How Agentic AI is Transforming The Startup Landscape with Andrew Ng

[MUSIC] >> Hi listeners, welcome back to No Pires. Today, a lot of Nyer here with Andrew Aing. Andrew is one of the godfathers of the AI revolution. He was the co-founder of Google Brain, Coursera, and the Venture Studio AI Fund. More recently, he coined the term "agentic AI" and joined the board of Amazon. Also, he was one of the very first people a decade ago to convince me that deep learning was the future. Welcome, Andrew. Thank you so much for being with us. >> No, always great to see you. >> I'm not sure we should begin because you have such a broad view of these topics, but I feel like we just start with the biggest question, which is, you know, if you look forward at capability growth from here, where does it come from? Does it come from more scales, come from data work? >> Multiple vectors of progress. So I think there is probably a little bit more crucial that the scalability limits it to be used to hopefully consume the pros there, but it's getting really, really difficult. Societies, perception of AI has been very skewed by the PR machinery of a handful of companies with amazing PR capabilities. And because that number of companies drove scales in narrative, people think of scale first of this vector progress. But I think, you know, agentic workflows, the way we build multi-mold models, we a lot of work to build concrete applications. I mean, there are multiple vectors of progress, as well as wild cards like brand new technologies, like Confusion models, which are used to generate images for the most part, will that also work for generating text? I think that's exciting. So I think there'll be multiple ways for AI to make progress. >> You actually came up with the term agentic AI. What did you mean then? >> So when I decided to start talking about agentic AI, which wasn't a thing when I started to use the term, my team was slightly annoyed at me. One of my team members that will name me, I said, "Andru, the world does not need you to make up another term." But I decided to do it anyway and for weather reasons to stop. And the reason I started talking about agentic AI was because, like a couple years ago, I saw people who spend a lot of time debating, "Is this an agent? This is not an agent? What is an agent?" And I felt there was a lot of good work and there was a spectrum of degrees of agency, whether it's higher than those agents that could plan, take multiple sets of using, do a lot of stuff by themselves. And then things that were lower degrees of agency, where we're prone to now, we're affecting this output. And I felt like rather than debating, this is an agent or not, let's just say the degrees of agency and say it's our agentic to spend our time actually building this. So I started to push the term agentic AI. What I did not expect was that several months later, eventually marketers, we get a hold of this term and uses a sticker to stick about everything in sight. And so I think the term agentic AI really took off. I feel that the marketing hype has gone like that insanely fast. But the real business progress has also been rapidly growing, but maybe not as fast as the marketing. But do you think of the biggest obstacles right now to true agents actually being implemented as AI applications? Because to your point, I think we've been talking about it for a little while now, there's certain things that we're missing initially that are now in place in terms of everything from certain forms of inference time, compute on through to forms of memory and other things that allow you to maintain some sort of state against what you're doing. What do you view of the things that are still missing or need to get built or most sort of from that progress on that end? I think the technology component level, this stuff that I hope we're, for example, computer use kind of works often doesn't work. I think so the God rails, e-thousands, huge problem. How do you quickly evaluate these things and drive e-vals? I think the component is this room for improvement. But what I see is the single biggest barrier to getting more agent AI workflows implemented is actually talent. When I look at the way many teams built agents, the single biggest differentiator that I see in the market is does the team know how they drive a systematic error analysis process with e-vals? So you're building the agents by analyzing any moment in time. What's working? What's not working? What do you improve as opposed to less experienced teams kind of try things in a more random way than it's just six long time? And what I look at was huge range of business, small and large. It feels like there's so much work that can be automated through agent to work those, but you know, to tell in the skills and maybe the software tooling. I don't know. Just as in there, I did drive that discipline entry in process to get the stuff built. How much of that engineering process could you imagine being automated with AI? It turns out that a lot of this process of building agent to work flows requires ingesting external knowledge, which is often locked up in the heads of people. Unless we built AI avatars, it can interview employees doing the work and that's a visual AI that can look at the computer monitor. I think maybe eventually, but I think at least right now for the next year or two, I think there's a lot of work for human engineers to do to build agent to work flows. That's more the kind of collection of data, feedback, etc. for certain moves that people are doing is that other things that I'm sort of curious what that translates into tangibly versus. Yeah, so we have one example. So I see a lot of workflows like, you know, maybe I customise your document, you get to convert the document to text, then maybe do a web search for some compliance visa and to see your working event you're not supposed to and then look at the database, records, see the pricing, write, save it somewhere else and so on. This multi-state agent workflows kind of mixed-gen robotic process automation. So we implement this and it doesn't work. You know, is it a problem if you got to you invoice date wrong? Is that a problem or not? Or if you routed a message to the wrong person for verification. So when all of we implement this thing, you know, almost always it doesn't work the first time, but then to know what's important for your business process and is it okay that I don't know, I bought the C of the company too many times or is the C of me if it doesn't mind verifying some invoices. So all that external contextual knowledge often at least right now I see thoughtful human product managers or human engineers having to just think through this and make these decisions. So can an AI agent do that someday? I don't know, seems pretty difficult right now, maybe someday. But it's not in the internet pre-training data set and it's not in a manual that we can automatically extract. I feel like for a lot of work to be done building agent workflows that data set this proprietary. It's just not it's not a general knowledge on the internet. So figuring that out. It's still exciting work to do. What is the if you just look at this spectrum of agent AI? What's the strongest example of agency you've seen? I feel like bleeding age of agent AI. I've been really impressed by some of the AI coding agents. So I think in terms of economic value, I feel like the two very clear very apparent buckets. One is answering people's questions. Probably, you know, opening AI chat you can use it more clearly than that with Rolex. We'll take off lift off velocity. The second massive bucket of economic value is coding agents where coding agents like my personal favorite plot to right now is plot code. Maybe we change at some point, but I just use it. Love it. High-dead autonomous in terms of planning out, you know, what to do to build a software, building a checklist, going through one other time. So this ability to plan a multi-step thing, execute the multi-step plan is one of the most high-vetonizations out there being used that actually works. There's other stuff that I think doesn't work. Like something computer use stuff. Like, you know, go shop for something from me and browse online. Some of those things are really nice demos, but not yet production. I think that's because I was sort of criteria in terms of what needs to be done and more variability around actions or do you think there's a better training set or sort of set of outputs for coding. I'm sort of curious like why does one work so while there almost feels magical at times and the others are, you know, really struggling as use cases so far. I think, you know, engineers really good at getting all sorts of stuff to work, but the economic value of coding is just clear and apparent and massive. So I think the sheer amount of resources dedicated to this has led to a lot of smart people for whom they themselves are the user. And so also good instinct on product building really amazing coding agents. And then I think I don't know. You don't think it's a fundamental research challenge. You think it's like capitalism at work and then domain knowledge in a lab. Oh, I think capitalism is great at solving fundamental research problems. Yeah. At what point do you think models will effectively be bootstrapping themselves in terms of, you know, 99% of the code of a model will be written by agent code agents or the error analysis. So I feel like where I'm I started with slowly getting there. So some of the leading foundation model companies are clearly what they've said publicly they're using AI to write a lot of the code. One thing I find exciting is AI models using agent to work those to generate data for the next generation of models. So I think I think the Lama research we were talking about this with older version of Lama would be used to think for a long time to generate puzzles that then you train the next generation of the model to try to solve really quickly without me to think as long. So I find that exciting too. Yeah, multiple vectors of progress. It feels like, you know, AI is not just one way to make progress. There's so many spy people pushing forward in so many different ways. I think you have rejected the term vibe coding in favor of AI assisted coding. Like what's the difference? You know, I know that I mean, you do the latter. You're not vibing. Yeah. Vive coding leads people to think, you know, like I'm just going to go to the vibes and set all the changes a cursor suggests or whatever. And it's fine that sometimes you could do that and it works, but I wish it was that easy. So when I'm coding for a day or for an afternoon, I'm not like going with the vibes. It's like a deeply intellectual exercise. And I think the term vibe coding makes people think it's easier than it is. So frankly, after a day of using AI assisted coding, like I'm exhausted mentally, right? So I think over this rapid engineering where AI is letting us build serious systems, build products much faster than ever before, but it is, you know, engineering just done really rapidly. Do you think that's changing the nature of startups? How many people you need? How you build things? How you purchase things? think it's still the same old kind of approach, but you just have people to get more leverage because they have these tools now. So, you know, AI fun, we built startups and it's really exciting to see how rapid engineering AI system coding is changing the way we built startups. So there's so many things that, you know, would have taken a team of six engineers like three months to build, they're now today, one of my friends or I, which is building a weekend. And the fascinating thing I'm seeing is, if we think about building a startup, the call loop of what we do, right? I want to build a product that uses love. So the core iteration loop is right software, you know, software engineering work and then the product managers maybe go to user testing, look at it, go by God, whatever to decide how to improve the product. So when we go look at this loop, the speed of coding is accelerating and it causes falling. And so increasingly the bottleneck is actually product management. So the product management bottleneck is now looking to build what do we want much faster while the bottleneck is deciding whether we actually want to build at previously. If it took you say three week to build a prototype, if you need a week to get user feedback, it's fine. But if you now build a product and the day, then boy, if you have to wait a week for user feedback, that's really painful. So I find my teams, frankly, increasingly relying on gut because we're going to collect a lot of data that informs our very human mental model, our brains mental model, whether the user wants and then we often have deep customer empathy. So you can just make probably decisions like that really, really fast in order to drive progress. Have you seen anything that actually automates some aspects of that? I know that there have been some versions of things where people, for example, are trying to generate market research by having a series of bots kind of react in real time and that almost forms your market or your user base as a simulated environment of users. Have you seen any tooling that work or take off or do you think that's coming or do you think that's too hard to do? Yeah, so there's a bunch of tools to try to speed up product management. I feel like, well, the recent fake IPO is one, you know, really not about design, AI, hiding it and doing to the great job. Then there are these tools that I try to use AI to hold interview prospective users. And as you say, we looked at some of the scientific papers on using a flock of AI agents to simulate your group of users and how to calibrate that. It all feels promising and early and hopefully wildly exciting in the future. But I don't think those tools are set everything product managers, nearly as much as coding tools are set everything software engineers. So this does treat more of the bottleneck on the product management side. It doesn't make sense to me that my partner Mike has this idea that I think is broadly applicable in a couple different ways of like computers can now aggregate humans at scale. And so there's companies like Listen Labs working on this for like consumer research type tasks, right? But you could also use it to, you know, understand tasks for training or for, you know, the data collection piece that you described. When you think about your teams that are in this iteration loop has like the founder profile that makes sense changed over time. To me, there are so many things that the world used to do in 2022. They just do not work in 2025. So if I often I ask myself, is there anything we're doing that today that we're also doing in 2022? And if so, let's take a look and see if it's still going to make sense today because a lot of stuff, a lot of workflows in 2003 don't make sense today. So I think today, the technology is moving so fast. Founders, they're on top of Jenny, technology does, you know, tech oriented product leaders. I think are much more likely to succeed than someone that maybe is more business oriented, more business savvy, but it's not does enough to feel for where AI is going. I think unless you have a good feel for what the technology can they cannot do, it's really difficult to think about strategy whether whether the lead company. We believe this too. Yeah. Yeah. Yeah. I think that's like old school Silicon Valley even. Like if you look at gates or Steve Jobs slash was near or a lot of the really early pioneers of the semiconductor computer early internet era, they're all highly technical. Yeah. And so I don't feel like we kind of lost that for a bit of time and now it's very clear that you need technical leaders for technology companies. I think we used to think, oh, you know, they've had one exit before. So two exit even. So let's just back that founder again, but I think if that founder has stayed on top of AI, then that's fantastic. But if you know, and I think part of it is a moments of technological disruption AI rapidly changing. That's the real knowledge. So actually take mobile technology. You know, like everyone kind of knows what a mobile phone can and cannot do right when mobile happens is GPS all that everyone kind of knows that. So you don't need to be very technical to have a gut for connect build a mobile app for that. That's changing so rapidly. What could do with voice that would engineer what those do how rapidly found these models was a reason model. So having that knowledge is a much bigger differentiator, whereas knowing what a mobile app can do to build a mobile app. It's an interesting point because when I look at the biggest mobile apps, they were all started by engineers. So what's Apple star by an engineer, Instagram star by an engineer. I think Travis at Uber was was technical ish. Technically adjacent. In Stacarta, poor was an engineer at Amazon. Travis read the insight that GPS enabled a new thing. But so you have to be one of the people that saw GPS mobile coming early to go and do that. Yeah. You have to be like really aware of the capabilities. Yeah, I have to know the technology. Yeah, super interesting. What other characteristics do you think are common? I mean, I know people have been talking about, for example, it almost felt like there was an era where being hard working was kind of poop. Do you think founders have to work hard? Do you think people who succeed? I'm just curious, like aggression, hours work, like what else may correlate or not correlate in your mind? You know, I work very hard. The tears in my life where I encourage others that want to have a drink or whatever they like work hard. But even now, I feel like a little bit of nervous is saying that because in some parts of society is considered not politically correct to say, well, working hard pray, carless and repersonal success. I think it's just a reality. I know that not everyone at every point in their life is in the time of their work hard. When my kids were first born, that week, I did not work very hard. It was fine, right? So acknowledging that not everyone is in circumstances in work hard, just the factual reality is people that work hard accomplish a lot more. But of course, you need to respect people that on the Facebook page. Yeah, I'd say something maybe a little less correct, which is I less politically correct, which is like, I think there was an era where people thought like there was a statement that startups are for everyone. And like, I do not believe that's true, right? I think like, you know, you're trying to do a very unreasonable thing, which is like create a lot of value impacting people very quickly. And when you're trying to do an unreasonable thing, you probably have to work pretty hard, right? And so I think people, I think that got very the sort of work ethic required to like move the needle in the world very quickly disappeared. Yeah, so does that, does that, what's that whole, I wish I remember who said this, but was it the only people that would change the world are the ones crazy enough to think they can? I think it does take someone with the bonus, the decisiveness, they go and say, you know what, just say the world, I'm going to take a shot at changing it and it's only people with that conviction of that I think can do this. It's nice to be true in any endeavor. You know, I used to work as a biologist and I think it's true in biology. I think it's true in technology. I think it's true in almost every field that I've seen is it's the people who work really hard do very well. And then in startups, at least the thing I tended to forget for a while was just how important competitiveness are people who really wanted to compete in win mattered. And sometimes people come across as really low key, but they still have that drive in that urge and they want to be the ones who are the winners. And so I think that matters. And similarly, that was kind of put aside for a little bit, at least from a societal perspective relative to companies. Actually, I've actually seen, I feel I have seen two types. One is they really want their business to win. That's fine. Some do great. Some are they really want their customers to win. And it's so obsessed with serving the customer that that works out. I say early, there's a Coursera. Yes, I knew about competition blah blah blah. But I was really obsessed with, you know, learning this with the customers and that drove a lot of my behaviors that now that that's a really good framework. And when I say competition, I don't mean necessarily with other companies, but it's almost like with whatever metric you set for yourself or whatever thing you want to win at or be the best at. Well, when I found this in a startup environment, you just got to make so many decisions every day. You just have to go by gut the lot of time, right? I feel like, you know, building a startup feels more like playing tennis than solving calculus problems. You just don't have time to think, so make a decision. And I feel like, so this is why people that obsessed day and night with a customer, with a company think really deeply and have that contextual knowledge that when, when someone says, do I should probably feature a feature B that you just got to know a lot of time, not always. And it turns out there are so many to use your basis term, like two way doors in startups, because frankly, you know, you're very low to lose. So just make a decision that is wrong, change it a week later is fine. So I find, but, but to be really decisive and move really fast, you need to have obsessed usually about the customer, maybe the technology, so have that say the knowledge to make really rapid decisions and still be right most of the time. How do you think about that bottleneck in terms of product management that you mentioned or people who have good product instincts? Because I was talking to one of the best known sort of tech public company CEOs. And his view was that in all of Silicon Valley or all of tech kind of globally, there's probably a few hundred at most great product people. Do you think that's true? Or do you think there's a broader spot that people who are very capable at it? And then how do you find those people? Because I think that's actually a very rare skill set in terms of the people who are, you know, just like there's a 10x engineer, there's 10x product insights it feels. Boy, that's a great question. I feel it's got to be more than a few hundred great product people. Maybe you And just as I think there are way more than a few hundred great AI people, I think there are. But I think one thing I find is very difficult is that you use empathy or that customer empathy because, you know, to form a model of the user or the customer, there's so many sources of data, you run service, you talk to handful of people, you remark or rapport, you look at people's behavior on other parallel or competing apps, whatever. But there's so many sources of data, but they take all this data and then to get of your own hit to form a mental model for what you're right, maybe I do customer profile or some, some user you want to serve, think and act so you can very quickly make decisions serve them better. That human empathy, one of my failures, one of, one of the things I did not do well in early phase of my career, for some dumb reason, I tried to make a bunch of engineers product managers, I gave them product management training. And I found that I just foolishly made a bunch of really good engineers feel bad for not being good product managers, right? But I found that one correlate for whether someone would have good product instincts is that very high human empathy, where you can synthesize loss of signals, to really put yourself into the present shoes, to then very rapidly make product decisions and all the serve though. You know, going back to coding assistance, it's really interesting, I think it is like reasonably well known that the cursor team, like they make their decisions actually very instinctively versus spending a lot of time talking to users. And I think that makes sense if you are the user and then like your mental model of like yourself and what you want is actually applicable to a lot of people. And similarly, like I think, you know, these things change all the time, but I don't think Cloud Code incorporates, despite, you know, scale of usage, feedback, data today from like a trained loop perspective. And I think that surprises people because it is really just like what do we think the product should be at this stage? So it's also one advantage that starts up to have is while you are early, you can serve kind of one user profile. Today, you know, if you're, I don't know, like Google, right? Google serves such a diverse set of user proso now, so you really have to think about a lot of different user proso now. And that asks, complexity of the product changes. But we're starting to try to get you in the initial ways in the market. You know, if you pick even one human that is representative enough of a broad set of users and you just build a product for one user that you have one idea of customer profile, one, you know, hypothetical person, then you should actually go quite far. And I think that for some of these businesses, be it cluster or Cloud Code or something, if they have internal via mental picture of a user, that's close enough. So very large your prospective users that you actually go really far that way. The other thing that I've observed and curious you guys see this in some of our companies is just like the floor is lava, right? The ground is changing in terms of capability all the time. And the competition is also very fierce in the categories that are already obviously important and have multiple players. So leaders who are really effective in companies generation ago are not necessarily that effective when recruited to these companies as they're scaling, like because the pace of, it is a velocity of operation or the pace of change. It's interesting to see you say, like, I'm looking at what I was doing in like today and in 2022 and saying, like, is that still right versus if you're an engineering leader or go to market leader and you've like built your career being really great at how that's done, that may not be applicable anymore. I think it's a challenge for a lot of people. And you know, many great leaders in lots of different functions still doing things the way they were in 2022. And I think it, it's just got a change. When new technology comes, I mean, you know, once upon a time there was no such thing as web search today, we were, would you hire anyone for any role that doesn't know how to search the web, right? And I think we're well possible point that for a lot of job roles, if you can't use OMS in an effective way, you're just much less effective than someone that can. And it turns out, everyone in my team, may I find knows how to code for everyone is a good have a call. And I see for a lot of my team members, you know, when my, I don't know, assistant general counsel or my CFO or my friend, that's operator, when they learn how to code, they're not software engineers, but they do their job function better because by learning the language of computers, they can now tell a computer more precisely what they want to do for them and computer to do for them. And this makes them more effective at job, their job function. I think the graphic piece of change is, uh, just contributing to a lot of people. Uh, but I guess, I don't know, I feel like when the world is moving at this pace, we just have to change at the world, at the pace in the world. Yeah. To your point, show up in, um, higher as particular on product. So, uh, or product and design. So one sort of later stage AI company I'm involved with, they were doing a search for somebody to run product and somebody to run design. And in both cases, it's selected for people who really understood how to use some of the vibe coding slash AI, assistant coding tools because they said, they said, your point, it's like you can prototype something so rapidly. And if you can't even just mock it up really quickly to show what it could look like or feel like or do in a very simple way, you're wasting an enormous amount of time talking and writing of the product requirements document and everything else. And so I do think there's a shift in terms of how do you even think about what processes do you use to develop a product or even pitch it, right? Like what should you show up with to a meeting when you're talking about a product? The whole thing, apparently. Completely. Yeah. No, you should have a prototype in some cases. Actually, just give me an example. Resource engineering genius for a row and hire their, uh, interview someone with about 10 years of experience, you know, full stack. Very good resume. Also, interview the fresh college draft. But the difference was the person that changes of experience had not used AI tools much at all. Fresh college draft had, and my assessment was the fresh college draft that new AI would be much more productive and I decide to hire them instead to another great decision. Uh, now the flip side of this is the best engineers I work with today are not fresh college drafts. They're people with, you know, 10, 15 or more years of experience, but they're also really on top of AI tools. And that those engineers are just completely in the class of their own. I feel like I actually think software engineering is a harbinger of what happened in other disciplines because the tools are more advanced in software engineering. It's interesting. One company that I guess both of us are involved with is called Harvey. And I led their series B. And when I did that, um, I called a bunch of the customers and the thing that was most interesting to me about some of those customer calls was because illegal as notorious as being a tough profession for adopting new technology, right? There aren't a dozen great legal software companies. Those customers that I called which were big law firms or people who were, you know, quite far along in terms of adopting Harvey, they all thought this was a future. They all thought that AI was really going to matter for their vertical. And the main thing they would raise is questions like in a world where this is ubiquitous, suddenly instead of hiring 100 associates, I only hire 10. And how do I think about future partners and who to promote if they don't have a big pool? And so I thought that mindset shift was really interesting. And to your point, I feel like it's percolating into all these markets or industries. And it's sort of slowly happening, but as industry by industry, people are starting to rethink aspects of their business in really interesting ways. And I'll take a decade, two decades for this transformation to happen. But it's compelling to kind of see how people, like the earliest adopting verticals and something that the people were thinking deepest about it. And it should be really interesting. I think, um, yeah, I would share about legal startup calluses AI that AI fun help builds is doing very well as well. Um, I think, I think the nature of work in the future would be very interesting. So I feel like a lot of teams want to outsourcing a lot of work, right, partly because of costs. But with AI and AI assistants, part of me wonders is a really small, really skilled team, um, with lots of AI tools. Is that going to all perform a much larger, you know, and maybe lower cost team that may only not be able to. And they have less coordination cost. Actually, so some of the most productive teams on one, you know, I'm a part of now is some of the smallest teams than, than very small teams of really good engineers with lots of AI enablements, um, and very local, then you should cost as well as kind of person. So see, we'll see how the world evolves to early to make a call, but you can see where, um, maybe thinking the world may or may not be headed. I work with several teams now, um, one of which is called open evidence and has like a pretty good penetration, like 50% of doctors in the US now where it's an explicit objective in the company to try to be as small as possible, um, as they grow impact. And, you know, we'll, we'll see where these companies land because, you know, there's lots of functions that need to grow in a company over time, but that certainly wasn't an objective for like five years ago. I've heard that objective a lot. I've actually, I heard that objective a lot in the 2010s. And there's a bunch of companies that I actually think underhired pretty dramatically or stayed profitable and would brag about being profitable for gross what wasn't as strong as it could be. So I actually feel like that's a trap. How would you calibrate that? Yeah. Um, it's basically really, it's, it's almost are you being, um, laxidaisical or too accepting of the progress that your company's making because it's going just fine. It could be going much better, but it's still going great on a relative basis. And so you're like, oh, I'll keep the team small. I'll be super lean. I won't spend any money. Look at me. How profitable I am. And sometimes it's amazing. And sometimes you're actually missing the opportunity or not going as fast as you can. And, um, usually I think what happens is in the early stage of a startup life, you're competing with other startups. And if your way ahead, it feels great. But eventually if they're incumbents in your market, they come in. And the faster you capture the market and move up market the less time you give them this sort of realize what's going on and catch on. And so often five, six, seven years in the life of a startup, you're actually competing with incumbents suddenly and they just kill you with distribution or other things. And so I think people really missed the mark and you could argue that was kind of slack versus teams. And so that was, um, you know, there's a few companies I won't name, but I feel like they're so proud of their profitability and they kind of blew up. I guess on the design side, that was sketch, right? Remember? Yeah. Yeah, you know, they were based on another ones. They were super happy. They were profitable. They were doing great and then the Figma wave kind of came Do you think your company stay this small? What do you think your teams stay this small? Do they my team stay this way? Yeah? What do you mean in terms of just efficiency of like? Can you actually get to you know affect millions and billions of people with 10 50 hundred percent teams? I think teams can definitely be smaller now than they used to be but We over investing around the best thing and then also I think to your point Analysis of market dynamics right if it's a if it's a win it take all market then the incentives just Gotta go. Yeah, it's gotta go Minecraft I think when it sold the Microsoft was how many people like five people or something And it sold for a few billion dollars and it was massively used I think people forget all these examples right? It's just this oh suddenly you can do things really and you could always do something things lean before The real question is how much leverage did you have in headcount? How did you distribute? What did you actually need to invest money behind and then I would almost argue that one of the reasons small teams Are so efficient with AI is because small teams are efficient in general Even higher at 30 extra crafty people who get in the way and I think often people do that if you look at the big tech companies for example right now Many not all of them but many of them could probably shrink by 70 percent and be more effective right and so I do think people Also forget the fact that a there's AI efficiency be there sort of High-value capital being arbitrage into markets that normally wouldn't have them equals a good example great engineers And want to work in legal now they do because of things like Harvey And my health care or health care which again suddenly you have these great people showing up But I think also the other part of it is just small teams tend to be more effective And AI helps you argue other reasons to keep teams highly small and perform it which I think is kind of under discussed I feel like one of the other reason why that AI is things so important I wrote one week had two conversations with two different team members One person came to me to say hey, I'm gonna do this. Can you give me some more headcount to do this? I said no later that we I think independently someone else Very similar say hey, Andrew can you give me some budget so hire AI to do this? Yes, and then so that realization you hire AI not you know a lot more humans with this you just gotta have those instincts Yeah, it's interesting if you think of What's happening in software engineering as the harbinger for like the next industry transformations You spend a lot of time investing at the application level or like building things there what what do you think is next Or do what do you want to be next? I feel it's just a lot of At the tooling level. I feel like I actually prefer a ranked list Yeah, for all investing in this stuff Yeah, does it actually one decision one thing I find really interesting Which is a web economist doing all the studies on whether the jobs you know at highest risk of AI disruption I think I think you're skeptical actually look at them sometimes for inspiration for where we should find ideas to build projects One of my friends every brain also right his he and his company work he takes which we're involved in is very insightful and the nature yeah, good So I find talking to that sometimes useful although actually one of the less enough learned though is And the top down mountain analysis I think AI would have talked a racial environment just so many ideas and no one's working on yet because the tech of us so new So one thing I've learned is um AI fun we have a session with speed All my life will always send up session speed But now we have tools to go even faster than we could And so one of the lessons I've learned is um We really like concrete ideas So someone says I did a market analysis AI with transform healthcare is true But I don't want to do that But if someone is subject matter expert or an engineer comes and says have an idea look at this part of healthcare operations and drive vision All this they go great great. That's a concrete idea I don't know if it's a good idea or bad idea But there's concrete at least we could you know very efficiently figure out your customers wonders It's technically feasible and get going So I find it AI fun Um, we're trying to decide what to do. We've seen a long list of ideas. You try to select your small number that we want to go for on Um, we we don't like looking at ideas that are not concrete. What do you think investing firms or incubation studios like yours will not do two years from now Like not do manually. Sorry. I think there's a lot could be automated But the question is what are the tasks we should be automating? So for example You know, we don't make follow-on decisions that often right because of portfolio or some dozens of companies So do we need to fully automate that part of you not because we're sorry. Look at it. I'm very hard to automate um I feel like doing deep research on individual companies and competitive research that seems right for automation So I wish my I don't know I I probably use whether I open a eyes D researcher and other D researcher times the tools a lot So just to at least a cursory mockery research things LP reporting that is a massive amounts of paperwork that maybe you could simplify yeah I'm taking a strategy of general avoidance Besides you know basic compliance, you know one of my partners Bella she worked at bridgewater before Where they had like an internal effort to take a chunk of capital and then try to disrupt what bridgewater was doing with AI And it's like you know macro investing is a very different style, but I think But I think it probably gives us some indications where the Human judgment piece for business. I think it's not obvious like doesn't entrepreneur have the qualities that we're looking for when you know Your resume on paper or your GitHub or you know what minor work history you have when you're a new grad It's not very indicative and so people have other ideas of doing this like I know investors that are like You know looking at recordings of meetings with entrepreneurs and seeing if they can get some signal out of like Communication style for example, but I think that part is very hard. I do think you can be like Programmatic about looking at materials for example and it's like ranking, you know Quality of of teams overall does actually one thing. I feel like um our AI models are getting really intelligent But does it set the places where humans still have a huge advantage of the AI as often if the human has is the has additional context That for whatever reason the AI model can't get at it could be things like Meeting the founder and susting out there, you know Just how they are as a person and the leadership qualities the communication or whatever And those things maybe reviewing video maybe eventually we can get that context of AI model But I find that all these things like as humans, you know We do a background reference check and someone may send off hand comment that we catch that affects the decision Then how does the AI model get disinformation especially when you know a friend will talk to me But they don't really talk to my AI model So I find that there are a lot of these tasks where human have a huge information advantage Still because they're not figured out the plumbing or whatever is needed to get information to the AI model The other thing I think is like very durable is things that rely on are like a relationship advantage Right if I'm convincing somebody to work at one of my companies and they worked at a previous company And they trust me because of it or whatever reason like You know All the information in the world about why this is a good opportunity isn't the same thing as me being like Sally you got to do this. It's gonna work It remains to be seen whether or not company building is actually that correlated with investment returns But I do think that that side of it feels harder to fully automate. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. I think I think like trust because People know and you know people do trust you know I trust you right because you can say so many things It's very easy to lose trust. Yeah. So yeah, that makes sense Yeah, but actually one thing I'm sure is that you take on is um, you know We increasingly see um Heidi technical people try to be a first-time founders, you know Senator processes to to to set up first-time founders to learn all the hard lessons and all the craziness needed Right to be a successful founder. So it's a lot of time thinking through that. How to set up founders for success when they have You know 80% of the skills needed to be really great But there's another just a little bit that we can help them with. That's a very manual process Um, I don't sweat it. You don't sweat it. I just feel it as like a mix of pure groups Like can you surround people with other people who are either similar or one or two steps ahead of them on the founding journey And then the second thing is um, complimentary hire. I think in general one of my big learnings is um I feel like early in careers people try to compliment or try to build out the skills that they don't have In leading careers they lean into what they're really good at and then they hire people to do the rest And so if the company's working, I think you just hire people like Bill Gates would notoriously talk about His COO was always the person he'd learn the most off of and then when he does certain Leppell Scully Pires next COO. I see. Yeah. And so I must view it through that lens for founders. Yeah Confucius makes sense. I think the best way to learn something is to do it And so that therefore just go, you know, you'll screw it up. It's fine as long as it's not existential the business who cares So I tend to be very laxed asical. I probably think too many things are existential for companies Yeah, it's something it's like do you have customers and are you building product? Most of it. Yeah. Are you building a project that uses love right and of course Go to markets is important and all that is important But you just solve for the product for us then usually sometimes you can figure the rest to I agree with that Most of the time it out always. Yeah, I think there's lots of there's some counter examples But yeah, I generally agree with you. Yeah, sometimes you can build a sucky product Yeah, I have a sales channel you can force it through but I rather know that's not my default There's a lot of really bad technology that's big companies right now Okay, if you have these You know first time very technical founders with gaps in their knowledge or skill set Being like the core profile of folks you're backing again like do you augment them somehow like what's what helps them when they begin I think a lot of things that's just when they realize that you know at that they your friends, ventures to those, we do so many reps that we just see a lot that even repeat founders have only done like twice in their life or even once or twice in their life. So I find that when my firm sits alongside the founders and shares their instincts on, you know, when do we get customer feedback faster? Are you really on top of the latest technology trends? How do you just speed things up? How do you fundraise? Most people don't fundraise that much in their lives, right? Most founders just do it at the end of times. That helps even very good founders with things that because of what we do with more reps. And then I think, harming others around peer group, I know these are things that you guys do. I think there's a lot we could do. It turns out even the best founders need help. So hopefully, you know, VCs, Ventures Studios, we can provide that to great founders. A lot's wiser about this than I am. I mean, I can't help myself, but like want to specifically try to upscale founders on a few things that you have to be able to do like recruiting, right? But I would agree that the higher leverage path is absolutely like you can put people around yourself to do this and learn it on the job. Last question for you. What do you, what do you believe about broad impact of AI over the next five years? Do you think most people don't? I think many people will be much more empowered and much more capable in a few years than they are today. And the capability of individuals is probably But those in embrace AI will probably be five greater than most people realize. Two years ago, who would have realized that software engineers would be as productive as they are today when they embrace AI? I think in the future people will also do job functions. And also for personal tasks, I think people in braces would just be so much more powerful and so much more capable than they are currently even imagining. Awesome. Thanks, Andrew. Thanks, thanks, David. Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.