00:18:28

Sarah Guo: 2024 tech markets. What's coming? Will people be able to fundraise? Will funds be able to fundraise? Are customers purchasing?

00:18:37

Elad Gil: know, I think there's going to be basically four markets next year in some sense. One market is just AI, and I think AI will continue to run in different ways, and it'll look very expensive at the time, and a handful of companies will look really cheap in hindsight, just like with every other technology wave. And I think that's separable from the rest of tech that existed prior to the AI wave. For companies that fundraised in 2021, prior to being like AI companies, a subset of them, I think if I were to sort of divvy up that pie of those companies, sort of mid to late stage private tech companies, not an AI, and what's going to happen to them next year and in 2025, I think a third of them are just going to go under. Or a third of, I should say, unicorns will eventually just go under, be fire sales, whatever. They won't be able to ever raise money again. A third will be at the highest valuation they'll ever be at ever in the lifetime of the company. They'll reach their terminal value. And there's examples from 2014 of companies that went through that same wave. They raised in 2014, they went public a few years later, and then they never surpassed their market cap again.

00:17:19 Elad Gil: And it feels like AI really has a promise in both of these areas. And so I always worry about, You know, how do you make sure that this can get to market because it's so valuable, but there's going to be all these regulatory or safety obstacles that in some cases are merited, but in some cases may actually prevent the emergence of really important applications. So I think it's awesome that you folks are working on all this and are being so thoughtful about it. How do you think about what workflows this is going to be most useful for? So, you know, if you look at a lot of the bio or biomedical AI companies, for some reason they keep doing drug development. A, why do you think that is? Because this seems like such an important part of healthcare and probably the bigger driver of healthcare efficacy. And so A, why is everybody just going and building another protein folding model or molecular company? And B, where do you think are the best applications of what you've been working on?

00:18:10 (someone): Yeah, these are great questions. I think on the drug discovery front, there's a bit of a playbook here, which any new company here looking for some revenue in the short term can follow. And that could be a safe option. There are, for example, existing AI augmented pipelines for doing things like giving small molecule chemistry, predicting things like absorption or toxicity. And it's kind of relatively easy to see that some of the more modern models, if placed into these pipelines, could perform better. And so there's a relatively safe bet there.

00:02:45

Elad Gil: Are we off by 2x, 10x, some other number?

00:02:48

Sarah Guo: It's hard to say because right now, there's no way to explore like the price elasticity of these things, right? So, you know, just very specifically, like the industry is kind of looking at deliveries in small quantity in September, larger quantities in December, January. Most of the large cloud providers are sold out for any scale for at least through April of next year. And so you have, like, really interesting dynamics, like large cloud players who, you know, are the biggest consumers of these GPUs already, like a Microsoft going and buying from other providers for near-term supply, right? So I think one question that I ask you is like, hey, do you think this is a long-term thing? Do you think it's a very short-term thing? But I think it just goes back to, like, the fundamental dynamics are, do you expect the demand for these chips to continue increasing at a pace that outcreases the ability to scale a very physical like real world process, right? Just to even be more specific, one of the challenges like I was talking to Jensen about this and a bonder, like not part of the GPU itself, but like a critical tool in the manufacturing and assembly of GPUs is very specialized. And so the ability to build any of these tools as well to enable these processes is a blocker. If you look at the demand from large labs today to continue increasing model scale and training time by magnitudes, I think it's hard to see that dynamic going away.

00:21:48

Elad Gil: Yeah, I'm actually less worried about valuation. I think valuation is ephemeral, right? Effectively, every or roughly every tech company in public markets did a down round over the last year and a half, right? They all lost or many, many companies lost 30 to 90% of their value, right? And effectively, they just did a down round in public markets because every day you're repricing a public stock. I'm more worried about the people who burn tons of cash. And they don't have a lot of revenue to show for it. And then when they're going to go out to raise more money, people say, well, you burn $50 million. You burn $100 million to generate $5 or $10 million of revenue. And so the issue isn't that your valuation is off. We can always reset valuation. It's the fact that you burned all this money, and you don't have anything much to show for it. And that's where I think the real issues will happen. Because you can always reprice things, and people will be forced to. And it'll just happen. But I think it's the underlying business case and business model that's going to be the real issue.

00:22:39

Sarah Guo: Yeah, I guess like the the unforced error there for companies who actually have the time to make the decision is the thing you want to avoid is like not adjusting your cost profile or, you know, holding on to that valuation until it's too late.

00:22:53

Elad Gil: Yeah.

00:07:27 Elad Gil: 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, there may be terrible things that happen, but fundamentally it won't be any different from any other period. 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 seventies about population collapse. We're going to have too many people and the world will starve and we're going to have global famine and that didn't happen. And so, uh, 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. Um, 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 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.

00:17:19

Elad Gil: No, that makes sense. I think I would add one third piece to that framework you have, which is the research driven versus product driven. I think there's a third approach, which is infrastructure tooling driven. And that's where you're like, I'm not going to build the agents, but I'm going to build the infrastructure that allows anybody else to build them rapidly. Now, sometimes those types of businesses or approaches work really well. And sometimes those things are solely an outgrowth of a vertical product that works really well that you then open up the infrastructure for everybody else to use. And it's very case by case dependent. It's the difference between Stripe. where it's just like we need to build payments for everybody. Everybody keeps building it over and over again. And the Facebook off the platform, which only existed because you got to hundreds of millions of users, you could open up off as like a third party service. And so I think as people think through that third angle of building infrastructure for others, they need to understand whether that infrastructure will be an outgrowth of an existing product area and benefit from the characteristics of the market liquidity of that product. Or whether it's just a piece of infrastructure everybody keeps building over and over and therefore it's a really good thing to just provide to the world. So I think it's kind of an interesting future topic.

00:18:24

Sarah Guo: We are on a couple month bull run at this point. 2024 tech markets. What's coming?

00:13:46

Elad Gil: You needed a tool for that. But it feels like in the agent world, a lot of the people that I hear talking about ideas have these very broad sort of abstract ideas. And so an idea would be, um, I'm going to build an agent that is going to be your assistant. And you're like, okay, well, what is it going to help me with? And they say, everything. It's going to make you happy. And you say, well, I don't, I'm, you know, I'd love to be happy, but at the same time, you know, starting with a very targeted, focused initial use case tends to be the best way to build product. A, because you know who you're building it for. B, you can really nail the use case. And there's the old sort of YC-ism, which I think, which is really good, which is it's better to delight a small number of people than to have a very large number of people indifferent to your product. And so I think my bias for the agent world is if you're building an agent, start with something really targeted. If it's an assistant to help you, what exactly does the assistant do? Does it do background information searches on all the meetings you have that day? Does it specifically help with certain forms of scheduling? Does it help with other aspects of your day planning or synthesis of what you've done or follow up action items or whatever it may be, but choose one or two things and do them very well versus do everything.

00:24:37 Elad Gil: 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. 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 operational 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.

00:18:53 Elad Gil: I've done a bunch of things like agent sync and Medallion and other compliance-related companies in the old world. And so I think that's just an area that there's always going to be. Converting spreadsheets and offline processes and random checks and docs into code is really powerful. I think there's a lot to do on the app side. Actually, maybe on the other side of people who think that it's impossible to tell what's good. And you know, nothing's 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, 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, link chain is sort of hot one in the area, but there's everything from people exploring vector DBs like Chroma on through to other forms of infrastructure. And so, Lama Index and other things.

00:19:30

Elad Gil: And there's examples from 2014 of companies that went through that same wave. They raised in 2014, they went public a few years later, and then they never surpassed their market cap again. And then I think lastly, there'll be a third of companies that grow past it. And so I do think there's going to be a lot of carnage next year and a lot of companies going under. And as those companies go under, three things will happen. Number one, it'll be much easier to hire people. People are already seeing that at startups. It's easier to hire again. Second, it should have follow-on effects and ramifications for commercial real estate. And we'll see a second shoe drop there. And then third, the venture capital community will be impacted because a lot of the things that they've been using to fundraise new funds or do other things with will suddenly go to zero. Their big unicorn success will go from a multi-billion dollar or billion dollar company to basically a company that isn't worth anything. And so I think that's going to have knock on effects to the venture ecosystem. But I think that'll take like two, three years to play out because all these things are a bit time delayed. Um, but yeah, I think that other shoe still hasn't dropped in private tech markets. And a lot of it is just companies raise so much money in 2021, they still have lots of money.

00:19:51 Elad Gil: Pharmaceuticals are about 20% of that. And then drug development is a fraction of that, right? So really what you folks are focused on in terms of the types of models that you're building is at least 16% of GDP. Maybe it's more than that if some of the pharma stuff is more clinical decision-making around who gets a certain pharmaceutical. Do you view this as a technology that's initially a physician's assistant? Do you view it as something that helps with adjudication of medical claims and billing. There's so many places where this can sort of insert. I'm just sort of curious, where do you think you'll see this technology popping up first?

00:20:28 (someone): Yeah, I think we're already starting to see it in some clinical workflows when it comes to documentation and billing. I think there are a lot of companies and people thinking about taking models like GPT-4 and applying them in that setting. I think that is definitely going to be something. I think that is also going to be something where players like Epic are going to be able to partner with existing models and I think potentially deliver real value there. I think that's very exciting. I think that's something that also general domain models will be potentially quite good at as well. I think where there might be more of a need for specialized models, is when it comes down to kind of higher stakes workflows. And I think that might look in the short term more like a physician's assistant.