AI Vibe Check: Lab Wars, Why APIs Might Vanish & Future Predictions

Rob: Jacob.

Jacob Efron: I'm Jacob Efron and this is Unsupervised Learning. We've had a bunch of new subscribers, uh, over our last few months and so wanted, uh, to welcome you to the show. We basically probe the sharpest minds in AI on everything that's happening today, what's real and what's coming up, where the space is headed. And today's episode is one of my favorite formats. We do. It's an AI vibe. Check that. Uh, we do with Ari from Datology. Ari was a former researcher at DeepMind and Meta, now runs a really exciting AI startup. And Rob at Radical, uh, one of the great AI venture firms. The three of us talk about everything happening in the AI world today. We talked about Fable, of course, uh, and the reaction around the release as well as model capabilities. We talked about how close we are to rsi. We hit on some pretty spicy predictions, uh, including that the labs may actually get rid of their API business as the compute crunch continues. Uh, and we just got to hit on all the main topics of today. Uh, just really fun to sit down with two friends and great minds in AI. Uh, I think folks really enjoy this. Without further ado, here's our conversation. It's time for another roundup episode. I always love doing this with you guys. Ari and Rob. I feel like we had a ton of fun in the last one, but like, God, it's AI world, things have changed. Uh, I feel like we last sat down after Neurips and I think since then we've had IPO filings. We've had, you know, uh, models not launched and then launched. We've had, uh, you know, SpaceX becoming an AI infra company. Uh, no shortage of headlines to, uh, discuss here. So excited to have you both back on the show.

Rob: Excited to be here.

Ari: Yeah, thanks for having us.

Jacob Efron: So I think to kick it off, uh, you know, six months is an eternity in AI world, but I figured I'd start at the highest level. Uh, what's the single biggest thing that has changed in how you're thinking about the landscape since we last talked? And maybe, uh, Ari, I'll start with you.

Ari: Yeah, I mean, I think the most obvious thing that has changed over, uh, the last six months is starting to see the coding agents really start to work. At least longer time horizons. Um, right. I think that was just starting when we recorded our last episode at the end of, uh, 25.

Jacob Efron: Everyone went away over Christmas break and was like, holy crap, these things really work.

Ari: Yeah. And I think it starts to show how there Are these thresholds where if you go beyond a threshold it can become a lot more valuable. Um, and obviously that's driven the massive rise in token spending and the whole token maxing idea and all this stuff. Um, but I think we're really starting to now see the shift of engineers, at least kind of almost all moving from ICs to managers of agents. Um, that's been something that's been very noticeable within datology. For example, um, over the last number of months is seeing more and more people starting to now context switch between managing different agents, uh, rather than just kind of working on the one thing and that is enabled by having these um, agents be able to run long enough and actually be useful in various ways.

Jacob Efron: Everyone likes to ask, uh, top AI researchers like yourself, how much more productive has it made you in uh, your work?

Ari: I think that it's interesting. It makes you a lot more productive in some ways. Right. But it also um, produces a lot of challenges as well. Like one of the things that we're struggling with is now um, it's a lot easier to produce a massive amount of code that can do something. But now you have this pretty massive understanding gap and it's a lot easier to put slop uh, into your code base. So it's definitely um, made us more productive. I think A lot of times though, the kind of top line numbers tend to be overestimated, um, because it doesn't take into account some of these like later costs of like we now have big bottlenecks on reviews, um, and we don't want to go fully to like oh you just like, well my agent will review your agent's output.

Jacob Efron: You know, the bottlenecks just seem to shift, um, you know, whatever. Uh, it's hard to improve on an entire process uh, because of, because of that. What about you Rob?

Rob: There are early signs that seem to suggest over the past six months that make me question whether open weight AI is going to continue to be a really meaningful uh, force in the ecosystem. Going for at least like near frontier.

Jacob Efron: It's coming for the jugular. Ferrari. Like uh, right off the top here. I like it.

Rob: Yeah, we can dig it in more detail. But I think six months ago or for the past few years, my kind of working assumption had been that the closed source proprietary models would advance the frontier. And there are a lot of structural reasons for that. But the open frontier would only be a few months behind, uh, and that gap might widen a little bit. I didn't think it would shrink altogether, but like I thought it would persist as being relatively small. And I think there are signs now that like, it seems like there's a real risk of near frontier open weight AI falling off altogether. Um, I think Meta, which historically has been the open weight champion in the west, is pulling back and it seems likely that they're not going to continue with their open source strategy. And then uh, more recently obviously the Chinese labs have been the ones driving state of the art open research. And there, it seems like there are strong indications that they may also be pulling back from that. And they're uh, you know, whether it's Quinn, uh, or Deep Seek, um, or others, their most high performing models, they're now keeping proprietary behind an API and just open, open sourcing, open waiting, you know, smaller, less performant versions. And I think there is like real compute incentives behind that. Like it's just very expensive to, to uh, service these open uh, weight models with no revenue coming in. Uh, and I think there's also geopolitical and competitive considerations. But it's interesting to contemplate what a world might look like where if you want real frontier artificial intelligence, you have to pay a company for a proprietary model as opposed to being able to have access to the weights yourself or build your own.

Ari: Uh, but I'd actually agree with that Other one I was thinking about is what changed. I don't think that we've seen a major change in the capabilities or the trend line of the capabilities of the open weight models. I um, actually think if anything we started to converge and we continue to see that. Um, I do think though the economic decision making around building and releasing open models has definitely changed, um, over the last uh, six months, which I think is what Rob was really getting at. And I am a lot more bearish on how many open models there will be going forward. It seemed like, you know, there was uh, this kind of cornucopia of open models that was only ever growing in 2025. And I think we're now definitely starting to see that we probably hit the peak number of open models and it's now going to kind of get less and less. And because the financial incentives just don't make sense, once you've already kind of achieved credibility, it makes sense to invest a lot of money to do that. But after that point you want to start selling hosted inference of your model, um, and opening it up just fully undermines your business, which. So I think we are going to see like other Chinese labs start with like big open models, but then probably close up after that once they've kind of gotten enough press and pr.

Jacob Efron: Is there a business model for an open source model company or is it literally just, it's marketing, uh, to, you know, as you're on your way to the frontier and then once you're, once you're kind of close, it makes it inevitable to want to go closed source.

Rob: I don't think there's a business model, honestly. Uh, there, you know, different things have been tried, the kind of like freemium enterprised, like uh, you know, red hat, um, model. But I just, I don't think that it, it works in AI given the just the massive upfront investment required to get to the frontier, close to the frontier in the first place. We'll see. Well, I like, uh, you know, we can come back and revisit this soon. But I'm very curious to see if first of all for reflection, when reflection releases a model and uh, what business model they aim to pursue with it. Um, but I remain skeptical that a great business model exists for open AI. Open source AI.

Jacob Efron: It's funny because I was going to say one of the trends we've actually seen happen in the past month or two is finally forever. I think people have been like, everyone will use smaller, cheaper open source models for tasks that those models can do. And uh, it felt like the vast majority of usage in tokens was still just at the frontier, pushing capabilities like figure out what models can do in different industries. And then finally I feel like we have a uh, movement now toward like, geez, these bills are pretty expensive, uh, or usage is pretty high. Like wouldn't it be nice to have something that was, that was cheaper and faster and smaller to use? Uh, but it's funny that it's happening at the same time that we're kind of seeing this real, uh, you know, the closed source models, you know, run ahead of open source models. And Ari, I know you spend a lot of time thinking about this stuff, like how do we think about those like countervailing forces that seem to be happening at the same time?

Ari: Yeah, I mean I think that first off I've definitely seen a lot of the former that you were talking about. It's been very interesting, um, the combination of the extreme compute constrained environments that we're operating in, um, the rise of capabilities of uh, the coding models in particular, um, and then even just seeing from release to release models, changing their token efficiency, their output, token efficiencies, um, has resulted in, you know, a lot of companies that were happy using frontier models, all of a sudden even just going from like Opus 4.6 to 4.7, there was a big difference in token efficiency, um, and a lot of people's bills just doubled overnight. And you've now, I'm now starting to see, talking to a lot of enterprises in particular, really strong desires to start cutting the cost of using the models. Um, and I think that wasn't there nearly to uh, the same extent a, ah, year ago, um, because the models weren't being used at a scale where those costs were meaningful enough. But now they've really reached that point where they are meaningful enough. Um, and you can just consume budgets so quickly because the models think for a long time, um, and that's now driving a lot of demand to say, okay, how can we make this much cheaper? I think that you can do a lot of that with open models as well. I think one very notable thing is that a lot of people were able to reproduce, um, the same or a similar level of, you know, vulnerability finding, um, that Mythos used with open models by just putting scaffoldings around. I think that's another one of the big changes, right, is that a model is not just a model anymore. It's the model combined with the harness and the scaffolding. And a lot of innovation is happening on the harness and scaffolding layer. I think that's also possibly how open source models can have a business model. You open source the model, you don't open source the scaffolding in the harness, um, and you then have an API where people can access the full system. Um, I think that could potentially work. Kimi's kind of doing that. Right? Like Moonshot I think is at a couple hundred mil of ARR through the API and kind of the chat interface. Um, and that's to some extent what they're doing. Um, so I think that we will continue to see strong, um, open models, um, but probably fewer and fewer, um, and I think that this is going to motivate a lot of folks where they have to now think, how do we survive in an era where there aren't going to be reliable open models? And um, I think that pushes people more and more towards actually starting to think about, okay, how do I make sure I have the capability to build a model, um, in some way to maintain a model, to do that repeatably. Um, I think that's a huge part of the reason that Nvidia has been pushing so hard, the Reflection and others. Right. To uh, have open model providers in the West.

Jacob Efron: Yeah, I mean certainly like the more model providers, the MERRIER for uh, for them. Um, and it's interesting, I mean it kind of almost feels like, uh, you know, you saw an early manifestation of this with Meta, right, where it's like early on the idea of having this controlled by a bunch of companies that weren't them was kind of like would never work for them as a business. And so it's like we have to have something here to be competitive even if we're not going to be the uh, leading model provider just for our own business. Like we want to have something uh, that we can use ourselves.

Ari: If you are one of these companies that's potentially threatened by this, you kind of have two options. Option one is that you uh, go and work with a Frontier model provider. You share your data and your domain expertise with them. They use that to improve the model with you. Maybe you're able to sign some agreement that gives you some long term certainty, uh, around that. But eventually they just out compete you, uh, because you've given up any of your proprietary advantages. The only other option is that you try to really compete on the niche that is your focus and where you have uh, unique capabilities. Um, and I think we're starting to see that across the board. Right. Look at what's happened with Cursor, look what happened with lots of other folks where they're realizing, okay, we have to kind of move away from this. Um, there's also a margin perspective from that as well. Right. Where just like fundamentally if you're competing against somebody who has better margin than you do, um, then it's very hard to win.

Jacob Efron: Yes, they will always give themselves better rates than they give you, uh, as a, as an app company on top. Rob, Rob, what was your whole reaction to the, you know, the apps are cooked and SaaS apocalypse, uh, you know, narrative of the past months?

Rob: Yeah, that's interesting. I think like market narratives have a way of like swinging so far in one direction or the other to the, to the extent that a lot of the nuance is lost. So I do think like a couple things are true simultaneously. Like I do think a lot of traditional software companies and even categories potentially are at real existential risk on account of the Frontier Labs and their, and their product roadmaps. And so I think like a lot of that rerating was rational. Um, and there are, I think there are a lot of big companies that have big revenues and big customer bases that are in real trouble based on how OpenAI and Anthropic are executing and performing. I also think it like for sure was an Overreaction and an oversell and like painted with too broad of a brush. And I think like, you know, in our field of venture capital, uh, it certainly is like the kind of prevailing narrative is apps are so challenging to invest in, stay away from them. Like deep tech and hardware and so forth have all become more appealing and consensus over the past few months. And again, I think there's a lot that's real there and there are a lot of deep tech categories that I'm super excited about that we're both investing in and I'm sure we'll talk about. But I also think there's still, I think just fundamentally there's no way that one or two or three companies will win every single important market and important category in the world. It just no company can have that span of influence and execute excellently so broadly. So for sure there are some categories that I think uh, I would bet on OpenAI and anthropic being very well positioned and coding obviously is the first and there are others which we can talk through. Um, and I think that these sort of like horizontal cross cutting areas are the ones that make the most sense for the labs to focus on and go after versus like, you know, vertical software for pet stores or something like this.

Jacob Efron: I do wonder if VCs are just pushing themselves into a corner of damned if you do, damned if you don't. If you're only going to go invest in pet stores because that's small enough for the models. I don't know if that actually ends up being a uh, super interesting end category. But it turns out no matter, anywhere you look in AI world there are existential questions, you can't avoid them. There's no just maybe if you want to go invest in a data center builder and you're like they will just build data centers and there will be demands. But sure, then you still have to figure out like, you know, how, what makes you a better data center builder than the other 10 people that will compete with aggressive financing for the same site.

Rob: There's no question that we are at a period of max uncertainty and volatility. And yeah, I mean I totally agree with you on the hard tech point. Like again, I do believe there are a lot of really exciting opportunities in categories in hardware and deep tech where there's a lot of interesting innovation happening right now. But yeah, to your point, it turns out hard tech is also very, very hard and like the failure rates are much higher and there's a lot of unsolved problems and like that there's going to be a lot of, I think, pain and lost money down that road in the years to come. Um, and then, yeah, on the application side, I think, as you said, like, I think there's, there's still tremendous value in that last mile and like the labs are also starting to try to tool up and stand up these deploy codes and, and lean in on the implementation side. But I think there will be so many pockets and opportunities for companies that are delivering applications to also thrive and continue to grow.

Ari: I think also it's interesting to ultimately, why do startups ever win, right? Why didn't Google do everything right? Um, it's hard for a massive company to do many different things. Well, fundamentally a counter argument to this is actually OpenAI shuttering or, uh, suspensing their video efforts. Right. Um, that was very surprising to me, I will say, uh, given their access to effectively infinite capital, um, and effectively infinite talent and they had a great team there, um, that was leading that. But they had to make the hard choices here. Now, uh, a lot of that is likely driven by compute constraints. Fundamentally, video training is very expensive relative to text training and so on. Um, but it goes to show that they can't do everything.

Rob: They needed to focus and strip down and just focus on acquiring TPPN and adding that to the company.

Jacob Efron: I heard actually was that they, uh, listened to our last episode and read your article and they were like, oh, Rob's predicting, Sam's out. Like, uh, this is really, uh, you know, that spicy prediction has gotten to our hearts. We really need to make sure, uh, we avoid that. How are you feeling about that one six months into the year? I guess you got six months still to be right.

Rob: It's looking a lot more likely today on June, you know, mid June than it was when I shared it with you guys. Uh, it's been interesting. Yeah. I mean, when I, when I unveiled that prediction in December, um, I feel like everyone was like, what are you talking about? That makes no sense, including you guys. You're like, oh, that's interesting, but that doesn't seem very likely. And yeah, I mean, like, you know, needless to say, like, the vibes have shifted against OpenAI this year and there's been this realization. Whereas this time last year it was like, oh, OpenAI is doing everything. They're such an amazing company. They're going to win in chips and data centers and robots and everything. Now I think now there's a realization like, oh, we really do need to focus. And I think there are a lot of elements of Sam's leadership that are increasingly under question. I think that the Elon Musk trial, even though, uh, Elon Musk lost, I think that, like, was damaging of first Sam's reputation, uh, and his trustworthiness and so on and so forth. Uh, so, you know, who knows? I like. It was. It was a provocative and like, purposely somewhat low likelihood prediction at the beginning of the year. I would say odds have gone up for sure. One thing that, One thing that's changed is at the time that I made and I published that in Forbes, my hypothesis was that Fiji was the obvious most likely successor and kind of was being groomed to be the next CEO. Um, and she obviously has now had to take a step back, given some health concerns. And so, uh, I think that the question around who the successor would be is different. I am actually, I think this theory, which I'm sure you guys have heard made the round, make the rounds around Brett Taylor, I think is quite plausible. Um, you know, for folks who aren't familiar, Brett is the chairman of the board at OpenAI, the CEO of Sierra. Ah, you know, one of the most respected and revered leaders in Silicon Valley. Um, and I like, I think it would just honestly make so much sense for OpenAI to acquire Sierra and to make Brett the CEO. I think it, I think it would be in the best interest of OpenAI's shareholders, honestly. And like, to this discussion around, uh, the vibes have really shifted against OpenAI and towards Anthropic in the lead up to the IPO. That is a decisive change that I think could be a total game changer for OpenAI because people just trust Brett and respect him and admire him and think he is an admirable leader. And I think if someone like that was at the helm of OpenAI, I think it would do a lot to change their fortune. So, anyway, we'll see. There are many months to go, but I don't know, I feel like that one might actually come to fruition. We'll see.

Ari: I think also this notion of OpenAI going to an Alphabet, like structure seems, uh, a lot more plausible, uh, in general, now that they shift to a holding company. Maybe Sam stays CEO of the holding company and then you have somebody take over OpenAI or maybe ChatGPT becomes, uh, its own product or something to that effect. Uh, that would make sense as well.

Jacob Efron: Yeah, no, I mean, you mentioned it, but obviously I feel like these past months, the dominant vibe, it's unbelievable to see anthropic on this unprecedented vibe run. Right. I Think, uh, they've kind of had, uh, just completely sucked up the oxygen. Probably the most consensus around one company we've had. And um, obviously that's come at the expense of OpenAI in many ways. Um, you're maybe starting to see some light cracks in that, uh, in this week with some of the reaction to the release of Fable. But I'm curious for both of you, like, do you think this is a Vibes trend that continues or like many things in our culture, are we inevitably going to have some sort of backlash or flip in Fortune of these two companies? Uh, in the next three, six months?

Ari: There'll definitely be some amount of backlash. There's always backlash to whoever's winning. Right. That's just like a truism of life. Um, I would say. Um, that said, I think Anthropic is also potentially starting to make moves that will alienate people, I think clearly with limiting the use of Fable for anything to do with AI development. And I think in particular I don't think people are incredibly upset at the core with just a limitation. It's the fact that it's a silent limitation that I think people are really upset about. Right. That it doesn't give you a refusal. It doesn't say, I'm not going to help you with this. It just does a poor job on that, um, without you knowing, um, they can say that's because of safety. That's tenuous. I would say it seems pretty clear that's a competitive positioning, uh, move rather than a safety move. Um, and I think to that end, I think I mentioned earlier that a lot of people were able to find many of the same vulnerabilities in zero days that Mythos found with open models and good harnesses. Um, so I also think there's not necessarily a unique safety risk, um, to this, um, but, but I've seen more people who have been incredibly bullish on anthropic and positive anthropic, um, truly pissed off, uh, as a result of just the Fable of the last day than I had ever seen. Um, and it does seem like a bit of a step change. So I think if they continue to make moves like that, we will definitely see more and more of a shift, um, I think against Anthropic.

Jacob Efron: I mean, obviously so many open source models have been trained on just like distilling, you know, anthropic models. Like, do you think this will have an impact on the, on like the open source community?

Ari: Depends if they litigate it. Right. It's not going to do Anything in China to limit anybody there. Um, and then for US models I think people tend to be very careful. Datology did all the Data Curation for RC's Trinity Large Model and we were very cognizant to not use any closed source APIs at any point. Um, in any of the development there also only public data. So you can build a really powerful model without that. I think also looking at the Mai, uh, model that they just released, right, they made a huge point of um, going so far as I could not use any synthetic data at all to avoid um, any ability to kind of sneakily be distilling um, from another model. And then I think they've said now they're going to start using synthetic data from those models to kind of bootstrap them. Um, but uh, I don't know if it's going to fundamentally change it. So long as people can actually use an API to get to your models, you can't actually stop them from trying to do some amount of distillation. Um, that all said, I think this claim is overblown to some extent. It's true. But also you can build still great models. You don't need to distill to build a great model. I think that the notion that, oh, the only way that any open model can catch up with a closed model is by effectively distilling or stealing from it reads uh, a little bit like Copium to me.

Jacob Efron: I feel like when these models come out, everyone's on Twitter trying to figure out what happened. And you obviously have a lot of people on the anthropic side saying, hey, this is the biggest step change in capabilities I can remember in a while? Um, I'm not sure uh, to the extent you've played around with it, but what are your early reads on how much of a step change in capabilities this really is?

Ari: I only um, played with it a little bit last night since uh, Fable released. Um, I didn't personally see massive differences from, from where 48 was. Um, and talking to people, it seems like the takes have been quite varied. Uh, around that it's pretty hard to tell honestly.

Rob: My take on Fable is I kind of feel like it is and also is not that big of a deal. I think it's the latest state of the art model that was released that happened to be released yesterday, like a day before we're recording this. But if we recorded this three weeks from now or seven weeks from now, there would probably be another model from another provider that we would be talking about. Um, I do think Just based, again based on the benchmarks and the quantitative data, which is far from perfect. Um, but it does seem like it is a meaningful step change improvement relative to the previous state of the art, which I think is meaningful. Again, not because it's some discontinuity. I think this gradual improvement will continue going forward. But I think it's meaningful in the sense that I think it really does undermine this narrative that people have already shifted away from it. That had a lot of currency a year or so ago that pre training is really hitting a wall. Things are plateauing now. It has to be rl, uh, and test time compute to carry us forward because we've hit this data wall. I just think that's clearly not true. And uh, the gains are continuing to come in very richly and I don't think there's any good reason to think that they will plateau anytime soon.

Ari: Yeah, I think we also saw a lot of pushback to that narrative over the last uh, six to nine months. And it's pretty clear that pre training did not hit a massive wall.

Jacob Efron: We were just waiting for new chips.

Ari: I think some of that, I think also just naive scaling doesn't work. Yeah, if you just take exactly the same stuff that we were doing and you just multiply it by a factor of 10 or 100, um, which was kind of like what 4 or 5 was uh, aiming at or llama 3, 405B at the time that didn't work extremely well. Um, but I think people took a much over generalized take from that especially because one of the things that's actually really challenging about deep learning I have found, um, is that uh, you really need to get all the details right often for something to really work. If you have kind of like 95% of it right, um, it rectifies to just not working. Um, a lot of the time. Um, there are a couple methods that are robust, but you can be doing almost everything right and not get no real improvement. And then you tweak the last knob and now all of a sudden you get a step change. Um, that happens a lot in deep learning. And one of the things that's challenging about that is it just makes it fundamentally difficult to interpret a negative result. M okay, you tried scaling everything up and it didn't work. Is that because scaling doesn't work or is that because you just did one thing wrong? Um, and that often happens. It also makes it very hard to decide when to abandon a project. Uh, because it's easy to also then always be like, well, maybe if I just make one more tweak, this thing will work. Um, oftentimes that's not the case, I

Jacob Efron: guess to hit the other players, um, in the space. I feel like Rob, in our December episode you said, which I think is a feeling all of us have, that Google is incredibly well positioned. Right. I mean obviously they've got talent, they've got Compute. Um, it doesn't feel like things have gone super well for Google in the last six months. Um, you know, feel free to push back but uh, what's, what's going on over there and like why haven't they been able to catch up on coding? I guess most notably.

Rob: Yeah, I think I, I think I would disagree a bit that like Google has fallen behind or isn't executing as well. Like I. Again, I think it like in part goes to this reality that I think the three labs are all kind of in this process of leapfrogging one another continually. In any given month, any one of them may have the like quote unquote, state of the art model.

Jacob Efron: Been a while for Google though.

Rob: I think there's no question that they're behind on coding and I, and I. But I think that's just reflects prioritization. Like, it's clear that Anthropic like leaned in on that as their North Star for years and that proved to be obviously an incredibly savvy move. Um, that has really catapulted them and OpenAI more recently has really doubled, tripled, quadrupled down on it. And you're seeing a lot of positive love for Codex and I think it just hasn't been as much of a priority for Google. Um, but I feel like everything we talked about previously, I still stand by and feel very confident in the sense that Google has an incredibly deep bench of talent. Unlike OpenAI, Anthropic, Google has this massive cache machine. And then lastly is computes. I think Google has benefits so much from being totally full stack. They design their own chips, they have their own cloud, no one has access to infinite compute. And Google is also. Would love to have more, but they have a massive.

Jacob Efron: Would they love to have more? I don't know. They sold some of it off to Anthropic.

Rob: Yeah, I mean, yeah, uh, they have a cloud, an external cloud business, but I think they're better positioned when it comes to compute than either Anthropic or OpenAI. So yeah, I continue to be very bullish on Google.

Jacob Efron: What do you think, Ari?

Ari: I tend to share the same view. I am a little surprised that they haven't uh, improved as much uh since 3.1. Um, I would have expected a bigger launch at I O uh and I wonder if in a couple of months we'll find out there was one planned and then something went wrong and it was scuttled or whatever the case may be there beyond um, just the Flash model. But I do think they have all the structural advantages. I think having access to the money printing machine. I think that's a really interesting way to think about the XAI SpaceX merger um is like it's a way for X to get attached to a money printer to the extent that SpaceX is a money printer which less so certainly uh than Google or Meta is. Um, so I think they are well positioned there. Um, I think also like I don't know if, if Google's market is the same as what Anthropic is going after like with respect to the focus. Right. Like fundamentally models are going to be commoditized for consumers. Like I think that's just like quite clear that for the consumer use case of I'm just asking my M model a question about you know, the world or having it be a tutor or any of kind of the standard things that most consumers are going to use that's going to be commoditized and people are going to use the model on their phone. I think that just seems very clear.

Jacob Efron: What about some of the computer use stuff? Uh, it feels like there's still frontiers of consumer uh that models can't do yet.

Ari: I think there will be certainly um, and I think you will see more of that. But I think first off most people will not use that level of uh, of software. I think like the power users will but like you know most people are just going to be using it um, as an answer engine. Um and Google's now quite well optimized to be the kind of default provider both on Android phones and iOS phones um until you know eventually Apple uh builds its own models there which I've been pushing a lot there. So I think Google's actually going to be in a strong position to that even if they don't necessarily have the best model. I think that's another big part here. It's like I don't know if the best model necessarily wins in the consumer space.

Jacob Efron: Well what about even in the coding space? Right. I mean this has honestly been most surprising to me is like I actually think Codex is clearly an amazing product. It doesn't seem to be making a huge dent. I mean obviously it's growing But Claude code remains the dominant coding tool and I wonder, it's interesting to see kind of like this first mover, like hey, you introduce people to the paradigm advantage in, in like, you know, I always thought of developer tools as like the most meritocratic, like you know, would switch on a uh, uh, in a second. Like best model always wins. I don't know what you guys make of, of, of, of, you know, of that.

Ari: I think that's generally still true. I think, I do think that developers are the most mercurial, uh, consumers in some ways and they are going to switch to whatever the best thing is. That said, like there's a question of how much better is it? Uh, and I think Claude and codecs have stayed close enough and there hasn't been a massively compelling reason why you need to switch to Codex, um, such that a lot of people have stuck with Claude. I expect that at least amongst AI developers, um, you're going to see a massive set of shifts now to Codex from Claude, uh, given the way uh, that they're limiting Fable and whatnot. Um, I think that honestly this is a pretty nice gift for OpenAI, uh, with respect to a lot of the people who are the loudest voices on Twitter and whatnot, um, becoming will likely be spending a lot more time with Codex in the immediate future.

Jacob Efron: But I mean it's open. So when 5, 6 comes out and OpenAI, presumably to counter position, is going to have to give people more access, are they just ultimately going to eat way worse margins and for vibes and for usage or what structurally is going to allow them to provide better access as a model that's on parity?

Ari: A good question. Um, I mean I think ultimately it's like in any market where you have kind of a couple players, like so long as the models are close enough, um, that can make a big difference. It could also be compute access. Right? Anthropic just got a lot of access here. But I think that's another one of the things circling back to earlier conversation around how there might be fewer open models, uh, going forward. I think part of that could also actually be that there could be fewer closed models, uh, access going forward. It is not hard to imagine a world in which Anthropic is so compute constrained that they actually cut off the API, um, because obviously they're going to prefer Claude code to the API with respect to how much money they make. Um, and you start to see this now with OpenAI starting to sell futures of hey, you get guaranteed access to inference tokens uh, going forward, that's actually a huge existential threat uh, to anybody that builds on top of these models. And I think that was not really a plausible thing six months ago, but now feels very plausible that actually the APIs go away, um, not as a business decision but just purely because of compute constraints.

Rob: Yeah.

Jacob Efron: And what do you think of that Rob?

Rob: Yeah, I think it's totally feasible. Um, I think a few different things could happen. I think um, cutting off their API access altogether would be an extreme move. But you can imagine for instance, uh, OpenAI or anthropic rather uh, than not open, sourcing their models but only making them available via API. You could imagine them actually not even making their most powerful models available to anyone publicly and reserving them for internal use. Um, uh, and yeah you could um. So much of this depends I think on the compute bottleneck and how long it stays this acute, um, which I think is interesting to reflect on. And again this gets to some of these deeper tech topics which I think are becoming increasingly relevant. There are uh, on the horizon there are efforts uh, and startups that are trying to break the semiconductor supply chain wide open, build cutting edge fabs in the US challenge, ASML challenge, TSMC et cetera. Elon Musk obviously has this terrafab concept which is fascinating and honestly I feel like people should be talking about more uh, just given how transformative it would be if he pulls it off. So I think a lot of those variables will, will influence how long is compute the limiting factor. And that in turn I think if it remains this compute constraint, I think some of these possibilities that Ari is sketching out, you could totally imagine them being real.

Ari: It's hard to imagine though how we actually unblock if we continue even remotely on this trajectory. Uh, it's hard to imagine how we relieve the compute constraint uh, within the next handful of years. I think.

Rob: Yeah, I think it's not a two to three year thing where tsmc, um, is not displaced but augmented by many other players that can provide comparable chips. But you can imagine over a file, it doesn't have to be the case that there's only one company in the world that can make cutting edge. It's actually kind of crazy that that's the current market structure that there's one company that knows how to do this and no one else can do it, and that one company that can do it. The most important machine that goes into the process is made also one company and no one else could do it. It doesn't have to be that way. And I don't think it will be that way for them.

Jacob Efron: What a wild version of, uh, the many worlds that we, uh, could live in, that we live in now. Uh, it is pretty nuts.

Ari: Are there clear upstarts going after asml? M. There are a lot going after tsmc, but I haven't seen as many going against asml.

Rob: There are, yeah. It's an interesting new area of research. Uh, in a nutshell, asml, their focus is obviously extreme ultraviolet lithography. Euv. Uh, and EUV is starting to hit physical limits in terms of how small of, uh, transistors it can print onto chips. And so there are a couple of really interesting new research directions. One is, um, rather than using light, like moving away from using light altogether and instead using matter, it's called atom lithography, where you use a beam of atoms to print, uh, these features onto chips, which lets you get way lower resolution. There are a couple startups doing really interesting work in atom lithography. And then there's also a handful of startups, um, that are basically looking to leapfrog EUV and move even further out on the electromagnetic spectrum to the thing that has even shorter wavelengths, which is X rays. So this whole concept of X ray lithography is getting a lot of momentum. There's a couple startups in each of these buckets that have raised a ton of money and are running at this. And to state the obvious, they're still very much in development mode. And it remains to be seen whether either of these will prove to be commercially viable. But if they work, honestly, I think especially atom lithography, there are so many advantages in terms of the machine can be way simpler, way fewer parts, way cheaper, way smaller, obviously much better resolution. So anyway, yeah, I do think there may be real technology disruption coming here.

Ari: It looks like that's at least five years away, probably, given where it is right currently.

Jacob Efron: I think this thread is fascinating to pull on. What are the actual implications of a year or two from now? We're even more compute constrained than we are today. And obviously, you know, it feels like there's lots of, you know, people, what they call like using a Ferrari to go down the street to the grocery store. Like there's probably like overusage of, of, of powerful models for, for what people need today. But even if we figure all that stuff out and route people perfectly to their, you know, uh, dtology built models or however the world ends up working, like, feels like we'll still be in this kind of, you know, uh, Overall compute shortage. And you know it's fascinating. One implication of that is, is obviously the labs themselves, you know, prioritizing first party products or prioritizing their own development. Um, any other implications? I don't know, I'm just thinking on the spot here. But I'm curious if there's other implications that come to mind for either of you, uh, of like what that might mean for even businesses in competitive industries that get access or don't get access

Ari: or it pushes towards efficiency. Right. Like generally, especially the frontier labs have not cared too much about efficiency because of said infinite capital access. Uh and like you will get to physical constraints where you have to figure out how to be m more efficient. So I think it'll drive a lot more interesting innovation frankly. Um, one of the directions that I've just always been very bullish on and I think we've seen consistently is that like you do not need trillion plus token parameters in order to achieve the capabilities that we currently see. Um, in the abstract. Like at the moment you need it to get that frontier level. But we consistently see that smaller and smaller models can match the largest models of even one to two years ago.

Jacob Efron: Right.

Ari: Um, so I think that will only accelerate ah, as a result of that will be more and more push towards how do you make models as small as possible. Um, you'll probably see a lot more investment in areas that can help like that, like distillation, uh, and things like that to try to get towards really reducing um, the inference costs. Um, I think if you can do that that can alleviate a fair amount of this. I would still expect that the usage is going to grow faster than what you can do to alleviate this.

Rob: Yeah, I definitely agree with Ari on the efficiency point. I think another interesting implication of this massive supply constrained world is it will be a very good thing for other chip providers other than Nvidia. Um, I think basically pretty much everyone would probably prefer to use Nvidia GPUs than anything else maybe other than people at Google. Uh, but there just aren't enough GPUs to go around. And so you're already seeing companies are doing what they have to do to adapt to use AMD GPUs and to use Amazon, Trainium and Cerebrus obviously is seeing massive tailwinds because of this. I think basically any chips anyone can get their hands on will uh, be in massive demand. And so I think it's not a bad thing for Nvidia, but I think it's a good thing for other chip

Jacob Efron: Players actually do think that's one of the big themes of the last uh, six, nine months is like the rise of these as inference has dominated and you can kind of split these inference workloads into pre fill and decode and other things. You really can use heterogeneous chips. Um, the question I have is do these other chips really help us on the compute shortage given this exact thing we keep talking about? You keep shifting bottlenecks. And so it's like there is a bottleneck upstream of the chips which are like the components that go into the chips or the production of the chips at tsmc, um, you know, asml, all this stuff like does having more chips actually solve that fundamental bottleneck energy, all these things, um, or is it just like uh, similar to every space we look at, all these vendors are very happy to have uh, more players than just Nvidia. I don't know if the rise of all these other chips actually gives us more compute. I'd be curious for your thoughts on that.

Ari: I think that intuition makes sense because if you imagine a world in which there's no cerebras, there's no D matrix, there's no one else who's making competitive chips, presumably Nvidia just eats up the TSMC capacity in that world, um, and the total number of chips stays constant uh, in the world. So likely it just accrues value to not Nvidia as a result of that, uh, rather than actually changing the situation fundamentally.

Rob: Yeah, I think the alternative chip providers aren't a solution to the compute constraints, but will be a beneficiary of the compute constraints in the sense that uh, Nvidia doesn't get all of TSMC's uh, production capabilities and all of the supply chains production. And so in a world where there is a surplus of chips, everyone would rather use Nvidia. People can buy Nvidia. There just isn't that much demand for these other chips. But in a world where you just can't get your hands on enough Nvidia chips, then people are going to pay a lot for AMD chips and pay a lot for Amazon chips. And it won't to your point, the um, overall supply of chips won't be changed as a result of them. But I do think they'll see massive tailwinds.

Ari: Right. TSMC doesn't want a monopsony totally.

Jacob Efron: But it's a fascinating parallel to the Neo cloud world too. Right. Um, where you basically uh, the same thing. It's not like you're not necessarily increasing the Number of overall chips, but they've certainly been shuffled among uh, a bunch of different players. And as long as the uh, capacity constraint is there, uh, every one of those is very solid business. Do we actually, given what's happening expectations, uh, you know, there not to be a compute constraint anytime soon. Are we really talking about like 2035 or like, you know, hey, in 15 years maybe someone will figure out what happens to these businesses.

Ari: It's probably not in the next couple of years. Yes, like I would be surprised if anything happens before 2030. That said, like when, when all of the different methods that people are working on to try to relieve these bottlenecks all start hitting at the same time, which is probably what will happen, right? We'll have like a several years where it'll be like several big unblocks. Probably in the early 2000 and 30s would be my guess. Um, that's probably when the merry go round, uh, starts. I think the other question is as more chips are produced, to what extent can we start to continue to get a lot of value out of older chips? Um, and that can also. I think one huge thing that has been the leading indicator of this has been that H100 prices reversed their drops. And that happened I think right around when we recorded the last episode was when that started to happen in December. But like H100 prices have gone up dramatically, uh, over the last number of months. So I think there's also a lot of, uh, there are a lot of chips there that can be used. But um, I do wonder what happens when the merry go round stops on that and they ultimately are all selling the exact same product with minimal differentiation.

Jacob Efron: Well, speaking of what, uh, the new cloud space. I mean, obviously I think the biggest headline in that world is SpaceX, right? And like just, you know, coming into the space in a huge way. Uh, what do you guys like, make of, uh, what is the future of xai, do you think? I mean, obviously, do they just lean into continuing to do this at scale? Do you think the model business has, uh, any legs? I guess not a bad time to bring up the uh, cursor potential, uh, acquisition and how that fits in. Um, but would love to hear, uh, one of you riff on that.

Rob: Yeah, it's a good question. Um, I would say I'm not super optimistic for xai's future trajectory in terms of them being a frontier lab. And I do like, I think the, you know, these massive deals that SpaceX signed to rent compute anthropic, um, into Google. I think on the One hand, yes. It's like padding the numbers, uh, of the, padding the revenue numbers ahead of the ipo and that like that obviously plays a role. But I also do. It's hard not to interpret it as a signal that like the company's foremost priority is not doing frontier AI research and fueling X research. Because if it was it just to this, the last 20 minutes discussion around how much uh, the world is compute constrained. Like you just wouldn't be giving away

Jacob Efron: uh, pretty hard business to IPO if all those chips were being used, uh, for, for training. Right. Uh, without a, without like much revenue to show for it. On the, on the uh, product side,

Ari: Elon companies aren't valued on fundamentals. Like, I don't know, it's totally different with an Elon company. So who knows?

Rob: And kind of to Ari's point, I think that the thing that Elon Musk is amazing at and his companies are amazing at is incredibly operationally intense real world atoms. Not just bits, uh, undertakings. Obviously we've seen that with Tesla, we've seen that with SpaceX. And so it's not surprising to me that in the world of AI, the one wedge where this SpaceX XAI behemoth is going to have a real durable advantage I think will be on the data center side. I think they will Excel, like and they already have Excel that's standing up massive, massive clusters super fast, getting them up and running. And I think that will be a great business for them and it may turn them into like the world's biggest cloud. Especially, as you know, in the years ahead the company starts putting more and more compute into orbit. Um, but I don't, I don't know, I guess I just, I don't. It doesn't seem to me like organizationally being at the frontier of the AI model race is a priority or is necessarily realistic. Obviously we've seen just like the insane attrition uh, from XAI over the past year or so and I think it makes sense for the overall Elon co to have a model ARM that's doing model stuff. But I guess I'm not. If I had to bet, I wouldn't be super bullish that they will crack back into the top echelon alongside Google OpenAI anthropic.

Jacob Efron: So what are they doing? Uh, why cursor?

Ari: I think why cursor is to get all the traces. Um, like, I think that would be the. And to have a hedge against the fact that they have um, struggled to produce a very competitive coding Model, uh, fundamentally.

Jacob Efron: So are traces for coding worth $60 billion?

Ari: I'm not sure if they're worth $60 billion, frankly. Um, but if you think that that's what can leapfrog you and accelerate you to having a strong coding model, um, I can see where that comes from. 60 billion is probably quite high relative to that. Um, but I don't think Elon has given up the ambition probably of trying to, of wanting to have the best models. I do think that when you look at the practical aspects of it, it's hard to. If your pure goal is to build the best models, then you would not be giving away massive amounts of compute, um, not giving away, selling mass amounts of compute. So I think that it does go to Rob's point that it's not their top priority. And cursors, especially given the way that that deal was structured, like it is an option. It's kind of like, hey, we want to maintain some amount of optionality for the next, ah, number of years.

Jacob Efron: Yeah. Until we see, uh, if traces actually are the most important thing to make a really good coding model.

Rob: And on the cursor point, I mean, I'm sure you guys all saw and laughed at the SpaceX TM charts in the S1 where I think they estimated the total space.

Jacob Efron: I never laugh at a big TAM chart. You got to love the ambition.

Rob: You just salivate.

Jacob Efron: Um, 28 trillion sounds good to me.

Rob: Oh yeah. All of space was like, I don't know, 5 or 600 billion and then comms, basically. Starlink was like 1 or 2 billion and then, yeah, enterprise AI was like 20 trillion or something.

Jacob Efron: I think it's always good when like all of space is, you know, not, uh, close to, you know, uh, like, you know, it's like a few percentage points of your, of your tam.

Rob: Yeah, yeah, exactly. So anyway, I think that points to Y cursor. Like they certainly like the narrative. Again, a lot of this goes into the ipo. Narrative is around like we're going to win enterprise AI and an asset like cursor, because before adding cursor they really had no like application or product surface area. Whether or not they'll actually succeed in that undertaking I think is a lot less clear. Uh, but I think that's the. I think so much of it is just that, like the positioning and narrative.

Jacob Efron: We've made it through a good chunk of the episode here. We haven't mentioned anything Andrej Karpathy related, which I think is, you know, I think we've done it pretty much every Episode. He's like, he seems to be the unifying theme. You know, first he says that, uh, everything slopped. Then he decides to go join one of the labs. Um, you know, obviously he went to join this, this recursive self improvement team. And it feels like there's, you know, I think both labs have been very vocal. Hey, we're going to have, you know, air and D by 2028. Um, it feels like, you know, there's all these Twitter vague posts about, hey, we're getting really close. Um, how close do you guys think we actually are? Um, and like, you know, is Andre deciding it's time to go back in? Like, how much of a signal is that?

Ari: I think we're closer. My estimate is that we're much closer now than we were six months ago. Um, I would say so. I do think this is something that has changed and that I've become one of the places where my mind has changed a little bit, where I become more bullish on this direction. I do still think that the bottlenecks are in compute fundamentally. Um, I think we talked about this in maybe the last episode, that ideas are not necessarily the challenge. Even execution is not necessarily the challenge. Um, you have to actually then go and run the experiments. Um, and that takes compute. Um, so I do think there's going to be some bottleneck on the pace of improvement. Um, but we are clearly getting to the point where models can improve themselves. We've started to do a number of experiments here around just having agents do, uh, the curation itself, uh, in various ways with various amounts of guidance, and seeing far more promising results out of that than I would have expected. Um, so there's a lot in this direction. I think there is a lot of reason to be very excited, um, about it. Um, I do think though that lots of people will be able to do the same thing. So I think the flip side of this is there's this view of RSI where it's like, okay, now there's one player that's going to go super duper fast, run away and run away, and then nobody can compete with it. Um, I'm still very skeptical of that view, uh, because I think there are just fundamental compute bottlenecks that can prevent the speed. And Also there are 10 companies at least at this point, that have the funding, the talent and the know how to work on this. Um, it's not something that I think is going to be like, fundamentally limited to a small group of people.

Rob: I was expecting you to be more skeptical, Ari, on The whole notion of recursive self improvement. But it's interesting that you are a believer in it and yet you don't buy into this kind of like takeoff narrative. Like what would that look like for, for a lab to crack true RSI and yet not have this sort of uh, exponential takeoff. Is it just the compute as a limiter?

Ari: Basically, I think that's where a lot of it compute is a fundamental limiting factor with respect to this. And also having just more humans only makes you go faster to a certain point. Um, and I think there is a question of how much better than humans is it going to be. It's clearly coming to the point where it can be comparable to, to a junior AI researcher. Um, having just an army of junior AI researchers gets you so far. So will it continue past that? Um, and then I think there will be some speed limitation. But I will say I was a lot more skeptical uh, of it uh, six months ago. And I think we have seen some clear progress here. I think also you look at some of the actions that some of the labs are taking. Anthropic is slowing down on hiring a lot of junior folks. Um, that starts to drive, uh, you know, more confidence that this is possible. But I think it's gonna be a lot slower than people say.

Jacob Efron: Well, I always like to end our sessions with like a, uh, a rapid quick fire where we get your, you know, to drop the last bit of spice which provides fodder then for the next episode. So uh, I figure, you know, maybe, maybe to start, uh, here's both of you. Like what do you disagree with most? That's kind of a common trope in the, in the broader discourse right now.

Rob: Um, I'm happy to start. Um, I think this is a bigger picture observation but relates to a lot of the discussion we've been having around the chip shortage. The capex build out the gigawatt scale data centers. Um, to me it seems so clear that in say five to 10 years we're going to look back on the current era of AI and it's going to be laughable how resource inefficient today's AI systems are. Like the fact that we need to build two gigawatt scale data centers, that's like twice as much power as all of the city of San Francisco to run these state of the art models. And to return to one of my favorite hobby horses, compare that to the human brain as an existence proof and the fact that human intelligence, which is what we're trying to achieve at the end of the day with AI runs on 20 watts of power. Um, I just, I think that like, and I don't think there's going to be one silver bullet breakthrough, but I think there will be massive advances at the hardware level. You know, things like Naveen Rao's company that he's working on, uh, analog computing, you know, efforts like that that lead to fundamental breakthroughs and the energy efficiency of chips. I think for sure there'll be a lot of uh, uh, algorithmic breakthroughs and optimization breakthroughs. But I think like, and I think it has complex uh, and not obvious intersections with this question of like the demand shortage. How long does it last, the capex build out?

Jacob Efron: Yeah, it's interesting obviously, like, I mean two things, you know, that come out of that. One is, you know, if things do go that way, then the moats on the leading model providers are far less compelling. Right. I mean obviously a huge part of the moat today is just access to capital and scale. Um, and so you know, you've had all these like folks spin out of NEO labs and like, I think a lot of them you have similar inspiration of what to go chase. Um, and I've never been convinced that even if they do figure that out, it ends up being like a great business. Um, because to Ari's point we've always talked about is like these ideas do diffuse and other people, it's very unlikely that for a long period of time you'll have an idea that looks fundamentally different than these other folks. And so uh, it'll be really interesting if the world does go that way. And then I think the second point, and Ari, you always hit on this, I think in our first episode you talked about this in the context of the Chinese open source models is like uh, constraints free that innovation. And I wonder, you know, you like, the incentive if you're at OpenAI or Anthropic right now is just like keep pushing the current. I mean the current paradigm and getting better at it is so, so valuable. And so you've had folks like Jerry Torrick spin out of OpenAI and be like, well I want to focus on something that feels more akin to what you're talking about, Rob. And it'll be fascinating to see whether uh, these advances, the next big advance around this happens in one of the big labs or uh, maybe in an ancillary place. I don't know if you have a portion on that Ari.

Ari: I think you'll see it come from many places simultaneously. Uh, there's this notion of uh, multiple independent discoveries uh, where, how many times are things discovered independently? And there's a really interesting Wikipedia page about this, uh, where you can go and look, there were a lot like basically every major scientific discovery was discovered by several people simultaneously. Um, and then as communication bandwidth increased, um, uh, and latency decrease, where now you know what's happened across the world the next day that uh, got a lot faster and now you see that a lot less often. But I think you clearly see that ideas are just ready. OpenAI didn't invent the idea of test time compute. Many people were working on test time compute.01 just came out first. Um, I think for anything we're seeing with recursive self improvement or any other aspects, you're going to see the same sort of thing. The space of ideas isn't that massive and ideas do become ready, so whatever ends up working. I would bet that uh, several other people will be working on the same thing simultaneously. So I think it'll be uh, a little bit of both.

Jacob Efron: And what do you disagree most with in the broader discourse right now?

Ari: Probably the thing I disagree with the most, although this could be cope potentially, uh, is the notion of the permanent underclass. And just like this idea that um, all AI is going to take, uh, all human jobs, AIs listening to this

Jacob Efron: in a decade are really going to laugh hard.

Ari: Uh, yeah, I'm doing poorly at Roko's Basilisk here, but it's kind of interesting that anthropic is even going out and saying this very, very visibly and so on. Um, the reason I tend to think that this is overblown, um, is uh, that fundamentally just humans are slow at dissipating things through the economy. It's going to take a long time for even the tools we're seeing to really fully percolate. And we're seeing that now, uh, and so much of business and everything is actually about human to human interaction and trust and so on. Those are barriers that I think technocrats tend not to consider. Um, and I think a lot of the folks that are projecting kind of the really fast timelines around this, uh, are underestimating how slow the world can be, um, in various ways. Um, so I tend to think that that's overblown. That's probably the thing I disagree with most. But it's also possible that I just want humans to still matter for longer.

Jacob Efron: Yeah, um, I think Ari, you mentioned kind of, you know, what you changed. I think actually both of you had mentioned changing your mind on uh, open source models and uh, kind of how many players there would be actually in that space and going after it. Anything else that I feel like we're constantly getting new information and having to shift things like anything else in the last few months you feel like you've shifted your perspective on.

Ari: I'm more bullish on RSI I think than I was. Ah, that's a change. Um, but I think that. And then yeah, I think this notion that the open models are going to go away to some extent, uh, I believe quite strongly now. And I think I uh, didn't really see that coming six months ago in the same way that I didn't see the rise of the open source models, uh, coming. I think we were all very surprised by that Pas llama. Uh, I think now the opposite is the same, but nothing beyond this.

Rob: Uh, one thing I've changed my mind on is uh, I feel like I've really pulled in my timelines in terms of the performance and improvement rate for robotic AI and robotic models. Um, I would say six months ago, whenever we chatted last, I probably would have been in the camp of this is inevitable and it's massive market opportunity, but who knows how long it's going to take. It could be 18 months or it could be 5 years to this sort of mythical GPT3 moment for general purpose robotics AI. And I feel like in just the past handful of months these models have really crossed a threshold in terms of how well they're working. Um, there's obviously still a long way to go, but I think they've like robotic foundation models have reached a point now where they are capable enough to be commercially viable across a lot of different use cases. And as they start to be deployed with customers and that data flywheel starts to spin, I think it's just going to accelerate. So I do now think that we aren't far from this like so called GPT3 moment in robotics.

Jacob Efron: I mean music to my ears, but certainly I think uh, I think it's been really exciting to see uh, a bunch of the progress in that space and obviously um, yeah there's been a ton of activity around, you know, obviously in robotics, I mean I think in biomaterial sciences I think people are definitely feeling uh, a lot of progress in some of these other spaces and so will be fascinating to, to see these next years. Um, I guess my last question for you guys is just, you know, I'm shameless now going to ask for an additional spicy prediction for the second, the back half of the year, um, uh, of something you think by the end of the year, uh, we'll all be realizing, uh, was actually true.

Ari: You know, I'll take the API bet. Actually, this is a bit aggressive for it to happen this year versus next year. I feel a lot more confident saying that by the end of 27. But I think there's a very reasonable chance that we see probably Anthropic, um, but it could be OpenAI, suspend, uh, API access for some period of time, or otherwise heavily limit API access, um, for a brief period. Um, and that maybe is like, the precursor to starting to see this happen more frequently, uh, down the line.

Rob: That is a spicy take. I like that one. Yeah. It seems so unlikely today that if that does happen, that'll be a prescient call.

Ari: Timing is hard on that one. I'm confident that'll happen at some point, whether it'll be in the back half of 26. This is the downside of options pricing,

Rob: but, uh, it's gonna be hard to top the predicting. Sam Alvin's ouster. Yeah.

Jacob Efron: Who else is out?

Rob: Yeah, I think my predict, Daria will still be at Anthropic at the end of the year. No, I think this is this. Yeah, this is a less, um, uh, dramatic one, and maybe this is probably less spicy these days because I think it's becoming increasingly evident to folks. But I think by the end of the year, it will be, like, very obvious that Anthropic is like a fledgling, uh, juggernaut in the making in the life sciences and biology. Like, it seems clear to me that that is, like, the next big direction that Anthropic is focusing on. And Anthropic has rightfully gotten a lot of praise and admiration for its focus and for, like, being so dialed in on coding and just knocking that out of the park and using that as, like, the stepping stone where to leapfrog OpenAI. Whereas OpenAI was kind of all over the place. Um, and so I think that focus has been really valuable. I do think that it's clear that life science is their next big bet. And so on the one hand, you could say, does that represent a fragmentation of focus that's going to be problematic for them? I think that it's their next big chapter after coding, and it will be for the next several years to come. Um, Ian, I think the really interesting question is, and I know, Jacob, you are deep in the healthcare world, and I'm sure you have a lot of thoughts on this, but I think really interesting question is how far down that value chain is anthropic planning? To go. There are plenty of rumors that I'm sure folks have heard that Anthropic is setting up their own wet lab facilities uh, to run their own experiments and collect data. And is that just to collect data to train models or are they thinking about, or will they eventually be thinking about developing their own assets? Uh, and it obviously as is generally the case in this space, it'll be a progression over time. But I do think that maybe this is m more suited for a three year prediction. But I think uh, in the fullness of time Anthropic will become one of the most important m life sciences companies in the world.

Jacob Efron: How much of an advantage do they have from what they've already done to date? Uh, within life sciences? Obviously you can use LLMs to be uh, a co pilot for research but um, it seems like the models themselves are actually quite different. Right?

Rob: Yeah, I don't think they have a huge advantage today. I think their only advantage is they're arguably the best AI research organization in the world. And if they choose to point that capability at ah, if only there was

Jacob Efron: a visionary CEO, uh, at a top AI lab who had been interested in bio for some time and actually spent some time on it and started a company on it years ago, uh, that

Rob: might be pretty interesting to uh, I'll revise my prediction. Anthropic and Isomorphic Labs will be two of the most important life science discoveries because you're right about ISO, but I think Anthropic can get there and I do think Dario has also been passionate about biology for a long time and who's a neuroscience uh, Ph.D. and so forth.

Jacob Efron: Totally. I mean I think like the thing I do take in your, that is real is I think bio actually probably is one of the only opportunities that is large enough. I mean obviously the Anthropophos are interested in it from a mission perspective. They've been so consistent in that and it's one of the coolest impacts of, of AI from like an impact. It uh, also is probably one of the only TAMS that is large enough to justify like that level of you know, you got code, you've got like your general copilot for knowledge work and like I don't know, there's not that many that are, is uh, that are, that are as large. And so I also think the, you know the opportunity could be massive. We'll see. The you know bio is hard, um, the feedback loops on data and actually getting longitudinal data just has a, you know, a time to it that is uh, that is, isn't even something like robotics is much easier because you can at least immediately get the feedback loop on whether something worked or not.

Ari: Yeah, I think domain expertise also matters a lot more than many AI folks often think. So I think there's a question of how anthropic approaches this. Does anthropic approaches by going to hire a bunch of really fantastic, um, biologists and then pair them with the really strong AI researchers and do as much there, um, versus just trying to throw the AI researchers at it. Uh, I think that is one thing that uh, Demis did very well with Alphafold and then isomorphic is not assuming that kind of the AI researchers alone can do that. And I've seen lots of other labs kind of make that mistake. Um, but I think the domain expertise matters a lot. I think it's going to be a hard one for anthropic to do, but if they can do it, if they can hire that team, it's obviously a huge, uh, boon. But I would bet probably more an isomorphic right now.

Jacob Efron: Well guys, this has been a ton of fun. I really appreciate you, uh, you, you both taking the time to jam on this and we had to do better, you know. No, no, six months in between this one and the next hour. You're closing too many candidates. Uh, next time we'll, uh, we'll, we'll do it sooner.

Rob: R is the only one with an actual job that we have to schedule.

Jacob Efron: Yeah, I don't know. You came in with some, some really good takes on uh, on lithography there. I was like that. Someone's been doing some work over there. I was out of left field.

Ari: Yeah, seriously. Til I, I, I, I learned, I learned a lot about alternates, alternate approaches.

Jacob Efron: I'm Jacob Efron and this has been Unsupervised Learning, a podcast where I get to talk to the smartest people in AI and ask them tons of questions about what's happening with models and what it means for businesses in the world. As I hope is clear. I have a ton of fun doing this. It's a nights and weekends project in addition to my day job as an investor at redpoint. But our ability to get these incredible guests on really comes from folks like you subscribing to the podcast, sharing it with friends. It's really what ultimately makes this whole thing work. And so please consider doing that. And thank you so much for your support and listening. We'll see you next episode.