AI Product Creators

Zach Hanson is an expert in artificial intelligence and machine learning product management, with experience developing AI solutions for Fortune 500 companies including IBM, Brightcove, Capital One, and Wells Fargo. He holds degrees from the College of Charleston and Johns Hopkins University. In today's episode, We discussed power of AI, Zach discusses how it aids in tasks like content parsing, summarizing, and producing video trailers. He also explores the interconnection of different AI models, and the rise of content generation through freeform speech. We discusses how AI technologies, like ChatGPT and GitHub co-pilot, can streamline creative content creation and refine stories or code. Finally, for those looking to enter the AI product management or creation space, Zach advises building something to understand core product fundamentals and getting comfortable with data. Tune in to hear Zach Hanson's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Zach Hanson: • LinkedIn: https://www.linkedin.com/in/zachary-hanson-a1a761a3/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt Transcript:-

Dhaval: Hey, Zach, tell us what you've been up to.

Zach: So everybody, I'm Zach Hanson. Dhaval it's great to see you again. You and I used to work together in a, past life in the AI field but What I've been up to lately is pushing for AI innovation within video, specifically at Brightcove, which is a great company, and working around how we actually build out and better experiences for our customers in the video space.

Dhaval: Oh, wow. That's like the most cutting edge space in AI right now. The video and AI generated videos and all of that as we speak in April of 2023. What specifically is Brightcove's mission? And, yeah, if you could share a little bit about the, what is the product, what pain is it solving, and what are your customers, who are your customers? And then a little bit about where you are in the product space, like in the journey. Like, are you a startup? Are you, have you already found the product market fit? Are you enterprise? So yeah, any of that? So, a lot of questions, but just trying to understand the product story.

Zach: So it is interesting, right? So we're a very unique company. So Brightcove's goal, to answer your first question, has become the most trusted company in streaming. So that's our goal, right? That's the big headline. That's what we're pushing and we kind of run the line of being a media company and an enterprise company. So from a competitive landscape perspective just to frame where we're at, would be the Vimeos of the world, Kaltura and some other companies that are providing OTT services to brands around the world. And Brightcove has actually been in the game for over 15 years. So to answer another piece of your question is we're a publicly traded company. We've been around for over 15 years and we've been providing these services for live streaming, for video on demand OTT for years, and it's pretty amazing. So we're one of the biggest little companies you might not have heard of. But with that comes a lot of responsibility around data because we actually ingest around one to two petabytes of video data every month. So we have an absolutely enormous catalog and data warehouse of videos, audio, all sorts of content that is being leveraged by our customers. Now, to answer one of the other questions rolled in there about our customers and some of the ones we can talk about, we help deliver video, great quality video with our encoders for the Olympics the year ago. Wow. Yeah, we've done that. We work with south by Southwest. So folks, if you've watched conference video from there, that's Brightcove under the hood masterclass for instance. So we have a huge list of really amazing clients who are doing All sorts of different things with video. And that's what we're trying to enable. Now, the other piece of your question, where are we at in the product journey? Are we a startup? Are we a mature company? And I would say from the delivery of video, we're very much a mature company. But when we start to think about machine learning and leveraging Models that are out there building our own models, we're really much more in that startup phase where we're trying to find the appropriate product market fit for the different types of models we might build or leverage the really immense amount of data we have to train models and do some really cool things.

Dhaval: Wow. Thank you. Thank you so much for answering all those questions. I threw a barrage of questions at you. Wanna dive in a little bit on your last answer here on the topic of being new to AI ml. And what I wanna understand, Zach, is what is the customer trying to do when they want to use the AI ML capabilities for Brightcove? What is the thing that's going on in their head when they are like trying to use your specifically AI ML features?

Zach: So this is where it's also like an interesting story because there's a lot of stuff that we're focused on from a machine learning perspective, kind of under the hood, things that our customers might not know is being powered by machine learning. So some of that has to do with encoding and how we get the video to the actual end user in a very efficient manner or in an efficient manner. As far as doing CDN optimization and making sure that the ultimate end user, which is our customer's customer, whoever's watching video. Has a great experience and that's where the bulk of our effort's been. But when you think about pain points, as we think about becoming more of a media company, when we think about enabling producers of content to be able to do some really cool things there's really this kind of crawl, walk, run approach. One is when somebody uploads content to our catalog or their catalog through Brightcove, there are sorts of metadata that should be tagged in those videos. Oftentimes people are having to do that manually time stamping stuff or putting this as a certain piece of a sub catalog within their overall experience. So we're trying to do some automation through their of automating tag management to suggest to our customers tags they might need in order to ease the burden of some of the metadata management, but then you go up the chain to content itself, the video, and we start to think about object recognition and video. We start to think about segmenting video. So you can easily cut and pull out specific elements of a video. For instance, if you were watching a soccer match or football match I grew up in the United States, so I'm a little bit more used to American football and I've become a bit more of a fan of the universal football in the years, in the past few years. But you might have an hour and a half long game and only have two goals or none. So the ability to be able to search through a video and find that really intense moment where somebody actually scores a goal, be able to rip that out really quickly and repurpose that content for marketing is very powerful. And there's a lot of startups actually playing in that space and then you have the bigger players like ourselves, Brightcove. Then you also trying to play around with segmentation of video. So it really runs the whole gamut where we've been focused mostly on backend support, leveraging ml. All the way to that kind of front facing customer content production type of use case.

Dhaval: Yeah, I, that's amazing. You have, I can think of so many use cases. I was at a photo shoot video shoot event this weekend where I was hosting it, and we have like terabytes of video content that we created and now I want to create recap videos for that event. And I can imagine being able to feed something like that to your platform. Is that, am I getting it right? Would that be a potential use case? Is that how you It is parts out valuable clips.

Zach: Exactly. And that is part of a potential use case that we're exploring. But this is where it goes back to being in that kind of pseudo startup space. Like with all the data we have. With the great customers that we have, there's a lot of opportunity there and we're still in that feeling out phase of saying, what are those pain points to your other question. And like the use case you just gave, that might be something at the top of mind for a lot of our customers. And that's where we're just starting to put the feelers out and understand how we might be able to build some of these things out and make sure we have the right product market fit before rolling something out to our broader customer base.

Dhaval: Yeah, that's very interesting. Just like thinking for like content creators like myself. That event is an example of a use case. This podcast is an example of a use case, parsing out insights from this video, insights, and then publishing them. And then for courses that I create on product management and artificial intelligence, it's the same thing. I can imagine being able to give you a whole course and create a trailer for that. So there are a million use cases that you can be going after. Are you thinking of like any big use cases right now that you are willing to share with us, that you may wanna pursue in near future?

Zach: You know, none that I want to talk about specifically for Brightcove. But there are a lot of really interesting things in that segmentation space that just interest me and that might be wrapped into something we end up doing with Brightcove. It might not, but meta just came out with a paper on Segment. Anything. Have you heard of this model that they've built? Again, it's in that segmentation space on video or, things like that. But with all the other models, stable diffusion. People are starting to piecemeal these different models together to come up with really cool use cases of really just actual content generation where you could take freeform speech to say what it is that you want to put together. Like for instance, it could be a thumbnail. Even a video thumbnail for the conversation we're having now and with stable diffusion, with Segment, anything you might be able to pull an appropriate clip, you might be able to change it to meet and give a visual for this conversation that you know, you would normally have to go and pay an artist or get a graphic designer to pull together in the snap of a finger. So there's a lot of Git repositories out there that's like edit anything which pieces all these models together and they're just a lot of really interesting work being done there in this space. That makes me excited and makes me excited to see how we might be able to leverage these different technologies just in video in general.

Dhaval: Yeah. You are in a hot space, like what you just said is like so much happening there and being able to. Creators are living in such a good time right now. Like they can have great idea, great creative ideas, and the execution is streamlined to the max. Almost to the max, right? So as we go forward with this progression of technology in this space, Zach, Do you envision a future where creativity is a lot more valuable thing? Or do you see that being, co-piloted with ai. What is the value of creativity as we progress with this technology that is trying to commoditize it at the same time?

Zach: Man, that is a philosophical question that is hard to answer now. I consider myself a creative, like you can see behind me, I like to write books. So, from that perspective, I like to flex the creative part of my own brain with coming up with stories for fiction books or writing non-fiction books. And as ChatGPT has come around, I of course have kind of played like giving certain prompts to see if it would give a similar storyline as to something that I've come up with. And of course, When you prompt it in the right way, it can get pretty close, but there's always a little bit of a gap. I think there is a premium on human creativity that, I'm not as bullish on thinking that it's going to disappear with the advent of a lot of these great technologies. I think there will always be that value for that human perspective. But like with GitHub co-pilot, with ChatGPT, I think what is happening and what will happen is the. Ramp up or the curve to get creative content out there will probably be reduced. Meaning if you can leverage it in an appropriate way to bust through rider's block to Refine a story a little bit more quickly than you would if it was just pen and paper. Same with code. If you can leverage co-pilot in a way that allows you to get 80% there and then really buckle down to make sure you have the finer points really well fleshed out, I think it's amazing. So I think it's going to actually more unlock the ability for people to be creative. A lot more at least more during the day. So I think it's actually a great tool. So I'm optimistic, I'm not a pessimist.

Dhaval: That's great, man. I think in the same way, I think as this tools advance, we will be seeing, a breakthrough in creativity for people who don't think they're creative. It's gonna create that lane for people they're not necessarily seeing themselves as creators or creative people. It will enable and empower them to be creative and we'll see a whole breakthrough in creative potential of humanity as this unfolds. I'm looking forward to that future myself.

Zach: And, on top of that, so similar to you, like I teach a course in data product management at Boise State University yesterday in my class, which is all senior level software engineers, computer science students. A lot of them are working right now and just doing school part-time. And one of my students talked about chatGPT, and he had a non-technical product manager on his team who ended up using copilot to just try to explore a little bit more and understand what his team was doing. And he said it was actually amazing, was co-pilot. He was able to get 80% there. Now the product manager lacked a few of the key core computer science fundamentals to get there. But it was just a little bit of a push from my student and his co-worker to get them all the way. And he was like, it was amazing. He's like, now our product manager has a better understanding of what it is we're doing. So I think it is also another opportunity for product managers and as product HQ to be able to. Empathize with their developers, more empathize with machine learning engineers because they can go out and explore on their own with a little bit more ease. And I think that's an amazing potentiality for growth in the product space.

Dhaval: Yeah, I love what you just shared. Thank you so much. Changing the gears a little bit, what are some of the product market fit lessons you are learning as Brightcove? Or any of your other advantages is starting to integrate AI into your product.

Zach: Yeah. I have a lot of these just from years past. Right. Overall, A lot of companies and you and I have been in this game for a decade, right? So it's now becoming I think easier to integrate ML into products, expose that to customers and have them appreciate it. But, my experience has been in the past five, 10 years is that oftentimes. Coming in as a machine learning product manager, you tend to push for things that are machine learning related, when oftentimes a simple set of rules might do the exact same thing and be more cost effective. So I think in past roles I've pushed for ML when really it might not be needed, or we might be a little bit ahead of the curve. So I think there's even today something to be said about. Hitting the pause button as you're thinking about implementing ML into any product you have. Like, is that the right solution? And oftentimes now it might be, but taking a step back and saying, can we do this more simply in a more cost effective way, that's not gonna take up more compute power. It's not going to need to have a whole model development and deployment pipeline tied to it. So, thinking about it from that perspective and making sure the market is actually looking for an ML solution is key. Because I've been hung up on that several times in the past where we overbuilt for something that was actually quite simple.

Dhaval: Yeah. And like you said, now it may make sense to. Do ML or AI because of the ease of doing it. Right even then though, like, I like what you just said, which is still take a step back and see if you really need it. Are you just getting sucked into the hype or does your customer really benefit from you doing it? And that's an important distinction, like that's a really important distinction you are making there. Thank you for that. What are some of the, specific challenges that you are running into in terms of working at this large organization that is trying to find this new foothold with this highly innovative use cases? You may reframe them as opportunities rather than challenges. But yeah, however you can, share that. I would love to hear that.

Zach: Yeah. The answer is data, data, data, data. And you know, it is both opportunity and it's both a little bit of a road blocker. companies that have been born into the cloud era that have been born with data first principles you can see are really getting ahead right in the ML space because they have. Well kept data with great metadata tied to it. They have strong taxonomies. It's well annotated so you can build models relatively easy or build features to put into models and it's great. One of the things that I've struggled with across the board and a lot of the companies and I've been a big financial institutions even with Brightcove, that's a 15 year old company now. Is making sure that we're treating data as a product. That's a big thing for me. It takes part of the data mesh principles, which is if you are building a product and deploying it within a company, it could be Brightcove, it could be IBM it could be anywhere. Oftentimes, that product that you're gonna be developing is also gonna have data as an output. And that cannot be an afterthought because what happens is things start to metastasize and you get bigger and you move on to the next product, and then that produces data. And if you don't have technical product managers and data engineers really evaluating the outputs of those data. And making sure that it has a strong taxonomy, making sure that the metadata tied to it is clear, concise, usable, traceable. Then you're gonna be at a disadvantage down the road when you actually want to implement machine learning practices and machine learning models. Because you're gonna spend a lot of your time going back and trying to clean that data or introducing a third party vendor to come in and either clean it, annotate it, and get it in a position where you and your machine learning engineers can take advantage of it. So for me, it's all about data, and that is an opportunity for everybody, is to sit back, look at the data that your product's already produced, and what you have available to build off of, and making sure that it is as clean as you can possibly have it. Implementing governance across the board to make sure your teams are treating that data well. And that way you've heard the, the trope about now people have data lakes and then often turning into data swamps because of that lack of governance. You wanna avoid that. And that's the biggest opportunity that we have. And the biggest opportunity, I think, really any company across the board could have if you want to take advantage of machine learning practices as you grow.

Dhaval: Yeah, data is still as relevant as it was 10 years ago and it's never going to be less relevant no matter how advanced AI gets. So one follow up question is slightly, slightly different question rather, is, what are some of the opportunities for. Brand new people like trying to get into this space who don't have prior AI ML background. I know you have been working in the space for 10 years. You've been doing this for, before. It was cool before you and anyone knew that there was something called AI product management. You were an AI product manager. Right? But for people who are just like oh this is not going away. I'm stuck with this field forever. Now I better learn it. It's like it's the new software. Right? So what are your. Recommendations for folks like that who want to learn about getting into AI product management or AI product creation space?

Zach: Yeah, first and foremost, I got into AI product management by happenstance. You know, for me it started out as being an entrepreneur. So I still think the greatest base for getting into product management, whether that's a data product manager, an AI product manager, is go build something. Get down core product fundamentals, which is for me, design thinking, understanding how to go from a broad problem space, refining that in the personas that are affected by it, understanding what they're doing today, what you might be able to change to make their lives better, and then executing on a product. So that's was my journey, was building a product that ultimately failed. Before I went into industry and then found my way into the machine learning space. But number one, build something. It doesn't have to be a unicorn. It can be something very small that either sell or give to friends, family, whatever. Build something on top of that. If you are keen on making sure that you are gonna be doing ai product management is get comfortable with data. Again, we just talked about data being the core of everything. Yamak Dajani has a great book out around data mesh that covers a lot of the principles that roll over into machine learning and ai. It's making sure you have federated governance around your data. It's treating data as a product, so applying product fundamentals to the data you might use in machine learning. It has all the other elements that you might need to be a good AI product manager with core data fundamentals. So I think those two things are what I would suggest anybody getting into it. So build something and really understand data.

Dhaval: Thank you. Thank you so much Zach. It's pleasure to have you on the show. Looking forward to have you back on the show when we have more things to share and when you publish your next book.

Zach: We look forward to it. Dhaval

Kylan Gibbs is the CPO and Co-Founder at Inworld AI. He is the former Product Manager at DeepMind, Consultant at Bain and also Co-founder at FlowX. In today's episode, Kylan Gibbs shares his experience working in AI startups and consulting, as well as his time at DeepMind working on conversational AI and generative models. He emphasizes the importance of iterative processes, adapting to market pressures and user feedback, and the need for creativity in defining good content in the AI space. He advises aspiring product creators to focus on building something that validates their value rather than teaching others before learning. Tune in to hear Kylan's insights and experiences in building Inworld AI and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Kylan Gibbs: • LinkedIn: https://www.linkedin.com/in/kylangibbs Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt Transcript:-

Dhaval: This founder and his team built a platform to create life-like characters for immersive experiences like games or books. In this show, Kylan Gibbs shares his thoughts on generative AI and how to manage products where you create immersive experiences. He also discusses his career journey. Kylen is a former product manager at DeepMind. He was also a consultant at And a co-founder at Flow X. Now he's a C P O and co-founder at In World AI.

Hey Kylan, welcome to the show. Tell us about where you are in your product journey, a little bit about your product as well.

Kylan: Awesome. Yeah. Thank you so much for having me. Super excited to be here. So At InWorld, we're building a creative suite of tools that allow people to build these AI characters and then integrate them into immersive experiences, games, entertainment, enterprise experiences as well. And we started just over a year and a half ago. And basically where we're at now is we have this studio where people can come in and actually craft their characters. We got integrations with things like Unity, unreal, where they can bring them into games, as well as Node for actually bringing these into web experiences. We've also then got sort of this arcade and, basically, which is a way to actually share these characters directly to the web. And then a suite of experiences that we're gonna be releasing this year that are self-produced. So we've got, for example, in world origins to sort of show off the power of the product. And this is all to sort of say that this is kind of crafting that end-to-end user journey of being able to build these characters and then bring them into worlds. And basically where this has all been going is kind of setting the foundations of actually being able to not just create the characters, but, build them into experiences and deploy them scalably. And I think, compared to a lot of the things you're seeing in generative AI right now, like it really is production ready. And we've been focused on that. And so we've already seen a lot of developers starting to churn out games and experiences now that are integrating in world characters. So it's super exciting to kind of see that. So, of course still early on, I'm excited to see where it goes, but already seeing hundreds and thousands of users using it, plus, actually seeing live experiences is pretty magical.

Dhaval: Wow. There's a lot there. So let me just quickly ask you a follow-up question on how you support game creators. Is that right? Or do you support all kinds of creators, like writers, novelists, or just focus on gaming experiences at this?

Kylan: We're supporting really any type of creator. So ultimately, we have users who are, for example, very, well-known science fiction authors who are using their characters to iterate on experiences and potentially write new books with, we have people actually building like AAA games and these types of experiences where you're building multiple characters and integrating them into worlds. We have entertainment companies who, you imagine integrating these into parks or like live experiences where you're actually interacting with. And then we also have enterprises who are using these for things like, brand representation, corporate training. So really we're open to any types of creators. Of course, we're really focused on narrative oriented, immersive experiences, and our product is best built for that. So, you know games, narrative, entertainment, you can think about the adaptation of movies and IP to these experiences. And that's really what we're targeting. But we're really open to any types of creators, and we're finding new ones every day.

Dhaval: Yeah, I picked up a keyword there. Narrative oriented, immersive experiences that could be representing multiple customer segments. What is your product journey like? How do you go about defining your product capabilities with such a broad range, range that you know you could be?

Kylan: I think that, so abstracting out of our specific use case for a second, like I think when you're building a developer tool, you always have two customers. You have to think, keep in mind, one is your creators, your developers, and the other is your end users. And so for us, our creator journey is really people coming in. They have ideas either their building an existing experience or they're ideating on a new one and they're using the studio and our characters to basically iterate on that and then integrate it through things like Unity and Unreal to actually bring those to users. And so when we think about success in that, it's basically: is this person able to create the character that they love and like, and kinda ultimately represents the vision that they have and fulfills the purpose? And then are they able to integrate that and deploy that successfully. Then you have the actual end users, which are the people actually interacting with the characters that the developers built. And that is really like, is the interaction enjoyable? Is the person you know staying around to talk with this character? Are they finding out what they need to progress through this experience? You can imagine throughout a game you have someone that's a guide or shopkeeper and they have to fulfill a particular role. Are they doing that successfully? And so you really have to balance the two of those. I think that's true for most developer tools, but it's kind of unique here because, ultimately there's a key part here, which is like, it's the generated content. So it's almost like we are allowing developers to create characters that are fulfilling the wants that they have for the users in the end? And so it's always a little bit of an art and a science.

Dhaval: Yeah, there is a lot of art there, especially when it comes. The narrative, the experience, right? The character creation and then narrative and the experience. What are some of the ways you create these characters that actually immerse themselves and are conducive to the experience? Is there a difference in product creation? How do you actually adapt to the narrative or the experience that the creator is trying to have?

Kylan: Yeah, I guess there's two points, which is like design time and run time here. So at design time, you could take for example, Arah LA a large language model and generate sort of responses that are aligned with a particular character. But getting them to do that reliably and stick to a story and actually fulfill goals and actions is very difficult. So we actually allow users to come in and they can specify, for example, scenes for their story, and then the characters will actually stick to basically, the kind of motivations and goals that they have for that specific scene. Then we allow them to specify, what is this character supposed to accomplish in this? How are they supposed to speak? What types of things in the world might they be reacting to? And all of that sort of is going towards controlling and biasing the character behavior in a specific moment within a specific story. So they're fulfilling what they're supposed to. And that's all kind of the design side. When you're actually interacting with the character. Then we introduce, for example, emotions. So the characters actually react with emotionality. We have voices that also integrate that emotionality, so the characters can actually, you know, you could hear when you upset a character, for example, and you can react to that. We then control gestures and animations. The characters can actually react to you, or you can ask them to perform a particular action and they can actually act on that. And so, that ability to actually have, and we'll be releasing this soon, and we have a new system where you can actually, for example, give a character a goal and a series of actions that they can use to pursue that goal. And they'll autonomously pursue the goal until they've accomplished it, which is like a pretty magical thing if you think about the ability to actually create this living thing that's kind of, pursuing goals and motivations. But of course all of that comes back to the ability to actually drive this story or narrative forward, whether it's something like Assassins Creed or Far Cry or one of these games. Or you can think about even in an enterprise experience where you're trying to usher a user forward through sort of a brand experience. All of it is really the key point is that the characters are filling a specific purpose in some broader experience. Yeah. And so we've got a huge amount of controls that are available to enable.

Dhaval: That's very interesting. You mentioned that you are able to add emotionality to a character. Wow. That blows my mind. Is there anything more you can share about how you go about doing that? I'm personally interested in that because a few years ago I created a startup to inject emotionality in writing. And that time there were no elements or any of that available, and it was pretty challenging, but I would love to hear how it's done in 20 23, 20 24.

Kylan: Yeah, so right now our system is really composed , so when you as a user are interacting with a character, you're taking all of that input. So whether it's audio, visual sort of event side, so you imagine a you unreal, you have these events that happen and all of that get fed through basically a series of, you can think of 'em heuristics models or models that are representing different parts of function. So for example, you have a goal-oriented module, you have emotions, all these kinds of things. And so emotions are literally kind of, you can think about as an engine. Is taking in the character's current state, taking in all those inputs from the user, updating the current emotional state, and then that feeds out emotion as basically one input to the overall behavior of the character. So then they'll respond to you incorporating that emotion into their conversation or their visual representation, all these kinds of things. And so literally the emotion engine is effectively this module that takes in inputs from different. You know, updates basically this emotion, which is represented as a continuous state, and then outpays, a discreet state, which is like, happy, sad, frustrated and distressed, these kinds of things.

Dhaval: Okay. We are gonna switch up the gears a little bit. I think you have, this is not your first AI startup. I think you have created many AI startups in the past. Tell us about your journey leading up to this involved experience. And what were the learning lessons that you had to share with aspiring product creators? What are some of the, so first, set the context and then tell us your learning lessons.

Kylan: Yeah, so my background was pretty, atypical. I didn't set my trajectory to become an AI product leader. It all really started with like, I was actually starting to go into politics, and then realized that nor to aggregate people's interests and insights and actually understand what they really needed. You couldn't do that manually. You need to do something like data science or machine learning. And so I actually started using it, for that purpose as basically working with the city to understand, you know, population interests. And then, my first steps into the startup world where after I finished my masters, I worked with a few friends to basically create a company where we were applying machine vision to CCTV, which is like security cameras. And basically extracting out information, and the idea there was to be able to feed that then into traffic systems. You could actually optimize traffic systems in real time in cities in the uk. We were working with Singapore and the UK at the time. And that was a super interesting first learning experience because of course it was like, going through the, straight out of university doing the startup life was very different than it was this time around. But it taught me a lot about, I think like that, the importance of. I think one, accepting the technical complexity of what you're working with, which I think can be very Dramatically underestimated, and really being realistic with yourself about what you can do and how much you can accomplish, without a team. And then also thinking a bit more scalably, for example, working with the public sector is a big challenge, so maybe it's not the best place to start out a machine learning product and these kinds of, And then I left that and went to consulting. So I worked at Banning Company and helped set up machine learning practice there and worked as a data scientist cross consultant, which was very interesting and got exposure to a lot of industries. I think the key thing was at that point, which is several years ago now. Getting in uptake from a lot of these large organizations, especially around innovative things like machine learning at that time especially, was very challenging. And also the way that consulting firms or large corporate work sometimes is amenable to building really hardcore tech. Whereas, you're basically focused on churning out client projects versus actually building something foundational. And so you have to put the time into that. Then. I was lucky enough to meet someone who was moving over to DeepMind while I was at Bain and transitioned over to DeepMind. While at DeepMind, I first started working, helping set up the research strategy and doing, you know, basically we were doing a meta-analysis of research across, you know, the industry to inform like research directions. And then around that time, sort of G P T three came out and a lot of these sort of large language model efforts came out. And I was working then as the product lead for conversational AI and generative models, as they pertain to sort of your language tasks and these kinds of things at DeepMind. And so I was working across Google, to basically integrate DeepMind tech into Google products. So it's super interesting, you know, basically learning about, even within Google, where are the challenges of applying generative models and like, you know, this more advanced AI, which is a lot more challenges than people would probably anticipate from the outset. And then that also informed, how I thought about, okay, the benefits of being in a large company versus being in a startup or working in product in a small startup where you have a lot more capacity for experimentation when you launch something, it doesn't have to work for a billion users from day one. You have that capacity to work with a smaller group of users to refine a product and then actually be able to scale that up. So while I was there, I met Mike, who is our current co-founder and c T o, who is leading AI and ML at Cloud conversational ai. And then Ilia, who previously founded Dialogue Flow or API at ai, which became dialogue flow when acquired by Google. And so that's how Inworld was born. Yeah. And then I think there's of course, this has been sort of a rocket ship, so there's been a lot of journey learning along last year, so I can pause there, but I'm keen to share as well. But a bit about what we've learned in the last year or so.

Dhaval: Wow. There is. Five careers in that one career. Thank you for sharing that. What do you recommend, what are some of the things that you recommend avoiding new brand new pm just thinking of becoming a product person or a product creator. What are the things they should avoid doing? Absolutely. If they want to be thriving in the aI space.

Kylan: Yeah, I would say, to boil it down, I would say like, don't philosophize. What I see a lot of people doing, I think because they're mimicking what, so when people have been successful towards the end of their career, they start to, philosophies and they go on Twitter and they go on, post blog posts about all their philosophies and learnings. And so what I see people doing at the beginning of their career, Is basically starting out with that versus actually building something that validates what they've actually done and proves that they're valuable. So don't start by trying to teach people lessons before you've actually learned anything. But that also translates, I think, into actually how you build products. And I think it's increasingly where. The differences between even software development as it existed 15, 20 years ago, and what we're doing now with a lot more AI and ML is that things need to be a lot more iterative because outcomes aren't as deterministic. So you don't know, for example, if a feature is going to be successful. One, because the actual development of a new machine learning model doesn't directly translate into something specific. It's not like you're adding a button to a page that you know has some outcome for a user. It may or may not improve, increase, performance and quality in some other ways. And so what I frankly learned the hard way was, you shouldn't set out, for example, like long-term visions or roadmaps. This is like a very classic thing that I see being told to a lot of early PMs is like, build out your roadmap. Build out your five year roadmap, break it down to one year, and then six. I think that the process is a lot less sexy, but is ultimately far more important. And I see so many PMs, like across their career base, like, oh no, I don't wanna be involved in any organizational stuff. Like I don't want to be involved in the Jira tickets or these kinds of things. But I think a really good PM. Is basically able to take the vision that they have in their head and then turn that into basically a machine or a process that naturally pushes people towards the direction that you're working in, but is able to adapt to things like the market pressures, the reactions, the user feedback. Because if you basically plant a point on the map that you're working towards, you're gonna be so biased by every time that you perturb ba from that point that I think you're not gonna end up in the optimal state. Whereas what you really want to do, and this is coming from the AI ML world, in AI and ml, you have this idea of gradient descent, which is basically you're doing an optimization problem where you're trying to find like the lowest point in a space, I think of good product building as basically inverse gradient descent. So you're trying to always find the highest point. And if you say, I'm gonna, getting to the top of that hill is my goal. Hands down, that's all I'm gonna do. You might be missing the fact that there's like a mountain on the other side of that. And so you should build yourself, you should build your product, you should build the organization and the process around your product all to be able to constantly take in that feedback and shift dramatically. So for example, I've now set up our organization to be more focused on smaller iterative cycles. So, breaking down, you have like larger term, longer term efforts, which you know, are important and thematic. And then you break those down into two week increments. So it's like standard, more standard sprint planning. But the challenges that With A N M L products, it's a bit more experimentation. So it's not like you're necessarily saying we're gonna have this launch and have this impact in two weeks. I'm gonna run this experiment within the next two weeks because I think it might have this outcome, and if it's successful, we'll build on that. And so it's really like taking that long-term vision that you have, setting the metrics, setting the sort of general directions that you have, setting your feedback channels from. And then being able to constantly turn that into experiments that you wanna run, both with developing hardcore tech. If you're doing a deep tech startup or, turning that into, user research or user experiments. You've been doing something that's more front done based.

Dhaval: Very interesting analogy there with gradient descent. I often use the same analogy, but I use a different term for it. Global maxima, find your global maxima. Yeah, I love that. Let me ask you this question. Where do you. This is going, this whole, generator AI especially. And then if you wanna narrow down to your specific space, I would love to hear that.

Kylan: Yeah, so I, I generally think where we're moving towards is, In the same way that you had basic automation of things like databases and things over the last 20, 30, 40 years. I think we're moving to a place where a lot of like hardcore knowledge tasks are going to be automated. And ultimately, this means things like you imagine as a PM creating a P R D, you can probably generate a pretty good PRD of chat GPT, barely better than most starting PMs to be honest. And, you could probably, you can create, there's like slide generators now for people who are in consulting. There's a lot of these things. So knowledge tasks are becoming more automated and I think it puts a lot more pressure on ideas and creativity as like the actual gold of what actually humans contribute to this whole process. And so what I see happening over the next 10 years is, A lot of the things that people held as basically their like unique value are going to be eroded as what happens like with any technology innovation. But what that means is, so let's say that was only 1% of the population, now those skills are accessible to 80% of the population. So the overall value created by everyone goes up dramatically. But it takes away the feeling, the niche, sense of value that small group once had. And so what I think needs to happen is for people who are very, you, Highly technical knowledge oriented that feel like this is impacting their work. The question is not "oh no, how do I react to these things being taken over?" It's like, how do I also now become an expert in taking advantage of these tools? So, for example, in our case, so you have. Amazing game developers, narrative designers, writers who are creating video games. And it's not the fact that creating these AI characters who are able to basically generate dialogue is gonna take away from that. It now means that a single writer could create a hundred characters and create all the dialogue for them in the time that it would've taken them before to create one character. And what this really means, I think, is you're just gonna have an explosion of content overall. So both in terms of games and things that are directly impacted by us, but of course there's also the image generation and text generation, and I see music generation happening now, and so. I think we're about to enter an era for probably the next five years of just like a content explosion where there's just gonna be so much of everything and a lot of it's gonna be complete noise. And so I think the key thing for like those people who want to continue to be leaders and product leaders in this space is not what can you create, but. How can you filter out the noise? So how can you ultimately define what good is? Because it's gonna be a lot easier to create something. And so I think that like over the last, while we've seen the benefits of people who are just able to build some hacky prototype. Building a hacky prototype is gonna get really easy. So now you've gotta figure out like, what are you actually creating that is completely unique and like applying some creative sense, not just necessarily some technical or general sense, or applying some framework because guess what? All these generative models have now taken in that framework and know it, so you don't really have anything special. So you have to come up with something actually new now. And I think that's where it's all going is like a content explosion that biases us to really thinking about what the content is that we want to create, not just the fact that we can create content.

Dhaval: Yeah. I love that. Thank you so much Kylen. Thank you for making time for. Really appreciate it. Looking forward to having you back on the show once you have a few more lessons to share with us.

Kylan: For sure. Thank you so much. Yeah.

Suman Kanuganti is the Co-Founder and CEO at Personal.ai. Previous to Personal.ai, Suman also founded Aira. Suman holds his BE degree in Engineering, MS in Robotics, MBA in Entrepreneurship, and ten patents in emerging technologies as well. Suman also founded Aira. Personal AI is a GPT implementation designed to mimic an individual's behavior and to speak like them. In today's episode, Suman shares his goal is to create AI systems that understand and replicate users' communication patterns and cognitive abilities. Suman emphasizes that their language model has time awareness, allowing it to adapt its knowledge base depending on the user's age or point in their timeline. Suman also shares his some learning lessons for AI product creators. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Suman Kanuganti: • LinkedIn: https://www.linkedin.com/in/kanugantisuman/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt Transcript:-

Dhaval: So today we have Suman Kanuganti on the show. Sunman is the co-founder of personal AI, a product that he will talk about and, we'll learn a lot more about where his learning lessons bear, what his learning lessons bear and all that other stuff. So yeah, let's get started. Suman, would you mind introducing yourself and tell us a little bit about your product, AI product?

Suman Kanuganti: Sure Dhaval. Thanks for having me. I'm Suman Kanuganti. My background is in engineering. Over 10 years ago I started creating companies. This is my second company. Previously I built a company called Aira, A-I-R-A. My philosophy always has been how do use technology kind, solve, hard human problems. Aira was about using technology to fill the gap of missing visual information for people who are visually challenged, such as blind and low vision. And personal AI is about augmenting people's mind where memory, cognition, and our time is limited. And we would want to augment that using technology by creating a personal language model of every individual that essentially learns to behave and act and learn to be you. So that's a little bit of my background and who I am.

Dhaval: Wonderful. Thank you, Suman. Thank you for that introduction. Tell us a little bit about what personal AI's narrative is like. What is the, who is your target customer? What kind of a problem, specific problem you are solving with that product? And what is the overall narrative? How is it different from your competitors?

Suman Kanuganti: Yeah, totally. As individual people, like on a day-to-day basis, we create and consume a lot of information. Like, you know, a lot of experiences have a lot of conversations. But obviously, 80% of that is lost or forgotten. Our goal with personal AI is to be able to create a model. That actually learns your knowledge, your style, and your voice, more or less like to be a digital version of you. Imagine being able to surface relevant pieces of your own knowledge, on demand whenever you need it. Or imagine, you having conversations in a, chat or text message. Are people talking to you? Where relevant pieces of information when we start facing as you speak. So our intention is to be able to augment, humans with an extension of their own mind. Because one cognition is limited and then two time is also limited. You mentioned about the target market. Our goal is to actually go after everyday consumers. Our intention is to have everybody have their own personal AI. That is trusted by them. The data belongs to them. The model gets trained over a period of time and innovate kind of grows alongside with you. Unlike, public or general models that exists such as open AI, such as, Google or Alexa, which is mostly like trained on public internet of data. Personal AI is a unique model that also uses similar architecture such as GPT, but actually trains on individual person's data. And it does so stylistically, relevantly authentically to replicate as you would. So we are trying to essentially like replicate, your thought process and your mind and give you an extension of your.

Dhaval: Wow. So it is adding the stylistic and tonality and the personal attributes to your, to the replica that you are building for someone which is not there in current GPT or any of those products, right? So there's a bias that. GPT has very confident answers, but doesn't necessarily align with your style or may not align with the way you communicate. And what you are saying is that not only does personal AI helps you do that, but also creates the model in the first place using your personal attributes , did I get that right Simon?

Suman Kanuganti: Yeah, totally. So you will create what we refer to as a memory stack, which is essentially taking all your unstructured data that you ever have in your digital world. Let's say you're having conversations online, you are texting with people. You probably have written a bunch of different knowledge pieces out there. And then we create this memory stack, which is essentially like a digital representation of your memory vault. In other words, we basically break down this idea of structuring your data into these blocks that is associated with time. And imagine over a period of time, as you create an as you learn. Your AI technically would also be training alongside with you. So it's kind of how. Conceptually we have architecture system.

Dhaval: Got it. Now, there are, if you were to dive a little bit into your products architecture or the core engine, the AI, since the audience of this show are the people who aspire to either create an AI Product. Or wanna add AI to their existing product, for their knowledge. If you were to share a little bit without getting into the confidential details about what does a product stack, what does it take to build memory stack? What does it take to build a knowledge replica, a knowledge brain summons, human brain, and human personality into something that you were doing, like what do you call that thing? The entity that.

Suman Kanuganti: Yeah, I'll try to provide answers and then I'll try to provide some contrast technologies that exist out there so that we can wrap our heads around. The first thing is at the core, we are essentially an AI first company. We built an algorithm called. Personal language model. So we call it personal language model. This is in contrast or like kind of opposite in concept to a large language model. If you think of a large language model such as GPT 3 or any other open Language models that exist out there close to around like one 70 billion parameters in our case. Our language models around one 40 million parameters. It revolves around at the core individual's data and not public's data. And you can keep on adding the data to your model so that way it gets more sophisticated in regards to the purposes of your mind. You can go abroad and you can also like, go deep into specific topics itself. So yeah, at the core we build this personal language model for every individual to essentially like mimic the behavior, knowledge, and style of an individual person. And the transformer that we have developed, we call it generative Grounded Transformer. And if you think about like GPT as a generative pre-train transformer, the subtle difference of our transformer is that it is grounded in the personal data of you. And whenever I refer to personal data, is nothing bad. The memory is tag that I was explaining earlier, So every AI response that your personal language model actually generates, it has an attribution, and the attribution is nothing but attribution Back to the data or what"s? Data elements of what memories in within your stack is responsible for creating a particular response? One of the challenges for large language models is that there is no attribution, primarily because it is driven by aggregation of the data. And there is quite extensive anonymization that is involved. So technically, you cannot create that attribution and it's extremely hard to create that attribution. And our goal is exactly the opposite. We would want to have. That attribution, we want to have the ownership and we want to create that value to every individual consumer by creating their own individual model, at the foundation, it's personal language model.

Dhaval: So this generated you, grounded trained model that you are referring to is that, Adaptive with human changes, human behavior changes over the lifespan of the user.

Suman Kanuganti: Exactly. The transformer also has a sense of time. For example, let's say, if you're talking about AI maybe three years ago, How you refer to your transformer, how you refer to your technology, maybe different from your latest and greatest creations or thought process around your ai. So it has a time to decay component. So when you are indeed chatting with your own AI, it normally anchors around the latest and the greatest thought process of. How you would respond. However, let's say if you indeed are contextually trying to fit something from the past that happened like 10 years ago Then, we are probably talking about the first autonomous car, right? And my experience with the first autonomous car, then it will. It'll go back in time and be able to fetch that response for you. So kind of like designed to work very similar or akin to how a human mind would function. You can think about potentially being able to drop your AI at a certain period in time. Given these are small models and given as the data is going in on a day-to-day basis, there is a new version of the model. We are technically able to time travel. Your model, like let's say 2, 2020, and then when you are having the conversation, it would like replicate the information density as if you were to be functioning at that time. If that makes sense.

Dhaval: That makes sense. Yeah. So with this language model the way you communicate is it's having a time sensitivity to it. It has a time awareness to it. So it, if you go back to your younger age, you would have smaller, it would have smaller set of parameters. You look at. Depending on, the size or depending on the scope, it may change depending on at what point in the timeline you put it. So this is, what are the use cases? What are the use cases of something like this or an average consumer? What would they benefit?

Suman Kanuganti: yeah. I'll give you a few different use cases, but I'll also tell you the focus of the use case that we are going after. The few different use cases could be. Anywhere from simply being able to remember everything that happens in your life and be able to recall. It's almost like a data store where you're not searching for the data, but you're actually interacting with yourself. And being able to, recall pieces of information and facts as well. So that's fine. Our personal language models also has different capabilities, meaning as increase your stack size and get more data in it moves from being able to simply answer questions to being able to generate content for you and being able to have a conversation like you as well. So that kind of unlocks into. Being able to create or generate responses or draft emails for you, write tweets for you and even large form content as well. And over time it could technically be a conversational mind that exists on the internet for anybody to interact 24/7. And an asset that could label potentially on the internet forever for your future generations to even have a conversation as well. So it's designed to be an digital asset that essentially grows over a bit of time and they own it. The use case that we are going after is to be able to draft responses in a human communicates with other humans currently in multiple different platforms. And one of the downsides is, everything is kind of lost and you are pushing the data to large, big tech companies. And our intention is to create a, personal AI chat system where people would communicate. But every conversation that people communicate in within that system is trained upon. So if they, give a piece of knowledge or information once. It'll be useful or re useful at later bit of time, given the particular context again and again. So think about as like a automatically drafting solutions almost all the time. And you can potentially put your AI in a co-pilot mode or a auto-pilot mode that is communicating on your behalf with other people if you would choose to all the time. So it saves time, but it also saves, the idea of augmenting your cognitive capacity. Because it's very hard to process all the information all the time that is needed.

Dhaval: Wonderful. Thank you Suman. One, couple of quick questions follow up and then we'll wrap is what are some of the learning lessons you had from your product journey? That's a second. The more important question is where are you in your product journey. Have you launched your MVP. How many users you have? Where are you in the stage of the company? If you could share that and then if you could share any learning lessons in this journey, any big learning lessons you had for other AI product creators?

Suman Kanuganti: Yeah. We had a little less than three year old company. We've been focusing mostly on creating our language models and testing with a few groups of people and experimenting to figure out like what our market is. So we would be launching our. The first version of the product in March there is a version of the product that people would come in and train their AI to, essentially remember and recall information and train model over a period of time. But technically the first launch, the personal AI chat application. We call it personal AI to that, or we'll be launching in March. The learnings are interesting, so the learning. First of all, like, almost two and a half or two years ago, even when we talk about personal AI and creating a digital version of your mind it was almost like too hard to delay. let's think about it. I am not sure if it is there. And then the GPT three came along, and when GPT three came along in 2021 it was good for AI awareness. And then it was taught as a almost like a marketing tool because it was really good at content generat. There are good number of, startups that are evolved who are building on top of GPT three which is content generation tools. We were heads down essentially finishing our development on the tech as well as on the product to create this personal language model. It has the generated capabilities but it also has conversational capabilities. One of the insights was we went after like content in Generation. But the most interesting thing is large language models are so great content generation. The content creation normally happens with new content now, I found people who would want to create content from the existing knowledge. The appetite is very low or maybe it's like a different use case. So our, purpose of the personal AI is more valuable where. The existing knowledge is more valuable, the existing interactions and experiences are more valuable. And that's where we landed on identifying our PMF, like where it's everyday consumer application for being able to augment your conversations and communications and make it more human to human and stuff like human to ai because human to Ai. Is a very, like a bot conversation. And our goal is to create this ai be like you and not necessarily talk to your bot. So there are like several nuances around how we create these experiences, which is which was exciting in a way. So yeah, so essentially where we are driving towards right now is Chat GPT came along. The awareness around the AI essentially being able to chat. Is more acceptable because people saw the taste of what is possible and it is great because it's all trained on public data. So when we talk about personal ai, which is essentially chat GPT, if you will, but for personal data the promise is very exciting and there is awareness and there is accept. So essentially here we are basically going to the market, creating a, not even creating, basically launching our personal AI chat application, which is in of development for past one year. And excited to give it to everyday consumer and people, and everybody will have their own personal AI that will essentially grow with them and it'll be theirs, right? It can make money for you. It can live in the cloud. While you are sleeping, it can do work. So yeah, that's where things are headed to.

Dhaval: What are you most excited about for personal AI in 2023?

Suman Kanuganti: Most excitement is this. We've been at it for a while now, and finally things are coming together. What that means is we are finally able to kind a fulfill this idea of democratizing AI. Where everybody will have access to their own AI. And we are also excited about this core idea of access between people. If you think about what does it mean to be able to have access to the loud ones that you know potentially passed away are people who has like knowledgeable but we do not have access to. So there is a lot more, interaction gap that exists. Information gap may not exist because internet exists. So I think personal AI is the next level of, almost like Internet 2.O that will kind of unlock this exchange of information in a much more meaningful yeah.

Dhaval: And you say that AI, generative AI is over a trillion dollar. Where does personal AI fit in this market space, and how much of it do you think belongs to personal ai?

Suman Kanuganti: I think it fits in the everyday consumer market space. it would likely be sitting next to, any of the Google Assistance services. We are more focused around day consumers to let say, you know, your personally would be living in your mobile phones. So communication like human to human communication and establishing those connections with and increasing access between people and reducing basically burden of being able to remember, recall and generate response. So I think it's somewhere between a communication augmentation tool within the consumer space and not necessarily not necessarily in the customer support are business engagement where general AI are pretty good at. So we really want to tap into this idea of, personal nature of personal AI.

Dhaval: Now if I were to use personal AI in my work communication, is that part of your vision or is that considered professional work and business AI.

Suman Kanuganti: You could nothing's talking about. And then at the end of the day, I think every company that starts with consumer application or consumer focus eventually penetrates into the business markets. So we want to build this like ground up rather than going after like existing like business application data and building the models over there because there are technologies who does that really well, right? We don't have to do it. There are not a lot of technologies who are actually like, really good at working on like smaller amounts of data and people's data. So eventually if you would want to use it for work, there is nothing stopping. In fact, there are people who use it for like specific projects, the conversation and augment their conversations, within the professional setting. But I think when I talk about like everyday consumer, we are essentially not going to start off with aggregating of the data in a business setting. We are going to start off with a personal data and individual data starting off with an individual consumer.

Dhaval: Got it. Well, fascinating conversation soon. Thank you so much for making time for. Chatting with me and helping other AI product creators cut their learning curve. I really appreciate that. Looking forward to chatting with you again once your product is launched and you have a few more things to share with us. In the meantime, I wish you all the best with your launch coming up here and we'll be keeping up with your news.

Suman Kanuganti: Thank you Dhaval.

Martin Pichlmair is the CEO of Write with LAIKA, Associate Professor at ITU Copenhagen and Co-founder of Broken Rules. He Holds an PhD degree (Department of Informatics) in Vienna University of Technology. In today's episode, Martin explains that LAIKA is designed to make AI-generated writing more accessible and user-friendly, with the AI and the user working in a tight interactive loop. Martin highlights that their product uses a "no-prompt" system, which means users don't need to be skilled in prompt engineering to get meaningful results from the AI. Instead, the software handles most of the prompt engineering behind the scenes, making it easier for users to interact with the AI. Tune in to hear Martin's insights and experiences in building LAIKA and how you can apply these lessons to your own product. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Martin Pichlmair: • LinkedIn: https://www.linkedin.com/in/martinpi/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt Transcript:-

Dhaval: welcome to the call, Martin. Thank you for joining. Tell us a little bit about your product.

Martin Pichlmair: Okay, so I'm Martin. I'm the CEO of Write with LAIKA. And our product is a kind of creative writing tool that is using large language models, in our case, quite small, large language models to, support writers when they get stuck or when they need more text, that is influenced by their previous writing.

Dhaval: Wow. Okay. So when the writers get stuck or when they. Interested in continuing with the style and the tone of their previous work. They can use your product including the contents, the storyline, or anything along those lines. Martin Pichlmair: Yes. How LAIKA works is that you you upload existing writing. You have when you get, for example, stuck in a murder mystery because you don't know who the murder is. Funnily, we had that case twice already with users. And then you upload what you have written before and our, system fine tunes a language model With your text and then you can prompt the model to continue writing in your voice, in your using your characters. You mentioned using scenes you have been writing about in the past and very much sounding like you. Now you can do that with your own text. Or with the text of famous writers, we have, for example Dostoevsky in there and Jane Austen in there. And a lot of, all of them, of course, dead and out of copyright writers that you can also collaborate with in a similar way by asking them how they would continue a sentence, for example.

Dhaval: Wow. So it has memory and context as well as style and the personalization built into it. So is that. Large language model that's very different from Chat GPT 3, which would spit out very confident phrases very long phrases. But they're also having the same style. Is that, how is that different from the large language models? You said that you have used large language models or you have you built on top of them or like, help us a little bit on how have you built this.

Martin Pichlmair: So we've built this on very small, large language models. They're still in the same architecture and come from the same family, but they're very small because that gives us the ability to fine tune them very quickly. It takes like five minutes. If you upload , a half done book, for example, takes five minutes and you get your own, we call them brains because that's a nice metaphor. Your own brain based on your writing to interact with. Now, of course it has an understanding of the context, but it's not always super, like it doesn't have an actual understanding. It can just play with probabilities of words, just like all of those language models do.

Dhaval: Wow. Very cool. Let's dive a little bit into your product journey, is this your first startup? Is this your first AI product? Tell us a little bit about your background, Martin.

Martin Pichlmair: So I have a weird background. I did a PhD in computer science originally at the University of Vienna, at the tech university, and then worked in academia for a couple of years. I got a little bit, I don't know I wouldn't say bored, but I wanted to do something differently. So I started a video game company and then after a year started another video game company because the first one didn't work out. It didn't work out, but it also didn't not work out. It was fine. It was just not meant to be a longer existing thing. The second one actually is still around. It's called Broken Rules and makes awesome in the games. But I'm not involved anymore because I decided at some point to go back into academia. So that's where I spent the last seven years until last year where I just realized with my partner, That we have a huge connection between what I was doing in research, which was using generative AI to create systems for video games and her background, which is writing for video games. So we sat down and, uh, started workshops during the Covid Pandemic when everyone was sitting at home. We started online workshops where we introduced writers. To, the newest possibilities in language models, using very, very clunky tools at that point. And after three or so of those workshops, we realized the workshops are always poked out, but it's really hard to work with the tools that are there. So we decided we have to make our own tool, and that became a research project that was funded by the Danish state, uh, in the beginning. With the intention of turning it into a product. And since last November, we founded a company and turned it into an actual university spin off that is based on yeah, research that is now working on a product that we will commercialize within the next month

Dhaval: Very interesting background. You do have like a very traditional computer science background, making you very competent in this area. Right. So, quick question. You mentioned that you launched this in November, but you haven't commercialized. It doesn't mean that the product has not launched yet.

Martin Pichlmair: Yeah. We have a wait list and we have, a data with, nearly 2000 users. So there are a lot of people using it every day, but, it's not a commercially launched yet. We're still only free for select users.

Dhaval: Very cool. Is this. Is this a self-funded or have you bootstrapped this whole thing? Are you intending to, or have you raised capital? And are you intending to raise capital as you move forward?

Martin Pichlmair: Well, we got some funding from Danish State again. The program that we're in that funded turning research into a product last year that was still in the context of my university has a follow up program that funds your salary basically. So we are kind of weirdly half bootstrapped. We have no investor, but we have, our salaries covered by the Danish State but a very low salary. But still, it's good enough to, know that we'll be, we'll be around for another year at least while this funding runs. we're looking for investment in the moment. It's, we are talking to a lot of VCs. It is. It just takes a while it seems.

Dhaval: Yeah. how is that playing out in this current market? Like how is that, can you give us have you done this before? And if you have, like how is it compared to the current market, if you can speak to that.

Martin Pichlmair: So I haven't done it before, but a good friend of mine has a very similar company, actually a very different company, but also an AI company also in Denmark. And he also has an academic background. It's otherwise very, very different because it's B2B and started out much bigger than we are. But it looks like they, the climate they saw two years ago is very different to what we have now. So I'm getting all my tips from him and half of them don't work anymore. The climate is not good in the moment. Even in the hype space of creative ai, there is a lot of chicken egg problem, happening in the sense that investors wanna see. They actually want pay traction very often .They want to see some pay traction or immense numbers in weightless users or something like they wanna have proof of actual viability very early on. But it's such a new area that you are actually creating a market. So it's very hard to say where this whole journey is going because the whole, like AI is not super new. But generative AI is really something that is only a thing since like a year or so. It's very hard to say where the journey goes in the moment and, like it could all still just be overhyped. Then I would understand the need for having paid traction, but it could also be that we are just opening, creating a completely new market here, and then a little bit of trust would be nicer than having to prove things too early.

Dhaval: Yeah. Yeah. Where are you in the product stage in terms of product development? Are you close to? I know you mentioned you were ready to launch in a few months. Are you close to finishing your product development? Is is that almost there? Like, if you can share that. Cause my follow on question's gonna be on, what were your top learning lessons around creating an AI generated product? What was that experience? What was the top learning lesson there?

Martin Pichlmair: So I think the whole idea of finishing a product is not really how it works with software as a service anyway, but especially in this extremely fast moving area of, AI in general, but especially generative AI where new technologies come out on a sometimes weekly basis. There's a lot of competition, but there is also just a lot of speed of development. I don't think we will ever be at the moment where we say, now we are done with this. Instead, what we are, where we are trying to get is to a point where we can say, this is our 1.0 version and we hope to be there in the month actually. And, from then on we of course continue building. and one of the main challenges to my surprise actually, and maybe that was the main learnings, is since what we're working with is so new, it is really, really very hard and crucial to explain to users what we even doing here, because they are very curious, but they have a very hard time understanding these new ways of interacting with the computer. And, it takes. Nearly as much time to make the product as it takes to actually package whatever you're building in a way so that an everyday user can understand what is even going on here. So that's quite challenging and that is something that we also are constantly in the process of figuring out.

Dhaval: Yeah. One of the biggest challenges for generative AI products is gonna be around user experience and managing that human AI interface. What has been your biggest learning lesson in that area for your product?

Martin Pichlmair: So we have a nearly no prompt system. So most, in most AI systems, how you interact with what a generative AI systems. If you interact with them, you do some kind of prompt engineering as they call it, so you get better and better at formulating what you wish to get out of the model. More and more precise over time and you just, it's a learning process. You prompt them with text usually, and you just get better at describing what you want in the way the machine understands. which is an interesting interaction, but it's actually surprisingly technical. And our approach is a little bit, since we are all about making this more accessible, these systems is our approach hides that. We are, of course also prompting in the background very much from the user and tries to make sure that you get a higher percentage of. Good results automatically instead of giving you this huge space of freedom that you have with more technical systems. But those systems exist so if someone is technically inclined, they can just use a different system. It's just not what we are making a software for.

Dhaval: Yeah. You mentioned something about prompt engineering and how does that play out when users are engaging with the product, do they get to like decide exactly what's gonna be the better prompt to ask, or is that something done behind the scenes?

Martin Pichlmair: Most of it is done behind the scenes. So the base use case of LAIKA is, you upload your materials to train with your voice, and then you just write half a sentence or a full sentence and a question mark, for example, and then you ask the system how it will continue from there. Now, of course, technically you are just prompting. But in practice it feels like you are writing and now, and then you give a little bit of control to the machine and then you override the result immediately and work in this very tight interactive loop with the system. So it's a bit of a, yeah, more. There's a lot of discussion about generative AI in the moment among artists that, uh, somehow do not appreciate the fact that there are so many machine generated images out there and what we are doing is very different because there is no result. That is trust. What comes out of the generative ai, it is always the author with. Their own thoughts remixed by the machine creating something. So it's very much 80% is human authored and what is even made by the machine vanishes, hopefully. So it's a slightly different way of interacting. It's using the same base technology and the same post model base modalities of how you interact with a machine learning model. But it hides them and it makes the loop even tighter than existing systems.

Dhaval: That's where the magic is, making that loop tighter. Thank you for sharing all your knowledge. Martin. Anything else that you wanna share about the future of your product? Where do you think it's going? Do you have a big vision around where it's gonna be in about 10 years from now?

Martin Pichlmair: About 10 years. Oh boy. Well, in the end I think that I actually have a big vision how, where it will be in 10 years or where society will be in 10 years. I wrote a blog post about it recently. That's why I have. The thing is I actually think that a lot of interacting, interaction with generative models is not so much about getting results and much more about the experience. So what we are doing is we are interacting with cultural, the cultural heritage of humanity, in a live and, uh, uh, back and forth ways. So we synthesize something that uses images that are up to thousands of years old, like the photos of those images, but still human expression that is hundreds, thousands, and so on, years old, and we synthesize them into something new. And that activity is actually a very interesting activity of interacting with the, what humanity is with all of our culture. And I think it will become just something very normal for us to do. That we're not only interacting with the way a specific historian has recorded our history, but in a much closer, again a tighter loop, interact with past images, past writing, past words, past figures, maybe historical figures and so on in a, and of course, synthesized way, but still very much informed by who humanity was in the past. And I think that just will lead to a different understanding of who we are. And that will maybe take 10 years, but it's gonna be interesting. That is a really big vision. I know.

Dhaval: Yeah. That's amazing. I, I look forward to get to there as well Martin Write with LAIKA 2000 beta testers, how many people who have signed up for the wait list.

Martin Pichlmair: Well, not many more.

Dhaval: Not many more. Okay. And you are ready to launch your product in about a month's timeframe and, you have exciting, future ahead. I am looking forward to following your journey. Martin any last thoughts on anyone who wants to start on this journey? AI, product creator who wants to build an AI product. In generative AI space using text as their, as their tool. What would the, your advice be for them? any tools that you would recommend for them to use to get started?

Martin Pichlmair: I think the important thing is to find a niche, because I think in the very near future, like next year, most likely, Microsoft will trust, integrate generic functions for creating text into all of their products. And then that's just gonna cover 90% of the use cases. So if you want to get started in text, it's like you have to find a very, very, very specific niche, maybe in the business to business sector, to be able to still be around in a few years when that will just be a standard function of word processors. So I think that is my main tip to just. Yeah, keep it, keep it focused.

Dhaval: Now in terms of execution to keep it focused, would that be custom models? Would that be built in tune with GPT 3 or GPT 4, whatever is the next version? How would you suggest that?

Martin Pichlmair: I do like our autonomy. We own nearly all of our stack, all of our core stack is owned by us. And that gives us the ability to define our own policies of how people can interact with our system it gives us safety. It makes us independent of the server costs of other people. Of course we are hosting it in the cloud. It is a little bit cheaper also in the moment. So this autonomy is something that works very well for us. Now, I think it's of course, expensive to build a product, using your own stack, but I think there is will just be more and more need for diversification of what you're doing. And we can have more diversification than any product that builds on the vanilla GPT 4 version or 3 or 3.5 or whatever.

Dhaval: You cannot really have that niche if it's built on top of a vanilla version. Well, thank you so much, Martin. It's been a pleasure having you on the show. I'm looking forward to following your journey,Thank you so much, Martin. Have a great rest of your day.

Boyang Niu is the Co-Founder of Stylized. He holds Bachelor and Master Degrees in Computer Science from the University of Pennsylvania. In today's episode, We discussed his journey and vision for the AI-powered e-commerce platform. Boyang shares the long-term vision for Stylized, which is to become an asset-first e-commerce platform, simplifying the process of setting up an online store by taking care of all abstractions, from website building to SEO. Boyang emphasizes the importance of understanding one's strengths, whether it's distribution, core ML, or UX, and iterating quickly to create a high-efficacy product. Tune in to hear Boyang's insights and experiences in building Stylized and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/boyang-niu/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt Transcript:-

Dhaval: This former Dropbox engineer built an AI product to create stylized professional product photos for people running e-commerce shops. Boyang is a founder of Stylized ai. He Figured out a way to leverage depth extraction, AI and 3D rendering to empower e-commerce sellers to transform their phone photos into professional assets for everyday use. One of the biggest lessons I gleaned from this conversation is how he approaches product development by focusing on areas of growth and applying the user mindset to product creation.

Hey, Boyang , talk to us. Tell us about your journey. How did you come to, identify this, space? And, tell us a little bit about you first and then we can talk about the product.

Boyang: Yeah, absolutely. Great to be here. Dhaval I joined Hive. So Hive was a social media company back in 2015, building like a Twitter clone. We pivoted hard to enterprise computer vision SaaS. And so that's where I first like learned about ImageNet, um, did all this AI stuff built like a trading pipeline and all of this stuff. And then after that, worked in some productivity tools at Dropbox, worked at e-commerce at Square, and then for the latest venture, I like put all those things together. And really it's about targeting the market that I care about, which is e-commerce sellers, which is a huge market, right? You get a lot of customer iterations, a lot of customers to go after. You don't have to really be scared to approach any particular one. And that's a great feeling when you're just getting off the ground, right? Because you need that iteration cycle. And so we, we. Sort of putted around me and my co-founder, looking for ideas that really resonated. And one of the things was like, oh, people want to take photos, right? People, when they sell things, they need photos of the thing. that's where buyer decisions are made, and they pay like 35, $50 per photo professionally for these images that they're putting on Shopify. And so people are coming up to us, they're being like, oh I waited like six weeks for this photo set. For, 250 photos, it cost me 10 K or 5k, whatever. And we're like oh this is interesting, right? Because, there's this new image stuff going on and maybe we could really leverage that, to make this workflow better.So that's really where we came up with the idea for, what is now Stylized, Stylized.Ai. What we're building is professional product photos, for people running e-commerce shops in under 30 second

Dhaval: oh wow. So tell us a little bit about where your product is at this stage and have you launched, is it in the pre-launch stage? Is it in the wait list, stage, et cetera, et cetera.

Boyang: Yep. We have launched, we are soft rolling out a launch with this is a prosumer product, right? And so we're really concentrated on the B2C motion of go to market. We're doing like organic seo. we're running a bit of ads on the side. And so this is all just to build up , a brand name and also to get really fast iteration on what the product surface is. So we have soft launch. We have about 200 customers right now. That's growing probably at a rate of, I would say, like 15 to 20 per day. Which is pretty good, right? It's only been like, I think since we opened the beta. To one and a half weeks. So we are pretty happy with that. And I think the goal for us really is to get that distribution and get in front of people into their workflows such that we get embedded. And because no one really knows, like honestly, no one really knows where the AI models are going. And so by in the next three months, someone could do some really magical stuff, right? And we want to be able to put that magic in front of customers. And to do that, you have to have a big audience. So that's our goal, right now

Dhaval: That's awesome. Focusing on distribution first, that's novel. Most of the founders and product creators, they get their heads down and they start building the product and it. They spend months and months and months before even thinking of the first interaction with the customer. And as you already know, it never goes as per the plan. Right. So what is the main value proposition? What is the main customer pain point that your product solves for?

Boyang: Yep. Customer pain point. I have a product. I'm trying to sell it. I have a Shopify store. I need good images. Right now my options are I get like a light box set up, which is, they can be pretty complicated, right? I need to set up a photo studio area in my house. I need to take pretty meticulous pictures. I need to then learn Photoshop and edit those just the way I want them. Or I go to a professional studio and get my photos back to me, in a couple weeks. And so that's my blocker right now. I can't get it on Shopify. So what we do is, you. An iPhone image, and as long as it's like pretty good neutral lighting, like anyone can do this. I've done this many times and I'm bad at taking photos. Right. So I do that in my product. In 30 seconds I get a virtual light box. So this is a staged, 3D rendering actually. And the technology is relatively, I wouldn't call it simple, but it pieces together a bunch of existing models, right? To render your product in 3D. And then you get to adjust whatever you want about that rendering such that you know your product is professionally lighted. You get to change all the backdrops as if you are in a product studio or in a photo studio with like different types of materials or Hey, I want this on marble or slate, or all of that stuff. But you get to do that from the comfort of your computer and the iteration time is, on the order, five seconds versus two weeks. so that's what we're going for.

Dhaval: Wonderful. Yeah. I've been a product owner for e-commerce companies and that finding good stylized photographs of your product has been the biggest game changer. Like experiencing, showing people experiencing the product has been the biggest game changer. So you're solving a real pain point. You said you are in a prosumer space. If you can unpack that a little bit, why is that presumer and not just e-commerce sellers. How do you differentiate?

Boyang: Yeah. So for us, the biggest differentiation is whether we are b2b, which would be selling to e-commerce platforms. And we've talked about this as well as whether we could go to Shopify and say, Hey we have an api, or we have a third party tool that. You could purchase for your sellers to make their shops more efficient. Whether that's the route or if we want to go directly to the customers themselves. So we see that as more presumer because it's self-serve one, we're just launching to anyone. You can come in, you enter your email, um, you upload a picture and boom, it's there it's free to use. You just get these premium add-ons. And that's how you are introduced to that product at first. So we're calling it prosumer mostly because all of our customers are independent shop owners, and they really get to make the decision about their own product.

Dhaval: Very cool. Yeah. So your product roadmap could be either build up your distribution, get a lot of, Prosumer e-commerce, use your product, and then become down the line, become this extension or plugin for all the e-commerce outlets that are out there. Is that something you're thinking of?

Boyang: Yes. I guess I won't go into too much detail, but we do have a Shopify extension that's coming out soon. As I said, we're focused really on hitting that distribution and just nailing it, getting as many customers as possible. And one of the, one of the benefits here is like we're solving one. Very individual problem, right? So it's, Hey, I need a photo. There's a very clear input, like I take a photo, there's a very clear output, I get a better photo back, right? That takes 30 seconds, 15 seconds, whatever it might be. We're solving that pain point. So it's very easy to get in front of people and say, Hey, look. This is what you're getting from us. and it's easy to onboard. And then from there, I think the strategy here is if we get many customers, we can start building up, catalog extensions. We can say, Hey, put your entire store catalog with us, like we will optimize it. Or you get better photos through all of your store. And then we can really start to leverage all of the newer AI things that we see coming out in the future. So if one day there are, very good AI models for just creating catalog webpages, we could leverage that. We could then let you know our customers create their own webpages directly from our surface. And that is really the expansion route that we're foreseeing. It's like an asset first, website.

Dhaval: Very cool. So we have talked about your journey. We have talked about your product. We have talked about your potential future roadmap. What I would love to dive into now is unpacking the product stack a little bit for people, for product managers or product owners who want to either make an AI product or wanna add AI to their product. What was your journey like when it comes to building this AI product, what was the stack like? Anything you can share that would really be tangible that would really help our audience.

Boyang: Yeah. I think it's, let's go back to August, right? So in August my co-founder and I were Playing around with a whole bunch of ideas.One of them we settled on was this 2D textures for games. So he has a background in, in 3D games and at Facebook running Oculus. And so what we did was we just made a very simple, page with you enter something and you get back a texture, that book like that thing. so that was our first foray into any of these newer diffusion models. And from there we started. Seeing the same sort of, using the same backend, toward different customers. And this was really just week after week of iteration where we strategized and we said, Hey, what is the thing we wanna focus on? Oh we believe that workflows are the thing that really matter, right? You have to get embedded into your customer's workflow. So if you make something where they come and they get their thing and they just like immediately leave you can get a lot of people coming to your site, but it's not it's not gonna grow into anything. Substantial, All right. So we targeted the workflow we started targeting the customer, which we said was e-commerce mostly for the large market. And then the actual product stack itself. We sort of device an engine to create apps very easily where it's the top level is UI that we build with Chakra. So it's very fast for us to just iterate on this thing.

Dhaval: What was that Chakra?

Boyang: Chakra is a, like a UI toolkit. For React. I think it probably supports other frameworks now. And then on the backend for us, uh, we have essentially a, a lot of APIs that go to different, ML models that we've stood up ourselves. And so as we keep building, different services, we stand up more of these models. And then we just have access to so much flexibility at this point where we're like, Hey, we need to upscale this image. But we already have that, right? We built this for another reason entirely, but as long as we can leverage the same flows, um, that will just work. Yeah, I think if that answered the question.

Dhaval: Yeah, that was very helpful, Boyne. Thank you. So there has been this debate about building on top of existing ML models versus standing up your own ML models and your own ML infrastructure, which route? Should a product creator take if she wants to build an AI product, what is the best, what is the best way to test your hypothesis? And at the same time, what is the best way to actually create a high efficacy product? If you were to offer some input there?

Boyang: I think it depends on where you think your talents and expertise are. For us, we're both technical. It was pretty easy to set up these models on our own side. We are not training them ourselves, right?And so , we're using open source packages, but it's easy to set that up if you believe that your expertise is in go-to market and, SEO and you just want to set up things that provide value very quickly. But you don't care about really optimizing the underlying stuff as long as you can get the users. Then Sure. I think that route is really good. I would just say the most important thing is for us to understand what our strategy is. And the strategy has always been, hey, like we are two technical people and we're focusing on distribution because that's sort of our weakness at this point. So we're trying to short that up. And that's why we made decisions we did. But if you are, coming from product and you just wanna stand something up these days the models are so good and there's so much out there in terms of SaaS that just lets you, get value immediately and get APIs immediately. That I think is the better route if you were to want to iterate really fast.

Dhaval: Very cool. So either distribution could be your advantage, or the core ML could be your advantage. You gotta figure out which path you want to take. It could be both, but you have to choose one at a time and then go forward and then iterate from there. Did I get that right?

Boyang: Absolutely. I think for us we very early on we said, Hey, if we're going into this ML space neither of us is a PhD, right? We're not truly confident in our ability to produce net new models or like net new ways of doing things, however, We are very confident in generating better UX in like allowing people, and this is the whole ChatGPT argument, right? Like it's better UX on top of something that is roughly the same and didn't really take that much, but it was a step function, difference in value. And so that's been our thing, like we're gonna leverage this ux and build as fast as we can to get the most users. And then the models will come. They'll come later.

Dhaval: Wonderful. Now, changing gears a little bit, are. Generating revenue as of now. What is the price point? How did you come to that conclusion? Are you pre-revenue, post revenue? Tell us a little bit about your revenue stuff.

Boyang: Yeah, pre-revenue. We're basically concentrated on gathering users. so we have built up a, an email list, of users. And the point of this is like to get iteration as fast as possible to come to a good pricing model. And we have one in mind. We're building it out. I think we're gearing up for this product hunt launch that will come in like the next week or week and a half. so I think that is the point at which we will find more legitimacy in saying, Hey, we have value to offer, that you can pay for. Right now it's a bit early and it just seems like. If we were to ask for the money, it'd be, we're not really providing that much, so there's not much to pay for. so that's the revenue argument. I think also for us, if distribution is so important we don't really wanna slow it down with anything. we would rather people see us as, Hey, we remove backgrounds really well and we give you like a flat white picture with nice shadows of your product and that's free. And if a million people use that, then we're very happy with where we.

Dhaval: Wonderful. Tell us a little bit about your venture, uh, funding strategy. Are you, have you raised capital? Are you raising capital? How is it compared to your past startups in the current environment? Yeah.

Boyang: I think funding We know the funding environment's a bit tough. it might not be as tough as it was, like the end of last year. we are not seeking money right now. I think we want to be in as strong a position as possible while going into that fundraising. And for us it's like we have milestones that we know we want to hit, before we really had conviction that our product is good and, you know, will have product market fit in the future if we throw money at it.. So right now, money's not the problem. we want to get to enough users, enough retention enough growth on the payment side, in order to say, Hey, like we know now the equation is if we go seek fundraising, we get this like $3 million. Here's what we are gonna throw it at and this is how it will help us grow.

Dhaval: Yeah. So becoming clear on where you want to invest is where you are at right now. So do you have any funding, have you done any seed rounds or, is it all bootstrapped until this point?

Boyang: so, last year my co-founder and I joined a, I would call it an accelerator. It's a community, sort of like Y Combinator, it's called South Park Commons. been really great for us as like a networking opportunity. and then that is where we got our umbrella. It's like a pre-seed. it's really just a parachute for us to, you know, jump off a cliff. So both of us were coming from relatively big tech jobs, so pretty cushy jobs. And, that was what gave us the confidence to just be like, Hey, let's let's quit. Let's do this instead. Um, and yeah, that, that money has gotten us to where we are. We're not really, we're not paying ourselves very much, so we have plenty of runway. and you know, for us it's really. Like we're not looking for like the big investment dollars. I think it really makes us happy to just build things that people want.

Dhaval: Wonderful. Tell us about the future vision, like really long-term future vision of, stylized.

Boyang: Yeah, so we want stylized to be an asset first e-commerce platform. And what this means is I can imagine a future where you wanna start a shop. And you don't go to Shopify because you need a website builder or a domain name or any of those payments options. You come to us because you have the thing in mind that you want to sell. And you can immediately launch the shop of your dreams, right? That is online. And so all of that virtual stuff, I actually don't think, and a lot of shop owners have told us this. The boiler plate of setting up what your shop, looks like necessarily on, in virtually is part of what they want to do. They're passionate about making good product, getting customers selling that product and like iterating on that. And that's what makes them happy. And we want to take away like, All of the abstractions up until that point, right? So we want you to say, Hey, I have a good product, or even I have an idea for a good product. What should my pricing model be? How do you do the SEO for this? What should it look like? How do I set up a website that is visually enticing to anyone who comes in? And once we have, I think these better image models, we'll be able to even A/B test the actual images themselves, which is something I think up till this point no one was able to do.You can't just run to a photo studio every week and be like, Hey, give me like five sets of different photos I want to try out on different users. But what were you willing on is like, Hey, this seller likes these photos. We'll just, on demand, we'll generate those of your product, and present it to them. And I think that is the real e-commerce experience that we're going for.

Dhaval: Wow. Okay, so you're starting with The seller's mindset. The seller's product mindset first, which is, I have an idea of selling this product. Help me launch a store, get a website, get the product page up, get the content up, and I can just focus on the product innovation. Did I get that right? Are you focusing on like taking over? All the abstractions, including the e-commerce, including the website, including all the marketing, including all the content. Did I get that right? Is that your vision?

Boyang: Yeah, that's the vision. And I think the way that it's been working so far, in the last 20 years is we've been able to abstract away some of these things. As a startup founder, I didn't have to set up my own HR ops or any I used gusto, right? And so these layers exist now and I think they don't quite exist from the retail to e-commerce side. You can't, you can see that transformation just about to happen and we want to be, To really transform e-commerce into something where you can say, well, like I wanna stand up an e-commerce store, and I click a couple of buttons and I tell you my idea and now I have it.

Dhaval: Wow. I'm excited about where you're going Boyang. And looking forward to your launch here. Good luck with your product launch. We'll be keeping an eye on awesome things you're working on and thank you so much for being part of this show. It's an honor to have you on the show.

Boyang: Yeah, absolutely. It was great to be here.

Andrew Palmer is the Co-Founder and CEO at Bertha Ai. Single Dad of a Twenties daughter He love to travel, play golf and attend WordCamps around the world. He love supporting Plugin and theme developers across the globe as it helps them get a profile and earn a living from their knowledge of WordPress, AI Content, PHP, Laravel and JavaScript. In today's episode, We discusses his journey and the development of Bertha AI, an application layer built on top of OpenAI's GPT-3. Bertha AI is designed to help website owners create and manage content efficiently. Andrew shares his advice for new entrepreneurs interested in creating AI products to start by fine-tuning their prompts and understanding what they want to achieve with AI. Tune in to hear Andrew's insights and experiences in building Bertha Ai and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Andrew: • LinkedIn: https://www.linkedin.com/in/andrewpalmer/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: [00:00] Introduction to Andrew Palmer and Bertha AI [00:04:20] How Bertha AI uses GPT-3 for content creation [00:07:47] Andrew's entrepreneurial journey [00:13:58] Advice for those looking to create AI products using GPT-3 [00:17:17] Staying motivated as an entrepreneur [00:19:31] The future of Bertha AI and content industry Transcript:-

Dhaval: Hey, Andrew, thank you for joining the call. I would love to hear from you on what your product is, like who does it serve, and what are the things that it solves for?

Andrew: Well, Bertha.Ai solves a multitude of problems. In WordPress, for instance, we have web developers dealing with clients that really just can't get write together own content because they're not concentrating on what they should say on a website. They're concentrating on what they should say to their direct clients. They're speaking to every day on the phone or in day-to-day meetings, sales meetings, networking, that type of thing. So WordPress has a content gathering issue with Bertha.ai, when you are developing a website, the developer and all the client can use Bertha.ai to create interesting content around what the services, the particular website client provides. It can also help them write blog posts relevant to their particular industry. It can basically help you get the right words out there at the right time and much, much faster. So you're going to increase your productivity as a web developer and also if you're a client building your own website, you are going to be able to increase your productivity and get more ideas about what people actually want you to talk about within your services. So that's what Bertha.ai is all about really.

Dhaval: Wonderful. Is that a content management system or is that a plugin for Wordpress?

Andrew: So Bertha.ai is a plugin for WordPress, which is an application layer built on OpenAI, which everybody's hearing about the moment, probably one of the best viral campaigns out there with GPTchat. With Bertha.ai, we've got a number of modules in there that help you get your unique proposition together. You can write a full on about page. As of this week, which is January, 11th of January, so coming soon is GPTChat to Bertha.ai as well. And as of by the end of January, we'll have a Google extension out there as well. So you'll be able to use Bertha.ai everywhere. And the whole point of being able to use it everywhere is that not every website is built using WordPress. So you'll be able to use it in Shopify, Wix, those kind of proprietary website building tools.

Dhaval: Wonderful. So you just answered my question that you are expanding for being a word, from being a WordPress only product to more of a open access for all types of content management systems.

Andrew: Yeah, using it everywhere. So when, if you're in your Shopify site, you'll be able to just invoke Bertha .ai and write fantastic product descriptions or enhance your product descriptions. You'll also be able to get page content that is relevant to your users. So you'll, you'll ask one line question and Bertha.ai will be able to give you the answer to that question. And then you can copy and paste that, edit that to make it more human-like if you like. And then you can post that in your Shopify website or your Wix website or Squarespace, whatever you're using.

Dhaval: That's wonderful idea.Tell us a little bit about where you are in your journey. When did you start first? How many customers you have? If you can share revenue. What is the split between developers versus end clients? From a business point of view, tell us or give us a few clarity on where you are in your journey.

Andrew: Where we are is we're nearly a year down the line or just over a year down the line. Actually, we launched in September 2021. So, just the MVP version, and we launched that for basic WordPress plugin. So we're a year down the line. So we're way down the line. We've made the plugin faster, more accessible, easier to install. Getting the customer journey right is very important when you are building a plugin that's got a lot of things in it. There's also a learning curve as well. So we produced in just under a month, we produced 54 learning videos which are on our YouTube channel, which take you through every single module. With the hype around GPT-3, obviously lots and lots of people, you've got a million users in under three weeks, I think on GPT-3. So people are understanding how to use AI, how to ask the right questions, and obviously with images, image generation as well. So Bertha AI put image generation in there about a month ago. And that's flying. People are really kind of intrigued about how they can create unique images for their blog posts, from CVD products to computer products to any kind of product for florist, interior design, kitchen design. You can really get some great imagery there. If you want a modern kitchen design designer or a modern interior design, just ask Bertha create an image and it'll produce that image of a beautiful laid out sitting room or a house with chandeliers or whatever you like. So the point is that we came from the WordPress design or website design business. So we understand how people want to build websites. It's kind of a generic way to do that. And with the developments that we've had, we've got something like around 10,000 registered users. Some of them are website owners. In fact, I had a Zoom call with a guy today who's a website owner. They're not a developer or anything. And he wanted to use Bertha AI to better describe his products to his wider market. In fact, most of the meetings I've had with Bertha AI I love doing one on ones with our clients. And they've been with people that aren't web developers. They're the website owners. And that's the people that we really like to target. Because as I said, website owners have a real problem getting content together and writing blog posts. So Bertha helps you with all that content. But revenue terms, we're okay. We're fine. It's in profit. It's running itself. It's quite nice, we're able to expand. Development team based in UK is upon part of the development team as well. And the majority of the team are based in Kolkata in India. And they do a great job.

Dhaval: So then quick question. You mentioned something very interesting. There are a couple things you mentioned. I want to glean them out. One is the customer journey. The audience for this podcast are people who are product creators and they're specifically interested in either adding AI to their existing product or they want to create an AI product. With what you just described about customer journey, what was that experience like for you? Like, understand the customer journey for your website owners. And how did you productize that? If you can share a little bit about that.

Andrew: Well, it's still happening. with the WordPress plugin, we are lucky it's on the repository and you can install that directly from your WordPress dashboard. With the extension, that's going to be a harder task making sure that customer journey is OK to actually install it in a Chrome extension. We're talking about millions of users that maybe don't know how to install a crime extension from scratch because it's not going to be on the Chrome extension store for a while. It's just going to be downloaded. Once you have an account within Bertha, you'll be able to download the Chrome extension and install that. So we're building education around that about how to install a Chrome extension and maybe make it not so difficult for people to understand that it's quite easy. It takes about 30 seconds to install the Bertha extension. So it's about education, but it's also about what we want as a customer journey. There are so many extensions out there. There's so many plugins. There's so many SaaS products out there that involve a learning curve. And what we've learned is that people don't actually want to learn. So what we have to do is almost automate that process .

Dhaval: I'm one of them.

Andrew: Yeah, exactly. You don"t actually want to learn how to drive a car or drive a new car, let's say. So if you've got a new car and it's great, but the indicator arm is on the other side, you forget, don't you? You put your windscreen wipers on. So, There's a learning curve around everything if I really simplify it. But at the end of the day, it's up to us as product developers to make sure the customer journey is as seamless as possible to immediate use. And that's where we're having difficulty. And we've had difficulty, but we're getting better and better by that every single day.

Dhaval: Now, you were a website agency website development or marketing agency, and that's how you identified this content problem that your customers were having. And that's how you ended up with this idea . Am I getting your entrepreneurial journey right?

Andrew: I mean it was my co-founder Vito Peleg who has a collaboration tool with wordPress as well, and with websites now is a whole, where people can leave feedback actually on the website. And he said, well, wouldn't it be nice to have some AI generate text automatically so the customer doesn't have to worry about what text to put in a particular place in a website? So we developed it for that purpose initially and then thought, well, you know what? We can make this work with every single page builder out there. Gutenberg, the classic editor for WordPress. And that was a learning curve as well. I come from a thing called Divi theme community, which is by elegant themes. So my first task was to make it work with Divi theme. And I've also got something like 16 plugins out there that work with the Divi theme. So we know that theme pretty well and how to make things work on the front end. And the nice thing about using page builders, most of them are front end. The user interface is front end. So what we've done is we've made Bertha integrate with every single page builder that people use so that when they're building their sections together, they can actually put the text in the same time as they're building their sections. So that was the key to us to make it user-friendly. Page builder friendly. Certainly Gutenberg friendly with WordPress, because the way that Gutenberg is progressing and systems out there like the kadence system for WordPress. It makes it easy for people to use blocks now because kadence is really the lion of where blocks are going. So we had to make sure that it works with every single theme out there, every single page builder as well. And there's some page builders that have just come out recently that we're still working on, but it works. One of the most helpful people out there was a company called Visual Composer who helped us integrate Bertha into their system. And that then led us to realize what we were doing wrong with other systems that were built on React and other technologies other than just the normal page builders. So that helped us a lot. And, Yoast.com as well. They helped us integrate it into the Yoast plugin for SEO as well. So there's some good teamwork out there. And I think that's what product makers need to realizes is that the way that they can really help their products progress is to get involved in partnerships or relationships with successful products out there and make sure that you keep that conversation going. That then leads to them once your product works seamlessly with their product that makes them help you promote it as well. So that's a good tip.

Dhaval: Wonderful You just gave a golden nugget here. One thing I eased out from this is that Most of the people who want to start out as a product creator, they end up choosing something that they already worked in. For instance, in your case, you chose something that you already had experienced in and you found gaps and you were able to solve that problem by creating partnerships with other people. So it's a lesson for the audience, if you are starting out, look closer, look close at your home. Where are you? Like where in your work, in your current work. There is an opportunity where you can use AI. And that's the starting point, like you did. Now big question for you is it seems like you were able to successfully bootstrap this and you have revenue and you have users, 10,000 users. That's a success story. Did I get that right? Or did you raise capital or did you use your agency money to build a product? Help us understand, how did you financially drive this forward?

Andrew: We've bootstrapped from the very beginning. One of the things that's prevalent in our industry or the plugin industry is to do a lifetime deal. So once we had the product at a stage where we knew that it was a flyer or we knew that it was gonna go along, we did a couple of lifetime deals. So that was kind of self-funding. Our lifetime members really invested in us as well. If you think about it, it was a good lifetime. It's a great lifetime deal. And we're always trying to improve it. We don't do lifetime deals now. So that gave us traction.

Dhaval: Now where this Your agency clients who signed up for your lifetime deal or did you go out and did some marketing campaigns?

Andrew: We did some marketing campaigns and just said, This is where we're at. We're pretty well known in the Facebook groups where I get on podcasts. So for instance, the time now is three o'clock in the morning. I don't mind when I'm going to talk to people on a podcast because I'm based in the UK. Bertha is an American company. We registered in the US. But the point is that you've got to get out there and say, let me talk about this product. Let me talk about the benefits of using AI, even though there are some negatives to it because of People are saying there's repetitive text. But again, once you educate yourself as a human, how to use generative text within AI, then you're going to produce great content and you're going to be a leader in getting really informing, educating, and entertaining your customers. And that's the whole point about generative AI. But what we wanted to do was totally bootstrap. I was very lucky. I had a business called Elegantmarketplace.com. I sold that to in motion host and that gave me some financial backing as well. So I didn't really have to worry about income for a little while. So that meant that I could concentrate with my team and I on developing Bertha. But I've still got my web agency in in the UK and I service around 500 clients on hosting and maintaining websites. And we pick up the odd website here and there. But Bertha AI is really pretty much my full-time job these days.

Dhaval: What would you recommend for someone who is starting out and has interest in creating a product, an AI product on top of GPT-3 or any of the future versions? Would you say that's like a solid strategy to put a user interface on top of GPT-3 or would you recommend building your own models and having that own core machine learning? What is the trade off there? If you can speak to that please.

Andrew: Well, the trade off, I mean, is you can do some fine tuning. We've done some fine tuning on our prompts. I mean, the whole point about Bertha AI was. It was developed specifically for website owners. So the prompts come from our life experiences within developing WordPress sites and writing and copywriting with WordPress websites. So we did a fair amount of fine tuning, which can be get expensive. But we also knew what we wanted to get out of Open AI. So we've been playing with it since May 21, and we didn't develop stuff or we didn't launch anything until September 21. So We had a real good session, a few months of learning how to write prompts that worked properly and developing prompts and producing new items like ask Me Anything. We've got a module within Bertha AI. Now, which is effectively chat GPT, where you can ask Bertha anything. You can ask it to write a song. Write a bit of code. The code may not be right, so you should always double check. But write a 500 word article on SEO, for instance, or product development or whatever you may want to do. Now we are adding GPT-3 chats. That's got even better. So we're keeping the asking Me Anything module. We're keeping the long form. We're keeping all the other modules that we've got. And also OpenAI helps you write in currently we're baked in 27 languages within Bertha AI as well. So when you are thinking about producing a product, make sure that you internationalize it. That's one of the keys. Make sure that you've got a global audience rather than a local audience. There are plug-in developers out there product developers out there that really niche down. There are people out there that are selling to enterprise only with their extensions or with their product solutions. But if you want to be able to get out there to the masses, one, cost it properly. Make sure you understand your costs completely. And two, price it correctly so that you understand where your profit margins are going to be so that you can continue to make a difference to people's lives. Really, that's the key. It doesn't matter what you develop, whether it's an AI product or whether it's a theming solution or helping people meditate properly or be more spiritual or less spiritual or whatever you like. You can use your intelligence. Let's go away from AI at the moment. The human intelligence is you just do your own research or what are people using and why are people using it? And if you want to use AI to help you build it, that's fine. Let's not forget the mRNA vaccine wouldn't have been produced in a year without the power of AI. So there's plenty you can do. You can go into healthcare, you can go into well-being, you can go into fitness. AI will help you build a product around anything. Which is great.

Dhaval: Andrew, I have two last questions and we'll wrap from here. So one is you're obviously not in your early 20s. You're not a single guy or a single gal who has a lot of time and a lot of energy, right. How do you keep your energy and your fire stoked as an entrepreneur?

Andrew: Okay. I get asked that question a lot. I don't really have an answer, apart from if you love what you do, What you do will love you back. So if you love what you do, what you do will love you back. I am an open source advocate. I'm a community advocate. I love what goes on in the community of certainly around WordPress, in the community of AI. I'm an anti keyboard warrior guy. I can't stand keyboard warriors. My goal in life is to calm people down, basically, make sure that we've got the facilities to be helpful. Bertha AI is essentially, So it's nice to be able to let people know that they can use it free completely forever. There's no restrictions on use currently. So I just try, I just know that what I'm doing in my life and in my business. I know that I'm doing it for profit, but I'm also doing it from an altruistic sense of what's right. And that's been learned over a period of years. It's not easy to get to give away things or get to give away your time. As I say, one of the most rewarding aspects of my as an entrepreneur is actually helping other people. I have a coaching business and many times I look at people that are in trouble with their business or anything, and they've come to me and I charge an hourly rate for that and, You know, I just don't charge. I just go, okay, you can have that one on me. Because, and it's not because I feel sorry from it, it's just because I feel I just want to give them an opportunity to come back. And, conversely, what then happens is when they do become successful, because they do, they come back and they pay me my hourly rate on a weekly basis. So, there's give and take in this . So basically wake up in the morning wanting to give rather than take. And I think that's the best entrepreneur that you can have. Because in the end things will come back to to you.

Dhaval: That's beautifully you said, I love your quote as well. One last question and we'll wrap. What is the future of Bertha? Where do you see it going?

Andrew: The future of Bertha is going to continue to be an application layer. We may very away from Open AI. What Open AI have done with GPT chat is almost a kick in the shins to the people who have built application layers on top of Open AI. They gave us a bit of a chance, but if you think about where open AI is going to go, they're going to probably get another massive investment from Microsoft. They're going to get another massive investment from anybody else. So our job as Bertha is to make sure that we exploit in the best possible terms of that word. We exploit what Open AI are doing, what other people are investing within Open AI and make sure that our application layer is as proficient and efficient as possible and brings new things. So one of the new things that we are doing is obviously bringing So I'm not going to go too much into it, but something that will really help the podcast industry, which is a billion dollar industry out there. So we're developing things that will help people really make sure that their podcasts are transcribed properly, got the right SRT fields files, and with video transcription as well. So all that kind of stuff moving down the line. So making Bertha AI from an extension and a plugin, a WordPress plugin. Let's not forget there are millions of WordPress websites out there that need good content or need their content looked at. So we're just going to continue to promote the benefits of Bertha AI to website owners. We're also going to promote it to podcasters, to people like you that are taking your time out to interview people like me and making sure that you benefit as much as you can from using AI and hopefully you benefit from using Bertha AI. We'll keep innovating as much as we can rather than following. That's the key.

Dhaval: That's exciting, Andrew. I can't wait to hear all about what you're working on when you come back again on this podcast a few months from now and hear about all your success. Thank you so much for your time. Andrew: Thanks For having me

James Clift is the Founder at Durable. He is the former Founder at KarmaHire, WorkStory. VisualCV and Holopod. He building businesses since 2005. In today's episode, We discusses the benefits of large language models (LLMs) like GPT-3, emphasizing their ability to generate human-like text that can be used for a wide range of applications, including content creation for websites. He also emphasizes the significance of having a data-driven approach to fine-tune the models and make them more effective for different business categories. James shares his experience of finding partners with AI expertise, highlighting the importance of networking and being involved in communities like South Park Commons. He believes in sharing work and attracting like-minded individuals to join the venture. Tune in to hear James's insights and experiences in building Durable and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find James Clift: • LinkedIn: https://www.linkedin.com/in/jclift/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:50 - The story behind Durable and its AI-powered website building platform 00:07:22 - The importance of user feedback in AI model training 00:10:49 - Fine-tuning and prompt engineering for LLMs 00:11:22 - Best practices for partnering with technical teams 00:12:27 - Durable's growth, funding, and team size 00:13:20 - Remote work culture at Durable 00:13:49 - Finding AI expertise and attracting technical partners 00:15:16 - The value of sharing your work and attracting like-minded people 00:16:20 - The future vision for Durable Transcript:-

Dhaval: This founder built an AI product to replaces your employer with ai. Yes. You heard that right? Your employer, James, is our guest in today's show, he shares his learning on how he built a product in a hyper-competitive market space. James Clift is a founder of durable. Durable, makes owning a business easier than having a job. He's a former founder of KarmaHire WorkStory, VisualCV and Holopod. He has been building businesses since 2005.

Welcome to the call. James, tell us about your product.

James Clift: Awesome. Yeah, so Durable is the fastest way to build a website on the internet. In three clicks, in 30 seconds, you can generate a business website using ai. And not only can you make a website, we've got the rest of the stack as well to operate a business, we've got a CRM, an invoicing tool a financial account. So essentially everything you need to start and grow your business in just a few click. .

Dhaval: Wow, that's very powerful vision where do you sit in your market space? are you serving a specific customer segment?

James Clift: Yeah, so we're totally focused on solo operators, so anyone that runs a solo business. So primarily those are service-based companies, so everything from a web designer, marketing contractor, copywriter to more traditional physical service companies like Lawn care, home Services. Plumbing contractors, skilled trades. So essentially anything where you're trading your hours for dollars or your hours for projects we're a great fit for if you're selling goods on the internet or you have a brick and mortar store, we're not the best solution there, but solo service-based companies is our primary market right now.

Dhaval: How did you differentiate yourself in this crowded market space? I believe it may be high competition market space. But You have, you seem to have found reasonable amount of success based on what I have seen about you online. How did you create that differentiation for your product?

James Clift: Yeah, I think there's a lot of ways to look at markets like most markets are very large on the internet, and for us it was a few things. So one is bundling, so providing all the tools you need to run your business under one login. So that was a big value add from the start. So you don't have to learn five different tools. You don't have to pay for five different subscriptions or 10 different subscriptions. It's everything you need under one platform. And then the other piece is what can you actually do 10 times better than everybody else? And for us it's the speed of actually getting a website. Out to market. So instead of taking, typically it's weeks to get a website live, if you're really good it's days. If you're really, really good, it's hours. We actually do that in minutes. So there's this order of magnitude that makes that thing faster, or that business process faster. And it actually unlocks a lot of creativity, a lot of, just makes it more fun and playful, not stressful. And I think you open up these brand new markets by just anytime there's an order of magnitude step change. Something's 10 times faster, 10 times better, 10 times more intelligent. That creates these huge opportunities. And I think AI as a platform is definitely one of them that we're seeing. yeah, I think customers are super excited about the speed, the simplicity, and then the bundling aspect of the platform as well.

Dhaval: Very interesting. So you brought this up, AI and this particular discussion and discussions like. That I host are focused on people who are either interested in creating an AI product or infusing AI in their existing product. Tell us about your infusion of AI into your product. When did you decide that, was it AI first from the ground up? And if it wasn't, when did you decide to bring AI into the user?

James Clift: Yeah, I think, so I've ran SaaS companies for a while now, so probably about 15 years. And, it's always, I mean, the goal of any software company is how do you make processes easier and make your products easier to use? So, the long-term vision of us and AI is really, How do we replace your employer with AI and just let you focus on your core competency. So that's your skill, right? So a lot of the time if you have a service job, you have an hourly rate that you're then getting marked up for by your employer. So, in a perfect world, you just meet that reach, that market demand. So, hey, you're a lawyer making, I don't know, call it 500 bucks an hour that your employer charges you out. You're making 200 bucks an hour. So that's a market opportunity for you to go independent and do your own thing. But what the law firm brings to you is a brand customers, some back office services. So the way we're thinking about that is what can we actually replace? And I think brand is changing a lot. Like the brand matters less, the individual matters more, and the back office piece can be solved with technology. So essentially . Everything can be automated except for the thing you're really good at, and that's really how we're thinking about ai. So from a product standpoint, we built the platform first, and then we built the AI on top of the platform because we've got a lot of features and it was always this idea of, how do you make those features easier to use, more accessible, more interesting, and just more intelligent. So the, our customers can just focus on what they're good at. so that's always been part of the strategy. Definitely it's accelerated in the last few months here with all these new technologies and APIs and libraries that have come out, and been super incredible and are moving really quickly. So definitely, the primary part of the strategy moving forward as well.

Dhaval: One thing, one thing that I always hear from other product creators in the space is about finding the balance between building on top of existing AI capabilities. That other companies have created versus building your own AI capabilities? Where do you draw that line in your product?

James Clift: Yeah, I think it really depends on what kind of company you want to be. Are you really good at marketing? Can you repackage these libraries and build a good user experiences around them? And you can accelerate really quickly? Are you deep technologists? in that case then you should build the underlying infrastructure layer. For us, I think the advantage, and I think. These core models are really, really powerful, but I think you have to train them on your own data sets. Otherwise you're gonna lose your competitive advantage really quickly. So if you're just re-skinning chatGPT and it's a slightly different user experience, but the same data, That's gonna be a race to the bottom pretty quickly, because everyone can do that. But if you have a user that is unique that you can build data sets around, then you can train these models to be more effective. So we're doing both, we're using the existing models, but we're also training them. Pretty specifically around our category of customers. If you think about a solo business owner, there's a set of activities that you need to do. Even from a marketing standpoint. It's okay, you've got your website. How do you optimize your seo? How do you create your ads? How do you create your marketing copy, your newsletters? Once you have customers in, like, where do you get more customers from? How do you measure your channels that are effective? When you send invoices, what is the value of that invoice? How does that tie to your accounting system and your customer database? So there's just a lot of things that we can actually build more proprietary, unique data sets around and workflows and processes that we can optimize. So I think as long as you own that customer journey and lifecycle, then you have the ability to train your model and make it better. But yeah, if you're just re-skinning an API, I think. There's some that will do well. So like re-skin the API in the category. I think one or two of those will succeed in every category because it is great tech. And if you're good at marketing and acquiring customers, there's opportunity there. But if you're the, I don't know, the 20th company to try and build a copywriting app with ai, I think that's gonna be more challenging.

Dhaval: Thank you. Thank you for sharing that within your customer journey. Where do you bring ai? Is it from the get-go or is it, how do you find that balance to delight the customers versus, make them anxious?

James Clift: Yeah, right from the start, I think, It's just a really cool technology to see, and from what our customers see when they're getting their websites built by us, you can actually see the copy being written and it's always wow, this is surprisingly good and really cool. So I think it's just like exposing them to the magic of this technology as early as possible, but it's not, but not trying to make it inaccessible. Right. I think the beauty of ai. Is that you see the output, right? It's not scary because you can see, oh, this is just copy. These are images you chose. This is a marketing strategy that you came up with. It's more just impressive than scary. and I think, yeah, for us it's really giving them that front and center and the first part of the user experience that's really delightful and giving them something to work from that then the human, I think humans are a lot better at editing than creating. A lot of the time. So give them something to play around with, to edit, to regenerate, just to make it fun as opposed to this tedious thing that you have to learn. It's more of a playful experience, I think is what we're really trying to build for our customers.

Very insightful: So making it playful is one of the ways to pull customers in so that they, and also making it transparent so that they see it happening in front of their eyes and it's a magical experience rather than. Behind the scenes, here's what we are doing, take it or lose it, or any of that. One thing, one thing often comes up is the thing that you brought up is fine tuning on top of existing LLMs. Do you have any tools for other, do you recommend any tools for other AI product creators in this space who want to embark on that journey? What are some of the quick ways that they can fine tune on top of existing LLMs?

James Clift: Yeah, I think that's the challenge now is that space is so new that, tools are popping up every day, but nothing's really. Taken over as the category leader yet. So I'd say just the more you actually, I mean, prompt engineering is a career now, so being able to figure out how to write good prompts for open ai, I think is a really interesting way of thinking about it. We're building a lot of that in-house as well. Like our own like model optimization is all gonna be in house at this point. I think when there's third party services, we'll definitely look at them as well. But I would say just use. Underlying technology. and then under, the more you understand it, I think the more you can optimize it as well. So play around with it, test it out. If you wanna read the papers, they're actually really interesting as well. I think from a. Like a pure software product. There's not a lot that I've seen yet. I think it's all coming out and people are building it. There's prompt analytics and stuff as well. That's really interesting. But for now, I think, yeah, that's where the expertise comes in, right? Is how deep can you go and how deep can your team go on these different models and training and all that.

Dhaval: Now as a founder, are you heavily involved in the product at this point or have you gotten to a point where you're focusing more out, focusing out? Or are you focusing in on the product and you have a separate product manager leading the product?

James Clift: It's, myself and my team leading the product. So we don't have any product managers or project managers right now. So yeah, we're still really heavily involved in all product decisions. We've got an incredible UX and product designer, incredible engineers, incredible marketing folks. So I think it's more, it's an, all the whole team's working on it right now. I think it's led by, the group right now and at the end of the day, it's me that's making the final decisions on product here.

Dhaval: Any learning lessons you wanna share around fine tuning and prompt engineering when it comes to building on top of LLMs. How has that experience been with your engineering team and what do you recommend others to, what are some of the practices you recommend other PMs or founders to model if they wanna establish that strong partnership, with their Technical partners, to fine tune and create a strong AI product that creates differentiation. What are some of the best practices for that partnership? What does that look like?

James Clift: Yeah. I think it's really just having this culture of experimentation and iteration and testing. It's really just volume. How much can you test different models and outputs and questions and, gathering feedback from users as well. So having a mechanism to gather feedback around those prompts. And then having your data. So if you say, Hey, we're. We're doing this right now as an exercise. we've got thousands of categories of business being created and we're saying, okay, we're really good at creating lawn care websites, but we're not so good at personal trainer websites. Why is that? Maybe the images we're choosing are off, or the prompts we're using don't apply as much. So it's really just training your own data set and having that data available and also the simple answers look at the output and decide if it's good as either get the feedback from the user, get the feedback from yourself. But I think being, pretty honest about like, okay, this is actually good or not. If not, what can we change and I think that's what we're finding, with some of our categories. Like, wow, this is totally off. And then you really dig in and evaluate what went wrong there, what you can fix. But really it's just volume iteration. And just, yeah, playing around with the outputs and the options. It's pretty much it. Put in the work basically. No simple answer. ,

Dhaval: tell us a little bit about the size of your team and a little bit about where you are in terms of product. Have you raised capital? Are you bootstrapping? Are you looking to raise capital? How many users you have? What is your revenue like, and what is the size of your team.

James Clift: Yeah, so we raised capital in May. We're eight people now. Raised a 6.2 million seed round from some awesome investors. We are at about 200,000 websites built, in the last three months. So we launched in October, and that's just been doubling month over month or tripling. So yeah, lot of users, lot of, a lot of, we got some revenue coming in the door now we're not disclosing that. But it's growing very rapidly as well. So yeah, all systems go right now. And the plan is to grow the team right now as well. I've got a few new hires coming on board pretty quickly, so lean team right now. But yeah, definitely operating very efficiently and shipping product very fast. Right now,

Dhaval: are you a hundred percent remote hybrid? Where are you hiring?

James Clift: Yeah, we're remote by default. So we've got, team members in South America, Vancouver, San Francisco. And then we do have small offices in those locations as well. So kind of your hub so you can come in, do work, or work from home as well. So we design our whole culture to be remote first, so a lot of async, not too many meetings, but some crossover time for collaboration. and then, yeah, we've got a space you can go into and work, but it's not required it's optional

Dhaval: as an AI product creator, did you have that AI domain knowledge or did you find partners who could fill in your entrepreneurial gaps for that area?

James Clift: Yeah, it's definitely, it's always been an interest, but not an expertise. So it's something that I've like it's been a hobby, but not part of any business that I've done before. So it's really just, yeah, finding the partners and the technologists in the community that, is on the cutting edge of this stuff. And yeah, there's a few out there that are great. and I think like one, one of my early investors is called South Park Commons. So they're a community of pre, before it's a zero to one stage. So before you actually start a business, it's a place to experiment with different ideas. And that's a really strong AI community that we've really drawn on for hires and expertise and just learning as well. So I think having a community to learn with is super valuable. and just, yeah, again, being curious and being obsessed with it to some degree, always comes in handy. And I think if you're gonna bet on a technology, it's a pretty interesting one for the next big cycle of software and technology and yeah. What the world might look like.

Dhaval: What was that process like for you to bring someone with an AI expertise on board? Did you meet them at South Park Commons, by the way? Great Community. South Park Commons is the. Where I have gotten a lot of guests from, and yeah, great community. So is that, would you say you, you attracted your technical partners there or what does that look like? How did you pull them into your vision?

James Clift: Yeah, I think it's just, again, volume, like having as many conversations as possible. My CTO I knew from my last business as well, so I worked with him before. And he's got his own network as well, so I think when you get one. Really excellent person. They typically bring more excellent people. So that's the value in hiring with and partnering with amazing people. And then just, yeah, getting the, doing all the stuff that seems not that important. So going to the demo, like the little demo days, like getting your name out there, just getting as much exposure on to what you're doing. people who are interested will reach out as well and want to communicate.So not building in a vacuum, but really. Really getting your name out there, putting yourself out there, showing your work. So even if it's not totally done, just ship something cool. And that leads to a lot more than you'd expect. It's the, you can't predict it, but typically the more you're out there, the more people will discover you and the more conversations you have, the better people that you will work with that will buy into what you're doing.So it's not trying to convince someone of what you're doing, but. have a good thing that you're doing and talk about it, and then people will be convinced. The right people will be convinced anyway.

Dhaval: Yeah. There is, I think there is this famous quote from novel, which is if you share what you're working on, you'll automatically attract people who are interested in that.

James Clift: Exactly.

Dhaval: Very interesting. Very interesting. What I wanted to ask you next is the future vision of your product. James, where do you see, where do you see your product going?

James Clift: Yeah, I think I touched on earlier, but essentially, can we replace your employer with ai? So how can we abstract and automate every part of running a business except your core skill? I would love to see. Someone who has a skill sign up for durable and they have a job scheduled in their calendar tomorrow, they show up, they do the work, their invoice is automatically sent and paid for their accounting's automatically done. So you have this total control over your schedule and flexibility. We tell you how to price your service how can you make a business as easy as renting your house out on Airbnb? So really just leaning in on the automation and the AI and just letting someone focus on their core skill. And there's so many problems to solve there. So we're starting with the, the marketing piece. So even from a, like on a website builder standpoint, there's a hundred features we wanna build to make that process much better, much easier. And then once you have your website, how do we help you market? So automating your advertising, your customer outreach, your marketplace connections, your lead generation, your follow ups, then it goes into scheduling. So we're kind of working our way down, the operations stack of a solo business. But yeah, the end goal I think is really, and what we're seeing in the world is hundreds of millions more people that are, that are their own boss, right? They're running their own business. They're more in control of their lives, they're pricing, their freedom, their flexibility, and they're living happier lives because of technology. And that's really where, what we wanna build for.

Dhaval: Thank you so much for sharing this vision, James. I wish you all the best in achieving that vision. I've done service businesses and I know how difficult it is. You are doing some amazing work there to help individual service business owners achieve their full potential, right? That's where the innovation comes from. So thank you for taking on this vision and thank you for coming onto this podcast and helping us learn from you. Thank you. Yeah,

James Clift: Thanks. Awesome. Appreciate it take care.

Abhi Godara is the Founder & CEO Rytr. He is also the Founder & CEO at HelpTap. Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost! In today's episode, We discusses the initial stages of his startup, where they utilized organic channels like LinkedIn, Facebook, and Reddit for marketing. He also discusses acquiring training data and recommends strategies depending on the domain, mentioning that GPT can work with a limited number of examples. Abhi highlights the importance of user experience in differentiating his product from competitors. Tune in to hear Abhi's insights and experiences in building Latitude and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Abhi Godara: • LinkedIn: https://www.linkedin.com/in/abhimanyugodara/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction 00:03:25 - Abhi's motivation for building an AI copywriting tool 00:05:15 - Strategies for acquiring the first thousand customers 00:09:40 - Differentiating the product in a competitive market 00:12:21 - Acquiring and using training data for AI models 00:13:50 - The story behind the acquisition by Copysmith 00:15:08 - The future of AI in content creation and advice for AI creators Transcript:-

Dhaval: This founder built an AI writing product that serves 4 million customers, and it got acquired in two years from Founding Date in this episode, we discuss his product development approach that differentiates his Gen AI writing product from the plethora of other gen AI writing products in the market space. We discuss his product differentiation strategy, his training, data gathering approach, and how he got his company acquired. Today my guest is Abhi Godara. He's the founder and CEO of Rytr and AI writing assistant that helps you create high quality content in just a few seconds at a fraction of the cost.

Welcome to the show, Abhi tell us about your product. Where are you at with it? what's the four 11?

Abhi: Right thanks Dhaval for having me. so I'm founder and CEO of Rytr one of the largest and probably the first one in the market AI writing platform. We have been there since last couple of years now. now we are serving close to 4 million customers all over the world with with close to perfect ratings pretty much on all the platforms. So it's been an amazing journey in terms of how, the platform has scaled which allows a lot of these content creators. Marketers And professionals to create really high quality copies across a range of use cases, purely through ai. So things like email writing, blog writing product description ads, you name it. Everything can be generated through our platform.

Dhaval: When did you found the company?

Abhi: So this was back in 2021 actually when we started working on this. although I've been in the AI space for a long time. but this idea took off only when OpenAI came to life back in 2020. So I was following that closely. And then when GPT 2 and then GPT 3 came out, and we bounced on that seemed like a great opportunity to build something like this and just to give you some background to that. Again I've been an entrepreneur for most of my career. And, when, one thing I've always found that content creation is a pain, especially when you're a small team just starting it's a fact that many startups and professionals fail because they do not possess the effective marketing and copywriting skills. While dabbling with GPT 3 on another sort of chat bot project, I realized the potential of this technology and the market it could address. And at that time we looked around and evaluated existing platforms and found the experience a bit frustrating. And decided, okay, let's give the market what it deserve. And that's how the AI writing tool was born. I think we were probably in the first six months of this technology when it came out. We launched this and yeah there is no looking back since then. From zero to almost 5 million customers now.

Dhaval: Wow. 5 million customers in less than two years. Did you bootstrap this? Was this venture funded? Tell us a little bit about the financial side of the business, if you may.

Abhi: Yes, absolutely. So the funny story is , it was completely bootstrap zero external financing or capital reached. We had a acquisition as well, last year now part of a bigger umbrella company called copysmith. And yeah, it was always a small team and even. As of today, we are just four people. It's a very, very small lean team. And for the first six to, I think nine months, it was just two of us, me and my co-founder, and we were just doing pretty much everything. So yeah, it's been a lean journey completely bootstrapped and even as of today we are a very small team that is focused on product and high quality customer support.

Dhaval: Okay. We'll switch to gears a little bit on. Where did the AI kick in for your customer experience? Customer journey? How did you make that decision that in this point of customer journey will be infusing ai? What was that decision making process like?

Abhi: Yeah, so I think the whole product itself was like, The foundation was ai, right? When GPT 3 came out, like it, it allowed people to create all kind of content and copies by just giving some examples or you can see, training data so when I played around with the technology, I could see the potential. Wow. What if I can turn it into a delightful experience for end users who can create all kinds of copies. So we did a lot of our own training data in terms of the different kind of copies that people would like to generate we trained the, the underlying sort of models which were provided to us by GPT 3 OpenAI. And then yeah, so the whole product was basically built on that technology from day one. AI was always there. It is an AI writing assistant, it's natural that AI is there. So yeah, so it was always AI first product, AI first pocket you can say. And when we launched, this was just heating up, this space was like just coming to life, I think now. AI and GPT 3 chatGPT all over the news, but maybe a couple of years back it was just a very sort of em embroiling, technology. Not many people knew about it. So yeah, but we decided, well, something like this can really make a difference. So that's how I think we bounced on it.

Dhaval: Yeah. You mentioned something about you fine tuned the models that you got from open AI. For new and aspiring product creators who may or may not have deep expertise in ai, is that a preferred route? Is it easy to fine tune existing foundational large language models that you get from OpenAI? If there are any tools you could, you would share with?

Abhi: I mean it to be honest with you, yeah, I think it's, uh, they've made it very easy. So it's not even a non-technical person can feed in some examples and have the AI. Produce content, which is of high quality and aligned with what the user is expecting. so it's not a highly technical of course you can fine tune to the extent that you can provide like thousands of examples with your own custom domain or maybe industry. And then the model would be like very, very customized to your needs. But , we didn't go that far and I don't think majority of the use cases need to go that far unless you're working with enterprises, I guess. so in our case it was, and this was like a couple of years back, now it is matured even further. So you can actually go in with chatGPT or any other such similar technology and with the zero short learning they're able to give you the output that, it's pretty decent. So, yeah. So I think even non-technical founders they're looking to get into the space. I think with some fair like industry experience, they should be able to train the underlying model, which doesn't require any sort of technical expertise. But if they're working with, I think, bigger clients and companies and enterprises, I think that's where maybe they would have to fine tune it a bit more.

Dhaval: Wonderful. What was your biggest learning lesson in terms of finding the product market fair, especially with AI capabilities? When was that light bulb like, yeah this is happening. I know you are an AI first product, but uh, just in terms of okay, yeah, this is where we are starting to see the fit. What was that? How, what was the learning lesson there?

Abhi: I think I, a lot of it was like market being at the right place at the right time as it's the case most of the times having been in the AI space for the last five years building like so I've been working on this AI chatbot tool for individuals and influencers. But then the tech wasn't there at that point, to create any sort of. Custom implementation, you would have to train tons of data and even then the responses wouldn't be anywhere close to what the user would expect. So having been through those struggles, and then suddenly when open AI release, GPT 2 GPT 3 I could see the remarkable differences in the output quality. And that's when like I said, I realized, okay, well this could be packaged into a much better, bigger product for a lot of these copywriter and marketeers. And to be honest with you, that that was, that seemed like the first, logical use case of this technology. Now you can think about a lot of other things, but at that time, I think content creation creative kind of copy generation was probably the first thing which would come to your mind, and that's how we got started with this. I think that's when it hit us. Hmm. This could actually do a lot of good things for small businesses and startups.

Dhaval: Yeah. So for you it was more of a, just making sure that the, the idea and the insights that you had around being able. Use a large language model for the end users and being able to implement them. That idea and follow through itself was enough for you to achieve that initial market pull. There were not a lot of hiccups in terms of like your journey itself. Once you had a great idea, you had a good execution and you just found the fit, there wasn't a lot of like oh, we did this and then we had to pivot and we had to do this and that and that. None of that stuff. Am I getting that right?

Abhi: I think for this idea, yes, but I wouldn't say that it was like a smooth journey from day one of course we had to struggle going from zero to let's say first thousand customers, that's always the biggest challenge, right? I think that part, like first one or two months, like literally had to burst our assets to actually go and get first set of earlier adopters and users. That part was tricky and I think it had less to do with the product and more to do with the marketing side, the kind of customers personas that we are targeting. And yeah, I think we just, uh, pushed through that challenge and that's how I think those first two or three months were so crucial in getting the foundation right. Building that initial set of audience and then from there on we didn't look back so from product side and market side, we didn't pivot at any point. But yes, the distribution was always challenging, in early days.

Dhaval: Yeah. Tell us a little bit about how you went about acquiring your first thousand customers, Abhi.

Abhi: Yeah. That's always actually zeal for most of the startups. But, we mostly used organic channels and even today we spend actually zero on marketing, as such so you know, initially it was mostly LinkedIn, Facebook, Reddit you know, these communities and groups that we targeted made up of, like mostly copywriters. Digital marketers getting them on board as beta testers inviting them to give us feedback, giving some sort of coupons and discounts. And then we had a very strong focus on email marketing as well. Like we had set up our own funnels to convert these visitors leads into retained users and probably paying customers. And then the whole SEO was like a huge channel for us, which we had invested heavily in from early days. Again, I have a fair bit of experience on, on that side so we went all in on creating content, optimizing it for seo building quite a few I would say, authority in this space, AI writing space. So that led us to acquire a lot of organic traffic from searches. Yeah, so I think these two or three things worked really well. And of course we had partnered with a lot of these early stage deal platforms like AppSumo and all, which gave us a lot of feedback and traffic in the early days, which helped. But yeah, it's, it was a mix of quite a few things, so I wouldn't say that, oh, this is the only thing which worked for us and that

Dhaval: lot of hustle. what was the differentiation for your product? How did you compete with other such AI copywriting tools? at that time.

Abhi: That's a great question actually. So yeah, we had a fair bit of competition, when we launched it wasn't as noisy as it is today, but, there were still like few people who were fairly established in the space. But like I said, I think one of the reasons why we did what we did was we realized there is still a gap in terms of user experience. A lot of these tools were like very cumbersome hard to use output quality wasn't good. They were using probably different set of models or training data. And we spoke to ourselves and said, well, we can do a better job at it. We, I have a product background and I, we really take pride in building delightful products, which really, you know make the customer happy and they should have a smile when they're using the product. So it's that level of I would say finesse that we want to provide in the whole, into an experience. When we embarked on it, we wanted to keep it super simple, intuitive, easy to use, and high quality outputs that people can get at a very, very, I would say, fair price point. So that is to date our value proposition the easiest most intuitive air writing platform with the most value for money for the end user. Yeah. So I think that was our differentiator. And Today that has been working very well, I would say

Dhaval: Abhi you mentioned fair price point and high quality output, and you also mentioned training data. One question I have for you is how did you acquire your training data and what do you recommend other AI product creators do when they're thinking of training or fine tuning the models? What is. Some of the best strategies to acquire training.

Abhi: It depends, on the domain to be honest, that you're working in. So like in our case, if you're writing copies for like, emails, blogs social media, I think it's easy to get that sort of data, right. Again, from my experience, I Have tons of, like you can say swipe files which I could use as like high quality examples of what I would expect the AI to generate and then tweak it a little. And do lots of permutations and combinations in terms of the kind of prompts you wanted to generate for the ai so I think that experience helps some domains have that knowledge freely available. And the good thing is with the GPT, you don't need that many examples of training data. It's, it can work with few examples as well. So we use that, we leverage that knowledge and some of our experience. But I think if you're working in different domains, let's say maybe finance or healthcare I think that's where it gets a little tricky because you need like very specific, factually correct, data and which is very, very customized to a given client or an industry. I think that's where, again, if you don't have, let's say, roots in that space, it would be very hard to go out and acquire that training data or knowledge base

Dhaval: you got acquired by Copysmith. And that's a public information. One question I have for you Abhi, is how did that acquisition happen? Like did you choose to get acquired or were you revenue positive? And the Copysmith team wanted to expand their operations. tell us a story about that acquisition.

Abhi: Yeah, it's interesting and amazing folks we have with the with us now. But yeah, I think it was mostly to do with how the vision and the products aligned, to be honest. And, the team at Copy Smith is incredibly good. the the CEO Shegun again, one of the very sort of visionary founders who has a very clear vision of the industry, the way the market is headed and what he wants to do. So it was more like a complimentary sort of brand and product for Coppersmith to add to portfolio. they have their own focus. We have our own focus. So it's it's like collective of AI. Writing or content generation products that we are building as part of this umbrella entity. So it sounded like a very natural match, for us. And again when it comes to scaling it, we would really benefit if we had somebody like coppersmith and bigger sort of people helping us and providing us the resources that we need. So yeah, so that's how it worked out. And again, good people to have on your side. I would say. Any day.

Dhaval: That's awesome, man. Congratulations on that. Final question. For AI creators who are just starting on this journey. They're just getting started. What is the future look like in your point of view? What is that?

Abhi: Yeah that's a great question Dhaval and to be honest with you, I think about it a lot and it's hard to open your phone or Twitter or any other app and not see any openAI chatGPT or AI related news these days. It's everywhere. And and my view is like it's getting noisier and noisier. it's a lot of every other day there's like hundred different companies and products which are coming up so my fundamental belief is that, again, going back to the first principles, if you are somebody who wants to venture into this space, think about the fundamental needs that will never change for the end user. Whether it is the customer or if you're talking about businesses or enterprises, it's about those industries. So if you're looking at it from that perspective, I feel aI will be mostly a feature in those products, existing products and services. So it'll be an embedded kind of experience. Which most of the products will provide at some, in some shape or form. So how you can, build something which can compliment their existing products is what I would do. If I were looking at this space or if it is something which is like completely new, which doesn't exist today. And that can only be powered by this kind of technology. Then that is another lens to look at it, right? So something like let's say copywriting and content generation, which there was no other company which was doing this. So it made sense to create something like this but let's say if I'm a finance company or maybe a healthcare company, I want to have AI embedded in my existing, suite of services, then I think it makes sense to do something for those enterprises instead of establishing, let's say, standalone features or products.

Dhaval: Thank you. Thank you so much for sharing your knowledge being here. Looking forward to staying in touch with you. Hopefully invite you back to the podcast once you build your next startup or grow this startup and share your learning lessons. Thank you, Abhi.

Abhi: Thank you. Dhaval thank you.

Nick Walton is the CEO of Latitude, an AI-gaming company known for creating the first-of-its-kind AI-generated text adventure, AI Dungeon. A builder at heart, he created the first version of AI Dungeon in early 2019, a revolutionary experience that had 100,000 users in its first week after being launched. Along with continuing to develop AI Dungeon, Latitude is re-imagining what games could look like with AI and is working on a platform that will enable creators to make their own unique AI powered games. In today's episode, We discusses the early stages of his startup, focusing on the challenges they faced and their approach to overcoming them. They also explored optimization techniques for model deployment and AI provider evaluation. Nick emphasizes the importance of focusing on fundamental human values and needs when developing AI-driven products, rather than novelty and short-lived appeal. Tune in to hear Nick's insights and experiences in building Latitude and how you can apply these lessons to your own business. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Nick Walton: • LinkedIn: https://www.linkedin.com/in/waltonnick/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: [00:01:00] - Introduction to Nick and AI Dungeon [00:03:45] - Initial challenges faced by AI Dungeon [00:09:03] - Energy expenditure in the early days: dataset and fine-tuning [00:10:05] - The role of ML operations in AI Dungeon [00:11:44] - AI Dungeon: A game company that uses AI [00:12:05] - Adapting technology and unit economics [00:13:03] - Balancing user expectations and cost [00:14:02] - Optimizing operations and advice for aspiring AI entrepreneurs [00:16:23] - Tapping into instinctual user needs [00:17:43] - The future of AI in gaming Transcript:-

Dhaval: Welcome to the podcast, Nick. Tell us a little bit about your product.

Nick: Yeah, our main product is AI Dungeon. So it's an AI powered role play adventure game where people can jump into variety of different AI experiences and make choices that result in a fun and interesting story. So, traditional text adventures where there's a limited set of options you can really select and you go down a path that a developer pre-imagined with AI dungeon every time the story is unique written by an AI. And so you have freedom to create all kinds of different stories that the developers us would never have imagined possible.

Dhaval: That's amazing so. Is this like a video game? I've never played Dungeon before. So what is this for people who have never played Dungeon? Is this a video game? Is this like a regular board game? Tell me a little bit about that.

Nick: Yeah, it's like a video game like a classic text adventure game where the game shows some story of where you're at and you type actions and then there's a result. It's a little bit more towards the end of a creative sandbox than it is a really structured game with lots of mechanics. So it's more on the story roleplay side.

Dhaval: Got it.Wonderful. So how do you serve your customers? Is this a mobile app? Is this a web app?

Nick: Yeah, We are on mobile and on the web both.

Dhaval: Wow Okay. And is there a specific segment of your customers, of video game players or other players that you address with your game?

Nick: Yeah, I think for ours, we're especially targeting kind of role play gamers. So gamers who are interested in role-playing games like DnD and tabletop ones, as well as our traditional game RPGs, like Skyrim and things like that. Where you get to kind of decide who you are and what direction, and what actions you want to take in a more open environment

Dhaval: wornderful Tell me a little bit about your own journey. Are you a game developer or are you a techie who got interested in games? Or are you a business person who decided to try a new idea? Tell us little bit about your personal background.

Nick: Yeah, definitely the techie category. So, I come from a machine learning background. Before I ended up doing this, I spent a couple summers at self-driving car companies. I was going to go work at Aurora. And this side project that I'd been working on AI dungeon, just kind of took off on the internet and I realized there's something cool here that people are excited about. So, I pivoted from where I was going to be a founder. So I have learned lots about kind of game industry and the business side since starting Latitude. But going in, I just had the kind of tech machine learning AI background.

Dhaval: And Is Latitude already generating revenue? Is that a revenue positive organization?

Nick: Yeah, we just reached profitability pretty recently.

Dhaval: Wow. Congratulations. That must be a big deal. So if you could share any information on your revenue or your number of users, or any capital that you may have raised that will help us understand the context of your product.

Nick: Yeah. We've raised over 4 million. Since we've reached profitability, we haven't had to raise in the last little while. We've had millions of users download AI dungeon and we have a pretty active excited player base.

Dhaval: Wow. Millions of users profitability. And when was the last time you raised?

Nick: End of 2020.

Dhaval: Wow, more than two years ago. So you are a fairly successful case study here in terms of building an AI product. I would love to dig in a little bit here in terms of what was your journey like in terms of building the product? So you said you are a techie, you have background in machine learning. Did you find some video game players, video game creators? Did you have a passion in this space yourself? How did you go about creating a business model and a product canvas for your capabilities?

Nick: Yeah, it's kind of funny because I think I very much came at it from like hacker creator who got kind of surprised. And so I feel like it's taken me a while to catch up on some of these other things around understanding our product, what it is kind of game industry space. So the way it really got started was just at a hackathon. I was just playing around with the smallest version of GPT-2 had just come out. So it was a hundred million parameters, a thousand times smaller than the language models of today. But even then I could see how this kind of AI's ability to do dynamic storytelling was really fun and interesting. So I got super obsessed with it. Worked on it over the next like nine months. I would have my friends play and I'd just like watch them and see kind of what their experience was, what was challenging and what made it more interesting. And so put it on the internet, not expecting that it would really launch off and be a product. And, so I was kind of surprised of scramble after that to figure out how do we make a product out of this so that people can continue playing. Because when it first launched, it was on Google Colab. And there was so much traffic. I knew that they could not let it be there for very long. So, I wanted to get an actual app and product place where people could continue to play it and not rely on Google Colab's GPU generosity for more than a little bit.

Dhaval: Wow. Colab, for people who are not aware of is like a script writing tool. Is that right? It's like one of those web editors in which you can write code. Is that true?

Nick: Yeah, the thing that was cool about it is Google generously lets people use it and run on their GPUs for free. And they have paid plans as well. So it was a way that. People can run machine learning models and try them out themselves, and so that's how AI Dungeon was first distributed was just as code that would run in these Google Colab notebooks.

Dhaval: Wow. So what was the journey like for you when you finalized the product and you are ready to launch it? Did you partner up with other business co-founders or are you the sole founder of this product? Tell us a little bit about your partnership.

Nick: Yeah, once the game really started kicking off and I realized there was something interesting here. I pulled in my older brother who's worked at a bunch of tech startups to co-found a startup around this with me. So for the first couple months, it was just us with a couple people advising or helping out here and there. And then we raised our first round of funding and got kind of initial team together. And then we started going forward from there. So that's kind of what it looks like.

Dhaval: Got It. So when you started this, you built this on top of GPT-2, which was a thousand times smaller than what we have now. And when you say a hundred million parameters I'm sorry, 10 million, did you say 10 million?

Nick: Yeah. It was the smallest version of GPT-2. The very first version, the version that took off was a billion parameters. So it was about a hundred times smaller.

Dhaval: Got it.Now, did you create a lot of custom capabilities on top of the foundational model or was that built in and you just used it in a clever way?

Nick: Yeah, we did fine tune the model on second person kind of story adventures so that we would have an understanding of what that format looks like. Especially because there's not a huge amount of that kind of text format on the internet. And so I don't think the base models had a great understanding of what that was, especially earlier on when they weren't as smart as they are today.

Dhaval:Got i t

Nick: There were also a lot of things around how you manage what you feed into the model, what it comes out, how you alter the output so that it would feel more like a chat experience where the AI is telling you a story rather than stopping mid-sentence on the way

Dhaval: That was also a big effort in your engineering, in your output engineering for the prompts. Is that what you're saying?

Nick: Yeah. In the early days. Getting that piece right.

Dhaval: Yeah.Where did you spend most energy in the early days? Fine-tuning the model, getting the data, training the model with the fine-tuned expectations. What was that like? What was the energy expenditure in the early days like?

Nick: Yeah. So Pre-launch it was a lot on the dataset. Changing the data set, like kind of getting it into the right point and fine tuning it so it worked well. After launch, a lot of people nowadays use language model APIs, but back then there was not language model APIs. We had to spin up clusters of hundreds of GPUs serving GPT-2 and people weren't really doing that yet. And so we spent a lot of time just managing those, trying to keep them up, trying to request more and more GPU quota from Amazon AWS because they don't like it when you want a ton of GPUs really quick. And a lot of the early days was just managing and adapting to the huge amount of traction we had early on.

Dhaval: Interesting. So, would you say that falls in the category of machine learning operations, orchestrating all the different pieces together to run an app together that has a foundation of ML? Am I getting it right? Was it ML ops or something else?

Nick: Yeah, I think you could call it ML ops.

Dhaval: Okay. And then your strength is in building ML models or is that in making the business case out of the ML models? I believe it's a former, but I just wanted to unpack that a little bit.

Nick: Yeah. I think we've even kind of changed how we've thought about it over time. So we used to consider ourselves more of like an AI startup. And I think in a lot of ways we are, especially compared to some, but at the same time we don't really think of ourselves as an AI startup anymore because I think that's it's easy to think of like the hammer you have relative to the nail. Really what we care about is helping users have a rich, like meaningful role play experience where they can jump into the shoes of a character, be someone they're not in normal life, and go on you know exciting fun adventures. And we use AI to enable that much more effectively. But AI is not the goal. It's just one of the big tools we use. Like If there's lots of other work we do to enable that kind of fundamental human desire that aren't AI. And so i think now like we certainly have a lot of things we've learned about how to leverage AI models to do that, but I think we're especially focused on that user desire and need and fulfilling that and using AI where appropriate.

Dhaval: Yeah. Interesting distinction. You are not a AI company, you are a game company that uses AI. Interesting distinction.

Nick: Yeah.

Dhaval: What was the journey like for you in terms of experiencing all the growth? What changes did you make in the fundamental technology once you reached profitability or once you reached a certain level of confidence in your product?

Nick: Yeah. I think one of the hardest things was, has always been the unit economics. So, anyone who has worked with these large language models, especially especially GPT-3 size models, knows that they're very, very expensive and especially for a consumer use case and a game where people don't expect and want to kind of get limited and say like, you can't play anymore because you don't have enough. I think adapting to that and user expectations was very, very challenging and figuring out how to make the unit economics work so we could provide a unit consumer experience at a price that was within consumer ranges because you can't do 50 or 100 dollars a month for consumer subscription because that's out of the ballpark of what the majority of consumers can afford or imagine paying

Dhaval: And where did you acquire that? How did you come to a place that was a happy place for you in that area? A lot of testing, a lot of iteration or something else .

Nick: Yeah. A combo of playing around with kind of the structure and our monetization and pricing structure over time, as well as just working on the how we deploy the models. So for our free models, we deploy them ourselves on GPUs because that's how we can get the cheapest cost possible so that we can support free to play, which I don't know how many like yeah it's hard to do free to play with kind of the cost that go into these models. And then with, I think the other thing is just testing and evaluating different AI providers and just finding ones that match. You know needs and what we can do for our users. So I think those are kind of the combo of those things. We've just iterated on as well as there's some other optimizations in terms of like and how and when you call the models, right. Like if you do one call with multiple generations versus one call with only one generation, like you can cash generations and there's some things there you can do to get some gains as well.

Dhaval: So it has been a lot of optimization in the operation and the unit economics. What advice do you have for game creators or for product creators who want to use the new technology to build a product? Like, any advice for new and up and coming entrepreneurs using AI.

Nick: Yeah. So, one of the things and I mentioned this earlier, one of the things I see and we had ourselves for quite a while was I think a lot of products and things you see are somewhat like people love trying them out and they play them for a few days, or they like play around with it a little, but then there's like, there's not staying power. It's not really a product that someone will use long term as much as it is kind of like a fun toy they play around with and set aside. And I think the generative AI tech lends itself very well to like, oh, look what you can do. And it's kind of fun. But I think the challenge in the space for people who want to build big businesses will be what is the like fundamental human value that you're solving here, Right? like One of them mental models I like to use is like, what's the like caveman equivalent of what you're doing, right? Like for us it's role playing where for thousands of years people have played in a way where they step into the shoes of another character and kids do that kinda like, it's an instinctual human thing. I think if people can understand that really well, like a good example is chatbots. Right like I think that there's a lot of AI companies doing chatbots, but I think the problem is a lot of the ways they do chatbots is as a toy and as a novelty, not kind of a fundamental human need, right? And so I think companies who will be successful with those won't approach chatbots as kind of that fun to chat with this a thousand. You know, these a thousand different interesting chatbots, but rather it would be It will try and match the fundamental human experience of like building a relationship with another being and like the progression in the relationship, how you start more guarded or unfamiliar and you develop greater kind of like emotional connection over time and feel this closeness. And I think that will happen with only one or a couple characters rather than this wide swath where you try one, set it down, try another, set it down. So anyways, that's one thing I've thought a lot about in this space.

Dhaval: Yeah. So going back to the value proposition or any product creator who's interested in creating an AI product using some of the fundamental foundational models, it's not the fact that you are doing something cool or something novel that's going to make you successful. It's a fact that you saw a real pain point of your users. That's gonna make you successful.

Nick: Yep yep exactly,

Dhaval: Yeah. And I love how you have have this analogy of tapping into the instinctual user needs. That's even like more powerful than just a simple pain point, right? So if you can tap into those instinctual needs as a product creator, you increase the retention, the stickiness, the relationship with your user needs.

Nick: think the pain point model works well when you're building a tool. Right. Like I think pain points are like humans have been using tools for thousands of years, right? Like fire and weapons and all these things. And so I think it works well there I think it isn't always as applicable for consumer things that aren't a tool but are are more of an experience or fill some other need besides helping you accomplish something else you already want to do.

Dhaval: Yeah. Very interesting distinction there. Thank you. Thank you for sharing that. Wrapping it up, I'm wondering what do you think is the future of your product? Where do you see it going?

Nick: yeah so , two things that we are really excited about in the long-term, enabling with AI in the gaming space. One is creating and enabling people to create games that are dynamic and alive in a way that they haven't been before, right. So past games, everyone goes on the same quest and everyone is kind of the same hero you know you have three options you can choose, right. So there is kind of projection of choice of choice But your actual choice is fairly limited. And so we imagine games of the future where you have this like living dynamic nature where you might have individual quests that are just for you. You might have conversations with characters that you build deep, meaningful relationships, right. And so I think there's this whole class of games that we've imagined as humans, but we've never been able to make before. And we'd love to see those come to reality. The second thing that we're really excited about is the ability of AI to democratize game creation. So you think about what the digital phone did for creators who wanted to make video or picture content right?. They spawned entire platforms platforms like Instagram and and just this wealth creation that before could never happen because it required kind of a professional Company level. And I think we're going to see the same thing with games where games have traditionally been somewhat hard to make. And I think AI will lower the bar for that. And so we'll see a lot more. Everyday people be able to have a cool idea and be the kind of creative director with an AI team to bring it to life.

Dhaval: Wow. I love that. I love that analogy that you just made. Thank you so much for coming out to our show Nick. And I learned a lot just from our brief conversation. Looking forward to have you back on the show once you have a few more learning lessons to share with us. Any last thoughts to share with us?

Nick: I think there's a lot of kind of like fear and worry with some people in kind of the AI space. And I think technology, new technologies are always hard. The way I see the future is not that AI really replaces people's jobs, but rather, I think the best example is with chess Right. So like for a long time, humans were the best at chess, and then a computer finally beat the best human at chess. But now the best kind of competitors in chess are not human or AI alone. It's human AI teams that work together. That are more successful than human or AI by themselves. So that's kind of how I see the future of AI and technology is not that AI is like replacing us as humans but rather that it's augmenting us so that we as a team can do much more than either can alone, because I think human will always have huge advantages in how we can think and our flexibility and our ability with ambiguity that machines won't. And I think machines can continue to kind of automate the less ambiguous You know sides of creation, enabling us to do more interesting things.

Dhaval: Thank you. Thank you so much, Nick.

Amit Gupta is the Founder at Sudowrite. He is the former Founder at Photojojo, He sold Photojojo at 2014. After selling his first company Photojojo in 2014, he started traveling, writing sci-fi, and more recently, building Sudowrite. In today's episode, We discussed the benefits of using AI to assist writers, including increasing their productivity and providing creative inspiration. He shares insights on building AI products using pre-trained models like GPT-3, as well as the challenges and costs involved in training custom models. Amit also reveals the future vision for Sudowrite, aiming to help writers create more content and monetize their intellectual property, effectively turning them into franchises. Amit shared about building on top of pre-trained models vs training custom models, the importance of personalization. Tune in for valuable insights into the exciting world of AI-powered writing tools. Find the full transcript at: https://www.aiproductcreators.com/ Where to find Amit Gupta: • LinkedIn: https://www.linkedin.com/in/amitgupta/ Where to find Dhaval: • LinkedIn: https://www.linkedin.com/in/dhavalbhatt In this episode, we cover: 00:00:00 - Introduction to Amit Gupta and Sudowrite 00:02:37 - Sudowrite's capabilities and how it helps writers 00:04:12 - AI writing assistance: Assisting vs. Replacing writers 00:08:41 - Sudowrite's goal of supporting writers in their entire journey 00:09:17 - Building on top of pre-trained models vs. training custom models 00:10:58 - Fine-tuning pre-trained models for new AI products 00:11:42 - The future vision of Sudowrite: Turning individual writers into franchises 00:13:08 - Sudowrite's seed round funding and bootstrapping experience 00:14:11 - Inspiring new AI product creators to start their own projects Transcript:-

Dhaval: This founder built an AI product to help writers beat creative block. He's a former writer himself, and in this episode we talk about how to approach building AI products in 2023 and beyond. Specifically, Amit shares his journey of how he launched in August of 2022 and generated a profitable company within a short amount of time with long enough runway. To allow his team and himself to chase the product vision without having to raise multiple rounds of capital. Amit Gupta and his team are building. AI writing tool called Sudowrite. He's a former founder of Photo jojo, a product that he grew to 10 million a year and sold in 2014. After taking a break for a few years, he launched Sudowrite in August of 2020. An app to help create a writers use AI to beat writer's blog. Talk to us, Amit. Tell us about your product, your market segment, your ideal users. I would love to hear about it.

Amit: Sure. Our product is called Sudowrite and we're building an AI writing tool for creative writers, people who are writing novels, screenplays, any kind of creative story-based narrative content. And today our audience is those individual creators, and I think in the future, We really want to create a story engine, something that helps anyone who wants to tell a story. So that could be any of the people in the world who always thought they had a novel inside of them. It's too much work. It's too hard to get that novel out. We want to make it easier.

Dhaval: Very cool. Tell us a little bit about how you got onto this idea. I know you are a very prolific writer yourself. How did you come up to wanting to solve this problem? And tell us a little bit about your background that you were willing to share with us.

Amit: Sure. Yeah. I've started a couple companies in the past. The last company I started, I sold in 2014. Then I decided to take some time away from Tech. I left San Francisco, I traveled and I really decided I wasn't going to do the same thing again. I was going to try something different. So I ended up writing and I spent a few years writing fiction, which is where I met my co-founder in a writing group. And as we were writing fiction, we were bemoaning how much harder it was in running startups because, Man, you get no feedback. It's in a startup, you launch something, right away if it's working, your users will tell you, you'll see it in the numbers. And with writing, it can take weeks or even months to get a few pieces of subjective feedback on a longer form piece. So it's a very different world, much different kind of Learning curve. And we wanted to find a way to bring some of the rapid iterations and the ideation, some of the collaborative elements of working in tech or on a startup to the writing process.

Dhaval: Very interesting. What was the process like for you to build your MVP? Did you start with wanting to build an AI first creative workflow, or was it writer first and then add AI to it? Tell us a little bit about how you did your MVP.

Amit: Yeah, we were definitely AI first. My co-founder James started playing with GPT-3 when he came out a couple years back. A few years back. And originally we weren't planning to start a company. We were just making a tool for ourselves and for our writing group. And then we shared it with some other writers that we knew and respected. And we just got incredible feedback. So we kept tinkering with it. If I think about a year before we decide to really turn it into a company, It was for that year just a hobby project. A lot of writers that are we knew were using it. And we didn't focus on a lot of the features that most writing tools had. We focused on the things that were unique Sudowrite, the places where we could bring AI to the process and do something new.

Dhaval: Very interesting. Did you build on top of chat GPT-3 or was that a private beta at that time? How did you get access to that? Tell us all the juicy details of how did you integrate AI in your workflow? And what was the product market fit experience like that like for you?

Amit: Sure. So this was back in 2020. GPT-3 had just come out. It wasn't widely available. I think we DM Greg Brockman to see if we could get access and we just wanted it to play with as writers for our writers' group. So he gave everyone in the writers' group access, and started playing around with it. Eventually we started building on top of it.

Dhaval: So where are you in terms of your product? Do Have you launched your MVP do you have revenue? Do you have certain number of users? Yeah. Tell us a little bit about that.

Amit: Yeah, product-wise, we started building it in. I think like August or July of 2020. And we started letting people in shortly after that. We started charging people, I think last year or late the l year before, not that long ago. So it's been on the market for a bit now. And I think it's grown steadily throughout the history. As soon as we started charging for it, people started to sign up. We don't have a freemium options it's all paid. And we've really kind of seen it hit product market fit in the last couple months, or at least what we think is product market fit. It's in the past couple months where I think we've seen a lot of people signing up, a lot of people kind of entering the community, asking for things, suggesting things that are rate where like it no longer feels like we're pushing it up a hill. It feels like we're chasing the ball down the hill trying to catch up. So it's a, that's a cool feeling. It's a very quick reversal.

Dhaval: Yeah. And do you have any numbers that you can share with us? The number of users you have, your revenue and daily active users or any of that data points? Cause we do have investors also listening to this. So, perhaps they can connect with you if you're willing to share that. Totally up to you. I understand that.

Amit: Sure. Yeah. I don't wanna share too much, but we did share recently that we hit 500K in ARR in December, and I think we're above 750k right now.

Dhaval: Wow. Wonderful. Do you have any data points around the number of users you have or market shares or rather market segments that you are going after? Any of that information?

Amit: Yeah. In terms of market share, I think we obviously have hit like a very, very, very tiny fraction of the market so far. These are really the early adopters. The folks that are willing to experiment with new technology. I think there's a lot of grounds still to be covered. A lot of things, frankly, we have to improve about the product before it's really going to be something that someone who is used to a very polished writing experience is going to want to use. But today it's useful to a lot of people. And I think the market is You know all these people who consider themselves writers, but like I said, in the future, I think that almost anyone who reads has a story in them. So there's a lot of additional people we think we can bring into the market.

Dhaval: Yeah, writing is such a fundamental thing, right? Once you figure out writing, you can create many other things on top of it. So I have like two questions that come to my mind. I'll ask the first question first, which are your writers using your product for instance, notion, like it's designed for short form documents, type of work, Google Docs notion word or are they using your product for writing? Like find five hundred page, a thousand. Novels or short documents? What type of short stories or long stories do you have a specific workflow in mind for writers? And I'll let you answer that. I have another question about product management. I'll get into that in a second question.

Amit: Yeah. So I think the place where we differentiate is that we're really heavily focused on long form. And that means that our writers are people who are writing novels or screenplay stuff that can go up to like a hundred thousand words or even longer. That's something that's very challenging to do with today's AI models. It's something that requires a lot of kind of like work behind the scenes to condense things, summarize things to, to make it work. But I think where a lot of the value lies because it's very easy to generate short snippets of copy or taglines. It's much harder to create something Longer and more meaningful and substantive. But that's where we're working towards.

Dhaval: Interesting. And this lends my second question, which is in the product experience, in the customer journey, in the user journey, how did you come to know , this is for AI product creators who are trying to infuse AI in their product. So this is for them, for your product. Amit when you were building it, when you were trying to find that product market fit. At what point was the moment when you decided, okay, at this point in the user journey, we'll be infusing AI . Tell us a little bit about how did you decide at what point in customer journey was the AI part of.

Amit: Well, Sudowrite is a AI first product, so there's really no reason for it to exist without AI today. And I think so even in the onboarding, you'll start to use AI. You'll start to experience what it's like to write with AI. It's from the very first moment after you sign up.

Dhaval: Got it. So as if a writer wants to write a book and right away you'll be like prompting them to get information data points that allows the product to support them in their journey.

Amit: We'll show them right away, right after they sign up, what AI can do for them in their process. And I think the eventual goal is that we can help with the whole process from ideation to first draft, to revision, to publication, and just be able to be there with every step in the process. And I think some writers are really good at various pieces and they hate other pieces. So we want something that can help take the drudgery out of writings, wherever that drudgery comes for each writer. because it's different for everyone

Dhaval: That's interesting. Yeah. I've talked to many of the AI founders in the last few months, and this is the point where I hear different opinions every time, which is, if you're a new AI product, if you're interested in creating a AI product, right? You're using one of the pre-existing tools, right? Where do you find that personalization? Do you build your own personal models on top of? Pre-trained models like chat GPT-3 or do you go a hundred percent custom? How do you find that differentiation, etc. What are your thoughts on building on top of pre-trained models versus training your own models and building a groundbreaking product from ground up? Where do you like and what are your thoughts there?

Amit: Yeah, it really depends on the company what stage they're at, what second on the market and what kind of AI is useful for them. I think with, for instance, stability, there's a lot you can do as a relatively small company trading that model to do interesting things for you because of the way they've sharded it down in size, and I think with large language models, that's a lot more challenging to run a large language model like GPT-3 requires a lot of infrastructure investments just to have the machines capable of running the thing. And so it's quite expensive to just run inference before you even get to the concept of like training it, which could cost millions. So I think there's definitely a lot to be gained from training custom models, but at this point in the evolution of them they're Iterating and developing some rapidly that a lot more of the gains come from things like fine tuning or developing models that sit on the layer above or below GPT-3 or otherwise finding ways to really get the best quality out of GPT-3.

Dhaval: So fine tuning on top of pre-trained model is the way to go for a new AI product creator. However, If you're working for one of those organizations that has the data available and has the resources available, building your own model may be an option.

Amit: Potentially, you'd have to see how much additional value you really think that custom model will bring, and if it's worth the expense. I think even if you are able to build it and have the data, it may be the case that still doesn't make financial sense to build an LLM yourself today. Because as soon as GPT-4 comes out, it's going to leapfrog whatever you've run and then you're going to have to train a whole new model to just to keep up with the new kind of state of the art.

Dhaval: Very interesting. Okay, tell us a little bit about your future vision of the product. Where do you see this going? Where do you see Sudowrite going ?

Amit: So today I think it's very useful for Sudowrite very useful for people who know how to write and who want to write faster, who want to get unblocked, who want to keep in the flow. And those writers come to us and they tell us that PseudoWrites able to help them double their word count so they can publish twice as much stuff. And they're very excited about that. We're in a world where as soon as you finish watching a TV show on Netflix, you want the next season. And the same is true of books. So as soon as a reader finishes reading a book, as soon as a writer has published a book, the next day they're hearing from readers that they want the next one. And of course, you can't deliver that, but I think in the future we want to create a system that helps the writer deliver that helps them. Serve their readers, produce more work, produce more artifacts aside from the novel that take place in the world and in the story that they've created, and basically find new and interesting ways to monetize their IP. In the same way that bigger pocketed creatives today, like the Marvel Universe, or Star Wars or whatever, are able to like create a lot of storylines in the same world, a lot of different product offerings. We want to bring some of that power to the individual.

Dhaval: Wow. Turning individual writers into franchise. Wow. That's a powerful vision. Amit, have you raised capital? Is this a bootstrap startup? Have you raised a seed round and if not, are you planning on doing it soon?

Amit: Yeah, we raised a seed round late 2021 $3 million. We raised it for primarily from individuals, so people who are in the creative fields, in other related fields, and we have no immediate plans to raise more money. We have quite a bit of runway.

Dhaval: Would you say that a AI product creator who is interested in getting their feet wet and want to build something bootstrapping to validate the idea and validate the hypothesis may be the best way to go forward? Or is there too much a bar? High bar for them to do that. Trying to bootstrap Were you at all at any point bootstrapping this and tell us a little bit about that experience?

Amit: Yeah, it was Sudowrite bootstrap for the first year. I think there's absolutely no limitation on anyone who wants to get started with generative AI today. Building a startup from the ground up with just one person and your spare time, I think the tools are incredibly powerful and there's a wide open field for so many different solutions that can still be made.

Dhaval: Thank you for sharing that. That's very inspiring to hear because you're a hundred percent right. There are so many remain to be optimized. And it's up to the creative potential of the individual who's trying to create that product to decide where they want to go after what they wanna go after. Yeah, that concludes all my questions. Thank you for being so on the point, man. Thank you so much.

Amit: Yeah, sure. Glad to do it. Happy to meet you.