Listener Q&A: AI Investment Hype, Foundation Models, Regulation, Opportunity Areas, and More

This week on No Priors, Sarah and Elad answer listener questions about tech and AI. Topics covered include the evolution of open-source models, Elon AI, regulating AI, areas of opportunity, and AI hype in the investing environment. Sarah and Elad also delve into the impact of AI on drug development and healthcare, and the balance between regulation and innovation. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Show Notes: [0:00:06] - The March of Progress for Open Source Foundation Models [0:06:00] - Should AI Be Regulated? [0:13:49] - Investing in AI and Exploring the AI Opportunity Landscape [0:23:28] - The Impact of Regulation on Innovation [0:31:55] - AI in Healthcare and Biotech

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

• 0:05Sarah Guo?

  Hey, everyone. Welcome to No Priors. Today, we're going to switch things up a bit and just hang out and answer listener questions about tech and AI.

• 0:12Speaker 01

  The topics people want us to talk about include everything from the evolution of open source models to the balkanization of AI, Elon AI, which I think will be super interesting to cover, regulating AI and AI hype in the investing environment. Let's start with the march of progress for open source models. I guess, Sarah, what have you been paying attention to and what are some of the more interesting things that you view happening right now?

• 0:31Sarah Guo?

  Yeah, so there's nothing out there today in open source that is like GPT-4, 3.5, or anthropic Claude quality, right? So there is a, there's one player out in front, and that's OpenAI. But I think the landscape has changed a lot over the last couple of months. Like, Facebook Llama is quite good. Many startups are just using it despite its licensing issues, assuming Mark won't come after them. And then you have a number of other releases that have happened, right? Tomorrow just released a pre-training dataset, which seems quite good. Stability just refused, released Diffusion XL in the image gen space.

• 1:03Sarah Guo?

  And so I think the larger dynamic is that there's been an increasing number of people and teams that now know how to train large models. The cost of a flop is only going to go down. There's a lot of investment in like distilling models. And a lot of researchers would claim that you and I know that it's going to be 5X cheaper to train the same size model the second time around, like, once you've made your mistakes and know what you're doing. And then you have these other accelerants, like you can use these models to annotate your datasets and increasingly do advanced self-supervision. So if VCs are going to continue to fund foundation model efforts, including open source, foundation model efforts, like, if I were a betting woman, and I am, I'd bet

• 1:46Sarah Guo?

  there's a 3.5 level model in the open source ecosystem within a year. And I didn't personally believe that would be true, like, a few months ago.

• 1:54Speaker 01

  I guess that puts it about two to three years behind when GPT-3.5 came out, though. And so do you think that's going to be the ongoing trend? That there'll be a handful of companies that are ahead of open source by, you know, one or two generations?

• 2:06Sarah Guo?

  Yeah, I think that's like the status quo. So if we just straight line project, I imagine that will continue to happen. And the real question is like, can you stay in the lead if you are OpenAI and like get paid for that? Or is that the is that the objective of the organization anyway? Like, I think, you know, if you have a great leader and a lot of resources and a lot of really talented people, that's not something I want to bet against.

• 2:32Speaker 01

  Is there anything you think is coming in terms of other big shifts in the model world, either on the open source side or more generally?

• 2:41Sarah Guo?

  Yeah, I mean, we should also talk about just like stuff that you're interested in investing in and generally paying attention to. But I think the the big idea that's been very popular over the last few weeks are autonomous agents, right? And I don't think that that's like a, I want to hear what you think about this, too. I don't think that's necessarily an architectural change. But for our listeners, the basic idea is to orchestrate LMs in this like iterative loop towards some high level goal, where they're doing planning and if memory and prioritization reflection. And so you're you're not necessarily changing the architecture of the LM itself. But this orchestration allows you to like do many new things, possibly. The classic example being like make money on the internet for me.

• 3:23Sarah Guo?

  And there's a good number of hackers trying to figure out how to make agents that, for example, like analyze demand, find a supplier, set up a drop ship Shopify store, generate ads, then promote that store on social, right? The whole loop being like one call to an agent with this high level goal of make money on the internet for me. Do you think this stuff is interesting around autonomous agents?

• 3:47Speaker 01

  I think it's I think it's super interesting. And I think there's the old saying that the future is here, it's just not equally distributed. And I feel like that's one of those things that people in the AI community have been talking about for a while. And there have been very clear ways to do it. And then I think there's one or two people that went and implemented interesting things there in terms of AutoGPT or other things. And then everybody's like, oh, my gosh, this can this can happen. And I think a lot of people in the community are like, this is really cool. But at the same time, yeah, of course it can happen, you know? Because effectively you have some form of context as an AI agent is acting. And then you use that context to inform the next motion and sort of update, you know, the prompt or what the model is going to do. I think there's other forms of memory that people have been talking about that are super interesting.

• 4:29Speaker 01

  Like, how do you make that a bit more of a cohesive part of how an LLM or AI agent functions? Because right now, effectively, every time you start a new instance of ChatGPT, a new chat, you've lost the context on all the other sessions you've had. And so a lot of what people are thinking about is how do I create ongoing context? So that whatever chatbot or whatever API I'm using remembers everything else I've done with it over time, or perhaps everything it's done with every other user over time. And then that becomes really powerful because you're effectively crowdsourcing an understanding of the world and then integrating it into an AI system and agent. And so suddenly you have global context. Like, imagine if you, as a person, understood the life of every other person who's lived,

• 5:13Speaker 01

  and then you had all the context around what that means in terms of just how you operate in the world, right? And so I think-

• 5:18Sarah Guo?

  I wouldn't operate anymore, Alad. We'd be hive mind.

• 5:22Speaker 01

  Yeah, exactly. It's just the hive mind. So I think, I think that's where we're all heading. So you heard it here first.

• 5:27Sarah Guo?

  Okay, fine. While we're on this topic of directionally AGI, there's been a lot of call for regulation of AI from, you know, Sam Altman to Satya to Elon Musk. Do you think AI should be regulated?

• 5:40Speaker 01

  You know, I think the first question is why do people want to regulate to begin with? And I think there's, you know, two or three reasons. One is, if you're an incumbent, it actually really benefits you to get lock-in. And one of the best ways you can get lock-in is in industries to have regulators get involved, because they start blocking innovation and creativity and new efforts. And if you, basically, there's that famous chart of where prices have gone up by industry and where they've gone down. And they've largely gone down in areas that have been unregulated traditionally. That's things like software or certain types of food products or other things. And then there's areas where prices have gone up dramatically, and that's education, it's healthcare, it's housing. It's the most regulated industries. So regulation tends to lock in incumbents.

• 6:24Speaker 01

  It means you have fewer people making drugs and you have fewer people doing all sorts of things that could actually be quite useful. So that's one thing. The second is, I think, some people are just scared.

• 6:36Speaker 01

  And in some cases, you could say, well, there's reasons to be scared, right? Like, what if the AI is used to unleash a virus? Or what if the AI is used to cause war? And if you look at the history of the 20th century, humans have done that pretty well on their own already, right? It's not, it's not a new concept that bad things will happen. And often, they're driven by other people versus technology, right? And of course, technology can have accidents or can be misused. But fundamentally, usually, people have driven a lot of the really bad things that have happened over time. And there's a really long history of doomers who are wrong, right? And I should say, by the way, on AI, I'm a short-term optimist, long-term doomer, right? I actually think eventually, there may be an existential threat from AI. But I think in the next 10 years, you know, everything will be okay. There may be accidents or maybe terrible things that happen, but fundamentally, it won't be any different from any other period.

• 7:21Speaker 01

  But if you look at the doomerism in the past, it's things like, you know, public intellectuals worried about swine flu, and nothing happened. Or, you know, a lot of people worried in the 70s about population collapse. We're gonna have too many people, and the world will starve, and we're gonna have global famine, and that didn't happen. And so we have a lot of examples of people in the past kind of predicting doom, and nothing happened. And we also had that during COVID, where a lot of people said COVID is the worst thing that ever happened to the world, and then they would be hosting dinner parties, unmasked inside with large groups, you know, later that evening. And so I just think you kind of have to look at people's actions versus their words. And fundamentally, you know, my view would be, let's not regulate right now, at least most things. I think the things that maybe should be regulated

• 8:06Speaker 01

  are things related to export controls. So there may be advanced chip technology that we don't want to get out of the country. And we already have those sort of export controls on other capabilities. We may want to limit the use of AI for certain defense applications. You know, do we really want a really smart, hyper-intelligent AI agent driving swarms of offensive drones or weaponry? And so there may be some need to do some sort of global regulation for things like that, or at least, you know, something like what we've done for chemical weapons, so like. And then I think in the long run, we may want to think twice about advanced robotics and their implications as AI becomes more of an existential threat to humanity. But overall, like, if I had to choose right now, I'd say don't regulate in the short run, except for those areas that I mentioned.

• 8:51Speaker 01

  And then I, I think that the big pivot point for regulation may actually come during the 2024 election.

• 9:00Speaker 01

  Because I think that's the moment that people will show examples of AI being used to influence the election or influence voting behavior, just like ads influence voting behavior, right? But AI could write better ad copy or do other things. I worry a bit about that becoming the reason that people claim that they should not regulate things, just like they got really aggressive about social networking. So I don't know. What do you think?

• 9:22Sarah Guo?

  I largely agree with that. I feel like it's worth describing what I think are the two more rational cases. By the way, I don't think, I think it's too early to regulate. I just want to make sure that's very clear. But I think the two rational cases I've heard, because I keep asking smart people, that I don't think are taking cynical actions, why they're afraid, or, you know, short term afraid, or why they think this makes sense. And the two things I've heard are, one, you know, this is unlike the past because of the speed of progression, like this hard takeoff idea of like, especially if you, and you know, I see you nodding and smiling, but when, when very, very smart people who are working at the state of the art tell me that they're concerned within

• 10:06Sarah Guo?

  a 10-year band for humanity because of the ability of this current generation of models to be used to train the next generation of models, and we're all very bad at thinking about compounding. I'm like, okay, that's not a like completely unreasonable point of view. I think the other is more of a, it's more of a tactical thing for the industry, which is, as you said, for, you know, whether it be the election or some other trigger, like, there's a version of the reaction to this from, you know, people who are afraid or from, you know, political opportunists to go in two directions. One is like mass surveillance, right? Or one is like complete lockdown, right? So I think the tactical thing is to like, try to create a democratic process that gets ahead of it with something,

• 10:51Sarah Guo?

  something that's a reasonable path forward. But I largely, I'm just, I feel as far, it's very early to be figuring that out. And then you also have the problem of like, if you're talking about the more existential risks, or sort of the AGI risk, like, alignment research is very tied to capability research, right? And so it's sort of impossible to be like, we're going to stop making any progress on research, but figure out how to control this stuff.

• 11:18Speaker 01

  Yeah, absolutely. And I think related to that, you know, I think it's really important to your point to separate out almost what I consider technology risk from species risk, right? Technology risk is there are some bad things that can happen due to technology being abused, right? And that could be a nuclear disaster, or that could be an AI being used to shut down a pipeline, or to crash a flight, or to do something really bad. And those sorts of things already happen. But you know, you could imagine it could accelerate it. In that case, you could literally turn off a bunch of servers, right? You could turn off every machine on the planet if you really needed to, and humanity would keep going. And it'd be a reset, but we'd reset fine. Separate from that, there's species level risk. Like, is there an existential threat to humanity? And that's like an asteroid hits the planet and kills everybody. And if I think a lot of the the people who talk

• 12:03Speaker 01

  about these things mix those two things. And I think the true doom review is while AGI eventually becomes a species and we compete with it, and then it wipes out all humans. And in order for an AI to rationally want to kill everybody, you'd need some replacement for the physical world, because eventually all the hard drives would burn out and the AI would die, right? If it existed as a species or a life form. So you need physical form for the AI in order for it to truly be an existential threat. And that's why if I were to focus on an area, it'd probably be robotics or something like that. Because that's where you suddenly give physical form to something. And if you're like, I was in a great if AI can I build my house and AI can now, you know, build a data center and now build a solar farm and you're eventually now build a factory. You've basically created

• 12:47Speaker 01

  an external system that no longer needs people. And that's when I think there's real risk. And that's why on the 10 year time horizon, I'm not that worried because robotics and atoms in the real world takes a lot of time. So even if you have this hyper intelligent thing running, the reality is if you really needed to, you could turn off every server on the planet.

• 13:04Sarah Guo?

  Yeah, I agree with the embodiment being like a key piece in this theory of the AIs are going to kill us. And we're pretty far away from that. Okay, so one question we got from listeners, and then I'm sure you get all the time is there's a ton of hype in the AI investing in startup world right now. What do you, what do you think of it? Is it justified? Is it appropriate?

• 13:25Speaker 01

  Yeah, I think we've both lived through a couple of different hype cycles now, right? There was hype cycles around social and mobile and then the cloud and then, you know, multiple crypto hype cycles. And the reality is, out of all those hype waves, interesting things emerged, right? And maybe in the standard hype cycle, 95 or 99% of things fail, but there's still like the 1% that work, or maybe it's 5% work and 1% end up being spectacular. And I think the hard part usually is to know what's actually going to work because so many things seem so overlapping and similar. And so I remember when the mobile wave happened, or mobile and social at the same time, a bunch of different people I know started mobile photo apps. And each one of those things took off. And so you'd suddenly see something go from zero to a million users in a week, just literally,

• 14:09Speaker 01

  it just spread virally. And none of them stuck, right? They all burnt out its cycles and the only one that really stuck was Instagram. And in part, that's because Instagram emphasized filters, which things like Camera Plus already had. And then in part, it emphasized the network. It's like, let's have a follow model like Twitter. And that's the thing that really worked. And so it feels to me like if you'd gotten excited about the overall cycle, you were right, but if you got involved with the wrong set of photo apps or you built the wrong thing, then you were wrong in some sense. I guess you were right about the trend, but wrong about the specific substantiation of it. And it seems like the same thing here. And so I think often it's that question of, you know, Peter Thiel has a good saying, which is you don't want to be the first to market, you want to be the last standing. And so I think it's a similar thing here. How do you end up being the last person standing or last company?

• 14:53Speaker 01

  And it may be the same thing as being the first mover, right? It's Amazon and books or things like that. But sometimes it means you actually do something a bit smarter and you come later in the cycle and it's fine. So, I mean, are there specific areas you're most excited about in this wave or cycle or opportunities that you think are these things that are obviously going to happen or important to happen?

• 15:12Sarah Guo?

  Absolutely. I mean, well, and I want your ideas, some of them are shared ideas, to be fair. But I would agree, it actually, at a data point, I was just over at OpenAI yesterday and they're biased, perhaps in a way that I'm also definitely biased, but a friend was saying they actually think that investors are being somewhat wary at the application level right now because they can't figure out what's going to be standing, right? It's a very different competitive dynamic.

• 15:43Sarah Guo?

  But the market is extreme for researcher-led foundation model companies because everybody is pretty sure OpenAI is going to be around. Right? And I agree, the applications are going to be non-obvious. But as one example, like any investor that claims they knew image generation from text was a killer use case like a year or two ago, besides you, is just empirically wrong, given David's completely investor-free cap table and amazing business. Right? So David, in case you're listening, I still love Midjourney and want to invest. But that's why this podcast exists. But you know, in terms of like specific things that I'm interested in now, I'd say like, I think there are a lot of things on the application side that are exciting.

• 16:27Sarah Guo?

  So to start with some of those, I think, you know, voiced into this, some dubbing are going to be just a huge unlock for like content providers and publishers. Like, I'd like to back something in that space. I was just talking to some people at a very large financial, and they said the like biggest potential cost savings, you know, on order of tens of millions of dollars a year for us, is in turning every line of code we have into explanations for a regulator. And that's at once like pretty specific to them, but also not, right? I think the areas of audit, tax, compliance, accounting, reconciliation, like there's a lot of natural language understanding

• 17:08Sarah Guo?

  that could be better served by semantic understanding. And so I think that's an obvious area. I think annotation is changing again, right? And we can use, this is like a very specific idea, but we can use LLMs too much more here. We talked about agents. And then this is like, this isn't necessarily a specific company idea, but I think Architecturally, retrieval is a field of active research, but the idea of personalizing LLMs with enterprise data is an important but like very tricky one. You have to do data management. You have issues in scalability, sync, access control. You likely want to

• 17:49Sarah Guo?

  apply traditional IR. If you own both retrieval and the model, you can do very magical things. And so I think like the the ChatGPT retrieval plugin is super cool, but it doesn't just serve a whole host of use cases. And I think this entire like half of the stack is still missing. So those are like a couple of the things that we're sort of explicitly like hunting around. But what are you paying attention to?

• 18:14Speaker 01

  Yeah, I mean, I think we have a lot of overlap, as you know. So I'm super interested in sort of voice synthesis, dubbing and related, both in terms of infrastructure, but then also in terms of application areas. And so I think that's going to be a really big sea change that perhaps people aren't paying enough attention to. I'm actually quite long on compliance in general. Like, I've done a bunch of things like Agent Sync and Medallion and other compliance related companies in sort of the old world. And so I think that's just an area that there's always going to be, you know, converting spreadsheets and offline processes and, you know, random checks and docs into code is, you know, really powerful. I think there's a lot to do on the upside. I actually am maybe on the other side of people who think that it's impossible to tell what's good and, you know, nothing's

• 18:59Speaker 01

  defensible and everything's just a wrapper on GPT or whatever. And I actually think there's tons and tons to do there. I mean, Harvey AI, which we're both involved with, I think is a great example on the legal side. But I think there's, there's, you know, two dozen things like that to build over time. And it probably takes five years for all those things to get discovered and built and substantiated. So I don't think it's like this year, there'll be 12 of them. But I think like every year, there'll be a couple of really interesting ones. And then there's probably a lot to do on the tooling side, right? Obviously, LangChain is sort of a hot one in the in the area, but there's everything from, you know, people exploring vector DBs like Roma on through to other forms of infrastructure.

• 19:37Speaker 01

  And so I, you know, Lama and Dax and other things. So I just think there's a lot that to be done at every level of the stack. It'll be interesting to ask like what happens on the foundation model side because to some extent, the question is, have we locked in a few of the leaders or is more to come? And I think the Elon Musk startup, this rumored to exist is sort of an interesting example of a new entrant. And back to regulation, you know, Musk was asking for a six month moratorium on progress, which seems to me very self-serving if you're simultaneously starting an LLM company, you know?

• 20:09Sarah Guo?

  Just hold off until I catch up, right?

• 20:11Speaker 01

  Yeah. And if I was in that position, I'd do the same thing. Don't get me wrong, you know? So it's not, it's not meant as a this is just meant as a, you know, remember people's incentives.

• 20:21Speaker 01

  But I do think there may be some interesting things to do on the foundation side. And I do think some people are doing that in a vertical specific way. They're saying, hey, we're going to build a healthcare specific model and we're going to build a, you know, Bloomberg did like their, their Bloomberg GPT or whatever it was called on the financial side. And so I think you can clearly see these verticals emerge and a lot of people obviously are debating, will a general purpose model discover all those use cases? Are you going to have bespoke sort of vertical models and what, what parts of the actual logic and synthesis and sort of magic of these AI models comes from the fact that you've trained on a massive amount of data and language, and then you're applying it to a specific area with potentially a unique data sets overlaid? Or is it something that's just, you know,

• 21:03Speaker 01

  that can be dealt with vertically specific and you don't need that broad-based understanding of the world? So I think that's a really interesting area of like exploration and I have no idea what to predict there. I don't know if you have any thoughts on that?

• 21:16Sarah Guo?

  Well, I would agree. I think it's, I think there is real opportunity for vertical specific models where you can imagine that control for either a compliance or a safety or a just performance, like reliability of input data makes sense, right? As well as like if there are architectural differences because, for example, you have multimodal data in healthcare and pharma, right? If you are looking at protein structures and radiology and healthcare records, it's not clear that.

• 21:54Sarah Guo?

  You would want to do that, train that in exactly the same way as a general web text model, right? So I think that makes sense. On the broader foundation model question, I, you know, we were talking about open source at the beginning. I think that OpenAI will continue to be a leader. Anthropic is very dangerous here, like really talented team. But the number of people who know how to train large models and the cost of a flop goes down, right? And so I think there's like just a lot of incentive in the ecosystem for additional players to, to compete. What do you, what do you think is the opportunity for incumbents? Or how do you, how should they, how should they react to all this?

• 22:38Speaker 01

  Yeah, I think, you know, obviously with every technology wave, there's a differential split in terms of where market cap, revenue, employees, innovation, et cetera, goes in terms of incumbents versus startups. And, you know, every wave is a little bit different, right? The internet wave was almost, you know, it's probably 80% startups in terms of value and 20% incumbents. And then mobile was sort of the other way around. It was 80% incumbents and 20% startups, right? The big platforms for mobile were the, were Google and Apple. But then you had a lot of interesting apps like Instagram and Uber and others emerge. For crypto, it was like 100% startup value, right? And it feels like in this wave, it's probably 80/20 again, right? It's probably, Google will probably become a player, right? OpenAI is closely aligned with Microsoft.

• 23:22Speaker 01

  And then, you know, Salesforce with AI is probably Salesforce, right? It probably is a new company, might be, right? I actually think certain companies are vulnerable for the first time because of these capabilities. And that includes everything from ERP providers where there's like a defensive mode through integrations. And obviously this could make integrating your data into multiple things really easy and fast. Instead of six months to roll out SAP, maybe you could have a next gen approach where it takes, you know, a day or two on a new product, right? To do all the integrations that you would have spent six months on consulting fees for. And so there may be certain types of thing, companies that are vulnerable, but the reality is, I think, in most cases. You know, if an incumbent is already doing something and they're quick to integrate it, then it works great. The one area that may be really interesting is almost like there's probably room for

• 24:06Speaker 01

  a new private equity approach, where if you think about how private equity companies bid on things, they basically look at cash flows and costs and all the rest of it. And if you can radically decrease costs for people-heavy businesses by using LLMs as like a replacement for certain types of work, or at least an augmentation, then you can differentially bid on companies as a private equity shop. And so I think like people who do buyouts could have this as a strategy. I don't know that any of them will, because most of them tend not to be very technology savvy, but I think there's really interesting alternative things to do at scale there that tend to be kind of under discussed. The healthcare side that you mentioned earlier, I think is kind of fascinating, because if you look at the cost of developing a drug, for example, say it's a billion or $2 billion to develop a drug, whatever it is, most of that early-stage development is in the tens of millions of dollars at most.

• 24:51Speaker 01

  And so I think a lot of the default focus of people who don't understand healthcare very well is to say, I want to use this for drug development. And it may help with certain aspects of drug development later, but usually I think the places in healthcare where this will really get applied fast is on the more operation or services-intensive related side. It's healthcare delivery, it's lowering the cost of a doctor visit or telemedicine, it's making payments easier and more streamlined if you're dealing with insurance reimbursement. And so I think there's really exciting things to be done there, like Color, a company I co-founded, is, for example, thinking about different application areas. And I just think that that's like a real wealth of fruitful areas for people to explore if they're healthcare savvy. And of course, with healthcare, the technology usually isn't the issue. Usually the go-to-market is the hard thing, right?

• 25:36Speaker 01

  So I think market access is really hard there.

• 25:37Sarah Guo?

  Yeah. I push back on that a little bit. I'd start with saying I agree on just the operational friction in healthcare that we can take down, right? There's so many processes, like if you look at prior authorization, it's a battle on two sides to fill forms and like compare like EHR data and clinical recommendations against a policy, right? And so there's a piece of that you can't get rid of, because you know, insurance company has incentive not to pay and like hopefully providers trying to provide the best care. But there is a piece you can get rid of, right? Like, we have models that can. You know, read data, try to understand it, fill out form. And so I, I think that there are lots of interesting applications there.

• 26:21Sarah Guo?

  The, the, the minor pushback, and you know, much more about healthcare and pharma than I ever will. But, you know, VC is the job of having opinions anyway. And I think if this wave of AI can change the cost curve in drug development, it's because it, you know, you're not actually impacting the 10 or 20 million dollars upfront on what's traditionally considered like research. You're increasing the probability that you're right. Right? And so like all of the cost of, you know, expensive recruiting and clinical trials, it is more efficient because you're right more often. You would just understand more about biology.

• 26:55Speaker 01

  Maybe, yeah. I think, I think the hard part is that a lot of, um, a lot of drug development ends up being, hey, this works great in mice and let's try it in people now. And to your point, there may be things that you can learn heuristically in terms of when do things translate versus not. Uh, but I think one piece of it is just, is just basic biological differences. And then the second piece of it is, this is back to the point on regulatory capture. To some extent, the incumbents have an incentive to drive up the cost of drug development. So no new startups can actually ever enter in terms of actually making it all the way to a launch drug. Oh yeah, but you know, it's interesting. Like it's, it really is this weird regulatory capture. And so if you look at, um, the last time a biotech company, outside of Moderna, which I think is, you know, an exception because of COVID.

• 27:39Speaker 01

  The last time a biotech company hit, I don't know, it is 30, 40, 50 billion dollars in market cap, something like that. The last year such a thing was founded was in the late eighties. So it's been, uh, at this point, what is that 35, 40 years? Without a new major biotech company started. In terms of biopharma, actually developing drugs, that's shocking, right? In tech, during that same, that same time period, there's dozens of companies. And if you actually look at the aggregate market cap of the entire biopharma industry, and as a reminder, healthcare is 20% of GDP and pharma is about 20% of that, right? Or 10% of that. If you add up the top four or five tech companies, their market cap equals the entire industry for biopharma.

• 28:23Speaker 01

  And that includes Pfizer and Eli Lilly and Genentech and Amgen. And all these companies, as well as all the small startups and all the mid cap companies and everything else. And so then you ask, why is that?

• 28:35Speaker 01

  And these are very profitable companies, right? They have software-like margins in some cases. And so as you start digging into the industry, you realize, wow, like, there's strong reasons for incumbency to remain as incumbents. And there is this regulatory process that really delays things quite a bit. In some cases rightfully, in some cases wrongfully. And if you look, for example, at the COVID era, we were able to develop multiple vaccines and do clinical trials on multiple drugs really, really fast. Part of that was we had a lot of patients, but part of that was we removed all the regulatory constraints. And we didn't have mass scale adverse events and bad things happening to people. We just moved really fast. This actually also happened during World War II.

• 29:17Speaker 01

  Winston Churchill wanted a way to treat soldiers in the field for gonorrhea. And so they rediscovered and developed penicillin in nine months. They again removed all the regulatory constraints and boom, nine months later, they had a drug that worked really well that was safe. And so I think it's something to really think about deeply in terms of what are the incentives that we're driving against? And how are we thinking about cost benefits societally? But also the second you start adding a lot of regulation, things slow way down. And innovation goes way down and costs go way up. And that's the reason that, you know, for the earlier conversation, I think regulation of AI for most things, you know, export controls makes sense, a few other things make sense. But for most things, it's probably a really bad idea right now.

• 29:58Sarah Guo?

  I would agree with that. I do think that there is

• 30:02Speaker 01

  That was my rant, by the way.

• 30:03Sarah Guo?

  No, no, stay on the soapbox. Learn something about gonorrhea today. But, but I think like, you know, if you think about the power of government, and I'm strongly on the like, reduce regulation, encourage innovation side. You also have these wartime examples of production of airplanes in World War II, going from a few hundred planes to 6,000 in also less than a year, right? And that you're here, we're fighting like atoms, not bits, right? You have to build plants and like for all these engineering processes. And so, you know, I think that there are ways in which like, from a industrial policy, national security perspective, like

• 30:47Sarah Guo?

  if we wanted to be winning in AI in a really durable way, like, I think the paths are pretty clear, actually. Like people need compute and like, we have to make it a priority in the United States. But I would also say in the field of pharma, I remember like asking you like, I don't know, seven, eight years ago, like, hey, Alad, like, I know you're interested in aging and like, weight loss and the intersection of areas where like, where the demand is very consumer driven, right, you might break out of, and demand and also like, the ability to access different solutions that are on the edge of like, consumer purchase,

• 31:29Sarah Guo?

  right, especially as we have more like, web diagnosed, doctor network diagnosed prescriptions, right, do you think this is interesting? And I'd send you a company or two, and you gave me the same extremely consistent view, which was like, hey, despite the PhDs, like, you know, the data-driven person investor inside me says, like, don't do this, just do tech companies. So, no change.

• 31:55Speaker 01

  You know, I think that the healthcare services and operations side is super interesting right now due to LLMs, and so, you know, that's an area where I think there's lots and lots of room to do interesting things. And I have invested in some software-related companies in the past, like Benchling or Medallion, in these areas, but I think it's really about what's the healthcare infrastructure that can be served through software, and then how can LLMs accelerate it? I think drug development can be extremely useful societally and really important and impactful, and obviously, there can be really great outcomes for people, as well as financially, it could be a really great thing. But it just comes back to like, why hasn't anybody built a generational company in a really long time in the area? And there's all sorts of reasons behind that. I mean, we tried that when I co-founded Color,

• 32:40Speaker 01

  right, the whole focus was trying to make healthcare more accessible to people. And I still really believe in that mission. So, it's more just, you know, what are the obstacles to getting there for different types of companies, and do you want to take on those obstacles? And if nobody takes them on, then society really suffers. And so, it's almost like, how can you make sure that you remove as many obstacles as possible, while still safeguarding the public, right, so that people don't get hurt by this stuff? But at the same time, perhaps these things have gotten too extreme, and that really, you know, strangles the ability for the industry to innovate in ways that it could otherwise. So, it's a really interesting area. Are there any other topics that we should cover from the audience?

• 33:18Sarah Guo?

  I'm good. What do you think, Alad?

• 33:20Speaker 01

  I think we got it all.

• 33:21Sarah Guo?

  Thanks to everyone who submitted their questions.