No Priors Ep. 72 | With Sarah Guo and Elad Gil

1. 0:00 – 0:16

   Introduction

   1. SGSarah Guo0:00

      (instrumental music plays) Okay. Hi, listeners. Uh, today you just have me and Elade shooting the ... What's the appropriate term here? Shooting the breeze.

   2. EGElad Gil0:12

      Shooting the breeze.

   3. SGSarah Guo0:13

      Um, yeah. But I want to start

2. 0:16 – 5:10

   Old school operators AI misunderstandings

   1. SGSarah Guo0:16

      this shooting the breeze session, uh, by talking about this Goldman Sachs report that everyone's reading which essentially says ... I- I'm just gonna get on my soapbox for a second here. That it, um ... The title is something like Calling Th- The Top on AI. Um, 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 Daron Acemoglu, um, and Jim Cavello. They're respectively, like, MIT professor and the GS Head of Global Equity Research. And Daron is arguing essentially that AI is going to impact less than 5% of all tasks, and the, like, trillion dollars of CapEx that people are spending on training models, um, is, is a waste because AI will be unable to solve the complex problems. It's, it's not built to do that. A- and Jim argues, you know, he argues th- that in contrast with the internet where you are, um, disrupting something expensive from the beginning even early on versus having a very expensive solution that then becomes democratized, uh, you know, AI is very expensive from the very beginning. And then the other argument he makes is that any efficiency gains ch- from AI will be competed away anyway, and so, um, like, you know, none of the companies are gonna gain from this. Um, and so if, if we just, like, talk about Daron first, Daron's arguing about something he doesn't understand. Like, he ... His claim is, you know, how do we know scale works? More data won't make customer support reps better. I, I think, like, that's just a fundamental, like, misunderstanding of the technology and also objectively of what has happened over the last, um, several years of scale and data, uh, improving capability and quality of model outputs.

   2. EGElad Gil2:10

      I think a lot of these folks do, by the way, are just kind of stuck in the old AI world. Like, I haven't read the report, so I'm not talking specifically about these authors, but, um, 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, um, transformer-based models or other architectures that effectively are, um, both highly, uh, scale-dependent but also provide different types of functionality and features than, you know, y- you're sitting there, and you're, you're munging some data and effectively doing fancy regressions in some sense. So, um, I think that's the other issue here in terms of ... A- a lot of what I hear... This, 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." (laughs) You know, sometimes data is useful, and well-labeled 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, I think there's just this broader misconception in terms of how this stuff works and what it means and, and all the rest of it.

   3. SGSarah Guo3:03

      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's state of what's happening on the ground. Like, there are absolutely things that are cost-effective to do today. Um, that's why you get actually a series of companies that are democratizing capabilities and really more on the prosumer side, but, m- 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. Uh, I, I think, like, as you said, one, one sort of problem with this framework of thinking is, you, 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. Um, and I think the willingness to predict 10 years into the future of, uh, insignificant improvement is ludicrous when literally all of the people working on this tech are unwilling to, um ... Like, you and I are probably unwilling to predict two years into the future, much less 10 years.

   4. EGElad Gil4:01

      Well, I mean, the thing I'd predict two years into the future is that there's gonna be- be even more broad-spread applications of it. So (laughs) -

   5. SGSarah Guo4:07

      Right.

   6. EGElad Gil4:07

      ... 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. Um, 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, um, 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 th- uh, the AI-centric, prosumer companies, ChatGPT, Perplexity, et cetera that are, um, that ha- they've provided functionality at scale so far. And so the, 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.

   7. SGSarah Guo4:52

      Yeah. I, 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, uh,

3. 5:10 – 6:09

   Tech history is repeating itself with slow AI adoption

   1. SGSarah Guo5:10

      I, I do think one of the, like, flawed assumptions in Jim Cavello's, um, 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 and you get Borders, and you wanna be Amazon or you wanna be Klarna or, you know, whoever is actually, um, changing their cost structure dramatically here.

   2. EGElad Gil5:37

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

   3. SGSarah Guo5:59

      Mm-hmm.

   4. EGElad Gil5:59

      So thi- this is an old story. It's, it's old wine in new bottles.

   5. SGSarah Guo6:02

      Mm-hmm. Time to, time to break the bottle. Um, what else is, uh ...... like, inspiring you or bothering you from a markets perspective?

4. 6:09 – 8:48

   New AI Markets

   1. SGSarah Guo6:09

   2. EGElad Gil6:09

      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, um, three sort of drivers of that. One is we have new capabilities, um, and in particular, we have new capabilities across multiple fields. You know, biology and, uh, robotics and obviously language and image gen and video and things like that, with a lot of emphasis right now on, on language and image gen. Um, so these things are very generalizable and you train one model to do lots of stuff versus doing bespoke, uh, mini models, which was sort of more the norm before. Um, second, you can access it through an API anywhere in the world, which means, um, 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. And third, um, a lot of companies and organizations have a mandate to do AI, 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.

   3. SGSarah Guo7:08

      (laughs)

   4. EGElad Gil7:08

      You know, those three things are driving unexpected behavior in terms of how you can address markets. And, um, what I and, you know, the, the small team that works with me have been looking at is, um, market by market, say you look at services and AI-transforming services, and I think I talked about this before where, you know, um, software spend in the US is something like a half trillion dollars a year. There's probably five trillion in, um, 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. And, um, as we've gone through market by market, um, we've basically been looking at, you know, are there companies that exist? If a company doesn't exist, should you incubate something or other, uh, situations where you actually want to do a buyout, uh, 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, um, using AI. And so I think for the first time, uh, two things really make sense. One is incubation, which usually is a terrible idea, you know? (laughs) Um, but there's a few things that, uh, you know, um, I and my team have started actively working on. And the second is, um, AI-driven buyouts, and I've now backed, uh, one or two of those with the idea that you can suddenly do things with AI that you couldn't before and you can kind of change the game in a market. And so, uh, and that's beyond, you know, obviously all the really exciting stuff happening in terms of model, model architectures and infrastructure and all the rest. But I just think from a markets perspective, which is sort of our topic today, um, there's really odd things we 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, it's a very exciting time from that perspective.

   5. SGSarah Guo8:47

      Yeah, I want to come back

5. 8:48 – 13:03

   AI-backed buyouts

   1. SGSarah Guo8:48

      to incubations in a second, having done a few of these. Um, with buyouts, like, just to articulate, like, some, you know, what I think is the premise here 'cause we, we've also looked at these, is you, um, you are shortcutting the change management process for industries that can be, like, dramatically automated with these new capabilities, um, by just controlling them, right?

   2. EGElad Gil9:13

      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. Um, 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 headcount 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. And there's lots and lots of, um, industries where a lot of the cost is associated or leveraged on time. Um, in other words, you can keep the same, uh, employee base, you can just make them 10 times more effective in terms of the set of customers they can serve, where a lot of, um, 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. Um, and some aspects of that are now suddenly, um, accessible or automatable via this new type of gen AI.

   3. SGSarah Guo10:28

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

   4. EGElad Gil10:56

      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, um, 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 it was a technology company and effectively what you're doing is you're arbitraging, um, a tech valuation in order to buy other sort of EBITDA-rich assets. You're looking for cash flow, right? (laughs) But you're buying it, um, 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 acts 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, 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 a technology company, the, the market cap and multiple would come down and normalize, right? It would lose value the later stage it got because the investors became more savvy about what was really happening. Um, that's different here where the leverage on the technology is dramatically higher than just adding a thin 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 we're hiring in, you know, or working or co-founding with, with, 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 you're really doing, uh, 'cause otherwise, you're just doing a rollup but you're calling it something else, which again, is fine, right?

   5. SGSarah Guo12:30

      Yeah, but you just need to have some rollup. Like, you need financial engineering DNA and, uh, investor DNA as well. Um, uh, 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 of, 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, uh, "Man, like, Sara, Elad, the automation works and the distribution is the problem, going acquiring the customer relationships."

   6. EGElad Gil13:00

      Yeah, and so you're buying the distribution. Yeah, exactly.

   7. SGSarah Guo13:02

      Yeah.

   8. EGElad Gil13:02

      Uh, you're just

6. 13:03 – 17:18

   AI incubation

   1. EGElad Gil13:03

      buying the whole thing.

   2. SGSarah Guo13:04

      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, um, like, you really, like, have visibility into an opportunity set or a customer set that others do not and the, um, the, like ro- the expertise set required to make a company work, like the DNA is unlikely to get it to, to, to get together organically. And so, like, that's the alpha. Like, how do you see it?

   3. EGElad Gil13:33

      Hmm. I think incubations usually are a terrible idea, and, um, most firms who, uh, or groups that incubate companies tend not to work very well, and there's counterexamples 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, or they have a captive customer base, base they can sell to, or they know how to roll together assets, right? And so, um, I think the way that incubation has tended to work historically is if you have some form of deep expertise in relationships-

   4. SGSarah Guo14:16

      Mm-hmm.

   5. EGElad Gil14:16

      ... that you can leverage, and most people just didn't have that. They would just go and try and start a company. And most, you know, the reason, uh, to some extent the startup ecosystem works is you have 5,000 different founders simultaneously doing a, 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 ten companies a year that actually matter out of the thousands of founders, right? And so, um, you know, a lot of these approaches traditionally have not worked very well, and there's counterexamples. You know, Snowflake, uh, 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? Um, uh, right now because of AI, the odd thing that's happening is that certain subsets of the market are incredibly crowded. Um, 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, um, 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, um, incubation that I think are possible now. And so there, there, 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. Um, the last incubation I did was about a year ago where I worked with, um, Ankur Goel who, um, had, had started a company that, uh, Figma acquired. I worked with him on a company called Braintrust, which is sort of Eval and, and, and, uh, Prompt Playground and a few other products all kind of, um, rolled together. Um, and that was just driven by the fact that, you know, uh, he and I had been sort of riffing on like what does a, what, what does a big enterprise need to use in order to adopt an, um, AI. Um, 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.

   6. SGSarah Guo16:11

      One of the things that I, I think makes this make sense right now is, uh, 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, um, that you describe of like some really obvious open markets. And so I, 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, um, you know, the first incubation I ever worked on was a company called Awake where it was actually, you know, applying last generation machine learning techniques to network data for security use cases. But I, I think be it that or other com- It tended to be like, hey, a technologist from a different domain is, um, is looking for the use cases that best match which can be very dangerous. But if you know what it is, um, then it becomes a unique match, and so I, I, uh, you know, I think that is exciting. Um, what are you, what are you paying attention to just

7. 17:18 – 18:26

   Exciting incubating applications

   1. SGSarah Guo17:18

      in terms of like new applications you think should exist, uh, stuff you want to incubate, whatever it is?

   2. EGElad Gil17:22

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

   3. SGSarah Guo18:09

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

   4. EGElad Gil18:16

      So I guess we talked about the really early stuff, um-... you know, which is incubation and, and all that kinda stuff. Um, the, the flip side of it is public markets, right? Like,

8. 18:26 – 22:20

   AI and the public markets

   1. EGElad Gil18:26

      and one question I've been increasingly getting is like, what do you... What do you buy in public markets given AI? Or would, would you change how you think about portfolio construction given AI? Or how do you think about, you know, existing companies and the, their, you know, the, the risk of AI to those companies and things like that. So, um, do, do you have any sort of thoughts or picks in terms of things that you think are exciting that are, you know, much later companies relative to AI or how to think about that?

   2. SGSarah Guo18:52

      Uh, I mean, I'm, 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, um, that, uh, 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, um, that's not... Certainly not every company. I mean, we've talked to a number of, like, amazing founders who have attacked the opportunity really aggressively because they see the, um, the way to grow revenue or make the businesses higher quality. But, um, but I think that isn't gonna be most companies at sort of the mid to late stage private, um, set. Um, so, so I think that is, that's something I would really think about in terms of portfolio rationalization. What about you? What, what in the public markets are you paying attention to?

   3. EGElad Gil19:47

      You know, I think of it as less just public market specific, but more like what are things that, you know, um, are durable in the, in, in, in the coming world? And there's almost two versions of this question. It's like, what do you want your kids to be able to own someday (laughs) or whatever, if you can afford it. And then, um, there's w- what portfolio would construct today in tech? And, you know, the, the crazy thing is if you go back, um, 10 years ago, uh, there was kind of this era where people were talking about FAANG, right? The- those were the four companies you were, you were supposed to just buy and hold, and that was like Facebook, um, Alphabet, uh, Netflix, and, um, Amazon, right? That kind of morphed now. Nvidia's part of it, and they, you know, they keep kinda changing their rubric. But to some extent, um, 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, y- whatever you wanna call it, new FAANG or new... Yeah, I can't remember what they call it. The, the, the great eight or the something seven. I can't remember the, what it's called anymore.

   4. SGSarah Guo20:51

      Yeah, the Magnificent Seven right now.

   5. EGElad Gil20:52

      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 that may just keep compounding for the next 10 years. Um, so that's one segment. Second segment is potentially some of these preexisting 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, um, iPhone replacement cycle just for AI. Oh, we have a new chip that runs these... You know, they, they mentioned that they're gonna 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. Um, so there may be some big tech. Um, third is, uh, what I call like AI durable companies. Like, what are the companies where AI just doesn't matter for... You know, is it railroads?

   6. SGSarah Guo21:42

      Mm-hmm.

   7. EGElad Gil21:43

      Is it certain SaaS companies, right? Where it just, it just doesn't matter. You add AI, who cares? Um, that actually shows durability. Like, if AI doesn't improve the product that much, it's not really a competitive factor. Uh, so you can imagine, um, a basket of stuff there. Uh, and then obviously there's like, what's the, what's the new AI index besides Nvidia, right? Nvidia is a core piece of just a way to index the market. Um, but there may be other companies, maybe that's scale, maybe it's something else, right? But I, I think, um, I'd kind of think of it across those four segments. You know, big tech, uh, subset, what's the new Magnificent

9. 22:20 – 25:14

   Staffing AI companies

   1. EGElad Gil22:20

      Seven, what's durable in the face of AI, and then what's your AI index? Uh, you know, as we build out this whole AI wave, how do you think about, uh, staffing AI companies and post-AI teams, and what does all that mean?

   2. SGSarah Guo22:33

      Uh, a, a while back you said that like, you think the founders right now, um, both, you know, existing and new, they've, they've been like high quality, motivated, ambitious in a way you haven't seen any- in a while. Um, just really, really good. I think one specific trait that keeps, um, or specific belief that keeps happening is like, especially second time founders, but, but both types like, uh, uh, I've seen founders be really ambitious to have like a great pure metric of, um, revenue per employee, right? They're am- ambitious to have really small teams. And I, I think that's interesting. Like, people talk about like the one person billion dollar company or whatever. But I, I do think this is kind of the purest version of like actually doing startups, right? Because you, 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, um, feels like just an extension.

   3. EGElad Gil23:34

      Yeah. I feel we're... It feels like we're a couple years away from that though, right? Like, um, there's some things that we can become dramatically more cost-efficient on, but, you know, at least the existing coding copilots and other things give you some efficiency, but not, you know, 5X. And maybe as some of these, um, you know, AI, uh, 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, um, you know, I think, uh, 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, a 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.

   4. SGSarah Guo24:43

      I don't think it's at that stage, and I, I would agree. Like, the models in this generation in, uh, uh, they're just not smart coherent enough and we're still fighting this uphill battle to get them to do tasks that are valuable. And, like, I don't think you're gonna eliminate headcount in the, the first ten 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, um, minimum viable quality already today. But when I ask, like, I, I'd be curious what you think, when I ask founders what functions they think shrink headcount first and, like, how soon

10. 25:14 – 29:12

    Competition and shrinking head count

    1. SGSarah Guo25:14

       was your question, um, I do ask all the time and I kind of get, like, like, we're two to three years from it. And then I, I, uh, um, for, like, actual headcount impact first, going back to an example you brought up, I get SDRs and I get support. You know, there is real volume there as you get to hundreds-

    2. EGElad Gil25:31

       Yeah.

    3. SGSarah Guo25:31

       ... of people. I, 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 adapt. 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, um, you know, you have this, like, increasingly aggressive race. And so it seems quite important to be 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. Um, and on the other hand, like, hiring these leaders tends to, you know, th- they tend to come from more experiences that they have a natural pace that is often quite different from, like, the, um, initial early-stage startup team. But especially for the prosumer companies that, you know, we've been both involved in, uh, having that go-to-market leadership is not quite as in the driver's seat quite as early, um, 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, uh, instead of bending to experienced leaders. 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. Uh, I think it goes back to, like, big companies suck and, um, 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, um, structurally from how, uh, companies are oriented or what the teams look like is that there are a, uh, in rare set of companies today that are not initially AI companies, where the products, the whole company are really a manifestations of the founders' product taste, these really design-oriented companies like, um, uh, Ivan Zao and Notion, Slack and Stewart Butterfield. But in, in AI companies today, models are such a big piece of the product experience and that the, uh, 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, um, Midjourney, HeyGen, Ideogram, Suno, Udio, Pika, like these, um, these products especially in the creative field, they really 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 company.

    4. EGElad Gil29:01

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

Episode duration: 29:12