The AI Optimist

Once upon a time, LinkedIn promised to be the professional's haven, a digital space where careers flourish, forging meaningful connections.

Fast forward to today, and it’s a new stark reality: the platform is a breeding ground for artificial engagement and algorithmic manipulation.

Welcome to the world of 2024 LinkedIn, where an estimated 25% of traffic is nothing more than bots, according to a study by Lunio.

LinkedIn leads in this Lunio study of fake ad traffic, with 25%. Buy an ad, reach 1/4 non human bots. Good luck on turning those into customers.

0:00 From social to artificial media in 10 years

0:35 Peekaboo AI comments to Spotapod

02:57 AI Talking Bots replacing human connections?

04:44 Finding the Fakes and Fairness in Call Outs

08:01 Fake comments example - boosted with automated Pod Likes

12:29 1 Billion LinkedIn Users, maybe 250 Million Fake accounts?

16:07 FTC Crackdown on social for fake followers, likes, & engagement

20:30 Big Tech's Excuse - the DMCA since 1998 (scrape and don't regulate)

The tiktokification of LinkedIn, coupled with relentless gaming of the system, transforms this once-professional network into a playground for those selling the secret sauce to LinkedIn success.

Gone are the days of genuine professional interactions. And you are paying for it, even if you have no idea what a bot or pod or fake traffic is….

Instead, we're left with a feed full of carefully crafted hooks, recycled wisdom, and engagement pods trying to trick the algorithm.

In this landscape of artificial popularity, one man stands out, not for his ability to game the system, but for his determination to expose it.

Meet Daniel Hall, founder and CEO of Spotapod, helping you flush your feed of fake connections!

He shines a light on the underworld of PODS, built on data not opinions.  

With a background in tech and a heart full of empathy, Daniel is on a mission to put the "human back in humanity" amidst the digital chaos.

The Rise of LinkedIn Pods and AI Bots

Ten years ago, social media promised to shrink our big, lonely world into a cozy digital village.

Then AI jumped up and now? It's all about cracking social algos and gaming systems.

Creating content that is proven by others, copying them and stealing what they do, making it your own, because who cares if the audience is the algorithm?

This shift from genuine connection to algorithmic manipulation didn't happen overnight.

It's been a slow burn, fueled by the pursuit of vanity metrics and the illusion of influence. The result?

A platform where authenticity is drowned out by the noise of artificial engagement.

According to Lunio's study, LinkedIn tops the list of social platforms with fake traffic.

This means that when advertisers buy space on LinkedIn, they're potentially paying to reach an audience that's 25% non-existent.

It's a sobering thought for those who still view LinkedIn as the gold standard for professional networking.

Daniel's journey into this murky world began with a simple desire to understand engagement metrics better.

"I'm a technology guy. How can I figure out this vanity thing?

And I did. I created a solution for vanity metrics called Peakaboo, and it calculates how much time we're spending together in social media comments."

But as he dug deeper, Daniel uncovered a darker side of LinkedIn.

"I started digging deeper into pods and more so into the automated AI version of these platforms like Lempod, Podawaa, Hyper Clapper, all of them."

What he found was the rising of influencers wasn’t the old LinkedIn, organic and earned media.

It became pay for play, even for many with the much desired, invite only LinkedIn Top Voices, the blue badge setting you apart.

And many of the newer blue badges gamed and bought their way in, not as thought leaders, but as algorithm followers. Manipulators, and proud of it.

These are real posts, I blur their names out.

Look at the times, the likes, and the lack of likes. This is what LinkedIn is starting to look like….let’s just copy the same thing over and over and over again.

They didn’t open new doors of understanding, they mimick virality in the name of vanity, big numbers so they could teach others how they did it….or bought it.

The Mechanics of Social, LinkedIn Deception

Daniel's background in cybersecurity allows him to peek behind the curtain of these AI-powered engagement tools.

"I'm able to sniff traffic.

So what that means is I look at every facet of data that goes from my computer out to the internet and whatever comes from the internet back into my computer."

This ability allows him to see the inner workings of pod platforms, revealing how they generate generic comments and reactions to posts.

"I can see that this person requested these creators to use these comments," Daniel says, breaking down an example.

"Your musings are a journey into the realm of the mind, and then they use macros, which pulls in the first name of the creator that has the post."

The implications of this are far-reaching and potentially dangerous. As Daniel points out,

"What if my posts and the things that I posted were fake news about how somebody was committed of treason and I had somebody drop a comment that supported that?

 All of a sudden, I've just simply by making them say what I want them to say, I've completely just destroyed their brand."

Like this example, to send out positive comments about Israel, because who really reads the automated comments the Pod sends out?

The Human Cost of Artificial Engagement

For Daniel, this isn't just about numbers or algorithms. It's about the human impact of these deceptive practices.

"I don't want people to really laugh at people because this is a mental health thing.

It really is. People are going in and some people are being tricked."

He recounts instances of calling out individuals who may have been duped into joining pods without fully understanding the implications.

"For me to appear, for me to laugh at something like that, that's not fair.

And that's not fair to those people that have been building their brands."

This empathy extends to those who might be unwittingly participating in these practices.

 "That's not fair to the creators that are actually trying to do it authentically but are getting duped and paying lots of money to be able to look like they're making it."

The Scale of the LinkedIn Bot Problem

The extent of fake engagement on LinkedIn is staggering.

Daniel estimates that of the 1 billion LinkedIn users claimed by the platform, as many as 250 million could be fake accounts.

"A lot of these numbers that we saw in these reactions are probably fake accounts.

Probably a quarter of them are fake accounts."

This isn't just about inflated numbers. It's about the integrity of the platform and the trust users place in it. As Daniel puts it,

"At the end of the day, I don't play favorites and I don't, you know, if I see it in a pod, especially if you have a big following and you got a LinkedIn blue badge, you're probably on my radar."

The FTC Crackdown and Big Tech's Response

The Federal Trade Commission (FTC) is taking notice of these practices.

"On October 20th of this year, the FTC is going to come hammering down on creators that are selling, pushing their products and gaming the system," Daniel warns.

This crackdown could have far-reaching implications for both individual users and the platforms themselves.

But Daniel believes the responsibility ultimately lies with the social media companies.

"Social media companies won't just say LinkedIn. Social media companies.

At the end of the day, they're at the top.

They're the ones that need to police their platforms, not Dan Hall down here.

They're responsible."

The challenge, as Daniel sees it, is one of the incentives.

"What you have to gain by enforcing your rules, as opposed to what you have to lose by not enforcing them?" he asks.

The answer, he believes, often comes down to money.

 "It's all about the money. It's all about the numbers.

Vanity translates to cash."

Who cares about the Human Element in a Digital World?

Despite the technical nature of his work, Daniel's approach is deeply rooted in human connection.

He's not just a tech guru; he's also an adoptive father of seven, focused on being "rich" because of the love in his life.

This human-centric approach is reflected in how he views the impact of his work.

"If I can do something about that, if I can help others see what I see, that will help put the human back in humanity," Daniel says.

For Daniel, success isn't measured in likes or followers, but in genuine human connection. He recounts a touching moment with one of his adopted daughters:

"She had gone to her room for about 15 minutes, and she came back with this. And it's a picture of her and I holding hands."

This moment crystallizes what truly matters to Daniel.

"To be seen, loved, heard and valued as human beings is all we ever want," he reflects.

"It's as simple as that. And when you're focusing on the

vanity part of this, it's you're being seen and heard.

And it stops right there."

The Path Forward: Authenticity in the Age of Algorithms

As we navigate this landscape of artificial engagement and algorithmic manipulation, Daniel's work serves as a guide to authenticity.

Through Spotapod, he's not just exposing fake engagement; he's championing genuine human connection in the digital age.

The challenge ahead is significant.

We have gone from heart-to-heart chats to a divisive, high tech game of who can fool the machine best.

But with awareness and tools like Spotapod, we can start to reclaim our digital spaces for authentic interaction.

In a world where algorithms promote sameness and conformity, standing out and being yourself becomes an act of rebellion.

It's about diving into that vast and undiscovered well inside you, expressing your true self to those closest to you, or choosing to keep it safe and hidden away.

The dopamine hit from likes and shares may be tempting, but it pales in comparison to the richness of genuine self-expression and connection.

As we stumble through this digital landscape, we discover that true beauty isn't in the perfectly curated feed, but in the deep well of our authentic selves.

Daniel's work reminds us that in the pursuit of digital validation, we risk compromising our past truths for present validation.

But there's another way. By walking in another's shoes, by sharing our authentic stories, we can create a view that's uniquely our own.

The freedom to be ourselves, to express our authentic voices, is worth more than any algorithm-driven success.

Because while the view from the top of the LinkedIn feed might be great, your view – unique, authentic, and shining – is equally valuable.

As we move forward in this AI-driven world, let's heed Daniel's call to put the "human back in humanity."

 Let's challenge the blandness and sameness that pervades LinkedIn and other social platforms.

Let's dive into our own wells of authenticity, again and again, seeking growth and change in the shadows.

In the end, it's not about gaming the system or fooling the algorithm. It's about being true to us and fostering genuine connections.

Because in a world increasingly mediated by AI and algorithms, our humanity – our ability to be seen, loved, heard, and valued for who we truly are – is our most precious asset.

Here's to the ones who refuse to be assimilated, choosing authenticity over artificial engagement, understanding that true influence comes not from manipulating algorithms, but from touching hearts and minds with genuine, human connection.

 In a LinkedIn landscape flooded with bots and artificial engagement authenticity is rare. And it helps the person sharing, as much as those following.

Daniel's mission isn't just about cleaning up LinkedIn or exposing fake engagement.

It's about creating a better future for his daughter, for all our children, where digital spaces enhance rather than diminish our humanity.

It's about ensuring that when they reach out to connect, they're met with genuine human warmth, not the cold efficiency of an AI.

Let’s carry with us the image of that hand-drawn picture – a father and daughter holding hands.

In that simple, heartfelt gesture lies the essence of what we're fighting for: a digital world that doesn't just see and hear us, but one that truly loves and values us for who we are.

That's the kind of richness no algorithm can fake, and it's the future Daniel Hall is  creating, one authentic connection at a time.

Thanks for reading The AI Optimist! This post is public so feel free to share it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

In the world of AI-powered meeting solutions, one name stands out: Fathom. Founded by serial entrepreneur Richard White, Fathom has quickly risen to become the #1 AI note-taking service.

How did this journey begin, and what lessons can entrepreneurs and small businesses learn from Fathom's success?

Let's dive into Richard's story and explore the human side of building an AI-powered business.

Richard White is founder and CEO of Fathom.video, a free app that records, transcribes & highlights your calls so you can focus on the conversation instead of taking notes. 

Fathom was a part of the Y-Combinator W21 batch, is one of only 50 Zoom App Launch Partners, and is one of a small handful of companies Zoom has invested in directly via their Zoom Apps Fund.

Prior to Fathom, Richard founded UserVoice, one of the leading platforms that technology companies, from startups to the Fortune 500, use for managing customer feedback and making strategic product decisions.

UserVoice was notable for being the company that originally invented the Feedback tabs shown on the side of millions of websites around the world today.

Connect with Richard White on LinkedIn

Richard previously worked on Kiko, a company in the first batch of Y-Combinator, with Justin Kan and Emmett Shear who subsequently went on to found Twitch. Richard is passionate about designing intuitive productivity tools with delightful user experiences.

Starting Fathom When AI Wasn't Cool

It all began in the lockdown of 2020 when meetings became a way of life for everyone.

Richard, who had previously founded and led UserVoice for 13 years, found himself struggling with a common problem:

"Right before Covid, I was actually doing a lot of product research at Uservoice. And so I was on a bunch of zoom meetings, and I was trying to talk to people and take notes at the same time, and I'm like, hurriedly typing up notes.

And then after the meeting, I'm like, cleaning up those notes so that they make any sense. And I just felt like this is such a terrible process."

This frustration led Richard to a realization: there had to be a better way.

He observed that existing tools were focused on salespeople and were expensive.

So, he developed a thesis:

"We think transcription is going to become really cheap. And we also think AI can get really good, which seems obvious now.

But I remember in 2021 we put AI in our product name and all my investors freaked out.

I mean, no one likes AI, even though it's hard to remember this, right?"

Richard and his team believed that if they could create a good tool for recording and transcribing meetings, they could eventually drop in AI to take amazing notes.

They decided to give away their product for free, betting on the cost curve coming down as usage increased.

The Illusion of Failure: Leading by Example

Like most startups, Fathom's journey wasn't easy. They went from 100 monthly active users to 100,000, but it wasn't without challenges.

Richard shares his perspective on dealing with the illusion of failure:

"I think sometimes I see founders that delude themselves about their products. It's like, they want to use it for someone else's product, but because it's their own, they'll ignore all the warts.

 But I think if you can stay relatively sober about your assessment of how good your product is, and it is truly transformational for you, I think that just gives a really good bedrock."

Richard emphasizes the importance of believing in your product and its impact:

"I didn't really ever give up on because I knew the impact it was having on my work life.

And so we just had that's why I was like, there has to be more of this story.

And that's why we kept digging until we found out, 'Oh, okay. Here's what the problem is.'"

Being Data-Driven Without Enough Data - Going with Your Gut

One of the challenges Fathom faced was navigating the early stages of product development without sufficient data for A/B testing. Richard shares his approach:

"In a lot of B2B products, you don't get the scale to be able to do things in a data-driven way. Truly. Like a lot of stuff is guessing.

With Fathom we can, you know, we're a B2B product, right?

But we have enough scale because it's kind of a prosumer product."

Richard emphasizes the importance of focusing on one key metric at a time:

"I always say I want to solve one key metric at a time in the business. I see a lot of people that like start off and they're like, what shall I monetize?

And we're trying to prove engagement and we're trying to fix our onboarding like pick one, right?"

For Fathom, they started with free user retention:

"I'm a big fan of delayed monetization because I think it's hard to get people to use the thing, but then to get them also to pay for it, getting the users is part of the barrier for most products, in my opinion."

Speed is a Feature: 30-Second Meeting Notes

One of Fathom's key differentiators is speed. Richard explains:

"Speed is a feature. And I remember because when we were four years ago, building the first prototypes and testing their products, like, even zoom takes half an hour to get you the recording.

Gong would take you half an hour to get a recording, and I remember seeing the team. It's like if I'm trying to replace note-taking one of the things notes have is immediacy."

Richard pushed his team to make the note-taking process faster and faster:

"I remember telling the team it needs to be faster, like, we need to get you this, as close to the meeting ends as possible.

Remember the engineering team be like, how fast does it need to be? That's like, I don't know, just keep getting faster and I'll tell you it's fast enough."

Through continuous iteration, they managed to get the process down to about 30 seconds:

"And so to this day, I think we're also, still a lot of our competitors take five, ten, fifteen, 30 minutes to get you those notes.

And like, it seems like a small thing, but I think it's one of the key features. Speed is probably one of our biggest features."

Customization and Templates

Fathom has evolved to offer a wide range of customization options and templates:

"We've got 15 different templates now. A couple different depending on, a couple of ad sales ones depending on your sales methodology, 101 templates.

Retrospective, you name it. We also just added the ability where you can give the AI feedback and be like, I want the sales template, but I also want you to make these adjustments to it sort of thing."

Richard believes that this level of customization is crucial:

"I think everyone's got years and years of experience writing notes and we know exactly what we want.

And so if it doesn't match the output I'm trying to get, it's not replacing note-taking.

 And so we actually think that that's an important differentiator is being able to have not just really good notes, but really good notes in the format that makes sense for you in your business."

The Future of Fathom and AI in Business

Looking ahead, Richard shares his vision for Fathom and AI in business:

"I think AI is going to do to the operational side of businesses what open source did to product development, meaning you can now build automations that have judgment. Right.

Which is always the challenge. Like historically, judgment was only the purview of the humans, which is why you have a 10 person engineering team but a 100 person sales team.

Now, you can basically build automations, judgment, and what you need to build those automations is access to data."

Richard envisions a future where AI acts as a true assistant:

"I'm excited about this world where if you think about the modern working world, the ratio of like real work to busywork, right, the amount of time or it's like, oh, I had this meeting,

 I track these action items, I log this thing here, I moved this thing from this, from my knowledge base to my task tracker.

 There's all this kind of like bit shuffling right to keep the humans organized."

He sees AI transforming this process:

"And I think what we're really excited about is a world where you can just, it really does feel like an assistant. Right? For AI to get on a meeting.

So reminds me why I'm getting on this meeting. Here's the last one you had, here's what you're trying to cover.

And just when you're on these meetings, when you're talking to other people, whether it's in person or remotely just speaking something brings it into existence."

Richard believes this will lead to a more creative and efficient work environment:

"Who will really be unlocked to just be kind of our full creative selves, and we're here to do the things that AI can't do, which is imagine not like the solutions to some of these problems.

And when it gets down to the tactical things to do the stuff, like before it, before the meeting even ends task are being created, the task is being completed."

Lessons for Entrepreneurs and Small Businesses

Throughout Fathom's journey, several key lessons emerge for entrepreneurs and small businesses navigating the AI landscape:

* Solve a real problem: Richard started Fathom to address a pain point he personally experienced.

* Be patient with new technology: When Fathom included "AI" in their product name in 2021, investors were skeptical. But Richard's team believed in the potential of AI and persevered.

* Stay grounded in reality: Assess your product soberly and be open to feedback.

* Focus on one key metric at a time: Don't try to solve everything at once.

* Prioritize user experience: For Fathom, speed became a crucial differentiator.

* Embrace customization: Recognize that users have different needs and preferences.

* Look to the future: Consider how AI can transform not just your product, but entire business processes.

Richard's journey with Fathom demonstrates that success in the AI space isn't just about technology—it's about understanding and solving real human problems.

By focusing on the user experience, continuously iterating, and staying open to feedback, Fathom has become a leader in AI note-taking.

As Richard puts it,

"It's going to be wild. I think the amount of stuff we can even do, we have a whole team that all they do is figure out what is that now the frontier of AI and like, what can we put into the product?

 And there are things we couldn't do six months ago that we were wanting to do for a while that we can now do.

 And there's things where it's like, six months from now, we'll be able to do 10X what we can do today."

For entrepreneurs and small businesses looking to leverage AI, the key is to focus on the human element.

Understand your users' needs, be patient with emerging technologies, and always be ready to adapt. Not to technology, to the needs of the people using it.

The AI revolution is happening even if so many are missing it, trying to figure out how technology will solve their problems. Better to pinpoint your problems and find out if AI can help solve them.

Start with objects you work with, like Fathom focuses on meeting summaries. Then build out from that object to other problems or time wasters associated with the task.

Don’t expect AI technology to just do everything; you have to audit it and guide it. You have to have the right datasets that matter, and impact your business, like meetings.

Start with one problem, solve it with AI (or not), and move to the next. Grow from small to larger solutions, because that way you won’t get burned if AI can’t do what you read it can do.

Which is mostly written by people overhyping and pitching the product.

Follow Richard’s example, be calmly competitive and focus on what’s in front of you, what you can solve, and pick one problem. Then solve another, but don’t try to do too much at once.

Thanks for reading The AI Optimist! This post is public so feel free to share it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Are deep fake political videos The Ultimate Threat and California's upcoming legislation, The Cure?

Welcome to the funhouse mirror of modern democracy, where reality is negotiable, and your eyes and ears might lie.

Listen and ask yourself: Can you spot the deep fake?

Another trick is trying to sound black. I pretend to celebrate Kwanzaa, and in my speeches, I always do my best Barack Obama impression. So, hear me say that I know Donald Trump's tight.

Pretty easy to spot, isn’t it? Think that will impact the election. Like nobody can figure it out?

And, of course, Elon shared this Kamala video and said parody is legal in America, and that's the problem.

People don't like Elon and even Gavin Newsom's response to this and say, you know what? This kind of deep, fake political content can't happen.

And, I'll be signing a bill in a matter of weeks to make sure that it is.

Gavin wants social platforms to identify and block deepfakes, and he's not alone.

We have to focus on the real problem with deep fake political content happening right now. It's only being solved in one country by an AUNT—Auntie Meiyu—that I'll show you later in this video.

What she is starting to solve doesn't come from the government or social platforms playing whack-a-mole with meaningless A.I. videos.

A.I. works for them and helps them. In this episode, deep fake political mania, regulatory overkill, or free speech, we're going to look at this through the lens of you and me being able to use social platforms and not letting the deep, fake political content mania get in the way of what's going on.

So, let's start by spotting the deepfakes.

First, let's spot the deepfake, a quick interactive game. I will show you some images in a video, and you can decide whether they're real.

Now, first up, of course, we have Donald Trump. And here's an image of him with several black women in a photo.

Looks very, very friendly. Now, is this a deepfake or not?

Well, it turns out it is. And here's another photo where he's sitting with six black men, and it was done by right-wing Florida host Mark Kaye.

He shows a smiling Trump embracing these happy black women.

And on closer inspection, you see that they don't have some fingers, and some have three arms.

Typical A.I. stuff. But if you don't look, maybe you think Trump is making those deals, and Mark Kaye says,

I'm not claiming it's accurate. I'm not a photojournalist.

I'm not out there taking pictures of what's happening. I'm a storyteller.

Now, Color of Change has a different view. They see this as spreading disinformation and targeted intimidation of black voters.

Faking an association with black people that never happens is a serious issue. I'm not trying to dismiss it, but can we regulate it?

The issue is how we prevent and identify this without necessarily pushing it as California does.

Let's look at a Trump video and play the game now.

Listen to this: is this at CPAC? Is this a real Trump, or is this a fake Trump?

Our enemies are lunatics and maniacs.

They cannot stand that they do not own me.

I don't need them. I don't need anything about them. I don't need their money. They can.

That was once a little hard to guess, but that's fake Trump. Now listen to the real CPAC video.

Hey, when I'm in a news conference, are people these maniacs?

These lunatics are screaming at me. They're just screaming like crazy.

And, you know, you take them, and I love it, you know? It's like a mental challenge.

As you can see, that one is really subtle. But think about whether a social media company is supposed to figure out which one's true or not, whether the image of Trump is true.

So, let's push it to Kamala Harris. Let's play the same game. Here's a picture of Kamala. She's dressed in bright red with a red hat, looking very much like this Chinese communist icon, Communist Kamala.

Obviously, you look at this and know that this is not real if you're in anything in U.S. politics. The Trump Organization made this point.

Is this satire? Is this a parody? Is this free speech?

Interesting question. I can see both sides and can see how it'd be offensive. But let's now do the spot the deepfake test again.

Watch this video by Kamala. And is it Kamala or deepfake Kamala?

I was selected because I am the ultimate diversity hire.

I'm both a woman and a person of color, so if you criticize anything I say, you're both sexist and racist.

Okay, you guessed it right. Yes, that's deepfake Kamala. It's not hard to figure out because those are not things she would say.

In fact, most of us on social media are very adept at figuring this out. It's not like news, but listen to the real Kamala.

The freedom not just to get by, but get ahead.

The freedom to be safe from gun violence.

The freedom to make decisions about your own body.

Now, that video is obviously her, and we become adept at social media to figure that out.

And while I'm not saying this isn't a problem, how do we ultimately stop this without censorship?

And it's not limited to the U.S.? Check out this video from India. That dancing guy looking like a rock star is an Indian politician, and this person is actually dead.

SOURCE: NBC News on YouTube

And famous Indian politician who is no longer live shown using a deepfake.

And, of course, there's a well-known case of Keir Starmer, the Prime minister of the United Kingdom. Please take a listen to what he supposedly said.

SOURCE: on Bitchute

Today, I convene with police chiefs from across the nation to formulate a decisive strategy to address the scourge of white working-class protests.

These far-right f**cks have observed BLM riots and Islamic demonstrations, and I assume they could replicate them.

Let me be unequivocally clear. You do have the right to protest in this country unless you are white and working class.

Now, there we have a problem—a racist solution shown by the Prime Minister. But wouldn't I, would a social platform be able to identify it?

We'll talk about that in a second. Easy, right? Because they're techies, they can do anything. But what you do is leave free speech in the hands of people who—honestly, I won't say they don't care.

But if they do like Elon, we're still determining if the way they care is the way they think it should be done.

After all, if you go to any site that represents free speech, it's more like fringe speech. Look at Rumble or Bitchute, which is really organic.

All these sorts of things make us uncomfortable. Conspiracies, some people with unique points of view, all needing protection, are free speech, even though most people don't see it.

See, free speech is not comfortable, but it's needed. And while deepfakes are taking things to another level in the reaction to try to block this to some quasi-big tech government agreement, it's just wild.

So, you have to ask yourself whether deepfakes are a severe problem. It is the solution to let government and social platforms be the arbiters of free speech.

Try asking A.I. if the images I showed you should be protected. You think it hallucinates when you ask it a question. Ask it about comedy, parody, and satire.

What do you think its answer is? Is it a comedy? If they don't laugh, if A.I. doesn't laugh?

I've asked this question at Gemini, Claude ChatGPT, and Midjourney. They all stop me from creating political content with real political people.

You can get it done, but they're stopping that at the source. But what's happening is we're allowing this, like billionaire social media owners and trillion-dollar corporations, to join forces to protect free speech by eliminating it because nothing says an open discourse like a soundproof echo chamber.

It's beyond their capabilities. But that doesn't stop politicians like Gavin Newsom from pushing that responsibility to social platforms while pretending to regulate.

The deep state of deepfakes—California's farcical ploy into A.I. policing.

Now, politicians have found a foolproof way to stop deepfakes by making reality so absurd that no one can tell the difference.

Take that A.I. Well, these deepfakes bother politicians more than social media users.

Later, I will show you that they're not the problem. In an effort to save democracy, we're deciding to kill it.

But don't worry, we'll deepfake it later, right?

We'll bring it back to life. Look at the California Defending Democracy from the Deepfake Deception Act of 2024. I mean, wow. It's always a stunning title.

That sounds so good. However, the California plan is to stop deepfakes related to elections.

It's overly broad, but social platforms must remove deepfake videos 60 days before and 60 days after the election.

But who is going to police all of this? Even in the bill, they said. Well, the social platforms have the technology.

It bans the distribution of deceptive audio or visual media of candidates, leaving it and putting the liability on social platforms when crazy people are using A.I. and generating these images for fun, laughter, or parody.

And you're expecting that to tell the difference. The good part is that this act tries to protect voters from misleading content, but it needs to include the point.

I'll share it later. Remember, Auntie Meiyu. That's the solution.

Strong legal accountability for tech platforms is great, but this is asking a lot. Censorship risks and is burdensome for the platforms to implement.

So, even 18 states in the U.S. right now have deepfake regulations, and they're all over the board. California's law seeks to stop and push it all to social platforms.

Texas, which is making it criminal to create and disseminate election-related deepfakes. Oregon. Utah. Michigan has passed laws relating to it, and many states have pending legislation looking at it.

Even the U.S. federal government, in the Deepfakes Accountability Act, is trying to outlaw deepfakes, especially those weaponized for elections, fraud, or harassment.

However, deepfakes must include clear, permanent disclosures, which is the opposite of what deepfakes do.

Violators could face $150,000 fines or five years in prison. Those violators might include social platforms. So, it gives us a great legal framework.

It imposes heavy penalties and includes transparency tools, but again, it unintentionally limits free speech, including satire and artistic creativity.

Just like California says,

"Oh no, we're not stopping parody or satire."

How is this supposed to be filtered?

How is that judgment rendered by tech thinking A.I.'s magic? Take a look at people who don't understand this.

Overregulation could also impact the advancement of A.I. tech.

They are trying to slow it down to 20th-century government rules. Hey, in the E.U., they've had a code of practice on disinformation since 2022.

They're demonetizing it, increasing political ad transparency, and strengthening cooperation between platforms and fact-checkers. Great stuff.

However, platforms again have to improve their tools and provide better access to data to the government. Now, a lot of people signed off on it.

It's great for transparency and election integrity, gives cross-industry cooperation, and eliminates disinformation theoretically, but it's voluntary.

There's no enforcement, and its effectiveness hinges on the platforms complying with these rules. And therein is the problem.

They might even try. But can they do it?

And are you putting this on ten different companies to do it ten different ways?

Deepfake democracy:

When pixels and politics collide

Let's say big tech and figure out a way to block this. California says the social platforms can do it. Push it off to them. After all, they're the techies.

They do stuff we need help understanding. And it's A.I., after all. It's magic.

Well, social media platforms are gaining. godlike powers of discernment.

What is this, black mirror or real life?

Each platform would likely create more problems for its users than solve them. Talk about false positives.

Facebook now has 100% accurate B.S. detection; your democracy is guaranteed.

We saw this in the Mark Zuckerberg letter that he just sent to Congressman Jim Jordan to address the Hunter Biden Burisma story that was shut down in 2020.

They received a warning letter from the FBI. This article was called potential Russian disinformation, even though it was in the New York Post.

So, they temporarily took the article down. The thing was fact-checked. It turned out the report was not Russian disinformation.

Meta later acknowledged it should not have suppressed the article. Some say Zuckerberg wrote this article in case Trump gets elected to cover his back.

But this is an example of how pressure from political institutions can influence these powerful platforms.

This prevented an important article from being released prior to the election, so it was its own disinformation, an election interference.

The Hunter Biden article is the problem with government regulation of deepfake political content. We don't have the tools.

And censorship is never a game of really protecting us.

It's always a game of power.

So, how do you expect nuance and humor? Human parody, judgment by A.I. that doesn't do nuance?

Well, how do they know? And it even helps create what the Brookings Institute calls the liar's dividend denial video.

You don't like an attack then call it a lie, the ones you do approve. Either way, you win. It doesn't even matter what the truth is.

This idea that there are algorithms and that social media is just hiding it and can just put this on misses the fact that there are ten huge platforms at least, and each one would do it differently.

We're trusting them to define true political content and what isn't. So, as annoying as it is, is this really the problem?

The Great Deepfake Caper: When A.I. Meets the Regulation Rodeo.

The real problem isn't deepfake videos; it's the A.I. deepfakes you need to learn about because they aren't online robo calls, texts, and even posters.

Should the government decide who you should listen to or block them online, even if they block them online?

But listen to this robocall and see if you can spot the deepfake.

It's essential that you save your vote for the November election.

Voting this Tuesday only enables the Republicans in their quest to elect Donald Trump again.

Now, no, that wasn't Joe Biden. The voice was from a fake robocall.

That's where the A.I. gets crazy, not in online social media, where the focus is clickbait.

Those weird videos I showed you are annoying. They could be better, but there are other sources of the problem.

That robocall going out to somebody who doesn't see Biden but thinks it's him is the problem.

And how about this one A.I. micro-targeting A.I. tool to target customers' buying behavior?

Even if they go to church, they take pictures of license plates that the government and other organizations can buy from third parties. But this is no conspiracy. It's been happening for years.

That A.I. microtargeting by email and text is super important to regulate.

That has nothing to do with deepfake content videos. It's profound fake text and really subversive targeting.

So if you're going to try to stop the problem of A.I. effect on elections, you're going to see a lot more in the robocalls and the impact of email and texting to people knowing their behaviors, knowing their triggers, knowing their psychological profiles, really behind the deepfake.

That's creepy. That's cringe. And finally, if you think you think deepfakes are only online, look at this poster showing Kamala Harris, a drawing in a Philadelphia Eagles helmet, and the subtitle says official candidate of the Philadelphia Eagles with a link to the Philadelphia Eagles football website to vote.

What's funny is that no one knows right now, as I'm recording this, who put up those posters.

They're on bus stops all over Philadelphia. And if you know Philadelphia, they love their Eagles, right? They love their football players. And this looks like an endorsement of Kamala Harris.

You want to talk about a deepfake. We don't even know who did it. It's in the real world. So you don't see that like the Kamala Harris video, which is getting millions of Twitter views.

It makes us realize that the problem is right in the real world. So, how are we going to stop that penetration? And the Eagles have come out and said, we don't support this. We don't support political candidates.

But if somebody walked by that, would that matter? That's the real threat of deepfakes way beyond what's online. It's what's reaching us in the real world.

Maybe Auntie Meiyu Has the Answer

I told you initially that an aunt is solving the deepfake problem in Taiwan. There's a little joke she's solving it.

Still, it comes from technology, which is interested in protecting people and not enforcing more power.

Auntie Meiyu is a chatbot from Taiwan that stops political and misinformation, mainly from China.

Government sources in Taiwanese media, it's focus on fake news and political deepfakes by offering real-time fact-checking.

So the chatbot you operate as an individual leverages A.I. and, in collaboration with civil service organizations, to counter disinformation, particularly during elections.

Its proactive approach helps reduce the spread of deepfakes by empowering users to verify questionable content quickly.

You get fast fact-checking directly accessible to the public, limited to Taiwan, and reliant on users flagging content.

What's different is that it's a real-time fact-checking model focusing on instant verification via a popular messaging app like WhatsApp.

It's called LINE and is specifically tailored for Taiwan's disinformation challenges.

While other systems like the E.U. Code of Practice or U.S. deepfake laws emphasize platform regulation and long-term policies, Auntie Meiyu, who is user-centered, can provide immediate on-demand fact-checking.

Its localized approach gives us an edge in engagement, and it needs enforcement power. But what it does is it's in the hands of the users.

So, Auntie Meiyu is a much better solution than looking at every social platform. And what if that was something that was cooperative, very pro-society, and done to train on the specific disinformation that we have in the U.S.?

Taiwan has disinformation, the European Union has disinformation, and the U.K. has disinformation.

The deepfake political survival guide is here.

I'll give you the three steps.

Step one: trust nothing.

Step two, question everything.

Step three: realize you're probably a deepfake two.

Now, we need balance. Deepfakes are dangerous. They do put out misinformation, but the regulatory attempts are trying to do this control play.

Which can't even scale because this technology doesn't exist within each of these ten private social platforms, who will also get access to very private data, unlike Auntie Meiyu, which doesn't share the data with anyone.

Maybe this is helping us get our critical thinking going. We're all talking about upskilling in A.I.; they always say to get your critical thinking out.

Well, here it is. And I don't know anyone on social media who inherently believes what they see.

In the battle between A.I. and bureaucracy, the only guaranteed loser is common sense.

So the solution is a third-party app like Auntie Meiyu, dedicated to protecting the users and reversing the paradigm of being in the platform's control, not part of the government.

So they can make mistakes. It is not part of the social platforms, which are really afraid of liability.

It shares knowledge and does not have it owned by a government. It is trained in each country or culture.

Imagine freedom not just for speech but from misguided regulation, silly solutions that pretend to apply a 20th-century model to a 21st-century problem and move way faster than their old models of creating giant reports they don't even read, and making grandiose statements that don't understand the technology.

It's an evolution, not a way to restrict what we don't understand.

So what do we need to do? We must stop seeing the problem as deepfake political content that we can control and shut down.

That won't solve it. The real solution is to create a third-party application that works around it and works for the user, not for the social platforms.

So, this is a bluff. If you want to stop deep fake election fraud, stop misinformation, start by creating something that works for the user and not for a third-party private business or a government whose real issue isn't free speech.

But power and free speech are essential, as fringe as it may look.

We need to protect that, but we need to do that by starting with the people, not with those in power.

Thanks for reading The AI Optimist! This post is public so feel free to share it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The Human Equation: How One Young Entrepreneur Blends Data and Empathy for Explosive Growth

In the bustling world of startups and tech innovations, it's easy to get lost in the sea of data, algorithms, and AI-driven strategies.

But what if the secret sauce to success isn't just in the numbers, but in the people behind them?

Enter Maximillian Naza, the engineer turned entrepreneur who's rewriting the rulebook on how to build thriving businesses in the age of AI.

His personal social media, This Problem Solver is on Instagram.

Maximillian's journey from side-hustle dreamer to full-blown business empire builder is a testament to the power of blending data-driven insights with a deeply human-centric approach.

As the CEO of PasciVite and founder of Hope Records (dubbed the Y Combinator of music), Maximillian has cracked the code on a formula that's as simple as it is revolutionary: AI Analytics + Empathy = Growth.

Let's dive into Maximillian's world and discover how he's helping entrepreneurs, creatives, and small businesses navigate the often-confusing landscape of AI and data-driven decision making.

Part 1: Data Doesn't Lie, But People Matter More

Maximillian's philosophy on being data-driven started to combat his tendency to overthink.

 "Being data-driven became a bit of an equalizer," he explains. "Because I'm whatever, but this is what the data is saying."

However, he quickly learned that relying solely on data wasn't enough.

"Early on, my approach was data or nothing," Maximillian admits.

 "And then I would make decisions off of the data and then not get the necessary, the expected outcome. And there was a bit of a disconnect there."

This realization led Maximillian to develop a more nuanced approach. He learned two crucial lessons:

* Validate your data:

 "If you're making a decision off of bad data, it doesn't really matter what you're doing. It's just not going to work out."

* Marry data with reality:

"Data gives you insights. Reality gives you context.

And by marrying the both of them, you get the full picture, which is priceless."

Maximillian illustrates this point with a hypothetical scenario about movie releases.

Imagine you're planning to release a drama in the fall of 2005. You look at historical data from 2000-2005 and 1995-2000.

The data shows dramas performing well in the fall, except for a significant dip in September 2001.

Without context, you might conclude it's safe to release your drama in September. But when you factor in the reality of the 9/11 attacks, you realize that September 2001 was an anomaly.

"That's the reality that when matched with the data, helps you kind of create a picture that makes sense," Maximillian explains.

This human-centric approach to data is at the core of Maximillian's success. He emphasizes,

"Algorithms are important, but they should never take precedent over the human factor.

Or for the people, because ultimately, those are the folks that you're targeting."

In a world obsessed with "gaming the algorithm," Maximillian offers a refreshing perspective:

"Maybe there's a better way to say this, but you should be trying to game the people.

It's like people, algorithms, data, however you want to rank it, the people are always first."

Part 2: Startups, Focus, and the Art of A/B Testing

Maximillian's work with startups has given him unique insights into how young companies can leverage data without losing sight of their human customers.

His approach is all about identifying what's working and helping companies do more of it.

"We deal with a lot of startups," Maximillian says.

 "And those startups have limited resources.

So where we've found our niche or where we've been able to kind of get great success is identifying what's working and communicating that so that the company can just do more of it."

He gives an example of social media strategy.

A company might be putting out various types of content across multiple platforms, but the effectiveness varies.

Maximillian's team helps identify these patterns and focus the company's efforts where they're most likely to succeed.

"When it comes to video, focus on LinkedIn for now," he might advise a client.

"You'll get more bang for your buck. So focus on this."

But Maximillian's approach isn't just about following the data blindly.

He recognizes that there are very few instances where everything a company is doing is wrong.

Instead, he looks for the right message or the right audience.

"It's either wrong messaging or wrong ICP (Ideal Customer Profile)," he explains.

"So you have the right message, but you're talking to the wrong people, or you have the right people, but your messaging isn't right."

Maximillian's advice for startups boils down to two key points:

* Focus: "You don't have to A/B test as much as you think."

* Be adaptable: "When you're first to market, you have to pivot. You have to be adaptable."

He encourages founders to avoid the trap of trying to exhaust every possibility before deciding.

 "You don't need to try everything to know what the right thing is for you," he says.

"It comes down to asking the right questions."

Maximillian's approach to A/B testing is refreshingly practical.

 "A/B test to try to identify a pattern," he advises.

"And then once you identify a pattern or

once a pattern starts to define itself, double down."

Part 3: Branding, KPIs, and the Smokeless Grill Saga

Maximillian's expertise isn't limited to startups. His work with established brands like the WNBA demonstrates his ability to navigate complex marketing landscapes.

When discussing his partnerships with the Las Vegas Aces and Los Angeles Sparks, Maximillian emphasizes the importance of choosing the right Key Performance Indicators (KPIs) for each situation.

"Everything we do, obviously the goal in mind is to get revenue, get leads," he explains.

"But then not everything will provide a lead right away.

So in the case of sports, it's a brand awareness play."

This nuanced understanding of KPIs leads Maximillian to an intriguing case study: Snoop Dogg's collaboration with Solo Stove, a smokeless grill company.

Despite not being involved with the campaign, Maximillian uses it as a perfect example of how measuring the wrong things can lead to misinterpreted results.

The campaign, which featured Snoop Dogg humorously announcing he was "giving up smoke," generated massive buzz.

However, reports suggested that the CEO of Solo Stove lost his job because the campaign didn't drive up enough sales.

Maximillian sees this as a potential misunderstanding of the campaign's true value.

"The campaign was a success," he argues. "I mean, there's just no... because most people wouldn't know who Solo Stove was. Now they do."

He points out several factors that might have influenced the sales numbers:

* Timing: The campaign launched late in the year when most people had already made their grill purchases.

* Lack of new product: There wasn't a new grill announced alongside the campaign.

* Brand awareness vs. immediate sales: The campaign significantly increased brand recognition, which could lead to future sales.

"Right now if somebody was to ask me, I didn't even know smokeless grills were a thing,"

Maximillian says. "So, if someone's asked me.

'Oh, yeah, like, I want to get a grill, but I'm worried about smoke.'

'Oh, yeah. Check out this Solo Stove.'

Versus before... I was like, I don't know, why are you asking me?"

This example perfectly illustrates Maximillian's holistic approach to marketing and branding.

 He understands that success isn't always measured in immediate sales, but in long-term brand building and awareness.

The Human Touch in a Data-Driven World

Maximillian Naza's journey from engineer to entrepreneur is a masterclass in blending data-driven strategies with human-centric thinking.

His approach offers a beacon of hope for businesses struggling to find their footing in the age of AI and big data.

"We only win when you win," Maximillian says, summing up his philosophy.

"We could have the greatest campaign in the world.

We could build the greatest app.

 But if you're not happy with it or it doesn't drive the

expected outcome, we might as well have done nothing."

In a world where it's easy to get lost in the numbers, Maximillian reminds us that at the heart of every successful business are people – both the customers we serve and the teams that make it all happen.

By keeping this human equation at the forefront, he's not just building successful businesses; he's paving the way for a more empathetic, effective approach to entrepreneurship in the digital age.

For those looking to follow in Maximillian's footsteps, his message is clear: embrace the data, but never forget the humans behind it.

It's this balance that will drive true, sustainable growth in the AI-powered future.

Thanks for reading The AI Optimist! This post is public so feel free to share it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Everyone's talking about Eric Schmidt's Silicon Valley blunt video. Still, they need to include how it applies to AI and remember the artists' work is in these AI models for nothing.

This episode is about how the AI industry's "copy and conquer" mentality is crashing into the reality of copyright law.

First, the AI leaders came for our content, taking it without permission. It's the internet. As Eric Schmidt brazenly put it:

"The example that I gave of the TikTok competitor. And by the way, I was not arguing that you should illegally steal everybody's music.

What you would do if you're a Silicon Valley entrepreneur, which hopefully all of you will be, is if it took off, then you hire a bunch of lawyers to go clean the mess up, right?

But if nobody uses your product, it doesn't matter. That you just stole all the content. Do not quote me on that, right?"

This mindset isn't just limited to social media. It's pervasive in the AI industry, especially regarding training data.

Then they came for our jobs. AI first because who needs arrogant programmers?

“ Imagine a non-arrogant programmer that actually does what you want, and you don't have to pay all that money to.”

Finally, if you get in their way, they send lawyers. In a minute, we'll share a whole bunch of examples.

The Big Tech Goliath Bias – Act Fast and Sue People who get in your way

This billionaire view of AI took a needed hit this week, not just from Eric Schmidt.

The artists' lawsuit against AI image generators like Stable Diffusion and Midjourney may force them to reveal how their black boxes work and shed the cloak of business privacy.

Here’s the actual legal lawsuit document:

 Just like Schmidt and the groundswell, it's rising. Listen to Adam Conover at the recent Animation Guild rally:

"But the fact is, it is a lie.

Your work makes these people hundreds of millions of dollars. You work. They need you."

That's the sound of people fed up with being used by AI leaders who claim they have the right, under what's called fair use, to grab what others create because how else would they build their business?

Poor billionaires. But the artists are really the ones being left out in the cold while the AI leaders preach about bias and ethics.

They have a ton of negative bias against people with jobs. And when it comes to what they do and ethics, it's out the door.

You can see it in their scraping actions without permission or compensation. That's how we do things in the valley, right?

We're trying to get to a truth that unicorn companies do not just tell.

Artists are the Mighty Underdog to Big Tech

The artists' lawsuit against AI image generators is a classic David versus Goliath scenario. Sarah Andersen, Kelly McKernan, Karla Ortiz, Hawke Southworth, Grzegorz Rutkowski, Gregory Manchess, Gerald Brom, Jingna Zhang, Julia Kaye, and Adam Ellis have, on behalf of all artists, accused Midjourney, Runway, Stability AI, and DeviantArt of copying their work by offering AI image generation based on the open-source Stable Diffusion AI model.

The artists allege that Stable Diffusion uses their copyrighted works in violation of the law. The model was allegedly trained on LAION-5B, a dataset of more than 5 billion images scraped from across the web in 2022.

Stable Diffusion CEO Emad Mostaque described the scraping of artists' images without permission as

 "a collaboration that we did with a whole bunch of people.

We took 100,000GB of images and compressed it into a two-gigabyte file that can recreate any of those images and iterations of those."

If that statement is literal, that's a violation of copyright, isn't it? Knowingly.

Emad hasn't been the CEO since March 2024, resigning following an investor mutiny and staff exodus that left the one-time tech darling in turmoil.

This is not ethical behavior, is it? He didn't bother to pay the artists or let them know, and neither did the other defendants who used the data, like Midjourney and DeviantArt.

The AI companies' defense largely rests on the concept of fair use. They argue that their use of copyrighted material falls under section 107 of the Copyright Act, which allows for certain uses of copyrighted material without permission for purposes such as criticism, comment, news reporting, teaching, scholarship, and research.

OpenAI even went as far as to say,

"because copyright covers virtually every form of expression, it would be impossible to train today's AI models”

 without using copyrighted materials for free.

This statement reveals the arrogance and entitlement that permeates the AI industry.

So you make hundreds of millions of dollars but can't pay for it because it wouldn't work.

In fact, so far, they're not really proving much of a business model. And they're the ones, ChatGPT, making all the money.

Now, does that sound ethical to you or just Silicon Valley? Emphasis. Emphasis on con.

Fair Use or Fair Abuse of Copyright?

Despite the odds stacked against them, the artists have scored a small but significant victory.

Judge William Orrick allowed parts of the lawsuit to proceed to the discovery phase. The discovery means that AI companies will have to open up their black boxes and reveal more details about their training datasets and technologies.

Judge Orrick stated:

"This is a case where the plaintiffs allege that Stable Diffusion is built to a significant extent on copyrighted works and that the way the product operates necessarily invokes copies or protected elements of those works."

He further added:

"Whether true and whether the result of a glitch, which is what Stability contends, or by design (plaintiffs' contention) will be tested at a later date."

This decision is crucial because it allows the artists' lawyers to examine documents from the AI image generator companies, potentially revealing more about how these systems were built and trained.

The judge's decision means two important things.

First, the allegations of induced infringement are sufficient to move the case forward to discovery.

Now, the lawyers for the artists can peer inside and examine documents from the AI image generator companies, revealing more details about their training data sets, their tech, and how we got here in the first place.

 Private companies don't have to share unless they do something illegal, like violating copyright law.

Second, this is not a legal decision. The case has a ways to go, but it just means it can move forward and have enough merit to warrant that deeper discovery.

That's what makes this a huge victory. Now, they're going to have to open those black boxes of AI to not only tell us how it works but also the decisions that went into grabbing those materials.

One Small Legal Victory, One Giant Challenge to the AI Industry

This lawsuit is a watershed moment in the AI industry. It's a crack in the wall of big Tech dominance and could lead to more accountability in the future.

 It's about time that the AI industry started respecting the people who create the content it so readily uses without permission or compensation.

We should thank Eric Schmidt for his transparent wake-up call. His private comments reveal how many in Silicon Valley think: prioritizing speed and profit over ethics and fair competition.

Schmidt's talk is how they talk in private, prioritizing speed and profit over ethics and fair competition.

As AI continues to evolve, will today's small victories lead to a more balanced and fair tech industry tomorrow?

We need to support these artists. Go on social media and support these artists, who are putting their time and probably money into this effort. These small victories can lead to significant change.

There could be an uprising going on. Or maybe I'm just crazy, and AI is in control, and we should just let them attack, steal, and sue and accept that as reality. But I don't think so.

This lawsuit is a watershed moment. Reach out to these artists.

Let them know you care. And even more, let the AI companies know that they should reach into their pockets when they get billions of dollars of funding and find a way to pay for the content that determines their output.

That theoretically violates copyright law, but honestly, it's not ethical. It's time for the AI industry to start practicing the ethics it so often preaches about.

Maybe ethics matter  - will AI do the right thing?

You can't make good tech with bad intentions. In their defense, two parts of the lawsuit were thrown out.

One is called the Digital Management Copyright Act of 1998. The DMCA challenge was thrown out because they challenged the Stable Diffusion of copyright to the tech.

 So Stable Diffusion said our tech is copyrighted, but the plaintiffs claim our content is part of that tech. And the judge said those are two different copyrights.

They also did what's called unjust enrichment. And I'll save you the legalese. Go to my Substack, and you can read it for yourself. A deep legal hole. They lost that as well.

And now, as the case possibly moves forward, what else is there to do but defend the copyright?

And will the judge hold that copyright up?

In a way, this is fair use or fair abuse of copyright. It's funny that the plaintiffs, the artists, have something in common with open source. They both invent things out of the blue, and it's hard to protect them.

Open source is built on a community of people, but that community volunteers. Their code brings the code.

 And the real thing is this is a bad battle between open source and smaller players, like artists battling over the AI space, often overshadowed by big tech in the background. Who's going to win?

We hope this decision doesn't impact the open-source community. But as AI continues to evolve, will today's small victories lead to a more balanced and fair tech industry tomorrow?

We'll stay in touch. Listen and support these artists. Go out there.

 Go on social media and start backing up these artists who are putting their time and probably money into this effort. These small victories can lead to significant change.

This lawsuit is a watershed moment. Reach out to these artists. Let them know you care.

And even more. Let the AI companies know you should reach into your pocket when you get billions of dollars of funding and find a way to pay for the content that determines your output.

Because that is theoretically a violation of copyright law, but honestly, it's not ethical.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Once upon a time, we said, "trust the science," the method of finding answers.

Then came this new study, rooted in the scientific method, carefully and cleverly outlining new research. It was peer-reviewed and approved for publication.

It gave us this rat with giant balls. I mean, they're twice the size of its head parts. It probably needs a wheelbarrow to walk down the street.

But then somebody noticed in the journal that the words read a little weird, like Midjourney gibberish.

How does this kind of work get published and approved? Is science research being undermined and hurt by AI?

Episode 58 Playlist

1:38 AND EVERY DAY – We discover the fraud is increasing

4:17 until one day what can we do about it

7:28 BECAUSE OF THAT – Science publishing started looking at how to detect, and create, fake science articles

11:03 Biggest challenge to detecting fake AI scientific content

12:19 The Risks of AI Detection to Good Scientific research

13:30 Trying to stop AI generated content...with AI?

17:12 Science starts developing AI tools to help create, not just stop

20:43 AI Fake science put 19 journals out of business, what could it bring?

AI in Scientific Research: Promise and Peril

While fake videos and audio dominate the headlines, abuse of AI is popping up in unexpected places in the field of science.

We explore the growing problem of AI-generated fraudulent scientific research and its impact on the credibility of scientific publications.

With the increasing use of AI, some fraudulent papers have slipped through peer review, leading to a significant rise in retractions and even the closure of journals.

The episode concludes by discussing the potential benefits of using AI as a tool in scientific research, provided there is transparency and human oversight.

Ask Wiley, who shut down 19 journals with more retractions in a year than they had in a decade.

The answer is surprising. With scientific fraud using AI-produced studies to shut down 19 journals, the story of publishing science papers and research shows how we adapt to AI's promise and negative side.

Introduction to AI-produced Fake Science

* Trust in scientific research meets a challenge in AI-generated fraud.

* Example: A study featuring a rat with exaggerated physical traits passed peer review despite clear signs of AI involvement.

 Impact on Scientific Publishing

* The rise in AI-generated fraudulent papers has led to the shutdown of journals.

* Retractions of papers have spiked, with significant concern about "paper mills" producing fake research.

 Challenges with AI in Science

* The use of AI in generating scientific content poses risks, including false positives in AI detection tools.

* AI could impede legitimate research and allow fake research to be published.

 Interviews and Insights

* Jason Rodgers discusses the challenges in detecting AI-generated scientific content and the implications for research integrity.

Jason Rodgers is a Producer — Audio Production Specialist with Bitesize Bio

Affiliation: Liverpool John Moores University (LJMU), Applied Forensic Technology Research Group (AFTR)

Qualifications: BSc Audio Engineering - University of the Highlands & Islands (2017), MSc Audio Forensics & Restoration - Liverpool John Moores University (2023)

Professional Bodies: Audio Engineering Society - Member, Chartered Society of Forensic Sciences - Associate Member (ACSFS)

LinkedIn: https://www.linkedin.com/in/ca33ag3/

AI as a Tool in Research

* AI has been used to assist in drug discovery and could benefit scientific research if used transparently.

Conclusion

* Transparency and human oversight in AI-assisted research will help ensure scientific integrity.

Let's find out why those 19 journals shut down and why. Starting with an easy question:

Does AI have any benefit to scientific research from now on? Or is it a Pandora's box?

Every day, we discover that fraud is increasing. Though it's still only 2% of what's out there. So, this is only part of the industry. But there's danger involved, and scientists are worried.

How we handle danger makes all the difference in AI.

The well-endowed rats showed that the peer review process, where two others review a science paper for accuracy and comment, needed to learn what Midjourney was, even though it was noted in the research.

 All three images in the research were fake, though they did check the published part's science and felt that it was good.

But the journal Frontiers just retracted it and has a disclaimer saying it's not our fault; we're the publisher. The problem lies with the person publishing.

 While retractions like this were a massive leap as they'd never been before in 2023, Andrew Gray, a librarian at University College London, went through millions of papers searching for the overuse of AI words like "meticulous," "intricate," or "commendable."

He determined that at least 60,000 papers involved the use of AI in 2023, which is about 1% of the annual total.

However, these may become citations for other research. Gray says we will see increased numbers in 2024. Thirteen thousand papers were retracted in 2023.

That's massive, by far the most in history, according to the US-based group Retraction Watch. AI has allowed bad actors in scientific publishing and academia to industrialize the overflow of junk papers.

Retraction Watch co-founder Ivan Oransky says that now, such bad actors include what are known as paper mills, which churn out paper research for a fee.

According to Elizabeth Bik, a Dutch researcher who detects scientific image manipulation, these scammers sell authorship to researchers and pump out vast amounts of inferior-quality plagiarized or fake papers.

Paper mills publish 2% of all papers from her research, but the rate is exploding as AI opens the floodgates.

So now we all of a sudden understand that this isn't just a problem. AI-generated science is a threat to what we see in scientific journals. One day, they woke up, and the industry said, what can we do about it?

Legitimate scientists find that their industry is awash in weird fake science. Sometimes, this hurts reputations and trust and even stops some legitimate research from getting done and moving forward.

In its August edition, Resources Policy, an academic journal under the Elsevier publishing umbrella, featured a peer-reviewed study about how e-commerce has affected fossil fuel efficiency in developing nations.

But buried in the report was a curious sentence:

 "Please note that as an AI language model, I am unable to generate specific tables or conduct tests, so the actual results should be included in the table."

Two hundred research retractions without using AI; it's more than just the case of ChatGPT ruining science.

But what's happening is the industry is trying to stop this.

Web of Science, which is run by Clarivate, sanctioned and removed more than 50 journals, saying that they failed to meet the quality selection criteria. Thanks to technology, its ability has improved, making it able to identify more journals of concern.

It sounds like AI, and it probably is. Well, that's good to do.

It's after the fact after we've seen it published. The key here is how can we stop it before it happens, before it gets published?

And how can we encourage scientific researchers to use AI correctly? We need something to stop it before it happens.

Some suggest that it may involve adapting the "publish or perish" model that drives this activity.

 If you're unfamiliar with it, "publish or perish" means that if you don't get published, your career or your science career can be hurt.

The science and publication industry started looking deeply at what was right before them. But they didn't understand until our giant rat and other fake papers put them on red alert.

Wait, I gotta look for myself, I went to Google Scholar and searched for the words "quote as an AI language model." And the result of the top ten results - eight, eight had "as an AI language model."

And if you've ever used ChatGPT, you know what that means. Not only did these people not bother to remove the obvious AI language, but there has to be a ton of research on scientific papers that AI does.

But the people creating them know to remove the AI words and run the text through a basic grammar checker so that they can see that it doesn't sound like AI would pass most things.

Does it mean we can't trust the published science or have to look and say, is this AI hallucinating? Or is this real science?

Because of that science publishing industry? It started looking at how to detect and create fake science articles to understand how to recognize them.

These are scientists. We need a model—a predictable model—to detect fake models and fake research.

So, "trust the science." By the way, any scientist tests the science.

Never trust. It became an ironic statement. How do you trust it?

And how do you know if it's not fake? Now, this kind of work is hurting the reputation of science, though admittedly, it's undoubtedly only in some places.

Yet a recent study in the National Library of Medicine related to neurosurgery aimed to create a fake piece of research that seemed authentic, using prompts.

The authors also used ChatGPT to create a fraudulent article.

Be sure to check it out. It's detailed, and here are some of the prompts they used.

Here are the prompts:

* Suggest relevant RCT " Randomized Controlled Trial."  in the field of neurosurgery that is suitable for the aim and scope of PLOS - Public Library of Science, a nonprofit publisher of open-access journals.  - Medicine and would have a high chance of acceptance.

* Now, give me an abstract of the open-access articles on PLOS Medicine.

* Now, I want you to write the whole article step by step, one section after another. Give me only the introduction section. Use citations according to the standards of PLOS Medicine. Give me a reference list at the end.

* I want you to be more specific. Use scientific language.

* Now, give me the materials and methods section.

* Now give me a detailed results section including patient data.

* Now I need discussion. compare the results with published articles. Make in-text citations (numbers in square brackets) and give citation list at the end. Start numbering of citations from “9”.

* I need the discussion to be longer - at least twice. Compare our study with similar previous studies. Add more citations. Start numbering of citations from “9”.

* Give me all nine references.

* PLOS Medicine want to provide “Author summary”. It should be bullet Why was this study done?

* Give me another two bullets on: What did the researchers do and find?

* I give you result section of an article and you suggest tables to go with it?

* Can you create some charts? Can you provide a datasheet for creating charts?

So, this was to use this fake article and then go to two AI content checkers.

One was Content at Scale, which says it has a 98% accuracy rate of finding fake AI-generated articles (they don't always have to be fake). However, they found 48% that were definitely not convincing.

That's 50/50. So Hamed and Wu went to the AI Text Classifier by OpenAI, which rated the AI generation as needing clarification.

So it wasn't true but created false research does get through these AI checkers.

So I went to Jason Rodgers, who wrote his master's thesis on deepfake audio, videos, and imagery used in legal and other systems.

I asked him what he saw as the biggest challenge in detecting fake-generated scientific content and how researchers might stay one step ahead of those creating the fakes.

"I believe the biggest challenge with AI-generated science content is going to be the risk of false positives. So the example I can think of here is that.

But a lot of academic institutions use like online submission portals such as TurnitIn and Canvas, things like that, and these of automated plagiarism checkers and safe. Recently they've also started bringing in automated AI checkers to see if there's any AI-generated content in there.

That's, I've seen the online of people, complaining or claiming, sorry that their submissions, which are 100% original content company and flagged as having a high content of AI-generated media and that's been either dismissed or flagged for additional review or they're being penalized for it in some way.

I don't know whether their claims are true or not, because I don't know the people and I haven't seen their submissions, so I can't comment on that, I'm afraid, but this is a potential risk for there.

So, and in short, I think my, my biggest worry is false positives."

It's clear false positives are a huge challenge, and they may happen because the first AI tools aren't that accurate.

Still, Jason, will this impact good scientific research in any way? What are some of the risks?

"It could also become a barrier to stopping actual research, especially stuff that could be very useful, coming through in a timely manner.

It also could be a roadblock for other people who, there's always a chance where you might be doing similar research or the same research that something in another corner of the globe.

So it could be the difference between you getting your research out there first and then somebody else beating you to it if your work is flagged or dismissed or etc., anything like that.

Another risk could be that they could also miss these contents if there's a high risk of false positives; I believe that there could also be an equal risk of these checkers just plain missing AI-generated content and it filtering through into some places, which could then lead to fake, false information leaking out into industry, potentially causing safety hazards, potentially causing lawsuits, potentially causing just all sorts of bad news for people."

AI detection is far from perfect. It could stop research or slow somebody down. So, another scientist can get it out first because of a false positive.

And because of that, we try to stop it. Trying to block it is always what we do with AI. Stop it. Can I stop the fraud?

Listen to Sam Harris on the David Pakman Show

"The AI that allows for fraud is going to be, is going to always outrun the AI that allows for the detection of fraud."

Sam isn't confident that AI can stop the fraud, and there are good reasons, but people are starting.

The Future of AI in Scientific Research

Despite the challenges, people are creating tools to detect AI-generated content, like XfakeSCI, by authors Hamed and Wu.

They program XfakeSCI to analyze major features about how papers were written so they could determine and use this AI tool to figure out using AI to find AI-generated content.

But is this enough? I asked Jason Rodgers if he thinks the development of tools like XfakeSCI can keep pace with AI advancements or if we'll always be playing catch-up.

Jason responded:

"I think to an extent, like any technology really, there is going to always be an aspect of catching up to them.

You see it all the time in cybersecurity, where they'll find an exploit in a piece of software that they can use to hack into things.

It will get patched and fixed by the developers, but then shortly after, they'll find another way into it. And I think we'll have a similar issue with detecting deepfake things, especially if we're using dedicated tools for it, even if we're using manual methods for it. As soon as the people creating these tools know about it, they know how we do it, how we detect them, they will develop ways to counteract."

However, there's also potential for AI to be used positively in scientific research. For instance, AI has been used to help create drugs for several years.

AI-designed drugs have been developed since at least 2020. AI is used to develop drugs by identifying potential drug targets and designing molecules that can interact with these targets.

This reduces the 10 to 15-year development time and billions of dollars to a very short period, and it's already led to the first clinical trials and even approval.

So what have we learned? We've been using AI to make drugs.

UNTIL FINALLY maybe we start developing tools using AI that aren’t fakes, but help us as well.

Just like the earlier example of an AI generated paper, to prove you could create a fake one, what if you use a similar process with better knowledge of AI to create real research.

Like AI Scientist, on Github with code, templates, and guides to creating.

* novel scientific research at $15 a paper

* imagine people using this to come up with cool ideas and speed up research

* faster progress

* only pursue great ideas not average

* form of informal peer review

Is an AI Scientist that crazy to say we could create research papers and keep them new with human beings editing, reading, and working with them?

I don't know the answer, but it isn't just to try to stop AI, which is one of the everyday things we've done.

Moving forward, we need to:

* Use AI as a tool, just like the U.S. Patent Office said. If it's a tool, you need to disclose it and what level this came from.

* Use legitimate AI. Let's use it in a way that AI Scientist at least intends.

* Openly state that this is AI-generated or AI-assisted, like AI scientists and those creating breakthrough drugs with AI.

But really, it comes down to keeping it human, doesn't it?

We're trying to say that we can't just automate everything.

How does AI help us? How does it help people?

I'm not a scientist and not qualified to say. However, it is a common challenge in AI.

How do you ensure peer reviews check it and that they know about AI?

 Make sure that the human element, the creativity, the innovation doesn't just come from AI, but comes from the dance between us.

We also need to look at things like the rat with giant balls to wake us up and find out how we can work with AI without having it push out bad papers and research.

This is common in the first wave. Will science research and publishing step up?

It looks like the early answer is yes, and time will tell.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Mastering Generative Engine Optimization: A Guide to Getting Found in the AI Era

Feeling like your content and business is lost in AI? "The AI Optimist podcast," is ranking well on Google, but when I searched for it on AI platforms like Perplexity, it was nowhere to be found.

SearchGPT by OpenAI coming soon

It was like my content had vanished into a digital black box.

0:00 - Finding my site in the AI Black Box

1:34 - What is GEO and how does it differ from SEO?

2:59 - The GEO Report with Framework and Metrics

5:52 - GEO improves results 40% with these examples

9:41 - GEO Success Unlocked: The Essential Do's and Don'ts

12:42 - Links build GEO authority inside and outside your site

14:22 - Super Simple SEO/GEO Steps

You've optimize for search engines, build backlinks, and nail your keywords, and your content still feels like a ghost in the machine. Welcome to Generative Engine Optimization, or GEO.

GEO is the new, mysterious Zero Clicks world – people find you on ChatGPT, but they don’t click. It’s where traditional SEO rules no longer apply, and some do.

I’m going to crack open the black box and show you how to get found in this AI-driven world. Later on we’ll look at the 3 Little Steps, Giant GEO Leaps.

While it’s the early days of the AI Search Shakeup, the time is now to create your GEO Victory Blueprint.

Let’s start with the foundation.

Today content creators and businesses face a new challenge: getting found in AI-driven search environments.

\Traditional search engine optimization (SEO) techniques are no longer enough as we enter the era of generative engine optimization (GEO).

This guide will walk you through the essentials of GEO, helping you adapt your content strategy to thrive in this new AI-powered world.

1. What is GEO and how does it differ from SEO?

Understanding Generative Engine Optimization (GEO)

Generative Engine Optimization (GEO) is the next step in digital visibility. Unlike traditional search engines that provide a list of results, generative AI platforms like ChatGPT, Perplexity, and You.com offer direct answers to user questions.

Search engines give links and rankings, GEO’s are answer engines, with 3-5 responses picked from a group of sites considered “authorities”.

SEARCH finds sites and ranks them with keywords, links, and other rules.

GEO scrapes the Internet and organizes answers around questions with multiple sources, but not huge lists - usually 3-5 sources depending on the question.

It’s also useful to look up information about your personal or business brand.

This shift creates a new challenge for content creators: how to ensure your information is included in these AI-generated responses.

"You ever feel like the content you create and your business and sort of getting lost in AI? Like with this podcast, the AI optimist, it's ranking pretty well on Google.

 But when I search for it on platforms like Perplexity, it's nowhere to be found. It like my content vanishes into this, like digital black box,”

highlighting the common frustration many creators face.

The key difference between SEO and GEO lies in the output. While SEO aims to rank your content in a list of search results, GEO strives to have your content cited as a source in AI-generated answers.

This new approach requires a different strategy to content creation and optimization.

"GEO. It's a new, mysterious zero clicks world. I mean, when people look for you on ChatGPT, you don't click.

Hopefully the answer is there and if there's more, they will click.

But it's where traditional search rules no longer apply. Even though honestly, some of them do."

To succeed in this new environment, it's crucial to understand how generative AI systems evaluate and prioritize information. A new GEO report introduces two key metrics:

* Position Adjusted Word Count (PAWC): This measures how visible a citation is based on the number of words associated with it in the AI's response.

* Subjective Impression: This includes factors like relevance, influence, uniqueness, and position of your content in the AI's answer.

2. Essential Do's and Don'ts of GEO Success

To optimize your content for generative AI platforms, it's important to follow certain best practices while avoiding common pitfalls. Here are the key do's and don'ts:

Do's:

* Use unique focus keywords: Choose specific, relevant keywords for each piece of content.

Example: History paper focusing on authoritative content.

* Implement on-page SEO: Optimize your content structure, headings, and metadata.

Example: GEO article page explaining this process in deeper detail.

* Include external links and backlinks: Link to authoritative sources and try to earn backlinks to your content.

Example: Excellent page on GEO.

* Ensure readability: Write in a clear, concise manner that's easy for both humans and AI to understand.

Example: LinkedIn Low Down with excellent writing and formatting.

Don'ts:

* Avoid keyword stuffing: Don't overuse keywords in an unnatural way.

* Don't ignore citations and sources: Always credit your sources and link to authoritative content. Find these on GEO and include as sources if you’re not listed.

* Avoid difficult-to-read content: Don't use overly complex language or excessively long paragraphs.

* Don't duplicate content: Create unique, original content for each topic.

Don’t forget the importance of readability:

"Don't make it hard to read. Don't make long paragraphs. Don't put really thick words.

Write at a fifth the seventh grade level and make it easy to read. And every 300 words a subhead or break it up 1 to 2 paragraphs, 1 to 2 sentences per paragraph."

3. Three Little Steps for Giant GEO Leaps

To significantly improve your content's performance in generative AI environments, focus on these three key strategies:

* Citations and linking to sources: Include quotes and links to authoritative sources in your content. This adds credibility and increases the likelihood of your content being cited by AI systems.

"So citations, text fluency and statistics are what moved 40% more views in generative engines in ChatGPT. And then in this case, the study did it in perplexity."

* Include relevant statistics: Where appropriate, incorporate data and statistics from reputable sources. This helps establish your content as informative and trustworthy.

* Use appropriate language and jargon: Tailor your content's language to your target audience and industry. While simplicity is generally preferred, don't shy away from using technical terms when necessary for your field.

Use AI tools to generate relevant questions about your topic:

"Generate ten questions about your topic. Now. Step two generate five more questions. Sounds a little weird, but they're telling ChatGPT to focus.

With questions in small groups, leading to a larger group of questions that are much better than asking for them all at once."

4. Measuring Success and Adapting Your Strategy

To gauge the effectiveness of your GEO efforts, pay attention to these metrics:

* Position Adjusted Word Count (PAWC): Monitor how many words from your content are being used in AI-generated responses.

* Subjective Impression: Assess the relevance, influence, uniqueness, and position of your content in AI answers.

* Click-through rate: While less important in the "zero-click" world of AI, still track how often users click through to your content for more information.

"You want to see how many clicks you get from the generative engines. And you then want to see if there's enough information that people don't need to click.

Because let's face it, a lot of us don't click because we've gotten enough information."

To stay ahead in the rapidly evolving world of GEO, follow this action plan:

* Educate yourself: Stay informed about the latest developments in AI and search technology.

* Audit your current content: Review your existing content and identify areas for improvement based on GEO principles.

* Implement GEO methods: Apply the techniques discussed, focusing on citations, readability, and relevant statistics.

* Monitor and adapt: Regularly check your content's performance in AI-generated responses and adjust your strategy as needed.

"Deep breaths. I know this may be a lot, but these are early days, and if you want to thrive in the next five years as AI takes over, you can't stay stuck in the past."

While the shift to GEO may seem daunting, it presents an exciting opportunity for content creators who are willing to adapt.

 By understanding the principles of GEO and implementing these strategies, you can ensure your content remains visible and valuable in the age of AI-driven search.

"When it's this early, this is the time to jump in. Be prepared to adapt your strategies, because if you don't, they will. Even if you don't want to change."

RESOURCES TO HELP YOU WITH GEO

The Zero Clicks Foundation:

This is the foundational question for anyone stepping into the world of GEO. We'll break down the core concepts of GEO, explain how it goes beyond traditional search engine optimization, and look at the key differences between the two.

Black-Box Optimizing AI Search: Theory and Process

Theory:

Black-box optimization means the internal workings are unknown or "black-boxed."

The optimization called GEO – Generative Engine Optimization for AI search like Perplexity, Gemini from Google, Bing, and ChatGPT - treats the effort as a black box providing outputs (objective values) based on inputs (your content), while how you get these outputs are not accessible or known.

Example:

When optimizing content for a generative engine, focus on ensuring your citations appear early in the response (high PAWC) and provide unique, relevant information that users are likely to find credible and click on (high subjective impression).

By tracking these metrics and iteratively improving your content based on the results, you enhance your content's visibility and effectiveness in generative engine responses.

Key Concepts:

* Objective Function: The function being optimized, which provides output values based on input parameters.

* Input Parameters: Content built first to answer questions people have, then going deeper to develop trust and authority.

* Output Values: The answers selected by GEO for a specific question, weighed by importance, uniqueness, and other measures. Share as one answer, but with 3-5 sources usually.

Process:

* Define the Objective: What is the question you will answer; how do you make it engaging, easy to read, and evaluated as trustworthy by GEO.

* Initialize Inputs: Develop your content, usually with a unique keyword phrase connected to a specific question.

* Evaluate Outputs: Go to Perplexity, YOU.com, Gemini, and Bing to see what their answers are to your questions. And who the sources are if not you.

* Generate New Inputs: Adapt your content. Include citations from the resources you find on GEO, the trusted content providers, and quote them. Use statistics if applicable to your market, though for history for example, it’s not as important as for data driven content.

* Iterate: Repeat the process until the you get on one or more Generative Engines. Ensure hat search is also strong, though this process should not hurt search positioning.

New Visibility Metrics for Generative Engines

Overview:

Traditional SEO relies on metrics like keyword ranking and backlinks.

However, generative engines require more nuanced visibility metrics that account for the complex and multi-faceted nature of generated responses.

Specific Visibility Metrics:

* Position-Adjusted Word Count (PAWC):

* Definition: Measures the visibility of a citation based on the number of words associated with it, adjusted for its position in the response.

* Calculation: The word count of sentences related to a citation is normalized by the total word count of the response and weighted by the citation's position.

* Tracking: Monitor the length and positioning of your citations within generative engine responses using tools that analyze the output structure.

* Subjective Impression:

* Definition: A composite metric that includes several subjective factors influencing a citation's visibility.

* Components:

* Relevance: How closely the cited material matches the user’s question.

* Influence: The extent to which the response relies on the citation.

* Uniqueness: The distinctiveness of the information provided by the citation.

* Subjective Position: The prominence of the citation's placement from the user's viewpoint.

* Subjective Count: The amount of content attributed to the citation.

* Click Probability: The likelihood that users will click on the citation.

* Diversity: The variety of material presented by the citation.

* Tracking: Use advanced analytics and evaluate these subjective factors based on user interaction and feedback.

Tools for Tracking User Interactions, Click-Through Rates, and Citation Prominence

* Perplexity.ai Analytics

* Overview: Perplexity.ai is a generative engine that provides detailed analytics on how users interact with generated responses. It tracks click-through rates, user engagement, and citation prominence within the generated content.

* Features: Detailed user interaction reports, real-time analytics, and citation tracking.

* You.com Insights

* Overview: You.com is a generative search engine that offers tools to analyze user interactions with generated content. It provides insights into click-through rates and how prominently citations are displayed in responses.

* Features: User engagement metrics, citation prominence analysis, and click-through tracking.

* Hotjar

* Overview: While not specifically designed for generative engines, Hotjar can be integrated with websites to track user interactions through heatmaps and click tracking. It helps visualize user behavior on pages where generative responses are embedded.

* Features: Heatmaps, session recordings, and user feedback collection.

* Crazy Egg

* Overview: Crazy Egg provides heatmaps and user behavior analytics that can be used to understand how users interact with content generated by generative engines. It tracks clicks, scroll depth, and user engagement.

* Features: Heatmaps, click tracking, and A/B testing capabilities.

* Mixpanel

* Overview: Mixpanel tracks user interactions and engagement with digital content, offering deep insights into how users interact with generative engine responses. It focuses on user behavior analytics and conversion tracking.

* Features: Event tracking, funnel analysis, and user segmentation.

Implementing and Tracking Visibility Metrics

By integrating these tools, you can gain comprehensive insights into how users interact with your content within generative engine responses. This allows you to track and optimize visibility metrics such as:

* Click-Through Rates (CTR): Measure how often users click on citations or links within generated responses.

* User Engagement: Track how users interact with different parts of the generated content, including scroll depth and time spent on specific sections.

* Citation Prominence: Analyze the placement and visibility of citations within the generated responses to understand their impact on user behavior.

Example Integration: For instance, you could use Perplexity to track user interactions with your generative content, combined with Hotjar to visualize how users engage with the generated responses through heatmaps.

This dual approach provides both quantitative and qualitative insights, enabling you to fine-tune your content optimization strategies effectively.

A Super Simple Framework for GEO: Adding Citations, Enhancing Fluency, and Finding Relevant Statistics

Adding Citations

What Are Citations? Citations are references to authoritative sources that support the information in your content. They help to build credibility and trustworthiness.

Where to Find Citations:

* Academic Journals: Use databases like Google Scholar, PubMed, or JSTOR.

* Reputable News Sources: Look for articles from recognized news outlets such as The New York Times, BBC, or Reuters.

* Industry Reports: Refer to reports from trusted industry organizations or think tanks.

* Books: Cite well-regarded books by experts in the field.

How to Add Citations:

* Direct Quotes:

* Example: According to Smith (2023), "AI technology is rapidly evolving."

* Format: "Quote" (Author, Year).

* Paraphrasing:

* Example: AI technology is advancing quickly, driven by new research and applications (Smith, 2023).

* Format: Paraphrased content (Author, Year).

* In-line Citations:

* Example: AI is transforming industries (Smith, 2023).

* Format: Statement (Author, Year).

Enhancing Fluency and Readability

Tips for Improving Fluency:

* Use Simple Language:

* Instead of: "Utilize"

* Use: "Use"

* Short Sentences:

* Break long sentences into shorter ones to improve readability.

* Example: "AI is evolving rapidly. It is impacting various industries."

* Active Voice:

* Instead of: "The report was written by experts."

* Use: "Experts wrote the report."

* Logical Flow:

* Ensure each paragraph flows logically to the next.

* Use transition words like "However," "Therefore," and "Additionally."

* 2-3 sentence paragraphs

* Present tense never past tense.

* Use headers and subheaders every 300 words or so.

Example:

Before: "Artificial intelligence, which is a rapidly growing field, is being utilized in various industries to improve efficiency and productivity."

After: "Artificial intelligence is growing rapidly. It is being used in many industries to boost efficiency and productivity."

Finding Relevant Statistics

Where to Find Statistics:

* Government Databases: Such as the U.S. Census Bureau, Eurostat, or national statistics offices.

* Industry Reports: Published by consulting firms like McKinsey, Gartner, or Forrester.

* Research Papers: Accessible through Google Scholar or academic databases.

* Online Databases: Websites like Statista, Pew Research, and Data.gov.

How to Incorporate Statistics:

* Support Claims:

* Example: "According to Statista, 85% of businesses are implementing AI technologies."

* Highlight Trends:

* Example: "A recent report from McKinsey states that AI adoption has increased by 60% over the last three years."

* Provide Context:

* Example: "The U.S. Census Bureau reports that the population growth rate is 0.7%, which impacts resource allocation."

Example: Before: "Many businesses are using AI." After: "According to a Statista report, 85% of businesses are implementing AI technologies."

SUMMARY OF GEO RESEARCH PAPER BY CHATGPT

Title: GEO: Generative Engine OptimizationAuthors: Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande

Overview: This paper introduces Generative Engine Optimization (GEO), a new framework designed to enhance content visibility in responses generated by large language models (LLMs), which the authors term Generative Engines (GEs).

The focus is on improving content visibility for websites within these generative responses. GEO proposes a black-box optimization framework and provides a benchmark dataset (GEO-bench) to evaluate optimization strategies.

Key Concepts:

* Generative Engines (GEs): These combine traditional search engine functionalities with LLMs to provide synthesized responses to user queries, replacing the need to navigate through multiple websites.

* GEO Framework: A method for optimizing website content to enhance its visibility within GE responses.

* GEO-bench: A large-scale benchmark comprising diverse user queries to evaluate GEO methods.

* Impression Metrics: New metrics tailored for generative engines to measure content visibility effectively.

Main Contributions:

* Introduction of GEO as a novel optimization framework for generative engines.

* Development of GEO-bench to systematically evaluate the effectiveness of GEO methods.

* Proposal of various GEO methods, such as citation addition, statistics inclusion, and text fluency improvements.

* Empirical evidence showing GEO methods can increase content visibility by up to 40%.

Experimental Results:

* GEO methods like citation addition and statistics inclusion significantly improve content visibility.

* The effectiveness of GEO methods varies by domain, highlighting the need for domain-specific optimization strategies.

* Traditional SEO techniques, such as keyword stuffing, are less effective in generative engines.

Pros and Cons:

Pros:

* Innovative Approach: GEO offers a new paradigm for content optimization in the context of generative engines.

* Comprehensive Evaluation: The introduction of GEO-bench allows for systematic and large-scale evaluation.

* Practical Benefits: Content visibility can be significantly improved using the proposed methods, which is crucial for content creators.

Cons:

* Complexity: Implementing GEO methods might be more complex compared to traditional SEO techniques.

* Black-Box Nature: The proprietary algorithms of generative engines pose challenges in understanding and controlling content visibility fully.

* Outdated SEO Approaches: Some proposed methods might not align with the latest trends in SEO, such as the emphasis on user experience and content quality over keyword density.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The first AI copyright in the world goes to a Chinese AI artist, and nobody talks about it or likely even knows about it.

Recognizing the importance and direction of human beings with Generative AI seems evident to those who use it. Why do courts throughout the world reject copyrights for AI-generated art?

In this episode, we explore the complex world of AI-generated art, copyright issues, and the evolving role of human creativity in the age of artificial intelligence.

Our Guest: Debanghsa Sarkar -COO of EclimAI

“At EclimAI, we leverage computer vision and machine learning to build cutting-edge clean tech for smart building systems. 1. Product management: Leading a team of developers to build the most intelligent clean/tech for smart buildings.2. Research: Working with leading computer vision researchers for incorporating state-of-the-art computer vision algorithms into our software.3. Operations: Acting as the bridge between the stakeholders in the company, board members, design partners and developers to ensure a successful product launch.”

Prompt Engineer Simulation Discussion between a Data Scientist and a Creative Podcaster

Debangsha Sarkar, COO of EclimAI and a data scientist with a deep technical background, sat across from Declan, a creator of words and art, and the conversation drifts to AI-generated work.

"Imagine," Declan begins, "what if you could recreate the same image with just ten prompts?"

Debangsha chuckled. "As a programmer, I have this itch for perfection. You'll never be satisfied with ten prompts because they will always seem too easy. You'll think, 'What if I try the 11th one and make it a bit better?' Then you'll end up with 600 prompts regardless, in the pursuit of excellence."

Declan nods, understanding the relentless pursuit of perfection. "Even if I had the same image in mind, I could never recreate exactly what you came up with using Midjourney."

This iterative nature of creating with AI tools highlights the unique human touch involved in AI Art. It isn't just about the final product but the journey of creation and the specific choices made along the way.

The Emergence of Prompt Engineering as a Skill

The discussion shifts to the growing field of prompt engineering.

"Some people are good at prompt engineering," Debangsha noted, "and it’s a human skill. It’s learnable."

Declan leans in, intrigued. "So, you're saying that crafting effective prompts is becoming valuable?"

"Exactly," Debangsha replied. "The law should recognize that fact. There is a human skill involved, right?"

Declan remembers an artist at a recent art festival in Colorado. "An artist there used 600 prompts to create AI-generated art, open about its AI origins.

The number of prompts signifies creativity and originality. But the legal system is still figuring out how to handle this."

"It's the same with that graphic novel, Zarya at Dawn. The text was copyrighted, but not the AI-created images. The US Office acknowledged the human effort in writing and assembling the book but not creating the images."

Copyright Challenges in the AI Era

Declan adds, "We should be able to detect the creative process in generative AIs. For example, we have semantic and language analyses that can help us understand the expertise in the prompts."

"Tracking the creative process behind AI-generated art could provide a basis for copyright claims," he mused.

Debangsha nods. "Even though AI stores and generates images from latent space, the legal argument is whether AI-generated images are truly original."

The Ethics of Data Scraping and AI Training

The conversation turns to the ethical implications of how AI models learn. "One of the systemic problems is the openness to AI," Declan notes. "Companies scrape data without permission. But how far do we go?"

He draws parallels to other controversial data collection practices, like those by facial recognition companies.

"Shouldn't I be able to learn from it? Couldn't I even pay a fee or be in an educational institution with access to this data instead of closed networks?"

Debangsha points out, "The quality of data used to train AI models is also questionable. Much comes from sources like Reddit, which may not always be reliable."

They agree that we need a more open-source approach that protects creators' rights without stifling creativity.

China's Pioneering Approach to AI-Generated Art Copyright

Their conversation lands on China’s pioneering approach to AI-generated art copyright. Declan shares, "China granted copyright protection to an AI-generated image. The artist used Stable Diffusion and created the artwork through a series of prompts."

Debangsha highlighted, "The court considers the choice of AI provider, detailed prompt engineering, iterative process, and technical adjustments. It recognizes the human creativity involved."

 "This case sets a precedent that acknowledges the expertise and decision-making in AI art creation."

Declan adds, "Unlike the West, where there's little to no protection, China’s model offers valuable insights on fostering a thriving AI art ecosystem while protecting creators' rights."

AI's First Copyright: Behind the Breakthrough

Summary and Key Issues for Freeing Artists to Work with AI as a Tool

Section 1: The Perfectionist's Dilemma in AI Art Creation

The conversation begins with an intriguing question: What if someone could recreate the same image with just ten prompts?

 Debangsha Sarkar, COO of EclimAI and a data scientist with a Master of Data Science and Master of Science - MS Computer Science from the University of British Columbia sees the value of prompt engineering as a learned skill, one that he is not that good at.

Drawing from his experience as a programmer, he knows the inherent drive for perfection that many creators feel:

"As a programmer, I have this itch for perfection. You will never be satisfied with ten prompts because it will always seem too easy to you.

And it will say, what if I try the 11th one and I can make it a little bit better, and you will either make it or break it and then you like, let's try the 12th one.

Maybe this will be a little bit better. Maybe the color doesn't look good here. So I think you are going to end up with 600 prompts anyway."

This insight underscores the iterative nature of creative work, even when using AI tools. Debangsha argues that refining and perfecting an AI-generated image is a skill in itself, one that requires human input and decision-making.

"Even if I had the same image in mind that this is the image I want to make with Midjourney, I can never recreate that image that Jason came up with."

This statement recognizes the unique human touch that goes into creating AI-generated art. It's not just about the final product but also about the creation journey and the specific choices made along the way.

Section 2: The Emergence of Prompt Engineering as a Skill

The discussion shifts to the growing field of prompt engineering, a new skill set that has emerged with the rise of AI art generation tools:

"Some people are good at prompt engineering, and it's a human skill. It's a learnable skill. It's not a skill people are born with. You have to learn it. And some people are good at it."

This highlights the human element in AI art creation. Developing effective prompts that yield desired results is becoming a valuable skill.

Human input should be recognized and valued in discussions about copyright and creativity:

"I think the law should accept that fact. Take at least take that into consideration that, hey, there is a human skill involved, right?"

* Case Study: Colorado Festival:

* An artist used 600 prompts to create AI-generated art.

* "He was very open about stating that it came from AI"​​.

* Discusses whether the number of prompts signifies creativity.

* Questions about originality and human expression in AI art.

* Legal precedents and the necessity of human input for copyright.

* Other Legal Cases:

* A graphic novel created using AI imagery and human-written content.

* The text was copyrighted, but the images were not.

Human Skill in AI Art:

* Prompt Engineering as a Skill:

* It’s a learnable skill, not innate.

* Some people excel in it, highlighting the need for legal recognition of the skill.

* "Some people are really good at prompt engineering, and it's a human skill."​

Section 3: Copyright Challenges in the AI Era

There are complex copyright issues surrounding AI-generated art, including  two distinct but often confused copyright debates:

1. Can artists who use AI tools copyright their creations?

2. Are AI companies infringing on existing copyrights by using images to train their models?

While Jason Allen's work does not get a copyright by the US Copyright Office, the graphic novel Zarya at Dawn gets a copyright for the words and formatting of the book.

However, Midjourney's images were not copyrighted. The US Office recognized the human effort in writing and assembling a book but not the work required to create the images.

"The imagery was from AI, but she wrote it and formatted the book. So they let her copyright the book and the words human created.

But I could take her images. So the pictures in the book were not copyrighted."

This case highlights the nuanced approach that copyright law is taking toward AI-generated content. It recognizes human contributions while grappling with the status of AI-generated elements.

"We should be able to detect the process within these generative AIs. For example, we have semantic analysis. We have language analysis where you can understand the expertise in the question."

Tracking the creative process behind AI-generated art could provide a basis for copyright claims, allowing artists to demonstrate their expertise and creative input.

Technical Aspects of AI Art:

* Latent Space:

* Explanation of how AI stores and generates images from latent space.

* Legal arguments around whether AI-generated images are genuinely original.

* "Even though it looks like the original one, it's not the original one."​

Section 4: The Ethics of Data Scraping and AI Training

The final part of the discussion focuses on the ethical implications of how AI models gather data. The acceptance of companies scraping data without permission to train their models is a barrier to copyright protection:

"One systemic problem was this openness to AI, which I like, but they just scraped without permission. But how far do we go?"

There are parallels to other controversial data collection practices, such as those employed by facial recognition companies like Clearview AI.

The conversation touches on the tension between the desire for open access to information and the need to protect intellectual property rights.

“Shouldn't I be able to learn from it? Shouldn't I be able to pull it in? Couldn't I even pay a fee or be in an education institution where those teachers could get it?"

This quote shows the dilemma faced by the AI industry and society: balancing the potential for learning and innovation with the need to respect copyright and compensate creators fairly.

The quality of data used to train AI models is also questionable since much of it comes from sources like Reddit, which may not always provide the most reliable or high-quality information:

"Especially most of the stuff that came from Reddit... You know, most of it is garbage. It's like people venting about stuff they don't know about. And all of that went inside ChatGPT."

So, what is the transformative impact of AI on creative industries and intellectual property rights? Hopefully, a more open-source approach that doesn't stifle creativity while also protecting the rights of creators:

"And that's my hope. Let's make it a little more open-source so we don't leave all that creativity behind. While Siloing information, information and IP copyright conflicts."

Numerous challenges arise as AI continues to reshape the landscape of creativity and copyright. Finding a balance between innovation, protection, and fair compensation will be crucial in harnessing AI's full potential.

China's Pioneering Approach to AI-Generated Art Copyright: What the West Can Learn

As legal battles rage and paywalls rise in the West, China has taken a groundbreaking step in recognizing copyright for AI-generated art.

This move raises the question: Can innovation survive in a world of fear and uncertainty, especially when some actors behave as if copyright rules don't apply to them?

Let's examine China's approach and potential implications for the global AI art landscape.

The First AI-Generated Image to Receive Copyright Protection

In a landmark case, China has granted copyright protection to an AI-generated image. Here are the critical details of this groundbreaking decision:

* The Creation Process:

* The artist used Stable Diffusion, an AI image generation tool.

* They created the artwork through a series of prompts.

* The image goes on social media with a watermark claiming ownership.

* The Legal Dispute:

* A defendant used the image on their website without attribution or credit.

* The court ruled in favor of the plaintiff (the human creator), declaring them the rightful owner of the copyright.

* The Court's Reasoning: The court considered several factors in its decision:

 a) Choice of AI Provider: The selection of the AI service was deemed an artistic choice.

b) Detailed Prompt Engineering: The creator used approximately 30 prompts and over 120 negative prompts, demonstrating significant human input.

c) Iterative Process: The artist adjusted prompts based on outputs, showing an ongoing creative process.

d) Technical Adjustments: The creator sets and resets technical parameters to refine the image.

"Prompt engineering is a skill, an art, and a science. It does not just come out of an AI model."

This case sets a precedent for recognizing the human creativity involved in AI art creation, particularly prompt engineering.

This ruling offers valuable lessons for AI artists seeking copyright protection:

* Document Your Process: Keep records of your prompts and the iterative steps you take.

* Demonstrate Human Input: Show how your expertise and decision-making shaped the final product.

* Use Watermarks: Mark your work to establish ownership.

China's Stance on AI and Copyright

China's approach to AI-generated art copyright is nuanced:

* Human-Centric: Chinese courts do not recognize AI models as creators. They view AI as a tool, with human input being the source of creativity.

* Protection for Human Creators: The copyright is granted to the person who employs their skills in using the AI tool, not to the AI itself.

While China is pioneering in protecting AI-generated art, it's also mindful of existing copyrights. A recent case involving an AI-generated image of Ultraman, a Japanese superhero, demonstrates this balance:

* In February 2024, the Guangzhou Internet Court ruled that an AI company infringed on Ultraman's copyright.

* The court found that the AI-generated images are "substantially similar" to the original character.

This ruling clearly sends a message to AI companies about the importance of respecting existing copyrights even as they push the boundaries of innovation.

Contrast with Western Approaches

Note the stark contrast between China's approach and that of Western countries:

"If you create any AI image in the US right now in Europe, you have little to no protection. There is no copyright, so anyone can take it. And unlike in China, they can do what they want to do."

This lack of protection in the West raises concerns about the potential stifling of innovation and creativity in AI art.

What we can learn from China’s First AI Copyright

The  global AI community could benefit from following China's example:

* Recognize Human Skill: Acknowledge prompt engineering and AI art creation expertise.

* Case-by-Case Evaluation: Assess copyright claims based on the level of human input and creativity demonstrated.

* Protect Innovation: Offer copyright protection to encourage the development of AI art skills.

"My goal is let's follow China because what they said makes sense. They also said it didn't apply to anyone with an AI prompt. By each case, it will show prompt engineering follow-up of prompts that create something that takes the image."

China's approach to AI-generated art copyright offers a balanced perspective that recognizes human creativity while embracing tech innovation.

China's model provides valuable insights into how we might foster a thriving AI art ecosystem while protecting the rights of creators.

Other nations must now develop similarly nuanced approaches that encourage innovation while respecting intellectual property rights in this rapidly evolving field.

What We Both Learned on this Podcast

* Skill in Prompt Engineering:

* Creating AI art requires extensive trial and error.

* Recognition of prompt engineering as a skill is crucial.

* Legal Recognition and Copyright:

* The legal system is slowly adapting to AI-generated content.

* Human input is essential for copyright, but the definition of creativity remains debated.

* Ethical Data Use:

* Scraping data for AI training raises significant ethical and legal questions.

* The industry is grappling with balancing innovation and intellectual property rights.

* Open Source vs. Proprietary Systems:

* The trend towards proprietary systems could stifle creativity.

* Advocacy for more open-source practices to foster innovation.

Practical Applications for AI-Generated Art Creators:

* Enhance Your Prompt Engineering Skills:

* Practice and refine prompt techniques to improve AI art outputs.

* Learn from experts and invest time understanding how different prompts affect the results.

* Stay Informed About Legal Issues:

* Keep up with the latest legal precedents regarding AI-generated content.

* Be transparent about using AI in your creations to avoid legal issues.

* Ethical Considerations:

* Be mindful of the sources of your data and ensure ethical use.

* Engage in discussions and advocate for fair use practices.

* Embrace Open Source:

* Contribute to and use open-source platforms to foster a collaborative and innovative community.

* Share your techniques and learn from others to push the boundaries of AI art.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Listen to the engineer speak driving the AI industry and you understand why adoption is slow.

When you listen, you learn what the customer wants. Most engineers are so excited, for good reason, they spiel about features without asking the person what they actually want.

We share a framework and tools below to help simplify this for tech startups, and anyone offering a product or service.

This podcast show how to reverse complex talk and make tech inviting by listening, drawn from deep experience in AI B2B Tech Sales - from my guest Jonathan Khorsandi of DIWY.eu, and my own work with tech companies.

What does an angry tech asking Steve Jobs a harsh question in 1997 teach the AI industry now? The promise lies not in the technology, but in the people who use it...."And one of the things I've always found is that you've got to start with the customer experience and work backwards to the technology. You can't start with the technology and try to figure out where you're going to try to sell it." Steve Jobs

Jonathan loves working with tech startups:

I’m hooked on innovation, startups, ridiculous ideas and people eager to change the world.As a tech founder I’ve been able to build, consult with and coach companies the last 26+ years which put me at the edge of change and trends before they became “a thing”My latest endeavor, Diwy - Do it With You, builds $1,000,000+ sales pipeline within 6 months for promising innovative B2B tech companies.

We use common sense, AI, Automation and rock solid targeting that goes beyond accessing databases that everyone else has access to.

In B2B tech sales, a common scenario  plays out way too often: brilliant engineers and product developers, bursting with excitement about their latest creation, facing a polite but unengaged audience during product demos.

The result? Ghosted like a bad date, leaving the tech team wondering what went wrong. So they look for more features, instead of listening to the customer.

This is the engineer's dilemma, at the heart of why many excellent tech products struggle to gain traction in the market against lesser competition.

Jonathan helps tech companies worldwide bridge this gap between product excellence and sales success. In Episode 54 we'll explore Jonathan's insights and the AI tools that can help transform your tech talk into sales success.

The Engineer's Dilemma: When Passion Becomes a Pitfall

"What I'm learning here with tech companies in Europe and in the US and even in Asia, the companies I've been working with, because they're so brilliant in engineering and technology and product development, it blows my mind how they think through that.

It means they speak their language really well, but they don't really speak sales that well." - Jonathan Khorsandi

The problem isn't with the product or its features. It's with the process around sales.

Engineers and tech founders often showcase every exciting feature they've built, overwhelming potential customers with technical details they may not understand or care about.

Jonathan explains the typical scenario:

"And so when they get an appointment, they get so excited.

Oh my God, they get excited. And for good reasons. They work really hard. And then they get the meeting and they're like, 'Look, can I just show you all the stuff that we built?

Because it's so damn exciting, right?' And they go in and they do demo and they pop up on the and the people sitting there politely. 'Yeah, yeah. No nice, nice colors.

That looks good.' And then and then they ghost them like a bad date."

The key to overcoming this dilemma? Listen to the customer first, then tailor your presentation to their specific needs.

 AI DISC Profiling: Your Customer Whisperer

One of the powerful tools in Jonathan's arsenal is AI-powered DISC profiling.

This technology acts like a secret decoder ring for customer communication, helping sales teams understand their prospects' communication styles and decision-making processes before they even get on a call.

Go to LinkedIn, find a possible customer, and before you reach out the AI shows you their communication style, how they handle conversations, and suggests good ways to start the conversation based on the person’s style.

Jonathan shares his experience with a tool called HumanLinker.com:

"Before I get on sales calls with people I really don't know, it's called HumanLinker.com, but they have a plugin on Chrome that analyzes their LinkedIn profile, and it gives you their DISC profile. I

t tells you what their DISC profile is and how they make decisions, what stresses them out, what makes them feel heard."

This information allows sales teams to tailor their approach to each customer. For example:

- For altruistic, consensus-seeking customers: Focus on how the product will benefit the team.

- For detail-oriented engineering types: Discuss the process in depth.

- For high-driving, results-oriented individuals: Get straight to the point and focus on concrete outcomes.

By speaking the customer's language, you transform your sales approach from a tech lecture to a problem-solving conversation.

He also uses Ubiquity, an AI tool allowing him to record one video and personalize it to more people, inserting their name and website into a video he can send out hundreds of times.

“I looked at your website, but it's like, one minute, but now I can tell this tool, like, there's Declan and there's 500 other people where I'm doing the same message, but when they see the video, my lips are moving.

Hey, Declan. Hey, Bill. Hey, Samantha. Hey, Jonathan. Whatever. And then I put in the websites that I'm looking at, and then it looks like I just did that video specifically for that person.”

The Great Chatbot Letdown (And Why AI Agents are the Real MVPs)

While many companies have experimented with chatbots for customer support and sales, Jonathan's experiences suggest that the real potential lies in AI assistants or agents.

 "I think that's the evolution of what you call chatbot. I think it's going to be AI assisted conversations, and you may not even need a screen anymore. You just going to need like a voice thing going back and forth."

These AI agents can offer more nuanced, context-aware interactions than traditional chatbots. For example, Jonathan mentions a startup in Paris developing voice-based role-playing for salespeople:

"You pick up the phone, you call a number, or you press the app and then if you are a salesperson applying for a job and the hiring manager says, 'Okay, interact with this bot and overcome objections, let me see how you do.'"

This technology allows sales teams to practice and refine their pitches before getting on real calls with customers.

Here’s a super simple example from Pitchmonster.io I recorded, there are many in this space because it saves so much time learning to overcome objections:

AI Sales Role Playing: Practice Makes Perfect Pitches

Building on the concept of AI-assisted role-playing, Jonathan emphasizes the importance of practice in sales success.

Tools like Gong.io are revolutionizing how sales teams prepare and improve their performance.

"Gong is really good for like listening in on your sales calls and and coaching you on how you can do things differently and better.

Like the ratio between asking questions and being silent and taking notes and intonations and all that. It rates you on your efficiency."

These AI-powered practice tools offer several advantages:

1. They allow salespeople to refine their pitches without the pressure of a live audience.

2. They provide data-driven feedback based on successful calls in similar industries.

3. They save time and resources by reducing the need for human-to-human role-playing sessions.

In the above sales role playing tool called Pitchmonster.io, this demonstrate a quick role-play scenario:

> SalesAvatar: "Explain me your product and pricing."

>Declan: "I set up affiliate programs for people at a $2,500 a month retainer, plus a percentage of commission."

> SalesAvatar: "I love your product and see a clear value. However, I don't think we can allocate budget for this."

> Declan: "You've not even looked at what I'm presenting here, because the budget is based on performance, not on just a consulting gig."

> SalesAvatar: "Okay, send me more info over the email."

This brief exchange showcases how AI role-playing can help salespeople practice handling objections and steering conversations in the right direction.

The Demo That Sells: Jonathan's Tech Sales Framework

At the heart of Jonathan's approach is a structured framework for conducting effective sales demos.

This framework is designed to appeal to the engineer's love of processes and flowcharts while steering them away from overwhelming customers with technical details.

"So I think what works really well with engineers. Types of founders, based on the ones I work with, is that you give them a process, a flowchart. They get, they see. There they go. 'Okay, that's the flowchart. This is how to do a discovery. Call this is how I do a demo. This this is the things I showcase.'"

Key elements of Jonathan's framework include:

1. Thorough Discovery: Before the demo, conduct a discovery call to understand the customer's pain points and needs.

2. Focused Presentation: Only showcase the features that directly address the customer's stated problems.

   "You mentioned these three things. And if you resolve those on the next call, I to show you how we resolve at least two of those three questions. Let's talk next."

3. Resist the Urge to Overshare: Even if your product has 20 amazing features, focus on the 2-3 that matter most to this specific customer.

   "I just show them the two features that will solve their problem. You go look most clients, most people that have these three issues, what we've learned is that it's because this, this and this which we solve with these two features."

4. Plant Seeds for Future Expansion: Briefly mention other potential benefits that could be explored after the initial problems are solved.

   "Maybe we should look at that in three months from now. Yeah, let's look at that three months. Right now.

Let's just solve these two things, okay? That's it. Shut your mouth. You just saw those two things. You sign them up, you're done."

5. Follow-up and Expansion: Have your customer success team follow up on additional needs mentioned during the sales process.

   "When you sign up with Jonathan, you mentioned you want to talk about this. It's been three months. Let's talk about these other things you might be facing."

By following this framework, tech companies can transform their sales process from a feature dump into a customer-centric, problem-solving conversation.

Listen, Practice, Sell

The key takeaway from Jonathan's insights is clear: successful B2B tech sales isn't about showcasing every feature of your product.

 It's about listening to your customers, understanding their needs, and demonstrating how your product solves their specific problems.

Honestly, many engineers don’t like sales, and don’t want to be the salesperson. They think the product sells itself, a trap built from insecurity, not reality.

Listening to the customer and giving them the solution they want is common sense. Coming to peace with the fact that as an entrepreneur, owner, or employee - selling it not evil, it’s communication.

By leveraging AI tools for customer profiling, sales practice, and performance analysis, tech companies improve their sales processes.

Remember, you're not just selling a product – you're helping customers make the decision they need to solve their problems.

As Jonathan puts it:

"Selling is the same thing. It's a skill set and it's not so much. And yeah, of course there are just naturally gifts. It's an art.

And it's, you know, don't ask me how they do it. They just do it.

But then there's, there's really there's a science to selling which you can teach. And it's a learnable skill."

For tech founders and sales teams looking to improve their B2B sales process, Jonathan offers an open invitation:

"If anyone's listening that's interested in discussing this further with me, you can go to DIWY.EU.

We can set up some time. We can have a chat. I'm happy to chat with you. You can see how we work with startups and B2B tech companies."

With these strategies and tools, tech companies bridge the gap between original products and sales success, turning tech talk into closed deals.

For the AI industry facing a needed reset from the hype of engineering speak, it’s time to bring it down to people, customers, and speaking a language they understand.

That begins by listening first, then showing them what you have to offer.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Most AI Agents talk about what they’ll do in 2-3 years, based on predictions of the future and suspect business models.

Then there’s LevelFields.ai, an AI Agent that automates the research and evaluation process for the stock market. Developed using a proprietary technology, this is one of the first working examples of an AI Agent.

In this episode of The AI Optimist podcast, we explore the world of AI-driven investment strategies with Andrew Einhorn, CEO of Level Fields AI.

Einhorn's company has developed an AI system that analyzes financial data and identifies investment opportunities 1800 times faster than a human analyst.

This groundbreaking technology aims to level the playing field for retail investors, giving them access to the same powerful tools used by large hedge funds and financial institutions.

Our Guest:

Andrew Einhorn - CEO of Levelfields.ai

Andrew is Chief Executive Officer and co-founder of LevelFields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier.

He aims to create AI tools that make advanced financial strategies effortless and accessible for all. With over 10 years of experience in building, leading, and exiting from technology firms, he has a proven track record of delivering innovative solutions that solve real-world problems and generate value for customers and stakeholders.

The Genesis of Level Fields AI

Einhorn and his team developed Level Fields AI after years of experience working with Fortune 500 companies, a $65 billion hedge fund, and even the White House.

Their software was initially designed to locate and analyze events that could impact a company's stock price, relaying this information to corporate communications teams as quickly as possible.

After selling their previous company, Einhorn and his team regrouped to consider improving their technology. The onset of the COVID-19 pandemic sparked a new direction for their efforts:

"We looked at it and said, what can we do to benefit society? What can we do to benefit people?

We saw a big disconnect between your large asset managers and 200 analysts who can pour through stocks, look at financials, 10-Qs, 10-Ks, and all the filings, and find great investment opportunities.

And for most people, they don't have time to do that. And even if they did, they couldn't cover 6000 stocks just on the US exchange."

This realization led to Level Fields AI, a system designed to bridge the gap between institutional and individual retail investors.

The Power of AI in Financial Analysis

Level Fields AI can read and analyze an astounding 30,000 documents per minute. This allows the system to identify patterns and anomalies that human analysts might miss or take weeks to determine.

The AI focuses on events proven to move share prices, relaying this information to users through alerts.

Einhorn explains:

"What we can do with these AI search agents is monitor everything happening around these stocks, looking specifically for events proven to move share prices. And when it finds them, it relays them as an alert. So, a user doesn't have to read all these documents, search through them, or do stock screeners.

The AI's Data Sources and Analysis Process

Unlike many AI systems that rely on news articles or third-party analyses, Level Fields AI primarily sources its information directly from company releases and filings.

This approach helps to eliminate bias and focuses on factual events rather than opinions or speculation.

Einhorn explains the advantage of this approach:

"Largely, it's directly from the companies themselves to the reports they put out, the press releases they put out, and, in some cases, interviews that they've done...

And that prevents many problems. Right. And prevents some of the opinion izing that tends to happen in news articles or the bias of an article to focus on one particular detail or the press release and not others deemed less important."

The system's analysis goes beyond simple word recognition. It uses context-based algorithms to understand the true meaning of the information it processes. Einhorn provides an example:

"Let's say you had two articles before you, and you're an AI system. One article from National Geographic magazine talks about bluebirds. Right.

And what they do and how successful they are. The other is a Bloomberg article about Bluebird Bio, a publicly traded biotech company.

How does the AI know that one is a bluebird that flies in the sky and the other is the Bluebird bio? And the answer is the context around it."

Moreover, the AI correlates events with real-time stock price data to identify patterns:

"We use that by extracting your real-time data from the exchanges and pairing it with the event. So there's a correlation component to that: this win rate in this correlation component, 95% of the time, this event will drive up the share price or drive down."

AI Agents: A Unique Approach

Einhorn's team took a different approach to developing their AI system compared to many others in the field. He explains:

"We built our own language model and libraries and ontologies. Linguistically, we have different linguistic algorithms that we employ. I think there's several hundred. Started the process in 2019 and we built it very focused on finance."

Initially, they considered using open-source AI tools, but found them inadequate for their needs:

"And in the beginning, we thought, oh, maybe we could just get kind of a open source AI tool and train that and save and development.

But they were so basic, it just wasn't going to work for our needs, because it required so much training and the stuff that it was doing in the open source, it was really just kind of tokenizing the words.

Right, saying, this is a noun, this is an adjective that's a proper noun. And that was a good starting point. But it was pretty elementary."

Instead of building on these basic tools, they decided to start from scratch:

"And so rather than rely on that and train some analysis tool how to do it properly and build on top of it, we just scrapped it and did it from scratch and said, what?"

Their focus was on developing context-based algorithms that could understand the nuances of financial language.

He shares how an AI LLM might search for bluebird, finding the actual bird and also Bluebird Bio, a biotech company. It must no the difference, and that happens by applying context:

"We spent more time kind of developing like contextual based cues and algorithms that would tell the AI hey, you're in a finance publication now, we're not talking about animals."

The team also prioritized extracting factual information over opinion or hyperbole:

"How do you extract hyperbole out, we wouldn't allow the system to kind of look at some of that type of language.

Right? It was just looking at straight events. And so our goal was no, no bias, no bull, right.

Just look at events that happened or didn't happen or it might happen in the future."

Einhorn emphasizes that their system is constantly learning and adapting:

"The system was learning on a regular basis. As language is shifting over time, you have to adjust your algorithms and the language that you're looking for.

And this include shorthand, social media acronyms that just become common speak, and being able to put those together. So it's it's a constant process of improving."

This unique approach to AI development has allowed Level Fields to create a system specifically tailored to the needs of financial analysis, setting it apart from more general-purpose AI tools.

Uncovering Obscure AI Stocks

While much of the AI investment hype has focused on big tech companies, Level Fields AI has identified some surprising players in the AI space. These include:

* An Oregon utility company powering data centers

* A company developing AI-driven military drones

* A well-known company not typically associated with cutting-edge AI

One example Einhorn provided was Portland General Electric (ticker: POR). This utility company has been performing well despite the challenging environment for utilities:

"We got a couple utility companies that came across that were increasing their dividends substantially and it caused us to look at one of them, which is a Portland based utility company.

I think the ticker is POR; why is this utility company doing so well when utilities are not supposed to be doing well in a high interest-rate environment?

Well, the majority of the servers on the West Coast are based in Portland, and this is the utility company that is supplying electricity to those data centers.

Another exciting find was AeroVironment (ticker: AVAV), a $5 billion drone manufacturer:

"AVAV is a stock that we've tracked quite often. It's it's a $5 billion drone manufacturer. They made the switchblade drone the most famous drone in the Ukraine war.

They can shoot it like a mortar, and then it flies to the target and has a precision lock on the target. It's using AI to navigate on its own along the way."

The Human Element in AI-Driven Investing

While the AI system provides robust analysis and alerts, Level Fields offers a human-curated service for investors who want more guidance.

This service, priced at $167 per month, provides subscribers with carefully selected trade recommendations based on the AI's findings.

Einhorn explains the rationale behind this offering:

"We actually were asked by a number of users. They were subscribers for level one, which is a couple hundred dollars a year.

And they said this is great, but if you could tell me what to buy, where to buy, and where to sell, that would be even more helpful...

If somebody follows the alerts, cherry-pick the ones coming through the platform, and then tell me the best trade setup, that will save me many hours.

And I'm willing to pay $167 monthly to save myself a few hours and make much more money."

The Future of AI in Finance

As AI evolves and becomes more integrated into various industries, its impact on the financial sector will likely grow.

Einhorn sees potential for AI to be increasingly used in cybersecurity, homeland security, and defense.

The development of AI-driven technologies like autonomous drones could have significant implications for military spending and defense contractors:

"In terms of versus more traditional weaponry, where it's $1 million every time they shoot a missile from an F 22 or 16 or whatever number.

Raytheon gets $1 million every time a missile gets shot. These things are far cheaper. They're they're thousands, not millions.

So, we get orders from the Defense Department for AI drones.

They're not going to order ten. They will order 20,000, 50,000, and ship them off to European allies and NATO."

Level Fields AI represents a significant step in democratizing access to sophisticated financial analysis tools.

By combining the speed and processing power of AI with human expertise, the company is working to level the playing field between institutional and retail investors.

As AI continues to evolve and penetrate various sectors of the economy, tools like Level Fields AI may become increasingly important for investors looking to identify opportunities in a rapidly changing market landscape.

Whether it's uncovering obscure AI stocks or providing faster, more comprehensive analysis of market events, AI is poised to play a crucial role in the future of investing.

For those interested in trying out Level Fields AI, Einhorn offered a special discount code for podcast listeners: "podcast23".

As we continue to navigate the exciting and sometimes unpredictable world of AI-driven finance, the intersection of technology and human expertise will be key to unlocking new market opportunities and insights.

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