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

0:00Nothing 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 the industrial revolution. We're going through another revolution. We will not be able to call it something. Future people will call it something. But we are going through something. The number of solo entrepreneurs that this technology is going to enable has 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.

0:38The age of solo entrepreneurship powered by AI is here, but the path to full automation is messier than the hype suggests. Today, you'll hear from Adam D 'Angelo, founder of Quora and CEO of Poe, and Andrad Massad, 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.

1:16Plus, Adam makes the case for why Vibe 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, Amjad, welcome to the podcast. Thank you. Yeah, thanks for having us. So a lot of people have been throwing cold water over LLMs lately. Some general bearishness. People talking about the limitations of LLMs, why they won't get a stage EI. 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?

1:52Yeah, 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.

2:28And 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.

3:07I don't know how we 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 can be done by someone whose job can be done remotely, that's AGI. You can then say it doesn't have to be better than the best person in the world at every single job. Some people call that ASI. It doesn't have to be better than teams of people.

3:44You 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 you're not sensing the same limitations of LLMs that other people are saying, you think there's a lot more room that LLMs 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.

4:20I think even those you can sort of fake and maybe we're going to be able 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. Amjad, what's your reaction hearing all this? Yeah, 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 myself and with, I think, how things are unfolding.

5:02I 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 Alexander, someone else wrote, and then the situational awareness and all this hype papers that are not really science, they're just vibe. Here's what I think will happen.

5:45and the whole economy will get automated, jobs are going to disappear, all of that stuff is that, again, it's 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 LLMs are amazing, 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 R's are in this sentence I think I tweeted about it the other day which was like 3 out of the 4 models didn't get it and then GPT-5 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 LLM itself and the training data or in the infrastructure around and everything that we're doing to make them work.

6:57But that makes me less optimistic that we've cracked intelligence. And I think once we truly crack intelligence, it'll feel a lot more scalable and that the idea behind the bitter lesson will actually be true and that you can just pour 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, GPT-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 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 investment banking and so I 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 collecting as much data and creating these RL environments.

8:09It's going to take enormous effort 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. Cloud 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 Cloud 4.5. So there is progress. We're going to continue to see progress. I don't think LLMs, 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 two pool and be able to do it.

8:56Right now, there's no way for us to have machines learn skills like that on the fly. Everything requires enormous amount of data and compute 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.

9:40And 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 like 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.

10:33So I don't disagree with that. And it feels like we're in a brute force type of regime, but maybe that's fine. And yeah. So where's the disagreement then? I guess there's agreement on that. Where is the divergence perhaps? 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. Until we understand 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 LLMs in a way are distracting from that because all the talent is going there.

11:25And 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, 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 get into the fundamentals and as opposed to, like, there's a lot of industry research.

12:20Like, how do we make these things more useful in order to generate profit? And so I think that's different. And often, I mean, Thomas Kuhn, 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 about physics and how there are, like, these industry of, you know, 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 gotta wait until the current people retire that's right and have a chance of changing the paradigm very pessimistic about paradigm but i 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.

13:17And I bet, yeah, I guess I would just bet that you can keep doing different innovations within the paradigm to get there. So let's say we continue to brute force it. We're able to automate a bunch of labor. Do you estimate that GDP is something of, you know, four or 5 % a year or are we going up to 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 let's say you have LLMs that with an amount of energy that costs$1 an hour, they could do a job of any human. Let's just take that as a theoretical point you could get to. I think you're going to get to much more than 4 % to 5%.

14:13GDP growth in that world. I think the issue is you may not get there. So it may be that the LLMs 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 LLMs that 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 LLM still can't do or the, you know, building enough power plants to supply the energy or other bottlenecks in the supply chain.

15:02One thing I worry about is the deleterious effect of LLMs in the economy in that, say, LLMs, you know, effectively automate the entry-level job, but not the expert's job, right? So let's take, you know, QA, QQual Assurance. And it's so good, but there's still all these long-term events that it doesn't handle. and so you have a lot of really good QA people now managing 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? I don't think that many people are thinking about it.

15:58Yeah, for sure. 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, you know, companies paid a lot of 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, That problem also creates an economic incentive to solve the problem. So it may be that there's more opportunities for companies that can train people or maybe use of AI to teach people these things.

16:49But for sure, that's an issue right now. Another related problem is that since we're dependent on expert data data in order to train the alums and the alums start to substitute those workers but at some point there's no more experts because they're all out of 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 to think about is like once you get the first tick of automation, I mean, there are some challenges there. And so how do you go?

17:37How do you go? How do you go to the next part? Yeah. I mean, I think a lot of it is going to depend on how good of RL environments can be created. So, you know, on 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, some people say it's like everyone's an influencer, you know, or in some sort of caring field or everyone's employed by the government in some sort of bureaucrat thing or maybe training the AI in some way.

18:35You 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 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 people can afford to live.

19:18But that's a while away. and in the near term... Well, like 10, 15 years out. I don't know how much, but yeah. I'll put it in the at least 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. I would doubt it happening. I'm not certain it would ever happen but definitely not in the current paradigm.

20:12Now, 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 create humans, unless AI is actually embodied in human experience, then humans will always be the generators of ideas in the economy. Adam, we respond to Andrade'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.

20:55What are your thoughts on, you know, to what extent will we be relying on 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 often know a lot of things that are not written down anywhere. Tacit knowledge. And you call it tacit 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.

21:47You 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 recommender systems, the system that ranks your Facebook or Instagram or Quora 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 they produce something and 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 multidimensional taste vector space and like getting you the best content there.

23:42So 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 everything. 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 headed because I think part of it is will be slightly unstable, 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 polemic book. But the idea is, you know, in the late 80s, early 90s, are they economists?

24:37I'm not sure. I think they're economists or political science majors. Two people out of the UK wrote this book about trying to predict what happens 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. 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?

25:12And 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 capitalists going to be so highly leveraged because they can spin up these companies with AI agents very quickly. So 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.

26:03But when you have only, when you have massive automation and then you have a few entrepreneurs and very intelligent and generative people are actually able to be productive, then the political structures also change. And so they talk about how the nation state sort of subsides and instead you go back to an era where states are competing over people, over wealthy people, and as a sovereign individual, you can 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 it might be headed. Now, again, it's not a sort of a value judgment or desire, but I do think it's worth thinking about when people are not the unit of economic productivity, things have to change, including culture and politics.

27:11Yeah, I think there's a question with that book and some of this conversation more broadly of like when does the technology reward the defender versus the sort of aggregator or something like the, when does it incentivize more decentralization versus centralization? Like remember Peter Thiel had this quip a decade ago of like crypto is libertarian, is more decentralizing, AI is communist or more centralizing. and 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 it's like FinTech or 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.

27:58So, 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 is just getting much, much, much, much bigger. And there's like these edges, but anyways, that's all right. 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.

28:50And 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 tweets all the time about people who like with their jobs because they started making so much money. You're using tools like RapLead and it's really exciting. I think for the first time, opportunity is massively available for everyone. And I think 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 massive. That trend is obviously going to happen.

29:29As we look out at the next decade or two, do you think that AI is more likely to be sustaining or disruptive in the Christian sense? 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 Replit still counts as the latter, and so does Quarton to 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 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, there's enough competition that as an application level company, you have choice and you have alternatives.

30:25and 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 about right. So the terminology of sustaining versus disruptive comes from the innovator's dilemma. And it's this idea that whenever there's a new technology trend, there's this idea of a power curve.

31:15It 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 it disrupts even the incumbents so originally the incumbents don't pay attention to it because it looks like a toy and then eventually it disrupts everything and eats the entire sort of market so that was true of PCs when PCs came along the big mainframe 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.

31:59And so PCs were just a hugely disruptive force. But there are technologies that come along and really benefit the incumbents and really don't really benefit the new players, the startups. I think Adam's right. It's both. And maybe for the first time, it's kind of both. 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 hyperscalers, for the large internet companies, but it also enables new business models that is perhaps counter-positioned against the the existing ones. Although the, you know, I think what happened is everyone read that book and everyone learned how to not be disrupted.

32:57For example, ChatGPT was fundamentally counter-positioned against Google because Google had a business that was actually working. ChatGPT was seen as this technology that hallucinates a lot and creates a lot of bad information and Google wanted to be trusted. And so Google had ChatGPT internally. They didn't release Gemini until like two years after Chai GPT and Chai GPT had sort of already won the like at least brand recognition. And so there was in a way OpenAI 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 getting better with Google.