AI Consolidation, Biotech Opportunities, and World Models with Sarah and Elad

In this episode of No Priors, Sarah and Elad unpack the current state of the AI market - whether it’s consolidating, what’s enabling or blocking key mergers, and where the most promising untapped opportunities lie, particularly in biotech. They also explore the rise of world models and how AI’s novel methods for understanding complex systems may ultimately reshape how humans approach discovery and problem-solving. Show Notes: 0:00 Is the AI market consolidating into clear winners? + the physics of the current landscape 7:01 Why more companies don’t merge (even when it makes sense) 10:09 Exploring biotech’s biggest commercial opportunities and the challenges founders face 17:14 Building world models 21:34 How AI is expanding the way humans reason, design, and evolve systems

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  A lot what's going on?

• 0:06Speaker 01

  How you doing Sarah?

• 0:08Sarah Guo?

  I'm good. I can't tell if it is a very stable time in the market like it's crystallizing into known businesses and models or it's as fluid. What's your take?

• 0:18Speaker 01

  You know, it's interesting. AI is the one market in my career where I've sort of consistently said the more I learn, the less I know, right? Every other market, you kind of learn more, you know more, you keep advancing. And I actually feel like that's shifted in the last couple of months where I feel for a subset of areas, despite the rapid pace of innovation, all the really exciting new models and research findings and everything else. I actually feel like a bunch of markets have sort of consolidated. And it's kind of clear now who are the likely players or winners in like two or three big areas. And that may change, right? In three years, another new startup may launch and displace everybody or an incumbent may make a bold move or whatever it may be. But I feel like in the foundation model market, at

• 0:58Speaker 01

  least for LLMs, there is a clearer view of sort of what's important and what isn't. At the application level, I think it's kind of clear who the winners are going to be in sort of at least the first set of services for healthcare related, things like medical scribing or other flows. In coding, it seems like it's consolidated into two or three players. You know, maybe that's cursor, codium, cognition, and then Microsoft's like Copilot, right? But there aren't probably like two dozen companies that are all still competing there. In customer success, it seems like things are kind of consolidating against Sierra and Decagon. So you kind of go through market by market and you're like, okay, there's a bunch of markets where it's kind of clear who we think some of the winners may end up

• 1:42Speaker 01

  being, or at least the ones who are going to be important for the next two, three years. And then I think there's a set of markets where it's still wide open, right? So you look at sales productivity tooling, there's going to be something really important there. There's going to be some financial analyst thing that's going to be really important. There's going to be an accounting company that's really important. And the question is, has that not consolidated yet because of nobody yet doing the exact right product approach? Is it because the models aren't good enough and the capabilities have to get better? So it feels like there's a bunch of stuff that is still unknown, but it's way clearer than I think it was a year ago. I feel like for the first time in like two years or something, you know, when I first started investing in generative AI, you just went and you backed the things that the people seemed really good and the market seemed interesting because there wasn't a lot of competition.

• 2:26Speaker 01

  Right. So that's when I led the seed round for perplexity or invested in character or Harvey or some of these other things. That was, you know, pre chat GPT or pre mid journey.

• 2:35Sarah Guo?

  The good old days. Yeah.

• 2:36Speaker 01

  The good old days when nobody cared. When GPT three was out and everybody's like, this is kind of crappy. But the scaling law was clear, right? So I thought a handful of people, you know, you being included, kind of, I think we collectively saw that this stuff was going to be important. But then there was like a period of like uncertainty for two, two years or something like that, maybe three years, where there was just so much innovation and so much change and so much rapid growth. And I think now finally we're hitting a period where at least this upset of things are consolidating back down. And again, these may not be the winners five years from now, but they definitely seem to be emerging as the winners for the next two years. So I think it's kind of a, a nice breather in terms of uncertainty and kind of having a bit more clarity into what's going to happen. I don't know. What do you think?

• 3:18Sarah Guo?

  I feel a little bit like I understand some temporary physics of the market a little bit better. Right. It's like a race to find the verticals of relevance and then get something to, to work in a way that users actually want. Maybe you have to go get proprietary data sources that you can retrieve against and like get distribution. And then ideally have users that can create or derive or extend knowledge from that, like the companies you, you just named. And I don't think you explicitly said, but I put like a bridge and open evidence. I think they fit into that shape. And then one thing you and I have talked about is, um, I'm actually quite unsure about sales. I like, don't know how to think about how something wins there.

• 4:00Sarah Guo?

  Like you could, you could go at it from a, a data perspective or adoption perspective, but it's been a very fragmented market to date. But I agree with you on finance and accounting. I'd add pharma to that. Like there are some industries that are really document driven where you can see something just becoming really important.

• 4:15Speaker 01

  Something coming there. Yeah. There's companies like Blue Note working in pharma, for example, that.

• 4:19Sarah Guo?

  And call it.

• 4:20Speaker 01

  Interesting things.

• 4:21Sarah Guo?

  Yeah.

• 4:21Speaker 01

  Yeah. And so I think to some extent, it's been clear what markets will be interesting, or at least a subset of them. It just wasn't clear like who would win and how. And you know, coding is an interesting analog where there's probably four different approaches to coding that everybody was taking simultaneously. And I think some of those approaches will consolidate over time, but also the entry points now seem way more clear in terms of how do you actually win in that market? And before, two years ago, there was a dozen different ways you could imagine somebody. And I wonder if the analog there is for the sales stuff you're talking about where it seems a little bit less certain right now, but maybe in two years we'll be like, of course it was whatever that workflow was.

• 4:56Sarah Guo?

  We had a debate internally at my firm as to like what it would take for another new entry point to work. And I think it would take a lot. I'm open minded to it, but you know, what is still changing? Like you, you know, you increasingly have like open models and little models that like can do real things with code, right?

• 5:17Speaker 01

  Sure.

• 5:17Sarah Guo?

  Code stroll and I think you'll see more there. Microsoft open sourced Copilot, we'll see what the impact of that is, but it's like they finally decided they need to fight Cursor for eating his lunch with its own open source VS Code fork. There's some chance that like making specific workflows from engineering work that don't work at sufficient quality today, like can create enough distribution. That's interesting. And then it's not clear, like you have the sync, like IDE workflow and the async, right? And one question is how quickly does the quality of these like asynchronous code agents increase, right? OpenAI with Codex, they make it made a bet on async, like cloud based software engineering agent.

• 6:00Speaker 01

  And then they bought the IDE with Windsurf, right? So.

• 6:03Sarah Guo?

  It's true. It's true. You can believe both.

• 6:05Speaker 01

  Yeah, I think, I think a lot of these things will just consolidate over time. And so my view is that the market's going to see two types of consolidation. There's going to be product consolidation and there'll be actual buys. And the Codium slash OneSurf acquisition by OpenAI is the first sort of step in that. But if I was a number one or number two in a market and I was a startup, I'd consider merging with the other party if there were the two main startup players, because the real threat will be fighting the incumbents. And so I would kind of get ahead of it and say, okay, let's stop the startup to startup war and let's just focus on winning against the three or four incumbents that we have to go up against. And so, you know, or you could just keep fighting and getting distracted by the other, the other party, which is kind of what Uber lifted for a while. Or, you know, there's other precedents, the ones that did merge were things like PayPal, right?

• 6:48Speaker 01

  There was X.com, which Musk was running, and then PayPal, which Peter Thiel was running. And they decided to merge because they were like, why are we competing with each other when there's so much incumbent competition? I think both paths will happen, but it may be something people should consider as well.

• 7:01Sarah Guo?

  What do you think prevents companies from thinking through that or doing that?

• 7:04Speaker 01

  Well, it's two things. One is there's ego. Who's going to run it? Do they want to subsume myself? Sure, I'm the number two, but blah, blah, blah, I'll still beat them. Or what role would I play or whatever? And to some extent, it's like, put that aside and just go win, you know? It's like, who cares? Second is people worry too much about integration. What's the culture and what's the this and what's the that? And often it's just like, just merge it. And if it doesn't work, shut down parts of it and move on with life. Whatever parts, either in the buyer or the seller, it doesn't matter. But just, it's again, kind of a who cares pragmatically? Like, you can fix it all sorts of ways. Either the culture meshes or they don't. And if they don't mesh, you don't have to keep everybody, honestly, because everybody's going to do very well off the acquisition. You can do all sorts of like, thank you packages and move on with life.

• 7:47Speaker 01

  And then third is sometimes there's dynamics around how you value the things relative to each other for private to private companies. And sometimes the easiest way to do that is you just choose some metric and say it's divisible by that metric. So, for example, years ago, when I was at Twitter, I drove an attempt to buy a major social network that was up and coming. And the way we constructed that offer is we just took their users and our users, added them up and then divided the ratio and made that offer as a proportion of Twitter that we offered for the company. I think you can do that. Take your revenue plus my revenue, add it up. And then what's the, um, what's the ratio? Or maybe it's users, it's whatever the right metric is for your business. But I actually think you can do really simple things like that.

• 8:31Speaker 01

  And just say, look, fair enough, like plus or minus X percent isn't going to matter if we just all win. So people tend to overthink those things. They overthink role slash I'm giving up or ego or whatever. Culture slash what is the surviving thing look like together? And then what's the value or what's the relative value of the two pieces? Pragmatically, it's like, do you want to fight it out for the next five years? Or do you want to just go win? Or, you know, and then your battleground shifts to the incumbents versus another startup.

• 8:56Sarah Guo?

  Yeah, I've seen the, the, um, simple, uh, like, uh, relative metric, um, also work. I also think that this is both like founders and board members or investors, like they're just unwilling to put something like inside the overton window. I, I think people will feel like it is, um, it is capitulating, but it's capitulating in, you know, in service of winning. And so I think that's a big reason people just like, don't want to look like they're unwilling to, to go to war.

• 9:24Speaker 01

  Yeah, the pie basically gets bigger if you do that because you're focused on just winning the market versus competing with each other. But also your pricing dynamics shift, you're not competing on every deal with another startup. You know, like a lot of things kind of shift. And so I think there's all sorts of positive characteristics. Again, people will win in these markets without it. And also, um, some of these markets are really big and there is room for a number one and a number two. And maybe a number three or maybe incumbents or, you know, payments was that way, right? We have Adgen, we have Stripe, we have PayPal, we have, you know, a dozen other payment processors. It's a very big fragmented market. And so some markets also can sustain multiple players. And that's fine, too. I'm just saying, like, sometimes you, you, you just want to say, hey, let's, let's put aside our differences and go in together.

• 10:08Sarah Guo?

  Okay, some part of the market is consolidated, um, some could be better consolidated in terms of startups winning. There are areas that you and I have talked about where they feel like obvious commercial opportunities, but people are not chasing them sufficiently, I think. And, you know, we've talked about engineering as one that I think AI will absolutely change. Like you have a bunch of ideas in biotech, what's missing?

• 10:33Speaker 01

  Yeah, I mean, the biotech stuff I'm interested in honestly, isn't AI related, although there's obviously really cool things happening in terms of models. There's a, there's a whole separate thread of stuff I just think is neat. I'm not an active biotech investor. I'm the wrong person to bitch on things, et cetera. I mainly do software, AI, you know, et cetera, type of investing. As well as the companies I've started have largely been software-driven companies. I just think there's some really cool stuff now that the science in biotech is far enough along, or in basic science, and that nobody or very few people are working on, right? And I'll give you maybe two or three examples. One is there's some really good data now for fertility out of Japan,

• 11:13Speaker 01

  where you can basically take a cell, you can reprogram it to turn into either a sperm or egg. And they've made mice now with two fathers, for example. You know, you could differentiate one father cells into sperm, one father cells into eggs, and then you can have viable offspring. And that really opens up the capability for any adult to have kids with any other adult. So if a woman is over a certain age, she can suddenly produce either sperm or egg. You know, you can do it for different types of couples. So there's stuff like that where you're like, why are so few people working on this? An even simpler version is just, you know, girls are born with one to two million O sites, which are egg cells. By puberty, they end up with about 300,000, and then there aren't good technologies to basically mature those eggs.

• 11:58Speaker 01

  So if you're a woman, you should be able to mature your O sites at different points in your life, and you should be able to harvest tons and tons of eggs if you ever want to have lots of kids, right? And so there's a lot of stuff like that that just nobody's doing.

• 12:08Sarah Guo?

  Is the outcome of that, like, people choose differently? The inputs to them having kids, like, for example, sperm or egg donor market is very different? Like, we're all just having kids with Elon, like, you and me both?

• 12:23Speaker 01

  The crazy thing about that, honestly, is say that you meet Elon Musk or LeBron James or Taylor Swift or whoever it is somewhere, and you manage to swap some cells off of them, you shake their hand or whatever.

• 12:36Sarah Guo?

  Oh, no.

• 12:36Speaker 01

  You could potentially reproduce with them.

• 12:37Sarah Guo?

  Without permission.

• 12:39Speaker 01

  Yeah, no, seriously. So some of the ramifications of this stuff is pretty crazy, if you think about it, right? But also societally, it's so impactful in terms of what you could do with that. And to your point, suddenly, anybody could become an egg or sperm donor in any capacity. But it just seems like it has such big implications, even if you just say we're going to limit it. To women over a certain age, like, you know, something we can, or people just aren't reproductively viable otherwise, right? It's, it's a pretty big deal, in my opinion. But again, the science is there. They've worked through a lot of the pathways to get there. And now it's like, okay, I know one company doing it is driven by a very good founder, but one company, that's it, right? Another area would be you look at Botox,

• 13:22Speaker 01

  right? People are injecting a bacterial toxin into their skin to look younger, like literally a toxin. And that, that was a $40 billion company, you know, one and a half billion dollars a year in revenue just for, for cosmetic applications. Why isn't anybody doing actual real drugs and treatments for aging? There's all sorts of science around it. There's all sorts of biology. So there's nobody working on skin aging, balding, gray hair, all that kind of stuff. And then there's the stuff that's really impactful in terms of neurosensory, right? Like your, the muscle that holds the lens of your eye gets weaker with time. And so why don't you rejuvenate that? That's why everybody ends up with reading glasses in their 40s, or hearing loss. You know, there's pathways for that, or tooth regrowth. Like you have a cavity, why don't you just grow a new tooth? And there's pathways for

• 14:07Speaker 01

  it. Again, there's, there's a lot of the biology worked out. Maybe there's more that needs to be done from a basic science perspective. In many cases, for example, for dental stuff, there's genes like USA G1, which allow for tooth regrowth in certain animal models. So why don't we do that in people?

• 14:21Sarah Guo?

  What's your, like a hypothesis for why that, why there are areas of, to me, what seem like clear demand, if the science you suggest exists, like, why isn't it being funded?

• 14:32Speaker 01

  Yeah, it's massive markets. I think there's three reasons. Number one, the, the biotech or biopharmaceutical market for founders is very different from the tech market. And the overall market structure is radically different. So basically, if you look at biotech, the last time a $50 billion plus biotech company was started from scratch, excluding Moderna, which was kind of an accident of COVID, was in the 80s. I think it was Regeneron. And so it's been almost 40 years since we've had a de novo, like tens of billions of dollar company created. And so all these companies are 50, 100 years old. And so imagine if tech was basically IBM versus HP right now, and you didn't have any young founder-driven

• 15:14Speaker 01

  aggressive companies. We wouldn't have the iPhone. We wouldn't have the internet. We'd just be logging into IBM mainframes off of HP laptops. Do you know what I mean? There'd be no like, uh, progress or very little progress. Um, so that's one issue. The funding models also are ones that a lot of biotech money is either very early stage or very late stage. And a lot of the companies are started as incubations by biotech VCs. So they load up a company with $40 million, they buy 40% of it upfront, whatever it is. And then they kind of have to make it far enough that they can get, um, almost public market money effectively. You know, a lot of the crossover funds then kick in. And the way that these funds are set up, because they have so much ownership, um, they're really built to flip

• 15:57Speaker 01

  these companies into the arms of pharma. And that means you build against pharma pipelines. So if there's six or seven areas that all the pharma companies care about are biopharma, it's cancer and it's cardiovascular disease and neuroscience, you only build companies in those domains. Because your goal isn't to build a big standalone thing, your goal is to sell it to a pharma company. Uh, and so a lot of the dynamics are driven by that. And then there's a big regulatory capture. It also prevents a lot of innovation. You know, the FDA will ask for, they'll, they'll push hard on endpoints for certain things that may not exist, or, you know, so there, there's a bunch of, there's, those are kind of the three main factors that make it kind of hard to do anything else. But there's all the science just kind of sitting there. Right? And I guess the last piece, the fourth

• 16:42Speaker 01

  is for some of these things, um, the people who are scientists who would work on them are a little bit, they don't want to work on something that's too commercial. That's kind of the purity of science.

• 16:53Sarah Guo?

  Because it's low status?

• 16:55Speaker 01

  It's low status. So how, how dare you work on fixing wrinkles? Um, as a scientist, you need to be doing something that's much more pure, et cetera, et cetera. So there's also a little bit of that kind of, um, I don't know if to call it prudishness around commerciality that exists. I guess back to our regular programming.

• 17:13Speaker 01

  I had another question for you on the AI side. And in particular, I know you've been thinking a bit about world models and RL and sort of how these things are overall relevant to capability and scaling, the scaling of capabilities. Do you want to explain a little bit about what you mean by world models? Because I think, you know, we're, people who are in the AI world get all this stuff. It'd be great for a more general purpose audience to kind of walk through your thinking. And what do you think is interesting that's going on there?

• 17:43Sarah Guo?

  Yeah, I don't know that this is actually a great. Well, I think it's very important as an overall area in that if you zoom all the way out, I actually think this is a time of more open research questions than ever. Right? So scaling up model size and training data for big LMs has given us this like really powerful foundation of knowledge and pattern recognition. But, you know, everybody talks about agents, like what people want to do from here, like the way people think about AGI is not just predicting text. Right? They want to go to broader intelligence and to taking actions. And like practically, that could be actions. I feel like it's really important to describe what we mean when we say like reasoning or actions or something more concretely, because I don't know that everybody has a great

• 18:28Sarah Guo?

  mental model for these things. But it could be like planning or reading documents and drawing conclusions, using tools, receiving feedback, like going down different reasoning paths, evaluating your own work. It's like taking a series of actions in pursuit of a goal beyond just sequential text generation. And my understanding is that the labs have spent, some labs more than others, right? Have spent a lot of money collecting traces of humans doing sophisticated tasks. Like this is how a lot looks at Japanese stem cell differentiation research. Right? He does these tasks and he calls these people and then they try to do like

• 19:11Sarah Guo?

  behavior cloning, monkey see monkey agent do, but for software engineering or investment research or whatever. But it tends to be really brittle when you go off the path with the cloning. Techniques. Model a lot monkey presses some button that like a lot of the human never touched. It lands in out of distribution territory and then has no idea what's next and it fails. Right. It gets stuck. Then people are trying reinforcement learning broad like, you know, new generation reinforcement learning, which is broadly like trial and error training. I think a lot of people who are paying attention in AI, they have seen like agents play games, famously chess and go or like more complicated ones with human interaction.

• 19:52Sarah Guo?

  You're like taking actions in an environment and getting feedback in the form of a reward or a penalty. And then you like play until you're better at the game. Right.

• 20:01Speaker 01

  And for games, that's easy, right? Because you have clear rules. And so you know very easily how to either reward or penalize an action. Right? And that's very different, I think, from real world tasks in some cases.

• 20:11Sarah Guo?

  And so this is like exactly like the, the problem or the expense of using RL more broadly. Like what is the task if it's not just winning in chess or go? And then like, how do you make the environment in? Like you're trying to make a copy of the universe. Right? Or at least some little piece of it. That's rich enough to teach useful problem solving, but like cheap enough to run. I don't know how we get to the, the matrix. It's, it's, so it's very hard to design rewards and then you have a gap from reality. Right? And then you need diversity or you're just memorizing a path through your game, even if that game is like the game of a lot doing research work or the game of a software engineering project and you're overfitting instead of adapting.

• 20:53Sarah Guo?

  And so, you know, I don't actually have a, a, a ton of conclusions here, but I've spent a little bit of time trying to understand it. And there's an interesting set of researchers now who, you know, they're, they're working on creating like, you know, more universal environments, world models, or, or just trying to get better trace data. And so I think this is, I actually don't know that I believe that any of the sort of more immediate term, commercial applications of these models are interesting. People are like, oh, we can generate games or we'll have like gaming assets or we'll use the data for robotics training or some other thing. But I, I do think it is like really interesting as a conceptual path to, to more AGI.

• 21:34Speaker 01

  One thing that I think is kind of intriguing in what you said, and it's, it's one of the points that I'll, I'll over extrapolate. If you look at the way AI has done certain things, for example, in Go, because there is a utility function but no other constraints, it came up with all sorts of crazy moves that a human wouldn't have come up with, or at least hasn't come up with to date. And then humans started studying and copying these moves. They were completely out of the box, but they ended up with a superior outcome. And so I always wonder what that looks like for other areas of human endeavors. If coding shifted from, hey, let's copy how people write code into, let's just solve this problem. How different is a type of code that's written?

• 22:15Speaker 01

  Or what sort of traditional approaches are just broken that we can then learn from? Because you've created a utility function with a unconstrained approach to actually figuring it out. And that happens sometimes in biology, right? You'll do these molecular evolution experiments where you'll evolve a molecule to do something. And sometimes it'll do things in a really weird way that you just completely don't expect. You suddenly have this catalyst that works really weird, or this binding protein that doesn't do it the way you'd have expected at all. And it's because it's not designed, it's evolved. And so I think this whole notion of evolved systems or self-selecting systems can yield really weird insights. And so I'm really excited to see that kind of stuff in terms of the outcomes of that.

• 22:57Sarah Guo?

  Me too. One way I visualize this is just like, you know, model is looking in a part of the search space that like humans have not traditionally been taught by the Go rulebook or the prior games or whatever. And it could be in shape of protein or any other problem. Have you seen the TV show Pantheon?

• 23:16Speaker 01

  No, what is that?

• 23:18Sarah Guo?

  It's a TV show about AI and mind uploading. It's a kind of niche animation TV show. You should watch it, everybody should watch it. But I think it's really interesting because the uploaded beings at some point, like becoming your full self, or at least for us would be humans learning to think differently. It is breaking through your constraint of like how you might traditionally solve the problem or see yourself. And so I do think that thematically is one of the more inspiring things about AI.

• 23:47Speaker 01

  That's interesting. Yeah, I feel like there's a lot of sci-fi books where eventually you have like upload, you know, your brain is uploaded into the cloud or whatever. And then there are all sorts of controls you suddenly have access to that you didn't have before. So for example, you should be able to fine tune your emotions or your emotional state and dial it up and down, literally with dials. I think there's always these really interesting meta questions of like, If a human upload were to occur, what does a transhuman species look like? And what are the capability set that aren't like a priori obvious that you suddenly expose? I mean, obviously, you could also spawn instances of yourself and have those things go do shit for you and then merge back in and maybe some of them don't want to merge back in. And then who's the real identity and you know, all that stuff. So it's kind of fun.

• 24:29Sarah Guo?

  I'm told that the modulation of emotions and attention actually doesn't require upload. Like Fred and some professors we know would say it's just ultrasound devices coming soon to, you know, a consumer shelf near you, but we can talk about that on next year's episode.

• 24:44Speaker 01

  Yeah, sounds good.

• 24:45Sarah Guo?

  Okay, man. It's been good to hang out. Market is crystallized. See you guys next week.

• 24:50Speaker 01

  It's all crystal clear, except for most things.

• 24:53Sarah Guo?

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