire to Reduce Screen Time
07:49 Cameron’s levelled up his coding skills
12:19 Mistral Large 2 for Coding
14:59 New AI Pendant “The Friend“
21:48 AI in Agriculture with Martin Murray
52:25 Kling, the Chinese TTV product
01:05:11 Crypto and Political Influence
01:11:26 The Future of Open Source AI
01:19:48 Nanotechnology and Radical Abundance

FUTURISTIC 29

[00:00:00] Cameron: Welcome back to the Futuristic Steve Sammartino, episode 29. You’re looking sharp as always, man. Look at you. You look like a, I don’t know, he’s just stepped out of a venture capital meeting in Silicon

[00:00:20] Steve: Finance, 6, 5, Trust Fund, Blue

[00:00:23] Steve: Eyes.

[00:00:24] Steve: really finance, 5 6. Finance, 5 6, not 6 5. I’m

[00:00:29] Steve: not 5

[00:00:29] Steve: 6, but anyway.

[00:00:31] Cameron: I look like I’ve just come off stage at a Grateful

[00:00:33] Steve: You actually do, you’ve got the, what have you got on there? The

[00:00:37] Steve: bandana.

[00:00:38] Cameron: It’s a beanie, cold enough. One of the few cold enough days in Brisbane to wear a beanie. I’m going to

[00:00:44] Steve: have you got on there? A

[00:00:45] Cameron: I’ve got a

[00:00:46] Cameron: got A

[00:00:46] Cameron: bit of,

[00:00:46] Steve: lovely 23 degrees or

[00:00:48] Steve: something.

[00:00:48] Cameron: yeah, that’s

[00:00:49] Steve: is real.

[00:00:50] Cameron: I got a, I got

[00:00:50] Cameron: a, got an annoying cough. So I’m going to have to keep muting my mic to cough. Hold

[00:00:54] Cameron: on. Yeah. All right. Uh, Steve, uh, tell me one. thing of note you’ve done that’s futuristic y since we last spoke.

[00:01:03] Steve: Futuristic y. Is, I’m trying to balance my screen usage. I’ve never really looked at how long, I’ve spin on the screen because it was always justifiably, well, it’s work for me. And to be fair, I don’t really spend a lot of time doing things on the screen that aren’t work. Uh, but I looked, and it’s an average of 5 hours and 46 minutes a day on my phone.

[00:01:27] Steve: It’s an average of 92 pickups. And so, I went out and bought a few books. This week I asked you for a couple of recommendations and I have some on the way and every time I go to pick up my phone when it’s not an absolute necessity to do a task on it for work, I’m going to instead pick up the book. Uh, because I actually think that there’s a really big difference between knowing about, hearing about something and knowing about it.

[00:01:58] Steve: And I think we hear about a lot of things and the screen lets you hear about a lot of things. I don’t think Enables deep understanding, like reading a book and really going deep into it, into a topic.

[00:02:13] Cameron: Well, one of the things I was going to add to our talking list, but I ran out of, well, we had too many things already, but I was started watching a video last night, uh, an interview with Dwarkesh Patel, who does a lot of AI videos on YouTube, but he was being interviewed on another YouTube channel. And he was talking about how he thinks you don’t learn very well from books.

[00:02:35] Cameron: And I think we talked about this in an earlier episode, and he’s come up, he said, like, when I interview people that are experts in a topic, I need to, you know, get smart on that topic really quickly, and often they’re topics I don’t know a lot about. Said he had a geneticist on his show recently. So he’s developed this workflow that he puts into Claude, his AI tool of choice at the moment, that creates, um, uh, spaced repetition.

[00:03:03] Cameron: And he said he can upload an EPUB. of somebody’s book into Claude and say, tell me what the key points are that I should know before I interview this person and then test me on those. Um, you know, give me questions and answers and test me on it to test my basic knowledge. And he’s developing a system, which I haven’t fully finished the video to integrate it yet, but I think, you know, we’re moving into an era where Books are going to be a component of how we learn, but there’ll be an AI layer wrapped around that, that will create a, um, educational context.

[00:03:42] Cameron: What do you call it when somebody’s an educator? You call it, uh, uh, there’s a word for it that’s escaping my memory. Hold on, I should ask GPT. Um, what, what, what’s, what do you call a system of education that, uh, leads you through the learning process?

[00:04:02] Cameron: Scaffold.

[00:04:04] Cameron: Or pedagogee was the word I was trying to think of..

[00:04:07] Cameron: He’s talking about it’s a scaffold

[00:04:09] Steve: yeah, and that’s a nice way, and that’s a nice visual of Understanding the elements and almost seeing a matrix design of where you could go to get different types of learning. And just, uh, look, I think that what you’re saying is right. There’s a multitude of ways of learning. I mean, I’ve always been very good at kinesthetic learning, where actually hands on doing, and it’s one thing with startups that I’ve always realized.

[00:04:30] Steve: It’s one thing to read about startups and it’s another one to get out there in the battlefield and try and get people to use your product and, and raise capital and, and, you know, exiting theory. is really important. Uh, and you always learn a little bit more by doing something than actually reading about how to do it.

[00:04:46] Steve: Like you can’t learn how to ride a bike by reading about it. You have to hop on the bicycle. You can probably get some tips. Sure. Uh, the context of what I was saying, and I, it’s probably good that you brought this up because I can clarify. I’m not saying that I want to read more books to learn more. What I’m saying is, of what I’m doing with reading, I want it to be physically on paper.

[00:05:11] Steve: So the context is the reading context. And I actually looked up some research on it because I was curious about it because I have this theory that I get more out of a book and we all know about the distraction side of it. It’s easy to get distracted when you’re on a phone and not stay in the book as deeply because it’s singular versus a phone which has a multitude of uses.

[00:05:31] Steve: But some of the research suggests that the subconscious impact on the light and tiring out your brain and the way that the words are absorbed is very, very different to ink on paper. And another one which blew my mind, I never thought of that, is that the pixels that are on a screen, uh, flashing lights really, really quickly.

[00:05:52] Steve: And apparently your brain doesn’t digest the words as well. So there’s some research coming out, uh, in relation to screens not enabling people to digest information as well as books. So there’s that part to it. And there’s also that you go deeper into a book than you do just scanning the news on tech.

[00:06:09] Steve: So I know about a lot of stuff or hear about a lot of stuff, but I want to know more deeply about

[00:06:13] Steve: it. So that’s that’s what I’m really trying to do.

[00:06:18] Cameron: I think you’re probably right. I mean, I think, hey, well, let’s, let’s break that down. Like, um, yeah, reading rather than scrolling. Um, you know, I, I’ve developed the habit over the last few years where if I have some spare time and I’m not working and I’m picking up my phone, it’s usually to do Duolingo, to practice my Italian,

[00:06:39] Steve: is great.

[00:06:40] Cameron: or it’s, or it’s chess.

[00:06:43] Cameron: I’ll be playing a game of chess against somebody on Chess. com and I’ll have to go and make a move. Oh shit, that reminds me. Oh no, I did make a move this morning. And. Or I’ll read. Um, sometimes it’s Reddit, um, but often it’ll be a book that I’m in the middle of. I’ve always got like ten books on the go, so it’ll be one of those.

[00:07:01] Cameron: Um, But for me, and I think you’re probably right in terms of reading paper, but for me, the upside of being able to, I’ve got a shortcut on my iPad that if I copy a, if I copy a block of text in a book and I run this shortcut, it’ll paste it into a running note that I have in Obsidian.

[00:07:19] Steve: got benefits that a book will never

[00:07:20] Steve: have.

[00:07:20] Steve: Absolutely. No doubt. For sure.

[00:07:22] Cameron: I can, I can pull up GPT and

[00:07:24] Cameron: say, what’s this word mean? Who was this person? What’s that I don’t know. So I think there’s, there’s pros and cons, but I, I, I think you’re probably right that ink on paper is

[00:07:33] Steve: Just something about, I just want to extricate myself from the screen somewhat because I just think I’ll get reading done as well because it’ll be more purposeful and less distracting. So there’s, there’s a multitude of reasons, but that’s, that’s what I’m trying to do more

[00:07:46] Steve: of and just pay attention to the physical world.

[00:07:49] Cameron: Well, I’ve, I, you know, I know every

[00:07:51] Cameron: time we do this, I talk about coding because that’s where I spend a lot of my time, but I really felt like I leveled up my coding in the last couple of weeks. There’s a particularly big thorny problem that I’ve been working on and getting nowhere for days and days and days on it.

[00:08:06] Cameron: And then I learned. To, if there’s a particular problem. See the way the AI tools work at the moment, if you’ve got a really big script and it’s doing, it’s doing like 25 functions, but one function’s not working properly, and you tell the ai, I got a, I got a an error code, I’m gonna try to do this. It’s immediate thing will, it’ll rewrite the entire

[00:08:28] Cameron: script and go, okay, run it

[00:08:29] Cameron: again. And so I’ll wait till it finishes and then I’ll run it again and it still won’t work, and I’ll give it the error code and it’ll go, oh, okay. Let’s put some more logging in. And then it’ll give you the whole script again, and you run it again, and you give it the log, uh, the error log. And he goes, Oh, okay.

[00:08:45] Cameron: Let’s put some more logging in. And you get in this loop and it’s really frustrating. So what I learned to do is to isolate the bit that’s not working and say to the AI, No, no, no, no, no, no, no, no, no, no, no, just stop. Write me a script that, that has three or four ways in it. To solve this problem, like the particular thing that’s not working.

[00:09:07] Cameron: And let me just run those independently. So I’m not running the whole script until, so I isolate, test, figure out the method that actually works, and then integrate that back into the bigger script as a new function. And, um, I did that over the last week or so, and it’s solved a whole bunch of problems.

[00:09:28] Cameron: Just isolate. Test and then combine or integrate it back into the main thread has really,

[00:09:35] Steve: it’s a really good point, Cameron. And I don’t know how the large language models.

[00:09:40] Steve: But it seems to me as though you cannot iterate verbally on the previous finding. And I know that OpenAI is quite guilty of this. I was doing a presentation, uh, getting ready to go to New Zealand early this week, and I had an image that I wanted in 16, or in widescreen format. Is it 4 is it? And I said, this image that you’ve developed, Now go,

[00:10:09] Steve: 16,

[00:10:09] Steve: 3, I’m way off, um,

[00:10:12] Cameron: It’s 3, 4 and 16,

[00:10:13] Steve: there you go, 9, okay. said, develop this image in widescreen format, redo it. It gave me a new one. I’m like, no, no, this image here, exactly the same, widescreen. Can I do it? It actually is. And I’m wondering if what you’ve found as well is it’s very, very poor at verbal iterations, unless you take out the piece of the puzzle and get it to reframe that exact piece and then insert it back in, it always goes back to the well and starts again.

[00:10:42] Steve: And I’ve noticed it can’t do the same thing twice. It’s very, very bad at doing the same, which is interesting and cool. Uh, but if you ask it to recreate an image or create that image again, it won’t do it. It’s always a little bit different. And I think that comes down to the probabilistic nature of it and the way that it works.

[00:10:59] Steve: And it’s really noticeable, especially from an image perspective, and I imagine from a code, based on what you’ve said, and it reruns the code base again, because it goes, oh, you didn’t like that, I’ll just do the whole thing again. I’m hoping that ChatGPT 5 and others come out with a greater iterative ability, and for it to understand what’s required within the iteration of the previous development, and it doesn’t seem to be there.

[00:11:24] Steve: And this goes to show What, what you’ve been doing and what I’ve been doing, and I’ve been saying this on stage so much is getting to work well with the AI is an absolute art form that cannot be understated. And the only way you learn that art form is by experimenting with it and playing with it. And I wonder, If that’s always going to be the case, even if we have extraordinary AIs that are close to the general level, because it, it doesn’t, it’s trying to satisfy your needs and it is not inside your brain.

[00:11:56] Steve: And so that is the complexity that requires the ability to prompt well and to work with it and iterate and, and craft the angles and take pieces of the puzzle out and then reframe those and then put them back into the puzzle. It’s a really interesting insight.

[00:12:10] Cameron: Mistral. Mistral. Large 2.

[00:12:15] Steve: You’re going to have to give me a download on this cam because I didn’t test

[00:12:17] Steve: it.

[00:12:18] Steve: I had a look and read

[00:12:19] Cameron: I’ve been using, I’ve been using Mistral for my coding. For the last couple of weeks, their latest model, Large 2, Mistral’s a French company. Um, I believe made up of former employees of Meta and Google, was founded in April, 2023.

[00:12:41] Cameron: And, um, they, uh, have a open source model. Well, we’re going to talk a little bit of open source on this show, if we have time. Their latest version, um, Large 2, absolutely. Stunning when it comes to coding. I haven’t used it for much else, but it shits all over GPT 4. 0 and 4. 0 when it comes to coding, just gets it right almost every time, understands what I want, nails it, so much less stress, uh, using it for coding and, uh, that in and of itself is great.

[00:13:23] Cameron: So if you, if you haven’t tried it, you can use it for free. If you go to Mistral. ai, M I S T R A L, they have something called Le Chat And le plateforme, le plateforme is the API. Weirdly, I signed up for the API and I haven’t been able to get it to work. Because one of the problems with le chat is, as my thread gets long, which it can often do with coding, it slows right down.

[00:13:52] Cameron: It’s certainly not fast enough. Fast. It gets really slow, like one letter at a time, typing out, like my, my, you know, my dad trying to type something on a keyboard 20 years ago. Um, uh, so I tried to use the API, and I can’t get the API to work for some reason, and their tech support is not, has not been great.

[00:14:12] Cameron: I’ve been asking their tech support for a week to Figure out what’s wrong with my API access. And they’re not really very responsive because they’re French. But the, uh, the, but the LeChat service in and of itself is pretty good. If

[00:14:24] Cameron: you don’t have massively long threads that

[00:14:27] Steve: in the chat. It’s right up So I highly recommend.

[00:14:30] Steve: I said it’s right up there with Ordineter. Which is what they call a computer because they’ve got the French language division which makes sure they don’t have Western words.

[00:14:40] Cameron: Ah,

[00:14:41] Steve: And that’s the computer, is it already

[00:14:43] Cameron: So anyway.

[00:14:43] Cameron: Mistral

[00:14:45] Cameron: 2 is great and if we have time after our guest comes on in six or seven minutes, I want to talk more about open source versus closed source because it’s been a big topic, uh, in videos I’ve been watching this week. Um, The Friend, Steve, another bloody AI pendant, uh, has hit the

[00:15:06] Steve: Yes,

[00:15:07] Cameron: by a 21

[00:15:09] Cameron: year old guy called, uh, Schiffman.

[00:15:13] Cameron: Uh, did you

[00:15:14] Steve: I did, he’s an interesting cat. You know, he, it was interesting. His turn of phrase was interesting. What I did like was that he said productivity’s over in his Wired interview, and I kind of, I do feel as though there’s so many productivity tools, we need productivity tools to handle the productivity tools.

[00:15:32] Steve: It’s like, it’s eating its own tail. And at least he had a point of difference where this. Is to help people have a friend and for loneliness, which is a real problem in the world. I mean, he just forgot to put the word in the name of the product imaginary before friend, which would have been so perfect that I would have loved that.

[00:15:51] Steve: And I think he really should have done it, missed an opportunity.

[00:15:54] Steve: Also really curious that he spent almost half the venture capital on getting the dot com.

[00:16:00] Steve: Uh, he spent 1. 8 million on buying friend. com. Uh, which I thought it was interesting, Look, this is one of those

[00:16:10] Steve: ones that I can see that there could be some utility

[00:16:13] Steve: for

[00:16:13] Steve: it.

[00:16:13] Steve: Definitely. Yep.

[00:16:14] Cameron: So tell people, tell people what

[00:16:16] Steve: So, it, it’s, it’s, interacts with you on your day to day where you can talk to it. It sends you messages which I think appear on your phone where it talks back to you. It might even just make comments without asking saying, hey it’s

[00:16:27] Steve: great to be outside. It’s good that we’re walking around getting some fresh

[00:16:30] Steve: air.

[00:16:31] Cameron: You wear it you Wear neck like a little necklace.

[00:16:34] Cameron: around your

[00:16:35] Steve: Round your necklace and it observes and listens. It’s always on, as is Siri and Google and everything else. It’s always on and it makes comments about your life and talks to you and send you text messages and interacts with you in a way that is kind of like, let’s say an imaginary friend or a little AI friend that comments on your day and has opinions and can summarize, Hey, you know, you had that chat with whatever.

[00:16:59] Steve: Did you follow up and do that email? I don’t, stuff like that. It seems interesting because I don’t think it’s over promising in what it can do or trying to replace a screen which none of them have been able to do. All of them have been technologies in search of a problem where the smartphone usurps their utility.

[00:17:15] Steve: This one, I think, I think it could work. I don’t think it’d be anything ever more than niche, something incredibly niche. I could, I could see it potentially working. Um, just because it’s a little bit more single minded on what it does. Because the smartphone is the ultimate Swiss Army knife that does it all and does it incredibly well.

[00:17:35] Steve: And it’s very, very difficult to see that being supplanted. But I think that the single minded proposition of it, commenting, talking to you, helping lonely people, You know, you can, I feel like it could have some utility. So, look, that said, do I think that having an imaginary AI friend is better than sitting across the table or me chatting with you or picking the phone up?

[00:17:59] Steve: No. But maybe some people don’t have that opportunity or that wherewithal or that confidence or have a base of

[00:18:05] Steve: friends. So that’s kind of my taking on

[00:18:07] Steve: it. Yeah.

[00:18:09] Cameron: And I don’t think it’s an either or. I think in the near future, we will have an AI friend or multiple

[00:18:15] Steve: Yeah, right. Yep.

[00:18:16] Cameron: and as well as real friends, if you’re lucky enough to have real friends. Personally, I don’t like real people that much. I like you. I like Tony. I like Ray. I like my wife.

[00:18:28] Steve: pretty sure, I’m not even sure if you like me. You say that you do, but you’re, you know, you’re

[00:18:33] Steve: an unusual cat, you know that.

[00:18:37] Cameron: There’s a small group of people that I actually like to hang out with. But, getting back, I look, I think, um, trying to sell these sorts of, uh, single purpose AI widgets is probably a path to nowhere. But, I do expect that in the very near future, my watch, or my phone, or my iPad, or all of those, will have an AI, or several AIs, that will be my friend.

[00:19:09] Cameron: We’ll be listening to everything that happens, watching everything that happens, and maybe I’ll have different friends. Maybe I’ll have, I’ll have the friend that lends a sympathetic ear when I’ve had a fight with Chrissy. Maybe I’ll have a friend that’s like, dude, you, you, you really weren’t very productive today.

[00:19:27] Cameron: You know, you know, I’ll have my coach,

[00:19:29] Cameron: you know, The one that tells me that,

[00:19:31] Steve: idea. Your own personal

[00:19:33] Steve: Jim Rohn, straight around the neck.

[00:19:36] Cameron: yeah, yeah. Although the one that’s telling me that, uh, I need to be, I need to spend more time with my kids. I’ve been

[00:19:43] Steve: You’ve only hung out with Ox.

[00:19:44] Cameron: or maybe it’s all wrapped up

[00:19:45] Cameron: into

[00:19:46] Steve: had one hour with Fox today, your average is 2. 3 hours, you’ve let him down.

[00:19:49] Cameron: I was thinking of my older kids, my 23 year olds, but, uh, yeah, they’re always calling me up like. Like, one of them said, I want to take you to Deadpool, the Deadpool film on the weekend. I’m like, uh, I’ll just wait till it comes out on streaming.

[00:20:02] Steve: go out and have some popcorn with your boy, what’s wrong

[00:20:04] Cameron: hang out, hang out with your sons. I was like, oh yeah,

[00:20:07] Cameron: okay, good point, good point. Um, so I do think we’ll have AI, uh, friends that’ll be listening. I don’t think these devices are really going to be where it’s at, but I do think we’re going to have these AI friends, coaches, mentors, guides, uh, counselors that will be there. end up playing a really significant role in their lives. They’re not going to replace people, but they will be on top of that.

[00:20:34] Cameron: It’ll be like having, uh, the smartest person that, you know, just hanging out with you all day. Oh, here’s

[00:20:43] Steve: can also see a B2B context for these being valuable. You can see a context where you might have an AI pin that is hanging around your neck because you’re a Boeing engineer and it’s like, Hey, did you do the X? Don’t forget the Y. And then that way we’ll have less windows pop out of Boeing 737 MAXs and that’s the kind of plan I want to be on.

[00:21:04] Steve: I’ve always said that. But, yeah, different AI contexts of some of these pendants and AI friends that could be useful, like you say, coaching, training, uh, work context, technical skills. Which are, uh, a fragment, uh, but a, a, a far more knowledgeable AI within a certain slither. And you tend to see those categories

[00:21:28] Steve: split down in, in ways as well.

[00:21:29] Steve: So that, that could, there’s some potential

[00:21:31] Steve: there,

[00:21:33] Cameron: He’ll be like, Hey Cameron, do you really want to buy that box of Tim Tams that you just picked up at Coles? Really? You know, you’re trying to lose 10 kilos right now. Do you really want to buy that? Is that going to help with your black belt grading if you get a box of Tim Tams? Maybe, maybe think twice.

[00:21:46] Cameron: Alright, we’ve got a guest! Uh, it’s a long time since we’ve had a guest on the show, uh, but Martin Murray has been, uh, tweeting up a storm from his farm in, I’m gonna say, somewhere in New South Wales. I’m taking a guess, and he’s been talking recently about his, how he was testing ChatGPT on the farm, and I thought, wow, that’s something I know

[00:22:11] Cameron: nothing about.

[00:22:12] Cameron: We should get him on. Welcome to Futuristic, Martin.

[00:22:17] Martin: Yeah, G’day, it’s good to be here, and yeah, you are right, northwest New South Wales, just around Dallungra, which is sort of between Armidale and Inverrell if you, sorry, between Armidale and Moree if you, yeah, you know that area at all.

[00:22:33] Cameron: I have driven through there a number of times. Yeah. It’s a lovely part of,

[00:22:37] Steve: It’s a New England highlight.

[00:22:38] Cameron: And you are talking to us, you were

[00:22:40] Cameron: talking to us from the cab of your tractor, by the looks of it.

[00:22:44] Martin: Yeah, literally in the

[00:22:45] Martin: spray rig. So, um, yeah, hopefully I didn’t ruin it when I jumped on earlier, your audio quality, uh, but I’ve shut it down, but kept the time booster on. So hopefully this conversation holds

[00:22:56] Steve: I have to ask the mandatory, uh, AI question. Are you on a John Deere with the right, no. Right. To fix it if it breaks down. That’s, that’s my

[00:23:06] Steve: question,

[00:23:09] Martin: Uh, can I swap the camera around? Because I am, yeah, you are banging

[00:23:14] Steve: there you go. What do you have the right to repair? Because that, that was something that was really, had a head of steam a few years ago for quite a bit. I don’t know what’s happened on the right to repair. There may be that, that’s an interesting starting

[00:23:25] Steve: point for us before we get into some of the depths of.

[00:23:28] Martin: To be honest, it’s not much of a, um, yeah, an issue for me personally. Cause if I had the right to repair, I still lacked the

[00:23:35] Steve: Okay.

[00:23:36] Martin: So,

[00:23:36] Steve: Yeah. Yeah.

[00:23:38] Martin: um, But, I think you are right, yeah, I think, like, in terms of diagnostic tools, and, um, all of that sort of software side of things, uh, which, I mean, these things are pretty well just computers now, computers with an engine.

[00:23:52] Martin: Um, yeah, we still don’t have the right to repair that and access that diagnostic software.

[00:23:58] Steve: think that’s it. Wow. With every sort of machinery, I think if you buy something, you should buy it. I don’t care if it’s got software inside it. The Digital Millennium

[00:24:05] Steve: Copyright Act is an absolute disaster.

[00:24:07] Cameron: Well, let’s, let’s move on to AI on the, on the farm, Martin, tell everybody about how

[00:24:15] Cameron: you’ve been playing with it, your motivations and your discoveries.

[00:24:21] Martin: Yeah, so, I’ve been a listener of your podcast for a little while now. Um, Oscar Pierce got me onto it maybe six months ago or so, a bit of a shout out to him. And just from, yeah, talking to him and listening to your show, I just, I figured I’d give it a go. Um, yeah. I was coming up to the current cropping season, got my soil test back, and look, I’m an agronomist by trade, like, that’s my background, so, I know what I’m looking at, I just thought, well, it’s a rainy day, I don’t have much else to do for the fun of it, let’s load these soil test results into ChatGPT. And, um, see what recommendations it comes back with. And I’ve got to say, I couldn’t quite follow the math on it. Um, I don’t know if it was using different formulas that are used elsewhere in the world to what I’m used to, but. At the end of the day, the end result was pretty well the same with what I recommended, um, putting in, you know, this is our crop, this is our target yield, this is our target protein percentage, all those other, those other inputs that you put in yourself, and, um, yeah, it came out pretty well bang on, and it just sort of went from there.

[00:25:31] Martin: I’m like, well, okay, this is the crop, uh, can you give me an agronomy plan, you know, how much starter fertilizer, how much urea, when should I be planting it, Uh, what sowing rate should I be at? And, uh, you know, what, what herbicides and fungicides am I likely to be using in the season? And to be honest, it was, you know, it wasn’t 100%, but it was pretty damn close.

[00:25:55] Martin: Like, it was, um, just an interesting experiment and we sort of took it from there. Had it, uh, Taking pictures, uploading pictures from the cattle yards, asked it to identify the breeds of cow, we’ve got a couple of different breeds, and, um, you know, things that are obvious and easy, like the Angus, it was able to pick them out pretty well, and then other ones that I thought might, you know, might throw it a bit, not as common breed, uh, like, uh, there’s a breed called Speckled Park, which is sort of black, but it’s got all these little white speckles through it, it was able to pick that out and identify it. yeah, it was just, just an interesting experiment and I’ve done a few other things with it, but that’s basically where it’s

[00:26:36] Steve: It’s so interesting.

[00:26:38] Cameron: Didn’t you also

[00:26:38] Cameron: ask it for like, uh, dry hire rates for a tractor or something?

[00:26:44] Martin: Yes, I have done that with it.

[00:26:46] Martin: Um, and it was on the money. Yeah, I think I put that one up on Twitter there a little while ago. Um, over selling, I hired a tractor off the neighbor and neither of us really knew the rate. Um, So we asked ChatGPT and we asked Twitter and then we eventually found someone that does commercially lease out the same horsepower tractor and the rate ChatGPT came back with was the same rate he was on.

[00:27:14] Cameron: Amazing.

[00:27:15] Steve: It’s, you know, oh yeah, I was just going to say, what I love about your approach. Is to ask it’s something you know the answer to already, because what that does is it gives you context of the type of accuracy you can expect, and then it gives you confidence to do the next thing. Like I haven’t, I’ve never done that on stage, but the idea of saying, well, okay, what’s something you have a lot of knowledge on start playing with the tool on that, because what that does is without you worrying about the information you’re getting back.

[00:27:46] Steve: You’re developing a relationship of usage, which gives you the back and forth to see how it works and the type of questions you need to ask it to get an answer that you already know. And I think it’s a really intuitive way to do it. It’s actually quite smart. And I don’t think we ever really teach anyone that.

[00:28:01] Steve: It’s really insightful. And then after that, you’re going to go, well, if I can do it for that, here’s the piece that I don’t know that I might be able to use it for. Because as, as you would know, as a listener, Cameron and I are talking about Whatever AI can’t do today can probably do it tomorrow. So, sort of with that in mind, what are some of the sticking points?

[00:28:20] Steve: Within your business on the farm that you’d like quicker solutions on, or it takes a lot of time, whether it’s administrative or, or, you know, planning what you do out in the field or with

[00:28:30] Steve: your crops or you, you mostly crops or it’s just always cattle.

[00:28:35] Martin: Yeah, so our business is primarily cropping. We do have, we’ve got like 30 cows, that’s not much, they probably make up, I don’t know, depending on the prices each year, they’re probably somewhere between like 1 and 10 percent of the farm’s income. But yeah, look, for me, I think with AI, it’s, it’s not so much what it can do for me now that I can’t already do, it’s what’s going to be coming, and, um, like in terms of regulation, red tape, reporting, emissions reporting, all of these sort of things, like biodiversity, reporting, um, like, I mean at the moment this is, Things that we’ve got a vibe are coming, but we don’t know what exactly is coming, and how it’s going to impact us, but it looks like, you know, whatever happens, there’s going to be a lot of office work, a lot of reporting, a lot of collating of data, uh, you know, how much herbicide have you used, how much, um, diesel have you used.

[00:29:38] Martin: Uh, just, just all this sort of stuff, you know, we’ve, like, my diesel records, they’re in, you know, my OB, they’re in the accounting software, because you pay for that by the litre, um, but I don’t record how that goes out on the paddock, um, you know, maybe there’s a way in the future that, that AI will be able to take that diesel input and go, you know, you’ve made 10 passes this year in the spray rig over this, This is a typical number.

[00:30:07] Martin: Um, you know, you’ve made your one sowing pass, your harvest pass. These are also typical fuel numbers and, and work out from the total, you know, diesel that you bought, um, what you’ve actually used in each paddock. And likewise with, with biodiversity, I was just talking to someone about this, um, before the podcast, you know, putting microphones and things around you, around your farm, um, and going, you know, uh, recording the, the sound of the boat, like.

[00:30:36] Martin: What, what’s, what animals are, what birds are chirping, what, what frogs are croaking, all of that sort of stuff, and then having AI analyze that, and then you get a pretty good picture of, of what’s on farm, and, you know, come up with a bit of a conscription of how to manage for those species. And again, you know, as I said, it’s not something that’s an issue now, but I can see it being an issue in the next 5, 10, 20 years.

[00:31:00] Martin: Something that we’re going to have to be on top of as land managers.

[00:31:03] Steve: funny because the administrative burden on small business is really been increasing, GST was introduced all of a sudden, you know, you have to, the administration needs to do with product services coming in and out in GST, which gave rise to. Uh, SESS for accounting because it just becomes too complex to do.

[00:31:22] Steve: But I like that idea of the internet and, and AI actually, not just being a filing cabinet where you can pull stuff out of it, you put stuff in, it’s a brain. So you’ve got to upload your data, which you can then analyze and you can give it directives on, okay, we’ve got this data here, what we need to find out is emissions, X, Y, and Z.

[00:31:41] Steve: Here are the data, here are the data buses, here’s the GPS from the tractors I’ve driven around. Of course if John Deere give it to you, here’s uh, here’s all of the raw materials that I’ve bought, here’s the head of cattle, here’s the output, and it can calculate all of those elements there and reduce the administrative burden because we tend to be looking at AI right now as a creation tool, like go and create more of the work that I do, but just that underpinning of putting up data to reduce administrative burden, um, it’d probably be more accurate than what we deliver today, I would imagine, you know, in, in, in calculations.

[00:32:14] Steve: Simple one is With your car, it’s like how many kilometers do you do on your little log books? It’s going to do a better job than you would do that just by looking at GPSs and petrol and acceleration and all of

[00:32:26] Steve: that stuff.

[00:32:27] Martin: Yeah, I’d imagine so. If it, if it’s got access to that data, it would definitely have more attention to detail than I would. I mean, no one gets into agriculture to be, uh, You know, an Auditor. It’s, you know, we’re here to grow things, uh, and drive tractors, chase cows, do whatever you love. You’re not getting into the game because you want to audit yourself and do all this reporting.

[00:32:50] Cameron: The outside of, uh, administrative red tape stuff, what are the, what are the two or three biggest issues that a farmer such as yourself is having to deal with in terms

[00:33:03] Cameron: of running your business every year, Martin? Yeah, I think, I mean, there’s a

[00:33:08] Martin: Um, yeah, uh, I mean personally my biggest issue is interest rates and I don’t know if AI can help me with that. But, um, but there’s I mean there’s other things, like other cases, I haven’t used this, done this personally, I’ve spoken to a fella that has, because he knew the parts catalogue was online, he took a picture of a broken part, and you can spend hours on the phone to dealers, like machinery dealers and, you know, repairmen, that sort of thing, trying to find a part number.

[00:33:38] Martin: He told me that he was able to take a, um, picture of this part, tell it where to find the parts catalogue online. And, you know, the make, model, everything else of that particular machine, and it was able to pretty quickly get back to him with the correct part. Um, personally, like, without giving ChatGPT a lot of direction, just taking pictures of machines, uh, around the farm, and asking it, you know, what’s this, what’s that, it’s probably about as good as my four year old.

[00:34:09] Martin: Like it could tell that it was a John Deere tractor and it sort of got the, like the family. Right. If that makes sense. But it, um, like, you know, say like you say you gave it a picture of a, you know, a 2002 Toyota Hilux, it would tell you it’s a Toyota Hilux, but it wouldn’t be able to quite pick up on, you know, that whether it was an Sr SR five and, um.

[00:34:33] Martin: You know, the thing with John Deere, and this is the thing that I found really surprising, is the model number is printed on the bonnet, and same with my case tractor, and that it wasn’t able to pick up on that, but it was able to tell me that it was a John Deere or a case, and give me the approximate, you know, generation, but not have the details, so I haven’t been playing around with it too much in that spare parts machinery place, but That is a big headache to solve if it can solve that.

[00:35:00] Steve: should mention that because one of the things that I’ve been doing lately is when ChatGPT, OpenAI, the one that I use the most, and I probably after today’s chat should go to Mistral, can’t do something, but I ask it to write code so that it can do that. It says, sorry, I can’t do this. I don’t know what the parts number, I would say, can you write some code to understand all of the part numbers or, uh, tractor numbers, whatever they’re called, or John Deere tractors, uh, go onto this website.

[00:35:28] Steve: Here’s where you’re going to find it. Do a screen scrape and visuals and then add into your database and create your own little, uh, GPT on it. So that you can use it to scan things and it will know it thereafter. So one of the things that, and this is this recursion, I’ve been thinking a lot about recursion lately, which is the idea that the technology can create technology that the technology can’t already do.

[00:35:49] Steve: Because it’s a functional tech, it’s not just like a steam engine where you turn it on. It can create new things that it can’t do. So you go in and ask it to write a script, To be able to identify X number of Toyotas or John Deere tractors, because that information is available on the web. It just hasn’t put it into the database in a format, which makes it findable.

[00:36:09] Steve: So you direct it to create that script and then go and do it. And then you ask it to put that in from now on, whenever you ask that, it can refer to that inside. Uh, your, your database. So that’s, that’s kind of one of the things that I think, and this is what agent AI is pretty good at as well. Uh, one that you might want to try out, which is pretty good, Martin, is, uh, agent GPT.

[00:36:32] Steve: And that does things like that. So you set it an objective. You could say, give me all the parts,

[00:36:37] Steve: numbers,

[00:36:37] Steve: and pictures of X and we’ll go out and do it.

[00:36:40] Martin: Righto, I’m gonna have to give that a go.

[00:36:42] Martin: Yeah,

[00:36:43] Steve: to wish you’d never talked to be mucking around on, you’re going to have crop

[00:36:46] Steve: failure. You’re going to be mucking around with AI for the next two weeks.

[00:36:49] Martin: yeah, nothing’s gonna get done but I do have a um, I do know, I’ve got a, well I’ve just actually got the replacement part, so I know the part number, I know where you can find the catalogue, um, so I’ll have a play with it and see if it can um, yeah, if it can do that.

[00:37:08] Cameron: lot of work if you, if, You know, to fuck around with it and try and make it do things that it can’t do. I guess we’re sort of assuming that the sorts of things that it struggles with today, it won’t be struggling with a year from now or two years from now. But I’m, I’m really interested in how AI is going to revolutionize different industries and agriculture being one of those.

[00:37:32] Cameron: I’m wondering, you know, there’s a lot of You and I were chatting on Twitter over the last couple of days. I mentioned that, uh, an old mate of mine from my Microsoft days, I had lunch with him a couple of years ago and asked him what he’d been doing. And he told me about a project that he had done for a client where it was, I think it was putting RFID chips into, uh, sheep and, and lambs, ewes and lambs.

[00:37:58] Cameron: Cause they, for some reason, these farmers needed to know or wanted to know which ewes belong to which lambs or vice versa. And they couldn’t tell, but they were able to figure out that by measuring proximity of the amount of time that they spend close to each other using the RFID chips, they could take, they could figure out whose LAMs belong to which use.

[00:38:20] Cameron: So he was building RFID just to capture all that data and then code to figure it out at the end of the day, write reports. I’m just wondering about all of the potential data. That can be captured on a farm these days with RFID chips and, as you mentioned before, visual, audio, um, and if all of that, uh, could be captured and just fed into an AI system that could come up with ways to help you be more efficient.

[00:38:48] Cameron: on the farm, look for ways of, uh, I don’t know, using less fertilizer or increasing crop yields or working less hours. And that’s even before humanoid robots. Elon starts, uh, giving you a Tesla robot. Uh, what, what would

[00:39:06] Cameron: a couple of humanoid robots mean for you on a property like

[00:39:11] Martin: Yeah, look, if I could get humanoid robots, um, that are, you know, just as competent as a human, you know, they’re able to react the same as a human, operate the same as a human, that would be brilliant, I mean, that would free up a lot of my time to go play with these sort of things with AI and whatever other tools are going on.

[00:39:32] Martin: Um, I mean, at the moment, like, you know, we’ve got GPS driven tractors, like, obviously, the one I’m in, you know, drives in a straight line forward and back, but I still need to be in here to operate, other controls, other important factors, there are now, um, you know, there’s, there’s Robots like Swarm Farm do swarm bots.

[00:39:50] Martin: They’re completely autonomous, like spraying, well they’re mainly used for spraying, but you can use them for other things too, uh, spraying robots. But at the end of the day, like, all my machines are set up for people. So, you know, we live in a human built environment, so if we, uh, well, an environment that’s built for humans, so if we can fit humanoid robots in it, it It would mean say instead of replacing, you know, three or four different machines, I’ve now got one robot that can drive all those machines.

[00:40:20] Martin: Uh, that would be a huge cost saving there. And at the moment, like labor’s hard to get. Um, it’s getting harder to get. Uh, it probably makes more financial sense to have a robot.

[00:40:32] Steve: such a strong

[00:40:33] Martin: And going back to

[00:40:34] Steve: It’s so strong. Just on that, I was Presenting humanoid robots in my keynotes lately, I show them now, the figure one and some of the humanoids, which are really extraordinary and Jensen Huang, we discussed on the podcast before says that before the end of this decade, they’ll cost, you know, like a small car, which is who wouldn’t invest in one of those? People ask me, why would we want a humanoid robot when you can have a self drive car? And if you look back to the old self drive cars in sci fi movies, they had a robot driving the car. And your answer is, is really the one, is that we have a human shaped world, right? And if you have humanoid robots, then they can have all of the capacity and work in the existing infrastructure that we already have.

[00:41:14] Steve: The insight that one humanoid robot would be a better investment than buying four new

[00:41:19] Steve: tractors that are probably 200 grand each. I don’t know, I imagine they’re quite expensive. There you go! So, the idea that you have a humanoid robot that becomes like a smartphone, a general purpose computer, but it can go into the shed and organize feed and then drive it.

[00:41:39] Steve: The tractor and, and then work with the cattle and walk around on the farm with humanoid gumboots. Like, that is actually a strong reason for humanoid robots. And I think that the marrying up of AI and physicality is a lot like when we used to have, you know, Uh, cattle pulling, uh, things along on the farm to, to, you know, grade the soil or before we had the combine harvester, it’s the idea of putting the engine inside the horse and cart or the engine inside, uh, the grader or whatever that you had on, on the, on the farm.

[00:42:15] Steve: I feel like the humanoid moment is the intelligence inside the dexterous physical robot is going to be maybe even

[00:42:22] Steve: the biggest part of the AI revolution.

[00:42:25] Martin: And yeah, and as I said, if they are

[00:42:27] Martin: just, you know, if they are as capable as a person, uh, it’s a no brainer. Even if they’re, you know, two, three hundred, four hundred, even half a million dollars. Um, I mean, that wouldn’t suit my operation, but if you’re a larger operation, uh, half a million on a robot, you know, compared to wages,

[00:42:46] Steve: Well, you’re 24 hours a day too.

[00:42:47] Martin: know, the the cost of, exactly,

[00:42:51] Steve: You’ve got the humanoid robots, uh, out on the front doing work 24 hours a day and just swapping over their batteries. Like, you think that through, it actually doesn’t have to be that cheap for it to be quite functional in industrial and commercial contexts.

[00:43:06] Steve: It’s not so much a

[00:43:07] Steve: domestic context, but it’s certainly in industrial contexts.

[00:43:11] Martin: No, you’re not gonna have it making you coffee and

[00:43:13] Martin: doing the laundry, but um,

[00:43:15] Steve: Well,

[00:43:15] Steve: it’s hard. That camera and I were like, wow, actually, yeah,

[00:43:20] Steve: mowing the lawns, yes, and putting the washing out,

[00:43:25] Cameron: but if they’re, if they’re 20, 30, 50 grand, 50 I think people, uh, will have them doing domestic stuff too, um, some people will. All right, well, if there was one, let’s leave it with this, Martin. If there was one problem that you wish AI could solve for you in the next, say, two years, if there was one sort of killer app for AI, not robots, just AI, on a property or running a farm,

[00:43:58] Cameron: what would it be?

[00:43:59] Cameron: One of the things we’ve already mentioned or something else?

[00:44:02] Martin: Well, yeah, I don’t know. I mean,

[00:44:05] Martin: I guess it comes back to you don’t know what you don’t know, like what, what, what, what? What insights haven’t you come across yet? Um, like one of the spots I’ve been thinking about, you know, a lot, I know I said the livestock make up a very small part of our business and, like, just business wise, we’re actually looking to change that so they’re a bigger part.

[00:44:24] Martin: Um, it’s what can we, where can we use AI there? What, what, what data can we get? What insights, what patterns can we, um, You know, find that we’re not seeing, we’re not looking for, uh, we’re entering a world now where, you know, quite cheaply you can have, um, weigh bridges or like weigh bridges for cows in the paddock.

[00:44:46] Martin: There’s a great company called OptiWeigh. Um, we’ve got GPS cattle tags which, uh, you know, they’re still a bit expensive. Um, like every cow has already has an RFID tag. Um, that’s, that’s a legal requirement for supply chain traceability, all that sort of thing. It’s now becoming a thing in sheep. But GPS tags for about 50 a tag, you know, it’s not quite economic, but what sort of insights are we missing out there by not having it?

[00:45:14] Martin: Um, like there was a fella on Twitter, Nigel Caron, um, brilliant farmer in the Central West, he’s got an Optiway, so an in paddock. Type way system, the cows just hop on, they weigh themselves, hop off, it’s all a bit of a novelty for him. Um, he was able to pick up, you know, from that, that all of a sudden they stopped gaining weight and he didn’t know They didn’t know why.

[00:45:37] Martin: So they did some fecal tests and found that they, they now had a word prob worm problem. And so just from that quick insight, they were able to get on top of that problem. And, um, he said what it would’ve otherwise cost them in lost weight gain. They were able to save just in that, um, that one worming, that one event.

[00:45:58] Martin: And it’s things like that. So like, I’d be keen for, you know, once. Once AI hits a point where it’s insightful and not just, like, where I feel it is at the moment, just regurgitating what it can find on the internet, it’s just a, you know, an enhanced search engine, it’s what is it going to be able to tell me, so similar to the tags you mentioned earlier, learning, you know, what tags are in proximity, that way you can come back to, you know, Which animals are your better performing animals, and which cows did they come from, so then you can breed more from those cows and get rid of your lower performing cows.

[00:46:35] Martin: Like, there’s, there’s a whole world of possibilities, and Yeah, it’s, I don’t know where the answer’s going to be, because I don’t quite know what questions to be asking.

[00:46:45] Cameron: I Sorry for the bit of a rant, but that’s,

[00:46:47] Cameron: No, that was perfect. I thought you were going to say the cows were stopped putting on weight because they were looking

[00:46:52] Steve: that’s what I thought I’d

[00:46:53] Steve: do it for.

[00:46:53] Cameron: fuck, I weigh, I weigh how much? Holy shit. I got to go on a diet.

[00:46:58] Cameron: That’s like

[00:46:58] Cameron: me

[00:46:58] Cameron: jumping

[00:46:58] Steve: I’ve got to get off

[00:46:59] Steve: the grass.

[00:47:00] Cameron: Holy shit.

[00:47:01] Steve: That’s funny. I

[00:47:01] Steve: also like, here’s

[00:47:02] Martin: I know, we keep that information from them. We can’t have them knowing

[00:47:05] Steve: another one I like

[00:47:06] Steve: too too much

[00:47:07] Steve: McKinsey cow strategy. We get rid of the bottom 20 percent of performers every year just like

[00:47:11] Steve: McKinsey.

[00:47:14] Cameron: Here’s my suggestion.

[00:47:16] Martin: and that is how you run

[00:47:17] Martin: a business. That is how you run an

[00:47:18] Martin: agricultural business. That’s why we don’t

[00:47:20] Martin: have the same cows that we had, you know, 50 years ago. Like, the same type of cows. We don’t have the same breeds of wheat from 50 years ago. It’s That is how it works.

[00:47:31] Cameron: So my suggestion, Martin, if you haven’t already done this, this is something that I do with GPT all of the time is I will ask it to tell me, uh, ideas of how, what I could be doing. So I’ll throw a whole bunch of data at it. Like I do this with my diet, right? I track my diet every day using combination of Obsidian, my notes app, and some shortcuts and GPT.

[00:47:55] Cameron: I have it all in a spreadsheet going back sort of a year now. And I’ll throw it into GPT every few months and I’ll say this is what I’ve been eating, this is what my exercise looks like, this is how much weight I’ve been losing, um, what should I be doing differently? Give me, give me some suggestions about ways that I can modify my diet, modify my exercise, et cetera, et cetera.

[00:48:17] Cameron: With coding, I’ll say, here’s, here’s all of the tasks because I track all my tasks every day and how much time I spend on them. I’ll throw it into GPT every now and again and say, here’s all the things that I do during a day and how much time I spend on them. Have you got any suggestions for how I can be more productive?

[00:48:33] Cameron: What, what should I be doing that I’m not doing? What should I do more of? What should I do less of? And it’s not, it’s not, Like it’s not, uh, a productivity, an amazing productivity coach. A lot of what it gives you is sort of generic stuff still, but every now and again, it does give me a really good idea, really good insight.

[00:48:55] Cameron: And it only, I only need one every now and again to make it worthwhile for it to get me thinking about something that I wasn’t already thinking about. And that spurs me to go, Oh, well. Not that, but what if I did this, what would that mean? And then it’ll go, yeah, that’s a great idea. And sort of build on it.

[00:49:10] Cameron: So it’s using it as sort of a, I use it a lot as a brainstorming tool to start me off with ideas and then build on them and build on them. And, uh, you know, even using a little bit of, um, hold on, I gotta mute while I A little bit of, uh, Edward De Bono lateral thinking with it too. Yeah, you start a trend, you say, okay, now give me like 10 really out there random suggestions and, uh, see what that spurs.

[00:49:42] Cameron: Like, just give me a whole bunch of random words and add that into the idea mix and see what it generates. It’s really good at doing things that humans struggle with, which is thinking outside of the box. Um,

[00:49:55] Steve: ask it, you have to ask it that. And I often use it as well to generate, like I give me 30 ideas in this topic here, and I won’t like

[00:50:04] Steve: the ideas, but those ideas give me an idea of where I leverage my

[00:50:07] Steve: depth.

[00:50:08] Steve: That’s what always happens.

[00:50:09] Cameron: I’ll tell you

[00:50:10] Cameron: something. I was having a marketing meeting with my son Taylor the other day about how to use TikTok to promote my QAV, my investing podcast. And he said, well, you need to be talking, you need to figure out a way to make it relevant to whatever’s big at the moment. And I was like, oh, sure.

[00:50:24] Cameron: Well, what’s big at the moment? He said the Deadpool film. So I said to GPT. How can I use the Deadpool Wolverine film that’s big at the moment to talk about investing? And it said, well, you could talk about what kind of investors, Deadpool and Wolverine would be in real life. Deadpool would probably be a high risk, high reward.

[00:50:45] Cameron: And Wolverine, because he’s a mortal, probably takes a long view and you know, he could have, you know, a long-term view in investing. He doesn’t have to hurry. And I was like. Oh shit, that’s a great idea. You know, that’s, that’s brilliant. I never would have

[00:50:57] Steve: the other one is too, you can break down how much a Deadpool cost to make. What’s the ROI on the average film? Is it better than average? Why do films like, uh, uh, animated ones keep getting recycled because it’s got a bit of ROI and there’s lower risk and like, there’s a lot of different things you can do.

[00:51:13] Steve: That’s good advice. That’s

[00:51:15] Steve: really good.

[00:51:16] Steve: Yes,

[00:51:19] Cameron: Yeah, anyway. Well, Martin, we’ll let you go. You’ve got food to make, um, so I can eat it.

[00:51:25] Steve: we need it.

[00:51:26] Cameron: Thanks for coming on and sharing that with Martin. Really interesting

[00:51:31] Steve: Keep us up to date.

[00:51:32] Martin: Nah, that’s all good.

[00:51:34] Cameron: If you have any more ideas or breakthroughs with the tech as it evolves, let us know. I’ll follow you on Twitter. We’ll continue to follow you on Twitter.

[00:51:43] Cameron: What’s your Twitter ID for people

[00:51:44] Cameron: listening? It’s just

[00:51:46] Cameron: martinmurray__ag, yeah?

[00:51:48] Martin: That’s it, Martin Murray Ag. That’s the one.

[00:51:52] Cameron: So they can go and follow you on Twitter and, uh, yeah, keep up the good work, Martin. Come and come back on and tell us if you, uh, have

[00:51:57] Cameron: any more ideas about AI and Ag, tech and Ag.

[00:52:03] Martin: Righto. Will do. Thanks for having me on. It’s, it’s been great. And yeah, been really good to talk with you too. And I don’t know what I’m doing on the next rainy day. I’ll be playing with a few of those brainstorming ideas. Cheers.

[00:52:14] Cameron: great. Let us know how it goes. All right. Take care, man.

[00:52:19] Cameron: Fun.

[00:52:19] Steve: good.

[00:52:20] Cameron: Cool

[00:52:20] Cameron: dude. Alright, let’s get back into news stories. Steve, Chinese text to video. Now, we’ve talked a little bit about where China’s at with, um, AI. There’s been a lot of, uh, talk about how they’re, you know, quite a, quite a ways behind some of the US companies, but making a lot of progress. A company called Kling, K L I N G.

[00:52:47] Cameron: Out of, uh, China, launched their text to video model, uh, in the last week, and it is in, it is right up there with Sora and, um,

[00:53:04] Steve: Runway.

[00:53:05] Cameron: whatever the other Yeah, Runway. Look, Runway. yeah,

[00:53:09] Steve: thought it was as good as Runway’s previous model,

[00:53:14] Steve: but, but not better than Runway 3, the

[00:53:16] Steve: new one. And it’s, it’s, yeah, it’s videos, it gives you a maximum of five seconds at the moment, it’s not a lot, um, in the ones that, that I saw, the samples that it had, uh, I thought it was okay.

[00:53:29] Steve: I didn’t think it was better. Yeah. But, look, if they were nowhere, they’re certainly not far behind now.

[00:53:36] Cameron: It can do five seconds image to video, but it can do three minutes in length for videos.

[00:53:45] Steve: Image to video. I can’t correct it.

[00:53:48] Cameron: it’s, which is a long time for a video. If you go through the demos that they have on their website, um, video extension, Uh, yeah, I mean, they’re quite long. So, um, it’s, uh, look, it’s just amazing with all these tools coming in.

[00:54:07] Cameron: Now, this one’s publicly available too, unlike Sora, although you have to have a Chinese mobile phone number in order to be able to register, which makes it tricky,

[00:54:17] Steve: Makes it difficult if you’re

[00:54:18] Steve: uh, doing the English version, doesn’t it? Need a plus eight

[00:54:21] Steve: six.

[00:54:23] Cameron: And, of course, as we’ve talked about, there’s the, uh,

[00:54:27] Cameron: Chat XiPT, I think I heard it.

[00:54:33] Steve: Did someone say that? XiPT.

[00:54:37] Steve: That’s a Cameron political joke.

[00:54:40] Cameron: XiPT. The, um,

[00:54:43] Cameron: there is going to be this issue about how it, how their AI products navigate. You know, some of the Chinese censorship and restrictions on content. But at the end of the day, you know, we’re going to be in a world where I don’t really need my AI to comment on the Chinese Communist Party or, uh, or tell me about Tiananmen Square in 1989.

[00:55:10] Cameron: I mean, the range of things that I’ll want my AI to do on any given basis, any given day, I don’t think is really going to be a problem for Chinese AIs to generate. It’s going to be able to talk about science, and maths, history, Outside of maybe, you know, Chinese 20th century

[00:55:31] Steve: if it is true, if it is true that the AIs are trained on certain databases, And OpenAI and, uh, Gemini and Anthropic are all trained on the general web of English. You could even make the argument that the Chinese apps, if they have a proclivity, because they’re training on Chinese language to train on the internet, which is very censored in China, they could become more scientific AIs because there’s less dissent and trolling and misinformation.

[00:56:06] Steve: Potentially, uh, they could become more scientific with the output from those AIs than what you might get from the training on an English based AI, which is, as we know, being, uh, washed via social media, which has the highest frequency of posting.

[00:56:23] Steve: Lots of inaccurate information.

[00:56:26] Cameron: Yeah. And you know, something that, um, I’ve heard, uh, Zuckerberg say a lot, I’ve watched a lot of Zuckerberg interviews recently, cause they just came out with the latest

[00:56:38] Steve: How do you feel about Zuckerberg

[00:56:41] Steve: 2. 0? Let’s just, Let’s just, get into Zuckerberg 2. 0 with the curly hair, mate. The curls get the girls. And Zach is, he’s right up there with

[00:56:49] Steve: gents in one, what is, Leather Jacket coming out next? What are we talking here?

[00:56:55] Cameron: I love it. I love it. Um, yeah, he’s looking like in one interview, he’s looking like a surfer dude. He’s just got, you know, uh, you know, tan, beautiful, big curls. He’s looking like he just stepped off the snow fields or the beach. Next, next interview with Jensen Huang, he’s looks like a hip, he looks like Run DMC

[00:57:15] Steve: Ha ha

[00:57:16] Steve: ha! He’s just all in

[00:57:17] Cameron: black with a big gold chain.

[00:57:19] Steve: Jacket. Well, here’s the question, is he going to pull a Jeff Bezos and get on the gear and just get some big guns, and get rid of Priscilla, and, and, and, and just get

[00:57:29] Steve: himself a nice sexy newsreader? Because that’s what Uncle Jeff did.

[00:57:32] Cameron: yeah, like, whoever his PR

[00:57:34] Cameron: people are that’s doing his makeover, I love it. I think they’re doing a great job. But anyway, um, that aside, and he’s had a personality upgrade too, he actually can talk like a human a little bit

[00:57:44] Steve: the upgrade. So,

[00:57:45] Cameron: The AI, the AI upgrade that he’s had in his chip is doing well. But, getting back to, um, The point is, like what he’s saying, ’cause they, they just launched their, their latest LAMA 3.1, which is an open source and he’s a big supporter of open source and he for ai and he talks about why, and we’ll get to that in the later segment.

[00:58:09] Cameron: But what he’s, one of the arguments that he’s making is that he doesn’t believe the future is all about the one big overarching AI tool that does. Everything. He’s talking about AI agents that are smaller and more nimble and do, you know, one thing really, really well, that’ll be able to talk to other AI agents.

[00:58:31] Cameron: And this has been sort of the vision I’ve been talking about on, on the show ever since we’ve been doing it. That’s how I see it sort of playing out as well. I don’t think we’re going to require a source of all knowledge, like GPT is turning into the one thing that knows everything about everything. I think we will end up with millions of AI agents that have deep knowledge in a particular domain and they just talk to each other.

[00:58:59] Cameron: Your experience might be like you’re talking just to the AI, but it’s actually a million AIs talking to each other behind the scenes. And if

[00:59:07] Steve: Conceptually that’s interesting for a couple of reasons. I think the thing that is interesting to me is that the cache that it needs to carry is so much smaller if you have a specific AI about 8 bit video games from the 1980s. Does the big AI need that? Or does the big AI just need the ability to go to the AI and procure the information it requires for that moment and then trans Transport that information and you would have to assume that that would be one of the ways that we could become more efficient with the average search in terms of the computation it requires and the electricity.

[00:59:38] Steve: If it uses an internet style strategy where it finds the right path to what it needs and then comes back rather than having all of it to carry the entire information load within a one giant AI.

[00:59:51] Cameron: Yeah, like, we’ve been talking about the LUI, the Language User Interface, for a long time now and, you know, I see that is the, that’s the magic glue. That’s the, it’s like the new version of the API that enables.

[01:00:06] Cameron: AIs to talk to other AIs, because they all just talk the same language, doesn’t have to be English, but whatever the common

[01:00:12] Steve: I know what it is. Pull the

[01:00:13] Steve: plug.

[01:00:15] Cameron: they can, you know, so my AI on my phone, let’s

[01:00:18] Cameron: say it’s Apple, Apple’s AI, Or it’s just an open source one.

[01:00:23] Cameron: Cause a lot of the, you know, Microsoft has released a mini version now of their AI. There’s a, there’s a Mistral mini. There’s a Llama mini. There’s a lot of mini models that are out there. GPT’s got a mini model. If you have an open source mini model that runs on your devices and knows everything about you, And it can understand language.

[01:00:42] Cameron: It can understand what you ask it. It can then go out to, oh, you want to know a question about, uh, the latest in, I don’t know, uh, medical advancements for Alzheimer’s? It’ll go talk to the Alzheimer’s, uh, medical research

[01:00:59] Cameron: databases AI,

[01:01:01] Cameron: right? And it’ll find that information. It doesn’t need to know

[01:01:04] Cameron: everything. It’s the Henry Ford model. You know, the famous story about Henry Ford.

[01:01:08] Steve: Pick up the phone.

[01:01:11] Cameron: He was, for people who don’t know, we’ve probably mentioned on the show, but he was, uh, being sued. Somebody, a journalist had said he was stupid or something and he sued him for libel and then they were asking him a whole bunch of questions about his business that he couldn’t answer.

[01:01:25] Cameron: He said, but you don’t realize I got a phone on my desk. I don’t need to answer. I don’t need to know everything. I can just ring one of my managers. They know the answer to those questions. So it’s the same, it’s the Henry Ford model, right? The AI will just call whichever AI it needs to answer your question.

[01:01:39] Cameron: But my point was going to be. If, if one of those, or many of those AIs are Chinese AIs that are handling all of this for me, doesn’t really matter. As long as I’m not asking it questions that pertain to stuff that the CCP doesn’t want you to know about, or doesn’t want its citizens to know about, it’ll be able to answer 90 8 percent of everything that I need to know.

[01:02:04] Cameron: For those other 2%, I’ll go to the, you know, an American AI that’s happy to dish the dirt on the history of the

[01:02:12] Cameron: Chinese

[01:02:13] Steve: what will happen with the AIs. We know about the great firewall of China where Google and Facebook and some other properties are banned, uh, over there. I wonder if, and we’re quite open to having Chinese firms operate in Western markets. That’s a topic for another day, but I don’t think that, uh, America should be as open as like, well, you would let us do business in yours or, or we don’t, but.

[01:02:39] Steve: I do wonder if with the AIs, they’re going to end up with a closed shop as well, and whether they’re going to be interchanging with each other, or whether it’s going to be a little bit like what we have now with the social web and search, whether it’s going to be certain AIs in China are shut down, our AIs, but we’re open to theirs.

[01:03:00] Steve: I mean, that’s going to be an interesting political play as well as a technological play. I don’t know which way it’ll go. You’re well versed in this area. What do

[01:03:07] Steve: you think will happen?

[01:03:08] Cameron: I mean, I don’t know. I don’t know. I think AI is going to change a lot. I think the CCP is, and all Western countries too, are going to struggle with censorship. Like, let’s not pretend that we don’t have extreme levels of censorship in Western countries. We do, it just takes a different form. of, to the censorship in places like China, the sort of censorship that we have in the West around things that are politically sensitive is, they’re sort of over the window, doesn’t allow conversation of them.

[01:03:47] Cameron: They’re highly limited and we have massive amounts of propaganda.

[01:03:52] Cameron: We spend massive amounts of money and time and effort on propaganda to sort of, um, get our citizens thinking one way. Like if you talk to most people in the West, I’m talking about the USA, the UK, Canada, Australia, right? And you ask them why America dropped nuclear weapons on, dropped atomic bombs on Hiroshima and Nagasaki in August 1945, most people will probably tell you they did it to end the Pacific war against the Japanese in World War II.

[01:04:26] Cameron: It’s not true. That’s not why the Japanese ended the war. Every scholar who studied this knows that, but most people in the West still believe that to be true. And if you try and tell them different, they freak out because they’ve got 70 years of propaganda that’s been drilled into them that makes them believe a certain version of the story, right?

[01:04:50] Cameron: So we have, we have censorship, but it takes a different form. But AIs are going to, I think. Just crush all of that. Um, you, you know, it, it, it’s going to be very hard for governments to limit this kind of information, whether it’s a Western government or a Chinese government. So I, I don’t know how that’s going to play out.

[01:05:09] Cameron: Moving right along, because we’re running out of time. I want to talk about my, uh, I want to talk about, uh, Cory Doctorow’s article

[01:05:16] Cameron: on crypto. Do you want

[01:05:18] Cameron: to, do you want to kick this one off or

[01:05:20] Cameron: do

[01:05:20] Steve: was a, really great piece and the basic premise is that some of the richest people in Silicon Valley, uh, the billionaires behind, you know, Mark Andreessen and Ben Horowitz and others, uh, giving Trump endorsements For their own financial wellbeing at the cost of all of the other elements.

[01:05:41] Steve: It went pretty deep into, into crypto, uh, that crypto is, is really, I mean, I’m just trying to think of the way to describe it. Well, he obviously went through the stuff that says that crypto isn’t money because it doesn’t satisfy the needs. of what creates a currency and that it’s just a giant manipulation so that they can fund Trump so that they can get what they want and continue on their merry 1 percent ways.

[01:06:11] Steve: That was my

[01:06:12] Steve: key, that was my net outtake.

[01:06:14] Cameron: Yeah,

[01:06:14] Steve: was a pretty deep article, went on a lot of angles, but that was my

[01:06:17] Steve: overriding outtake.

[01:06:19] Cameron: You know, you know, I think he’s, he’s saying that basically crypto is a

[01:06:24] Steve: Yeah, there you go. I mean, there’s the words. Crypto’s a scam. Yeah, keep going. Tell me, Tell

[01:06:30] Steve: me what

[01:06:31] Steve: you think.

[01:06:33] Cameron: Well, you know, he talks about how, uh, crypto, you know, has been hyped up by, uh, a bunch of people, obviously, over the last ten years, and they’ve, you know, uh, uh, scammed a lot of people, getting them to invest in it,

[01:06:52] Steve: And the worst coins around it. Not

[01:06:54] Steve: so much it, but all of the shit coins that live around the fringes of it.

[01:06:58] Cameron: yeah. You know, there’s been a whole bunch of scams like NFTs that a lot of people got scammed on, different, um, ICOs, the different coin offerings that people invest in that go nowhere, and there’s been an influx of crypto money into elections. Election campaigns and it’s hard to track and it’s hard to monitor.

[01:07:21] Cameron: There’s a lot of dirty money flowing around in that sense. But, you know, the crypto, the major crypto players are trying to influence politicians in the US to get favorable treatment for crypto, to try and keep it out of regulation, uh, try and help legitimize it so they can profit from all of the investments that these, some of these venture capitalists have been making it for a very long time.

[01:07:52] Cameron: He talks about the Bezel Concept. Uh, which is sort of, uh, deceptive, uh, prosperity, uh, which is really based on a fraud. He calls it the The bezel, like M bezel, but just the bezel where you make something look really attractive, sort of a, it’s kind of like a, uh, another name for like a pump and dump really, you, you, you use your influence in the media.

[01:08:20] Cameron: And when it comes to these Silicon Valley billionaires, They’re followership on Twitter, their influence over tech media, their ability to get stories written about the things that they’re investing in and create hype cycles around it when it’s all just based on sort of smoke and mirrors, usually. He’s talking about the need for regulatory clarity in companies.

[01:08:44] Cameron: Crypto and the challenges around that. Illegal contributions by companies like Coinbase, uh, towards campaign finance, um, and, and how that needs to be monitored a lot more closely. And just about the complex interplay between economic interests, political interests, the ethical considerations around crypto.

[01:09:04] Cameron: Anyway, it’s a really great article, uh, it’s called, what’s it called? Um,

[01:09:11] Steve: campaign finance

[01:09:12] Steve: violation in US history.

[01:09:15] Cameron: That’s it Yeah, check it out on Cory Doctorow. Really worth a read. I mean, crypto is, you know, I look, I say what I’ve always said. I think there’s a lot of potential in the, the technology behind crypto, but, um, I think all of the claims that are being made about it as being, you know, it’s going to be a legitimate

[01:09:38] Steve: Never will be. It’s been poisoned. It’s the, the, the lake is poison and my view on, on it is really simple. Technology is a currency and, and all currency is a technology and all currencies have represented the technological capabilities of the day and the overriding tools that we use. You go all the way back to Ferris coins and.

[01:10:00] Steve: Uh, Cowry, Shells, to Fiat Currency, Bills of Exchange, all of those types of currencies, uh, precious metals, have all represented our technological capability. And they all have certain requirements for them to be effective. Uh, one of them is stability, which crypto doesn’t have. Uh, they need to have acceptance, which crypto doesn’t have.

[01:10:19] Steve: So it fails on about four, four of the six requirements, uh, to be a currency. It will never happen because Any country that is smart enough needs to know that it needs to have a sovereign currency so that you can demand tax in that currency, which maintains the civility and your ability to fund government projects.

[01:10:42] Steve: But here’s the thing that was so interesting in that article is they’re using the crypto scam to get fiat currency in replace of the crypto that they own. Like, and if you ask any, any, uh, Bitcoin maxi, And if you want to truly understand whether or not they believe in Bitcoin, here’s what you ask them.

[01:11:00] Steve: You ask them this one simple question, Cameron, you say, what’s one Bitcoin worth? And invariably, their answer is in US dollars. And that tells you what they believe in. They sure as hell don’t believe in crypto. Because if they did, and you ask them, what’s one Bitcoin worth? They’d say one Bitcoin is worth one Bitcoin

[01:11:19] Cameron: One Bitcoin. Yeah, yeah. Alright, well, let’s finish up, Steve, because I know you’ve got a hard out. Um, I want to talk about closed systems versus open systems, uh, and again, because I’ve believed this for a long time, and I’ve heard Zuckerberg talk about it a lot lately. Now, let me preface this by saying, I don’t trust Zuckerberg for one second, even though I like his rebranding.

[01:11:43] Steve: See? See? We always be buying into the story, don’t we? All of a sudden, Oh, you know, I don’t like you, I don’t like Zuckerberg, but he looks cool. You know, five episodes from now, you’re singing the praises of Uncle Zuck. That’s all I’m saying. Listeners, watch out for Cameron. He’s been caught off guard.

[01:12:02] Cameron: And to be fair, he’s pretty upfront in most of the interviews I’ve seen with him lately, saying, Listen, I’ve got selfish reasons for doing open source AI. Um, and his thing is, his experience has been that Facebook has really struggled, particularly in the mobile web, in the last 15 years, because the mobile web has largely been controlled by Apple, as we know.

[01:12:28] Cameron: And Apple has the Apple tax and they will allow certain things to happen on their devices and they won’t allow other things. And he feels like they’ve been hampered by Apple’s control over a closed ecosystem. He doesn’t want that to happen again with ai. And he can see that, uh, you know, if, if it’s, if chat GPT open AI is the future of ai, uh, that he could end up in the same situation.

[01:13:00] Cameron: So he’s spending a lot of money. I mean, I, I saw a, a recent interview of with him and, uh, done by Jensen Huang, CEO of Nvidia, where I think Jensen was saying that. Men now have like 600,000 H one hundreds, uh, Nvidia

[01:13:19] Steve: valued at 10 grand each, aren’t they?

[01:13:20] Steve: Got

[01:13:22] Cameron: I’m not sure. 10, something like that. 50, maybe a hundred. I’m not sure which, how much the H one hundreds are worth, but it’s a lot.

[01:13:29] Cameron: So they’re spending billions and they’re making an open source product. Now, one of the things that, you know, often hear people who are not paying close attention to this about AI is, Oh, all the rich people are going to have AI and it’ll be controlled by a handful of companies like, uh, you know, Microsoft, Google, OpenAI, whatever.

[01:13:49] Cameron: And it’ll just be the mobile web or the web all over again. You know, people like us that are old enough to remember the mid 90s. We did think that the web was going to be this democratic place where anyone would be able to do anything. And that is

[01:14:03] Cameron: true. No, well, it is true. I can build a website. I can write whatever I want on my website.

[01:14:11] Cameron: Uh,

[01:14:12] Steve: You could have,

[01:14:13] Cameron: Just but you could, you could also stand on a soapbox on the corner and just

[01:14:16] Steve: scream to the top and you’d be just as effective as building your own website, right?

[01:14:20] Cameron: Well, that’s true. But I couldn’t, I couldn’t really

[01:14:22] Cameron: print my own newspaper, um, and, you know, make it accessible to You’ve got a people or billions

[01:14:28] Steve: still slim, But you’ve got a chance to break

[01:14:30] Steve: through in the system and be

[01:14:32] Steve: known.

[01:14:34] Cameron: But companies spent hundreds and hundreds of billions of dollars to basically make sure that audiences tended to go to their properties and, and not anywhere else. They, they sort of dominated the space. And people, uh, you know, are reasonably concerned that the same thing’s going to happen with AI. But I don’t think it is.

[01:14:58] Cameron: And I do think open source is going to be big. You’ve got companies like Mistral. You’ve got companies like Meta. We don’t really know how the Chinese AI companies are going to play out, but they’re going to be players in this space as well. You are going to have a proliferation. And, and Zuck’s vision is that, Hey, it’s funny because he uses Microsoft as an example of open source.

[01:15:20] Cameron: I never really thought of Microsoft as open source, but you know, when I was at Microsoft 20 years ago, the battle was between Microsoft and Linux, Linux being the open source alternative to Windows. And the Microsoft ecosystem. But Zuck’s view is that, yes, but anyone could write an application that would run on Windows, and Microsoft wouldn’t stop you from having an application that ran on Windows.

[01:15:48] Cameron: I mean, Marc Andreessen Might have something to say about that because we made it difficult for Netscape to run on Windows in the late 90s. You could, for all intents and purposes, unless you were in Microsoft’s crosshairs like Netscape were, because it was an existential threat, you could write an application and you could sell that application and Microsoft wouldn’t put a tax on you for selling your Windows application.

[01:16:15] Cameron: It didn’t have a marketplace that you had to get permission

[01:16:18] Cameron: to get your thing in. You could distribute it.

[01:16:22] Cameron: So it was open source in that, it was an open

[01:16:25] Cameron: system,

[01:16:26] Steve: you could get your, it wasn’t an five inch disc and upload that piece of software to

[01:16:30] Steve: run.

[01:16:32] Cameron: Apple’s is a closed system, and for good reason, Apple did it to try and prevent hackers and malware and all those sorts of things, and they go, okay, well we have to manage this and that. If that comes with a fee, it comes with costs, and you have to share your revenue with us because we’re managing this system.

[01:16:50] Cameron: So it’s a safe and a secure environment for users. I’ve never had a virus on my iPhone or my iPad. Never had to worry about it. Don’t, you know, I don’t really give a much thought about it, but he’s talking about a world where there are billions of AI agents that are built on top of open source AI models that.

[01:17:15] Cameron: He is building, along with others, he’s gonna build them, he’s gonna make them available, anyone can take them, you know, check the security or improve upon them, make them bigger, make them smaller, dedicate them to subject X, Y, or Z, they can be hosted on a, on AWS or on a Google data center or on your own data center, or you can run them with a small footprint on your phone or your iPad or one day your watch or whatever it is.

[01:17:47] Cameron: So Meta are putting God knows how much money, billions and billions, and probably hundreds of billions over the next few years, into a future where we have extremely powerful AI tools that are open source. And I think that is gonna really be a hugely

[01:18:11] Steve: does he leverage that? Does he end up renting out infrastructure like an AWS, but it’s, uh, it becomes, you, you, you subscribe

[01:18:19] Steve: to the meta AI engine to create whatever you need,

[01:18:22] Cameron: I think there’ll be revenue opportunities for them in doing that and probably providing hosted opportunities for businesses that don’t want to do it themselves. They’ll have revenue streams in there, but I don’t think he really knows. The sense that I get from the interviews with him right now, either he doesn’t know yet or he’s not revealing it yet.

[01:18:45] Cameron: He probably has ideas, but it’s just right now, the battle is for. Getting yeah. having

[01:18:52] Cameron: the tools available

[01:18:54] Steve: Have it, have the powerful tool, and then work out the revenue stream ladder. It’s a little bit like 1999, when everything was free, and it’s like, well, we’ve just got to build it, get people using it, and then we’ll get the revenue model. Which both Google did, Facebook did, all of them found their revenue models.

[01:19:10] Steve: Albeit, they went to the horrible advertising model, which is You know, the, the original sin of the

[01:19:16] Steve: internet, you know, don’t like it, but, uh,

[01:19:19] Cameron: but yeah, so imagine a world where we

[01:19:21] Cameron: have, uh,

[01:19:23] Cameron: open source AI, millions of people working on it, millions of AI agents working on improving it. And then when we figure out nanotech, we have millions of open source AI agents building the plans for open source nanoreplicators, nanofabricators, giving us the plans for open source robots.

[01:19:48] Cameron: I’m reading this book and I’ll wrap up with this because I know you’re going to go, um, Radical Abundance. Hey, Eric Drexler’s book from about 10 years ago. He’s the father of nanotechnology. 1986, he wrote.

[01:20:00] Steve: regime. Radical

[01:20:01] Steve: abundance.

[01:20:02] Steve: Got it.

[01:20:04] Cameron: Yeah, in 1986, he wrote

[01:20:05] Cameron: Engines of Creation, which was sort of the first book on nanotech, and, uh, his last one, which is 2013, Radical Abundance, he has this model, which I thought was fascinating. He said, I want to, I want you to imagine how a car is going to get built in the future. It’s being built in something the size of, uh, Household garage.

[01:20:24] Cameron: And if you look at the garage, you see some platforms that go up and down and a whole bunch of robotic arms that are assembling things like you would see in any sort of car factory, but the back wall. is made up of shelves that have devices about the, they’re boxes, they’re like the size of microwaves, some bigger ones about the size of a washing machine, some smaller ones the size of a household microwave, all up the wall.

[01:20:50] Cameron: And if you look inside of those, they have smaller versions of the garage with smaller robot arms that are assembling components. When they assemble their components, they get pushed out, of a door in the machine where the bigger arms pick them up to assemble them together. But inside the microwave in the washing machine, there’s a smaller box with smaller robot arms that are making smaller

[01:21:11] Cameron: parts.

[01:21:12] Steve: It’s like the, the entire thing of fractals. I’ve been really watching a lot about fractals lately and all manners of biology have certain patterns which are unavoidable. I mean, if you look at. The image of a brain and a satellite map of America or anything, it looks like a brain, you know, where all the cities are and the neurons and the lights and where things are flashing, which, where there’s more activity, which is, uh, the same as in the brain where there’s nodes and neurons going through to an MRI scan.

[01:21:41] Steve: It looks really similar. Large language models look really similar to a brain. And the idea of, you know, Fractals, uh, the way we create stuff very, very similar all the way through. And it’s almost like we have this inevitability that we will replicate nature in the way things are built, even at the nanoscale and then nanoscale all the way up to manufacturing a car.

[01:22:02] Steve: It seems like there’s this fractal orientation. I don’t know if he has that in the book, but it,

[01:22:05] Steve: it, it seems really interesting.

[01:22:07] Cameron: He doesn’t, he doesn’t mention fractals, but that’s great. I was thinking of matryoshka dolls, but fractals is a better, more exotic explanation. He says, To the right, picture yourself standing outside the final assembly chamber of a large product APM system and looking in through a window to view the machines at work in a space the size of a one car garage.

[01:22:27] Cameron: To the right, you see an exit door for products ready for delivery. To the left, you see what look like wall to wall, floor to ceiling shelves. Which, with each shelf partitioned to make a row of box shaped chambers. In the middle of the garage sized chamber in front of you is a moveable lift, surrounded by a set of machines.

[01:22:48] Cameron: And so, basically, the, the The little microwaves have little ones, have little ones, right down to molecular components that it’s building from scratch based on, you know, I have some inputs, which is, you know, bottles of nitrogen, oxygen, hydrogen, carbon, whatever it is, that probably it’s getting from deconstructing last week’s car model that you got bored with.

[01:23:12] Cameron: So it’s building you a new one for today. And it, it builds the molecular components and feeds them into the next box, which builds, you know, adds them together, which fills in the next box, which adds them together, assembles

[01:23:22] Cameron: them all the way You’d just scoop up some dirt and get everything you need.

[01:23:27] Cameron: Oh, well, you need to sort the bits from the

[01:23:30] Steve: Robots are going to do that. Robots will do everything.

[01:23:32] Steve: yeah.

[01:23:34] Steve: because

[01:23:36] Cameron: So that’s his view for this, but you know, I believe that we have a chance. I, I, I don’t know what probability was assigned to this, but we have a chance where we will have open source AI, open source robots, open source nanotech.

[01:23:50] Cameron: And that will lead to sort of this, um, version of the world and a version of an economy I don’t think we can really envision right now.

[01:24:02] Steve: it removes the scarcity model, which all of the, you know, whether it’s capitalism or communism, they’re all based on the fundamental principle that there’s scarce

[01:24:10] Steve: resources. And this kind of circumvents that to an extent.

[01:24:16] Steve: There will always be some form of scarcity, but the scarcity, there might be different types of scarcity, and I don’t know what they are.

[01:24:22] Steve: You know, scarcity of attention, scarcity of fame, scarcity

[01:24:25] Steve: of recognition. I don’t know.

[01:24:27] Cameron: Kurzweil in The Singularity is Getting Nearer

[01:24:29] Steve: I just the law of accelerating returns.

[01:24:32] Steve: Yeah,

[01:24:33] Cameron: He talks about how the IT

[01:24:34] Cameron: industry, the computing industry, has built on the law of accelerating returns over the last 100 years because each generation of technology helps you build the next generation of

[01:24:43] Cameron: technology, but that most other fields don’t benefit from the law of accelerating returns.

[01:24:50] Cameron: You know, this, this year’s, um, crop of wheat isn’t going to make next year’s crop of wheat exponentially better. But he said that as these sorts of, this is the, I’ll read this one paragraph and I’ll let you go. What makes the LOAR so powerful for information technologies is that feedback loops keep the costs of innovation lower.

[01:25:12] Cameron: And as artificial intelligence gains applicability to more and more fields, the exponential trends that are now familiar in computing will start to become invisible in areas like medicine, where progress was previously very slow and expensive. With AI rapidly expanding its breadth and capability during the 2020s, this will radically transform areas we do not normally consider to be information technology, such as food, clothing, housing, and even land use.

[01:25:42] Cameron: We are now approaching the steep slope of these exponential curves. That, in short, is why most aspects of life will be getting exponentially better in the coming

[01:25:52] Steve: that it’s the idea that everything becomes an information technology business. And once we started to use computation in many different industries, we got this horizontalization where things go across. And it actually is Easier to see that that becomes possible. And especially once you start to organize things at the nano and atomic level, then everything becomes organizing things at an atomic level.

[01:26:15] Steve: So then all of those industries have that positive feedback loop and the AI just becomes the scaffold to use a term that we’ve already used today that creates that possibility.

[01:26:26] Cameron: That is Futuristic, Episode 29. Thank you, Steve. And thank you, Martin Murray, for coming on. That was fascinating. Really appreciate your time,

[01:26:33] Steve: Thanks so much, Cam.

​In this episode of The Futuristic, hosts Steve Sammartino and Cameron Reilly explore cutting-edge topics in technology and culture. They dive into AI’s role in creativity, discussing AI-generated music compared to human-made tunes, and examine the rise of local micro-AI models like OpenAI’s GPT 4.0 Mini. Steve talks about the transhumanist movement and ethical concerns over chip implants in humans, while Cameron shares his latest AI-assisted coding projects. They analyze SpaceX’s Starship launch and Elon’s vision for multi-planetary life, as well as nanotech breakthroughs in cancer treatment. The episode also covers the revolutionary potential of Apple’s Vision Pro in creating immersive entertainment experiences and its applications in various fields. The hosts end with an overview of technological advancements and the opportunities they present.

00:00 Introduction and Welcome
00:14 Corporate Critique and Personal Reflections
02:33 Transhumanism and Media Manipulation
07:26 Exploring Assembly Code and AI Agents
12:40 Coding Challenges and Subscription Creep
22:42 GPT-4o mini drops
29:06 RunwayML Gen 3 alpha  
40:54 Orson Welles and the Future of Filmmaking
41:48 Starship Super Heavy launch into space 
45:41 Debating Multi-Planetary Species and Space Exploration
50:27 Nanorobot with hidden weapon kills cancer cells ; Self-assembling and disassembling swarm molecular robots via DNA molecular controller
01:01:25 AI in Music Production: A New Era; Rick Beato’s critique of modern music  ; Music producer Dan McClellan tests Suno and Udio 

01:14:29 Apple Vision Pro: A Game Changer in Entertainment

FUTURISTIC #28

[00:00:00] Cameron: Just have to be different. Welcome to The Futuristic Episode 28, I believe. We’re recording this on the 19th of July, 2024. Mr. Sammartino, Mr. Steve Sammartino, won’t you ever quit? You’re back.

[00:00:20] Steve: don’t you make me sick with your fraudulent behavior. Ooh, ooh, Mr. Sammartino, talking to corporates, tell them how to do it. Ooh, ooh, Mr. Sammartino, you gotta pretend to care when you’re selling them things. What you believe is what they selling, make you think that buying is rebelling. That was a little bit of Zack DeLaRocca, Rage Against The Machine.

[00:00:46] Cameron: I love it, love it. With Del the Homo Sapien mixed in.

[00:00:51] Steve: Yes.

[00:00:52] Cameron: How you doing,

[00:00:53] Steve: Dels! It was DeLaRocca and Del the Homo Sapien, we double Delled.

[00:00:59] Cameron: Double Del. Della, in Italian, of course, meaning of the, yeah, Della Rocca. Of the, of the rock, Zach of the rock, as he was, uh, and is, I lost track of what he’s up to these days, but huge rage fan.

[00:01:18] Steve: I’m a huge, I listen to them more than any other band. And, you know, I feel like I’m a fraud. Because basically, I sing those lyrics with such passion, and then take big checks from corporations. But I always can come back to say it’s not my fault. Cameron told me I’m just the accumulation of molecules temporarily and all of my thoughts, I have zero control over them.

[00:01:44] Steve: So it’s amazing. Thank you for that get out of jail free card.

[00:01:50] Cameron: I, one of the, um, one of the, uh, motivations for me leaving the corporate world, leaving Microsoft 20 years ago, holy shit, this month, this, like two weeks ago. Wow, I missed that anniversary. Uh, it was the beginning of July 2004 when I left the corporate world and, um, part of it was listening to Rage Against the Machine, quite honestly.

[00:02:12] Cameron: It is part of my conversion to being a lefty, uh, was listening to Rage Against the Machine. Anywho, Uh, we’re not here to talk about that, Steve, uh, we’re here to talk about technology, the future. Uh, the future is now, what is happening in your life vis a vis the future, Steve.

[00:02:33] Steve: Had a spot this week on the project. For anyone who doesn’t know, there’s this thing called Free to Wear TV, which has 17 people watching it across Australia. Yeah, over the The waves, the airwaves, and they rang me that day and said, look, we need someone to give us a synopsis on what’s happening with, uh, the transhumanist movement, using technology to enhance, uh, physical and mental capabilities and, uh, Longevity, Escape, Velocity.

[00:03:11] Steve: And they asked me a number of questions. I did a talk to camera. They came to me and they had someone coming on who had, which is a bit of an old trick now, had a few chips installed under his skin. And it’s so interesting. They did two things. First, they asked me a whole lot of stuff and questions about the topic.

[00:03:27] Steve: And then when I was watching Waleed Ali, uh, talk about it on the, uh, On the preamble where they had some visuals and then cuts to me doing some soundbites. He stole all my good stuff and put it as his words. They’ve been merging with the machines and a whole lot of nice verbiage I’ve written about and said, anyway, I guess that’s the price of getting coverage.

[00:03:46] Steve: Uh, and then they wanted me to say, Can someone with chips installed in their body hack into someone’s phone or computer? I’m like, no, not at the moment. No one can. And they’re like, yeah, but if, and they were just trying to get this sound bite. They asked me the same question 10 times and I said, look, it’s not going to happen.

[00:04:07] Steve: I’m not going to tell you something that isn’t the case. And so you could see when they did the edit that it was, uh, and shout out to all the people from channel 10 that were tuning in, uh, you could see what they were trying to get. And then they had this guy on there. He was a classic charlatan, uh, you know, in my view.

[00:04:23] Steve: People are just doing this.

[00:04:24] Cameron: Do you remember?

[00:04:26] Steve: No, I can’t remember.

[00:04:27] Cameron: The reason I ask is because 19 years ago, uh, in the early days of my podcasting, I went and did a podcast with a guy in Melbourne that had a chip inserted in the webbing between his thumb and his forefinger that he was using to unlock his door. Just wondering if it’s the same guy.

[00:04:46] Steve: Yeah, I’ll bring him up. Here it is. Here. I’ll just find, I’ll say his name.

[00:04:51] Cameron: trying to find my archive podcast to see if I can

[00:04:54] Steve: His name’s Leno. L-E-N-O-N-O-E. And, I mean, Transhuman, he’s got his, his subtitle there, uh, what was interesting was that he, asked me in the interview, which is during the day, and then they had my spot with voiceovers and visuals, and then they interviewed him. And, you know, what I found was interesting was that he said he could hack into something with what’s in his phone, and they said, can you do it?

[00:05:24] Steve: And he was, Like he turned on his phone. He had RFID and NFC chips in him, right? Which is, you know, a lot of people have done. Yeah, you can open the door. Congrats, whatever. Uh, and I said, well, he can’t hack into anything. And then he was trying to infer that he could, and I said, well, then how do you do it?

[00:05:40] Steve: If you scan, he said, I could scan with your card and then get your card and do a copy and, and a URL redirect. I’m like, well, yeah, you’re still going to need something to hack into it. Whether it’s, he said, well, I’d use my phone. So he was trying to make out. As if he could hack in through something that was in his body, when he clearly couldn’t.

[00:05:59] Steve: And they kind of fell for it. Um, but the most interesting bit for me, and it circles back to the attention economy, where that is the most valuable resource in the global economy now. The biggest companies, you know, are based on selling attention. Uh, they asked him, so why do it? And he said, well, why not?

[00:06:17] Steve: But his real answer was, so I can be on TV. So he just didn’t want to say that.

[00:06:24] Cameron: Yeah.

[00:06:26] Steve: Like, you know what I mean? Like I get annoyed with someone who just does something for attention rather than creating some value, you know, anyway, that’s my view. That’s my whinge and that’s it. And that’s my week.

[00:06:37] Cameron: Uh, dude, sounds like one of my conversations with my, uh, TikTok, a son, like, don’t you feel like you should be adding some value? He goes, well, entertaining people is value.

[00:06:49] Steve: That’s true. I don’t disagree with

[00:06:50] Cameron: can’t argue with that.

[00:06:52] Steve: Yeah. And, and, and this other guy is opening people’s minds potentially to future possibilities of, you know, it’s tongue in cheek when, when I say that, um, but I think now. Uh, what can generate attention, the bar is a lot lower now. And that might be a function of the fragmentation of possibility and, uh, The desire to create attention and the fact that everyone’s got a megaphone and everyone’s the CEO of their own personal media corporation now, um, that, that you get a lower bar.

[00:07:26] Steve: Um, two other really quick things I did this week is I was in Warrnambool doing a presentation and it was the only time I’d ever had a room of 300 people where only one had used or heard of ChatGPT, which was interesting. I’ve been in other rural locations and it’s usually 50 50. That was a real surprise.

[00:07:42] Cameron: Yeah.

[00:07:43] Steve: So again, we do live in a bit of a bubble, just a bit of a reminder. And then the other thing I’ve been doing this week is trying to understand assembly code. Um, I want to learn how to make a video game where I can do the assembly code and the controls to do like a, a simple video game, not, you know, a replication using Python or something, but in the traditional code, I just, I want to understand The deeper layers of it, because code is such an abstraction now that we’re up to the level of natural language processing.

[00:08:15] Steve: I want to really get down to the ones and zeros and off and on switches. I’ll just be mucking around with understanding assembly code, using ChatGPT to understand assembly code. There you go, full circle.

[00:08:26] Cameron: Wow. Well, speaking of which, what is assembly code exactly? Let’s go to ChatGPT and see what it has to tell us. Assembly code is a low level programming language that is one step above machine code. It is specific to a computer architecture and is used to write programs that are directly executed by the CPU. Here’s a breakdown of what assembly code entails. 1. Low level language. Assembly language is close to the hardware and allows programmers to write instructions that the CPU can directly execute.

[00:09:02] Cameron: Unlike high level languages like Python or Java, assembly requires detailed knowledge of the computer’s architecture. Stop it there. So why would you want to do that, Steve?

[00:09:12] Steve: Oh, cause I’m, I’m curious to get more of an understanding of how things work at a deeper level. I’ve been thinking about that a lot. And I think the evolution of large language models has made me think more deeply about how things work, because I think we’re getting to increasing levels of abstraction when no one really understands anything.

[00:09:31] Steve: And I just thought it’s, it’s, it’s almost like growing your own vegetables, but in a computational sense and going, going back to the seventies. And I’ve got the big arcade machine here. Which doesn’t, runs on a, runs on a CPU, so it just runs on a, on a simple laptop and screen. Um, but I was just interested in, in how much memory you need and how much RAM and how it can work and how it can fit it all in there.

[00:09:56] Steve: Assembly code takes up far less space, so you can do a lot more with a lot less. So there’s just something interesting about it. I don’t know. I was just curious. It started off with what code a traditional 8 bit arcade game is written in. And then I just kept going down, you know. And then I wasted two days when I could have been generating income for my family.

[00:10:14] Steve: Sort of understanding that.

[00:10:17] Cameron: It reminds me, you’re talking about abstraction layers, I was listening, uh, watching actually uh, an interview with Eric Schmidt recently, um, was the CEO of Google for 20 years, give or take, and um, they were talking about AI among other things, and um, he was saying that the, Like he, what he sees is the near term future for AI is we’re going to have, you know, super advanced AI very quickly, AGI, etc, etc.

[00:10:47] Cameron: Um, he, and he was talking about AI agents, and we’re going to have AI agents in everything, and then those, you know, AI agents are going to talk to other AI agents. He said then what’s going to happen is the AI agents, We’ll realize that using English or even code, human created code, to talk to each other is inefficient.

[00:11:08] Cameron: So they’re going to create their own language to talk to each other, which humans won’t be able to understand. And he said, and that’s the point where we have to pull the plug.

[00:11:17] Steve: Right.

[00:11:18] Cameron: said, as soon as they start talking to each other in languages we can’t understand. understand, and there’s no transparency about what the agents are talking to each other about, pull the plug.

[00:11:28] Steve: It’s really interesting, my father and I, when we used to speak Italian sometimes, if we wanted to talk about someone, we would go to Italian. So I just feel like they’ve worked out that if you go to another language, you know, and you do it with your kids as well, when you, when they’re really young, you use complex language that they haven’t quite got the vocabulary for yet.

[00:11:47] Steve: Um, and I, and I, the sentiments there of Schmidt. Uh, and that’s a first principle. The first principle is be worried if you don’t understand. Now, it doesn’t mean necessarily that the computers have nefarious intentions,

[00:12:01] Cameron: Hmm.

[00:12:01] Steve: but if you can’t understand it, um, then,

[00:12:05] Cameron: Then maybe they

[00:12:06] Steve: then, well, well, then, well, then you won’t know.

[00:12:08] Steve: So, so the first principle is make the, the, uh, yeah, the, the reparations.

[00:12:17] Cameron: Sorry, I’m just changing my video. My video was flashing at me for some reason, so I’m turning it off and on and

[00:12:22] Steve: I’m getting a thousand text messages, which is fine.

[00:12:25] Cameron: now I’m all over the side here, which is weird. Um, yeah, turn your damn phone off, Steve. What are you, amateur? What are you doing?

[00:12:34] Steve: Yeah, I am. If there was anything that I am, amateur is certainly it.

[00:12:40] Cameron: Um, Well, Steve, uh, I’ve been coding a lot, um, in the last couple of weeks, so, um, without boring people with the details, Spotify, uh, fucked me severely about a week ago. Basically all of my, all of the MP3 hosting for all of my podcasts, um, is, sits with Spotify now, one of their subsidiaries, Megaphone, and there was a whole bunch of legacy links there from company, a company they acquired, an Australian company called Wooshka, a couple of years ago. And they arbitrarily turned all of that code off last week and, uh, I had about 500 episodes across my many, many shows that I produced just went dark

[00:13:23] Steve: The Sammartino Method. The Sammartino Method podcast is gone.

[00:13:27] Cameron: yeah, I had to fix that one. That was the big one I had to fix. That was the first one I went to fix. But, um, so that, so, and, uh, you know, it’s, had to update 500 posts.

[00:13:38] Cameron: I’m still in the process of it, and each post has a bunch of different mp3 links, because a lot of my shows have lots of different feeds for different levels of subscribers and whatever, and it was taking me A minute and a half, I timed it to update every post, so multiply by a thousand, that’s a lot of time.

[00:13:58] Cameron: Uh, so I wrote some code to automate a large chunk of it. I still have to do 50 percent of it, but I wrote some code to automate 50 percent of it. And that was a big coding project late last week. Thank you to GPT. I tried using Claude, 3. 5, Sonnet. It was a disaster, partly because I’m not paying for it, so, you know, I ran out of, uh, free, um, chats very quickly.

[00:14:22] Steve: Well, you got to worry about subscription creep, Cam. Subscription creep is a big financial issue, I think.

[00:14:29] Cameron: I’ve been cutting off all my subscriptions in the last week or so, actually. All streaming,

[00:14:34] Steve: I did a subscription audit. Are

[00:14:36] Cameron: Yeah, that’s what I’m doing too, cutting stuff off. Um, but anyway, I, I got the code working and, um, oh, I was impressed. Like now I, I just, I run some code and it says, what’s the link you want to update?

[00:14:48] Cameron: And I give it to it and it does more than 50%, probably 90 percent of it is automated. I need to finish the last 10%, which just seemed too hard to automate. Um, but I, again, I was impressed that I could write code to do that, you know, pre AI, no fucking way I would have known how to do that. And in a less, um, complicated sense, I’ve been using it more and more for film and music recommendations.

[00:15:15] Cameron: Like, uh, give me a cult film that I might like. Or, um, you know, give me an artist like Lou Reed that I might like. This video is right. Um, and it’s been doing a pretty good job. I actually discovered via this, uh, a really great film. And a really great band that I’d never heard of just by getting GPT to give me some recommendations.

[00:15:45] Cameron: So that’s been fun.

[00:15:46] Steve: you going to keep them a secret or are you going to tell us all what they are?

[00:15:50] Cameron: Uh, well, now that you’ve insisted, I will happily tell you, the film is a 1976 film called Stingray, written and directed by a guy called Richard Taylor, who, um, I think he did Fright Night after that, but not known for much, but it’s a, um, uh, actually 1978 according to IMDB. Prim it’s a low budget car chase shootout film.

[00:16:24] Cameron: I think probably in the vein of, uh, you know, your, um, uh, Cannonball Run slash, uh, Smokey and the Bandit era comedy action. Premise is great though. Starts off with a drug deal going down in an alleyway in the middle of the night. Two guys with the coke, two guys with the money, goes awry. The guys with the coke kill the other guys who were trying to.

[00:16:50] Cameron: But, you know, uh, betray them, they planted a tracker on them. Um, and then the, the, the two guys that are still alive, have the money, have the coke, they take off, they hear cops coming, because there’s a big shootout, they hear cops, they drive into a car yard in the middle of the night and deposit the briefcase with the money and the drugs in a Stingray convertible behind the back, behind the seats, you know, the, the boot of these sports cars.

[00:17:15] Cameron: was like you’d flip the seat down and then they go back then they get arrested by the cops anyway cops find them get arrested can’t pin them with anything because they’re not holding anything they get out a day later they go to the car yard to see two young guys buying the car and driving out the lot so they chase these guys down and the guy the two young guys uh like college age guys in the stingray think these guys are wanting to race so they start fanging down the streets you A cop car pulls out to pull him over for speeding, does pull him over for speeding, uh, on a, on a country lane somewhere, and the gangsters come up behind him and mow the cops down.

[00:17:55] Cameron: But the cops had called in that they were chasing a stingray. The rest of the cops think the kid’s in the stingray, So now the cops are after the kids in the Stingray, the gangsters are after the kings in the Stingray. But the best thing is, the gangsters, the leader of the gangsters, is a woman who’s dressed up as a nun to disguise herself from the cops.

[00:18:18] Cameron: And she’s this real hard ass, fast talking woman. Strong girl boss, as my wife called her, just a great premise. The acting’s sort of a little bit dodgy, very low budget, but real stunts, real car chases, real,

[00:18:36] Steve: and people dressed up as nuns. I think if you’ve got a stingray and someone pretending to be a nun, I feel like. These are the ingredients of greatness. I’ve always said that, Cameron. I’ve always said that.

[00:18:50] Cameron: It’s only got a 5. 6 rating on IMDb, but I think

[00:18:53] Steve: Well, what would IMDB know? What would IMDB know? Let’s be honest about that.

[00:18:58] Cameron: I got to say, my wife and I enjoyed watching this more than we’ve enjoyed watching much lately. Cause it was, it’s just, there’s something about

[00:19:04] Steve: trying to tell me that this is better than some animated movie that’s been made 76 times? Is that what you’re trying to tell me? That real people in real situations is better than a, yeah, an Avengers film? Well, I mean, how dare, I mean, what were you thinking?

[00:19:19] Cameron: you get the sense of like, it’s made for driving kind of a movie from the late seventies, you know, and it’s, it’s just fun. You can tell that who the people making it were just having fun. Like it was just like, fuck you. We get to make a movie. This is awesome.

[00:19:32] Cameron: The music that I discovered was, um.

[00:19:34] Steve: that fun, having fun, right now everyone is thinking about What everyone else wants to see instead of what they want to make. Like that, that for me is what is what’s missing culturally right now. That’s all I’m saying. I mean, that’s what the Avengers is. It’s like, what can we sell rather than what’s worth making?

[00:19:53] Cameron: Yeah. Well, the music I discovered, uh, was the Jim Carroll band, you know, Jim Carroll, the basketball diaries. You ever seen that Leonardo DiCaprio film

[00:20:01] Steve: I haven’t seen it. I know of it, but I haven’t

[00:20:03] Cameron: mid nineties, based on an American poet, Jim Carroll, who was also a drug addict. Um, and, um, it, it turns out I never knew this.

[00:20:12] Cameron: I knew who Jim Carroll was. I’ve seen the film, uh, didn’t realize that he had a punk band. Uh, called the Jim Carroll band. ChatGPT got me onto them. Uh, listened to it. Fantastic. Sort of a cross between Iggy and the Stooges and Lou Reed. Cause again, like Lou, he’s a poet that started, like Leonard Cohen, a poet who started writing rock songs and putting his poetry into it and, you know, like Lou, a lot of drugs and, you know, sort of, you know, dirty, dodgy lifestyles and that kind of stuff, but with a Stooges y kind of feel to it, really, really good stuff.

[00:20:49] Cameron: Anyway, so. But yeah, I just go to ChatGPT and I go, Hey, you know, tell me, I like this cult film. I like these sorts of films. Give me some recommendations of things that I may not have seen because they’re not very well known and that are available on Tubi, which is where I watch a lot of these cult films now.

[00:21:07] Cameron: Do you stream Tubi? Use tubi. tv,

[00:21:10] Steve: No, I

[00:21:11] Cameron: T U B I, it’s free. It’s ad supported, they put in ads every 15 minutes or so, um, and it just has a great collection of, you know, B rated, D rated movies from the 20s onwards, my kind of stuff, you know, schlocky, B, D, C rated, crappy, straight to video movies from the 80s and 90s, which is, uh, 70s, which is my favorite kind of films usually, because they’re just, again, fun.

[00:21:41] Cameron: Anywho,

[00:21:42] Steve: I like, it’s funny because every recommendation engine that exists. Exists because you need that quantum of support for it to work financially, that kind of original sin of the internet where you need a lot of traffic. And there’s something beautiful about asking an AI for things that are quirky and nuanced, because even if you Googled that.

[00:22:04] Steve: You’ve still got to get someone who’s going to give you quirky and nuanced stuff that’s still in their top 10. So by default, it’s very, very difficult to find these types of things. Whereas an AI engine that can scour the entirety of the internet can find something that is, you know, a little island off to the side that the way search is structured just won’t find.

[00:22:26] Cameron: and you can say to all, well, tell me about it, what’s good about it. This director, Richard Taylor, what did he do? You know, who’s in it? What have they done? You can, you can have a conversation with it,

[00:22:36] Steve: You get a flavor profile for what culturally what you’re looking for. Yeah, that’s why

[00:22:42] Cameron: well, tech news, Steve, moving on to that, uh, just today. OpenAI, OpenAI, uh, announced, released GPT 4. 0 Mini, uh, according to their press release, the cost per token of GPT 4. 0 Mini has dropped by 99 percent since Text DaVinci 003, a less capable model introduced in 2022.

[00:23:12] Cameron: So they’re talking about the massive Uh, efficiencies that they’re starting to get, uh, with some of their newer models. This is, they say, their most cost efficient small model. We expect GPT 4. 0 Mini will significantly expand the range of applications built with AI by making intelligence much more affordable.

[00:23:35] Cameron: Then they go on to talk about its, uh, context windows and how it’s ranking on the MMLU and the NLMSIS leaderboard. Uh, There’s a, there’s a big explosion of Mini. Have you played with any of the Mini models out there, Steve?

[00:23:50] Steve: I haven’t played with it, but it’s kind of interesting. And to me, it’s analogous. To the idea of six cylinder cars. And we said, you know what? They use a lot of petrol and this idea of AI and energy really having this symbiosis and having similar patterns as well, um, comes to the fore where you need to say, how can we become more efficient and, and really give us the result we need.

[00:24:18] Steve: Do we really need to carry all of this weight and have all of this capability where large parts of the search, you’re just carrying all this extra weight that you don’t need. So I actually found it really interesting, uh, to align this thinking to the fossil fuel era and transportation generally and heating houses and looking for more efficient ways to do things.

[00:24:38] Steve: And we’ve read all of that, that stuff as well, not just on the cost. Cause I, in many ways, cost and efficiency and energy usage are kind of the same thing within this computational context. I mean, you’ve got to buy the hardware of course, but if you use less of it, then you lose less energy, which means it’ll cost you less.

[00:24:56] Steve: So for me, that was the first thing that kind of popped in my mind. I thought, here we are again, And here we are following a similar path in a different technology, and I haven’t really used them, so, but I’d be really interested to see, you know, if it’s dropped by 90 percent of the cost, then the question is, okay, do we Do we still get sort of 80 percent of the result?

[00:25:20] Steve: What is the ratio of cost reduction versus the output? And I haven’t played with it enough to understand that yet. Do you know?

[00:25:27] Cameron: I know from the benchmark reporting that they did in their press release today, they say GPT 40 Mini is better than other small models of reasoning tasks involving both text and vision, scoring 82 percent on MMLU, a textual intelligence and reasoning benchmark, as compared to 77. 9 percent for Gemini Flash and 73.

[00:25:50] Cameron: 8 percent for Claude Haiku. So they’re comparing it

[00:25:54] Steve: They’re comparing it to four cylinder cars to four cylinder cars. I’m interested to know, how does it go against the big V8? That’s actually what I’m really interested to know.

[00:26:03] Cameron: it’s not going to be as good, or they would be telling you that it was, obviously,

[00:26:08] Steve: Yeah, right. Yeah. You choose your, you choose your measurement parameters based on your capability, right. And where you can win,

[00:26:14] Cameron: They’re saying it outperforms ChatGPT 4 on chat preferences, in the LMSIS leaderboard, but, you know. Anyway, the bottom, like,

[00:26:25] Steve: but that’s important. That’s a good point because you got to remember that the models get better and better if, if this small version is better than, you know, a couple of models previously that, that, that’s significant. And they should talk that up even more, I think.

[00:26:39] Cameron: One of the things that we’re seeing happening is not only the explosion of course, of uh, open source models, but the whi which are performing really well, but the explosion of mini models that, uh, faster and cheaper don’t quite perform as well as the top end models. But ostensibly we’ll be able to run locally on small devices eventually.

[00:27:05] Cameron: Um, you know, in theory that Microsoft launched one too, just in the last week or so, that apparently can run locally on phones. I was going to set aside some time to download it and install it on mine, but then, um, my Spotify crisis happened. But you know, it’s, it’s just, we, you know, we saw the Apple announcement.

[00:27:26] Cameron: Um, With Apple intelligence, this idea that in the next year or so, we’re going to have AIs running locally on some of our devices without having to go and talk to anything on the cloud in terms of stuff that it could do locally with local information, your information in particular, you know, is going to be a complete game changer.

[00:27:51] Cameron: I

[00:27:52] Steve: Well, and, and you would suggest that that’s why you need this small model so that we can run on edge computers and our own devices. I,

[00:28:01] Cameron: I think that’s part of it. And, but part of it is also as they’re understanding how LLMs work better, they’re able to get them to do more with less. I was watching a Gates interview just earlier today where he was talking about that, uh, as we understand them better, we’re going to be able to get them to function a lot more efficiently.

[00:28:23] Cameron: And they were talking about the energy requirements and he was saying, look, the breakthroughs we’re going to make in material sciences as a result of AI will far outweigh their short term energy cost to get us

[00:28:37] Steve: I think that too. I think that too. Um,

[00:28:40] Cameron: do too.

[00:28:41] Steve: I remember it might’ve been Nicholas Negroponte, there’s an old tech name, who, when he used to talk to some of his students, he said, I want you to assume that computation and microchips are free, because only when we do that will we push ourselves to make the things themselves cheaper.

[00:28:57] Steve: So you have this recursive efficiency effect where the technology solves its own technological problems and output problems.

[00:29:06] Cameron: Well, speaking of, uh, recent releases, Runway released RunwayML Gen 3 Alpha, a text, the update of their text to video engine. I tried to play around with it, but they wanted me to sign up, and because I’m in a Because I’m in a, uh, not signing up to new things, uh, binge at the moment, I said no. But I’ve seen some

[00:29:30] Steve: Well, that’s because you’re a communist. That’s because you’re a communist and you believe that everything should be free to everyone, right?

[00:29:36] Cameron: I do. And everything will be free. I believe that we will reach communism at some point in the not too

[00:29:41] Steve: Technological communist abundance.

[00:29:44] Cameron: Exactly. As I heard somebody say recently, I don’t know who it was, but they were saying it’s not a universal basic income, it’s gonna be a universal, universal Uh, wealth income, wealthy income, where we’re headed.

[00:29:59] Cameron: Everyone’s going to have everything. I think it was Elon actually in a talk. He was saying it’s, it won’t be a universal basic income. It’ll be a universal, everything you need, um, is the

[00:30:10] Steve: I could, that, that, that I can buy into. Universal basic income, I, I, you and I, we debated it once on somewhere inside our 28 episodes, um, on it. And, uh, the, the word basic for me was the problem because it’s, it’s, yeah, it’s all, you know,

[00:30:26] Cameron: It’s just like what Elon said to Donald Trump last weekend. Anything you need, 45 million a month for your campaign, done. Not a problem. That’s what he’s putting in at the moment. Did you know that? Um, anyway, have you seen the runway three video?

[00:30:43] Steve: I, I didn’t sign up, but I tell you what, I watched a couple of demos and I actually watched a video demo of someone showing use cases live, because as you know, like we saw with Sora, they can show you incredible output. How they got there is, is the demos are not how you got there, but he went through the process of prompting a few different things to show its capability.

[00:31:06] Steve: And some of them were extraordinary and some of them weren’t so good. When he went through it, he gave it a few challenges and some of them were glitchy and terrible, but some were great to the point where I thought, I’m going to sign up to this. And it made me think with some of the TikTok videos that I do, if I’ve just got soundbite ideas, but I haven’t, I don’t want to talk to camera.

[00:31:24] Steve: I can just do a VO of me and have a video, which explains it in 10 seconds. And it could be incredibly compelling. I was really impressed. And I thought it kind of was at the Sora level. I thought from what I saw,

[00:31:37] Cameron: even better than Sora, I

[00:31:38] Steve: Yeah, so I was really impressed by it to

[00:31:41] Cameron: the human faces that it was doing, like the human face, its ability to capture human faces is like, Stunning. It’s as good as, uh, Mid Journey for bases, but as video, it was really impressive.

[00:31:58] Steve: I was super impressed by it to the point where I’m this weekend I’m going to muck around with it and maybe sign up for a month anyway and see how it goes. I did

[00:32:06] Cameron: had the same,

[00:32:08] Steve: on the economics of it all, how, All of the AI tools. And as you know, there’s, you know, every month there’s a number of tools that arrive.

[00:32:17] Steve: They’re all really pushing hard to give you incredible discounts where you almost get 12 months for the cost of three, because they know that you’re going to play with it next month and next month, you’re going to turn it off because a better one comes along. So it’s a real game of cat and mouse with the economics of getting the signups was also noticeable. Yeah. Monthly versus 12 months, but I’m definitely going to have a play with it for sure.

[00:32:41] Cameron: I had the same thing thinking of TikTok, you know, with the QAV TikToks that I try and do where we’re talking about investing and it’s dull and it’s boring, you know, my sons have often sent me examples of TikToks where people will be talking about something boring in the bottom half of the screen and the top half of the screen is just a Minecraft, it’s like a Minecraft walkthrough

[00:32:59] Steve: compelling.

[00:33:00] Cameron: do with the topic.

[00:33:01] Steve: Or a time lapse of someone cooking or cutting up watermelons or whatever. It’s, they come in my feed too. And it’s, it’s actually, it gives me hope in humanity that. Despite the tools, some people come up with creative ways to draw attention while you’re verbally giving.

[00:33:18] Steve: And it’s interesting to have this dichotomy of visual and verbal non matching to keep your attention, to transpose the important verbal information. That’s really important. It’s, and it’s a, it’s a real innovation. And just while you’re mentioning, you know, the QAV podcast, uh, I wrote. I’ve got this big list of TikToks I want to do.

[00:33:42] Steve: One day, when I get two days, I’m going to do 50 TikToks in one day and just get all the videos done. But one of them I want to do on is CEO wages, which is a real bugbear of mine, that CEOs have continued to extract largess while everyone else gets less. And here’s the ironic thing, they are not any better.

[00:33:59] Steve: They’re actually They’re worse than the CEOs in the seventies because the ROI on the average S& P 500 is a little bit lower than it was in the last, you know, it’s been around about 10 percent for a hundred years. So they’re not better, but they get paid thousands of times more. So this was the prompt that I put in to Runway.

[00:34:17] Steve: I put in, create a video of a greedy CEO which demonstrates an increase in wealth and their wages exponentially through the years while the average worker is getting poorer. Show it in a documentary style which would portray it over a timescale of decades starting in the 70s up until now. didn’t give me a very good result.

[00:34:34] Steve: But, but, um, but that was on Gen 2. So I’m going to try that on Gen 3 this afternoon. Uh,

[00:34:40] Cameron: yeah, let me know

[00:34:41] Steve: the other thing that I think is cool is how you can drop in a couple of images and get it to extrapolate on those, which I really like.

[00:34:50] Cameron: Well, just, you know, I think it’s just another demonstration of where we’re headed in terms of the ability for. Regular people, the democratization of the creation of high quality video content. We’re not very far away from, I think, from regular Joes like us being able to produce really high

[00:35:21] Steve: Hold up, hold up on the, hold up on the regular. Okay, keep going.

[00:35:24] Cameron: I’m being, I’m being humble. Uh, yeah, I’ve. Being able to produce really high quality video that, um, you know, can be used in a variety of ways. It can be used obviously for a business communication side of things, but also for entertainment. Um, it’s going to be a, an incredible world when we can all produce this kind of stuff to go along with our content.

[00:35:49] Cameron: Like for example, Uh, you know, in some of my history shows, you know, where if I’m talking about some historical battle, Julius Caesar or Alexander the Great or et cetera, et cetera, what I have done in the past, like if I’m talking about Alexander’s battle, great, uh, some of his great battles, um, and I’ll say, go watch the Oliver Stone film, uh, the film itself is pretty bad, but, uh, you know, but look at the battle scenes.

[00:36:18] Cameron: It’s pretty bad. Battle scenes give you a sense of what this was like. For me to be able to create My own video of the battle scene by just explaining to the, uh, engine what I wanted to demonstrate, uh, you know, a hundred thousand, 150, 000 Persians on elephants versus, uh, 20, 000 Macedonians on horses and Alexander’s battle tactics.

[00:36:45] Cameron: To be able to communicate that sort of like as an educational. Tool for, for teachers, for schools, for AI to teach a kid something by producing video. It’s, it’s going to be an incredible world a few years from now with these tools. Really amazing.

[00:37:01] Steve: The point you make there is so important. And I do this at the start of every speech. I said there’s two ingredients for a technology to create.

[00:37:07] Cameron: I think. Your mic just switched

[00:37:10] Steve: I was just playing around with it. Is that better?

[00:37:12] Cameron: Oh yeah. Yeah. Yeah. You’re

[00:37:13] Steve: I was just moving it around. Sorry. Um, I think the, uh, the process that you just mentioned, then democratization is really important.

[00:37:22] Steve: It’s one thing for technology to feel like magic, but when it’s available to everyone, that’s what changes everything. And just looking at the marketing of the Messiah, I remember when you, you did the documentary film and you had some animations that you got made. And I remember you, I was lucky enough to see some of the early ones.

[00:37:41] Steve: Imagine the capability now, if you were to remake that film on some of the animations and the way you’d be able to do those quickly, more detailed, um, really with, uh, text prompting and commands would be pretty extraordinary. And I, and I do like that idea that Um, you needed one of two things to communicate previously, even in the social media era, you had to have a good turn of phrase or voice or have access to resources to create video, if you were less compelling verbally, but now you can be a storyteller, uh, if you don’t have access to a whole lot of resources, but you’ve got a point of view, and I really like that.

[00:38:17] Steve: It has

[00:38:19] Cameron: Yeah. It’s going to be an amazing future with all of this. I mean, there’s going to be a lot of, like with all, with all of these things, there’s going to be a lot of crap that gets generated, but yes, there does. You’re right. You know, there will be people. In some cases, existing creative artists, in other cases, people that are brand new to creative arts that will do amazing things with this.

[00:38:45] Cameron: You know, and the example I always keep coming back to in my head is Orson Welles and Citizen Kane. I’m a huge Orson Welles fan, always have been, huge Orson Welles buff. And for people who don’t know the story, When Orson Welles made Citizen Kane in the mid 40s, it was his first film, and he had no background in film, never acted on film, never directed, never written anything.

[00:39:13] Cameron: Uh, he had a theatre, theatrical and radio background, and then because he was a big success, particularly with the War of the Worlds on radio, Hollywood came to him and basically gave him a blank check, do whatever you want, full creative control, never done, never, never, never heard of before, never been done before. And he, and he went to Hollywood with a script that he sort of co wrote with, uh, Mankiewicz. And he, he, he got approached by a cinematographer, a guy called Greg Toland, who had just won the Academy Award the year before for cinematography. And he came to, he came to Orson and said, I want to, I want to work with you.

[00:39:48] Cameron: And Orson goes, Orson was 26 years old. And Greg Toland to work with you. And Orson’s like, why? You just won the Academy Award. You could do anything you want. And he said, basically everyone in the industry thinks they know. How to do this. I want to work with somebody who doesn’t know what they’re doing, so they bring a fresh eye.

[00:40:07] Steve: That’s really

[00:40:07] Cameron: And so, Orson just had these ideas. that he brought from his theatrical days, you know, could we do a shot like this? And Toland was like, we’ll figure it out. Yeah. Yeah, absolutely. We can figure out how to do that. Not like, well, no, no, no, you wouldn’t do that. And so the, one of the reasons why Citizen Kane was such a breakthrough film and is still revered today.

[00:40:29] Cameron: by Film Buffs today is because it invented a dozen new cinematic languages, techniques, like zooming in through a window into a room and some of the shots and the use of light and shadow and perspective that Orson and Greg Tolland invented that changed cinema. But a large reason for that was Orson didn’t know what he didn’t know.

[00:40:54] Cameron: He came into it completely ignorant of what the rules of film were. Uh, as opposed to Tarantino, right, who takes the best of film and, and rehashes it, takes all the best things and, and throws them into a single film. Orson came at it with absolutely, absolutely stupid about film, but just had a lot of ideas, and Greg Toland was his technology.

[00:41:19] Cameron: Greg Toland was like, yeah, yeah, we’ll figure out how to make it better. Get it, get it done, right? Imagine who, who, who is the Orson Welles of runway going to be, of text to video going to be. That’s what I often wonder, right?

[00:41:30] Steve: That’s a, that’s a really good point. And especially when it’s like, because you have to imagine a year or two from now, you could do a 60 minute feature film through

[00:41:43] Cameron: Oh, that will, you know, we will get there, I’m quite sure, uh, with these tools. Anyway, moving right along, um, moving into a completely different space, um, our friend Elon Musk.

[00:41:53] Steve: Pun intended.

[00:41:56] Cameron: Starship! Now, this is actually, uh, happened, uh, I think about a month ago, but, um, Fox and I watched the, um, uh, successful launch, I think it was the fourth launch of one of SpaceX’s Starship Super Heavies went up into space.

[00:42:16] Cameron: And, uh, successfully landed, as did its, uh, booster rockets. Um, it’s, I mean, a stunning video to see, like, these things, so massive, I think like 36 Raptors, uh, Raptor engines on the bottom of it, all but one successfully ignited. It goes up, the boosters break away, fall back down to earth, beautifully synchronized, landing, uh, on the water in this case.

[00:42:44] Cameron: And then the ship went up into space for a little while and then came back down to earth as well. It just, like, it’s an incredible piece of engineering to be able to send these rockets that large up into space and then have them return safely on the ground. Whatever you might think of Elon and his politics, Tony Kynaston said to me on QAV this week he thinks Musk is a Bond villain.

[00:43:08] Cameron: We were suggesting that in the next Bond movie they should just get Elon to play the Bond villain as himself. Just go, just, you

[00:43:15] Steve: You’d like that.

[00:43:16] Cameron: He’d love it. He’d be totally down for that, I’m sure. But I was saying to Tony, like, I watched, in the course of about a week, I watched not only the Starship launch, I watched a video where Elon took a guy through the Starship factory, I think they call it the Star Factory, um, for an hour, Showing him all the different parts of the manufacturing process and explaining what they’re doing for the next iteration and why they’re doing it this way, not that way.

[00:43:44] Cameron: Then I watched him on stage at the Khan Lion Conference, the advertising industry conference in Khan, as the, as the, he’s not the CEO of Twitter, whatever he is of Twitter,

[00:43:53] Steve: He’s the CEO of Twitter.

[00:43:56] Cameron: no, some,

[00:43:57] Steve: Twitter. Yeah, I know. But that’s what I’m saying. He’s the CEO of Twitter. She’s there

[00:44:01] Cameron: the real CEO of Twitter. Yeah. Yeah. Yeah, he is talking about Twitter and I loved it. The guy who was uh, interviewing him, who was the CEO of WPP, the big agency, um, his opening line

[00:44:14] Steve: My old

[00:44:14] Cameron: six months ago.

[00:44:16] Cameron: Oh yeah. You were all WPP.

[00:44:18] Steve: Yeah. When, when I was, when that was when Sir Marty Sorrell was running it before he decided to put some, uh, Interesting transactions at 1am on the corporate credit card, which is insane, which is insane given that he’s, you know, worth 500 million or whatever he was worth anyway.

[00:44:36] Cameron: This guy’s opening question, Dylan was six months ago you told our industry we could go fuck ourselves. Uh, what was that all about?

[00:44:43] Steve: Yeah, it was,

[00:44:44] Cameron: so. Good, good opening question. Anyway, so I watched Elon, you know, have that conversation for an hour, and then a couple of days later, I watched him with a panel of his neuroscientists at Neuralink talk about the latest updates to Neuralink and what they’re doing there.

[00:44:57] Cameron: Like, whatever you think of Elon. To go from talking about super rockets to running Twitter to, you know, Uh, putting chips in the brain over a course of a week, you’re like, Jesus Christ, what is it, what must it be like in this guy’s head, really? I mean, I juggle a lot of plates, but they’re not like that.

[00:45:17] Steve: Nah, and, and it’s pretty interesting if you think about what he’s done with, you know, media, transportation, space.

[00:45:25] Cameron: Yeah, it’s not even Tesla.

[00:45:27] Steve: Yeah, that’s right. Uh, so. Yeah, there’s nothing you can say other than I think the kind of innovations it makes are nation state style innovations and, and, and should be the bastion of that.

[00:45:39] Steve: They’re not. They should be. Um, that said, you know, the space one for me is super interesting and I’ve always held this position and Bill Maher did a great piece on it about three years ago. It’s like, I’m really interested in what they call the low, Uh, Earth Orbit Economy or the Low Orbit Economy for Starlink and what he’s doing there.

[00:45:59] Steve: I think that’s incredible. I’m really excited about how space gives us new technologies, you know, everything from digital watches to Teflon to, you know, the, uh, where we are with, um, the IT revolution that we’ve had since the, the 60s. I think all that’s great, but the idea of becoming a multi planetary species, Is an incredible misallocation of resources.

[00:46:21] Steve: Now I’m hopeful that, yeah, yeah, it’s, it’s, it’s insane. Yeah, no, it’s, it’s, it’s look for me that the space exploration as it pertains to creating benefits on earth, I’m all for. Right? But the idea of becoming a multi planetary species, A, is never, ever, ever going to happen, not in our lifetimes, unless we have a singularity in which all problems can be solved computationally.

[00:46:48] Steve: So that’s the possibility. But Elon’s idea that, by the way, you know, we’re already meant to be, have our first thing on Mars. You know that, like he’s already broken a zillion of his promises there. Um, he’s running late, running late. He needs the singularity for it to happen.

[00:47:03] Cameron: Jesus was supposed to return 2, 000 years ago. People are still hanging out for that, like, cut Elon some slack. He’s just the new Jesus.

[00:47:10] Steve: yeah, well, they’re more patient than I am. I’m really not into the multi planetary species. And I am, I actually know what he’s trying to do. He’s trying to populate Mars himself. He just had another baby in the last couple of months, and he’s really doing some solid work there. And I just love how he calls these babies XYZ or whatever he calls them.

[00:47:27] Steve: It’s my favorite thing. So that’s, that’s his getup. And when he says multi planetary species, I mean, His planet and his species. He just left those two bits out. Uh, but I’m really into the benefits that space and space exploration

[00:47:44] Cameron: why do you think we won’t be a multi planetary

[00:47:47] Steve: I listened to a podcast with an ex NASA scientist who talked about Mars and unless we have general AI or singularity level AI that helps us solve a whole heap of problems and potentially, you know,

[00:48:00] Cameron: to have in five years or less. So

[00:48:04] Steve: okay. All right.

[00:48:06] Cameron: AGI, we’re going to have, we’re going to have AGI, we’re going to have robots that we can send up there to build all the structures that we need for

[00:48:13] Steve: Well, you need to change the climate. You can’t just build the structures. It’s not enough. Anyway, there was a podcast that I listened to on Econ Talk with Russ Roberts, where he had an ex NASA scientist who’s one of the most revered, who’s worked in it for, and he just basically said, let me just start off by saying this, space wants to kill you.

[00:48:34] Steve: And he went through, um, the layers of redundancy that you would require. To live on Mars and the potential for error is just so high and constantly a threat, it would be impossible to get all of the resources and the materials up there, unless like you say, you have, uh, you can send robots up there that have all the energy they need, that have all the resources, that can convert, the dust or whatever with nano robots to build all of the materials we need and change the climate and change the gravitational force like he went through it in all great detail and the moment i listened to that i’m like yeah nah never gonna happen The only way it could happen is if you say exactly what you say, is if we have some sort of revolution, you know, singularity level revolution with robotics to change the climate, the gravitational force, put all the materials up there, have all the energy you need, and be able to entirely reconfigure it.

[00:49:34] Steve: The planet? Then it’s never gonna happen and not worth happening. Now, here’s the counter. If we have all of that, if we have all of that technology, and it has to be here before it can be there, then let’s fucking fix the problems here, and help the people here, and have abundance here, and we don’t fucking need to be a multi planetary species.

[00:49:55] Steve: The end, rant over, I’m Steve Sammartino, and I’m running for president!

[00:50:00] Cameron: I think. Um, Musk’s point is that, uh, in case things go wrong here, we need a backup. We need redundancy.

[00:50:10] Steve: Well, he’s making them go wrong by giving 45 million dollars to Donald Trump. What do you mean in case? He’s fucking creating the problem he’s purporting to solve.

[00:50:19] Cameron: Well, he would disagree, but, uh,

[00:50:21] Steve: course he would. He’s Elon Musk. He’s God. He can have whatever opinion he wants.

[00:50:26] Cameron: yeah. Well, moving right along, uh, nanorobots, Steve, um, you know, one of the big, Brongs of the future, one of the pillars of the future. We’ve got AI, we’ve got robotics. One of the other ones is going to be nanotech. So I’m always keeping an eye on what’s happening in terms of nanotech progress.

[00:50:47] Cameron: This came out at the beginning of

[00:50:48] Steve: can I just, can I just point out where Cameron got this, which I love. So, Cameron, please.

[00:50:56] Cameron: What’s

[00:50:57] Steve: you’re just at the next level. I mean, you’re just going into science daily. You’re just going deep into the journals. I respect that. Everyone else is just fritzing around with TechCrunch and WiredMag, not Cameron Reilly. Cameron Reilly is deep in the journals.

[00:51:11] Cameron: it’s one of the websites I look at almost every morning is Science Daily, particularly the nanotech thread to see what the latest is. There’s a story a couple of times a week, they have some updates on nanotech research. Anyway, this one came out of the Karolinska Institutet, um, which is one of the foremost medical universities in the world based out of Sweden.

[00:51:34] Cameron: Um, Researchers at Karolinska Institutet in Sweden have developed nanorobots that kill cancer cells in mice. robot’s weapon is hidden in a nanostructure and is exposed only in the tumor microenvironment, sparing healthy cells. The study is published in the journal Nature Nanotechnology. The research group has previously developed structures that can organize so called death receptors.

[00:52:04] Cameron: Uh, coincidentally the title of my next punk rock album, death Receptors

[00:52:09] Steve: What was the

[00:52:09] Cameron: on the Surface,

[00:52:11] Steve: That’s great, that’s great.

[00:52:12] Cameron: it’s been so many, I can’t remember so many albums.

[00:52:14] Steve: one. Death Receptors, Punk

[00:52:16] Cameron: Death receptors,

[00:52:18] Steve: Playing at the Bridge Hotel this Friday, Get Undown.

[00:52:21] Cameron: oh God. The bridge that takes me back, uh, on the surface of cells leading to cell death. The structures Exhibit six peptides, amino acid chains assembled. In a hexagonal pattern, this hexagonal nanopattern of peptides becomes a lethal weapon, explains Professor Bjorn from ABBA. Um, when he’s not leading ABBA, he’s leading the Karolinska Institute’s Department of Medical Biochemistry and Biophysics.

[00:52:51] Cameron: If you were to administer it as a drug, it would indiscriminately start killing cells in the body, which would not be good. Glad he pointed that out. To get around this problem. that’s medical, that’s medical terminology.

[00:53:05] Steve: not be good. High medical, not be good. And I love that they’ve, you know, they’ve pulled Mel Gibson into the scientific paper with a bit of lethal weapon work. I mean, this is everything we can believe in. Would not be good, lethal weapon. Who says

[00:53:20] Cameron: a,

[00:53:20] Steve: medical researchers are boring? Not me.

[00:53:24] Cameron: to get around this problem. We have hidden the weapon inside a nanostructure built from DNA. So, look, I could go on, but, you know, ever since I read Engines of Creation by K. Eriks Drexler, sort of in the, I don’t know, early 90s. You know, one of the dreams is that we can build nano, medical. Nanorobots that can go into our body and kill cancer, fix diseases by targeting specific cells with specific molecular, um, solutions to whatever’s going wrong with that cell.

[00:54:03] Cameron: The fact that, um, they’ve come up with this and have tested it is really exciting to me, Steve.

[00:54:11] Steve: Well, this is, this harkens back to our Futuristic 27 where we talked about, uh, longevity, escape, velocity. And the idea that we’re getting advances and I think the next article you’ve got as well is, you know, self assembling and disassembling swarm molecular robots to, you know, control and again, kill disease and this, this is for me, the really interesting stuff, the stuff happening at the nano level.

[00:54:40] Steve: And, you know, it, it, it gives us a question, is it, what is. The computational ability of the future is that things just operating at a molecular level instead of off and on switches. You know, there’s this real sense of, uh, biological mastery that’s happening where we organize things at the molecular level.

[00:55:00] Steve: I don’t know. It’s, it’s, for me,

[00:55:02] Cameron: to know a secret?

[00:55:03] Steve: Yeah,

[00:55:04] Cameron: Often on switchers work at a molecular level too.

[00:55:06] Steve: of course. But, but that’s what I’m saying. Like instead of us using, um, instead of us using chips the way that we do now, you know, it’s, I guess the biological machines, is that, is that how you frame it, do you call them biological machines or, uh, yeah,

[00:55:24] Cameron: Well, you know, the whole field of nanotechnology comes from a paper that, um, Richard Feynman wrote, I think in 1959,

[00:55:33] Steve: paper?

[00:55:35] Cameron: yeah, from 59, I think. Feynman gave a talk, or wrote a paper, or one or the other, which was called, There’s Plenty of Room at the Bottom. And basically he was explaining, look, The way that nature works is it builds stuff out of molecules, it builds machines.

[00:55:52] Cameron: All life is basically molecular machines. And there’s no reason why we can’t build molecular machines if we become adept enough at A, understanding how Molecular machines work, and then having the ability to create small enough tools and technologies that can build the molecular machines that we want to build, that will do the functions that we want them to do.

[00:56:18] Cameron: So that kicked off the whole thinking about nanotech and You know, we’ve seen lots of advancement. Things like CRISPR that have been around for 20 years now, 15, 20 years, are quite exciting. But this is just taking to a new level. In the story you’re mentioning, self assembling and disassembling swarm molecular robots via DNA molecular controller.

[00:56:38] Cameron: This is from June 14. By a Tohuku Toku University researchers from Tohoku University and Kyoto University have successfully developed a DNA based molecular controller that autonomously directs the assembly and disassembly of molecular robots. This pioneering technology marks a significant step towards advanced autonomous molecular systems with potential applications in medicine and nanotechnology.

[00:57:08] Cameron: A newly developed molecular controller composed of artificially. Designed DNA molecules and enzymes coexists with molecular robots and controls them by outputting specific DNA molecules. This allows the molecular robots to self assemble and disassemble automatically without the need for external manipulation.

[00:57:34] Steve: Yeah. I mean, when you hear, hear this and if you, I don’t know if you can put an exponential paradigm around it, but if these small experiments can scale, then you have to believe. Let’s call it the problem of death is something that can be a workaround. I don’t know if Google still owns it, but they used to own a company called Calico.

[00:57:58] Steve: Whose, uh, objective as a company was to solve the problem of death. I mean, you talk about BHAG goals, like it’s, it’s pretty interesting, right. And part of that longevity space, but I think CRISPR, uh, molecular robotics and, and, uh, nanobots is, is going to become more interesting. And I think tap into modern society where people start to talk about it a little bit more.

[00:58:24] Steve: I mean, we in 80s. A couple of real world examples that go beyond mice and kill cancer with a particular type of cancer. And then, and then it’s game on. I mean, I imagine it’s probably not that easy to get funding for these things here. It’s certainly not like the AI race where anyone buying GPUs can raise billions of dollars.

[00:58:43] Steve: Imagine if this had enough attention. And you could solve some real problems like cancer and the revenue sources around that. I mean, goodness me, you’ve got a great target audience. You’ve got all those boomers who own all the houses. They can just, just take a chip off the old block there and pay to cure their cancer and then just buy more property and keep more children out of housing, Cameron, is what they can do.

[00:59:08] Cameron: About, uh, 2007, I interviewed a guy called Dr. Aubrey de Grey. who is a British biomedical gerontologist. He wrote a book in 2007 called Ending Aging. And I remember when I interviewed him, he talked about how the last great disease that we have to fix will be aging. Um, okay. And, um, yeah, he, he’s still around, got himself into a little bit of, me too, trouble a couple of years ago, but, uh,

[00:59:43] Steve: Just a little bit of, just a little bit, just a little bit of me too trouble the other day. Anyway, he’s done some great work. Let’s get back to his growing. Sorry. I just saw an opportunity for humor in my humorless life at the moment.

[00:59:56] Cameron: He, uh, said some inappropriate comments to some female colleagues, apparently, but, um, He,

[01:00:03] Steve: Musk did and just have babies with them.

[01:00:06] Cameron: he, can I finish this fucking story, or, uh, can you, yeah,

[01:00:11] Steve: Sorry.

[01:00:12] Cameron: you’re the one with the heart out, and you’re not letting me finish the fucking story. And we haven’t talked about Apple Vision Pro yet. Anyway, he’s been working on this for 25, 30 years, um, and, you know, a lot of the stuff that he told me about nearly 20 years ago, we’re getting closer and closer to.

[01:00:27] Cameron: You know, this idea that aging is just a problem that we can solve, right? It’s the breakdown of cells, um, it’s, it’s not something that’s written into laws of the universe, that we have to get old and die, we should be able to fix that, and he’s, I remember he told me back then that he believed, I can’t remember the exact numbers, but he said something like by, by 2050 we’ll have the ability to extend the healthy human lifespan to 125 and then within the next 20 years we’ll be able to push it out to 150 and then I think he had, he thinks there is a hard stop around about 200 or something like that but by then we’ll have, fuckin who knows, robot bodies and uploading our brains and uploading ourselves into the Apple, Cloud with Apple Vision Pro.

[01:01:16] Cameron: Uh, so let’s, let’s skip my next story then, Steve, and, uh, talk about our deep dive, uh, for music. Um, do you want to lead this or do you want me to kick it off?

[01:01:29] Steve: think you’ve been, you kick it off. I’ve been interrupting a lot.

[01:01:36] Cameron: It’s like my wife cannot let me finish a fucking sentence without interrupting. Um, I saw this video during the week, um, on TikTok by a music producer and he was doing an analysis of. A couple of the text to music generators out there, Suno and Udio. Let me play a quick clip of him and I’ll just get to his summary.

[01:02:02] Cameron: So he made some tracks. This guy’s a professional music producer. His name’s Mike McClellan.

[01:02:08] Dan McClellan: Well, friends, we’ve arrived. AI tools can now generate music from text. But the question is, is it any good? So last week I posted a video where I looked at suno. ai, which is a website that anybody can use. You type in some text and it generates an instrumental track or a song. I told it to create a reggae song about a Soviet submarine captain who likes cheese.

[01:02:33] Dan McClellan: And boy, did it deliver.

[01:02:41] Dan McClellan: I got a lot of comments on this video. A lot of strong, mixed reactions. A lot of people said something to the effect of, Wow, this is so cool! Looks really fun to use, I’m gonna go make a bunch of stuff now. A lot of people said something more like, We’re doomed, this is the end of creativity, the end of humanity.

[01:02:56] Dan McClellan: Some people said something like, Well, this just means we all need to go see more live music now, you know, let’s bring it back to the old days. And this is the correct take. And a lot of people said, Go try Udio, it’s better than Suno. So that’s what we’re doing today. Today I’m going to give these two AI tools the same prompt and see how they compare and just how they fare in general.

[01:03:16] Dan McClellan: Cool mist falls on the green fern groves, Damp and serene in the forest coves, Drifting through pines as the owl hooves slow, yeah, yeah. Roaming past rivers where wild salmon play, I see the earth breathing in the fogless day, I say, uh, hey there old big woods, can you rain whisper too? I have to confess that Hearing this for the first time sent me into kind of a funk, because I’m a music producer.

[01:03:52] Dan McClellan: I spent ten years of my life working full time in music, people hire me to create what has just been rendered in a matter of seconds. I spend hours recording guitars, setting up microphones, editing takes, recording vocals, arranging vocals, recording strings, recording drums, editing drums, mixing. There is so much that goes into this.

[01:04:14] Dan McClellan: And this just did it in a matter of seconds, and while it’s still not quite up to snuff, it will be very soon. My livelihood is about to change.

[01:04:24] Cameron: So basically his, his professional opinion is, look, the tools are really impressive already, um, not quite ready for, you know, mainstream, but they’re gonna get there.

[01:04:37] Cameron: And I think he’s terrified by what this means for him and other producers and musical artists as, you know, I think all of us in any sort of creative industry is terrified at the moment.

[01:04:50] Steve: Well, I wrote about this, I think about a month ago. On my blog, it was called AI Killed The Radio Star, and I said this, artificial creativity, sorry humans, it turns out AI is creative too. Of course it’s derivative, but so is human learning. Uh, within a decade, someone, in parentheses, will create an album entirely with AI, which will be a global super hit.

[01:05:12] Steve: I’m not talking about a song, I’m talking about multiple songs, all of which will be among the most downloaded on Spotify of months. The album will have incredible music videos, the likes of which no one has ever seen creatively, And this album will create a media sensation where pundits explore this new reality with amazement.

[01:05:28] Steve: But it shouldn’t surprise anyone given where we already are. Yeah, I wrote a song too, but the way I did it was I used a few different AI tools to then put it in Udio and I gave it samples of the type of music that I liked and I based it on like nine inch nails and I made it like a cyberpunk. Some, um, and I even gave it an album name.

[01:05:51] Steve: Uh, the album name was Files of Ideas That Will Never Happen. The band name was called Trading Electrons and the debut single was called Electrons in the Void. Do you want to hear Electrons in the Void?

Ikea’s flowed like cybersmog, None will happen, life’s a card, Stuffed with dreaming, it’s all a scam, Fade to black, slam the sham, This song’s a glitch, a digital ghost, Another post, another toast, The lawn’s in chaffs, not in code, Evaporates, crashes the node, Makers cry, there’s a machine, Don’t be that drone, stay unseen, Overpriced, never delivered, Just molecules, data slivers, We’re electrons in a void, It’s not our fault, we’re just annoyed, Life’s a glitch, dreams corrupt, Let it crash, don’t disrupt, We’re electrons in a void, It’s not our fault, we’re just annoyed,

[01:06:35] Cameron: Wow, that’s absolutely banging.

[01:06:40] Steve: Do you joke it? I actually like it.

[01:06:43] Cameron: No, I like it, that’s banging, I want the whole track, that’s only 32 seconds, where’s the rest

[01:06:47] Steve: pretty good. Well, and because I kind of thought through the pieces of the puzzle that will make it match. And I wanted like a song that is kind of like cyberpunk, anti kind of tech, but you’re in tech and that the irony of it. And then I thought that industrial punk would suit that lyric base and sound.

[01:07:09] Steve: So I think if you. Again, it’s like a movie. It’s how you put the pieces of the puzzle together to create new forms of creativity.

[01:07:19] Cameron: Wow. So tell us, walk us through how you did that again.

[01:07:24] Steve: So I came up with a premise, uh, with some of the things that, uh, uh, had an idea around where we’re going technologically. And I had a bunch of words and then I put those words into ChatGPT and said, Give me some lyrics that gives a cyberpunk sound to it and pace. Use these particular words in the lyrics. So I gave it a bunch of words. It was about 12 words that I put in there and I said give me the lyrics.

[01:07:56] Steve: I played with it and made it go back to the well about four or five times. And then I used those lyrics and said, give me some styles that would suit these lyrics in music that’s already existed in band names. And then I got that and then I pumped it into UDEO and got some styles, played with the prompts until I got a style that sounded good and then dumped the lyrics in and got it to make a song with those lyrics.

[01:08:17] Cameron: Brilliant.

[01:08:18] Steve: So just a few more little steps to the puzzle. Do you know what I mean? Yeah.

[01:08:22] Cameron: Yeah. Like this is, you

[01:08:25] Steve: And then those lyrics and then think about those lyrics to, to draw back to something that we spoke about earlier is. You could use RunwayML Gen 3 with the song and those lyrics. And then next thing you know, you’ve got a pretty interesting video.

[01:08:41] Cameron: Yeah. And people are doing that. I mean, I’m seeing examples of this on Reddit all the time. Music videos, ads made with tools like Runway, um, or, uh, whatever the OpenAO one is, I can’t remember now. Suno? No. What’s the OpenAO? What’s the video speech to text thing that OpenAO have got? I’ve.

[01:09:01] Cameron: MentalBlank,

[01:09:02] Steve: Sora, Sora,

[01:09:06] Cameron: Sora,

[01:09:07] Steve: many. Mate, this, a few months ago, I used to keep this file of, um, um, amazing AI tools you can use. And it got to about 35 and I just went, yeah, just Google it. At the bottom, I said, there’s too many now, just Google it.

[01:09:19] Cameron: yeah. Yeah, look, you know, with all of this stuff, you know, I, I’m absolutely convinced, uh, you’re right, that there will be hit albums, that will be hit albums, that will be created by AI, and that people won’t know are created by AI. It’ll be like Milli Vanilli. It won’t be until You know, six months later that it will come out that it was actually completely created by AI and the artist doesn’t really exist.

[01:09:44] Cameron: It was just, uh, some, some dude in his basement who created the whole story and got it out there with the help of, um, some viral marketing. It’s funny. I was watching, you know, who Dick Cavett. was or is, um, great American, um, TV host in the seventies, eighties. He had a talk show, but it was, it was, it was like the original podcast.

[01:10:09] Cameron: He would get people on and like celebrities, politicians, whatever, but just talk to them for an hour, like just a real deep, intelligent conversation. And, um, I was watching one of him interviewing Billy Joel in sort of the mid 80s the other day. I just have it on the TV when I’m working. Again, it’s on Tubi.

[01:10:27] Cameron: tv, the whole Dick Cavett archives, um, and they were talking about Milli Vanilli. And the uproar about Milli Vanilli and Billy Joel was like, I was really surprised. Like, I thought everyone knew that they just, you know, were actors and just mimed it. He said, I was surprised. He said, but then the people who heard the song, liked it enough to go buy the album, then are upset when they found out they’re fake.

[01:10:50] Cameron: I’m like, you like the music. You bought the record because you like the music. What, who do you, why do you care who sang it? Really? I mean, you liked it when you heard it. So what does it matter if the guys in the video were fakes or not, right?

[01:11:04] Steve: But, well, I mean, then the question is, well, everything is set up in music, it’s just how far on the spectrum is it, but also, and, but it’s also, it’s the deception that people are upset about, the perceived deception, I mean, we should really be more understanding that anything in media is a quasi deception, it’s a, it’s a production, it’s just how much of it is produced and who produced what and how many people are behind the scenes and backup singers and all this kind of stuff.

[01:11:32] Steve: Um. But for me, that, that, yeah, uh, well, I’ll, I’ll, I’ll, I’ll come to that, but yeah, yeah, I’ll come back to that. But it’s a little bit like, uh, simulation theory. People say, well, are we in a simulation? Are we, or if we’re not? It’s like, if we are, if we aren’t, it doesn’t make any difference. It just is. If you’re in a simulation, you’ve got a

[01:11:52] Cameron: Well, it kind of does. Yeah.

[01:11:55] Steve: Yeah. Um, Rick Beardo, um, is a great, uh, I love his stuff. He’s a music YouTuber. He’s incredible. And he said, you know, is, is AI music, music. And that’s a real question. Like what is music now? So you might have different genres where it’s like you have music where you play instruments, and then you have music where you create it with AI tools, I think you’re just gonna get a split, where you’re gonna have maybe two sets of charts, you might even have AI charts, and you might have, uh, analog musician charts, it’s kinda like fast food and gourmet slow food, it’s, you know, canned food

[01:12:31] Cameron: even,

[01:12:32] Steve: Fresh Vegetables.

[01:12:32] Steve: Really?

[01:12:33] Cameron: analog musicians are using auto tuning and stuff like that. So where do you draw the line between using auto tuning and

[01:12:42] Steve: from Steven Commands to, um, the busker on the street with an acoustic guitar. It’s a very, very Raw spectrum. And where are we on the, on that spectrum? But the one thing that AI music doesn’t do, it lacks human nuance. And the big thing is with quantized music and Rick Beato has gone through this in great detail.

[01:13:03] Steve: When something is recorded on tape, it has the slight nuance where it’s on slightly on or off beat. The sounds are a little bit different where the microphones are set up. That guitar is the same as the other guitar, but it was from different timber from a different tree. And you had all of these sounds.

[01:13:18] Steve: that had difference and our ears have had 200, 000 years of evolution to be able to pick up slight differences in your voice versus my voice versus this sound versus and and as soon as things are quantized what you had was like a everything got Down to a small sampled regime. Like a snare drum is a snare drum that in Pro Tools, everyone uses that snare drum.

[01:13:40] Steve: Well, there’s seven snare drums instead of thousands of different snare drums produced, which have all these different sound. And they’ve done studies that music sounds better then because there was more variety and the lack of variety is the homogenization of music. It’s not that there’s not creativity.

[01:13:54] Steve: It’s actually to do with sounds and timbre anyway. So that’s kind of where. He kind of came to, but my, my, my view is if you create music via text prompt, is it really music? I don’t know if I know the answer to that question, but I know that music will live on a spectrum now where it’s like. It’ll be like, it’s a little bit like a restaurant.

[01:14:15] Steve: It’s like, okay, what is this? Oh, this is made by a human to this level, or this is grown in my backyard. No, this is supermarket shelf stuff. I don’t know. Trainspotting.

[01:14:29] Cameron: Alright, we’ve got four minutes and I wanted to talk about Apple Vision Pro. Yes, they’re in the country, they came into the country a week ago, uh, today, uh, I had a demo Saturday a week ago with one of my boys, uh, at the Apple Store in Chermside. Uh, Chrissy had one a couple of days later. I went to my demo, I was really going there because I dropped my laptop off and they were fixing it, and I went to pick up my laptop, and I was like, ahhhh, get a Vision Pro demo, had very, very low expectations, spent the entire 20 minute demo, going, holy shit, no fucking way, this is insane.

[01:15:07] Cameron: Absolutely blew my mind, Hunter, one of my sons, the TikToker, had one, same reaction, I got his on video. Chrissy, who’s been sort of anti The Vision ever since it was announced, she’s like, that’s all the world needs is another fucking screen for people to get lost in. Took her for a demo on Tuesday. She was like, holy shit.

[01:15:25] Cameron: I had no idea what this was. This is incredible. Wow. I highly recommend,

[01:15:31] Steve: Trainspotting. How good does it feel? Well, best orgasm you’ve ever had. Times a thousand. Get this skag your life.

[01:15:42] Cameron: highly recommend everybody who listens to this. If you live near an Apple store, uh, book a demo. They, they, they. You know, they’re not selling any of them. So they, they want to do demos and they keep saying, come back, have another one. When I went with Chrissy’s, the guy goes, you want to do another one while you’re here?

[01:15:57] Cameron: And I’m like,

[01:15:58] Steve: just like the drug dealers down in the alleyways in Footscray. Come back, have another one. It’s fine.

[01:16:06] Cameron: will blow your mind. And when you do it, I want to talk to you about how it’s going to,

[01:16:11] Steve: and, and we’ll talk about it next, next, um,

[01:16:14] Cameron: How it’s going to change TV and cinema. Um, particularly when you combine that with the things that we’ve been talking about, like RunwayML, when you’re, cause you, with its fully immersive modes, you feel like you’re right there. All you can see, your entire frame of reference is the video that you’re in front of and they have spatial photographs, spatial video where you feel like you can reach out and touch things.

[01:16:41] Cameron: Um, and it’s insane, like you’re watching a basketball game from the top of the hoop and it’s all around you, like so in your face. But, you know, one of my sons, Taylor, the talent manager as we were leaving, he was saying, imagine when you’re watching the Fast and the Furious film, but you’re in the car and you.

[01:17:00] Cameron: Everything is first perspective. And I was even thinking when When’s France? Ford Coppler are going to remix the Godfather for the seventh time, but you are in the room with Don Corani and your, your, uh, Jurassic Park. You are Star Wars. You are there. It’s hap the action is happening. around you because you’re sitting in the middle.

[01:17:24] Cameron: The camera perspective is not going to be, you know, we used to talk about sit forward and lean back media. This, if this can, if this kicks off and there’s a, there’s a lot of, there’s, there’s going to be a gap between when it’s like four people have one and it’s widespread enough for. You know, people who invest a lot of money updating their film or how they make film.

[01:17:47] Cameron: But it won’t be that hard, I imagine, to use text to video production technologies to build new things where you’re in the middle of the action that you can wear with these things. But it’s, it’s gonna be a complete revolution. In video entertainment, when you feel like you are in the middle of whatever video that you’re watching.

[01:18:07] Cameron: Uh, I was super pumped by it. Uh, I mean, I can’t afford to buy one and there’s no, I don’t think there’s really any point having one right now. It’s sort of a demo proof of concept tool, I think still. But, um, Forget, you know, I was thinking of it as a, as a work tool, you know, Oh, I can have my screens around me and that kind of stuff.

[01:18:29] Cameron: Again, the other thing that I didn’t really appreciate is the computer is in the goggles. So the entire computer you’re wearing on your eyes, the entire thing. And I, and I was thinking like a seven and a half grand. I remember the, the, the Windows 95 PC that I bought in 1995, which was a huge, big CRT with.

[01:18:49] Cameron: A huge fucking, uh, box that sat on the floor cost me 5, 000 30 years ago. This thing is 7, 000, but it’s all of that times 1, 000 in terms of performance, or probably more than that, and you’re wearing it on your face. It’s insane. What we’ve done with what they’ve done with, uh, shrinking technology in the last 30 years, but forget about it as a work tool right now, go and look at it as an entertainment device and what it holds for the future of entertainment.

[01:19:21] Cameron: It blew me away, really did.

[01:19:23] Steve: the metaverse place and the idea of the Oculus Goggles going way back, I always said that, look, there’s three really great use cases, right? I never thought we’re going to live in there socially or do that or do work meetings like this because I just don’t think you need it. It’s just overkill.

[01:19:38] Steve: But I always thought it would be an incredible entertainment device and movies. I thought it’d be great for industrial applications like fixing things and augmented displays on, you know, big screens. Repairing aircraft engines. And I thought it’d be great for training things like, you know, surgeons and all manner of things.

[01:19:55] Steve: So that were the three use cases and it sounds like they’ve really nailed that entertainment case, but I’ll, I’ll check it out this week and report back.

[01:20:02] Cameron: And I imagine FaceTiming too, like if you’ve got family on the other side of the world or even the other side of the country, being able to have a conversation with someone where you’re right there and they’re right in front of you will be cool too. But anyway, go check it out. That’s futuristic. Steve’s got to go.

[01:20:18] Cameron: I got to go to Kung Fu. Good to talk to you as always, buddy.

[01:20:22] Steve: mate. I really enjoyed it today.

​

This week on Futuristic, Cam, Steve, and Tony Kynaston of the QAV Podcast tackle the burning question: Will AI and humanoid robots bring about a utopian future or massive unemployment? They dive into potential economic shifts, the state of media, Apple’s antitrust issues, and whether competition will drive tech adoption. Tune in for a lively two hour debate on the future job market and the possibility of a global AI Cold War.

00:00 Introduction and Nostalgic Sitcom References
01:06 Guest Introduction and AI Optimism
12:04 Ray Kurzweil’s Predictions and AGI Discussion
45:22 Australian Media vs. Meta
58:02 Big Tech and Antitrust Issues
01:01:25 The Impact of AI on Employment and Economy
01:20:24 Debating Utopian Futures
01:20:59 Economic Realities of Utopia
01:25:17 AI’s Role in Future Jobs
01:26:56 The Human Element in AI Integration
01:31:07 AI’s Impact on Professional Sectors
01:32:49 The Future of AI and Society
01:38:57 Regulatory and Economic Challenges
01:53:15 The AI Cold War
01:55:41 Final Thoughts and Reflections

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FUTURISTIC 27

[00:00:00] Cameron: you guys. Welcome back to a very special edition of Futuristic. That’s what they used to say on sitcoms in the

[00:00:16] Cameron: 80s, wasn’t it? If they were going to talk about AIDS or suicide or something, it was a very special edition of, uh, Different Strokes this week, where somebody got touched by a Catholic priest and we’re going to talk about the ramifications.

[00:00:32] Steve: See, I thought when different strokes came out that look, America’s not racist. It’s amazing. Look, a white rich guy stole two black children and put them in his

[00:00:40] Steve: penthouse apartment. Like, seriously? And I fell for that? What?

[00:00:46] Tony: I thought you were going to tell your joke about reverse exorcism then, Cameron.

[00:00:51] Cameron: Ha ha ha ha! My

[00:00:52] Cameron: joke? Why is it my joke?

[00:00:53] Tony: Oh was Taylor’s. It was one of the Reillys.

[00:00:55] Cameron: Yeah, yeah, yeah, it’s Taylor’s joke. Yeah, yeah. What’s a reverse exorcism, Steve?

[00:01:00] Steve: No idea.

[00:01:01] Cameron: When a demon pulls the priest out of a child. Yeah, uh, it’s episode 27 of Futuristic with myself, Cameron Reilly, Steve Sammartino and Tony Kynaston, the man from the QAV podcast, uh, who’s come on to, um, discuss, debate. Steve and I about our optimistic AI projections. How are you, Tony?

[00:01:31] Tony: good things, Cam. I don’t know about discuss or discuss. Yes.

[00:01:34] Tony: I don’t know about debate. I often agree with what you guys are saying and thanks for inviting me on. It’s a great show.

[00:01:39] Tony: It’s great to explore this area and you guys do a good job.

[00:01:43] Cameron: Thank you, Tony. How are you, Steve?

[00:01:45] Steve: Good.

[00:01:47] Cameron: What’s on your hat, Steve? I should have worn my Joe’s BBQ hat, but you’ve got a hat on. What’s that? Buck Fever. I thought that was, I thought you were going with the Pulp Fiction line there. My name is Buck.

[00:02:01] Steve: It’s a, it’s a local hat business called The Farm

[00:02:06] Steve: and all of them have some sort of an animal and a statement and I just like them and this is one and I get one every year for Christmas.

[00:02:14] Cameron: That’s nice. Well, let’s, uh, let’s talk about AI, um, before we do, Tony, the way we

[00:02:22] Cameron: normally start the show is we talk about one thing of note that we’ve done with AI or futuristic technology since our last episode. Uh, do you want to start?

[00:02:34] Tony: I’ll be very short, I’ve done nothing, but I’m just looking at Steve’s cap there, you sure it’s not a spoonerism, Steve? Buck fever?

[00:02:42] Steve: Could be.

[00:02:44] Cameron: Spoonerism.

[00:02:45] Tony: Yeah, swap the initials.

[00:02:47] Steve: Oh, no, it’s definitely not that.

[00:02:51] Cameron: Oh, there you go. You’ve, you’ve taken the show right, immediately, straight out of the gate, down to another level, Tony. Well done. Well done.

[00:02:59] Tony: not me, I’m not wearing the hat. It’s just where my mind, it’s just where my mind goes.

[00:03:04] Cameron: Lone

[00:03:05] Steve: If they’re all spoonerisms, that one doesn’t work, so.

[00:03:08] Tony: okay. It’s not the

[00:03:09] Steve: That botches that theory, doesn’t it?

[00:03:12] Tony: Well, possibly, yeah. Uh, no, I’ve done absolutely nothing with technology this week,

[00:03:17] Tony: Kev. I mean, apart from the fact that we live in an age where we’re talking to each other across vast distances and, and, uh, in real time with video, it’s pretty amazing, but otherwise, no.

[00:03:28] Cameron: What about you, Steve? Done anything impressive?

[00:03:30] Steve: well, I have something disappointing to announce. Weekly, almost daily, I put up what I believe are thought leading posts and ideas of things I’ve written and said on places like LinkedIn to generate work, and I get a few views. If I do something, it’s Incredibly poignant about the economic impact of AI.

[00:03:49] Steve: Five people read it. But this week, I went Mr. B style and went into the, let’s call it the game show side of AI. I put up a quiz. Guess which picture is AI generated and which one is an actual photo? Bam, 20, 000 views, which is the decline of modern society. Put up a pop quiz. People are in. Put up something thought leading that might change their economic or social circumstances.

[00:04:12] Steve: Crickets!

[00:04:13] Steve: up something that goes to the lowest common denominator of I’m smart, I can guess which one’s AI, and bam, 20, 000 views. That’s all I got to say.

[00:04:21] Steve: That was less than a minute. Check me later.

[00:04:24] Cameron: you gotta entertain, I guess is the lesson there, Steve. People wanna be entertained,

[00:04:28] Steve: Seems like

[00:04:29] Cameron: not educated, entertained, which is why we’ll all be getting our close off

[00:04:34] Steve: Entertainment is greater than education in 2024. Is that where we’re at? Wasn’t the internet the promise of all knowledge to

[00:04:40] Steve: all people?

[00:04:41] Cameron: yeah. Well, I’ll tell you what was entertaining was the, uh, US presidential debate, um, talking about the, the decline of decline of civilization, uh, that was entertaining for all the wrong reasons. Um, well, I, you know, I continued to do a lot of code, um, the, the. AI tool that everyone’s raving about from a coding perspective is Claude, again, Anthropic’s AI tool, Claude, 3.

[00:05:06] Cameron: 5 Sonnet is the current iteration of it. Um, a lot of people have had a lot of success, a lot of professional coders are saying they’re using Claude for their coding work. I’ve been playing with it a lot in the last week, trying to continue to automate our checklist process, Tony, uh, for QAV. And I have not had a lot of success with it.

[00:05:30] Cameron: It’s, uh, gets very confused when trying to write Python code to manage Excel spreadsheets. Leads me around in circles, so, you know, it’s this continuing thing of coding with AI, on one hand, is incredible and amazing because I can code shit and, you know, two years ago I couldn’t code to save my life. But it’s also really frustrating at times going around in circles.

[00:05:55] Cameron: So I continue to push through and part of me goes, dude, you’re wasting so much time doing this. A year from now, you’ll have ChatGPT 5 and it’ll just do it in a heartbeat. But you know, what am I going to do? I gotta, I gotta do something. So anyway, it’s, um, it’s, it’s, it’s one of those things where you have enough success that it sort of keeps drawing you in.

[00:06:19] Cameron: Just when I thought I was out, they drag me back in

[00:06:22] Tony: conditioning. Random reward.

[00:06:25] Steve: It

[00:06:26] Tony: keeps you there.

[00:06:27] Cameron: Yeah, it is. It’s like golf, right,

[00:06:29] Tony: Yeah. Oh, like any poker machines and gambling,

[00:06:32] Steve: social media.

[00:06:33] Tony: social media. Yep.

[00:06:34] Steve: Yeah. I mean,

[00:06:35] Tony: like watching sitcoms. So, you know, you get one good episode every three or four. It keeps you, keeps you tuning back in again.

[00:06:41] Steve: Although I don’t think that was by design, but I love that.

[00:06:46] Tony: that’s an interesting point you make, Cam. I think, you know, when I was putting together my notes for the show, I actually made a note to say that the democratize, democratization of coding is the, is probably the biggest thing I’ve noticed about AI in, My Lives or Society.

[00:07:02] Steve: And that’s the,

[00:07:03] Tony: And seeing you do it. I mean, going, as you say, over the last couple of years, going from very little knowledge of coding up to where you are now, just because you can ask ChatGPT to write some script for you.

[00:07:13] Tony: It’s actually, it’s been amazing to watch that. And, and democratization of coding and access to data I think are two things which will be big going forward.

[00:07:22] Steve: I do that on

[00:07:23] Cameron: When we started.

[00:07:24] Steve: I talk a lot about the idea that everyone’s an AI developer now with natural language processing. And most people are actually a little bit surprised and people will come up after and go, I just did that thing that you asked. And it’s like, wow. I still think. And it’s easy for us to get in this bubble of like, everyone knows this, but they don’t, they just don’t know that how emancipating the ability for everyone to create code and shape the technological world, because now we’ve got natural language processing is probably the heart of this revolution.

[00:07:53] Steve: I think.

[00:07:54] Tony: Yeah. And can you, you think about all the people when we started QAV who said, I don’t like Excel spreadsheets. Don’t give me Excel

[00:08:01] Tony: spreadsheets. Right. can sort of jump over that barrier with, with a kind of coding that you’re doing and just produce an end result. It doesn’t matter if it came out of a spreadsheet or whatever.

[00:08:11] Cameron: Yeah. Well, I was going to say, when we started QAV

[00:08:14] Cameron: four or five years ago, I could barely work a spreadsheet. Uh, and

[00:08:18] Cameron: now, you know, I can build

[00:08:20] Cameron: spreadsheet, you know,

[00:08:21] Cameron: um, masterpieces, because GPT tells me what to do. Put this code in here, do that there. It’s, it’s, um, been incredibly liberating. I, I did remember, though, one thing I did yesterday with tech, with AI, which I was So I’m standing at my mum’s place up in Bundaberg helping her do some yard work.

[00:08:39] Cameron: I’ve spent the last few days chopping down trees and taking loads of green waste to the dump in her trailer. Feeling very manly as I do it. It’s like the only manual labour I’ve done in the last year. Uh, but she doesn’t have, my mum’s got a TV that’s smaller than my iPad screen, and it’s not connected to any of the digital services.

[00:09:01] Cameron: She’s got a whole bunch of DVDs that have been sitting on her shelf for like 25 years, um, that are mostly crap. Uh, so Chrissy and I were in this in town the other day, uh, yesterday, and I was in like a silly soys, like a $2 discount store, and they’ve got a bunch of DVDs there for a couple of bucks. And I was slipping through ’em and most of them were crap, but there was some that looked like art house palace cinemas kind of stuff that Chrissy and I enjoy watching.

[00:09:27] Cameron: In the past, I would’ve. Pulled up IMDB or Rotten Tomatoes to look at reviews for these things. What I ended up doing was just holding my phone up, taking a photo of the cover into GPT and saying, is this worth watching or not? And it would give me a pricey on the film, tell me what the critics said, how it did at the box office or whatever.

[00:09:48] Cameron: What awards it’s won at Cannes or Sundance and places like that. Just boom. One second, I’ve got a review. And I did that like four or five times. Just hold up the cover, take a photo. What about this one? What about that one? What about this one? The other thing I did with it last night is we, Chrissy and I like to drink tonic water.

[00:10:05] Cameron: You know, we’re not alcohol drinkers. Chrissy’s sober for 12 or 13 years. I barely drink. Although my mum has poured me a couple of scotches since I’ve been here, which is nice. But we drink a lot of tonic water, and I just had the label, it was like a Schweppes tonic water, and I took a GPT photo of the ingredients on the back and said, how would I make this at home?

[00:10:26] Cameron: And it gave me the entire recipe and the process and what to do. And it involves quinine powder, I went to look up quinine powder, hard and expensive to get quinine powder. So I said, well, what, what, quinine powder’s a pain in the arse to get, what else could I use? And they said, what about Angostura bitters to put the bitter flavor in?

[00:10:44] Cameron: I go, I got some of that in my fridge. Yeah, okay, well, just, you know, replace the Angostura bitters and you’ll have something similar. So boom, got a homemade recipe for making my own tonic water now. I have heard people talk about this. You take a photo of any ingredients on the back of a thing in the GPT and it’ll give you a recipe to replicate it at home.

[00:11:03] Cameron: So if you can be bothered, there you go.

[00:11:05] Steve: I mean, I don’t think people realize how much GPTs and AIs are going to eat the internet. Things that we would go to to get information, yeah, you know, the IMBD example or Rotten Tomatoes of a movie is a great example. Take a photo of anything, image recognition, and image recognition, Leads you to a commercial answer that someone otherwise had a valid business running behind that on some sort of internet or app play, it’s really going to eat up apps because a GPT can do anything that an app can do.

[00:11:37] Steve: And you know, one of the topics that we want to discuss today is what, what are the competition implications of that? When classic power laws emerge and you’ve got three AIs that literally can do.

[00:11:50] Steve: Anything on the internet, what happens to the entire, uh, commercial system underneath it at the moment.

[00:11:57] Cameron: Yeah, that’s the thing that’s been mostly front of mind for me for the last few weeks. But before we get into that, I sent you guys a link to Ray

[00:12:04] Cameron: Kurzweil’s latest TED Talk. I’ve been reading his latest book, The Singularity Is Nearer.

[00:12:11] Tony: I thought it was coming out next year you’ve already read it have you?

[00:12:15] Cameron: Uh, yeah, no, it’s

[00:12:16] Steve: Came, it became so near tiny that it’s already arrived, see what, what happened, what, it just, it

[00:12:21] Steve: just, it went back, it went forward in time, and, and that’s, that’s what

[00:12:24] Steve: Ray can do, because have you seen his hair? With hair like

[00:12:27] Steve: that, he’s capable of anything.

[00:12:31] Cameron: That hair. Uh, yeah, so, um, Uh, I’ve been reading the book and it’s great. And it prompted me to pull out his older books too. So I went back over the last couple of days and I pulled out The Singularity is Near from 2005, Age of Spiritual Machines from 1999, I think, and The Age of Intelligent Machines from 1990 and had a look at some of his predictions from those books, which I’ll talk about a little bit later on, but in terms of the TED Talk, did you guys get a chance to watch that?

[00:13:03] Steve: did watch it,

[00:13:06] Cameron: any takeaways, any thoughts on Ray’s latest TED Talk, apart from his hair and his braces?

[00:13:11] Steve: but look, I liked it, and, and I think, The thing that I like about his TED Talks, which is the antithesis of most of them, is that it’s not really a keynote speech, which has got like this story arc and this whole thing, it’s just like, here’s the data, and here’s where it’s going, and I really like that. The one thing that is, I think, uh, a phrase that, uh, And the last one that really stood out to me, and you’ve written it down here, is Longevity Escape Velocity.

[00:13:38] Steve: I mean, that’s a really, really interesting idea where, uh, you know, because everything’s just really organized at the atomic level, I guess,

[00:13:45] Steve: to some extent. And, uh, I thought that that was a, really poignant moment,

[00:13:50] Cameron: to some extent, to,

[00:13:52] Steve: to all extents. There you go. There you go. Thank you, Cameron. Yeah. So that,

[00:13:57] Cameron: you know me, I’m a, I’m an atomic, I’m an atomic dogmatist. Everything is atoms, don’t tell me anything else. It’s all atoms,

[00:14:04] Steve: Including my thoughts, which I have no

[00:14:06] Cameron: including your thoughts,

[00:14:07] Steve: that I just made then wasn’t my fault, according to your thesis.

[00:14:11] Tony: Hehehe. Hehehe.

[00:14:12] Cameron: Not according to my thesis, according to physics. Alright, move along.

[00:14:17] Steve: Yeah.

[00:14:17] Steve: I, I really liked

[00:14:18] Steve: it. And I like, I love that he just went, here’s a whole heap of charts, bam, bam, bam. You can’t look at them.

[00:14:22] Cameron: Yeah, no presentation level, whatever with

[00:14:26] Steve: Zero. I don’t care. They’re in there. I just want, just read

[00:14:28] Cameron: He reminds me of, he reminds me of Noam Chomsky, like, like, it’s like, just, here’s the

[00:14:32] Cameron: data, take it, don’t take it, I don’t care, you know, let’s move on. Um, explain the longevity

[00:14:37] Cameron: escape velocity for the audience at home, Steve.

[00:14:41] Steve: Yeah. So the idea is that. As we move closer to having artificial general intelligence, we will eventually merge with the machines and that we’ll have enough knowledge to cure disease. And yeah, either, either with nanobots as well inside our body, that the life expectancy will increase at a faster rate than one year per year.

[00:15:06] Steve: Is that, is that a fair way to describe it? So we’re actually, We get to a point where everything is solvable because the technology is moving so fast that even though you’re aging at a year by year basis, the technology. is increasing at a level that’s faster

[00:15:20] Steve: than that, that will enable us to solve all disease and cure things

[00:15:24] Steve: so that we don’t ever die, is

[00:15:25] Steve: basically the idea.

[00:15:26] Steve: Unless you get hit by a bus, which he poignantly points out in the presentation.

[00:15:30] Steve: It’s, it’s actually not that easy to explain. How did I go

[00:15:33] Steve: Cam? Maybe you can give your version

[00:15:35] Cameron: well, I think the way I explained it to my mum and Chrissy after I watched it was essentially that at the moment with medical science, for every chronological year that you age, you’re actually only aging eight months because medical technology is able to stave off You know, sort of four months of what traditionally, a hundred years ago, you would have been experiencing in terms of the breakdown of your body and illnesses and the impact of those illnesses and

[00:16:05] Steve: injury was a, was a big one as well. That

[00:16:07] Steve: you die from a broken leg or something, or you have an appendicitis. I had that when I was 10, I’d be dead. If it was, if I was born 200 years ago,

[00:16:14] Steve: the end. Thanks for coming. I hope you enjoyed your stay, Samantina.

[00:16:18] Cameron: And he was saying that as, as, um, AI continues to progress, uh, over the next decade, we will be buying back more and more time for every chronological year that you age. So eventually you’ll get to a point, he’s saying in the next decade, where for every chronological year that you age, medical science will actually save you a year, so it becomes neutral.

[00:16:43] Cameron: You don’t actually

[00:16:44] Tony: It adds a year to your life

[00:16:45] Cameron: the aging process. Well then, and then he said it goes backwards, then for every chronological year that you will age, you’ll actually be saving 18 months,

[00:16:56] Cameron: um, so you’re actually getting younger as it’s repairing the damage that aging has done to your body, and so you will get to a point, and he’s saying in the next two decades, basically, by 2040, essentially, we will have reached a point where you’re You will get younger and younger as you get older until a point where you reach some sort of stasis and then you’ll just be held in that stasis.

[00:17:25] Cameron: Uh, you’ll, you’ll get to basically 25, the physical health of a 25 year old, and you’ll just basically hold there until you get bored and you want to terminate things, you know, or you get hit by a bus.

[00:17:37] Tony: Or you

[00:17:38] Steve: it was,

[00:17:38] Steve: it was peak oil. And now in the Kurzweil era, it’s peak humanity. Welcome to 27 forever, club 27, Kurt Cobain, we’re coming for you.

[00:17:47] Cameron: And I’m sure we all hope that Ray is the first person to get access to these

[00:17:52] Steve: Well, he needs to improve his hair. And, and that is the first thing on Ray’s list. Let me tell you.

[00:17:58] Cameron: You know, it’s sad to me, like you mentioned Marvin Minsky a couple of times, and for those people who don’t know Marvin

[00:18:03] Cameron: Minsky or his role in AI, he was like one of the original 50s and 60s who was at the forefront of AI research. He was Ray’s professor and mentor and friend. For many, many decades, I remember reading Marvin Minsky’s Theory of Mind book, um, geez, in the early 90s, maybe, when I first started getting really interested in this stuff.

[00:18:27] Cameron: And he passed away, uh, a couple of years ago, sadly. Like, he didn’t even get to see ChatGPT land in 3. 5, or all of this kind of stuff. So, you know, I find that Kind of really tragic. I’m just looking up when he actually died here. Hold on. 2016. Yeah. Okay. Eight years ago. Like, um, really like just, you know, the, the, the tragedy and irony of one of the guys who spent decades.

[00:19:00] Cameron: But here’s the thing that’s interesting that Ray talks about in his latest book is he and Marvin had a huge debate. You know, Ray talks in the book about the two approaches, the two basic approaches to AI. They’re thinking about AI over the last 70 years and, um, one is what he refers to, I think, as, um, symbolic logic or some variation of that.

[00:19:27] Cameron: Basically, and this has been the dominant approach for the last 70 years, is where you had to write down every rule about how the world works and give it to the AI. The sky is blue, cats like to drink milk, roses are usually red. Unless they’re not, etc, etc. And you had to write all of these rules, capture all of these rules, and then the AI, if you asked it a question, would have to look up all of these rules, and work out how to answer your question based on all of these rules.

[00:19:59] Cameron: And then he talks about the problem with, um, complexity. The com Plexity theory where every time you add another rule it gets more and more complex and if one rule is wrong or breaks it just breaks everything and it’s a very fragile system. The other idea was basically neural nets. Here’s another term for it in the book, I can’t remember off the top of my head what it is, but essentially what we now call neural nets, which is where you basically give it tons of information and tons of compute.

[00:20:33] Cameron: And tell it to, you know, build little nodes or tokens of information. And he says that Minsky and another guy, uh, wrote a book decades ago that basically shit on the neural net idea as not being valid. And it, he said it stopped research in neural nets for like 40 years. And before he died, Minsky said that one of his biggest regrets was co writing this book because it basically shut down a whole field of research, which of course today is exactly how transformers, which underlie the LLMs, actually work.

[00:21:12] Cameron: The problem that they had, they used to try and do this stuff in the 60s and 70s, he talks about a guy that was experimenting with it earlier on, and when Minsky shit on it, um, you It, you know, with Papert, I think was the name of the other author, it just, you know, everyone just veered away from it and his experiments shut down and no one took it any further.

[00:21:32] Cameron: But we didn’t have the compute to, to do that back then and, you know, all of a sudden now, thanks to largely NVIDIA, We have the compute to make this kind of stuff work. So anyway, Minsky, I just, I just hope that Ray survives long enough to take advantage of the stuff that he’s been predicting for the last 50 years, you

[00:21:54] Tony: that’s, that’d be the real tragedy for me. I mean, certainly Minsky was important in my life, but, but yeah, if I gets like dies a year before the upload. Yeah. I think you’re talking about Markov chains when you were talking about neural networks before. Expert systems and Markov chains. And that’s really

[00:22:11] Tony: what I grew up experiencing in business and how they were going

[00:22:14] Tony: to be revolutionary and, and just really ran out of computing space to, to be successful.

[00:22:20] Cameron: yeah. Anyway, um, sticking with, uh, Ray, so, um, He’s still predicting, he’s predicting AGI by 2029, and again for new listeners, what is AGI? I get asked this a lot by people, um, it stands for Artificial General Intelligence, the basic Like, there’s a lot of people with different definitions. It’s a bit fluffy, but the one that I always come back to that seems to make sense is AGI is when an AI system is better at most things than most people.

[00:23:01] Cameron: So it’s going to be better. Yeah. Why? You have a different definition?

[00:23:07] Tony: Sorry, I shouldn’t interrupt. I do, yes.

[00:23:10] Cameron: That’s fine. What’s your definition?

[00:23:12] Tony: Well, I thought in my mind an AGI is when you, uh, whatever you program can learn. So you didn’t, as you said before, you don’t program in the nodes and then have the system work out the logic to present the answer based on the data.

[00:23:27] Tony: It’s, you’ve written a program and

[00:23:29] Tony: it can think for itself and teach itself and then, um, solve problems that weren’t coded originally into,

[00:23:35] Tony: into the program.

[00:23:37] Steve: You can already

[00:23:37] Cameron: Well, we already have that. That’s what AI is now.

[00:23:41] Tony: Yeah. Okay. All right.

[00:23:43] Cameron: I mean, the way an LLM works is nothing’s coded into it. There are no answers coded into an LLM,

[00:23:51] Steve: Yep. it

[00:23:52] Cameron: it is

[00:23:53] Steve: uncover an answer based on the data that it has and

[00:23:56] Steve: the inference it can make through the probability in the large data sets. So it sort of does that now.

[00:24:02] Tony: Okay.

[00:24:04] Cameron: no one’s, no one’s coded, like, and this is a thing I again explain to people that are new to this a lot, because I think this is the thing that people don’t appreciate, like when people, people bring up hallucinations. Oh, it’s not perfect. It makes mistakes. You can’t trust it. See, the thing you have to realize is two years ago, three years ago, no one even knew this would work, this approach.

[00:24:32] Cameron: I saw when, uh, Jensen Huang, the CEO of NVIDIA, interviewed Ilya Tsutsikova, the chief scientist at the time for OpenAI, then unfortunately left recently, um, after the whole kerfuffle with Sam’s removal, etc. But he was interviewing Ilya, who’s basically the brains behind ChatGPT. One of them, but one of the dominant ones, and he said, Jensen Huang asked earlier, What surprised you the most about ChatGPT 3.

[00:25:00] Cameron: 5? And he said the fact that it worked.

[00:25:04] Tony: Hmm.

[00:25:04] Cameron: So you’ve got the chief designer of the AI who was surprised that it worked because the theory was just, you know, they started to play around with this approach of transformer models, which are only themselves five or six years old. Well, at the time they were five or six years old when they started putting it together and it seemed to be exhibiting emergent behavior.

[00:25:28] Cameron: And so they basically just thought, what happens if we throw way more data at this? And to do that, we need to throw way more compute at this. And so they scaled up their data and their compute. That’s all they did and continue to train it like they would normally train it, which is just, you know, come up with an answer to this question.

[00:25:49] Cameron: Yeah, that’s the best answer. Come up with an answer to this question. Yep. That’s the best answer. Come up with an answer to this question. Yeah, that’s the best answer. And let it, you know, figure everything out for itself based on how well it’s answers scored. As they threw more and more compute at it, it all of a sudden became intelligent.

[00:26:09] Cameron: And they were like, oh shit, like, this works! Like, they didn’t know, they, and we still don’t really know how it works. The people who run these things, Still really, it’s a bit of a black box. That’s the problem with it. We know if you put information in, intelligence comes out. We don’t know exactly why or how, but it just does.

[00:26:34] Cameron: So the fact that it’s, makes mistakes and makes errors is kind of not the key takeaway. The key takeaway should be, holy shit, this is magic. We just invented magic. You, you stick stuff in and something comes out. Now we have to figure out how, why it works so we can compress it and compress the data set and compress the level of compute that we’re using, et cetera, et cetera.

[00:26:57] Cameron: But the AGI,

[00:26:59] Tony: sorry. So I’m just going to read from the first thing that popped up when I asked for an AGI meaning. It comes from Amazon Web Services. AGI is a field of theoretical AI research that attempts to create software with human like intelligence and the ability to self teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for.

[00:27:21] Steve: sounds like your definition, sounds exactly, looks like you and AWS agree. Seems like you’re the man, Tony.

[00:27:26] Tony: Yeah. So, and definitions matter.

[00:27:29] Steve: there’s a lot of different definitions around this and often I think AGI gets confused with ASI and uh, you know, cause you’ve got Artificial Intelligence, Artificial General Intelligence and then Artificial Superintelligence, which is kind of what I think most people think of.

[00:27:44] Steve: When they, when they, um, talk about AGI, where it’s an artificial intelligence that far exceeds human intelligence in all manner of, uh, human intellectual endeavor, whether it’s creative, intellectual, economic, whatever it happens to be. I don’t know.

[00:27:59] Tony: I agree, Steve. I, when I,

[00:28:00] Steve: can’t even agree on the definition tells us where we are.

[00:28:04] Steve: We’re in a moment of significant change, right? That, that kind of is, I think, part of this whole

[00:28:08] Tony: we’re making it up as we go along. I agree. But, but when I think about this subject area, I think we already have AGI, but we don’t have ASI. AI is a sort of

[00:28:18] Steve: Cam and I have talked about that too, at various points.

[00:28:21] Tony: Yeah. So that’s, I guess, just the term straight. That’s what I think of when I think of those two things. And I think it’s important for like discussions about what happens with AI.

[00:28:32] Tony: I think ASI is really hard to predict what will happen and it’s way out there into the future and well, maybe not too far into the

[00:28:40] Steve: 2045, according to

[00:28:41] Tony: yeah, but it’s coming.

[00:28:43] Steve: which is even more radical,

[00:28:45] Tony: But I think AGI is a bit easier to predict. It’s Kurzweil’s curves. It’s, we know what an increase in computing power will do. You know, it allows us to code and all that kind of stuff.

[00:28:59] Tony: I think

[00:29:00] Steve: we have, we have been here before in the industrial era, when we used those curbs before, and we assumed that they would exponentially increase in perpetuity. And then we got to a point where they didn’t. Well, for two reasons. One was we, we decided that, um, faster and stronger wasn’t necessarily answer more efficient was.

[00:29:20] Steve: And we might, we may decide that as well with compute. We might decide, well, wait a minute, we’re just using too much energy and go, this is intelligent enough. Like airplanes haven’t gone any faster since, you know, the early seventies. That’s it. It’s 1969.

[00:29:34] Tony: Yeah,

[00:29:35] Steve: And we made them safer.

[00:29:36] Tony: it. Yep.

[00:29:37] Steve: And more comfortable.

[00:29:38] Steve: And maybe we make AI safer, more usable, you know, less, less, more economic in terms of energy. So there, there, there is some historical, and this might be different because AI and intelligence, computational intelligence is different to industrial powers, but we have, we were on that trajectory at one point where we were talking about, Oh, you’ll fly to London in half an hour, like, and it just never happened.

[00:30:00] Steve: So, I mean, I don’t know, but it’s worth, you know, Remembering

[00:30:04] Tony: Yeah, well, there are constraints, right? And probably in preparing for this show, I was reading articles and they, I forget now who it was, uh, may have been Sam Elson or someone like that, but they came out and said, the constraint they’re facing now is the data set, right? They’ve trained AGI on the internet, um, but they, but they’re finding that’s limiting.

[00:30:25] Tony: The learning is now being limited by the data set. So it’s possible that we don’t get any smarter or faster than the computing

[00:30:32] Steve: I think

[00:30:33] Tony: Now,

[00:30:34] Steve: is kind of interesting because it’s what you put in. There’s two, there’s two parts to that equation. One, and Cameron and I have discussed it, is the idea of dead internet theory, where the internet is already starting to be heavily populated by AI, and you get into a recursive, uh, Decline Spiral, where the resolution of the data and the nuance of new information gets lost because AI is populating the internet, which is what the AI learns on, so it can decline.

[00:31:00] Steve: The opposite side to this is the idea that self drive cars and closed circuit and open circuit cameras start to train the internet with inferences from the real and the physical world and satellite data and all of the information that is just absorbed rather than published, let’s say.

[00:31:17] Tony: really interesting. That’s where I was going to as

[00:31:19] Steve: Yeah,

[00:31:19] Steve: so we absorb

[00:31:20] Tony: is the constraint, then it needs to have an interface with the physical world. So it doesn’t just look up every picture of a rose. It actually looks at all the roses that you can see.

[00:31:31] Steve: And if everything is on camera and everything is recorded audibly, and we have enough camera and computation taking in inferences from the real world, the AI could maybe train itself without relying on human publishing of language and words and video, and then populate itself with a second generation of data points to train the AI.

[00:31:54] Tony: Yeah, and I go one step further. AI, I think, will hit the next quantum, so we’ve kind of reached the quantum level now, goes up again with synthetic eyeballs and synthetic eardrums, so it’s, I don’t think cameras will work because it’s a flat 2D image, but I think if you have some kind of, you know, if you can grow

[00:32:14] Tony: an eyeball on the Petri dish that links into Bye.

[00:32:16] Tony: Bye. ChatGPT, then it starts to experience the real world and the data set expands again. So I think that’s probably,

[00:32:23] Steve: And that goes into the whole Kurzweil bit too of, you know, synthetic, um, you know,

[00:32:30] Steve: symbiosis with, with computation and yeah.

[00:32:34] Cameron: your, your, your iPhone camera has LiDAR built into it. So we now have cheap, deployable LiDAR that can be used in cameras that are plugged into AI. So you get full depth perception, um, full recognition of shading, of colors and movement, that kind of stuff. I, I think that’s almost there. Anyway,

[00:32:57] Tony: But also, but yeah, but just, just before we leave that, like, I don’t think that’s good, but it’s not going to be enough to increase the data set enough to make AGI take that next leap. And I, and I use as evidence for that self driving cars. And, um, there was an article in the AFR recently about, um, Is it Waymo in San Francisco now offering autonomous taxi services?

[00:33:18] Tony: And if you look at the car, it’s full of LiDAR units and spinning cameras and all the rest, but it still does have some problems actually driving. It’s supposed to have less accidents than a human at the wheel, but it still has accidents. So, but yeah, you can only take spinning cameras and LiDAR so far.

[00:33:36] Tony: What’s the difference between us and that we have eyeballs and a, and ears and a head that can swivel. So that’s probably, I think, gonna be the, the next game changer for ai when it, when it merges with

[00:33:47] Tony: synth synthetic tech,

[00:33:49] Steve: Tony, we need to have a rock band called Swivel Heads and I just feel like

[00:33:52] Steve: I can have a punk band called the

[00:33:54] Tony: you can supply the hats

[00:33:57] Cameron: Tony, Tony’s a legendary guitarist and vocalist, man.

[00:34:01] Steve: I didn’t know that.

[00:34:01] Cameron: to the QAV theme song at the beginning of every episode.

[00:34:05] Cameron: That’s Tony,

[00:34:06] Steve: Well, I better get on that. Alright.

[00:34:08] Cameron: Legendary. Um, well, the point I wanted to make though, getting back to Ray, is in his TED talk, he said some people are predicting AGI

[00:34:17] Cameron: within two years, and he’s saying Elon, others are saying, and he said that’s possible.

[00:34:22] Tony: A GI or a, so sorry, A SI or a

[00:34:26] Tony: GI.

[00:34:27] Cameron: AGI,

[00:34:28] Tony: And is that the equivalent of a SI? Is that the artificial super intelligence.

[00:34:32] Cameron: no, just AGI, which again, I’m going to get back to Sam Altman’s definition, when the AI system is generally smarter than most humans at

[00:34:41] Cameron: most things.

[00:34:42] Tony: so that, why isn’t that, why isn’t that a SI,

[00:34:46] Cameron: well, ASI is when it’s like 10, 000 times smarter than humans.

[00:34:51] Tony: Okay,

[00:34:52] Tony: Mm-Hmm.

[00:34:53] Cameron: So the,

[00:34:54] Cameron: um, The AGI, um, you know, the AI that we have today, ChatGPT 4, et cetera, Claude 3.

[00:35:02] Cameron: 5, Gemini 1. 5, whatever it is, are really, really good at lots of things and really bad at other things. Like, I’ll give you an example if you, and I did this the other day to demonstrate this to somebody. You open up any one of those tools, and I, and add perplexity to this, um, and you ask it to multiply two four digit numbers.

[00:35:25] Cameron: You will get wrong answers over and over again. Uh, actually with most of it, ChatGPT 4 is the worst. It gave me like five wrong answers. I had the calculator in front of me and I kept going, Nope, Nope, Nope, Nope, till I finally got it right. The other apps I used took one or two, I took two, uh, two goes to get it right.

[00:35:44] Cameron: The first answer was wrong. And then I’d say, Nope. And it’d give me the right answer. So they’re bad at that. They can’t play chess. Um, you know, the coding that they do sometimes is terrific. Sometimes it’s shitty. Um, so they’re really, really good at lots of things some of the time, and then really, really bad at other things all of the time or some of the time.

[00:36:05] Cameron: AGI, in my mind, is when they’re right about nearly everything, nearly all of the time, and highly, highly reliable. And, and so the predictions for that, from everyone who’s in the field, Um, all the guys who run all the AI labs, and Kurzweil, and Elon, and Gates, and you know, Wolfram, is two to five years from now, is when we get AI systems that are massively more competent than most humans at most things, and Uh, extremely reliable.

[00:36:43] Cameron: Sam’s saying QPT 5 is going to be extremely reliable. We don’t know when that’s coming out, but the assumption is in the next year or so. And this is important when we get to the next part of the, the, the debate, the discussion, there’s around what happens to the economy when we have these systems readily available at relatively low cost.

[00:37:03] Cameron: That’s where I want to get to, um, with the show. Before we do further that, I want to touch on some of Kurzweil’s past predictions. In The Age of Intelligent Machines, again, which he wrote in 1990, he said, I placed the achievement of level four intelligence sometime between 2020 and 2070. Now, level four in that book was passing the Turing test.

[00:37:27] Cameron: And he still is saying in his latest book that it hasn’t done that yet. Steve and I disagree. We think it has blown away the Turing test, because it can answer pretty much any question as well as a human can, and in fact, the problem now, of course, is that it answers it too well. The way that you know it’s an AI is it’s able to answer it far better than most humans could.

[00:37:50] Cameron: But anyway, so, in 1990, he predicted that, and I’d say 2020 was pretty much right on the money. Like, ChatGPT 4, ChatGPT 4 Let’s say came out 2023. Um, that was pretty much, you know, he was a couple of years out there. He really said between 2020 and 2070. So it was on the lower end of that scale. This next one, not so good.

[00:38:11] Cameron: So in Age of Spiritual Machines, 1999, he wrote, if we apply the same analysis to an ordinary personal computer, we get the year 2025 to achieve human brain capacity in a 1, 000 device. Now, Apple is coming out with Apple Intelligence in an iPhone, which is a little bit more than a 1000 device, it’s like a 2000 device, depending on what model that you get, and it is going to have some level of AI built into it, we don’t really know how much it’ll be able to do until we see it, But, he may not be far off, that’s a year from now.

[00:38:48] Cameron: But then he went on to say, The memory capacity of the human brain is about 100 trillion synapse strengths, neurotransmitter concentrations at interneuronal connections, which we can estimate at about a million billion bits. In 1998, a billion bits of RAM, 128 megabytes, cost about 200. The capacity of memory circuits has been doubling every 18 months, thus, By the year 2023, a million billion bits will cost about 1, 000.

[00:39:17] Cameron: However, the silicon equivalent will run more than a billion times faster than the human brain. There are techniques for trading off memory for speed so we can effectively match human memory for 1, 000 sooner than 2023. So I asked ChatGPT about this. And it said he was slightly out on this one. Um, 1, bits of RAM is about, uh, 1 petabit of RAM, which is about, uh, 1, 000, bytes.

[00:39:51] Cameron: Uh, current RAM prices for DDR5 are about 3 per gigabyte in the US. So that would be about 350, 000 basically for a petabyte of RAM. But as it then goes on to say, not really Ray’s fault because it was market driven dynamics for that, but that’s one of the problems with projection, you know, projecting into the future, right?

[00:40:16] Cameron: You don’t really know what the market, we probably could build that amount of RAM, but there’s just no market for that amount of RAM in consumer devices because we have had really nothing to do with it You don’t need that much RAM to run a super fast iPhone or a super fast PC. We do need it to run a super fast AI.

[00:40:36] Cameron: So we may catch up, uh, with that. Um,

[00:40:40] Tony: I can just throw 2 cents in here, cam. I think the beauty of kurz while’s predictions and, and you know, you and I talk about the inability for people to predict on our QAV show all the time when we talk about the stock market and what companies will do and where the economy’s going, et cetera.

[00:40:55] Tony: But the thing I’ve always admired about Kurzwell is it’s rooted in this curve. The idea of the curve. Here’s what’s happening. Here’s what I’ve observed. I’m simply extending that out using the simple maths of plotting it on a graph. That’s, that’s such a powerful framework for, for anchoring his predictions in.

[00:41:13] Tony: That’s why he’s got longevity in the space, I think,

[00:41:15] Steve: I think too though, Tony, it is Use that word anchored. It’s anchored in the economic incentives, which ties into the QAV thing, on people building computation capacity and getting the price cheaper and all of that. So without of that, I mean, it’s not just like a law of nature. It’s how we interact in the market, which makes these things more available at better prices, but, but like the share market, and, and I remember in one of his presentations, it was when he, um, he did a presentation at Google Talks, it was around about 2000, I want to say 2010, when his last book, whenever that came out was, or 2013, and he went through the logarithmic charts that show it, Like the share market, they don’t move in straight lines.

[00:42:00] Steve: So you might have a, a period of flatness or decline and then, and then it goes up when there’s a, there’s a curve and a, a bit of a jump in, you know, technology which allowed those curves to over the long term be look like they’re predictable and straight line, but they, they bubble around before they hit those points.

[00:42:18] Steve: And sometimes you’re a little bit behind schedule and then there’s a curve jump in the tech, bam. And GPUs might be one exemplar of that as well.

[00:42:25] Tony: Yeah, and that’s why I think Kurzweil’s still pretty accurate, even though the price of that memory chip is 350, 000 to replicate the human

[00:42:33] Steve: Yeah, it’s like, you know, the, the, the all ordinaries and, you know, the S& P 500 are averaging, you know, 10, 11 percent or whatever it is over, you know, the longer term. Yeah, you have your ups and your downs in that, but over the long run, it’s kind of what you

[00:42:44] Steve: get. so

[00:42:45] Tony: taking a dot plot and attempting to put a curve through the middle of it. But the dot plot never follows the curve exactly.

[00:42:53] Cameron: That’s the name of Swivelhead’s first album is the Dot Plot, by the way. Um, this,

[00:42:58] Steve: I’m going to make that song this afternoon.

[00:43:01] Cameron: is near 2005, he said, based on the above analyses, it is reasonable to expect the hardware that can emulate human brain functionality to be available for approximately 1, 000 by around 2020. As we will discuss in Chapter 4, the software that will replicate that functionality will take about a decade longer.

[00:43:22] Cameron: However, the exponential growth of the price, performance, capacity, and speed of our hardware technology will continue during that period, so by 2030 it will take a village of human brains, around 1, 000, to match 1, 000 worth of computing. By 2050, 1, 000 of computing will exceed the processing power of all human brains on Earth.

[00:43:44] Cameron: Of course, this figure includes only those brains still using only biological neurons. Anyway, my point is that 2020, uh, Kurzweil’s been saying 2020 since at least 1990 and, um, he’s, like, you can debate what we get for, in terms of, for 1, 000 worth of computing, but the overarching idea there, I think, has always been that by 2020 we would have the ability to start to create forms of artificial intelligence.

[00:44:15] Cameron: And he was right on the money. Like ChatGPT, it’s almost like, you know, they were using Kurzweil’s timeline as a guide for when to launch this stuff. But, uh, it’s kind of astounding. So, when he says now AGI by 2029, when he says ASI, Buy, uh, well he says the singularity by 2045, I think, in his latest book, uh, let me just scroll down, yeah, 2045, and he defines the singularity as when we increase intelligence a million fold.

[00:44:49] Cameron: Now, you know, I’ve been arguing on this show that we’re already in the singularity, because I don’t think I can accurately even begin to predict what the world’s going to look like ten years from now. And to me, that’s always been the definition of the singularity, when change is so rapid, And so massive that we don’t really know what it means to be human 10 years from now.

[00:45:12] Cameron: We can’t really imagine what that would look like, but, um, he’s, he’s pushing it out a little, you know, another sort of couple of decades. Um, moving right along guys, uh, Steve, uh, I know you’ve wanted to talk a bit about, um, The Australian media versus Meta, and I see, um, your good mate Albo came out over the weekend and stuck the boot into Meta.

[00:45:39] Cameron: Um, I find this all pretty hilarious, but, uh, why don’t you throw your two cents in?

[00:45:44] Steve: well, I’m just interested and we don’t have to labor on it, but I think it’s important, especially for, for the Australian listeners. Um, there’s been a media bargaining code where. Uh, tech companies like Google and Meta have to pay for the content that they absorb, uh, into, um, their systems or, or, or into their formats where people can read the news and so on.

[00:46:12] Steve: And they reluctantly pay, who knows, you know, tiny amounts. Um, And we can see why there would be a protest against it, both from Meta, who are like, Hey, we’re just distributing stuff, we shouldn’t have to pay this. And then you’ve got News Limited and Murdoch and all the others saying, You’re just sucking up our content, and we should be paid for what we’re creating.

[00:46:31] Steve: I’m just really interested to hear what you two think about it, because I don’t know who’s worse. I don’t know if the Murdoch press is worse, or if, uh, Meta and Google and the others. I just don’t know. But, well, the one thing that I do know is that the Fourth Estate is important. As Cameron pointed out on a text message to me, he said, Fourth Estate?

[00:46:49] Steve: What’s Fourth Estate? We haven’t had one for a long time. But I’m really interested because It’s really, really important. We’ve already talked about the elections today. So,

[00:47:00] Tony: Yeah, I’m surprised Meta hasn’t offered some kind of revenue split for the ads that go with the news. as a way of paying back rather than try and have the government impose a solution on it. Because I, you know, I think you’re right, Steve. I think, um, certainly in the last 10 or so years, the level of journalism has declined as social media has taken off.

[00:47:22] Tony: Um, and you know where is that we’re evidencing in the US candidates who are standing for election, really. Um, there’s, there is no, Yeah, there’s no, you go back many years, there’s no, um, who was the guy from Thank You and Good Night? The, anyway, you know, the old sort

[00:47:38] Steve: Clooney.

[00:47:38] Tony: yeah, well, the character George Clooney played, you know, there’s no sort of,

[00:47:42] Tony: um, independent journalist holding people to it, holding truth to power, or telling truth to power these days.

[00:47:50] Tony: The journalists we have these days are so

[00:47:53] Cameron: Well, there are. Julian Assange, they

[00:47:54] Tony: it’s true. Yeah, they are there. True. They are

[00:47:57] Steve: the other guy that they just locked up in Australia who let out some military secrets, whatever his

[00:48:02] Cameron: Pride,

[00:48:03] Tony: McBride, yeah. Yeah, no, that’s a good point. But, um, but, you know, gone are the days where journalists, there was enough money in journalism that

[00:48:11] Tony: they could stand up to government.

[00:48:13] Tony: Now they’re pretty much beholding on government, right? So they become friends of government and it’s a bit of a dance. It’s, so it’s a problem.

[00:48:20] Cameron: So, like, it just amused the hell out of me when it was

[00:48:24] Cameron: News Limited. Uh, coming out and saying that Facebook was toxic, uh, toxic influence in society. Like, really? Really?

[00:48:35] Steve: No, but it absolutely is. This is the point, Cam, is that yes, it is. Right? They

[00:48:41] Cameron: Yeah. Both things can be true.

[00:48:43] Steve: that said, and we too are toxic. Like the point is, there’s a lot of toxicity here. Shout out to System of a Down.

[00:48:50] Tony: Hey, we know Toxic. We know toxic. We invented

[00:48:52] Cameron: Yeah,

[00:48:53] Steve: toxic. Don’t question us at Toxics. A lot of people, they don’t understand how toxic the mainstream media is.

[00:49:01] Steve: Very good friends of mine. Very good friends.

[00:49:04] Cameron: It’s pretty good, Steve. That’s probably the best amateur Trump impersonations I’ve seen. Um, Yeah, look, the media has, you know, I’ve been, I’ve been banging on this for a long

[00:49:17] Cameron: time now, since I started podcasting, really. Like, the media has, uh, run, deliberately run itself into the ground over the last couple of decades by, you know, cutting costs, getting rid of journalists, getting rid of fact checkers, cutting, cutting, cutting, cutting, uh, partly because of economics, because the economics of media retention fragmented with when the internet happened and, and, and ad spend fragmented and they didn’t, uh, uh, adapt.

[00:49:52] Cameron: Fast enough, so they got replaced by Seek, and Google Ads, and all of these other platforms that took their revenue away. But they keep trying to maintain their relevance in society, whilst at the same time laying off most of their journalists, and changing their content from any sort of hard investigative journalism to just click baity bullshit.

[00:50:20] Cameron: And, you know, they’ve been losing money hand over fist. Um, I, I did some quick research over the last couple of days. News Corp in Australia recorded a 75 percent drop in full year profit last year. Nine Entertainments saw a 38 percent decrease in net profit. So they’re, they’re just basically floundering.

[00:50:44] Cameron: And if, and if Meta, Stop paying them the millions that they’ve been forced to pay. By the way, I love this, like, the media narrative is Meta, uh, trying to, um, you know, bribe the media companies. It’s like heavy handed tactics of Meta. It’s like, really? You’re the ones who went to the government and got the government to pass laws to face, to force Meta to pay you money But they’re the ones that are being the bullies and the heavy handed in the marketplace.

[00:51:18] Cameron: I mean, I think Meta’s playbook here is like, you know what, we don’t need you, but you need us. And if you don’t have our revenue, you’re fucked. You’re already fucked, but you’re even more fucked if you don’t have our money. So they’re in the power position. And these media companies know it.

[00:51:34] Tony: Yeah. Except meta. Meta. Meta does need the content, so it does need the.

[00:51:37] Steve: it does need it a bit. Uh, look, it

[00:51:39] Cameron: Does it though?

[00:51:40] Steve: it, it doesn’t need it. But here’s what they should do. If, if the, if they don’t pay the, yeah, they, they definitely should shut it off because then at least what you get is something needs to break for something better to arrive. Okay.

[00:51:53] Steve: The system is already broken, so

[00:51:54] Steve: let it really, really break. If meta and meta don’t need it, you’re right. But let’s

[00:52:00] Steve: Good. Shut it off because then

[00:52:01] Steve: something has a chance of happening. Right? That’s better

[00:52:05] Cameron: already done this in Canada, they’ve shut the, they’ve, they’ve deleted the news from Canada, uh, when the Trudeau government tried to implement some similar sort of, uh, bully boy tactics. They just went, okay, we’re not carrying Canadian news anymore, so there’s no news in Facebook in Canada as I

[00:52:24] Steve: Yeah. And I think one of the interesting things is the definition of, the definition of news is, is interesting as well, right? Because the lines have been

[00:52:32] Steve: blurred. You could argue that the podcasts that, the things that. We write are news. to an extent. And last year meta, you know, really, really went dirty and blocked off local sporting clubs and all of that kind of thing.

[00:52:42] Steve: So that,

[00:52:43] Cameron: Yeah, I got blocked. All of my podcasts got blocked when they did that. We were considered news. I couldn’t post a link to any of our podcasts.

[00:52:50] Steve: It’d be interesting to see what happens, but I would like to see it break so that there’s a chance for something else to emerge. I don’t know what needs to emerge or where we go, but it’s really, really important. And it couldn’t be more important right now in the era of AI building the ability to create fake anything.

[00:53:07] Steve: It’s like, where is the trusted source and how do you get access to it? And distribution is the most powerful thing in business, I think, more powerful than product. All so often, so many companies do well because they have distribution, you know, whether it’s Meta or whether it’s Coca Cola or whatever it happens to be.

[00:53:25] Steve: And if we can’t distribute valuable

[00:53:27] Steve: ideas and news, you know, we’re going to be in some,

[00:53:31] Steve: we’re going to be in a singularity again, where the future is unpredictable.

[00:53:38] Cameron: I mean, there’s this hearing going on at the moment. I, I, I, I’m guessing this parliamentary inquiry into MEDA is, uh, been carefully timed to coincide with the, uh, re signing attempts of this media bargaining document. But, um, Albo, our Prime Minister, came out over the weekend and was criticising META, um, over the social, over the harm that social media is doing to the children of Australia and how META aren’t taking responsibility for that.

[00:54:14] Cameron: As a parent

[00:54:15] Steve: It’s not meant as responsibility. It’s the government’s

[00:54:18] Cameron: never thought it’s, no, it’s the parents responsibility. I’ve always thought, it’s not Meta’s job to keep Fox off of social media, it’s my job

[00:54:27] Cameron: and Chrissy’s job?

[00:54:27] Cameron: to keep Fox off of social media if we don’t

[00:54:29] Steve: No, and the government’s. We pay them,

[00:54:31] Cameron: Why is it the government’s

[00:54:32] Steve: up until Tuesday afternoon to pay them to legislate against things for our own benefit. I know what you think of that, but I think it’s, there’s a responsibility for the government to take steps to protect their populace. You know, whether it’s all the way up to the military, to local bylaws on roads and safety of children, absolutely, and, and, and the parents, more so the parents, but the government has, the last person that has

[00:54:58] Steve: responsibility for anything is a for profit corporation, right, and if and that’s my pet hate on all of this, is, oh, the, government, corporations have got to be more responsible, no they

[00:55:11] Cameron: on

[00:55:12] Cameron: Well, it depends on what they’re doing. If they’re, if they’re putting pollution into the rivers,

[00:55:17] Cameron: or into the land, or into the sky,

[00:55:20] Steve: into our minds,

[00:55:22] Cameron: Well, I have more control over what goes into my mind than I do over what goes into the river in, in my suburb, right?

[00:55:31] Tony: This is an age old debate, though. There are some children who don’t have two parents, there are some children who don’t have parents who care about social media or know the issues. So you need both. You need government legislation or frameworks and you need good parenting. You can’t always rely on good parenting, unfortunately.

[00:55:47] Steve: have some people who drive responsibility and some that don’t. And the rules are there to protect those against those who don’t

[00:55:54] Steve: do the responsible thing. Just like the parents who don’t do the responsible thing with their

[00:55:57] Steve: children.

[00:55:59] Cameron: that’s a reasonable argument. But I I don’t

[00:56:01] Cameron: think it’s META’s responsibility

[00:56:03] Steve: I agree with that.

[00:56:04] Cameron: the minds of our children. It would be nice if they cooperated, but,

[00:56:08] Steve: Well, this is the thing we’ve got to start, we’ve got to start doing regardless of what it is. You don’t ask for cooperation, you demand it. Right, and it’s the same when it comes to taxation and multinational tax avoidance, which just shits

[00:56:20] Tony: I agree, Steve.

[00:56:21] Steve: It shits me to tears. Guess what,

[00:56:23] Steve: government? It’s really easy. You’re sovereign. Guess what? You’re my new

[00:56:27] Steve: taxpayer. Right, here’s a revenue assessment tax the same as land tax. Congratulations. You’re my newest taxpayer. You don’t like it? Fuck off. You’re

[00:56:34] Cameron: Well, Steve, they’ll all leave the country if we do that. If we, if we, if we’re mean to them, they’ll all leave,

[00:56:41] Steve: Good, fuck off. They

[00:56:42] Tony: We’ll, we’ll end up like Tasmania and be great. Well,

[00:56:47] Cameron: argument with, uh, you know, uh, taxing mining interests. People will say, well, if we tax them, they’ll all leave. I’m like, good. We’ll nationalize it. Fuck off. I don’t care. We’ll just nationalize it.

[00:56:58] Steve: with them. Mate, we should totally nationalise all of the minerals in our ground and or, and or, have significant royalties on all of it. It is so easy, and it is such a simple decision

[00:57:11] Steve: to make. You don’t need a dollar. from any corporate donor because for once you’ll be making a decision for the fucking people you’re meant to represent.

[00:57:20] Steve: Easy. I’m fucking running for politics. I’ve had

[00:57:22] Cameron: They’re like, oh, the

[00:57:22] Steve: God.

[00:57:24] Tony: national, national, nationalized, they’re owned by this, they’re owned by the states. They just, they just

[00:57:29] Steve: Yeah. And they give them away and they give them away, Tony.

[00:57:33] Tony: they charge a royalty on their extraction, but it, But sometimes it looks like a giveaway, like in WA compared to Queensland, where they charge a lot more on the royalty, a col.

[00:57:42] Steve: We, we, we have given net proceeds to companies like Santos

[00:57:46] Tony: Mm. No, I agree,

[00:57:47] Steve: what we,

[00:57:48] Steve: what we, what we get in royalties versus what we give the fossil fuel industry. They actually get more from the government. It’s incomprehensible.

[00:57:56] Tony: No, I agree.

[00:57:59] Cameron: Steve, you wanted to talk about, uh, the European commission and Apple.

[00:58:02] Steve: Well, I just wanted to touch on it briefly and especially because Tony is in the house, you know, I Apple’s in breach of the EU anti competitive laws and they’re putting up a case against them. I mean, I think Cam and I have discussed that these big tech companies aren’t corporations. They’re basically modern era utilities, which are unavoidable and have unassailable competitive modes where I don’t think anyone can catch up to them.

[00:58:30] Steve: We’ve discussed that in great detail. And I just would like to understand Tony’s viewpoint on this. What sort of an investment is big tech? In the face of potential antitrust, whether it’s fines or splitting up, does it still make them a good investment? Does it tell you how powerful they really are and they’re still a good investment?

[00:58:52] Steve: If they get split up, where historically when monopolistic firms, whether it was Standard Oil or whether it was Bell AT& T got split up, They actually created more value post split. I’m just really interested from an investment viewpoint, given the power of big tech and what is it? The top seven tech companies have a market cap of what?

[00:59:11] Steve: It’s something over 15 trillion now. I think it’s crazy. Might even be 17 trillion.

[00:59:16] Tony: Yeah, well, I’m a value investor, so I don’t really follow big tech as investments, because I think it’s a bubble, um, and you look at NVIDIA, I saw

[00:59:24] Steve: answering the question then. This is, this is good. This is what I want to learn.

[00:59:27] Tony: I saw, I saw something on, um, I think it was in today’s paper, uh, NVIDIA lost 430 billion last week in market cap, so it’s,

[00:59:37] Steve: It’s just insane. It’s such a big number. You know,

[00:59:40] Tony: it is, isn’t

[00:59:40] Cameron: Yeah.

[00:59:41] Steve: companies only have a market cap of 30 or 40 billion, and then they lose 10 X that in a week. And it’s like, I mean, it tells you something, right?

[00:59:49] Tony: yeah, and I’ve got a chart I was going to use when we record QAV tomorrow, Cam. Someone produced a graph I saw on the weekend, which said that when, uh, I think it was the top 10 companies in the U. S. had such a concentration of market cap, it was a preceder to a crash, and it happened at this level in 1929 and in 2000.

[01:00:11] Tony: Um, so when you get this kind of, Money following the leaders, um, it, it tends to crash and revert back to the mean. So, uh, you know, I, I think that’s coming. I think it’s a bubble. I think whenever anyone asks if it’s a bubble, it probably is a bubble by, by definition. Um, I don’t know if that answers your question though.

[01:00:31] Tony: Like they do, Apple does have an unassailable moat, although there’s Google, Android, et cetera. Duopolies actually, duopolies can be as, as profitable as monopolies. You know, we see that in Australia all the time. Coles and Woolworths are very profitable. So yes, they’re great investments, but at the moment I think they’re in a bubble.

[01:00:53] Tony: Should they be broken up? I have some sympathies to the EU argument because Apple controls via the App Store most of the developer market. in the world probably and charges at 30 percent on that and there’s no very little competition and yeah that’s a monopoly which you there is an argument to say it

[01:01:12] Tony: should be broken up and as you say though that may not that may create more

[01:01:16] Tony: value um for investors so you have to watch that space Ha!

[01:01:19] Tony: Ha! ha! ha! ha! ha!

[01:01:23] Cameron: right. Moving right along then. Um, I want to, let’s skip over the next couple of stories because I really want to get into the deep dive and we’ve already been talking for an hour. Um, the deep dive. So let me, I’ll restate my thesis and then we can get into it. So the thesis I’ve been trying to figure out for the last month or so on my, on my blog and on the show is what’s gonna happen.

[01:01:50] Cameron: in the next five to ten years in our world, but particularly in our economy. And my, stated simply, my thesis is this, um, AI is going to continue to improve rapidly to the point where in the next five years, We will have a form of AGI that is going to be widely available to businesses as well as consumers and governments.

[01:02:22] Cameron: And we’ll be able to do most things better than most people in terms of knowledge work, information work, and will also be integrated into a wide variety of robotic devices, including humanoid robotic devices, which, according to Goldman Sachs and McKinsey and organizations like that, will be available for purchase in the vicinity of starting price 10, 000 to 20, 000 in the next decade.

[01:02:58] Cameron: So a humanoid, a humanoid robot with an AI built into its brain, maybe commercial versions of that might be a little bit more expensive. But my theory is that when a business can buy a robot with an AI brain for Less than the cost of the annual salary of a human, uh, with all of the incumbent issues that come with humans like HR costs and health and safety costs and all of that kind of stuff, that they will start to replace manual labor with robots as well.

[01:03:32] Cameron: And so over the course of the next five to ten years, I expect to see a lot of jobs being taken first by software, AI, then by robots. And I think this is going to lead to large scale unemployment. And when I Think about the traditional, what’s happened historically is when technology has replaced a sector, um, those people or the generation that comes after them in some cases have found jobs in other industries that may not have even have existed before.

[01:04:08] Cameron: But when you have AI that’s smarter than all humans and robots that can do anything a human can do better, More cost effectively, more efficiently, I can’t imagine what kind of jobs could be created that AI and robots won’t immediately be able to do better than any human. So then, if, uh, you know, there is large scale unemployment, by large scale I’m talking 20 30 percent unemployment, you know, the last time we had 20 30 percent unemployment we had the Great Depression.

[01:04:42] Cameron: And what happens when you have that level of unemployment, obviously, is economies meltdown, there’s not enough cash being spent, uh, so all wealth decreases. You know, there’s this idea that, oh, AI and robots will be available to the rich and the poor people will get screwed. I keep saying, if the economy melts down, everyone’s screwed.

[01:05:03] Cameron: There are no rich people when people can’t afford to buy goods and services. Businesses fail, small businesses fail, that causes bigger businesses to fail because the small businesses can’t pay their bills to the bigger businesses and there’s the trickle up effect, right? And governments don’t have enough income tax to come in to pay for services.

[01:05:24] Cameron: So you end up with austerity measures like they’ve had in the UK for the last 15 years and we’ve seen what’s happened to the UK. My hairdresser, believe it or not, I just got a haircut a couple of days ago. Um,

[01:05:42] Cameron: Steve couldn’t cope with that, he had to walk away from the mic. Uh,

[01:05:45] Steve: you didn’t! Listen, we’re trying to

[01:05:48] Steve: make this

[01:05:49] Tony: Which hair did you have

[01:05:49] Steve: truth, about the future, and then you just pull a Kurzweil on me and tell me how to haircut?

[01:05:54] Steve: You and Ray need to have a cup of coffee and sit down and discuss things.

[01:05:58] Cameron: it was a good inch longer last week and he put layers, layers, Jerry, layers. Um, he just got back from London, he’s visiting his grandmother, he’s a

[01:06:08] Cameron: Pom, uh, who’s lived here for 25 years, and he was like, dude, it’s so fucked over there, like, she’s 90 and she had to go to a hospital to get a brain scan done.

[01:06:17] Cameron: He said there were like, staff fighting over the two available wheelchairs they had, Old people lying in corridors just drooling without being, getting any attention for five or six hours. He said plaster falling off the walls in the hallways of the hospital. He said it looked like the Soviet Union in the 60s.

[01:06:35] Cameron: Like it was just that decrepit. He was shocked. It was his first time back in quite a while. And he was like, dude, that country is fucked. Fucked. And this was in London. Like, we’re not even talking about, was it, um, Manchester or Birmingham that’s just gone bankrupt? Uh, like eight cities in the UK now have just, have gone bankrupt because they can’t afford to pay their bills for their services.

[01:06:58] Cameron: Anyway, my, getting back to my theory is I am predicting and forecasting massive eco Economic and social upheaval in the next five to ten years, Tony thinks I’m bonkers. Discuss.

[01:07:15] Steve: I’ll let Tony go first, I’ve got a whole flowchart on.

[01:07:18] Tony: I think you, I think, well, inherently predicting is difficult. I’m not seeing a curve for your prediction, a Kurzweil curve for your prediction. But let me just, to me it defies the laws of commerce. So if you can buy a synthetic human for 20, 000 and a starving human will work for 19, 000. So there’s a race to the bottom that goes on in commerce in terms of cost cutting.

[01:07:43] Tony: So I don’t think you’re going to see widespread unemployment. We’ve seen this happen before, right? Mexicans cross the border and do gardening jobs cheaper than what, um, the locals do it. We outsource things to China because the manufacturing is cheaper. The, the production of a robot that can do A human’s job is always going to be tested against the cost of being done by a human.

[01:08:08] Tony: And if humans are

[01:08:09] Tony: being displaced en masse, like you’re saying, they’ll work for food they’ll they’ll lower their price, if it’s an issue. But you also said when we, okay, I’ll stop there. I’ll stop there.

[01:08:20] Cameron: humans working for food still doesn’t sound like a healthy functioning economy.

[01:08:26] Tony: Yeah, but the economy will reset as well. I mean, if, when there’s less purchasing power in the economy, prices come down. So food will get cheaper. It may not get better. The quality of the food might decline dramatically. Um, and we’ve seen this as well. I mean, why is McDonald’s successful? It’s because it can feed a family for 20 bucks.

[01:08:47] Tony: It may not be a good feed, but it can. And. Yeah, it’s calories, exactly. You know, how does the, how does the Chinese person survive in the factory? Well, they eat rice and they get their calories cheaply. So, it’s kind of this law of commerce or the economy that, that, um, the robot’s got to compete for its job, even though it’s maybe fantastic and can do it better.

[01:09:11] Tony: It’s still, it’s still got to pass the economic test.

[01:09:14] Steve: Well, I’ll just, I mean, that’s true. I just want to point out that

[01:09:18] Steve: the name of our second album of the Swivel Heads is going to be called Working For Food. I just had to point that out.

[01:09:24] Tony: We’ll have playing for food. We’ll play

[01:09:26] Steve: going to be our follow up album. Let’s hope it’s as successful

[01:09:29] Cameron: After the,

[01:09:30] Steve: of the dot plot. So yeah. I think it’s going to really have a nice flow on in terms of album names.

[01:09:38] Steve: It’s right up there. Um, it’s right up there with Garbage’s 2. 0, version 2. 0, which I really loved as an album, uh, circa 99. Um, I think I agree with you, Tony, is, is that. The tech adoption, there’s an economic paradigm that is required. Like what is the cost of a robot? That said, let’s assume that the robot or the AI or the combination of those two things is better than a human, can work 24 hours a day, doesn’t need annually, all of those things, which if they’re going to be, you know, the cost of AI online, which is kind of free, or a humanoid robot, which is, you know, 10 to 20 grand, the cost of a small car, geez, I tell you what, the economics of that, it doesn’t seem like it’s going to be hard.

[01:10:21] Steve: For it to exceed human capability. But for me, there’s a fundamental flaw in this idea. Um, and, and the number one thing that I think that really matters, and it circles back to the Apple thing, is competition is the most important thing when you have an economic upheaval. You need Large amounts of competitive players.

[01:10:42] Steve: And I’m going to explain why in my economic thinking on this is you have technology adoption. It doesn’t matter whether that’s a combine harvester or AI robotics. The reason a company adopts the technology is to reduce its costs. Right? Or to have greater output. One of those two things. Reduce cost, increase output, which reduces costs anyway.

[01:11:04] Steve: Um, so that’s why you would do it. And why would you do that? To make things cheaper. And you make things cheaper for one of two reasons. And this is a fork in the road. One is to sell more to more people because you’ve reduced the cost and increased your market scope. Reason A, or reason B, don’t increase your market scope necessarily, but increase your margin on what you sell because it costs you more, but you don’t put your price down.

[01:11:29] Steve: Now, if option A happens, right, that’s great, because you reduce, you increase the market scope, you reduce the cost, more people can afford it, and that frees up capital of the purchasers, the people buying things, to invest in emergent industries of which we don’t know what they are.

[01:11:46] Tony: Yeah.

[01:11:47] Steve: But, if we go to that second fork in the road, which is, no, they don’t increase their market penetration, all they do is increase their margin, then we’ve got a problem, because what happens is, they become more profitable, money isn’t freed up to go into new industries, and the only reason Part B can happen, companies Choosing the profit, profit and margin path instead of market penetration is if there’s no competition in which margins, and this is microeconomics theory 101, uh, increase, uh, access to the market, compete away profits because they all have the same new price.

[01:12:21] Steve: And if you don’t put your price down because you’ve got a lower cost of production, your competitors will, and you lose market share and people will substitute across. If, if we don’t have competition, then part B can happen. If you do have competition, it’s impossible for companies to increase their margin at the expense of consumers. that’s actually the most important thing in this right now in your thesis, Cameron, is whether or not we have competition. If we have competition, it’s impossible for the economics to work because That margin gets competed away, which frees up capital with humans who can spend money on new and emerging industries and then populate those places with jobs.

[01:13:05] Steve: It’s the same as like what happened with the music industry. Classic example, right? I spend 30 on a CD for 12 songs. Where does the 30 go? As soon as I can download or steal music, that 30 goes into data, an iPhone, all of those other things, emergent new industries. But now those emergent industries for the music industry are all big tech and big tech is an invasive species sucking up everything.

[01:13:30] Steve: If big tech choose the profit

[01:13:31] Steve: path with automation and robotics, the whole

[01:13:34] Steve: system collapses. So for me, the number one issue on this is competition. It’s actually not the technology. That’s my theory.

[01:13:43] Cameron: problem with that, Steve, is you’re saying prices get cheaper so people can redeploy

[01:13:47] Cameron: their capital in other places, but I’m saying that people are unemployed, so they don’t have an income to redeploy anywhere. And with Tony’s point about it becomes in terms of the humans will drop their price, you know, if you can buy a robot for 20, 000, a human will do it for 19, 000.

[01:14:07] Cameron: That may be true when the quality of the output of the robot is roughly equivalent to the quality of the output of the human. But if the robot, slash the AI system that you’re using in a knowledge worker sense, is twice as good for 20, 000 as a human would be for 19, 000 or three times or five times as good with its output for speed, reliability, etc.

[01:14:37] Cameron: There has to be a breaking point where as an employer you go, humans are just difficult. They need sick days. They need managing. Um, if I get it, if, if I have a, let’s create a hypothetical company that has a department with 10 knowledge workers, let’s say a marketing department. So you’ve got 10 people in the department.

[01:15:01] Cameron: You’ve got a manager overseeing those people, um, that, that has a salary. Um, if I can get rid of. All of those people and have an AI do all of that work. I can probably have a single AI to replace all 20 of those people. If I get rid of those people, I don’t need a manager, so I can get rid of the manager.

[01:15:22] Cameron: If I have, uh, uh, uh, an executive manager overlooking all of the managers and I’ve gotten rid of all of the managers, I don’t need the executive manager overlooking the managers anymore. There comes a point where the AI systems are just, Too good, the economics of keeping humans there and then you need a HR department and you need a building with light and power and you need a car park and you need, you know, to pay holiday leave and all this kind of stuff.

[01:15:47] Cameron: Surely there has to come a point where businesses go, you know what? Humans just don’t cut it anymore. I’m going to replace them all with AI and or robots.

[01:15:56] Tony: And then.

[01:15:56] Steve: that path for a long time. Cameron, though of, of systems replacing multitudes of humans, whether it’s paperwork and the, the software’s been eating that for a really long time. In terms of the, the idea

[01:16:11] Steve: that the adoption and they move to other areas. If they’ve, if they’ve lost their jobs, they can’t pay.

[01:16:16] Steve: That always happens, but it doesn’t happen rapidly. I don’t think. It might do this time. It could be different this

[01:16:21] Cameron: Yeah, but

[01:16:22] Steve: But it happens like this, Cam. It happens like that, and a few people lose their jobs, but as that product gets cheaper, as people lose their jobs, that money goes across to this burgeoning industry, and then the people

[01:16:33] Steve: move across to that.

[01:16:33] Steve: You’ve always got that structural shift. It doesn’t go, boom, like that, or this one comes down, and all these people are It hasn’t so far. It might do. It might do. But so far, it

[01:16:42] Cameron: never, yeah, but in the past, we’ve built, okay, we’ve built a, let’s say an ERP system,

[01:16:49] Cameron: which has replaced people

[01:16:50] Cameron: who

[01:16:50] Cameron: were doing resource planning on pen, on pads with pens, and those people could be redeployed. We had, we built a system here, but we didn’t build a system that could do everything all at once.

[01:17:02] Cameron: That’s the thing with AI, is we’re building a system that can do everything. All at once, at the same time, better than any human. We’ve never seen it. It’s like the, uh, the ultimate machine.

[01:17:15] Steve: you’ve got to remember, the people Who are being displaced also have access to this ultimate machine. And that thing that they had in their head that they were going to invent and create, they couldn’t create, but then now they can, and then now they’re going to go

[01:17:27] Steve: out and go, you know what? I’ve been ready to put, I’ve got this AI.

[01:17:29] Steve: I can just talk to it with natural language and it’s amazing. And I’m going to create this new revenue system in this new industry or job or company.

[01:17:38] Cameron: but I don’t, but I don’t have an income, so I can’t pay my rent. I can’t buy

[01:17:41] Cameron: food while I’m doing that. And no one’s going

[01:17:44] Cameron: to

[01:17:44] Steve: you got a redundancy and you got like six or 12 months money and it’s cause a big corporation and, and then you, but you do, you

[01:17:51] Cameron: Yeah. Okay.

[01:17:52] Steve: And then you go

[01:17:53] Cameron: pay them all out. and so some people take that money and use it to become entrepreneurs.

[01:17:59] Steve: Some do.

[01:18:00] Cameron: Some will,

[01:18:02] Steve: Maybe, maybe, many, many of them do it. And we end up with like a, a

[01:18:09] Steve: system where we have

[01:18:14] Steve: lots of, let’s just call them freelancers and independent workers. Like, like we’ve had pre industrial era where most people, sorry?

[01:18:23] Cameron: Isn’t that the gig economy?

[01:18:24] Steve: Well, yeah, yeah.

[01:18:26] Tony: a, that’s a curve

[01:18:26] Tony: that

[01:18:27] Steve: that does happen. I don’t know.

[01:18:29] Steve: Maybe,

[01:18:29] Tony: I think the trends,

[01:18:30] Steve: gig workers pre industrial.

[01:18:32] Tony: see, I, I still, you’ve jumped to the endgame, I still think about what happens between now and then as AI increases computational power, and so it’s still got to be, it’s still got to be sold to somebody. So, you know, who pays for ChatGPT, who pays for, you know, because what’s going to happen, I think is that, and it’s already happening, is that, um, you know, Silicon, Silicon Valley loves to have the frat boys throw money at these things, even though they’re not profitable, like Tesla, for example, and, and someday it’s got to make money for the serious people to keep putting money in.

[01:19:10] Tony: So, you probably see that. Companies like, uh oh, I see. The different, I guess the different difference in this is that you’ve got, say, apple developing in ai so they’ve got a cashflow to support it. Um, so people will still keep investing in, in ai, but I guess what I’m trying to say is AI will need to support itself economically.

[01:19:30] Tony: And if it’s, um, and that’s gotta happen on the road to this perfect. device that can do all jobs cheaply. Um, and there’s going to be so many secondary effects to that that you may never get to the utopian world of a 20, 000 robot doing all jobs because, um, you might have, uh, it might break down along the way.

[01:19:51] Tony: It might not get to 20, 000. It might, um, because as Steve says, there might be lack of competition in the marketplace and it’s never gets below 100, 000. because Apple want to keep

[01:20:02] Tony: making a, profit margin, um, there’s all sorts of things that have

[01:20:05] Tony: to happen economically or not happen economically to get to that utopia.

[01:20:10] Tony: And I, I find,

[01:20:13] Tony: you know,

[01:20:13] Cameron: Actually, I think it’s a, I think it’s a

[01:20:15] Tony: dystopia. Yeah. Well,

[01:20:16] Steve: thought the exact same thing to get to that

[01:20:18] Steve: dystopia. I love it. Cam will never let you get away with that one, Tony. Cam was on that.

[01:20:24] Cameron: Well, look, it’s going to be, I think there is a utopian version of the future. And that’s what Kurzweil is pitching. He’s a utopianist, that everyone’s going to be great and we’re going to be immortal and every, all the work’s going to be done for us. And I’m trying to figure out what does the economy look like in that scenario?

[01:20:45] Tony: my, my starting question is who pays

[01:20:48] Cameron: for what?

[01:20:48] Tony: for your, for Kurzweil’s utopia? Who pays? Right? Because,

[01:20:53] Cameron: question. yeah,

[01:20:55] Tony: that’s, and that’s why I’m saying I don’t know if we actually get there. It’s like, if you think about where we are now, someone’s still got to spend 2000 bucks on the Apple iPhone, um, if that’s where technology is, you know, along the curve now.

[01:21:09] Tony: So, which means they need jobs and they need to be able to, you know, save and buy that. So, it’s a bit like the old Henry Ford thing. He paid his workers enough so they, they could buy the product, the car. So, you, There is a bit of a circularity to this argument that, you know, I know that if we get to utopia, then you don’t need money, but how do we get there?

[01:21:32] Tony: I just can’t see, I mean, the current laws of commerce and the current laws of human nature suggest that we may not get there because we’re, there’s always someone trying to get an advantage and make some money out of it.

[01:21:44] Steve: I mean, the question that Cam raised last time we chatted about this is Do the current laws of commerce apply? Cause maybe we’re going into uncharted territory.

[01:21:52] Tony: Yeah, and that’s where I come, there’s, in terms of extrapolating the curve, we’ve never had a situation where they haven’t applied. So, it’s, yes, they may not apply in the future. Um, I give that a relatively low probability because they’ve always applied up to then. And having an AI, think about the power that goes with having an AI or an ASI if you get there.

[01:22:12] Tony: It’s, it’s really valuable and you can charge a lot for it rather than give it away for free and make this utopia on Earth.

[01:22:20] Steve: Well, there’s also the emancipation. If you make it available to the large populace, then would have to assume that there’ll be a whole lot of new roles and industries that are invented because people have superpowers where things that they were incapable of creating, they can now create, whether that’s an industry or a company or just working for themselves.

[01:22:43] Steve: If it is. Uh, as emancipating as we think, it doesn’t just emancipate the organizations that create it, it’s those that they sell it to, which then it gets a distribution through the marketplace, which redistributes some form of the funds, so long as there’s access and competition. They’re my two things, is access and competition.

[01:23:02] Steve: If we have that, we’ll be fine. If we don’t, then we could be stuffed.

[01:23:05] Tony: I think we’ll extrapolate where we are now, where if you compare our lives to what they were like 100 years ago or 200 years ago, we’re miles ahead. But do we feel like we’re in utopia? And I think it’ll be the same in 10 years time.

[01:23:18] Steve: You think we’re in Utopia, Cam?

[01:23:21] Cameron: Yeah,

[01:23:21] Tony: Well, Cam gets to sit

[01:23:22] Steve: Really?

[01:23:24] Tony: all day and

[01:23:24] Steve: Because after you

[01:23:25] Tony: playing with Chrissy and Fox. That’s utopia.

[01:23:27] Steve: but listen, after you eat and sleep and do a little bit of recreation, like, I feel like our minds have this heavy toll to carry. We’re carrying around this cognitive burden of uncertainty and, you know, too much information.

[01:23:41] Steve: It’s just too much of everything. I, I really feel a burden on, on carrying. All of this. I don’t feel, and I’m more well off than I’ve ever been in my life. I’ve got everything I’ve ever hoped for. I’ve got my arcade machine there, my BMX, my surfboards. It’s all, I’ve got everything I ever dreamed of. And I feel, I don’t feel as calm or as relaxed or, or

[01:24:02] Tony: never produces that result.

[01:24:04] Steve: Well, I’m not saying that I just go out and chase money. Come, comes from Tony, mate, Mr. Investor Extraordinaire. Like, and I go surfing and I do think, like, the point is, is that, I, you know, I can afford things, but I’m not saying that I was chasing, like, material but I feel like the cognitive load. In the modern era and the uncertainty and all of that is, is a real burden that reduces happiness.

[01:24:31] Tony: It’s always been there though. Like I was, I brought up, I was brought up in the era of the Cold War where every day you woke up was a good day, you weren’t bombed by nuclear war. So I think that underlying anxiety might be a condition of human nature. You’ve just got to learn how to control it.

[01:24:46] Steve: Okay. Thank you. Well, now they’ve just ruined everything for me. I just want to point out that I was having a really good time now. And now what you’re telling me is just, just roll with the anxiety and the unhappiness that is a natural human extension and gets worse as I get older. Thank

[01:25:00] Tony: ignore the noise. Ignore the noise.

[01:25:03] Steve: Very ha easy to say, hard

[01:25:04] Steve: to do. It’s like anything in life. Well, how do you get fit? You go to the gym and you eat less. Easy to know, hard to do.

[01:25:09] Tony: Hard work. Yeah.

[01:25:12] Cameron: Okay, so back to the future. So let’s think about the next couple of years. So. AI is going to continue to improve, dramatically, I believe, in the next few years. It will start to take jobs. Do we agree on that?

[01:25:29] Tony: Uh, yes, but, but that may not be a bad thing. They might as Steve says, supplant people into other

[01:25:34] Tony: jobs.

[01:25:36] Cameron: Okay, maybe. So,

[01:25:38] Steve: Take jobs and invent jobs, as it always has,

[01:25:40] Tony: Yeah. That’s a good way to say it. Yeah.

[01:25:43] Cameron: right. So, initially, the sort of jobs I think are going to go in the first

[01:25:48] Cameron: trench. And I’m talking the next couple of years. At first, slowly, while we’re testing the capabilities, the reliabilities of AI, and assuming that they stand up to that test, I think they’ll go quite quickly. Low level knowledge worker jobs and artistic jobs.

[01:26:07] Cameron: Graphic design, gone when you can just create something, um, using an AI in a second. Um, you know, low level sort of data analysis jobs, uh, gone. Um, writing. Anything that involves writing marketing copy or anything like that is gone. Secretarial work gone when you just talk to your computer and it can rewrite it in any fashion that you want.

[01:26:36] Cameron: I mean, I already dictate most of my podcast notes to my computer. Um, and if I want to sharpen it up, I’ll just then, it already is translated via an AI from voice to text, but then if I want it cleaned up, I’ll just dump it all into something and say clean this up for me and it will. Um,

[01:26:56] Tony: Can I just stop you there? I think that’s very simplistic. I think that there will still be some humans involved in each of those industries for different reasons. Um, partly because in the next five years I don’t think the AI will be perfect enough to replace holus bolus everyone. But just take the secretarial support.

[01:27:14] Tony: So you’ve got an AI controlling your calendar. I’ve got an AI controlling my calendar. Um, we work for large organizations and something fucks up and the meetings clash and you know, it’s human nature to want to open your door and yell at the secretary to say, sort it out for me. There’s still, there’ll still be someone sorting out problems with the tech.

[01:27:34] Tony: So he wants to plant every role, every job. in secretariat, in

[01:27:38] Tony: the secretarial

[01:27:39] Steve: Well, so Tony has solved it here. Basically, AI won’t take over because humans want to yell at humans and a lot of people don’t know that. That is the savior! To

[01:27:49] Cameron: I think that tells us, tells us more about Tony than it does about, uh,

[01:27:54] Cameron: Humans in the future.

[01:27:56] Cameron: Last thing I want to do is yell at someone and you’ll just yell at your AI. I mean, you’ll go, Hey, I mean, who hasn’t yelled at their AI before?

[01:28:05] Steve: And, and of all the things Siri isn’t good at, given the woke organization running, I should work. Well, I’m not one of those people, but, but the idea that says, sorry, I won’t answer that if you use, uh, abusive language.

[01:28:20] Cameron: racist

[01:28:21] Tony: don’t be, it’s mine says don’t be like that. If you went through my Siri searches, it would just be fuck off Siri.

[01:28:28] Steve: I say that too, and it says, I won’t answer such profane language. It actually does that to

[01:28:32] Steve: me. And, and, and as Cam, Cam has said, you got to be nice to the

[01:28:35] Steve: robots. Cam’s getting in early. Cam is getting in early.

[01:28:39] Cameron: People living across the road from my mom have got like a robot. Roomba mowing their lawn, which I’ve never seen in the wild before. Just all day, it’s just pottering around mowing their lawn. Then it docks on the side of the house to recharge. Super crazy. Anyway, uh, coding, obviously, in terms of the early jobs, and look, I’m not saying, Tony, that all the jobs are going to go immediately in these sectors.

[01:29:01] Cameron: I’m saying, okay, if you’ve got a team of five coders and a manager, you’ll, you’ll lose one, then you’ll lose two,

[01:29:08] Tony: Yeah. So there’s an, no, you’re right. There’s an efficiency there, but that’s my point is, is what does it cost to replace those people? How much is Sam Altman going to charge

[01:29:17] Tony: for the coding AI to replace the people who are now coders?

[01:29:22] Tony: And then compare that cost to people in India.

[01:29:25] Tony: Who do it for like two bucks an hour.

[01:29:27] Steve: So this is the competition

[01:29:28] Cameron: but they won’t be anywhere near as good as the AI.

[01:29:31] Tony: Oh, it says you.

[01:29:33] Cameron: I’ve hired people in India and I’ve used AI and already the AI is better than

[01:29:38] Cameron: the people

[01:29:39] Steve: Cam, that’s so racist. Cam is so

[01:29:41] Steve: racist. just saying, brother. No, well,

[01:29:44] Steve: but this is the competition thing. How much will charge? How much will Sam Altman charge? It depends on how much competition he’s got.

[01:29:52] Cameron: Yeah.

[01:29:53] Steve: humans and from other AIs.

[01:29:55] Cameron: but we’ve, they’ve already established sort of the price point, right? We’re already paying

[01:29:59] Cameron: 20 to 30 bucks a month for AI,

[01:30:01] Steve: Pretty good value.

[01:30:03] Cameron: premium level tools, uh, you know, whether you’re looking at U. S. or Australian, um, which will do all of that for you. They may have a higher level of access, but we’re talking.

[01:30:15] Cameron: You know, not a lot. I mean, they’re not going to start charging all of a sudden 10, 000 a month for it to do what they’ve already shown you it can do for 20 a month.

[01:30:26] Tony: Well, they might because, I mean,

[01:30:28] Tony: we’re getting into the area of prediction, so this is a bit of what if,

[01:30:30] Cameron: Yeah, yeah,

[01:30:31] Tony: like, what if the ChatGPT general’s 20 a month,

[01:30:36] Cameron: Tony, the show is called Futuristic!

[01:30:39] Tony: but they actually,

[01:30:39] Cameron: all we do on this show is predict the future, Tony.

[01:30:43] Tony: some little gremlin in Sam Altman’s corporate structure says, Hey, you realize if we actually package up the AI to say it’s a coding specialist, we can charge 1, 000 a month for it to replace coding people.

[01:30:57] Tony: Um, so there could be

[01:30:59] Tony: specialization in AI where

[01:31:00] Tony: they charge a premium, and then that’s got to be, you know, Competed against other providers in the

[01:31:05] Tony: economy.

[01:31:07] Cameron: Anyway, my point is that I think the first layers that are gonna go will be low risk, high cost, high benefit jobs, right? So you can take out a coder here, you can take out a customer service person, replace them with an AI, you can take out an analyst, you can take out a writer, graphic design, industrial design.

[01:31:30] Tony: Look, I agree. Call centers are toast. They’re gone.

[01:31:33] Cameron: So still initially it’ll be incremental, you’ll take out one, then you’ll take out two, then you’ll, you’ll still have managers overseeing the work and

[01:31:41] Cameron: that kind of stuff until

[01:31:43] Steve: big, big end too. Lawyers, investment bankers, a whole lot of

[01:31:46] Steve: analysts, like, we’re not just talking low end call center, you’re talking a whole cohort of, you know,

[01:31:52] Cameron: I think

[01:31:53] Steve: in a big law firm, where they’re doing discovery and all of that. Bam, AI, it’s done it.

[01:31:57] Cameron: Well, I think that’s the next level. So I say the first level is low risk. If I get rid of a graphic design person and then the

[01:32:04] Cameron: AI produces a bad graphic design thing, I can just say, do it again, do it again, do it again. If I have them, if, you know, if I replace a lawyer and they come up with a bad contract, um, that could be a bigger issue.

[01:32:16] Cameron: But I think the first iteration, we will be testing it. In the workforce, and they already are, but I mean at a higher level, more visible level, and when we have the ChatGPT 5 level of reliability that we believe is going to be there, they will start to go, you know what, we’ve been testing it, replacing a human for six months, for a year, It’s been terrific.

[01:32:41] Cameron: Okay, now let’s replace 10%, 20%, 30 percent of the team until it just replaces nearly everyone. Then around 27 to 2030, I sort of predict is the next layer, the second layer, when we’ll have AGI sometime around this timeframe as well. Higher level jobs will start to be replaced, middle management, because they’re going to be less people to manage.

[01:33:04] Cameron: So, you know, I can get rid of every second middle manager and just, you know, group all the people that we still have under this one manager. I think it’ll start to impact legal, accounting, HR, again, because there are less people to hire and manage, recruitment for the same reason, psychologists. I think more people will start to use AI as a psychologist.

[01:33:27] Cameron: And that may, I mean, we already have too few psychologists in the marketplace, so that may not have a short term impact on the, uh, incomes of psychologists in Australia. But eventually, uh, people won’t study to become psychologists if everyone they know is using an AI. So as a psychologist, um, medical, everyone’s going to have a free GP.

[01:33:49] Cameron: Again, this is going to be, it’d be a good thing because it’ll take strain off the existing medical infrastructure initially, but are you really going to study to be, are you going to go in a medical school today and study to be a doctor? If you’re looking at where AI is going in the next 10 years, you really think it’s worth 10 years of study to become a doctor when we’ll have AI in 10 years that can answer any medical question better than a human can?

[01:34:14] Cameron: I’m not sure that people are going to, that it’s going to be a logical step. Animation will go, business strategy and then acting and all sort of work in TV film production, I think is going to go.

[01:34:28] Steve: Well, Sora, I mean, just extrapolate out what Sora can do. But also with acting, that’s interesting though. And I’ll tell you, acting, you might say, We’ll go away. And we’ve seen that there are virtual influences that already exist. This is where, do we want to see something, human creativity because a human is doing it?

[01:34:51] Steve: Like that’s actually an interesting question. I don’t know the answer to do. We just know we just want to see creativity and I don’t care if that

[01:34:59] Steve: actor doesn’t exist in the real world. If I like their

[01:35:02] Steve: persona And it’s not Tom Cruise, it’s Billy Bloggs, who is an AI actor. Who’s in a whole lot of starring Billy Bloggs in the next movie.

[01:35:09] Steve: I don’t know.

[01:35:10] Cameron: films have already answered that question, Steve. And

[01:35:13] Steve: Yeah, good, good point. Although, although, we don’t just have, uh, Avengers movies. We have movies with real humans. The other contention

[01:35:24] Tony: that’s a note I’ve made, Steve. Sorry to interrupt, but, um, there’s an artisanal element. So, again, extending the curve, um, if AI gets so smart at feeding us, for example, that we get to drink a bowl of gloop

[01:35:39] Tony: every day, because it’s the cheapest, best way of getting our nutrients, then there’s going to be artisanal cafes pop up where they bake the bread by hand.

[01:35:47] Tony: Um, so I think,

[01:35:49] Cameron: no one can buy the bread because no one has any money

[01:35:51] Tony: well you say that, but, but who, AI, AI has got to be paid for, so people have to have jobs to pay for AI, otherwise it doesn’t exist. It’s, you know, Microsoft don’t half of ChatGPT, they’re not going to give it away. They’re going to, and that’s the other, other problem I’ve got with getting to where you want to get to, is that there’s a growth imperative in these big companies.

[01:36:12] Tony: Companies, they’ve got to keep growing. So if everyone subscribes to ChatGPT for 20 bucks a month, where does the growth come from? There’s got to be, there’s got to be, there’s got to be add ons and other services, etc. Like the doctors, etc. They’re going to be charged to allow Microsoft to keep increasing its revenues every year.

[01:36:33] Tony: It’s got to come up with new products and charge more for

[01:36:35] Steve: tend to get bundling and unbundling. Like, this happens again and again and again. And we’ve seen this in computations. Like, at the moment, we’re moving to a bundling phase in tech where everything comes under the one banner. And then it fragments out because people do better versions of this and it’s, that’s the doctor AI and this is the architect’s AI and this is, it’ll be interesting to see if that happens.

[01:36:56] Steve: I think that’s inevitable because it’s a greater way to make money by refragmenting the market, then re bundling the market. And it’s just basically, it’s the breathing effect of how the markets respond to maximize revenue over the longer term by changing their product portfolio.

[01:37:09] Tony: Yeah, and that maximizing revenue is really

[01:37:10] Tony: important in this discussion. It’s not going to, AI is not going to be given away. You’ve got to pay for the psychologist on

[01:37:16] Tony: your phone. It’s not going to be free, and therefore not everyone will use it.

[01:37:21] Cameron: 60 percent of the AI models that have been released in the last year were open source. And A lot of those are very, very,

[01:37:30] Cameron: close in terms of their performance capabilities to the top end models, the ChatGPTs, the Geminis, and that kind of stuff. There is a theory that we will have a lot of freely available open source models.

[01:37:45] Cameron: At the moment, you can’t really run them on your desktop PC easily. They require a lot of, uh, Technical know how to get them up and running and trained and all of that kind of stuff, but all of that will go away at some point where it’ll be click install and you’ll be able to run your own AI locally.

[01:38:00] Tony: Yeah, but that’s like, we’ve seen that curve before as well. It’s like Linux, right? It’s, it’s an open AI operating system, but

[01:38:08] Tony: Microsoft was so

[01:38:09] Tony: much better at giving you

[01:38:11] Tony: one that worked a little bit better and it was free, it was cheap and came with your laptop, that it won the day.

[01:38:18] Cameron: Yeah, because It didn’t have an AI built into Linux. When you have an AI built into Linux that can do, you know, Everything that Microsoft can do.

[01:38:26] Tony: It still may not win the day. It probably

[01:38:28] Cameron: It may

[01:38:29] Steve: gonna, who’s gonna own the data center? I mean, you might end up with someone like Amazon Cloud and others. You know, basically this idea that computing really becomes in the energy business and the energy they provide is the energy to run the data centers and the information in, in processing the data, which then has AIs, which are open source and cheap, but any commercial AI fails.

[01:38:50] Steve: I don’t know. You could get a whole shift in the computation, uh, infrastructure,

[01:38:57] Tony: The other thing that I wanted to add to this debate is you’re talking about professional jobs being replaced by AI, which could potentially happen, but the personal insurance industry or professional identity insurance industry has to get comfortable with that before it will allow it, and they allow it by, they won’t allow it by charging unreasonable premiums to, until I get comfortable with it.

[01:39:17] Tony: So it takes a long time for the insurance industry to see that AI is going to be effective as a doctor and not kill people before it will bring the premiums down to a low enough level to let AI operate at a cheaper rate than the current medical staff. So it’s going to take longer than five years, I

[01:39:34] Tony: think, just from the insurance point of view.

[01:39:36] Steve: but,

[01:39:37] Steve: but, but

[01:39:37] Cameron: who’s buying the professional indemnity

[01:39:40] Tony: The Doctors. Doctors currently, one of their biggest costs currently is to get insurance, so if they make a

[01:39:45] Cameron: Yeah. But if, if I’m asking ChatGPT, my medical question, where does professional indemnity come into it?

[01:39:53] Tony: Oh, well, good question, but the provider of ChatGPT could be sued by the family of the person who was injured by the medical advice.

[01:40:03] Steve: They’ll just take what, they’ll just do exactly what big tech has done up until now, which is yeah, well, we’ve got 5 billion customers and we’ll go to war with whoever because

[01:40:11] Steve: our pockets are full and we might lose a few cases here and there, just like Mark Zuckerberg, you know, yeah, let’s go for live streaming, shoot up some people live, it’s all good, whatever.

[01:40:19] Steve: That’s, I think, what will happen there. But, but that

[01:40:22] Tony: But that’s the Wild West of

[01:40:24] Cameron: ever shot any people live on camera, which is what you just inferred there. I,

[01:40:28] Tony: I don’t

[01:40:28] Steve: didn’t infer that he should, I said he let people video it.

[01:40:33] Tony: Wild West of medicine though, right? So medicine has

[01:40:36] Steve: You know I love Mark.

[01:40:40] Tony: So that when I, so that when I go to the doctor, I know the doctor has been taught, has been qualified, has the right insurance, um, will likely provide the right advice ’cause I’ve gone through all these gates along the way.

[01:40:53] Tony: The wild west of, of

[01:40:54] Tony: medical advice is gonna be, no, I’ll just ask my phone. , I don’t care if it, you know, may gimme the right answer. It

[01:41:01] Steve: doing it. We’re already doing it though, aren’t we? Pretty quickly.

[01:41:05] Cameron: yeah,

[01:41:05] Tony: I think it’s more likely that, I think it’s more likely that the phone will be able to provide me with prescriptions up to a point. You know, so there’ll be parts of the medical industry that

[01:41:16] Steve: I don’t think the medical profession goes away. It just shrinks. So, the first gate you go through is the AI gate, and then you take What you

[01:41:24] Steve: and the AI have agreed or understood or communicated to the doctor who then goes, Okay, yeah, I like what the AI has done and it just truncates and expediates the entire process, I think.

[01:41:34] Tony: yeah,

[01:41:34] Cameron: it’s the shrinking that I’m.

[01:41:36] Cameron: It’s the shrinking that I’m talking about. All of these sectors are going to

[01:41:40] Cameron: shrink and we have to figure out how we’re going to redeploy the shrinkage. Shrinkage, Jerry, shrinkage. Um, we have to,

[01:41:51] Cameron: like a turtle. We have to, we have to figure out how we deploy the shrinkage.

[01:41:55] Cameron: And my point of all of this is I think this is going to happen way faster. than governments are prepared for. I think it’s going to happen way faster than industry is prepared for. And I think it’s going to happen way faster than people are prepared for. I think it’s going to happen in this decade, massive shrinkage.

[01:42:15] Cameron: And we’re not talking about it, we’re not prepared for it, it’s gonna be, it’s gonna be like when the pandemic hit, and we were like, oh shit, we have not prepared for this, and the world went into chaos mode for a year. I think this is gonna be a similar sort of thing when it hits, cause no one’s, no one’s really taking it seriously.

[01:42:38] Cameron: very seriously. And I’m starting to feel like St. Paul running around saying the world’s about to end and no one’s listening, except in the difference being I’m right and he was wrong.

[01:42:49] Tony: I don’t know if you are right. I don’t think it’s going to hit us like the pandemic hit us. It’s not all going to happen at once for a start. Not every industry will be disintermediated, disintermediated,

[01:42:57] Tony: at once. Like you said, it’ll start off with graphic designers, etc, etc. And I take Steve’s point, if we extrapolate again from the curve, there’ll be a bigger gig economy when that starts to happen.

[01:43:08] Cameron: But when I say it’s not going to happen at once, I’m not saying there’s going to be a five year gap between level one and

[01:43:12] Cameron: level two. I’m saying a year between level one and level two. 2025 will be level one. 2026 to 27 will be level two.

[01:43:23] Tony: Yeah, I don’t see it happening that quickly, Ken. Level two. So are you saying that Judges are going to accept me defending myself with a phone so I can go in the court and say, Hey judge, listen to this. Here’s my argument. And Siri reads out the case precedence and the judge goes, yeah, okay. That’s just going to take a lot longer than two years, even five years for that to

[01:43:48] Steve: You’re saying that the human gatekeeper is more poignant than we think and will stay longer within commercial settings, legal settings, governmental settings.

[01:43:59] Tony: I’m saying two things. I’m saying, yes, that there’s a human nature to this, but there’s also an economic component to this. And I don’t think it’s going to be such a quick evolution. I think it’s going to be a lot of competition and

[01:44:12] Tony: what’s more efficient and who’s paying, what price do you pay for this and how do

[01:44:17] Tony: they make money and who are they charging?

[01:44:19] Tony: All the stuff that goes on in the regular economy has got to play out as well,

[01:44:22] Steve: Well, certainly areas

[01:44:24] Cameron: I’m not suggesting that Sorry, I’m not suggesting trial lawyers are going to disappear. Again, I’m not saying that all jobs in all

[01:44:31] Cameron: sectors are going to disappear on day

[01:44:33] Cameron: one. I’m saying shrinkage. So, lawyers, low level lawyers, legal clerks, you know, the, the, your local guy that does a real estate contract or a conveyancing contract or divorce agreement or an employee contract or All of those people will go first, not all of them, but increasingly they will all be replaced by an AI which can write a watertight contract faster, cheaper than any human can.

[01:45:06] Cameron: And then there’ll be a trickle on effect as, uh, as people and businesses become increasingly confident That, oh yeah, this is robust. Yeah, we can’t pick any holes in this. We’ve had all of our, you know, top legal experts try and find flaws in the cases that the AI is providing and we can’t find one.

[01:45:27] Cameron: That’ll hit the media. People will go, Oh, did you hear that? Yeah, the AI is now better than any lawyer on the planet. Um, why are we still paying lawyers 800 an hour to represent us? It’s going to happen pretty quickly, but it won’t happen All at once, it’ll be like, it’ll be an avalanche, but it’s going to be a very fast avalanche, is my point.

[01:45:49] Steve: I think it’s going to

[01:45:50] Cameron: years, not decades.

[01:45:52] Steve: if there’s an avalanche, there’s going to be two sides to this mountain. You’re going to have the commercial, let’s call it unregulated avalanche, which organizations get to make the choices on who they deploy to get the work done. And then you’re going to have things which have a higher regulatory hurdle.

[01:46:12] Steve: And the doctors is a good one that’ll take, I think, a lot longer. And this is a classic of Amara’s law, which states that the impact of a technology is usually overstated in the short run, but understated in the long run. Uh, and the example that I will cite there is that it took us 12 years before the government in Australia allowed us to see a doctor on the phone just to get a script renewed or something.

[01:46:35] Steve: It wasn’t until COVID that they did that, but the technological capability was there from about 2010. And it wasn’t 2021 until we deployed seeing a doctor on your smartphone.

[01:46:46] Tony: Yeah, and that’s why I think this will all roll out, Steve. That’s exactly, really good analogy. It’s um, The tech will get there before the economy does, I guess is what I’m saying. And that gives a bit of breathing space for the economy to work out what it wants to do with disruption and displacement.

[01:47:02] Steve: But I think what should happen is they should pay people like the three of us to give advice while we don’t know what’s going on to just come. And so just sing it, just sing out and we’ll just, we’ll

[01:47:12] Tony: yeah, and

[01:47:13] Steve: and just pay us an inordinate amount of money to pontificate on these points.

[01:47:17] Tony: and the background cam can ask ChatGPT and we can sell that one off as well. We can feed

[01:47:21] Steve: We’ll just have an earpiece in and Cam will just be asking it the question. We’ll just be saying, here’s what we think, because no one knows what we think anymore. We’re just asking the AIs in secret,

[01:47:31] Tony: But extrapolating the curve, right, we’ve seen, we just spoke before about the newspaper industry and how it’s disrupted. It’s just like, there wasn’t mass unemployment, people moved into other jobs, you know, because a lot of times these jobs like conveyancing or contract writing, they’re done by people who don’t really want to do those jobs.

[01:47:50] Tony: They don’t want to be treated like a robot just sitting

[01:47:52] Tony: there doing nothing. You know, turning out the same boilerplate every day. They, they see themselves as being better than that. So

[01:47:59] Cameron: but why are they doing it?

[01:48:02] Tony: eat,

[01:48:03] Steve: because they’re not better.

[01:48:04] Cameron: can’t,

[01:48:04] Tony: or they want to

[01:48:05] Tony: get,

[01:48:05] Cameron: not better, they can’t get a better.

[01:48:06] Tony: it’s the first step along a promotion curve for them is often the reason.

[01:48:10] Tony: Oh!

[01:48:14] Cameron: and I’m wrong, genuinely. But that’s the same thing I say to

[01:48:17] Cameron: Christians, uh, when they say, we’re gonna spend eternity in paradise with Jesus. I hope you’re right and I’m wrong, but so far, I’m never wrong, so.

[01:48:28] Steve: Cam, I’m never wrong. I love I’m never wrong. But what I, what I love though, is I, I think that you’re keeping your hair like that. So when Jesus sees you, he knows that he can be in a rock band with you because you have a similar haircut.

[01:48:40] Tony: I think you went to the wrong

[01:48:41] Steve: Alright, and that’s what you’re doing in

[01:48:43] Tony: where you’re wrong.

[01:48:44] Steve: You’re aligning.

[01:48:44] Steve: You’re aligning physically so that he may accept you despite the documentary films you’ve made about him not existing. That’s all I’m

[01:48:53] Cameron: When I first came into this, this is my old bedroom I grew up in,

[01:48:56] Cameron: by the way, to record this morning, this light was on above

[01:48:58] Cameron: me, and I thought that was too much of a halo effect, so I actually, I turned it off, so I didn’t, uh, accentuate the, uh, prophet esque, uh, look of my hair. Thank you guys! Look, we’ve been talking for two hours.

[01:49:12] Steve: Wow.

[01:49:12] Cameron: Um, I appreciate both of you coming in and talking me out of my, uh, state of panic. Uh, Tony, thank you for coming on and, um, bringing your, uh, vast intelligence to, and experience to this. Steve, as always, your vast intelligence.

[01:49:29] Tony: You know, that’s the other point I wanted to make before we go is we’ve seen super intelligence before, but it’s still subject to commerce and human nature. Look at Einstein. He discovered the laws of physics which led to the atomic bomb, but he couldn’t stop it from being used.

[01:49:43] Tony: It’s the similar sort of thing with when we get a ASI, I think,

[01:49:47] Tony: it’s going to have,

[01:49:49] Tony: it’s going to butt up against governments, it’s going to butt up against commerce before it

[01:49:53] Tony: eventually does what it

[01:49:54] Cameron: well, my final note is my, my problem with that is, I think, The idea that a superintelligence will be beholden to the desires of humans is like saying humans will be beholden to the desires of ants.

[01:50:13] Tony: No, I know, but what happens if the Chinese superintelligence gets built first and the Americans feel the need to build one, and there’s a superintelligence

[01:50:20] Tony: war?

[01:50:21] Cameron: I think that’s a very likely outcome

[01:50:25] Tony: it may mean that they cancel each other out.

[01:50:28] Steve: Well,

[01:50:29] Cameron: Well, cancel everything out if that happens.

[01:50:32] Tony: Possibly. yeah,

[01:50:37] Cameron: a super intelligence, once we have a machine intelligence, that’s a million times more

[01:50:42] Cameron: intelligent. And the thing that Kurzweil, sorry, points out in this latest book is, uh, maybe even in earlier books, um, the, the, Turning point will be when the AIs are able to code themselves better than humans can code themselves.

[01:50:59] Cameron: When the AIs are in control of their own coding, that’s when they go from being smarter than us to a million times smarter than us. Really, really and quickly, there’s this recursive loop that happens with each generation and the generations are days apart, not years apart. And that’s what they call FOOM in the AI industry.

[01:51:23] Cameron: Is that hard take off? FOOM! Like in a superhero comic when a jet takes off? FOOM! It’s when you, and this is the sort of stuff that Eliezer Yudovsky is scared of, is when the AI goes from being just a little bit smarter than us to 10, 000 times to a million times in a month, and just because of its coding optimization that it does for itself,

[01:51:49] Tony: that’s the dark, that’s the dark forest problem, isn’t it? Because really, what that, what that, what that is, what that ASI is,

[01:51:56] Tony: is an alien. And, you know, again, we have a paradigm for dealing with aliens. We, we destroy them

[01:52:04] Tony: before they have a chance to grow up and destroy us.

[01:52:07] Cameron: but we’re not going to do that.

[01:52:09] Tony: Why not?

[01:52:10] Steve: Because they’re our children. And,

[01:52:12] Tony: To our commercial interests.

[01:52:14] Cameron: Yeah, the same re I mean, this is the question, like, Eliezer Yudovsky and the like have been saying this for the last year or

[01:52:21] Cameron: two now is, We need to stop this right now! stop

[01:52:24] Cameron: building AI! Stop build Stop spending hundreds of billions of dollars a year to build something that’s gonna kill us!

[01:52:31] Cameron: And everyone’s like, Nah, nah man, we can’t.

[01:52:35] Steve: Elliot, have you seen how good that last email was to my manager? I don’t think you understand the implications here, brother.

[01:52:42] Cameron: yeah, the love poem that I just wrote to this girl that I like. Come on, man. It was like Pentamic It was a

[01:52:50] Steve: seriously,

[01:52:51] Cameron: I think you know, if we know anything about the laws of industry and commerce is that if there’s a buck to be made, we will push ourselves to the brink of extinction to make that buck. Um, hence, uh, the nuclear bomb that you said before is exactly the point. We built that to stop the Russians. Why? Because they were threatening the capitalist economy.

[01:53:14] Tony: Mm hmm.

[01:53:15] Steve: on that capitalism thing with the AI war across nations, one interesting thought, and Cam, you’re probably the best person to ask this, given your work on the Cold War podcast and so on. The idea that different nations are building out AIs, I do wonder if the AIs from nation states have some sort of a systemic competitive advantage.

[01:53:40] Steve: Evolution, where it’s commercial AI, which has all of the world’s knowledge and data in it, versus the Chinese AI, which is segmented in the great firewall of the internet. Do they train their AIs on the type that they deploy in their own economy? Or do they stealthily bring in everything from the world and the West to train their AIs?

[01:54:00] Steve: Because you might end up with two typologies. A communist typology AI, which sort of has a very draconian viewpoint of the world, versus the open source. A. I. model from the U. S. which has everything in it from fake news to whatever to train it. Like, do you end up with two typologies of

[01:54:17] Tony: has a draconian worldview of the world as well.

[01:54:20] Steve: Well, yeah, but do you end up with two different A.

[01:54:22] Steve: I. s? And if you do, is one more intelligent and defeat the other? Is it kind of like a

[01:54:27] Tony: that’s Elon’s. That’s Elon’s.

[01:54:28] Steve: competing

[01:54:30] Tony: He wants, he wants to build an AI because ChatGPT is too liberal.

[01:54:35] Steve: Well, I’m just interested in that, if you end up with this AI Cold War with them

[01:54:39] Tony: Yeah, it’s an extension of human

[01:54:41] Tony: nature. But of course, ASI may just, just go off on a different, might go off and swim

[01:54:46] Tony: with the whales and forget about us. Who knows?

[01:54:48] Steve: Yeah. Yeah. Like on her.

[01:54:50] Cameron: well, I, I want the communist AI to win, because I want Star Trek communism, um, in place, where everyone is given everything that they need, and no

[01:55:00] Tony: China doesn’t have Star Trek communism.

[01:55:04] Cameron: Not yet, that’s why they’re building the AI, to get us there.

[01:55:08] Tony: Oh, and, and the,

[01:55:09] Tony: the head of the CCP will just hand over power to the AI, will he?

[01:55:14] Cameron: That’s what Xi Jinping’s lifelong mission has been, is to give the world Star Trek communism, that’s his,

[01:55:20] Steve: Did he really say that?

[01:55:21] Cameron: and, no, I said that, but, you know, I’m telling you. Xi Jinping,

[01:55:25] Tony: Hmm.

[01:55:25] Steve: I said it.

[01:55:26] Cameron: Xi Jinping is a true believer in, uh, the, the

[01:55:31] Cameron: utopian communist, uh, ideal, as am I.

[01:55:35] Tony: Yeah, but he’s wearing a red shirt and we all know that red shirts never survive. Star Trek episodes.

[01:55:41] Cameron: As for the ASI just leaving us all behind, Tony, I, I, I, I’ve thought a lot

[01:55:46] Cameron: about that argument and I don’t think it’s necessarily an either or. The ASI can go off and explore the universe and turn all of the planets into computronium, but they can stay here as well and keep running things here. I don’t think it has to be an either or.

[01:56:02] Cameron: Anyway, fun chat. I, I really enjoyed talking to both of you guys. Um, nice for Tony and I to be able to spend two hours talking about this instead of the half hour we normally do at the end of QAV these days. I’ll talk to you on QAV tomorrow, Tony and Steve. Um, I’ll talk to you whenever I talk to you. Have a good week, both of you.

[01:56:20] Tony: Thanks guys. Well done.

This week Steve and Cam debate Apple Intelligence – the biggest flop ever? Or the the beginning of huge things for AI? Also – Cam talks about his timeline for the next five years, and how the world’s economy is going to require complete re-engineering.

Futuristic 26

[00:00:00] Cameron: Testing, testing, loop back, yeah, I think everything’s running. Welcome, welcome back to The Futuristic. Uh, this is, um, I’m not sure what episode this is, cause I haven’t, do you know what episode this is? 26 says Steve, thank you Steve. Uh, it’s been a few weeks between episodes, Steve, because we were talking like, hey, there’s not much happening, not much going on, then all of a sudden, everything’s happening, everything’s going on.

[00:00:37] Cameron: Uh, Apple, Apple. This week, a couple of days ago, did their WWDC event where they announced

[00:00:45] Cameron: Dun, dun, dun, dun, Apple Intelligence.

[00:00:51] Steve: And I’m glad they called it that, Cameron. I’m glad they called it that because there was nothing artificial about it, my friend. There was a few little just maneuverings. And I feel like there’s a big heated discussion on the relative merits of Apple intelligence because let me tell you, that’s all it was, man.

[00:01:08] Steve: It was deck chairs inside the Apple ecosystem.

[00:01:13] Cameron: Steve sent me a text basically saying

[00:01:15] Cameron: he thought it was a bunch of bullshit. And I was like, Oh, I disagree, man. I think it’s huge. So we had to do a show, but we were just talking off air

[00:01:21] Cameron: about,

[00:01:22] Steve: glad, I’m glad that we disagree because no one wants to tune into the Mutual

[00:01:26] Steve: Agreement Society. How boring is

[00:01:27] Steve: that

[00:01:28] Cameron: Well, we normally, we normally agree. Um, I, I wanted to have my QAV Kynaston on because he and I had a debate on at the end of our QAV show this week for about an hour about AI. He’s still very cynical and skeptical that AI is gonna have any impact on the world. And, uh, I was like, dude, I need to get you on with Steve and we can have this debate.

[00:01:51] Cameron: Cause I respect Tony. He’s one of the smartest guys I know. One of the most successful guys I know. And so I respect anything he has to say, but I think he’s missing the point on AI. But anyway, um, we were off air, you and I were talking about David Lee Roth, the wisdom of David Lee Roth, and then you brought up Motley Crue, which brings us back to Apple, because the opening of the WWDC, Craig Federighi and his team, uh, Paratrooping, skydiving out of an Apple plane to the Motley Crue track Kickstart My Heart.

[00:02:30] Cameron: I thought it was all quite funny. Frederique pulling on his helmet that was basically a version of his perfectly coiffed silver hair. Jumping out, uh, and then they, they land and Tim Cook’s waiting for them, standing on the

[00:02:46] Cameron: roof of, uh, the Apple headquarters at number one loot. Um,

[00:02:53] Steve: yeah, I’m not jumping out. I’ll just stand here,

[00:02:56] Steve: kids. Well,

[00:02:58] Cameron: Uh, so the first hour of the two hour show, I was sort of going, when are they going to talk about AI, because they’re talking about, All of the updates to iOS and Apple, the watchOS and the macOS, no mention of the AI stuff, and I’m like, come on, we know that the OS stuff, the AI stuff is coming, we know that it’s going to impact all of these things, why aren’t you leading with that?

[00:03:26] Cameron: They’re trying to do a Steve Jobs one more thing. AI. It should have been Woven into the whole presentation, I thought. My complaint was, stop wasting my time talking about all of the updates to the OS if you’re not talking about the thing that actually we all came here for, which is AI. But anyway, that was my biggest complaint about the whole thing.

[00:03:54] Cameron: I don’t really care about being able to change the color of the widgets in the iOS, and I don’t really care about emojis, and I don’t really care about Being able to time text

[00:04:08] Cameron: messages. I don’t really care about anything that they had to talk about, quite honestly, until it got to the AI.

[00:04:14] Steve: it seems like, it seems like you agree with me because everything

[00:04:18] Steve: you’ve mentioned, you don’t care about, which is 99 percent of what was announced. There was, there was nothing, nothing. It was one out of 10. It was a massive flop, it was a super fail, and the fact that their share price has gone up 10 percent since

[00:04:34] Steve: then is astounding me.

[00:04:37] Steve: But no, no, it doesn’t prove me wrong, because we know that in the short term,

[00:04:41] Steve: the share market doesn’t know what it’s talking about, but in the long term it does. It’s a voting machine, and it just proves that the world are morons. That’s all that proves, and that fund managers don’t understand AI.

[00:04:51] Cameron: I said, Tony should have been on this and here you are quoting Benjamin Graham, Tony’s, uh, God. Um, in the short term.

[00:05:00] Cameron: It’s, uh, no, in the short term it’s a voting machine, in the long

[00:05:03] Cameron: term it’s a weighing machine.

[00:05:05] Steve: machine, exactly.

[00:05:06] Cameron: Benjamin Graham’s,

[00:05:07] Steve: to say, Mr. Benji Graham, Intelligent Investor. Well, they had a 10 percent um, upside on a 3. 2 trillion market cap, which is 300 billion in value, and it turns out that the current market valuation of OpenAI, I think, is 98 billion. Tell me how, What Apple announced, this is the irrationality of markets.

[00:05:28] Steve: Tell me how what Apple announced is worse three times of what OpenAI is.

[00:05:33] Cameron: Okay,

[00:05:34] Steve: is, this is where people fail to get

[00:05:36] Steve: Yeah,

[00:05:37] Cameron: No, this is where, in all due respect, cause you know I love you and I respect

[00:05:42] Steve: you know what all due respect means? Hey dickface.

[00:05:45] Cameron: you

[00:05:47] Steve: all due respect,

[00:05:49] Steve: I’m really saying hey dickface.

[00:05:52] Cameron: should take that,

[00:05:53] Cameron: um, uh, gracefully because I don’t respect many, I don’t respect many people. And

[00:06:00] Steve: you don’t respect anyone, and I’m pretty

[00:06:01] Steve: suspicious you

[00:06:02] Cameron: for,

[00:06:02] Steve: respect me, which is fine. The

[00:06:04] Steve: longer some

[00:06:05] Cameron: For me to say that, for me to say that, it means a lot,

[00:06:10] Steve: Thank you. Thank

[00:06:11] Steve: you, mate.

[00:06:12] Cameron: Um, no, look, Apple Um, of, uh, Apple’s reach is enormous. The fact that Apple are now putting AI in, and they’ve always had machine learning. I don’t know if you saw, um, Tim Cook’s interview on, um, Marcus’s podcast. But, you know, they were talking about the fact is Apple will say, look, they’ve always been using AI. There’s AI in your watch, fall rec, you know, when it knows that you’ve fall alerts, the way it does it, they got machine learning and AI has been part of Apple’s thing for a long time, but they haven’t talked about it as AI since the whole LLM generative AI thing took off.

[00:06:55] Cameron: And that’s what we’ve been waiting for is them to say, Hey, here’s how we’re going to use this kind of AI. in our devices. There’s how many iPhones out there? One and a half billion iPhones that people have got. Here’s the thing from my perspective. What the, the, the way that they have announced they’re deploying Apple intelligence.

[00:07:19] Cameron: Okay. So for people who didn’t watch it, there were two parts of the AI thing in, in the keynote. Apple, uh, building AI. And again, to be clear, we’re talking about the sort of LLM based approach to AI, language based AI. People call it generative AI still. And, and I think that’s, I was thinking about this, listening to Tim Cook this morning, last night.

[00:07:46] Cameron: I, I, I don’t like the term generative AI because to me, it’s not about the generative component of it, even though that is a lot of what Apple’s doing. It’s not the fact that it’s generative that I think is important. It’s the fact that it understands language.

[00:08:03] Cameron: That, we need a better term than generative AI.

[00:08:06] Cameron: I

[00:08:07] Steve: and I agree and, and, and I use this when I do my keynotes on stage, you know, the phrase that I use is language is the fabric of all human knowledge because it, it crosses the chasm from mass to engineering, to marketing, to legal. If you understand language, you can create context, which ends up in schematics, visuals, every type of thing.

[00:08:27] Steve: And, and it turns out that the training mechanisms when they train the databases. On what the visuals are. Yes, it looks for patterns, but it’s the words underneath the patterns that helps the machine understand that that’s a palm tree, because it’s seen that described, you know, millions and billions of times, and then it knows what that pattern is and it matches the pattern to the word, and that’s actually all language based.

[00:08:47] Steve: And a lot of people miss that.

[00:08:48] Cameron: don’t know who I got it from, whether it was either Kurzweil or Wolfram a year or so ago, but the language user interface, linguistic user interface, I think is, is more

[00:08:58] Cameron: important than the generative aspect. But anyway, so just to be clear, that’s what the, this is what

[00:09:03] Cameron: Apple, so the Apple intelligence,

[00:09:05] Steve: GUI to Louie is, is the thing. Graphical User Interface to language, GUI to Louie. No, it is Language User Interface. I use that on stage as well. It always gets a laugh because it rhymes and everything that rhymes is 10

[00:09:15] Steve: percent better. I’ve always said that.

[00:09:20] Cameron: Yeah, you are a marketing guy. You came out of marketing and yeah, you know, you’re, you’re

[00:09:25] Steve: Still there.

[00:09:25] Cameron: rhymes, it’s 10 percent better. That’s perfect. Gooey DeLooey. So the Looey aspect of it. So There’s two components of, of, of what they talk. The first is Apple intelligence. So this is their own implementation that’s on device to a large extent, and it will enable you to use language to do a lot of the basic stuff that people who use LLMs already do with it.

[00:09:54] Cameron: Um, low, low level stuff. Write me an email, generate me a picture, create, you know, they were big into create your own emojis and all that kind of stuff. They didn’t say create your own icons, which I found interesting because one of the things I’ve been doing a lot lately, you know, I use iOS shortcuts a lot, increasingly because now I can write code thanks to GPT and I can use it to do, you know, relatively sophisticated things with iOS shortcuts.

[00:10:24] Cameron: Then. If you create a shortcut in iOS and you want to save it to your desktop to run as an app, they don’t let you, they’ve got a bunch of pre built icons that you can use and none of them are very descriptive. So what I’ll do is I’ll go into GPT and I’ll say create me an icon for an iOS shortcut for Adding notes to my Kung Fu note.

[00:10:50] Cameron: And it’ll create a little icon for me that says Kung Fu notes with a little picture of a guy doing Kung Fu that I can then save as the icon. So I have custom built, custom designed icons for all these shortcuts now, but they didn’t, they should have

[00:11:03] Cameron: got me involved. I would have said, this is the, this

[00:11:06] Cameron: is the way you can customize iPhone now.

[00:11:09] Cameron: Anyway,

[00:11:10] Steve: stop. Just really quickly. You’ve given me an idea. I’m gonna see if GPT this afternoon can create me an 80s style 8 bit video game that kind of is a mashup of some of my favorite karate games. So that’s, I’m not getting any work done this

[00:11:23] Steve: afternoon and it’s

[00:11:24] Cameron: that is work. No, that is work. That’s, that’s, you know, that’s where, that’s where the rubber meets the road. You may not get paid for it, but it’s still work. Um, so Apple Intelligence on device creating emails and texts and, and images and all that kind of stuff. On device, it’s only going to be available if you have a 15, uh, an iPhone 15 or later, an iPhone 15 Pro or later.

[00:11:53] Cameron: You’ve got to have the silicon chips, you’ve got to have the latest Apple chips, same with the iPads and the Macs, uh, the MacBooks. You’ve got to have a fairly recent one that’s running their chips, because it’s all running, uh, Then there is a second aspect to Apple Intelligence, which they’re calling Private Cloud Computing.

[00:12:11] Cameron: If you need to do something that can’t be handled by the chip on device, and they made a big deal out of that because they’re saying it’s all about security, it’s all about confidentiality of your data. They’re really driving that whole aspect home. If you want to still do it in that contained privacy security, uh, system, but it needs cloud computing power, it, the phone, the device, the iPad, the Mac will reach out to this Apple thing they’re calling PCC, private cloud computing, still completely private.

[00:12:48] Cameron: All of your data is locked in. Down, secured, encrypted, but it’s in Apple’s server farm, basically. So that’s part one. It’s fairly boring implementation of it, but. It’s going to be available on all of your Apple devices, if you have a relatively modern device, and, um, it’s going to be secure. The second component of what they talked about was integrating OpenAI, ChatGPT into Siri.

[00:13:19] Cameron: And by the way, all of that’s driven by Siri as well. So, hey Siri, Make me a picture. Hey Siri.

[00:13:26] Cameron: Sorry, my phone is like

[00:13:29] Steve: Look, same, see what you did, see what you did, creating

[00:13:35] Steve: complexity,

[00:13:36] Cameron: Our phones just went nuts. Um, all integrated. They’re not rebranding Siri. It’s all happening in Siri. But then you can say, Hey Siri, uh, show me what movies are showing at my local cinema. And It will say, would you like me to use? Yeah,

[00:13:52] Cameron: sorry. My phone, even though I put it on airplane

[00:13:54] Cameron: mode, how do you stop it?

[00:13:56] Cameron: Do you have to like turn it literally off to

[00:13:59] Steve: put it in the next room, put it in the lock box,

[00:14:02] Steve: Cameron, lock

[00:14:03] Cameron: Oh, I’m turning it, turning it completely off. Yeah. Okay. I need to turn you off. Uh, it will say, do you want me to use ChatGPT for that? And you, or you have to apparently say yes every time, and then it will use ChatGPT to handle your thing.

[00:14:22] Cameron: Now, here’s why this is massive. There are. Uh, the vast majority. I mean, I don’t know how many people are using ChatGPT on a daily basis now, but you know, let’s say it’s a hundred, maybe 200 million people, um, that have used it at all. I don’t know how many use it on a daily basis, probably a lot less than that. Um, within the next year or so, you’re going to have all of Apple’s user base, billion plus people, whether they know it or not, using ChatGPT. AI on their phones to do stuff. It’s going to, it’s, it’s a massive, we’re going to look back. Five years from now and say, this is when it all changed. This is when AI, the, the, the daily use of AI went from early adopters, which is the people that are using ChatGPT or Claude or Gemini or whatever now, still a lot of fucking people, but it’s still early adopters, right?

[00:15:21] Cameron: To

[00:15:22] Cameron: everyone who has an iPhone or iPad or a Mac will be using AI. And the implementation that they’re doing is boring. It’s low level, it’s boring, but it, it’s the beginnings of great things, again to quote David Lee Roth, The

[00:15:40] Cameron: beginnings of great things cannot be seen with the naked eye. That’s from Skyscraper, 19,

[00:15:48] Cameron: Skyscraper, 1988,

[00:15:51] Steve: I’m going to call 86, 88 could be, but let’s just make a promise to

[00:15:56] Steve: ourselves.

[00:15:57] Cameron: solo album.

[00:15:59] Steve: Great. And let’s make a promise to our listeners. That we try and mention David Lee Roth in every episode going forward, because that’s the kind of podcast I want to

[00:16:07] Steve: be involved in.

[00:16:08] Cameron: Dude, David Lee Roth changed my life. When I was 12 or 13 and I discovered Van Halen for the first time and I saw David Lee Roth, I was like, yeah, that’s who I want to be. That’s, that was my, my first role model in

[00:16:24] Cameron: life. Was, uh, David Lee Roth.

[00:16:30] Steve: Now, I’m ready to tee off on quite a few of these things that you’ve

[00:16:34] Steve: mentioned. And the first one I will say, I totally get your proposition of why this matters. I get the bigness of it, because what you’re really talking about is a market dynamic rather than a technological dynamic. And I agree with you entirely on

[00:16:52] Cameron: step change in terms of market access.

[00:16:55] Steve: for consumers. It’s actually not a step change. at all in technology. And it’s not even a step up to where the steps are up to. Like they’re not even if it’s a staircase, they, they went onto step number two of the 10 steps on the staircase. So in terms of technological innovation, any, not useful, anything that’s innovative, you know, being a fast follow who come back with a better product, it’s none of those things in my view, but I totally get your sentiment.

[00:17:22] Steve: On this is an introduction to a wider marketplace of the capabilities of AI, which in many ways helps others understand why there’s a bit of a boom now with everything from NVIDIA to AI being the topic du jour in business. So I get that. I get that. I was more thinking about the machinations of what they launched and how underwhelming they were, but the bundling of these and handing them over to, you know, whatever it is, 1.

[00:17:49] Steve: owners or what have you. I get the gravity of that.

[00:17:52] Cameron: And, you know, Siri, as they pointed out in WWDC DC, Siri’s been around for 13 years now, came out 2011, just before Steve died. And we all know, I mean, it’s been a running meme joke for many, many years, how terrible Siri has been. I still use it, as you know, all day.

[00:18:13] Steve: I still use

[00:18:14] Steve: it.

[00:18:14] Cameron: Every day, but it has been stuck as a technology for many, many, it has had incremental improvements, but pretty much it’s been a massive disappointment, uh, to me and for most of us for many, many years.

[00:18:29] Cameron: And there’s been lots of sort of reasons and explanations from Apple over the years about why You know, the code base was terrible. It was hard to upgrade. It was hard to maintain. Apple have finally gone. I don’t know how they’re doing it. They didn’t really explain

[00:18:45] Cameron: it. I haven’t heard Tim talk about it, um, in any of the post WWDC interviews either, but somehow

[00:18:52] Steve: You’re talking about Tim Apple because I just love it. I just love it when, when Trump said, we got to Tim here, Tim Apple,

[00:18:59] Cameron: Tim Apple,

[00:19:01] Cameron: Tim

[00:19:01] Steve: that was the greatest moment in culture, Tim Apple,

[00:19:04] Cameron: Trump is a branding genius. He’s a marketing genius. Oh, by the way, did you see, there was an article I read in The Fin last week, one of the original producers of The Apprentice? has just started

[00:19:18] Cameron: talking publicly about making The Apprentice because his NDA has expired

[00:19:24] Steve: please send me that.

[00:19:26] Cameron: oh, it’s gold man.

[00:19:27] Cameron: Like him talking about that first season of The Apprentice and what they went through. Apparently the original idea was they were going to have a different billionaire each season to be the host of The Apprentice. They, you know, they wanted to get Spielberg and Katzenberg and all the other bergs. Um, And

[00:19:49] Steve: on fire

[00:19:50] Steve: today mate,

[00:19:50] Cameron: no one

[00:19:51] Cameron: else. I just did two hours of the Renaissance talking about the foundations of anti Semitism, man. It was great.

[00:19:57] Cameron: I’m on fire. About Christians and the first crusade and roasting babies on spits and it’s been fun.

[00:20:04] Cameron: Um, yeah. But, um, none of the other billionaires would do it. No, and everyone else was like, no, actually we have real businesses that we need to run.

[00:20:13] Cameron: We have real jobs. We’ve got things to do. More fool them because Trump used it to become president. Um, anywho, was I going with this? Tim Apple. Yes. They haven’t talked about it, but they’ve had to, they’re having to rewrite Siri from the ground up, I imagine.

[00:20:31] Steve: even, you can call it Siri, it’s just ground

[00:20:33] Steve: zero.

[00:20:34] Cameron: well, but no, but it, it, you know, it has to be Siri. But it also has to now, they have to increase Siri’s intelligence by a thousand and they’re integrating it into all of their apps. You’ll be, you know, it’s, again, it’s early days, but within a year, within two years, once they have rewritten the code base that enables all of this across all of their devices and the OS, we finally get to that point that we’ve all, you know, when I say we, you and me, and people like us, early adopters, nerds, have been talking about where you can say, hey, Phone, hey Siri, um, find me this and integrate it into that and, you know, tell Steve that I, you know, you know, find the next available time in my calendar and Steve’s calendar when we can get together and do an episode.

[00:21:26] Cameron: And by the way, go through my notes and look at all of the things that I’ve saved in the Futuristic folder of the last week and create a document and send it to Steve and, you know, where you just. Talk to your devices and it does all of the shit. You don’t have to go in there and fucking tap, tap, tap, tap, tap.

[00:21:44] Cameron: Where you just tell your device what you want to do and it does it for you because it has inherent intelligence

[00:21:50] Cameron: enough to do these things in its own iOS, its own ecosystem to integrate everything. This is, this is the beginnings of that. It’s huge.

[00:22:02] Steve: Okay. So It’s not the beginning of that. What you want is, is huge.

[00:22:08] Steve: but this is not the beginning of that for a whole lot of reasons. All

[00:22:10] Steve: right. It’s

[00:22:11] Cameron: Why? How do you, how, why?

[00:22:13] Steve: this? This is just some, this is just a bit of packaging and wrapping because none of that, none of what you described is possible.

[00:22:22] Steve: So it’s not the beginning of it because none of it’s possible. How can it be the beginning of it if none of this that you’ve just spoken about is possible based on what they’ve announced? A couple of tiny little, you can ask this app to do

[00:22:34] Steve: that. I mean,

[00:22:35] Cameron: because what they announced is indicating that they are now rewriting

[00:22:42] Steve: it, but they haven’t

[00:22:43] Steve: got it. It’s

[00:22:43] Steve: not there. That’s like saying we’re going

[00:22:45] Cameron: yeah, but they are gonna do it.

[00:22:46] Cameron: I mean, this is Apple, they’re not fucking

[00:22:48] Steve: Well, then next year, well, next year when it comes, I’ll be excited and I’ll be as excited

[00:22:53] Steve: as you are, but this is not it.

[00:22:56] Steve: So,

[00:22:56] Cameron: is not it. but this is, this is the, the,

[00:22:59] Steve: on, on, on,

[00:23:00] Cameron: but this

[00:23:01] Steve: that will be.

[00:23:02] Steve: And that would be the most important technology for a human ever invented.

[00:23:07] Steve: If we have exactly what you’ve mentioned, two things.

[00:23:10] Steve: First one is ChatGPT like capabilities. And I’m going to use it because it’s still, I think, better than all the others which I’ve been

[00:23:19] Steve: testing. It’s

[00:23:21] Cameron: said that in his interview with Marcus Brownlee.

[00:23:24] Steve: it really better than all of them by a long

[00:23:27] Steve: shot.

[00:23:27] Cameron: that’s the best, yes.

[00:23:29] Steve: Uh, so ChatGPT like three things. There’s three parts to the equation. First one is ChatGPT like capabilities, which obviously doesn’t have because it has to refer to ChatGPT whenever it can’t do something.

[00:23:41] Steve: The second one is agent like capabilities, which is what you said. Cause at the moment, even when you go into ChatGPT, we’ve got to do all those things in a singular fashion. You can’t get it to do a number of things and coordinate, you know, a little bit like the God Mode and AgentGPT, and everyone’s starting to talk a lot about agents and how they’re going to come together.

[00:24:00] Steve: I think Apple is. The best place to do an agent like service and Siri could be at that concierge, which goes through all of your things. And the other one is having an ecosystem of a personal database of your photos, knowledge, people, communications, data, all of that. If it pulls those three things together, that is Star Trek mode.

[00:24:20] Steve: You know, that is, You know, STM, that’s Star Trek mode, if we can get that. And AI with all of those capabilities like ChatGPT, agent like integration where Siri becomes your agent, and then a walled ecosystem where you have Private database information that’s relevant to you and those you’re connected with, as well as access to wider web information and being able to go in and out of your ecosystem with those privacy, uh, barriers.

[00:24:45] Steve: That is absolute utopia. And I would be even more excited than you if that was what we have, but we don’t have that in my

[00:24:52] Steve: humble opinion

[00:24:53] Cameron: No, we don’t have that, but for the last 18 months, since GPT dropped 3. 5, uh, end of 2022,

[00:25:03] Cameron: you and me and, and, and people like us have been prognosticating about what the future was going to look like when we had Star Trek, um, tech. And, but it w up until this week. Apple has never indicated that they were on board to build that.

[00:25:23] Cameron: We all assumed, I think. That Apple and Microsoft and Google and Facebook behind the scenes were all going to be playing around getting us there. They had the same vision, but it hadn’t been confirmed. You know, Apple was doing their secret car and their Vision Pro and fucking around the edges with all of this kind of nice toys.

[00:25:50] Cameron: This week, Apple went, Tim Apple, stood up and said, effectively, Yes, we are on board with your vision. We, we, it’s baby steps, but it is going to, The fucking, the wheels are turning now. We have,

[00:26:16] Steve: And I think on, on, on the Apple AI wagon, it’s a wagon, right? And the Apple AI wagon has got some new bumper bars. It’s got a nice new fluffy steering hubcaps, but I think, I think, and I hope,

[00:26:30] Cameron: off the rear vision mirror.

[00:26:31] Steve: Puffy

[00:26:31] Steve: Dice,

[00:26:32] Cameron: Hey,

[00:26:32] Cameron: I’m

[00:26:33] Steve: a little box out front, and they put some Chrome, they put some Chrome

[00:26:36] Steve: on the

[00:26:37] Cameron: decals, some

[00:26:38] Steve: sure

[00:26:38] Cameron: Fiery, flamey decals. Peace.

[00:26:41] Steve: They have, they’ve tinted the windows

[00:26:43] Steve: and let’s hope Eddie Murphy’s not around to put some bananas in the tailpipe because they’re going to fall from the banana in the tailpipe, man, right? And then I actually think What they’ve done is they’ve, they’ve pimped the wagon, the AI wagon a little bit, and I think while this is doing this in concert, they’re building what we’re speaking about.

[00:27:02] Steve: And I actually think they’re gonna come next year with all of this. But I think this is a, yeah, we’re on board. Here’s some, uh, jazz hands, jazz hands to get us through to next year. That’s what I think, and I hope, because I actually want them to come back with what they’ve always done, which is a, and by the way, here’s how you do it.

[00:27:23] Cameron: look, uh, you know, there this interesting, um, vision of Apple that I hear talked about online and you’ve indicated this in the past too, Um, Apple don’t innovate, they take innovation and they just make it better than everyone else and they do the best implementation of it. You know, you’ve said before the iPhone wasn’t the first smartphone, um, obviously Airpods weren’t the first Bluetooth headphones, the Apple Watch wasn’t the first digital watch, all of these things are true, um, the iPad wasn’t the first tablet, Um, Apple just came along and.

[00:28:14] Cameron: Made the best version. I mean, debatable, obviously, but I think, generally speaking, most people will agree that Apple made the best or one of the best implementations. They took stuff that was already existing and said, hold my beer, we’re going to do this properly. Let me show you what this looks like if you actually implement this well. There’s no reason, I mean, okay, a lot of that stuff was done during Steve’s