Nothing seems fundamentally so hard that it couldn't be solved by the smartest people in the world working incredibly hard for the next five years.

Humanity went through the agricultural revolution and the industrial revolution. We're going through another revolution. We will not be able to call it something. It's like future people will call it something. But we are going through something. The number of solo entrepreneurs that this technology is going to enable. It's vastly increased what a single person can do.

For the first time, opportunity is massively available for everyone. Just the ability for more people to be able to become entrepreneurs is Yeah, it's massive.

Adam, welcome to the podcast. Thank you. Yeah, thanks for having us. So, a lot of people have been throwing cold water over LLMs lately. It's been some general bearishness. People talking about the limitations of of LLMs, why they won't get us to AGI. Well, maybe uh what we thought was just a couple years away is now maybe 10 years away. Adam, you seem a bit more optimistic. Why don't you share your broad general overview?

Yeah, I mean I I actually honestly I don't know what people are talking about. I think I think if you look a year ago, the world was very different. And so just judging on how much progress we've made in the last year with things like reasoning models, um things like the improvement in code generation ability, um the improvements in video gen, it seems like things are going faster than ever. And so I I don't really understand where the the kind of bearishness is coming from. Well, I think there's some sense that we hoped that they would be able to um replace all of tasks or all all jobs. And maybe there's some sense that it's like middle to middle but not end to end. And maybe, you know, labor won't be automated in the same way that we we thought it would on the same timeline.

Yeah. I mean, I I don't know what the previous timelines people were were thinking were, but you know, I think I think if you if you go 5 years out from now, we're in a very different world. I think I think a lot of what's holding back the models these days is not actually intelligence. It's getting the right context into the model so that it can be able to to use its intelligence. Um, and then there's some things like computer use that are still not quite there, but I I think we'll almost definitely get there in the next year or two. And when you have that, I I think we're going to be able to automate a large portion of what people do. I don't think I don't know if I would call that AGI, but I I think it's going to satisfy a a lot of the critiques that people are making right now. I I think they won't be valid in in a year or two.

What is your definition of AGI? I don't know. Everyone everyone thinks it's something different. I think I mean you know one one definition I I I I kind of like is um if you say that you have a remote worker a human any job that could be done by someone whose job can be done remotely um that that's AGI you know you can you can then say does have to be better than the best person in the world at every single job some people call that ASI um does have to be better than teams of people you can you can argue with those different definitions. But I I think once we get to be better than a typical remote worker at the job they're doing, we're living in a a very different world. And I think that's that's effectively what people that that's a very useful anchor point for for these definitions.

So in summary, you're not sensing the same limitations of LM that other people are. You think there's a lot more room that LMS can can go from here? We don't need like a brand new architecture or other breakthrough. I don't think so. I mean I I think there are certain things like memory and learning like continuous learning that are not very easy with the current architectures. I think even those you can sort of fake and maybe we're going to be able to to get them to work well enough. Um but we we just don't seem to be hitting any kind of of limits. The the progress in reasoning models is incredible. And I think the progress in in pre-training is is also going pretty quickly. Maybe not as quickly as people had expected, but certainly fast enough that you can expect a lot of progress over over the next few years.

Amad, what's your what's your reaction hearing all this? Yeah, I I I think I've been pretty consistent and consistently right perhaps dare I say consistent with yourself or consistent with what I'm saying with with um with myself and with I think how things are unfolding that uh you know I started being a bit of a more public doubter of of things around uh the time when the AI safety discussion was uh was reaching its height back in maybe 22 23. Um, and I I thought it was important for us to be realistic about the progress. Um, because, you know, otherwise we're going to scare politicians. We're going to scare everyone. You know, uh, DC will descend on Silicon Valley. We they'll shut everything down. So my criticism of the idea of like AGI 2027, you know, that paper that I think it's called Alexander, someone else wrote uh and then um and the situational awareness and all this uh hype papers that are not really science, they're just vibe. Here's what I think will happen. Uh you know, the whole economy will get automated. You know, jobs are uh are going to disappear. all of that stuff is that again is just I think um it's unrealistic. It is not following the kind of progress that we're seeing and it is uh going to lead to just bad policy.

So my view is LMS are amazing amazing machines. Uh I don't think they are exactly human uh intelligence equivalent. You can still trick LMS with things like they might have solved the strawberry one, but you can still, you know, uh trick it with like single sentence questions like how many Rs are in this sentence. I think I think I tweeted about it the other day, which was like three out of the four four models couldn't didn't get it even. Um and then GP5 with high thinking had to think for like 15 seconds in order to get a question like that. So uh LMS are I think a different kind of intelligence than uh what humans are uh and also uh they have they have clear limitations and we're papering over the limitations and we're kind of working around them in all sorts of ways whether it's in the LLM itself and the training data or uh and the infrastructure around and everything that we're we're doing to make them work. Um but that that makes me less optimistic that we're we've we've cracked intelligence. And I think once we truly crack intelligence um it'll feel a lot more scalable and that you can uh and that the the idea behind the lesson will actually be true and that you can just pour more um more power, more resources, more compute into them and they'll they'll just scale more naturally.

I think right now uh there's a lot of manual work going into making these models better. In the pre in the true pre-training scaling era, you know, GPT2, 3, 3.5, maybe up to four, um it it felt like you you can just uh put more internet data in there and just it just got better. uh whereas now it feels like there's a lot of labeling work happening. There's a lot of contracting work happening. A lot of these uh contrived RL environments are getting created in order to make uh LLMs good at coding and becoming coding agents and they're going to go do that. I think the news from OpenAI that they're going to do that for for investment banking.

And so I uh try to coin this term I call functional AGI which is the idea that you can automate a lot of aspects of a lot of jobs by just going in and like collecting as much data and creating these RL environments. It's going to take enormous effort and money and data and all of that in order to do and I think we're yeah I I I agree with Adam that you know things are going to get better uh 100% over the next 3 months 6 months cloud 4.5 was a huge jump uh I don't think it's appreciated how much of a jump it was over over four there's really really amazing things about cloud 4.5 so there is progress we're going to continue to see progress I don't think LM as they can understand are on on the way to AGI.

And my definition for AGI is I think the old school RL definition, which is um a machine that can go into any environment and learn efficiently in the same way that a human could go into uh you can put a put a human into a a a pool game and you know within 2 hours they can like shoot pool and be able to do it. Uh right now there's no way for us to have machines learn skills like that on the fly. You know everything requires enormous amount of data and compute and time and effort and and and and uh and more importantly it requires human expertise which is the non bitter lesson uh idea which is you know uh human expertise is not scalable and we are relying today we are in a human expertise regime.

Yeah, I mean I I think that humans are certainly better at learning a new skill from a limited amount of data in a new environment than the current models are. I think that on the other hand, human intelligence is the product of evolution which used a massive amount of effective computation. And so this is a different this is a different kind of intelligence. And so because it didn't have this this massive equivalent of evolution, it just has pre-training for for that which is not as good. You then need more data to learn everything, every new skill. But I guess I think in terms of like the functional consequence. So like if if you're like when when will the world when will the job landscape change? When will the e economic growth hit? I think that's going to be more a function of when we can produce something that is as good as human intelligence. Even if it takes a lot more compute, a lot more energy, a lot more training data, we could just put in all that energy and still get to software that's as good as the average person at doing a typical job.

So, I don't disagree with that and and that's it is it feels like we're in a brute force type of regime, but but maybe that's fine. And yeah. Yeah.

So, where's the disagreement then, I guess? So, there's agreement on that. Where is the deer? I I don't think that we'll get to the singularity or I don't think that I don't think we're going to get to the next level of human civilization uh until we um we we we crack the true nature of intelligence like until we understand and have algorithms that are actually uh not brute force and and you think those will take a long time to come? Uh I I'm sort of agnostic on on that. It just does it does feel like the LMS uh in a way are distracting from that because um all the talent is going there um and therefore there's less talent that are trying to do basic research on on intelligence.

Mhm. Yeah. At the same time a huge portion of talent is going into AI research that used to previously wouldn't have gone into AI at all. Mhm. And so you have this this massive industry, massive funding, you know, funding compute but also funding human employees. And that is I guess I nothing seems fundamentally so hard that it couldn't be solved by the smartest people in the world working incredibly hard for the next 5 years on it. But but basic research is is different, right? like trying to um like trying to get into the fundamentals and as opposed to like there's a lot of industry research like how do we make these things more useful uh in order to generate profit and um so I I think that's that's different and often I mean Thomas [ __ ] this philosopher of science talks a lot about how these research programs end up you know becoming like a bubble and like sucking all the attention and ideas and like think think about physics and how there are like these industry of a string theory and like it pulls everything in and there's sort of a plug black hole of progress and you know

Yeah. Yeah. No, and I think I think one of his things was like you got to wait until the current people retire even have a chance at changing the paradigm. He's very pessimistic about paradigms. But I I guess I feel like the current paradigm, this is maybe our disagree, I think the current paradigm is pretty good and I think we're nowhere near the sort of like diminishing returns of continuing to push on it. Mhm. And I bet Yeah, I guess I would just bet that you can keep doing different innovations within the paradigm to to get there.

So let let's say we continue to brute force it. um we're able to automate a bunch of labor. Do you estimate that GDP is is something you know four or five percent a year or are we going up to 10% plus or what does it do to the economy?

I think it it depends a lot on exactly where we get to and what what AGI means. But so so let's say you have let's say you have LLM that with with an amount of energy that costs $1 an hour, they could do a job of any human. Let's just just just take that as a as a theoretical point you could get to. I think you're going to get to much more than four to 5% GDP growth in that world. I think the issue is you may not get there. So it may be that the LMS that can do everything a human can do actually cost more than humans do currently or they can do kind of like 80% of what humans can do and then there's this other 20%. Um and and I I think I do think at some point you get to also like I I don't see a reason why we don't eventually get there. That may take five, 10, 15 years. But I think until you get there, we're going to get bottlenecked on the things that the LM still can't do or the, you know, building enough power plants to to supply the energy or other bottlenecks in in the supply chain.

One thing I worry about uh is uh the delotterious effect of LMS in the economy in that say LM's uh you know effectively automate uh the entry level job but not but but but the but not the expert's job right so um let's take uh you know QA Q quality assurance um And uh it it's it's so good, but uh there's still all these longtail event uh you know events that it doesn't handle. And so you have a lot of uh really good QA people now like managing like hundreds of agents and you effectively increase productivity a lot. Uh but they're not hiring new people because the agents are better than new people. Uh and and and that that feels like a weird equilibrium to be in, right? And I don't think that many people are thinking about it.

Yeah. Yeah. For sure. Yeah. No, I I I think that's, you know, I think it's happening with um CS majors graduating from college, there's just not as many jobs as there used to be. And and um LLMs are a little more substitutable for what they previously would have done. And I'm I'm sure that's contributing to it. And then it means that you're going to have fewer people going up that ramp that, you know, companies paid a lot of money to to employ them and and and train them. Um and so I I think it's a real problem. I think it's going to I'm guessing you'll probably see some kind of like that problem also creates a economic incentive to solve the problem.

So it may be that there's like more opportunities for companies that can train people or maybe use of AI to to teach people these things. Um but for sure that's that's an issue right now. Another related problem is that uh since we're dependent on uh expert data in order to train the LLMs and the LM start to substitute um those workers but but but you know at some point there's no more experts because they're all out of jobs and and and and they're equivalent to the LLMs. If the LMS is truly dependent on on labeling data, expert RL environments, then how would they improve beyond that? I think that's something question for an economist to really sit down and think about is like once you get the first tick of automation, I mean there there are some challenges there. And so how do you go how do you go how do you go to the next part? Yeah, I mean I I think it a lot of it is going to depend on how good of RL environments can be created.

So, you know, in the one extreme you have something like Alph Go where just a perfect environment and you can just blast past expert level. Um, but I think a lot of jobs have limited data that anyone can can train from. And so I think it'll be interesting to see how how easy is it for research efforts to to overcome that that bottleneck.

If you had to make a guess on what job category is going to be introduced or explode in in the future um you know some people say it's like the you know everyone's an influencer you know or in some sort of caring um field or um you know everyone's employed by the government and some sort of bureaucrat thing or um you know maybe training the AI in in some way uh you know as as more and more things start to get automated you know what is your your guess as to what more and more people start to you know, doing art and poetry is

at some point you have everything automated and then I think people will do art and poetry and you know there's a data point that the people playing chess is up since computers got better at human than than humans at chess. So I don't think that's a bad world if people are all just kind of free to to pursue their their hobbies. uh as long as you have some kind of you know way to distribute wealth so that so people can afford to to live. Um but I you know in the near that that's a while away and in the near term well like 10 15 years out I I don't know how much but yeah in the in the I'll put it in the at least 10 years range. Um, I I think in the near term the job categories that are going to explode, the jobs that can really leverage AI and so so people who are good at using AI to to accomplish their jobs, especially to accomplish things that the AI couldn't have done by itself, there's just there's just massive demand for for that.

I think in the near term the job categories that are going to explode, the jobs that can really leverage AI and so so people who are good at using AI to to accomplish their jobs, especially to accomplish things that the AI couldn't have done by itself, there's just there's just massive demand for for that.

I don't think we're going to get to a point where you automate every every job. Uh, definitely not in the current paradigm. I would uh I would doubt it happening. I I I'm not certain it would ever happen, but definitely not in the current paradigm. Now, here's why I think because a lot of jobs is about servicing other humans. You need to be fundamentally human in order to you need to be actually human in order to understand what other people want, you know, and so you need to have the human experience. So unless we're going to uh create uh human humans, unless the m unless AI is actually embodied in the human experience, then humans will always be the generators of ideas in the economy.

Adam, respond to Andre's point around the human part because you created one of the most, you know, the best wisdom of the crowds, you know, uh platforms in in the universe. Um and now you've gone, you know, all all in with Po. Um what are your thoughts on you know to what extent will we be relying on um humans versus will we be trusting AIs to you know be our therapists be our you know caretakers in other ways. Humans have a lot of knowledge collectively and you know even like one individual person who's an expert and has lived a whole life and had a whole career and seen a lot of things they they often know a lot of things that are not written down anywhere tacet knowledge and um you call it tested knowledge but also also what they're capable of writing down if you did ask them a question I think there's still an important role for for people to play in the by sharing their knowledge, especially when they have knowledge that that just wasn't otherwise in an LLM's training set. Um, you know, whether they will be able to make a full-time living doing that, I I don't know. But if that becomes a bottleneck, then then for sure that's going to mean that all the sort of like economic pressure goes goes to that.

I don't in terms of the like you know you have to be human to know what humans want. I don't know about that. So like as an example I think I think recommener systems the system that ranks your Facebook or Instagram or Kora feed those recommener systems are already superhuman at predicting what you're going to be interested in in reading. Like if if if I gave you a task that was like make me a feed that I'm going to read, like there there's just no way. No matter how much you knew about me, there's no way you could compete with these algorithms that just have so much data about everything I've ever clicked on, everything everyone else has ever clicked on, what all the similarities are between all those those different data sets. And so I don't know, you know, it's true that as a human you can kind of like simulate being a human and that makes it easier for you to like test out ideas. And I'm sure that composers and artists are this is an important part of their their process for doing work is they or chefs or Yeah. Yeah. They they produce something and you know a chef will cook something and they taste it and it's important that they can taste it but I don't know you know they they just they have very little data compared to what AI can be trained on. So So I I don't know how that's going to shake out.

That's a that's a good point. I mean ultimately what recommended systems uh are they're like aggregating all the different tastes and then sort of finding where you sit in the sort of multi-dimensional taste vector space and like getting you the best content there. So I guess there's some of that. I think that's more narrow than we think like like yes it it's true in recommener systems but I'm not entirely sure it's true of of of everything. Um but so I I think the best prediction for where the world is headed and this is not a endorsement or necessarily like this is where I think the world's headed because I think part of it is uh will be slightly in uh instable unstable system but I think the sovereign individual continues to be I think a really good set of predictions for the future although it's not a scientific book or not. It's a very pyic book and um but but the idea is uh you know in the late 80s early 90s um are they economists? I'm not sure. I think they're economists or political science majors uh two people out of the UK um wrote this book about trying to predict what happens uh when computer technology matures, right?

They're like, you know, humanity went through the agricultural revolution and the industrial revolution. We're going through another revolution. Uh clearly, uh information revolution, now we call it intelligence revolution, whatever. I think we will not be able to call it something. It's a future people will call it something, but we are going through something. And so they're trying to predict, okay, what happens from here? And what they arrive at is that the um ultimately you're going to have large swaths of people that are potentially unemployed or economically not um contributing, but you're going to have the entrepreneur the entrepreneur capitalists going to be so highly leveraged because they can spin up these companies with AI agents very quickly.

Oh, because they have this because they're very generative. They have interesting ideas. They're human. They've uh they have interesting ideas about what other people want. They can create these companies very quickly in these products and services and they can organize the economy in certain ways. And the politics will change because uh to you know today's politics is based on um every human being uh economically productive. Uh but when you have only uh when you have massive automation and then you have a few entrepreneurs and very intelligent generative people are actually uh able to be productive then the political structures also change. Um uh and so they talk about how the you know nation state sort of subsides and instead you go back to uh to an era where um states are like competing over people over wealthy people and like they you know uh as a sovereign individual you can like uh negotiate your tax rate with your favorite state and so it starts to sound like biology a little bit and I don't think it is far from where I where it might be headed. Now again, it's it's not a sort of a value judgment or or desire. Uh but but I do think it's worth thinking about when when people are not the the you know unit of economic productivity, things have to change, including culture and and politics.

Yeah. I I think there's a question with that book and in some of this conversation more broadly of like when does a technology reward the uh you know the defender versus this the sort of aggregator or something or like the um when does it incentivize more decentralization versus centraliz like uh remember Peter Tiel had this quip a decade ago of like you know crypto is libertarian is more decentralizing AI is you know communist or more centralizing and it it um it's not obvious to me that that that that's entirely accurate. um on on either side AI does seem to empower a bunch of individuals as you were saying and then also you know crypto turns out is like fintech or it's like stable you know uh it does empower sort of uh you know in nation states we're talking about doing the sort of like you know the the China thing that they were going to do so yeah I think there's an open question as to you know which technology leads to who does it empower more the edges or the the center and I think if it empowers the edges it seems like the sovereign individual is is and and maybe there's a barbell uh where it's like both basically the big the incumbents just get much much much much bigger and there's like these edges but anyways that's

I'm I'm very excited for the um the number of solo entrepreneurs that this technology is going to enable. I think it's it's just greatly it's vastly increased what what a single person can do and there's so many ideas that just never got explored because it's a lot of work to get a team of people together and maybe raise the funding for it and get the right kind of people with all the different skills you need. Um and now that one person can can bring these things into existence, I I think I think we're going to see a lot of really amazing stuff. Yeah, I get these tweets all the time about people who like quit their jobs because they started making so much money. You're using tools like like rapid and um it's it's really exciting. I think uh if for the first time opportunity is massively available for for everyone uh and I think that that is to me the most exciting thing about this technology other than all the other stuff that we're talking about just the ability for more people to be able to become entrepreneurs is yeah it's massive

that that trend is obviously going to happen as we look out of the next decade or two do you think that AI is more likely to be sustaining or disruptive in the Christian sense to ask it another Okay. Do you think that most of the value capture is going to come from companies that were scaled pre OpenAI starting? Um uh so is so replet still counts as the the latter and so does court to some degree or or do um do you think most of the value is going to be captured by companies that started you know after let's say 2015 2016? So there's a related question which is how much of the value is going to go to the hyperscalers versus everyone else and I think on that one we are I actually think we're in a pretty good balance where there's enough competition among the hyperscalers that the um there's enough competition that as an application level company you have choice and you have alternatives and the the prices are coming down incredibly quickly. Um, but there's also not so much competition that the hyperscalers and the you know labs like Anthropic and OpenAI, there's not so much competition that they are unable to raise money and make these long-term investments. And so I actually think we're in a in a pretty good balance and and we're going to have a lot of a lot of new companies and a lot of growth among the the hyperscalers.

I actually think we're in a pretty good balance where there's enough competition among the hyperscalers that the um there's enough competition that as an application level company you have choice and you have alternatives and the the prices are coming down incredibly quickly. Um, but there's also not so much competition that the hyperscalers and the you know labs like Anthropic and OpenAI, there's not so much competition that they are unable to raise money and make these long-term investments. And so I actually think we're in a in a pretty good balance and and we're going to have a lot of a lot of new companies and a lot of growth among the the hyperscalers.

I think that's that's about right. So the terminology of sustaining versus disruptive comes from uh uh the innovator's dilemma. Uh and uh it's it's this idea that uh whenever there's a new technology trend, it sort of there's this idea of a power curve. It starts as a toy almost or something that doesn't really work or captures the lower end of the market. But as it sort of evolves, uh it goes up the power curve and eventually disrupts even the incumbents. So originally the encompass don't pay attention to it uh because it looks like a toy and then eventually disrupts everything and eats the entire uh sort of market.

Uh and so that that was true of PCs. You know, when PCs came along, the big main mainframe manufacturers did not uh uh pay attention to it and and initially it was like yeah, it's for it's, you know, for kids or whatever. Uh but we we have to run these large computers or data centers or whatever, but now even data centers are running on PCs and so on. Um and and so PCs were just a hugely disruptive uh force. Uh but there are technologies that come along and really benefit the incumbents and really don't really benefit the uh the uh new players, the startups. Uh I think Adam's right. It's uh it's both. Um and maybe for the first time it's kind of both like a a huge technology trend cuz the internet was hugely disruptive. Um but but this time uh it feels like it is an obvious supercharge for the incumbents for the hyperscalers for the large uh internet companies but it also enables uh new business models that uh that is perhaps counterposition against the uh the existing existing ones.

Al although the the you know I think what happened is everyone read that book and everyone learned how to not be disrupted. Uh for example Chad GPT was fundamentally counterposition against Google because uh Google had a business that that was actually working. Uh Chad GPT was seen as this uh technology that hallucinates a lot and creates a lot of bad information and Google wanted to be trusted and so Google had chatb internally. they didn't release Gemini until like two years after Chachup and Chachup had sort of already won the like at least brand recognition. Um and and so there there was in a way open AI came out as a disruptive technology uh but but now Google realizes it's a disruptive technology and kind of responds to it.

At the same time it was always obvious that AI is going to benefit Google at minimum. It's uh you know overview uh search overview has gotten a lot better. um all its uh you know workspace suite is is getting a lot better with Gemini. Uh their mobile phones, everything gets better. So it's it seems like it's it's both. Yeah, I I really agree. Like everyone read the book and and that changes what the theory even means because you have you've like all the all the public market investors have read that book and they now are going to punish companies for not adapting and reward them for adapting even if it means they have to make long-term investments. I think, you know, all the the management leadership of the companies have have read the book and they're on top of their game.

I think also just like the people running these companies are in I I guess I would say smarter I think than like the the companies from the generation that that book was sort of built on. and they're they're on at the top of their game and they are a lot of them are founder controlled and so they can make it's it's easier for them to sort of take a hit and and make these these investments. So that's I actually, you know, I think if if you had an environment more like we had in say like the '90s, I think this would actually be more disruptive than than the the current hyper hyper competitive uh world that we're in now.

One mistake that we as a firm have reflected on over the past few years, though of course I haven't been here for more than just a few months, is this idea of we've that we've passed on companies because we they weren't going to be the market leader or the or the category winner. And thus we thought, oh, you know, learning the lessons from from web 2, you have to invest in the in the category winner. That's where things are going to consolidate. Value is going to acrew over time. And um it seems so you why do the the next foundation model company if the first one already has a has a head start. Um but it seems like the market has gotten so much bigger that in foundation models but also in applications there's just multiple winners and they're kind of you know fragmenting you know and taking parts of the market that are all venture scale. I'm curious if this is a durable phenomenon or but um it that seems just one difference than than the web two era is just more winners um across more categories.

I think network effects are playing much less of a role now than they did in the web 2 era also and that that makes it easier for competitors to get started. There's still a scale advantage because you know if you have more users you can get more data. If you have more users, you can raise more capital. But that advantage is not it doesn't make it absolutely impossible for a competitor of smaller scale. It makes it hard, but it's there there's definitely like room for more winners than than there was before.

I I think another difference is that people are seeing the value um so strongly that they're willing to pay um early on in maybe a way that they the question with web two companies was how are they going to make money you know you were Facebook super early obviously you know Google etc was like oh how are they going to monetize and you know the companies here are monetizing from from the get-go you know your guys' companies included

yeah yeah and the I I think with the earlier generation of companies the monetization kind of depended on scale. Like you couldn't build a good ad business until you got to millions, tens of millions of users. And now with subscriptions, you can just charge right away, I think, especially thanks to things like Stripe that are making it easier. Um, and so that that that's also made it a lot more friendly to to new entrance.

There's there's also uh questions of geopolitics like you know it seems clear that we're not uh in this um globalized era and perhaps it's going to get much worse and so investing in the foundation in the open AI of of Europe might be a good idea and like similarly China being an entirely different different world and so there's um sort of a geo aspect of it that interesting

all of a sudden our geopolitics you know nerdiness is helpful is is useful. Um, Adam, you were talking about sort of human knowledge. Did you see yourself with Po kind of disrupting yourself in a sense or or talk about the the the bet that you you made with with PO and the sort of evolution there?

You know, I I think we saw Po more as just an additional opportunity than than as disruption to to Kora. Um the the way we got to it was we in early 2022 we started experimenting with using GBD3 to generate answers for Kora and and we compared them to the the human answers and sort of realized that they weren't as good but what was really unique was that you could instantly get an answer to anything you wanted to ask about and we realized it didn't need to be in public. It actually was your preference would be to to have it be in private and so we felt like there was just a new opportunity here to to let people chat with with AI and in private.

Yeah. Yeah. So it was also a bet on diversity of of model companies which took a while to play out. But I think now we're we're getting to the point where there's there's a lot of models. There's a lot of companies especially when you go across modalities. You think about image models, video models, audio models. Um especially like the reasoning research models are are sort of diverging. Agents are starting to be their own source of diversity. Um, so, so we're lucky to to now be getting into this world where there's there's sort of enough diversity for a a general interface aggregator to to make sense. Um, but yeah, it was it was a bet early on. We kind of

it's surprising actually that um even uh not particularly technical consumers actually do use multiple AIS. Uh like I didn't expect that like you know people only used Google. they never like looked at Google and then Yahoo or like very few people do it. But now you talk to just average people and they'll say, "Yeah, I use CHP most of the time, but Gemini is much better at like these types of questions." And it's like, "Oh, interesting. The sophistication of consumers have gone."

And even people saying that they have different personalities and they, you know, you know, sort of resonate with Claude more, you know, or whatever. the um I want to return back to this point you said earlier Adam about you're kind of talking about like dark matter about how we're going to you know brute force there's a lot of knowledge that people have that's you know sort of not um sort of categorized yet and it's not just task of knowledge it's actually knowledge that you could you know ask them about and they could describe it how you know because one question people have with LMS is like how much we've already trained the whole internet how much more knowledge is there um and so is it like 10x is it like a thousand like what is sort of the what is kind of intuitive sense of if we do brute force it and build this whole you know machine that gets all the knowledge out of humans onto sort of you know a data set that we can then you know implement how do we think about the upside from there

you know I think it's very hard to quantify but there's a massive industry developing around getting human knowledge into for the form where AI can use it so this is things like scale AI I Surge Merkore, but there there's a massive long tale of other companies just getting started. And as you have, you know, as intelligence gets cheaper and cheaper and more and more powerful, the bottleneck, I think, is increasingly going to be on the data and what do you need to create that intelligence? And so that's going to cause this that's going to cause more and more of this to happen. It might be that people can make more and more money by training AI. It might be that more and more of these companies get started. Um or it might be it might be that there's there's other forms of it. But I I think I think it's going to be sort of like the economy is going to naturally value whatever the AI can't do.

And what is the framework for what it can't like? what has meant a model for what it can't do? I don't, you know, you could you could ask a an AI researcher, they they might have a a better answer, but to me, there's just information that's not in the training set. And that is something that's inherently going to be, you know, going to be something AI can't do. There will be, you know, the AI will get very smart. It can do a lot of reasoning. It could prove every math theorem at some point. If it starts from, you know, some axioms that you that you give it, but if it doesn't know how did this particular company solve this problem 20 years ago, if that wasn't in the training set, then only a human who who knows that is going to be able to answer that question.

And so over time, how do you see Kora um interfacing with or like how are you running these in parallel? How how do you think about this?

Yeah, so I mean Kora, our focus is on human knowledge and and letting people share their knowledge and and um that knowledge may be helpful for you know it's it's it's helpful for other humans and it's it's also helpful for AI to to learn from. um we have relationships with some of the AI labs um and we're going to sort of play the role core will play the role that it is meant to play in this ecosystem which is a as a a source of of human knowledge. Um at the same time AI is making core a lot better. we've been able to make uh major improvements in moderation quality and in uh in ranking answers and in uh just just improving the product experience. So uh so it's gotten a lot better by applying AI to it.

Yeah. And and talk talk about your future as well. Obviously you know you had this business for for a long time you know focused on developers. Because at one point you're targeting you know u nonprofit. No exactly the edtech market I believe you did two or three million in revenue reported and then you know recently techrunch I know it's outdated but I think it reported something like 150 million. I know it's since you've had this incredible growth as as you've shifted the the business model um and and the customer segment. How do you think about the the future of replet? Um I think Kpathy uh recently said that it's going to be the decade of agents. Uh and I think that's absolutely right. It's um uh as opposed to like prior modalities of AI like when uh AI first came to coding it was autocomplete with co-pilot then it became sort of chat with chat

then I think cursor innovated on this composer modality which is like editing like large chunks of uh files but that's it. I think replet what Replet innovated is is is is the agent um and the idea of like not only editing code, provisioning infrastructure like databases, doing migrations, um you know connecting to the cloud, deploying uh having the entire debug loop like executing the code, running tests, um and so just like the entire development life cycle loop happening inside an agent and that's going to take a long time to mature.

So we're agent in beta came September 2024 and it was the first of its kind that did this both code and infrastructure but it was you know fairly janky didn't work very well and then agent v1 around December um it took another um uh generation of models so you go from claw 3.5 to 3.7 3.7 was the first model uh that uh really knew how to use a computer, a virtual machine. So, unsurprisingly, it was the first also computer use model. Um, and these things have been moving together. Uh, and so with every generation of models, we see we find new capabilities. And, um, you know, um, Agent V2 improved on autonomy a lot. Agent V1 could run for like 2 minutes. Agent V2, uh, uh, ran for 20 minutes. Agent 3, we advertised it as running for 200 minutes. just felt like it should be symmetrical, but like it's actually runs kind of indefinitely. Like we've had users running it for 28 plus hours. Wow.

Um, and the main idea there was that if we put a verify on the loop. I remember reading Deepseek uh a paper from Nvidia about how they um used Deepseek to write CUDA kernels and they were able to run Deepseek for like 20 minutes if they put a verifier in the loop like being able to run tests or something like that. And I thought oh okay so what kind of verifier can we put in the loop? Obviously, you can put unit tests, but unit test doesn't really capture whether the app is working or not. So, we started kind of digging into computer use and whether computer use was going to be able to test apps. Computer use is very expensive and um it's actually kind of still very buggy and like Adam talked about that's going to be uh a big area of improvement that'll unlock a lot of applications.

But we ended up building our own framework with like bunch of hacks and some some AI research and repless computer use I think testing models. I think one of the best. Um and uh and once we put that into the loop then you can put replet in high autonomy. So we have an autonomy scale. Uh uh you can you can you can choose your autonomy level and then it just writes the code goes and tests the applications. If there's a bug it reads the error log and like writes the code again and and can go for for for hours. And we've seen people build amazing things by letting it run for for a long time.

Now, that needs to continue to get better. That needs to um to get cheaper and faster. Uh so, it's not necessarily a point of pride to run for a lot longer. Like, it should be as fast as possible. So, we're working on that. Um a agent for there's a bunch of ideas that are going to be uh coming out. Agent 4, but one of the big things is you shouldn't be just like waiting for that one feature that you requested. you should be able to work uh on a lot of different features. So the idea of like parallel agents is very interesting to us. So you know you ask for a login page but you could also ask for a stripe uh checkout and and then you ask for an admin dashboard. The AI should be able to figure out how to paralyze all these different tasks or some tasks are not paralyzable but should also be able to do merge across the code. So being able to do collaboration across AI agents um is very important and that way the productivity of a single developer goes up by a lot.

right now even when you're using clot code or cursor and others that there isn't a lot of parallelism going on but I think the next uh boost in productivity is going to come from sitting in front of programming environment like replet and being able to manage uh tens of agents maybe at some point hundreds but you know at least you know five 6 7 8 9 10 agents uh all different all you know working in different parts of your your product. I also think that um UI and UX uh could could use a lot of work in terms of um right now um you're trying to translate your ideas uh into this like textual representation. I'm just like like a PRD, right? The what product managers do, right? Just product descriptions. But product descriptions don't it's really hard and you see it in a lot of tech companies. it's really hard to align on the exact features because it's l language is fuzzy.

And so I think there's a there's a world in which you're interacting with AI in a more multimodal fashion. So open up uh like a whiteboard and being able to draw and like diagram with AI and and and really work with it like you work with a human. Uh and then um then the next stage of that uh having uh like better memory better memory inside the project but also across project and perhaps having different instantiations of replet agent that uh you know that this this agent is really good at like um Python data science because um you know it has all the information and skills and memories of about my company what it's done in the past. So I'll have a data analysis like sort of rapid agent and I'll have like a front-end replet agent and they have memory over multiple projects and over time and over interactions and maybe they sit in your Slack like a like a worker and you can like talk to them. So again like I can I can keep going for another 15 minutes about a road map that could span like 3 to four to 5 years perhaps. and but but this this agent this agent phase that we're in is just there's so much work to do and it's it's it's going to be a lot of fun.

Yeah, it's a I was talking to one of our mutual friends, one of the co-founders of one of these uh you know big productivity companies and he leads a lot of their R&D and he's like man uh during the week these days I'm not even talking to humans anymore as much. I'm just like it's just you know using all all these agents to to build. So it's living in the future to some degree is already in the present.

There's something interesting about that and that are people talking to each other less at at companies and is that a bad thing? Um so it's a you know I think uh I I I'm starting to think more about these second order effects of of things like that. um uh you know will it make it awkward for like again the new grads I feel so bad for them like uh you know if if people are not sharing as much knowledge between each other or it's like it's not culturally easy to go ask for help because like you should be able to use AI agents uh there's something there's some cultural forces that I think need to be reckoned with.

Yeah, I think a lot of tough cultural forces for zoomers these days. Yes.

Um let's gearing towards closing here. Um obviously you guys are you know focused on running your companies but to stay current on the AI ecosystem. You you guys also make angel investments as well. Um where are you guys most uh most excited? Um you we haven't talked about robotics. Are you guys bullish on on robotics in the in the near term or any emerging categories or use cases or spaces that you're looking to make more investments in or you have made some? I just think vibe coding generally is just unbelievably like high potential. Um just the idea that all the you know this

you think underhyped even still I think so I I I think you know just opening up the potential of software to the mainstream of you know every everyone. I think that and yeah and actually I think one reason I think it's underhyped is that the tools are still very far from what you can do as a professional software engineer and if you imagine that they're going to get there and I think there's no reason why they wouldn't might it'll take a few years but um then it's like everyone in the world is going to be able to create any things that would have taken a team of 100 professional software engineers that's just going to massive open up opportunities for for everyone. So I think Replet is like a great example of this, but I think it's also going to that there will be cases other than just like building applications that that this also creates.

By the way, just on that note, if you were going to Stanford or Harvard, you know, today 2025, just entering, would you major again in computer science or just focus on building something or

I think I would. I mean I I I went to college starting in 2002 and it was right after the dotcom bubble had burst and there was a lot of pessimism and I remember my um my roommate his parents had told him like don't study computer science even though that was that was something he really liked. Um and I just kind of did it because I I liked it. And I think that I think that it's definitely like the job market is worse than it was a few years ago.

At the same time, I think having these skills to understand the sort of fundamentals of what's possible with algorithms and data structures, I think that actually really helps you in in managing agents when when you're using them. Um, and I I I'm guessing that it will continue to be a valuable skill in the future. I also think the other question is like what else are you going to study? And and every single thing you could imagine, there's an argument for why it's going to be automated. So, I think you might as well study what you enjoy and and and I think this is as good as as anything.

Yeah. I um I think there's a lot to to get excited by. One thing is maybe kind of random, but like I get really fired up to see like mad science experiments like the uh Deepseek OCR that came out the other day. Did you Did you see it? It's It's wild where um correct me if I'm wrong cuz I only looked at it briefly, but basically you can um get a lot more economical with a context window if you like have a screenshot of the text instead of the [ __ ] text.

Yeah, I'm not I'm not the right person to be correcting you on. than that. But like it's there's there's definitely some some really interesting things. Yeah, I saw another thing on hacker news the other day where um you know uh text diffusion uh where someone made a text diffusion model by instead of doing go saying dnoising he would take like a single BERT instance and like try to you know mask different words and uh and just predict like these different tokens and um and so we have a lot of components like I don't think people think a lot about that you know we have now the you know base pre-trained models. We have the all these RL reasoning models. We have the uh you know encoder decoder models. We have diffusion models. We have there's all these different things like just like you know you mix them in different ways.

And um and so we have a lot of components like I don't think people think a lot about that you know we have now the you know base pre-trained models. We have the all these RL reasoning models. We have the uh you know encoder decoder models. We have diffusion models. We have there's all these different things like just like you know you mix them in different ways.

Yeah. Uh I feel like there isn't a lot of that. I mean it' be great. It'd be great if like a new research company just like comes out and is like not trying to like compete with OpenI and things like that but instead uh is just trying to like discover how to put these different components together in order to create a new flavor of these models.

Yeah. In crypto they talk about composability and like mixing primitives together and and AI maybe there needs to be more exploation.

There's less playing around I found like there is like I remember in the like web 2.0 era when we were like playing around with JavaScript what browsers could do and what web workers could do whatever there was a lot of like really interesting weird experiments I mean replet was born out of that the original version of replet in open source pre pre the company which my interest was like can you compile C to JavaScript right that was like one of the interesting things that became WM by the time it was uh mcriptton and it was like such a such a nasty hack and um but I think there's so much I think We're in an era of Silicon Valley where it's like very uh very getrich driven and that makes me a little sad and that's partly why I moved the company out of SF. I feel like the culture in SF has has gotten maybe to maybe I I I wasn't there but like during the com era a lot of people talked about how it's sort of like get rich fast or the crypto thing. So I feel like there needs to be a lot more tinkering and I would love to see more of that and more companies getting funded that are trying to just do something a little more novel even if it doesn't mean like it fundamentally new new model.

Last question. Um Amad you've uh been into consciousness for a long time. Are are you bullish that we will um via some of this AI work or just some you scientific progress elsewhere make some progress in understand in in uh you know getting across this this hard problem or you know something happened recently uh which is interesting um uh cloud 4.5 uh seemed to have to become more aware of its context length. So as it gets closer to the end of the context, it starts be becoming more economical with tokens, it also it looks like its awareness when it's being redteamed or in a test environment like jumped significantly. And so there's something happening there that's quite interesting.

Now I think uh in terms of you know the the question of of consciousness it is still fundamentally not a scientific question and there is a sort of uh we've given up on trying to make it scientific but I think it I think this is also uh the problem that I talked about with all the energy going into LMS um uh no one is trying to really think about the true nature of intelligence, true nature of uh consciousness. Um, and there's a lot of really core core questions. Like one of my favorite one is uh the uh Roger Penrose um Emperor's New Mind where he wrote a book about how everyone in the sort of philosophy of mind space uh and perhaps the larger scientific ecosystem start thinking about the brain in terms of a computer. And in that book he tried to show that it fundamentally is impossible for the brain to be uh a computer because uh humans uh are able to do things that touring machines cannot do or Turing machines like fundamentally get get stuck on such as um uh you know just uh basic logic um puzzles uh that we're able to kind of detect, but like there's no way to encode that in a in a in a cheering machine. For example, like this statement is false. You know, those like old logic puzzles.

Um and uh anyways it's like a complicated argument but uh if you read that book or or many others uh there's like a core strain of arguments in the theory of mind about how uh computers uh are fundamentally different from from human intelligence and uh and so yeah I I haven't really I've been very busy so I haven't really updated my thinking too much about that But but I think there's there's a there's a there's a huge field of study there that is not being studied.

If you were a freshman uh entering college today, would you study philosophy?

I would do that. I would definitely study philosophy of mind. I would probably go into neuroscience. Uh cuz I think those are the core questions that are kind of become very very important as AI kind of continues to see more of jobs and economy and things like that.

That's a great place to wrap. I'm John. Adam, thanks for coming on the podcast. Thank you. Thank you. [Music]