This Week in Startups - Copy.ai CEO Paul Yacoubian on keeping up with GPT’s rapid evolution & AI’s potential impact | E1719

Episode Date: April 12, 2023

Copy.ai Founder Paul Yacoubian joins Jason to break down the evolution and progress that OpenAI has made with its GPT LLMs (2:38) before discussing the radical shifts that AI will cause across most industries (20:46). They end the show with a demo of Copy.ai and the legal issues surrounding training datasets (48:54). (0:00) Jason kicks off the show (2:38) The evolution of GPT-2 to GPT-3 (8:16) Mercury - Apply in minutes and get up to $5M in FDIC insurance at https://mercury.com (9:45) How GPT summarizes work  (13:39) Working with GPT-4 (19:14) Microsoft for Startups Founders Hub - Apply in 5 minutes for six figures in discounts at http://aka.ms/thisweekinstartups (20:46) Radical shifts in AI (29:59) Accepting AI   (36:59) Mayfair - Get 4.35% APY on your cash and increase your FDIC insurance coverage at https://getmayfair.com/twist (38:29) Working systems with ChatGPT (45:20) Who wins between Google and OpenAI  (48:54) Copy.ai demo (52:38) Licensing of the underlying data  (57:10) The demand for hardware when developing AI  (59:53) More on Copy.ai  FOLLOW Paul: https://twitter.com/paulyacoubian FOLLOW Jason: https://linktr.ee/calacanis Subscribe to our YouTube to watch all full episodes: https://www.youtube.com/channel/UCkkhmBWfS7pILYIk0izkc3A?sub_confirmation=1 FOUNDERS! Subscribe to the Founder University podcast: https://podcasts.apple.com/au/podcast/founder-university/id1648407190

Hey everybody, hey everybody. We all know the AI space is moving at a blistering pace right now. So we decided to do a little ad hoc miniseries on it. We're calling it innovators in AI for a creative. Every week, I'm going to interview a founder in the AI space here on this week in startups. And every founder that joins is going to be building something awesome. That is the criteria. We don't want just pontification. We want to actually see real products. And today we have the CEO of copy AI. We have a great conversation about the insane pace of AI, as I mentioned, and building products for a specific niche. In this case, writing, copy AI, right? They do writing on top of various LLMs. And he explains how he's dealing with the fact that, hey, he started, I think, with GPT two or three. Then they went 3.5.4.5 is coming out.

Bard is coming out from Google. So many different LLMs out there. How did you actually build a product that people are willing to? to pay for when these core platforms are moving so fast that they're going to absorb the innovations of people building on top of them almost in real time. This is a major issue for founders who are building in GPT or in AI with Open AI's products. So it's going to be a great and insightful episode. Please stick with us. This week in startups is brought to you by Mercury, where innovation meets peace of mind. Now more than ever, startups need a safe

place to put their cash. Mercury offers a simple way to manage bank risk and protect every dollar with up to $5 million in FDIC insurance and a money market fund. Visit mercury.com to apply in minutes. The Microsoft for Startups Founders Hub helps all founders build a better startup at a lower cost from day one. Startups get up to $150,000 in Azure credits, access to open AI APIs, free dev tools like GitHub, technical advisory, access to mentors and experts, and so much more. There is no funding requirement, and it only takes minutes to join. Sign up today at aka.m.s slash this week in startups. And Mayfair helps venture-backed companies protect and grow their cash automatically.

Diversify where you hold your cash, increase your FDIC insurance coverage, and earn up to 4.35% yield on deposits. Go to getmayfair.com slash twist to get started today. All right, everybody next up on the program is a company that's been doing generative AI, specifically in copy and words, for a couple of years. I met Paul a couple of years ago when he was raising money for copy.com. My bestie David Sacks from Kraft Ventures is an investor in the company. I remember meeting you, Paul, and Paul's last name is Yucubian.

Yucubian, right? That's right. That's right. You, I remember, were working on a couple of experiments when we met. One of them was like doing taglines for using. That's right. I don't know if you were using chat GPT at that point, but when did you first become aware of chat GPT and or generative AI for a copy? Good question.

So I started using GPT2. in the end of 2019. And we were using it to create startup ideas. So it was insanely creative, but it was really bad at telling the truth because it wasn't trained on as much data as GPT3 was. And it was immediately clear that the creative power was where the value driver would come from

with the generative AI models. And I thought it'd be seven years away from being commercially viable because it would just make stuff up and you had to really help constrain the outside. outputs of it to drive value from it. So I was creating science fictional products and just, like, had this explosion of ideas. What was the interface like for that chat GPT2 at the time?

Was it all like a dev tool? Because I remember it was a lot of startups were trying to get access to it. Yeah, GPTT2. It wasn't really available. It was actually open source. So somebody had to set up the model, host it, and then build a UI for it. And the simplest UI was really just, text box where you would type something in and then just click a button and

auto complete it for you. And so I was using that, but I wanted a lot of ideas. And so I kept hitting the button and then deleting what it printed out and then hit it again. And I was like, a search interface would make way more sense, like a UX interface for brainstorming. And so I thought it would be about seven years before the model would actually be good enough because only about 10% of the results even made sense at that time with GPT2.

and then fast forward until July of 2020, just only like six months later, and they launched GPT3, just a hundred times the size of GPT2, and that all the numbers flipped. So all of a sudden, most of the time it was making sense, what was coming out.

Ah. So when you were doing 2.0, this is really interesting to look at the history of it, and I'm sure people are going to study this rapid pace that this has gone on, there was no web-based service. There was no cloud-based service. You had to download it. And then did you have to provide a model,

or was there already some training in it based on some model? Yeah, they had pre-trained it, and they had open-source the model. So somebody else ended up hosting it and providing that interface. Got it. And so that's what I was using it. Were they up front about what was in the model, or did they just say, hey, we trained it on some stuff?

Or were they explicit? Hey, we downloaded the Wikipedia. Hey, we did Cora. hey, we did Reddit, where they at that time, because that's something that I've been trying to get my head around and hopefully you can educate us in the audience as to what exactly, how do you know what's been, it's been trained on?

Yeah, they, I think in the paper listed out a number of the sources. One is a common crawl dataset. It's just a huge, huge file that's open source. You can download it and you can use that to train the model. Wikipedia was a big component of it. Common crawl as in a common crawl of open web. So just a bunch of web pages that, are in an open source search engine of sorts.

That's right. They got Reddit, and I don't think they got Quora at that time. I think... Yeah, probably had a paywall or something, or a registration wall, so they couldn't get in there. Right.

I think since then, they have probably gotten access somehow to Quora's data set. So you see 3.0, and it goes from one in ten times to getting it right, to one in ten times getting it wrong. Yeah. I said another way,

nine and ten times. Dude. At least making sense. Like, it wasn't completely gobbly good, gibberish. Exactly. Yeah. So the first thing that came to my mind, I was like, this is the next big wave in tech.

Because you have all these new use cases that are unlocked. And I thought we would see, you know, immediately I tweeted this out. I thought it was a phase change of the internet. So this is the first time the internet can be trained and synthesized into a model and then talk back to you directly. Right. So that's a profound change in the amount of information that you have access to at any, any moment in time.

And given that you're kind of able to talk to the internet, I thought that conversational interfaces would be the most relevant way to access that information that's inside the models. So we built a SlackBot, and we asked

Open AI if we could launch it. And their safety team said, no, you can't launch that. It's too dangerous. And so we had to go back to the trying board. And we built the an app that could just summarize whatever you threw at it.

So we launched that. We got a mention in the Wall Street Journal for that. And then we realized, you know, people, that's like a sometimes use case. I mean, that is a component of a skill that the A.M models have, but that's not something that people will subscribe for

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When it's summarizing something, explain to a layperson. what it's actually doing. So you have a paragraph, let's say you have, I don't know, a movie review from the New York Times. It's a thousand words about some, you know, really detailed movie like TAR, right? Academy Award Oscar nominated Best Picture. But what does it do?

What is the, what is the, what is the model actually doing when it is summarizing? So there are different techniques for summarization for these models. based on neural networks, the neural net is learning skills. It has learned skills that you'd have otherwise needed specialty natural language processing algorithms to do. And that's really what sets it apart. So when you had 176 billion parameters in the model

and they're structured into layers, these layers get really good at doing certain things. And for the summarization tasks, it can learn, you know, what summarization means. Like, what does the word mean to summarize something? It can pick that up from the corpus that it's trained on, so all the text that it's trained on. And then that is a task.

It can do pretty well. Which to me, I was like, okay, now this one model we can use to do all of these natural language processing tasks without needing a bunch of NLP engineers on the team. It's literally one API call, and I can just prompt it in natural language and tell it what I want, and I don't want to give that back to me. So the first, that was a major unlock because it suddenly made the process of building an MVP, a software MVP that uses natural language technology, made that really easy.

So you could build a prototype and launch it. And so when we built our next prototype, it was called Taglines.AI. It was just a tagline generator. That took 48 hours from beginning to end to build the entire. entire thing. And we launched it 48 hours after saying, okay, maybe people would buy, you know, maybe marketers would buy words if we can generate them. And so within the first week, we had validated that not only would you have professional copywriters using these tools, but you'd have marketers and freelancers and small business owners, students. So, like,

that was a huge validation of the market size and the TAM potential because it was such a wide user base. And then we also validated that people would use a tagline generator to do like email subject lines, headers for websites, just all kinds of like PowerPoint, slide, you know, titles, and really validated that the number of use cases were really wide for that as well. And then threw up like a striped paywall and then validated people would sign up for a subscription. So we started charging $3 a month. We got some subscribers, raise it to six and then raise it to 10. And we continue to get subscribers coming in. So at that point, we validated one that it was

commercially viable, that you had a wide market and a wide set of use cases. Right. And that makes sense. There are copy editors in the world. You discovered this is working nine out of ten times. And if you can make them more efficient for $10 a month, if they, the average copyrighter, I think freelance probably gets paid 50 bucks an hour, 40 bucks an hour, 60 bucks an hour, some of that range. I mean, it's not even one hour of their time. So that's always one of those great tools for founders is to say, what does this person get paid an hour?

Okay, this attorney gets paid $1,000 an hour. Save them one hour a month. They'll pay you $1,000. Or they'll be okay with it, right? Exactly. So when we went through that exercise, it was really clear that from all the way from a student to a professional would find value in these AI models. And then the thing that we learned from going from two to three was that if you throw more data at it,

it's going to get even better and better at performing these tasks at an expert level or beyond. So now when we get to GPT4 to get back to these jumps, there was three, I think there was 3.5 and 4 in terms of major releases. You're working directly with the OpenAI team. You have some insider access to it, I understand. So you were kind of in touch with them and seeing what was coming, what was coming between 3 and 3.5. and 3.5 and 4. Yeah, so for 3.5, they ended up tuning it to human feedback. And so what that would do is it would allow you to kind of describe the intent that you had,

and you wouldn't have to prompt it with examples as much. So when we first built our tools, each tool, you had to give it examples of what good looks like, and you had to make sure that even the examples are very diverse. When they ended up fine-tuning it on human feedback, they kind of programmed that in across, a wide variety of use cases. And so that enabled the chat GPT experience where you can just tell it what you want and it will figure out

and actually create that content that you want. And so when you saw three, 3.5, 4, maybe you could describe for the audience, the step functions there. You said before, hey, you know, it's right one out of 10 times and it's right 9 out of 10 times. Now let's go to 3.5 and 4. 3.5, yeah, it was awesome.

It was like a big leap. And a lot of the complexity and prompting it kind of went away, a lot of even the use cases for more fine-tuning of models. So you'll see a lot of startups talk about how valuable their fine-tuned models are. Like, oh, we have proprietary models. Well, at the end of the day, there aren't that many models that are really relevant for you to have fine-tuned. Explain what a model is, again, to a layperson. And what's an incredible example of that, where you're, restricting it and building a model that maybe results in better output.

Yeah, so these are foundational models. So these are built to be general purpose. That means they can do a lot of things at a pretty solid rate. What they figured out was that the general purpose model could actually outperform specialized models if they kind of made them bigger and trained it on more data. and that is nuts. That's nuts. So I don't know if you've seen some of the GPT4 metrics,

but they said, oh, well, it passed the bar exam. It passed, like, all these exams, right?

It's getting better across every single category of measurement. Pause on that for a second. Yeah. You used to build in the industry, what was called narrow AI. Yes. Narrow learning. So,

hey, we're going to learn how to beat, you know, humans at poker chess, go. you know, pick a video game, Fortnite, and we'll really just work in that narrow subject area. Now, this more general model,

and a little confusing to use the word general because it's loaded because there's general AI as a concept, which is thinking like a human, but their average or their default model, maybe is a better word for it. Their default model is better at doing the L-Stat or the SATs or the bar exam than a model that would, was trained just to do the bar exam.

That's what we're, they're learning with four. That's right. And you seem to say, hey, that's insane. Why is that insane? Why is that insane?

It really does level the playing field if you have access to those, the GPT4 model. So you don't need a whole team of AI engineers. You don't need a whole team of machine learning engineers. You can actually build, you can just go straight into building the application. And it's a,

yeah, keep going. I was going to say something that I think larger companies are running into issues around is they've kind of delegated the, like, hey, AI team, go figure out this generative AI stuff. But the AI team, you know, the AI team comes back and says, you know what, we can't beat GPT 3 and a half. We can't beat GPT4. They'll get fired, right? Right. That's fascinating.

So if I was working inside of, I don't know, Amazon. I would just say Walmart and I said, hey, our internal team is a great example. Amazon's a great example. Yeah, I'm in Amazon. I want to optimize Amazon Prime members to show them better reviews and show them better Q&A.

So I take the Q&A section on an Amazon page and the review section and we'll summarize it and make it better for users. If GPT4 can do that better than the internal team, why is there even an internal team? Yeah, that's pretty, wild. Depends on the use case.

If you looked at the iPhone voicemail system, you know, try to transcribe a voicemail message, they had, I don't know how many thousand AI engineers at Apple.

And then OpenAI had a team of, I think, two or three people that figured out how to complete the voice-to-text task. And they ended up launching a model called Whisper,

and they open-sourced it. They literally gave away the technology. And it dramatically outperformed all of the existing technologies, all of it. All right, everybody. Our friends from Microsoft are here. Tom Davis, a senior director at Microsoft for startups.

And you're a former founder. You are here today to talk to us about the giant leaps that Microsoft has made in the AI space. What does this mean for startups? I see a ton of different tools. I've been playing with chat TPT for. I have a paid account. But I'm also seeing things happen with GitHub.

Absolutely. So the work that we've been doing with Open AI over the last few years has really set ourselves up with a foundation around. We've built this sort of AI supercomputer from the ground up. And we've been looked at everything from GPU configurations to networking and things. And really what we're now able to do is sort of allow startups to access all of this innovation through our founders hub. So we've been building this for to do something at scale. So startups can now build their own AI applications and build out and train LLMs as well. And this has really helped us to become a far better cloud for AI broadly. And being able to drive that down to the startup ecosystem is fantastic.

It's open to everybody. There's no funding requirement. You don't have to be anointed by a VC. Five minutes to sign up. You get six figures of benefits. Azure credits, GitHub, open APIs, which everybody's really having fun playing with. And so much more. So go ahead and sign up right now, aka.ms, slash this week in startups, aka.m.m.

slash this week in startups. Thanks so much, Tom. Thank you. So as we're looking at this, yeah, this is almost like the snake is eating its own tail kind of situation. We're all going, you know, copyrighters, you name of your company's copy AI, you got $12 million in reoccurring revenue. And your premise, which turned out to be absolutely correct, is, hey, this. This thing is advancing fast. Copywriters are going to use this tool to be willing to pay for it.

It's going to make them bionic, make them superhuman. Won't replace them, but it might replace the bottom third that are terrible at their jobs or make the bottom third great at their jobs or good at their jobs. Was that your central tenant about this? Because the replacing of a job or elimination of the job, in other words, I'm the developer or I'm the CEO. I'll just ask ChatGPT for copy and I don't need a copy editor. What is your belief now?

Let's say copy editing, your wheelhouse, is copy editing going to go away and the CEO or the sales team just does the cop as Chappi T to do it or will there be bionic copywriters who use this as a starting point? I think before we launched copy A, I had no idea what copyrighters even did.

So it was never about copyrighting. That was basically the first use case that I thought would be relatively translatable. against the models. But the thing that really motivates me and my co-founder is the idea that people have a high degree of creativity, especially kids. Yeah, they're super creative.

I've got two daughters that are extremely creative. And then we pre-trained them through school, right? Just like these models are now pre-trained on the internet. And all that pre-training kind of stamps out the creativity. and by the time they graduate, they're very tracked in the things that they want to do. So it's like, oh, I'm going to be an attorney or I'm going to be an accountant or I'm going to be a software engineer.

And I think that that whole process is going to come to an end here because GPT4 can do all of the liberal arts better than any expert could. Right. And now that's a tool that's in your pocket, which means we don't need to train people. on a lot of these very technical tasks that we used to because we've now trained computers to do that repeatedly. So there will be a very, you know, a challenging transition for a lot of industries, a lot of institutions like higher education, education in general. And we're going to have to

figure out, okay, what's the best use of a person's time? Well, that usually comes down to the individual. And what we, what I'm seeing, you know, the whole, my hope here is that people can be way more creative because each time they are creative, they have an outlet to actually build something. They can actually get things done. So a non-coprywriter, a non-writer, somebody's just not good at words, can now go in there and be good at words. So you take the 80% of people who are not good at that, now they can be good at it. I may not be good at drawing or illustrating, but I can pop up stable diffusion or any number of tools and I can be a good illustrator. So now you've got the 99% of people who have no illustration skill, can't draw or not,

and they can be creative. So it's not that everybody's job goes away. It's that everybody gets good at everybody's job. Yes. That's an interesting way to look at it. Right. And that's incredibly powerful, especially when you're looking at entrepreneurship. ship. So if you've ever tried to create your own website, for example, that takes a lot of time.

And it can be very stressful for somebody that's trying to get something off the ground. That is now, you know, going to be pretty much instantaneous. Like, oh, I need a website. Boom. I've got one. You know, I need a presentation. Boom. You know, I can just generate a whole, like, my whole deck. And so in that from there. So you're starting on third base. Yeah. And so. Which happens now. You can go out to. Squarespace, pick a template. And now you'd be able to, a future version of Squarespace will be, hey, make me a website and show me five different designs. I need one that's funkier.

I need one that's a little more avant-garde. Now, the challenge is, what kind of business are you going to create that's going to actually be able to compete against what the AI models can do? Yeah. Well, and to your point, the AI development. who are making this are literally working themselves out of a job. The better they do, the less they're needed. Because the general model can do better than the specific model. So at least in that case, the narrow specific models are being subsumed into the general

models. Yes. Yeah, you're seeing that. And then you're also seeing it in the software itself. So I'll give you a run through here. So software is, why is software valuable, Jason? because it makes people more efficient, which means it reduces the cost of any good or service.

Right. And why software is valuable is because it's reusable. So your investment to build it is a reusability. So we're now heading into a world where software can be generated on the fly. Right. And it's almost the cost of creating new software is going to zero. Right? That means that the returns on investing in that software should also go to zero, which causes some problems in the venture capital community.

I'm an angel investor. Yeah. You're an angel investor. So we really, this whole thing is going to get transformed from a technology standpoint. Fantastic. I mean, if you can build a company with two people instead of 10 or 10 people instead of 30, well, that means you need less investment. And the earliest investors are the only ones

who get to seat at the table. I'm fine with that. I'm fine with people who do Series B and C is getting blocked from having an opportunity to invest because the company's too profitable. Is that sort of your premise? It takes less money to run this just like cloud computing drop the cost of it?

Yeah, so one, it should expand the number of people that can build things, which should be good overall for entrepreneurship. Two, like you said, on the gross stages, is that's going to increasingly make, I think, a little bit less sense. And you saw YC shut down the continuity fund, which is their growth stage fund this week. And so when we look at the AI landscape, a lot of the repeatable tasks are in the sales and marketing space. So like personalization now can be done on a one-to-one basis at scale.

even things like querying databases, that's something that people had to do on their own. So you'd have to figure out, well, how do I write the right search query to get the right results, maybe on LinkedIn? So what you're going to see happen and this chat is a great example of this, we'll express intent, right,

which is what our query is when we type into chat. And then the AI will figure out what it needs to go do. to get us back what we're looking for. And so that is a world of AI agents, where we're letting the AI go and do things for us. And that world looks very different as well. So AI doesn't need an interface to go grab data, right?

It can just query APIs directly and go get the information it needs. If it's allowed to, if it has access to those, right? Exactly. So I had a tweet about this. I said the biggest, by the end of the year, it's my prediction, the biggest search engine on the planet won't be Google, it'll be Bing, and it won't be because people are using it's because of the bots

are using it. So so many bots are doing queries that it's just going to increase the size of the corpus and the learning model. The corpus, the corpus kind of grows when you publish things. And so there are going to be, you know, agent flows and bot flows that go and create more content and new content.

But for the, on the retrieval, side that a lot of those queries are going to get sucked into, you know, powering some result, right? And then that result gets re-indexed. It gets re-indexed. And now people are speculating it's going to pollute the search indexes to the point of absurdity. Somebody like yourself or some group of people offshore, just like they created content farms. They could literally just start creating, they could create an AI right now with chat GPT that registers 100 new domains a day. makes a hundred different recipes and then articles about those recipes and then does the same

thing every day until there are so many recipe sites that the internet is flooded with garbage and nobody knows which one actually works. That's the fear. Google will know. Yeah, Google kind of knows. How do they know? Well, they have the original index. They have, yeah.

And they can detect AI generating content. Overall, there. How do they do that? How do they know it's, um, at GPT generated. There are signatures in the outputs that allow you to calculate

it's a perplexity score sometimes and that's an actual measurement of how likely the next word is to follow the previous one. Got it. So it's too perfect. It can be too perfect and you can make it

not be as perfect just by giving certain instructions and say mix it up a little bit. I think this is how they they caught the chess cheater kid was he was doing the book move. Yes. And the book move is like,

yeah, how often does somebody do the book move? And I play in the chess.com thing and it will tell you like, this is the book move. This is the, you know, game theory optimal move basically.

And if everybody's doing game theory optimal, so now you have to put something in it or randomized to not do game theory optimal. So maybe at some point it figures that out. You know, it's interesting though. They said when the digital camera

came out, that this would change Hollywood forever, that digital filmmakers and Wayne Wang did a digital film and Bennett Miller had done a digital film. This is all on the VX-1000, I believe, was the name of the Sony camera. The Sony one, yeah. It became super popular in the late 90s in New York and all these. And skateboarding. And skateboarding. Well, skateboarders use it, right? Yeah. And it was like, this thing is so good that when you blow it up and you put it on a projector, you can't kind of tell, or most audiences can't tell. A cinematographer, obviously, good. And then there were filters in, you know,

different pieces of software that would make it look even better. Anyway, the point was, because you don't have to worry about film stock developing it, the cost just got sucked out of this, you could record forever. Even a bad actor could just keep doing different takes. You would get there, and it didn't actually change the world. I mean, it did create YouTube. It did create a lot of other content, so maybe a nuts extent it did, but it didn't change film. So what's different about that argument than your argument for,

you know, AI replacing everything. Well, the iPhone won, right? iPhone won the camera world. It destroyed it. It also won music. So my, my, like, base case is that Apple wins

probably this, too, to a large degree. One, because the phones are powerful enough and the models are going to shrink small enough that they can fit on a phone. And so even some of these image generation models, they can fit now on the phone text gen and same thing on a long enough time frame,

they will fit on the phone. And I think that, you know, that's always crazy, but then people will just get used to. And they're like, oh, yeah, iPhone 20 will have whatever it is, native. Well, just like laptops now have Wi-Fi built in. There was a time you had to add Bluetooth with a dongle or Wi-Fi with a dongle or a PCM CIA card,

whatever that was called. You know, you have different card slots on your laptop to put in different peripherals. Now it's all on a chip somewhere. and you don't have to do anything for it. Exactly. I have one more point here. So my kids are eight and five.

And they just think computers can generate images. It's just totally natural to them. You know, and in the, I think it's adults and people that have struggled to use computers, their adult lives. It's like, this is, these are the people that have, you know, a challenge, like accepting that AI is going to power computers to do these tasks for us. But kids and kids are like, yeah, of course, of course the computer. Yeah, of course it's going to. Yeah. I mean, it's just like, well.

It's so obvious that a computer could do it to them that it's not even special or interesting. You know, if you're of the age where you remember the internet happening in broadband, and there was a moment in time where I was on a college campus in the late 80s and I had seen higher speed internet speeds, Bitnet and ARPANET. and people only had dial-up, so I was using dial-up at home, but I was like, you know, this can get a lot faster.

There are faster systems than this, and I had installed token ring and banyan vines, and then eventually Ethernet in local area networks. I'm like, you know, this thing's going to be able to carry images and pictures and sound and eventually video. And so it's kind of the same thing.

Like, you're limitation right now that you're like, oh, it can produce interesting copy that's sometimes good, sometimes bad. Okay images. And once in a while, a three-second looped video. I mean, you're going to be able to talk to this thing and just make your own Star Wars story and it would be indistinguishable than, I don't know, the original series or whatever, 10 years ago series without any polish on it.

I mean, that basically is, it doesn't take much of a jump, actually. It only takes a huge jump if you haven't watched rapid change. Right. Well, I think in the next year, you'll see it probably just take over business in general. So we know it can generate business ideas. We know it can create websites from an idea. We know it can figure out who the core persona is for that service. We know it can access APIs that would retrieve, right, who the target contacts are.

We know it can write personalized emails to them, right? So it's going to come up with an idea? You make your SaaS startup and then it starts selling it for you. It's all in the same one of sequence of prompts. Yeah. That's all it is. So that's, all that is here today. It's just a matter of infrastructure.

And that's something we're working on right now. So what, I mean, what you've described means your own business becomes commodified. So are you saying that your own business is going to, or are you saying your business will be commodified every 24 months and you'll just have to extend it? No, I'd say like most businesses are not commoditized by that loop. you know, most businesses are up some kind of like real world thing happening, let's say like J.P. Morgan, right? That's not, you can't really replicate J.P. Morgan

with that kind of system. But there are a lot of one services that can be replicated, like specialists, consulting, a lot of that stuff can be replicated. A lot of software companies can be replicated. Yeah, I mean, you're talking about regulation. I mean, you could, I mean, somebody's going to put AI

and Web3 together and crypto and be like, make me a crypto that does X, Y, and Z. They're trying to, I mean, they're trying to shut, you know, shut all that down with regulation. Regulation. The crypto choke point. Yes, if you F around too much, you will find out what the government is capable of. When you're a founder priority number one is hitting your payroll, this means you need

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So I'll give you that same question, which is, hey, you know, the stuff you were talking about two years ago, to your credit, I literally put everybody in my company. I said, everybody has homework this weekend. We're a finance company. We all work six, seven days a week. If you don't like it, don't work at a finance company. And I said, just spend an hour or two playing with chat GPT. And tell me on Monday how it helps you do your job faster, better.

What did they come back with? The sales team started doing prospecting in it. And other people started doing marketing plans in it. And I haven't gone through everybody's responses. But my team on this podcast has been doing podcast notes with it. And that works really well, which we thought was a goof 60 days ago when chat GPT 3.5 came out. I was like, just ask you what the revenue of this, you know, Netflix was the last five years.

And it was wrong. And then chat GPT4 comes out. I was like, okay, it's right. So it's starting to things that we even noticed three months ago, four months ago, we're wrong. So you could, and then we asked it to take, you know, we will take a Zoom recording of a meeting with a founder, transcribe it. In this past week, we took the transcripts, which now Zoom is doing.

And then we had ChatGPt4 summarize it and summarize it in three different lengths. So now in our notion instance, not only do we have the video with the interview with the founder recorded, we have the transcript and then I guess two or three versions of it. So I could read the short version of it. And I was like, it's pretty. pretty accurate. I mean, I wouldn't rely on it to make an investment decision, but certainly would be interesting to read before you meet with the founder. What about GPT5? It could do it. You have to assume GPT5 will be extremely less error prone, and you will just trust it. Just like GPS. The first GPS, remember people ran off the side of roads, be like turn left and somebody would wind up in a cornfield. Like it said, turn left. And it's like, hey, dummy, you still have to look. Just all. also like autopilot.

It's like, it's going to stop at this red light unless like there's a Christmas tree at the red light, you know, confusing it. But it feels like you can trust it. I think trust is going to be the main issue is can I trust this by default? Yes. I trust it more than a human.

And I trust self-driving on a highway more than I trust a human today. Yeah. Yeah. It took a little while. And we went through the, that valley where the quality was lower than people can produce. And that was like the GPT3 Valley. And that's where a lot of people were saying, oh, well, the content's terrible and, like,

it's awful quality. And it's just going to pollute the internet. That's a very short duration until he basically can replicate all the steps that a person would go through to write a high-quality article. Just replicate that at scale. And I think one of the key points that even chat GPT3 is not, it's kind of missing right now is the scalability component. So in order for you to get value out of it, you have to manually query it.

So your marketing team has to say, okay, we're going to go do this or your sales team is manually configuring it. The area of opportunity for, you know, in our kind of viewpoint of the future is really scaled, fully automated workflows. So you give the AI, you kind of tell it what you want to do or over time, just give it access to your systems and then it will go and configure itself and it will try to improve your business for you autonomously. So you can't really get there from chat. But part of the back end infrastructure is very similar because in the world of autonomous business process agents,

that is like an agent building platform. And so you do need to say, okay, I want to do this. Here's the list of tools you have access to. Go do it. Now, instead of reporting back to me, report back to yourself and then figure out how you can improve it from there, right? So what you want to do is build these, you know, extend the data flows that you have coming off of your current software stack. So you have a newsletter, right? Yep. What do you do with the information, the data that?

comes out of the newsletter performance. Nothing. Don't care. Right. So is there value there? Sure. Right. And it's,

and it usually comes down to, like the team just doesn't have enough time to do everything. Right. It's an afterthought to think about and to coach yourself. There's no person on there. So you could say, every time the newsletter comes out at the end of the week, tell us which story's got the most clicks,

and then what are stories like those that we can do more of? Boom. And that's just set every week and it just sends them an agent. So it's like Google Alerts or Magic Leap had this idea that you'd have agents out there doing things for you. But if you take a Google Alert, which is on your last name or your company name, there's a Google Alert that can do more advanced things. Yeah, exactly. And even the newsletter, maybe you have like very high value contacts in your newsletter database.

Sure. Yeah. And you're like, hey, I actually want to personalize theirs. So I want to take this content and just literally personalize it down like one to one to them because I want every single message they get from us to be extremely high quality. Oh, interesting. Right. So those are systems that are going to be, you're going to be able to build. We're building out, we have pilot enterprise partners that we're working with now that are deploying some of these workflows into their existing business processes.

Fascinating. The thing that compounds is once we see. set up a workflow. Let's call it like the newsletter use case. Most newsletters would need it. And so that is the thing that we're going to copy. That would be a template in your library, like notion has templates or any other platform as templates.

And yeah, that gets super interesting in a creative. We did just as an example. Here's a couple of, you know, when we do, when our researchers are going to do a Google search. Like, I'm doing a live show and they're like, hey, when did Netflix launch? When did it pass 100 million subs? When did Disney Plus launch? When it lands to 100 subs? It's now getting that right. It wasn't getting that right previously. But here's examples of chat, Chippy T doing that. And it does seem like Bard from Google, which I've been playing with, has more recent information. So in your mind, how far behind is Bard do you think in reality?

probably the measure that matters is the user base for the chat programs. I think that would be the difference maker there. That ultimately will determine who wins. So then Google does win because even if OpenAI has tens of millions of users, Google has billions across Chrome, Android, YouTube, etc. So Google wins? I wouldn't count anybody out. I wouldn't count Google out.

I mean, Google has, they have all the data. sets that matter. So they should be able to go faster and have the users and have the browsers. They have to really get their vision together about what they're building. And for them, you know, their goal was to make, you know, all human knowledge accessible and useful. That was like their mission statement. And search does that, right?

It does that along one dimension, which is you can search for something. So it's like, I'm going to pull information out and they show you the list of links, which is great. However, the idea that it's making it useful, I think they're failing to deliver on that function. The output that you get back. That. And then also, just think about how much value is not sitting on the internet that you

don't know about that would create value for you. Right? And until now, we've never had a way like a technology. that could take all of that information and directly apply it and make your thing more valuable. Whatever it is that you've built and have been working on. Right. So even for you, you know, you're the media company, content company, you're trying to read a lot of stuff and figure out what's important, what's valuable. And I'm sure in the past, it's called four weeks, you see more launches than you can even keep up with.

It's crazy. I mean, the number of people launching verticalized AI. like if I had a nickel for every company that's going to respond to sales emails or customer support emails in a better fashion. I mean, it's in the hundreds now. Yeah. Right. And the thing we ran into, we were trying to build verticalized solutions.

And we realized that it's just basically the same back-in infrastructure that you need to do any of them. And so rather than a large, let's say, bank trying to evaluate 3,000 verticals, article-wise solution providers, they'll end up opting just for a platform. And you've seen this happen over and over again. So Segment was a CDP layer, customer day layer that they didn't have to build. Snowflake was a huge, like, data warehouse in the cloud. They didn't have to build that. Service now, one of the originals, right, just scaled so hard and they were very use case

agnostic. and that's that's very appealing for large companies because they're always trying to reduce the number of tools that they have at the company level yeah for sure so that's i mean Microsoft office perfect example you have bundling of stuff so show us your product and where you're at with it and what people are using the most and specifically how does it differ from chat chpT4 because as i said my premise was or my question to you was was like is this thing eventually collide or are you just in an arms race where they're building this sort of web based service and then they need people like you to build stuff on top of it. Yeah, it works both ways. I think there's plenty of room in this, you know, in these categories. This is our chat product. We've connected it to the internet. Let's ask it.

So this, you know, SVB got sold today. Yep. And so this would be, you know, a real-time query that we can run. on it. Most people we're using like our, I'll show these tools here.

So over time, all the latency drops and the cost drop. And so for the, for this result, you know, you're getting the actual articles.

Right. And the citations. You're surfing the open web. So you have Google News or some news feeds that interior. using chat TP4s like, hey, go check the web. We can do anything.

We could do any of it we want. So you can hit, like, you can do it in real time. You can do like a search API that you can add, add to the back end. You can use the Being API. That stuff doesn't really matter too much. Like, you do anything you want. And then you can bring all those results back in. And for this one, you know, it'd answer the question really fast.

Perfect. Right. Yeah. Right. And when GBT3, launched, it would make something up. It would just literally make up the wrong answer and be very confident in it.

Yeah. Right? What's the word for that? Is that what they mean when they say it's delusional or it's a hallucination? Hallucination. Yeah. It thinks it's right. It has no idea. Yeah. Okay. So this is impressive. What next? So what's happening in the back end is actually where the secret sauce gets built. So when you in, you know, when a user comes in a chat and they ask

question. In the Google world, it would just hit up its index. So the index would say, okay, what's the closest set of results that I should show to this user based on that query? In the generative world, you can build these agents so you can go try to solve the problem. And the more tools you give that agent, the AI agent, the better it can solve the problem. So it is choosing now what route to take. So these are not pre-programmed steps that you build into it. So it's saying I could go to Wikipedia. I could go to Google News or Bing News or I could search the open web.

I could do a web crawl. It could do, excuse me, any number of things. I search Twitter, search Reddit or Quora. And it somehow figures out, you know what, this news service is better. or is it blending the new service with the web search? It's going to do a little bit. It can do whatever it wants. Yeah.

So this is fundamentally a new way of building back-ins to software. The other thing that, you know, ours can do is it can recommend new steps. So it can, you know, we can figure out what tools it wants and we can give it access to more tools. So in some cases, people wanted access to like LinkedIn data, right? So we can go get an API that queries LinkedIn profiles and returns that. And it's like a JSON format, which is like a data format. And then now we can take that information and do something with it that's relevant to that user. This is going to make these large datasets invaluable.

And if you're going to use them, you're going to need to get a licensing fee from LinkedIn, Reddit, Quora. Maybe you could talk about what's going on in the back channel because you're probably facing this yourself with those discussions. I understand there's like multiple lawsuits about to drop on open AI for using various platforms data without explicit permission. And once you start charging for stuff and you take 10 billion from Microsoft, you can no longer be like, oh, it's an academic nonprofit, even non-profits. Like I don't know if you saw the way back machine, the internet archive lost its copyright case. That's a nonprofit.

So this is clearly unfair use. No, I don't know if it breaks, the fair use will have like a, there'll be a big debate coming up. But what's the back channel right now about who gets access to Cora, Reddit, Twitter, pick a dataset, and how will that hash out in your mind? What's the fair route there? That's a good question. I think over time, I mean, licensing tends to get figured out over time. Music industry, they figured it out.

Even in the image generation side now, I was at South by Southwest and at the shutter stock booth, and they had an AI generated image product. And even them, even though you're using AI to generate it, they would find the images that went into the generation and they will pay a royalty to the copyright holder of those images. Yeah, getting images suing stable diffusion right now. A bunch of open source developers are suing Microsoft's GitHub co-pilot. So these are dropping. Those are, to me, those were like very, they should get sued at least. for those. The code base, I think

I would at least want to see how those arguments play out. So if you open source your code, someone grabs it, trains a model, and it is replicating your code verbatim, and even in some cases like the script, the documentation script

that was personalized to you, I mean, that's very traceable, right? Yeah. There's no reason not to put citations in it. Why wouldn't you? Right. So that same thing happened on the initial stable diffusion dataset. So they grabbed images and you could see like Getty and the watermark.

The watermark. That was classic. So we kind of avoided doing image gen. I know other platforms wanted to launch that as products. But if any of these image generators, they'll produce copyright and content. And the way the copyright laws work is if you are the one that generated it on your servers and you're selling it, like, that's a, you're going to get deemed for that. Yeah.

And that's, it's a derivative product that you created. Now, if I were to load, uh, just like on my browser, I can take my browser. I can save a web page on my desktop. That's fair use. Um, and I can even edit the webpage on my desktop. It's when I choose to put it out in commerce that your copyright gets, uh, you know, has problems. But if I were to, if I were to download the original chat,

GPT open source and it's no longer open source? Is this like chat GPT4 is not open source now? When did it stop being open source? GPT3 was not open source. So three and a half, which powered chat, not open source, four not open source. And it's unlikely that opening I will open source. Is there, what's the number one competing product and how far behind are they? Because it seems the world needs an open source version of this.

And if I would have run the open source version on my cluster of server, or, you know, my desktop, my phone, and pointed out a data set that I had access to, well, that's for my own personal use. Nobody can stop me. Right. You can do that. And even the stable diffusion models,

they've shrunk them down. They can fit on your M1MACbook or your phone. So, no kind of copyright issue there. But if you try to monetize that, you do run into it. In terms of open source, open AI is still stay of the art for their, for their core foundational models. The advantage actually is not on the training side.

It's the computational intensity side. So it's how they optimize their hardware, how they optimize the training of the models. That's where you build a really compounding competitive advantage because the bottleneck across the world right now is GPU capacity and availability. So, Nvidia has back orders like crazy for their H-100, a GPU.

And no one can get it up. Explain what the H100 is. It's a really expensive GPU, so like a graphical, you know, processing unit. And it's, you know, it's what powers video games. It does a lot of kind of matrix math. And that's what, you know, these neural nets really need that at scale. And anytime a big company wants to launch an AI powered product, that's just more compute that's needed to serve that up.

And I think most people will have run into like outages. ChatGPT goes down or OpenAI goes down. It's a function of like the limitations of just not having enough of these GPUs in their server farm and getting overlooked. But isn't the tensor stuff all open source hardware as well? So other people should be able to build these and compete over time? No. Invidia has, you know, they make custom model. modified chips for folks, and they competed pretty hard.

Google did have the TPUs, the tensor processing units, but those have not really performed for the applications in the same way for other companies. This is not my area of expertise. No, I mean, I'm hearing people talk about the shortage now, and it does seem like it's an opportunity for, you know, massive competition to get an open source and other technologies. It's just the heart, you know, that hardware is made in, you know, like TSM, see, Taiwan semiconductor and

that's a whole geopolitical risk too and it's pretty amazing that we've been talking about this invasion of Taiwan now for

you know, close to a decade but that has gotten more and more heated the last couple years and that increased tension seems to parallel

the development of AI and COVID. Yeah, I don't know. That's, that is

It's like, you know, Taiwan built itself on the back of its semiconductor industry because that was the thing that would get the most protection from the U.S. Yeah. Over time. And that was a good bet. That was a good call on that. But I think even TSM is saying, hey, maybe we need to diversify a little bit and start opening up fabs in the U.S.

Oh, and they are. Yeah. India, everywhere. Yep. It's absolutely fantastic. So show me your product before we wrap up here and run out of time. Any other pieces you want to show? any other like things you're super proud of.

I mean, it's super impressive. And I understand now the difference. Yours is doing a lot more inputs. In the way it's kind of like Zapier or if this than that kind of is built into it natively. So it can go find more sources dynamically. Yeah, it can go do that. And then I think, you know, over time, and this is what I was getting to about software in general, you know, you bought software for your team before.

Yeah. Sure. And then what happens if the team doesn't use the software to its fullest potential? We unsubscribe at some point. Hopefully somebody remembers they put it on a credit card. But I cancel our cards every year where I have the cards that you can set the limit on, where I just say, okay, take all the cards down to zero and let's watch our phones ring off the hook as SaaS vendors are like, hey, your card's not working.

That definitely works. And one of the big shift here is when you move away from software and towards agents, the agents, the agents know how to use these tools better than people do. And so they're going to extract all the value out of these tools and that it could be APIs, that could be really anything. And you're seeing it

across the board and all these little point solutions. But imagine wrapping all those point solutions into a generalized platform. That's what we're building. Makes sense. Yeah. This is pretty crazy. Well, listen, you got a team of 30 or 40 over there.

35. We're going to lean. I mean, yeah. I mean, you raise a decent amount of cash. You got a decent amount of revenue coming in. Are you finding your clients are this is your, you're too far ahead of what they need, or they're just trying to understand this,

or now with chat, Chb, T.BT, 3,5, and 4, they're all like, oh, my God, we have to catch up. I know we paid for this. We need to now expand it. And we need to understand this because our bosses are breathing down our throats. It's a mix.

So, yeah, there's an AI maturity model. Gardner published this model in 2019, and they said, hey, look, we're going to start by companies kind of being interested in different, you know, potential with AI. Yeah. I've taken that and adapted their maturity model. And basically that phase one is like the end users are going to start bringing in different AI tools into their day-to-day workflows. So that would be like people using chat GBT, using our chat, you know, that's also powered by chat GBT. or using copywriting tools. I saw Canva, Adobe, you know, everybody's now long.

Notion. Everybody's got an AI feature, kind of a bullfair. Which was, you know, I think it took two years. And probably the 3.5 model was really the point where those companies would say, okay, we can, we feel okay trusting this and putting this out there for our customers. So that's really phase one is, are your, is your team using it? The second piece would be, is your team able to actually scale its usage of AI? And that's not something that chat GPT is going to really help your team do.

But if you wanted to make, you know, repeatable processes, build, you know, build actual components of your business. So focusing on the system rather than the manual tasks automation. So that's where, you know, we built into that. That's kind of phase two. And then phase three is when you connect, you begin to connect. enough of the workflows together so that the data feedback loop starts to really run like a flywheel. And at that point, now more and more of your business is going into like autonomous mode.

And as you as you go into autonomous mode, people have to actually build that system out. And it's not the AI team, but it's actually the business users. And so we're seeing process people, the business leaders. Even the functional people, a lot of people, want to actually build these systems out. And so you are seeing kind of two groups emerge. One, they're excited about, you know, building more efficient systems, being able to do more projects than their, you know,

their team could have ever done before. And they're the ones that are really AI-native. And that's who we build for. And then you have folks that are really hesitant to adopt any of the systems in tooling. And ultimately, those companies are not going to be competitive, kind of in the 12 to 18 month time frame. They're going to become rapidly uncompetitive. And then pretty much at every company,

the CEO, is like, hey, AI is here, like, what's our AI plan? What's our AI strategy? Right? And so then there's a really big gap between the end user tool and the CEO. Yeah. Right?

And so where we found success with our platform is selling the vision of the platform to the CEO, C-suite, and then the leaders, of all those groups because they say, okay, well, can you do this use case? Can you do this use case? It's like, yes, yes, yes. Because it's all basically just stacking these blocks together. And that'll cover that. I mean, that could be a 10-year time frame where that really gets adopted or it could be like three years, five years. It really depends on the level one of interest. And then two, it's like how fast is the underlying technology really going to improve?

right now the bottleneck is people don't even know what's possible with AI yet so there is some education happening and that's why I just told everybody to start playing with it and it's kind of like mobile you know one of the first steps people did in mobile was or even cloud was just give everybody a mobile phone so everybody had flip phones and they're like just get everybody on your team on mobile phone get everybody your team an iPad if you want to build iPad apps they got at least have iPad if you want to see what 5G and broadband's like just buy all your executives broadband at home and get them laptops and show them how it works, right? They need to really start soaking it and playing with it and then the use cases will come out and it's like anything else. Some people will go too slow like Microsoft did with mobile and they'll give mobile up to- They didn't miss this one. They didn't miss this one. I mean, this is like the makeup for missing mobile, right? You think about how badly they miss mobile and now how well they hit this one. They're like, we're not going to miss this one. We'll pay 10 billion. And then you look at like

Mark Zuckerberg with ARVR or maybe to a lesser extent, Apple, who has a device coming, maybe they just also made huge bets, and those huge bets are just not going to hit the mark. So it's one of the great things about technology, is how dynamic it is. All right, listen, Paul, you're amazing. Really great overview for people who don't know about it.

And if you're in the business of running a business, I think that's as plainly as you can say. Copy AI might be able to help you be more efficient. So go check it out. We have free plans. There's a free plan. Go to apply with it. And until your free plan,

when you use up all your credits. And then you go, or what do you do? Multiplayer mode is where you have to pay? We're doing, right now it's still credits because it's, you know,

yeah, it's a little pricey for us. Oh, it is pricey. Yeah, you have to. Yeah. I mean,

I thought Open AI dropped the price like 100% or 90% or something. They did. They, that saved, that saved us pretty good. Yeah.

Because that was, it was going to be quite expensive. And they'll do that again, I guess, I bet, right? Yeah. Over time, it's going to go to,

at zero. And so then you'll have the scale, the volumes will really start to work through the system. And that's another reason that I wouldn't want to focus on the end user point solutions because you won't have enough volume over time to really propel a business. Yeah. It's incredible. It's something to think about, you know, something to think about it because it's so easy to create companies and you're seeing this AI hype race, right?

but then the durability of a lot of the point solutions is really going to be questionable, I think. Well, I also think you have to also want and have the motivation. And when human motivation, this is a very hard reality for most people, especially woke people or people who want to believe the world is fair in some way. Like it really does break their brain when you're like, okay, now solve for motivation because I can take any course from MIT or Harvard or Stanford online for free right now. and so the world's unfair, and those Ivy League schools

have now given all their information out for free. Or the VX100, you can buy a used one and make a movie, and so Hollywood's holding you back, or you could get three or four of your friends together and make a movie this week and a short film for free. What's holding you back? And that's what I learned over time is that human creativity is all there,

but motivation, that fire in your belly, the desire to go do something, sometimes the unhealthy obsession and desire to go do something, that's one that we still, as much abundance as we have, can't seem to solve for. We can't make people motivated to go change the world. Some people are and some people aren't. My oldest daughter, I asked her like, what do you want to do when you grow up? And she's like, well, I want to breed dogs and I want to do art.

I'm like, sweet. I said, you can do that now. Like, you don't need to go to college. Yeah. She could do it now. A dog breeder and make art. And that sounds like a pretty dope life. She said, she said, I'll breed dogs and then I'll sell the dogs and I use the money to buy more art supplies.

And I'll do more art. Yeah. And buy more dogs. Right. So I think if I could have one wish, it would be that when we do go update the curriculums of schools, that the only thing that we instill is like that creativity and getting things done that you care about. And if you drive that passion forward, everyone will be successful because you'll have unlimited tools to make things happen. There's literally in a school of academia, so there's Montessori, and then there's something called Regio.

And Regio, one of the core concepts is, Reggio Amelia, is figure out what the child is motivated by and then have the curriculum taught through that. In your case, art, supplies and art, and puppies and dog breeding. So you could teach biology, obviously, through dog breeding, and you can teach math, and you can teach science through the science of oil painting or sculpture and the physics of it. You can teach anything through a lens in which somebody's,

you know, if somebody's into skiing, you could teach them physics of skiing. And they would learn all the physics concepts, but they would be super motivated. And then teaching them physics abstractly, they're not motivated. Turns out every kid could learn physics

or pick a topic if you just put it in the right wrapper. But yeah, the education system. We should just ask copy AI right now how to fix the education system. I'm sure it has a great answer. Well, you can do custom learning plans for people. People are already building those systems out.

And even, like, linking to the right YouTube video to watch. That's awesome. Right. Yeah, if it nails that, it's incredible. Yeah. At some point, it would be like, hey, what is this person stuck on? You know, give some sort of assessment. Nobody's going to be stuck.

Well, you'll do an assessment of somebody. Like, they're, you know, like some kids lost in life. They're like, oh, this kid's lost. to be like, okay, let's figure out some assessment tool. And then it's like, okay, here's what would motivate this kid. Watch these five videos. And it's like, watch this gladiator video, watch this like skiing video, watch this puppy video and then listen to this song.

And it's like, it's going to unlock their brain and unstuck them. That'd be awesome. That'd be pretty great. All right, listen, we'll see everybody next time on this week in startups. Bye, bye, bye, everybody.