How AI is opening up new markets and impacting the startup status quo with Sarah Guo and Elad Gil

This week on No Priors, we have a host-only episode. Sarah and Elad catch up to discuss how tech history may be repeating itself. Much like in the early days of the internet, every company is clamoring to incorporate AI into their products or operations while some legacy players are skeptical that investment in AI will pay off. They also get into new opportunities and capabilities that AI is opening up, whether or not incubators are actually effective, and what companies are poised to stand the test of time in the changing tech landscape. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Show Notes: (0:00) Introduction (0:16) Old school operators AI misunderstandings (5:10) Tech history is repeating itself with slow AI adoption (6:09) New AI Markets (8:48) AI-backed buyouts (13:03) AI incubation (17:18) Exciting incubating applications (18:26) AI and the public markets (22:20) Staffing AI companies (25:14) Competition and shrinking head count

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

• 0:05Sarah Guo?

  Okay, hi listeners. Today you just have me and a lot shooting the, what's the appropriate term here? Shooting the breeze.

• 0:12Speaker 01

  Shooting the breeze.

• 0:14Sarah Guo?

  Yeah, but I want to start this shooting the breeze session by talking about this Goldman Sachs report that everyone's reading, which essentially says, I'm just gonna get on my soapbox for a second here, that the title is something like calling the top on AI. And so for obvious reasons, I don't like it, but I do think it's worth decomposing for a second. I do encourage everybody to go skim this thing. So there's a bunch of interviews in it, and two of the core ones are from this guy Darren Asmoglu and Jim Covelo, the respectively like MIT professor and the GS head of Global Equity Research. And Darren is arguing essentially that AI is going to impact less than 5% of all tasks

• 0:59Sarah Guo?

  and the like trillion dollars of capex that people are spending on training models is a waste because AI will be unable to solve the complex problems. It's not built to do that. And Jim argues, you know, he argues that in contrast with the internet where you are disrupting something expensive from the beginning, even early on, versus having a very expensive solution that then becomes democratized. You know, AI is very expensive from the very beginning. And then the other argument he makes is that any efficiency gains from AI will be competed away anyway. And so like, you know, none of the companies are gonna gain from this.

• 1:41Sarah Guo?

  And so if we just like talk about Darren first, Darren's arguing about something he doesn't understand. Like he, his claim is, you know, how do we know if scale works? More data won't make customer support reps better. I think like that's just a fundamental like misunderstanding of the technology and also objectively of what has happened over the last several years of scale and data improving capability and quality of model outputs.

• 2:09Speaker 01

  I think a lot of these folks too, by the way, are just kind of stuck in the old AI world. Like I haven't read the reports, so I'm not talking specifically about these authors. But a lot of people are treating this like old school ML. And they don't seem to realize that there's been sort of a breakthrough in terms of these transformer-based models or other architectures that effectively are both highly scale-dependent, but also provide different types of functionality and features than, you know, you're sitting there and you're munging some data and effectively doing fancy regressions in some sense. So I think that's the other issue here in terms of a lot of what I hear. This happens a lot in healthcare. You know, in healthcare, they always talk about how data is the new oil. And you're like, data is not the new oil. You know, sometimes data is useful and well-labeled

• 2:51Speaker 01

  data can be extremely useful, but you know, a lot of it is also about the model and the application and everything else. And so I think, um, I think there's this broader misconception in terms of how this stuff works and what it means and all the rest of it.

• 3:03Sarah Guo?

  Yeah, I agree with that. I think this is actually a case of like this time it's different and also people lacking, you know, even the market state of what's happening on the ground. Like there are absolutely things that are cost-effective to do today. That's why you get actually a series of companies that are democratizing capabilities and really more on the prosumer side, but you know, beginning to see things even in, for example, healthcare, traditionally really slow industry where you go zero to five or 10 million of run rate in your first year. I think like, as you said, one, one sort of problem with this framework of thinking is, you know, you assume AI is like what it has been in the traditional ML world. The other is this assumption that the tech won't get much better fast and it won't get cheaper fast. And I think the willingness to predict 10 years

• 3:47Sarah Guo?

  into the future of insignificant improvement is ludicrous when literally all of the people working on this tech are unwilling to, like you and I are probably unwilling to predict two years into the future, much less 10 years.

• 4:01Speaker 01

  Well, I mean, the thing I predict two years into the future is that there's going to be even more broad-spread applications of it. So I think it's almost the opposite thesis, which is this is early days. And if you look at enterprise adoption of AI, most large enterprises are very early on. They think of it as three things. What sort of vendors can I buy AI-related tools from? What are my internal tools and how do I adapt them to AI? And then third is, how do I think about it from the perspective of external customer-centric products? Almost everything that's happened to now, up until now, has been like vendor buys, Decagon, Hervey, et cetera. And then the product side is very early and it's really the AI-centric for consumer companies, ChatGPT, Perplexity, et cetera, that are, that have provided functionality at scale so far.

• 4:45Speaker 01

  And so the big wave of enterprise adoption hasn't even happened yet, right? It's very early days. So all the impact is in the future.

• 4:52Sarah Guo?

  Yeah, I think the predictions I'm willing to make with strong confidence is models will get better across a bunch of domains. And like, we're very early in exploitation. And as you said, like, there's a sequence of enterprise adoption where we like, haven't really gotten there yet, because the planning cycle takes so long and like, you know, all these natural frictions. But I do think one of the like flawed assumptions in Jim Cavill's picture of the future is that the way you apply these advantages is standard across companies. That's like saying, oh, everyone will use the internet and therefore there's no economic gains to be had by companies when the internet happens. But like, you clearly get Amazon, you get Borders, and you want to be Amazon, or you want to be Klarna, or, you know, whoever is actually changing their cost structure dramatically here.

• 5:37Speaker 01

  Yeah. By the way, this is not a new story. During the internet era, there are people who said that the internet was meaningless. And the CEO of IBM in the day had this quote, "The internet companies were fireflies before the storm." And this storm was companies like IBM adopting internet, et cetera, et cetera. And then, of course, now the trillion-dollar market cap companies are Amazon and Google and the like. So this is an old story. It's old wine in new bottles.

• 6:02Sarah Guo?

  Time to, time to break the bottle. What else is like inspiring you or bothering you from a markets perspective?

• 6:09Speaker 01

  You know, I think the really interesting thing about AI is that it's opening up markets in unexpected ways. And I think there's three sort of drivers of that. One is we have new capabilities. And in particular, we have new capabilities across multiple fields, you know, biology and robotics and obviously language and image gen and video and things like that, with a lot of emphasis right now on language and image gen. So these things are very generalizable and you train one model to do lots of stuff versus doing bespoke mini-modes, which was sort of more the norm before. Second, you can access it through an API anywhere in the world, which means you don't have to build all the ML ops, you don't have to do your own model training, like, you can use it out of the box with a simple API call, so suddenly anybody can use it.

• 6:50Speaker 01

  And third, a lot of companies and organizations have a mandate to do AI.

• 6:57Speaker 01

  And that means the markets are suddenly open in ways that they haven't been in two decades, when the last time that happened was with the internet, where they said, oh, we have to do something on the internet. We have to be like an e-company, I think some people said. You know, those three things are driving unexpected behavior in terms of how you can address markets. And what I and the small team that works with me have been looking at is market by market. Say you look at services and AI transforming services, and I think I talked about this before, where, you know, software spend in the US is something like half trillion dollars a year. There's probably five trillion in headcount costs for services industries that could be transformed by AI. You know, things like legal or accounting or, you know, you name it, sales, et cetera.

• 7:40Speaker 01

  And as we've gone through market by market, we've basically been looking at, you know, are there companies that exist? If a company doesn't exist, should you incubate something? Or are there situations where you actually want to do a buyout that's AI-driven? Because it's better to have somebody take over the asset or the company and then dramatically change the cost structure or the leverage per employee using AI. And so I think for the first time, two things really make sense. One is incubation, which usually is a terrible idea, you know. But there's a few things that, you know, I and my team have started actively working on. And the second is AI-driven buyouts. And I've now backed one or two of those with the idea that you can suddenly do things with AI

• 8:23Speaker 01

  that you can before, and you can kind of change the game in a market. And so, and that's beyond, you know, obviously all the really exciting stuff happening in terms of model architectures and infrastructure and all the rest. But I just think from a markets perspective, which is sort of our topic today, there's really odd things you could suddenly do. And part of it is just the buying behavior has shifted because everybody has an AI mandate and they're willing to consider products that they wouldn't be able to consider without AI. So it's a very exciting time from that perspective.

• 8:47Sarah Guo?

  Yeah, I want to come back to incubations in a second, having done a few of these. With buyouts, like just to articulate, like, you know, what I think is the premise here, because we've also looked at these as you are short cutting the change management process for industries that can be like dramatically automated with these new capabilities by just controlling them, right?

• 9:13Speaker 01

  You're actually shortchanging two things. You're shortchanging adoption of technology, which in some industries may just not happen very fast. And in some cases, there's an incentive not to adopt it. You're shortchanging change management to your point. But third, you're actually taking over an asset and you'll be able to entirely rework the way that a subset of that organization functions relative to AI. And Klarna kind of did that to itself with this customer support team where they reduced Ted count there by 700 people by effectively using OpenAI and some custom workflows to do better customer support. And suddenly it was 24/7. It was, I think, almost 20 languages. It was higher MPS than the customer support reps. It was faster response time. It was lower repeat incidents.

• 9:57Speaker 01

  And there's lots and lots of industries where a lot of the cost is associated or leveraged on time. In other words, you could keep the same employee base, you could just make them 10 times more effective in terms of the set of customers they can serve, where a lot of the work is basically what people call email jobs, right? You're copying and pasting data from one spreadsheet to another. You're responding to emails. You're in a CRM, whatever it is. And some aspects of that are now suddenly accessible or automatable via this new type of gen AI.

• 10:27Sarah Guo?

  When you, and just to articulate like the case against it too, like I think it is, you know, managing services companies, identifying assets and the operational intensity of, you know, doing acquisitions as well is a different skill set than most, let's say, software engineering heavy startup teams are. And so I do think that is the question, like, you know, can, can you get the right people to run these things?

• 10:56Speaker 01

  Yeah, you kind of need both because the traditional playbook on what used to be called like tech-enabled buyouts, which largely didn't work, is you'd have a PE person come in, they'd buy a bunch of companies, they'd roll them up, and they'd put this really thin veneer of software on top of it so that they could claim as a technology company. And effectively what you're doing is you're arbitraging a tech valuation in order to buy other sort of EBITDA-rich assets. You're looking for cash flow, right? But you're buying it for cheap at a normal private equity multiple, and then you're raising money on the back of that revenue or cash flow using a tech multiple, which often is, you know, a few x higher. And so effectively, a lot of those early things were arbitrage. And the early investors and the founders of those things tended to do well.

• 11:39Speaker 01

  And the late investors and late employees tended to do poorly. Because as the thing increasingly became recognized as a private equity play versus a software company or technology company, the market cap and multiple would come down and normalize, right? It would lose value at the later stage of GAH because the investors became more savvy about what was really happening. That's different here where the leverage on the technology is dramatically higher than just adding up in software veneer. But, you know, one of the things I backed is actually driven by a software founder who's hiring in a PE team versus the other way around. And obviously, there's very good PE people who are hiring and, you know, are working or co-funding with ML people or AI people or software teams. But you need to make sure that you have the right mix and that you actually know what

• 12:23Speaker 01

  you're really doing. Because otherwise, you're just doing a roll-up, but you're calling it something else. Which, again, is fine, right?

• 12:30Sarah Guo?

  Yeah, but you just need to have some roll-up. Like, you need financial engineering DNA and investor DNA as well. I think one of the things that makes this, like, idea resonate with me is I have some friends running, let's say, the current generation, you know these people too, of like, sort of tech-eating services companies in traditionally very fragmented industries. And, like, in a small room, they would say, "Man, like, Sarah, a lot, the automation works and the distribution is the problem," going and acquiring the customer relationships.

• 13:00Speaker 01

  Yeah, and so you're buying the distribution, yeah, exactly. You're just buying the whole thing.

• 13:04Sarah Guo?

  Okay, let's talk about incubation. So my, you know, thinking on this historically has been like, okay, when does this actually make sense at all? It's when, like, you really, like, have visibility into an opportunity set or a customer set that others do not. And the, like, the expertise set required to make a company work, like, the DNA is unlikely to get it to get together organically. And so, like, that's the alpha. Like, how do you see it?

• 13:34Speaker 01

  I think incubations usually are a terrible idea. And most firms who are groups that incubate companies tend not to work very well. And there's kind of examples of that, right? There's a handful of firms that have actually done very good incubations. And often, but not always, they're kind of vertical, vertically specialized. And so they really understand the dynamics of BTC, or they really understand the dynamics of healthcare, or they really understand the dynamics of X thing. And they have proprietary access to customers or product ideas that they know will quickly take off and resonate, where they have a captive customer base they can sell to, where they know how to roll together assets, right? And so I think the way that incubation has tended to work historically is if you have some form of deep expertise in relationships that you can leverage. And most people just didn't have that.

• 14:18Speaker 01

  They would just go and try and start a company. And most, you know, the reason, to some extent, the startup ecosystem works is you have 5,000 different founders simultaneously doing a parallel exploration of multiple markets and multiple technologies until a small subset of them actually hit something that works, right? So there's probably like a handful, you know, three to 10 companies a year that actually matter out of the thousands of founders, right? And so, you know, a lot of these approaches traditionally have not worked very well. And there's kind of examples, you know, Snowflake was famously an incubation by Sutter Hill Ventures, et cetera. But, you know, most of the time, these things haven't done great. And there's other positive examples, right? Right now, because of AI, the odd thing that's happening

• 15:02Speaker 01

  is that certain subsets of the market are incredibly crowded. You know, you have a dozen companies all doing the exact same thing. And then you have these areas where it's really obvious to build stuff, and they're just wide open and nobody's doing anything. And then relatedly, there's a lot of very large enterprise interest in building things or adopting AI technology. And so there's all sorts of forms of incubation that I think are possible now. And so there's a handful of things that I'm working on right now from an incubation perspective, which, again, normally I wouldn't do very much. The last incubation I did was about a year ago, where I worked with Anker Goyle, who had started a company that Figma acquired. I worked with them on a company called Brain Trust, which is sort of Eval and Prom Playground and a few other products all kind of rolled together.

• 15:47Speaker 01

  And that was just driven by the fact that, you know, he and I had been sort of riffing on like, what does a, what does the enterprise need to use in order to adopt AI? But, you know, it's, it's, it's rare for me to do those sorts of things, but, but right now there's just a lot to do because there's so many just clear market opportunities or customers to work with or, you know, so there's, there's that customer pull or market pull that normally doesn't exist. So I think it's a very exciting time.

• 16:11Sarah Guo?

  One of the things that I, I think makes this make sense right now is the, the set of people who understand the technology are not commonly the set of people who understand the domain. Right. This is why you have this like mismatch that you describe of like some really obvious open markets. And so I think if you can get like great engineers and technologists who understand what's going on in AI and what the capabilities that are possible to actually apply them to where the enterprise problems are, that's exciting. And, you know, the first incubation I ever worked on was a company called The Wake, where it was actually, you know, applying last generation machine learning techniques to network data for security use cases.

• 16:53Sarah Guo?

  But I think be it that or other, it tended to be like, hey, a technologist from a different domain is, is looking for the use cases, that best match, which can be very dangerous, but if you know what it is, then it becomes a unique match. And so I, you know, I think that is exciting. What are you, what are you paying attention to just in terms of like new applications you think should exist, stuff you want to incubate, whatever it is?

• 17:22Speaker 01

  Yeah, there's a ton, you know, and if people are interested in working with me on something, they should obviously feel free to, to reach out through the network kind of thing. But, you know, there's, there's one thing that I've been looking at very seriously on the healthcare side. One thing I'm working on. That is sort of incubating against these large enterprise assets that I mentioned. And that's very exciting because then you have in-state customers. And we're in the process of pulling together a founding team for that. If anybody, again, wants to pigme or apply for it. And then there's one or two areas that I'd really like to work on, but we still don't have bandwidth. One is in the services world and one is more like a new type of model or a large-scale model for a specific application area.

• 18:07Speaker 01

  Are you working on anything in the area?

• 18:09Sarah Guo?

  Yeah, well, we just did a healthcare thing, so I'm very excited about it after many, many months of casting about.

• 18:16Speaker 01

  So I guess we talked about the really early stuff, which is incubation and all that kind of stuff. The flip side of it is public markets, right? And one question I've been increasingly getting is like, what do you buy in public markets given AI? Or would you change how you think about portfolio construction given AI? Or how do you think about existing companies and the risk of AI to those companies and things like that? So do you have any sort of thoughts or picks in terms of things that you think are exciting? That are much later companies relative to AI or how to think about that?

• 18:52Sarah Guo?

  I mean, I obviously focus on the early stage, but I'd be curious for your point of view on this. I do think there's a, like, if I could short something, I think that there is a mid to late-stage private company, small public company, that does not have the speed and institutional and founder or leadership will to go change the business when they see the writing on the wall. And I think that that's certainly not every company. I mean, we've talked to a number of amazing founders who have attacked the opportunity really aggressively because they see the way to grow revenue or make the business as higher quality. But I think that isn't going to be most companies.

• 19:35Sarah Guo?

  That's sort of the mid to late-stage private set. So I think that's something I would really think about in terms of portfolio rationalization. What about you? What in the public markets are you paying attention to?

• 19:47Speaker 01

  You know, I think of it as a lot just public market specific, but more like what are things that are durable in the coming world? And there's almost two versions of this question. It's like, what do you want?

• 20:00Speaker 01

  Your kids to be able to own some day or whatever, if you can afford it. And then there's what portfolio would construct today in tech. And you know, the crazy thing is if you go back 10 years ago, there was kind of this era where people were talking about FAANG, right? Those were the four companies you were supposed to just buy and hold. And that was like Facebook, Alphabet, Netflix and Amazon, right? That kind of morphed now and Nvidia is part of it. And they, you know, they keep kind of changing their rubric. But to some extent, you know, one question is what is that next set of companies that have multi hundred billion or trillion dollar potential? Right? And so there's, there's some basket of like, whatever you want to call it,

• 20:45Speaker 01

  new FAANG or new, yeah, I can't remember what they call it. The great eight or the something seven or I can't remember what it's called anymore.

• 20:51Sarah Guo?

  Yeah, it's the magnificent seven right now.

• 20:52Speaker 01

  The magnificent seven, right? So what are the, what is the next wave of that? And maybe that's SpaceX and Stripe. And you know, you can kind of think through, what do you think of these companies? It may just keep compounding for the next 10 years. So that's one segment. Second segment is potentially somebody's pre-existing big tech, big tech companies that will just keep going. Like, I wouldn't be surprised, for example, if Apple ended up with like another iPhone replacement cycle just for AI. Oh, we have a new chip that runs these, you know, they mentioned that they're going to be working with smaller models that run these smaller models locally, you need to upgrade your phone, et cetera. So at least for some period of time, Apple may have a bump from that. So there may be some big tech. Third is what I call like AI durable companies.

• 21:36Speaker 01

  Like what are the companies where AI just doesn't matter for? You know, is it railroads? Is it certain SaaS companies, right? Where it just, it just doesn't matter. You add AI, who cares? That actually shows durability. Like if AI doesn't improve the product that much, it's not really a competitive factor. So you can imagine a basket of stuff there.

• 22:00Speaker 01

  And then obviously, there's like, what's the new AI index besides Nvidia? Nvidia is a core piece of just a way to index the market. But there may be other companies, maybe that scale, maybe it's something else. But I think I kind of think of it across those four segments. You know, big tech subset, what's the new magnificent seven? What's durable in the face of AI? And then what's your AI index? You know, as we build out this whole AI wave, how do you think about staffing AI companies and post AI teams? And what does all that mean?

• 22:32Sarah Guo?

  A while back, you said that like, you think the founders right now, both existing and new, they've been like high quality, motivated, ambitious in a way you haven't seen in a while, just really, really good. I think one specific trait that keeps or specific belief that keeps happening is like, especially second time founders, but both types, like I've seen founders be really ambitious to have like a great pure metric of revenue per employee. Right? They're ambitious to have really small teams. And I think that's interesting. People talk about like the one person billion dollar company or whatever.

• 23:15Sarah Guo?

  But I do think this is kind of the purest version of like actually doing startups. Because you never quite had enough money to begin with. So you're always trying to solve the problem with the people in the building. And so, you know, solving it with fewer people in the building and more technical capability feels like just an extension.

• 23:34Speaker 01

  Yeah, I feel it feels like we're a couple of years away from that, though. Right? Like there are some things that we can become dramatically more cost efficient on. But, you know, at least the existing coding co-pilots and other things give you some efficiency, but not, you know, 5X. And maybe as some of these, you know, AI coding agents really start to work at scale, that's when you have this shift in terms of cost structure. Obviously, there's cost structure shifts that we talked about in terms of buyouts and customer support or BPO or other things. But

• 24:08Speaker 01

  You know, I think, I think there's a, one of the questions in my mind is like, when do we feel this really hit? And what is the GPT generation equivalent at which it happens for enough of the economy or enough of a tech company, right? Like, what portions of a tech company for a startup can be automated today? Because if you think about it, many startups are six to eight engineers and a designer or product engineer, and then, you know, a founder or two, and then sometimes like an early business person, right? So there just isn't a lot to get rid of in some sense for, for very early companies.

• 24:42Sarah Guo?

  I don't think it's at that stage, and I would agree. Like, the models in this generation, and they're just not smart, coherent enough, and you're still fighting this uphill battle to get them to do tasks that are valuable. And like, I don't think you're going to eliminate headcount in the, the first 10 people. You're like, you know, doing what we're doing, like, looking for the pockets of workflow or services, whatever else, where there's that minimum viable quality already today. But when I ask, like, I'd be curious what you think, when I ask founders what functions they think shrink headcount first, and like, how soon was your question? I do ask it all the time, and I kind of get like, like, we're two or three years from it. And then I, for like, actual headcount impact first, going back to an example you brought up, I get SDRs and I get support.

• 25:27Sarah Guo?

  You know, there is real volume there as you get to hundreds of people. I think the other observation on just like, what's happening with teams is the, the cadence of competition and change in the environment are, like, what, what matters in the environment for a startup? It is, you know, like here, what your supply chain is doing, like, what capabilities are out there, what your competitors are doing, and how quickly your customers adopt, right? Maybe I'm missing something, but like, those are kind of the external things. And, you know, customers will be customers unless you control them and buy them out. But the pace of change of the underlying technology has accelerated such that, you know, you have this like, increasingly aggressive race, and so it seems quite important to be

• 26:12Sarah Guo?

  velocity oriented. And maybe that's just axiomatic in startups, but I, I do think that there is this trade off where the dynamics have changed. And the trade off on one side is you still need to hire experienced leaders to, for example, help build certain go-to-market functions well and effectively.

• 26:35Sarah Guo?

  And on the other hand, like hiring these leaders tends to, you know, they tend to come from more experience and they have a natural pace that is often quite different from like the initial early-stage startup team. But especially for the prosumer companies that, you know, we've been both involved in, having that go-to-market leadership is not quite as in the driver's seat, quite as early, but you do need to existentially maintain this product velocity. And so on margin, I'd say, you know, for these companies that have this pull from the market in AI, you can choose that velocity instead of bending to experienced leaders,

• 27:20Sarah Guo?

  or at least be more selective about waiting for and finding and hunting the experienced leaders that like match the, match or enhance the natural velocity of the company and/or can step up to it. And I think it goes back to like, big companies suck and you can kind of like punt and avoid big company-ness for longer. And I think that's one of like the core dreams structurally of like, what does AI mean for teams and companies? I think another thing that has been happening structurally from how companies are oriented or what the teams look like is that there are a rare set of companies today that are

• 28:01Sarah Guo?

  not initially AI companies, where the products, the whole company are really a manifestations of the founder's product taste. These really design-oriented companies like Ivan Zhao and Notion, Slack and Stuart Butterfield. But in AI companies today, models are such a big piece of the product experience and that the, thus the like model design, and I do mean that in the aesthetic sense of the term, is quite important and often comes from the founder. And I think an example, a bunch of examples of that could be Midjourney, HeyGen, Ideogram, Suno, Udio, Pico, like these, these products, especially in the creative field, they really

• 28:45Sarah Guo?

  reflect like the type of output that the founders want, the type of data they're choosing. And I think some of the companies end up being more founder-driven in terms of product and model sense than maybe traditional software companies.

• 29:01Speaker 01

  Yeah, makes sense. Cool. I think we covered a lot of really great stuff on the market side. So thanks everybody for listening today.