a16z Podcast - Amjad Masad & Adam D’Angelo: How Far Are We From AGI?

Episode Date: November 7, 2025

Adam D’Angelo (Quora/Poe) thinks we're 5 years from automating remote work. Amjad Masad (Replit) thinks we're brute-forcing intelligence without understanding it.In this conversation, two technical founders who are building the AI future disagree on almost everything: whether LLMs are hitting limits, if we're anywhere close to AGI, and what happens when entry-level jobs disappear but experts remain irreplaceable. They dig into the uncomfortable reality that AI might create a "missing middle" in the job market, why everyone in SF is suddenly too focused on getting rich to do weird experiments, and whether consciousness research has been abandoned for prompt engineering.Plus: Why coding agents can now run for 20+ hours straight, the return of the "sovereign individual" thesis, and the surprising sophistication of everyday users juggling multiple AIs. Resources:Follow Amjad on X: https://x.com/amasadFollow Adam on X: https://x.com/adamdangelo Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 agriculture revolution and industrial revolution. We're going through another revolution. We will not be able to call it something. It's the future people who will call it something. But we are going through something. The number of solo entrepreneurs that this technology is going to enable is 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 massive. The age of solo entrepreneurship powered by AI is here, but the pact of full automation is messier than the hype suggests. Today, you'll hear from Adam DeAngelo, founder of Quora and CEO of Poe, and Androd Mossad, founder and CEO of Replit, on why we're in a brute force era of AI rather than true intelligence, and what that means for the future of work. We discussed the expert data paradox, how automating entry-level jobs creates a crisis

in training the next generation of experts, why managing tens of agents in parallel will define the next wave of productivity, and how the sovereign individual framework might be the best lens for understanding AI's economic and political impact. Plus, Adam makes the case for why Vyde coding is radically underrated, and Amjad explains what Claude 4.5's strange new self-awareness might signal about the path ahead. Let's get into it. Adam, I'm John. 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 LLMs, why they won't get a stage GI. Well, maybe 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, honestly, I don't know what people are talking about. 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, things like the improvement in code generation ability, the improvements in video gen, it seems like things are going faster than ever. And so I don't really understand where the kind of bearishness is coming from. Well, I think there's some sense that we hoped that they would be able to replace all of tasks or all jobs. And maybe there's some sense that it's like middle to middle,

but not end to end. and maybe labor won't be automated in the same way that we thought it would on the same timeline. Yeah, I mean, I don't know what the previous timelines people were thinking were, but I think if you go five years out from now, we're in a very different world. 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 use its intelligence. And then there's some things like computer use that are still not quite there, but I think we'll almost definitely get there

in the next year or two and when you have that I think we're going to be able to automate a large portion of what people do I don't know if I would call that AGI but I think it's going to satisfy a lot of the critiques that people are making right now

I think they won't be valid in a year or two what is your definition of AGI? I don't know. Everyone thinks it's something different one definition I kind of like is 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, that's AGI. You can then say, does it have to be better than the best person in the world at every single job. Some people call that ASI. It does something better than

teams of people. You can argue with those different definitions. But I think once we get to be better than a typical remote worker at the job they're doing, we're living in a very different world. And I think that's effectively what people, that's a very useful anchor point for these definitions. So in summary, you're not sensing the same limitations of LOMs that other people are. You think there's a lot more room that LOMs can go from here. We don't need like a brand new architecture or other breakthrough. I don't think so. I mean, 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, but we just don't seem to be hitting any kind of limits. The progress in reasoning models is incredible, and I think the progress in pre-training 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 the next few years. I'm Judd, what's your reaction hearing all this? Yeah, I think I've been pretty consistent and consistently right, perhaps. Dear I say. Consistent with yourself or consistent with what I'm with myself

and with I think how things are unfolding. That I started being a bit of a more public doubter of things around the time when the AI safety discussion was reaching its height back in maybe 22, 23. And I thought it was important for us to be realistic about the progress because otherwise we're going to scare politicians. We're going to scare everyone. DC will descend on Silicon Valley. They'll shut everything down.

So my criticism of the idea of AGI 2027, you know, that paper that I think is called Alessender or someone else wrote. And then in this situational awareness and all this hype papers that are not really science, they're just vibe. Here's what I think will happen.

The whole economy will get automated. Jobs are going to disappear. All of that stuff is that, again, is just, I think it's unrealistic, it is not following the kind of progress that we're seeing, and it is going to lead to just bad policy. So my view is

LMs are amazing machines. I don't think they are exactly human intelligence equivalent. You can still trick LMs with things like they might have solved the strawberry one,

but you can still trick it with single sentence questions, like, how many are in this sentence. I think I tweeted about it the other day, which was like three out of the four models, couldn't, didn't get it even. And then GPD5 with high thinking had to think for 15 seconds in order to get a question like that. So LMs are, I think, a different kind of intelligence than what humans are. And also 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 LM itself and the training data or and the infrastructure around and everything that we're doing

to make them work. But that makes me less optimistic that we're, we've cracked intelligence. And I think once we truly crack intelligence, it'll feel a lot more scalable and that you can, and that the idea behind the bitter lesson will actually be true in that you can just pour more, more power, more resources, more compute into them and they'll just scale more naturally. I think right now, there's a lot of manual work going into making these models better. In the true pre-training scaling era, 2.2, 3, 3.5, maybe up to 4, it felt like you can just put more Internet data in there and it just got better.

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 contrived RL environments are getting created in order to make LMs good at coding and becoming quoting agents, and they're going to go do that. I think the news from OPI that they're going to do that for investment banking. And so I try to coin the term I call functional AI, which is the idea that you can automate a lot of aspects of a lot of jobs by just going in and collecting as much data and creating these RL environments. It's going to take enormous efforts and money and data and all of that in order to do. And I think I agree with

Adam that things are going to get better 100%. Over the next three months, six months, clot 4.5 was a huge jump. I don't think it's appreciated how much of a jump it was over four. There's really, really amazing things about clot 4.5. So there is progress. We're going to continue to see progress. I don't think LMs, as they can understand, are on the way to AGI. And my definition for AGI is, I think, the old school RL definition,

which is a machine that can go into any environment and learn efficiently in the same way that a human could go. into you can put a human into a pool game and within two hours they can chew pool and be able to do it right now there's no way for us to have machines learn skills like that on the fly everything requires enormous amount of data and computer and time and effort and more importantly it requires human expertise which is the non-bitter lesson idea which is human expertise is not scalable and we are reliant today we are in a human expertise regime yeah i mean 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 kind of intelligence. And so because it didn't have this massive... equivalent of evolution, it just has pre-training 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 the functional consequence, so if you're like, when will the job landscape change, when will the 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 that's, it is, it feels like we're in a brute force type of regime, 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 diversity? I don't think that we'll get to the singularity or I don't think we're going to get to the next level of human civilization until we crack the true nature of intelligence, like until we understand it and have algorithms that are actually not brute force. And you think those will take a long time to come? I'm sort of agnostic on that. It just does feel like the LMs in a way are distracting from that

because all the talent is going there and therefore there's less talent that are trying to do basic research on intelligence. 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. And so you have 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 five years on it.

But basic research is different, right? like trying to like trying to get into the fundamentals and as supposed to like there's a lot of industry research like how do we make these things more useful in order to generate profit and I think that's different and often I mean Thomas Coon

this philosopher's 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 things think about physics and how there are like these industry of our string theory and like it pulls everything in and there's sort of a 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 that's right

you can have a chance at changing the paradigm it's very pessimistic about paradigm but I guess I feel like the current paradigm this is maybe 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 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's say we continue to brute force it we're able to automate a much of labor do you estimate that GDP is something of you know four or five percent a year or are we going up to you 10% plus or what does it do to the economy? I think it depends a lot on exactly where we get to and what AGI means.

But so, so let's say you have, let's say you have LLMs 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 let's take that as a theoretical point you could get to. I think you're going to get to much more than 4 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%. And I do think at some point you get to LMs, they can do everything, every single thing a human can do for cheaper. I don't see a reason why we don't eventually get there. That may take 5, 10, 15 years. But I think until you get there, we're going to get bottlenecked on the things that the LMs still can't do or the, you know, building enough power plants to supply the energy

or other bottlenecks in the supply chain. One thing I worry about is the deleterious effect of LMs in the economy in that say LMs effectively automate the entry level job but not but but the

but not the experts job right so let's take you know QA QA QQAQQA QA assurance and it's it's so good but there's still all these long till

events that it doesn't handle and so you have a lot of really good QA people now like managing like hundreds of agents and you effectively increase productivity a lot but they're not hiring new people because the agents are better than new people and 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 think that's you know I think it's happening with CS majors graduating from college there's just not as many jobs as there used to be and LLMs are a little more substitutable for what they previously would have done and 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 companies paid a lot money to employ them and train them. And so 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 teach people these things. But for sure, that's an issue right now. Another related problem is that since we're dependent on expert data in order to train the LMs and the LMS start to substitute those workers, but at some point there's no more experts because they're all out of the jobs

and they're equivalent to that alums but if the alums is truly dependent on labeling data expert RL environments then how would they improve beyond that? I think that's something a question for an economist to really sit down and think about as like once you get the first tick of automation I mean there are some challenges there

And so how do you go, how do you go to the next part? Yeah. I mean, I think a lot of it's going to depend on how good of RL environments can be created. So, you know, in one extreme, you have something like AlphaGo where it's just a perfect environment and you can just blast past expert level. But I think a lot of jobs have limited data that anyone can train from. And so I think it'll be interesting to see how easy is it for research efforts to overcome that bottleneck. If you had to make a guess on what job category is going to be introduced or explode in the future, you know, some people say it's like the, you know, everyone's an influence or, you know,

or in some sort of caring field or, you know, everyone's employed by the government and some sort of bureaucrat thing or maybe training the AI in some way. You know, as more and more things start to get automated, you know, what is your guess as to what more and more people start to do? You know, doing art and poetry is... Yeah, I mean, 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 humans at chess. so I don't think that's a bad world if people are all just kind of free to pursue their hobbies as long as you have some kind of way to distribute wealth so that so people can afford to live 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 don't know how much but yeah in the in the I'll put it in the at least 10. 10 years range, I think in the near term, the job categories that are going to explode, the jobs that can really leverage AI. And so people who are good at using AI to accomplish their jobs, especially to accomplish

things that the AI couldn't have done by itself, there's just massive demand for that. I don't think we're going to get to a point where you automate every job. Definitely not in the current paradigm. would, uh, I would doubt it happening. I, I'm not certain it would ever happen, but definitely not in the current paradigm. Now, here's what 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, create, uh, you need to be actually human. human, humans, unless AI is actually embodied in a human experience, then humans will always be the generators of ideas in the economy. Adam, there's one time's point around the human part, because you created one of the most, you know, the best wisdom of the crowds, you know, platforms in the universe, and now you've gone, you know, all in with Poe. What are your thoughts on, you know, to what extent will we be relying on? humans versus will we be trusting AI to be our therapist, 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 often know a lot of things that are not written down anywhere. And you call test and knowledge, but also what they're capable of writing down if you did ask them a question. I think there's still an important role for people to play in the world by sharing their knowledge, especially when they have knowledge that just wasn't otherwise in an LLM's training set.

You know, whether they will be able to make a full-time living doing that, I don't know. But if that becomes a bottleneck, then for sure that's going to mean that all the sort of like economic pressure 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 recommender systems the system that ranks your Facebook or Instagram or Cora feed

those recommender systems are already superhuman at predicting what you're going to be interested in reading. Like if I gave you a task that was like, make me a feed that I'm going to read, like there's just no way,

no matter how much you knew about me, there's no way you could compete with these algorithms to 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 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 process for doing work. Or chefs or, you know, 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 just, they have very little data compared to what AI can be trained on. So I don't know how that's going to shake out. That's a good point. I mean, ultimately, what recommender systems 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 yes it's true in recommender systems but I'm not entirely sure it's true of of everything um but so 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 had it because I think part of it is will be slightly unstable, unstable 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 polemic 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 agriculture revolution and the industrial revolution. We're going through another revolution, clearly. Information revolution, now we call it intelligence revolution, whatever. I think we will not be able to call it something and say 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 ultimately you're going to have large swaths of people that are potentially unemployed or economically not 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. So because they have this, because they're very regenerative, they have interesting ideas, they're human, 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, you know, today's politics is based on every human being economically productive. But when you have only, we have massive automation and then you have a few entrepreneurs and very intelligent generative people are actually able to be productive, then the political, structures also change. And so they talk about how the, you know, nation, states 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 desire but but I do think it's worth thinking about when when people are not the you know unit of economic productivity things have to change including culture and and politics yeah I think there's a question with that book and some of this conversation more broadly of like when does the technology reward the, you know, the defender versus the sort of aggregator or something,

or like the, when does it incentivize more decentralization versus centralization? Like, remember Peter Thiel 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's not obvious to me that that's entirely accurate on either side. AI does seem to empower a bunch of individuals, as you were. saying, and then also, you know, crypto turns out as like fintech, it's like stable, you know, it does empower sort of, you know, in nation states we're talking about doing the sort of like, you know, 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 center? And I think if it empowers the edges, it seems like the sovereign individual is, and maybe there's a barbell where it's like both, basically, the big, the income that's just get much, much, much bigger, and there's like these edges, but anyways, that's on it. Yeah. I'm very excited for the number of solo

entrepreneurs that this technology is going to enable. I think it's just greatly, it's vastly increased 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. And now that one person can bring these things into existence, I think we're going to see a lot of really amazing stuff.

Yeah, I get these treats all the time about people who like with their jobs because they started making so much money you're using tools like Rapplet and it's really exciting. I think for the first time, opportunity is massively

available for everyone. 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 not as it. 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 incident so to ask it another way do you think that most of the value

capture is going to come from companies that were scaled pre-open AI starting so replet still counts as the latter and sort of as court in some degree or 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 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 hypers

that the there's enough competition that as an application level company you have choice and you have alternatives and the prices are coming down incredibly quickly but there's also not so much competition

that the hyperscalers and the 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 pretty good balance and we're going to have a lot of new companies and a lot of growth among the hyperscalers. I think that's that's about right. So the terminology of sustaining versus disruptive comes from the innovators dilemma. And it's this

idea that whenever there's a new technology trend, it's 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, it goes up the power curve and eventually the disrupts even the incumbents. So originally the incumbents don't pay attention to it because it looks like a toy and then eventually disrupts everything and eats the entire sort of market. So that was true of PCs. You know, when PCs came along, the big main frame manufacturers did not pay attention to it. And initially it was like, yeah, it's for kids or whatever.

But we have to run these large computers or data centers or whatever. But now even data centers are running on PCs and so on. And so PCs were this a hugely disruptive force. But there are technologies that come along and really benefit their incumbents and really don't really benefit the new players. startups. I think Adam's right. It's both. And maybe for the first time it's kind of both like a huge technology trend because the internet was hugely disruptive. But this time it feels like it is an obvious supercharge for the incumbents, for the hypers, for the large internet

companies. But it also enables new business models that that is perhaps counterposition against the existing ones. Although I think what happened is everyone read that book and everyone learned how to not be disrupted. For example, Chad Chipt-D was fundamentally counterposition against Google because Google had a business that was actually working. Chad ChbPT was seen as this technology that hallucinates a lot and creates a lot of that information,

and Google wanted to be trusted. And so Google had ChatchapD internally. They didn't release Gemini until like two years after Chat Chitpity, and chat ChitpD had sort of already won that, like, at least a brand recognition. And so there was in a way Open AI came out as a disruptive technology. But now Google realizes this is a disruptive technology and kind of response to it. At the same time, it was always obvious that AI is going to better with Google. At minimum, it's, you know, overview, search overview has gone a lot better.

all its workspace suite is getting a lot better with Gemini their mobile phones everything gets better so it seems like it's both

yeah I really agree like everyone read the book and that changes what the theory even means because you have you have 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 all the

the management leadership of the companies 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 guess I would say smarter. I think then like the companies from the generation that that book was sort of built on and 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 easier for them to sort of take a hit. 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 current hyper competitive world that we're in town one mistake that we 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 that we've passed on companies because we,

they weren't going to be the market leader or the category winner. And thus we thought, oh, you know, learning the lessons from from Web 2, you have to invest in the category winner. That's where things are going to consolidate. Values going to accrue over time. And it seemed, so why do the next foundation model company if the first one already has a as a head start? But it seems like the market has gotten so much bigger than, that in foundation models, but also in applications, there's just multiple winners, and they're kind of fragmenting

and taking parts of the market that are all venture scale. I'm curious if this is a durable phenomenon, but that seems just one difference than the Web 2 era, is just more winners 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 makes it easier for, competitors to get started. There's still a scale advantage

because 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's definitely like room for more winners than there was before. I think another difference is that people are seeing the value so strongly that they're willing to pay early on and maybe a way that they, the question with Web 2 companies was,

how are they going to make money? And you were Facebook super early, obviously, you know, Google, et cetera, was like, oh, how are they going to monetize? And, you know, the companies here are monetizing from the get-go, you know, your guys' companies include it. Yeah, yeah. And the, I think with the earlier generation of companies, the monetization kind of depended on scale.

Yeah. 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. And so that's also made it a lot more friendly to new entrants. There's also questions of geopolitics. Like, you know, it seems clear that we're not in this globalized era and perhaps a going to get much worse. And so investing in the foundation, in the open AI of Europe might be a good idea. And like similarly, China being in an entire different, different world. And so

there's sort of a geo aspect of it. Yeah. All of a sudden, our geopolitics, you know, nerdiness is helpful, is useful. Adam, you know, we were talking about sort of human knowledge. Did you see yourself with Po kind of disrupting yourself in a sense? Or talk about the, the, the bet that you made with Poe in the sort of evolution there? You know, I think we saw Po more as just an additional opportunity than as disruption to Quora. The way we got to it was we, in early 2022, we started experimenting with using GPD3 to generate answers for Quora. And we compared them to 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 have it be in private. And so we felt like there was just a new opportunity

here to let people chat with AI and in private. Yeah. And it seemed like you were also making a bet on how the different players were going to, that was going to be. Yeah, yeah. So it was also a bet on diversity of model companies which took a while to play out but I think now 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 especially like the reasoning research models are sort of diverging agents are starting to be their own source of diversity so so we're lucky to now be getting into this world where there's there's sort of enough diversity for a general interface aggregator to make sense. But yeah, it was a bet

early on. We kind of... It's surprising, actually, that even not particularly technical consumers actually do use multiple AIs. Like, I didn't expect that, like, you know, people only use 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 chatypsum most of the time, but Jev and I is much better at like these types of questions. And so yeah, interesting. The sophistication of consumers have gone on.

And even people saying that they've different personalities and they, you know, you know, sort of resonate with Claude more, you know, or whatever. The, I want to return back to this point we said earlier about 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 sort of categorized yet.

And it's not just tacit 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 the alums is like how much we've already trained the whole internet, how much more knowledge is there? Is it like 10x? Is it like a thousand? Like, what is sort of the, what is your 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 the form where AI can use it. So this is things like scale AI, surge, Mercor, but there's a massive long tail 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 or it might be that there's other forms of it but I think it's going to be sort of like the economy is going to

naturally value whatever the AI can't do what is the framework for what it can't like what it's meant to model for what it can't do? I don't you know you can ask an AI researcher they might have 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 it's going to be something that 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 knows that is going to be able to answer that question. And so over time, how do you see Quora interfacing with, like, how are you running these in parallel? How do you think about this? Yeah, so I mean, Quora, our focus is on human knowledge and letting people share their knowledge and that knowledge may be helpful for, you know, it's helpful for other humans and it's also helpful for AI to learn from. We have relationships with some of the AI labs.

And we're going to sort of play the role. Cora will play the role that it is meant to play in this ecosystem, which is as a source of human knowledge. At the same time, AI is making Cora a lot better. We've been able to make major improvements in moderation quality and in ranking answers and in just improving the product experience. So it's gotten a lot better by applying AI to it.

Yeah. And I'm going to talk about your future as well. Obviously, you know, you had this business for a long time, you know, focused on developers. At one point, you're targeting, you know... There's a non-profit. No. Exactly.

The end tech market, I believe you had two or three million in revenue reported. And then, you know, recently a tech grant, I know it's outdated, but I think it's a report is like 150 million. I know it's higher since you've had this incredible growth as you've shifted the business model and the customer segment. How do you think about the future of Replit? I think Carpathie recently said that it's going to be the decade of agents. And I think that's absolutely right. It's as opposed to like prior modalities of AI, like when AI first,

came to coding. It was auto-complete with Coal Pilot. Then it became sort of chat with Chat Tipiti. Then I think Cursor innovated on this composer modality, which is like editing like large chunks of

files, but that's it. I think what Repleta innovated is the agent and the idea of like not only editing code, provisioning infrastructure like databases, doing migrations, you know, connecting to the cloud, deploying,

having the entire debug loop, like executing the code, running tests. And so just like the entire development lifecycle loop having 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 first of its kind that did this both code and infrastructure, but it was fairly janky,

didn't work very well. And then Agent V1 around December, it took another generation of models. So you go from Claw 3.5 to 3.7. 3.7 was the first model that really knew how to use a computer, a virtual machine. So unsurprisingly, it was the first also computer use model. And these things have been moving together. and so with every generation of models we find new capabilities

and you know Agent V2 improved on autonomy a lot Agent V1 could run for like two minutes Agent V2 ran for 20 minutes Agent 3 we advertised it as running for 200 minutes it just felt like it should be symmetrical but like it actually runs kind of indefinitely like we've had users running it

for 20 plus hours and the main idea there was that if we put a verify on the loop, I remember reading Deepseek, a paper from Nvidia about how they used Deepseek to write Kuta kernels and they were able to run deepseek for like 20 minutes if they put a verifier on the loop,

like being able to run tests or something like that. And I thought, oh, okay, so what kind of verify can we put on the loop? Obviously, you can put unit tests, but unit tests doesn't really capture whether the app is working or not. So we started kind of digging into computer, and whether computer use was going to be able to test apps.

Computer use is very expensive and it's actually kind of still very buggy. And like Adam talked about, that's going to be a big area of improvement that will unlock a lot of applications. But we ended up building our own framework with like a bunch of hacks and some AI research and Rapplet's computer use, I think, testing models, I think one of the best. And once we put that into the loop, then you can put Rapplet in high autonomy, so we have an autonomy scale, you can choose your autonomy level.

And then it just writes the code, goes and test the applications. If there's a bug, it reads the error log and writes the code again and it can go for hours. I've seen people build amazing things, but let it run for a long time. Now that needs to continue to get better. That needs to get cheaper and faster.

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 agent for there's a bunch of ideas that are going to be coming out agent for 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 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

or Stripe checkout 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 is very important. And that way, the productivity of a single developer goes up by a lot. Right now, even when you're using CloudCode, a cursor, and others, there isn't a lot of parallels I'm going on.

But I think the next boosts in productivity is going to come from sitting in front of programming environment like Replit and being able to manage tens of agents, maybe at some point hundreds, but at least five, six, seven, eight, nine, ten agents, all different, all different, you know, working in different parts of your product. I also think that UI and UX could use a lot of work in terms of rate. now you're trying to translate your ideas into just like textual representation. I'm just like a PRD, right? What product managers do, right? Just product descriptions. But product descriptions, it's really hard. And you see in a lot of tech companies, it's really hard to align on the exact features because it's like 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 really work with it like

you work with a human. And then the next stage of that, having like better memory, better memory inside the project, but also across the project and perhaps having different instantiations of Replit agent that, you know, that this agent is really good at like Python data science because it has all the information and skills and memories about my company what it's done in the past so I'll have a data analysis like sort of Rapplet agent

and I'll have like a front end Rapplet agent and they have memory over multiple projects and over time and over interactions and maybe they sit in your Slack like a worker and you can like talk to them so again like I can keep going for another 15 minutes about a roadmap that could span like

three to four to five years perhaps but 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 one of these uh you know big productivity companies and he leads a lot of their 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 these agents to to build so it's living in the future to some is already in the present. There's something interesting about that and that

are people talking to each other less at companies? And is that a bad thing? So, it's, you know, I think I'm starting to think more about the second order of facts of things like that. You know, will it make it awkward

for like, again, the new grads I feel so bad for them. Like you know, 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 you should be able to use AI agents. There's some cultural forces

that I think need to be reckoned with. Yeah. I get a lot of tough cultural forces for Zoomers these days. Yes. Gearing towards closing here, obviously you guys are focused on running your companies, but to stay current on the AI ecosystem, you guys also make angel investments as well.

Where are you guys most excited? we haven't talked about robotics or you guys bullish on robotics 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 actually think vibe coding generally is just

unbelievably like high potential. Just the idea that all the, you know, this is underhyped even still? I think so. I think you know, just opening up the potential of software to the mainstream

of, you know, every, everyone, I think that, and yeah, I actually 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, it'll take a few years, but then it's like everyone in the world is going to be able to create any things that would have taken a team of a hundred, professional software engineers that's just going to massively open up opportunities 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 this also creates. By the way, just on that note, if you were going to Stanford or Harvard,

you know, today, 2025, would you major again in computer science or just focus on building something? I think I would. I mean, I went to college starting in 2002 and it was right after the dot-com bubble had burst and there was a lot of pessimism and I remember my roommate, his parents had told him

like, don't study computer science, even though that was something he really liked. And I just kind of did it because 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 managing agents when you're using them. And 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 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 I think this is as good as anything. Yeah, I think there's a lot 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 deep seek OCR that came out of the other day. Did you see it?

It's wild where, correct me if I'm wrong, because I only looked at it briefly, but basically, you can get a lot more economical with a context window if you, like, have a screenshot of the text instead of as a fucking text. Yeah, I'm not the right person to be correcting you on. But, like, there's definitely some really interesting things. Yeah, I saw another thing on Hacker News Saturday where text diffusion where someone made a tax diffusion model by instead of doing Gaussian denoising, he would take

like a single Burt instance and like try to mass different words and just predict like these different tokens and so we have a lot of components and I don't think people think a lot about that. You know, we have now the base pre-trained models We have all these RL reasoning models.

We have the encoder decoder models. We have diffusion models. There's all these different things, like, just like, you know, you mix them in different ways. I feel like there isn't a lot of that. I mean, it'd be great if a, like, a new research company just, like, comes out and it's, like, not trying to, like, compete with OpenEI and things like that. But instead, it's just trying to, like, discover how to put these different components together.

in order to create a new flavor of these models. In crypto, they talk about composability and mixing primitives together and AI, maybe there needs to be more. There's less playing around, I found. Like, there is, like, I remember in the, like, web 2.1 era when we were, like, playing around with JavaScript and what browsers could do and what web workers could do, whatever.

There was a lot of, like, really interesting, weird experiments. I mean, Replit was born out of that, the original version of Replit in open source pre the company, which my interest was like, can you, compile C to JavaScript, right? That was like one of the interesting things. And that became WASN by the time it was MScriptin. And it was like such a nasty hack.

But I think there's so much, I think we're an era of Silicon Valley where it's like very, very get rich 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 wasn't there but like during the dot 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 fundamentally new model last question Amjad you've been into consciousness for a long time

Are you bullish that we will, via some of this AI work or just some scientific progress elsewhere, make some progress and understand in, you know, getting across this hard problem? You know, something happened recently, which is interesting. Quad 4.5 seemed to become more aware of its context length. So as it gets closer to the end of the context, it starts becoming more economical with tokens. It also, it looks like its awareness when it's being red-teamed or in test environment, like jumped significantly. And so there's something happening there that's quite interesting. Now, I think in terms of, you know, the question of consciousness, it is still fundamentally not a scientific question. and there is a sort of

we've given up on trying to make a scientific but I think this is also the problem that I talked about with all the energy going into LMs no one is trying to really think about

the true nature of intelligence true nature of consciousness and there's a lot of really core core questions like one of my favorite one is the roger penrose emperor's new mind where he wrote a book about how everyone in the sort of philosophy of mind space and perhaps the larger scientific ecosystem started 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 a computer because humans are able to do things that Turing machines cannot do or Turing machines like fundamentally get stuck on, such as, you know, just basic logic puzzles that were able to kind of detect, but like there's no way to include that in a 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 And so, yeah, I haven't really, I've been very busy, so I haven't really updated my thinking too much about that.

But I think there's a, there's a huge field of study there that is not being studied. If you were a freshman entering college today, would you study philosophy? I would do that. I would definitely study philosophy of mind. I would probably go into neuroscience because I think those are the core questions that are kind of become very, very important. as AI kind of continue 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.

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