No Priors Ep. 128 | With Andrew Ng, Managing General Partner at AI Fund

Hi listeners, welcome back to No Priors. Today and I are here with Andrew Ing. Andrew is one of the godfathers of the AI revolution. He was the co-founder of Google Brain, Corsera, 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. Andrea, thank you so much for being with us. >> No, always great to see you. >> I'm not sure where we should begin

because you have such a broad view of these topics, but I feel like we should start with the biggest question, which is um you know, if you look forward at capability growth from here, uh where does it come from? Does it come from more scale? Does it come from data work? >> Multiple vectors of progress. So, I think um there is probably a little bit more juice out of the scalability lemon to be squeezed. So, hopefully we continue to make progress there, but it's getting really really difficult. um society's 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 scale the narrative people think of scale first of a vector progress but I think you know agentic workflows um the way we built multimodal models we did a lot of work to build concrete applications I there are multiple vectors of progress as well as wild cards like brand new technologies like can diffusion models which are used to generate images for the most part will that also work for gener ing 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 uh decided to start talk 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 won't name, he said, Andrew, the world does not need you to make up another term. But I decided to do it anyway. And for whatever reason, it stuck. And the reason I started to talk about agentic

AI was because um uh like couple years ago I saw people were will spend a lot of time debating is this an agent is this not an agent what is an agent and I felt there's a lot of good work and there was a spectrum of degrees of agency where there are high autonomous agents that could plan take multiple sets of reasoning do a lot of stuff by themselves and then things that were lower degrees of agency where would prompt an alarming output and and I felt like rather than debating is this agent or not

let's just um say the degrees of agency and say it's all agentic. So you spend our time actually building this. So I started to push the term agentic AI. What I did not expect was that uh several months later a bunch of marketers would get a hold of this term and use as a sticker to stick it on everything in sight. And so I think the term agentic AI really took off. I feel like the marketing hype has gone like that insanely fast. But the real business progress has also been you know rapidly growing but

maybe not as fast as the marketing. What do you think are 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 are certain things that were 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 the things that are still missing or need

to get built or will sort of ferment progress on that end? >> I think at the technology component level there's stuff that I hope will improve. For example, computer use, you know, kind of works often doesn't work. Um I think so the guard rails eval is a huge problem. How do we quickly evaluate these things and drive eval so I think the component is there's room for improvement but what I see is the single biggest barrier to getting more uh agentic AI workflows implemented is is actually talent. Uh so 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 to drive a systematic error analysis process with eval. So you're building the agents by analyzing at any moment in time what's working what's not working what do you improve as opposed to uh less experienced teams kind of try things in a more random way that just takes a long time and we're looking across a huge range of businesses small and large it feels like there's so much work that can be automated through agentic workflows but you

know the talent the skills and maybe the software tooling I don't know just isn't there to drive that discipline engineering process to get this stuff built >> how much of that engineering process could you imagine being automated with AI. >> You know, it turns out that a lot of this process of building agent workflows, it requires ingesting external knowledge which is often locked up in the heads of people. So, until and unless we build, you know, AI avatars can interview employees doing the work and better visual AI that can look at the computer monitor. I

think maybe eventually you know 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 um to build more Asian workflows. that's more the kind of uh collection of data, feedback, etc. for certain loops that people are doing. Is that other other things that I'm sort of curious like what that translates into tangibly versus >> Yeah. So, one example. So, I see a lot of workflows like um you know maybe a customer emails your document. You got to convert the document to

text and maybe do a web search for some compliance reason to see you're working with vendor you're not supposed to and then look up a database record see the pricing right save it somewhere else and so on. multi-state region workflows kind of nextgen robotic process automation. So you implement this and it doesn't work you know is it a problem if you got the 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 when you implement these things 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 bothered the CEO of the company too many times or is the se doesn't mind verifying some invoices. So all that external contextual knowledge um 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 a 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 a workflows that data set is proprietary. It's just non it's not general knowledge on the internet. So figuring that out is still still exciting work to do. >> What is the um if you just look at it the spectrum of agentic AI what's the strongest example of agency you've seen? >> I feel like bleeding edge of agentic AI I've been really impressed by some of the AI coding agents. Um so I

think in terms of economic value I feel like there two very clear and very apparent buckets. One is answering people's questions. uh pray you know OpenAI chat GP seems to market leader to that with real grow takeoff liftoff velocity. The second massive bucket of economic value is coding agents where coding agents like my my my personal favorite cloud develop to right now is cloud code. Maybe it will change at some point but I I I I just use it love it uh highly autonomous in terms of planning out you know what to do to build

the software building a checklist going through it one at a time. So disability is a plan a multi-step thing. Execute the multiple steps of a plan. Uh is one of the most highly autonomizations out there being used that that actually works. Uh there's other stuff that I think doesn't work like some of the computer use stuff like you know go shop for something for me and browse online. Those some of those things are really nice demos but but not yet production. Do you think that's because of looser criteria in terms of what needs to be

done and more variability around actions or do you think there's a a better training set or sort of set of outputs for coding? I'm sort of curious like why does one work so well or almost feels magical at times and 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 um uh 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 a lot

of smart people for whom they themselves are the user. So also good instinct on product building really amazing coding agents. Uh 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 um models will effectively be bootstrapping themselves in terms of you know 99% of the code of a model will be written by agentic coding agents >> or

the error analysis. >> So I I I feel like we're I I I certainly suspect we're slowly getting there. So some of the leading foundation model companies are clearly well they've said publicly they're using AI to write a lot of the codes. Um maybe one thing I find exciting is uh AI models using agentic workflows to generate data for the next generation of models. So I think I think the llama research paper talked about this but older version of llama 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 need to think as long. So I find that exciting too. Um yeah multiple vectors of progress. It feels like feels like you know AI is not just one way to make progress. There's so many smart people pushing forward in in 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, um I I I >> assuming you do the latter. You're not vibing. >> Yeah. Vibe coding

leads people to think, you know, like I'm just going to go to Vibes and accept all the changes that 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

of it as 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 approach things, or do you think it's still the same old kind of approach, but you just have people that get more leverage because they have these tools now? >> So, you know, AI found we built startups and it's really exciting to see how uh rapid engineering AI

assisted coding um is changing the way we build startups. So, there's so many things that you know would have taken a team of six engineers like three months to build that now today one of my friends and I we're just building on a weekend. And the fascinating thing I'm seeing is um if we think about building a startup the the core loop of what we do right I want to build a product that users love. So the core iteration loop is write software you know that software engineing work and then the product managers maybe go

do user testing look at it go by got whatever to decide how to improve the product. So we go look at this loop the speed of coding is accelerating the cost is falling and so increasingly the bottleneck is actually product management. Uh so so the product management bottleneck is now you can build what do we want much faster well the bottleneck is deciding what do we actually want to build and previously if it took you say three weeks to build a prototype if you need a week to get user feedback it's fine but if you

now build a prototype in a day then boy if you have to wait a week for user feedback that's really painful so I find my teams um frankly increasingly relying on gut because um we go and collect a lot of data that informs our very human mental model, our brain's mental model of what the user wants and then we often, you know, have to have deep customer empathy can just make product decisions like that, right? 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 um 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 that almost forms your market or your user base as a simulated environment of users. Have you seen any tool like 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 been a bunch of tools to try to speed up product management. Um I feel like uh well the the recent Figma

IPO is one you know great example of design AI Heidi and you know Dylan did a great job. Um then there are these tools that uh are trying to use AI to help 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 a group of users and how to calibrate that. It all feels promising and early and hopefully wildly exciting in the future. I don't think those tools are accelerating product managers nearly as much as coding tools are accelerating software engineers.

So this does create more of the bottleneck on the product management side. It does make sense to me that my partner Mike has this idea that I think is broadly applicable in a different a couple different ways of like computers can now interrogate humans at scale. Um and so there's companies like Lucen 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 um 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 that just do not work in 2025. So, in fact, often I I I 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 still even makes sense today because a lot of stuff, a lot of workflows in 2020 don't make sense today. So, I think today um the technology is moving so

fast. founders that are on top of geni technology thus you know uh tech oriented product leaders I think are much more likely to succeed than someone that maybe is more businessoriented more business savvy but is not doesn't have a good feel for where AI is going. I think unless you have a good feel for what this technology cannot do it's really difficult to think about strategy and whether where to lead the company >> we believe this too. >> Yeah. Cool. Yeah. Yeah. Yeah. I think that's like old school Silicon Valley even like if you if

you look at uh Gates or Steve Jobs Waznjak or a lot of the really early pioneers of the semiconductor computer um early internet era they were all highly technical. Yeah. >> And so I almost feel like we kind of lost that for a little bit of time and now it's 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 or two exits 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 um in moments of technological disruption which AI rapidly changing that's the rare knowledge. So, so actually take take mobile technology, you know, like everyone kind of knows what a mobile phone can and cannot do, right? What a mobile app is is GPS, all that. Everyone kind of knows that. So, you don't need to be very technical to have a gut for can I build a mobile app for that. But AI is changing so rapidly. What can you do with voice act,

what workflows do, how rapidly foundation models, what was the reasoning model. So, having that knowledge is a much bigger differentiator. Whereas, you know, knowing what a mobile app can do to build a mobile game, right? It's an interesting point because when I look at the biggest mobile apps, they were all started by engineers. So WhatsApp was started by an engineer. Instagram was started by an engineer. I think Travis at Uber was was technicalish >> technically adjacent. >> Technical adjacent um Instacart was an engineer at Amazon. >> Yeah. And Travis had the insight that GPS enabled

a new thing, but so you had to be one of the people that saw GPS or mobile coming early to go and do that. >> Yeah. You have to be like really aware of the capabilities. >> Yeah. You have to know the technology. Yeah. It's super interesting. What what other characteristics do you think are are common? I I know um people have been talking about for example uh it almost felt like there's an era where being hardworking was kind of poo pooed or do you think founders have to work hard? Do you think people who

succeed were I'm just sort of curious like aggression uh hours work like what else may correlate or not correlate in your mind? You know I work very hard. There periods in my life where you know I I encourage others that want to have a great career of an impact like work hard. But I even now I feel like a little bit of nervous is saying that because in some parts of society it's considered not politically correct to say well working hard probably correlates to personal success. Um I think it's just a reality. I I know

that not everyone at every point in their life is in a time where they work hard. You know when 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 and work hard. >> Just the factual reality is people that work hard accomplish a lot more. Um, but of course you need to respect people that aren't in a phase where they >> Yeah, I'd say something maybe a little less correct which is I uh less politically correct which is like I

think there was an era where people thought like there was a there's 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, there

was a there was a was a quote I I wish I remember who said this, but uh 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 to go and say, you know what, just say the world, I'm going to take a shot at changing it and and and it's only uh people with that um conviction uh uh that I think can do this. >> Strikes me as being 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 um 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 or people who really wanted to compete and win mattered and sometimes people come across as really low-key but they still have that drive and that urge and they they 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 uh from a societal perspective relative to companies >> actually I've seen I feel like I've seen two types. One is they really want their business to win. That's fine. Some do great. somewhat they really want their customers to win and they're so obsessed with serving the customer that that worked out. Uh I used to say early days of course era you know yes I knew about competition blah blah blah but I was really obsessed with

you know learners with the customers and that drove a lot of my behavior instead. >> No 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. One thing I found is in a startup environment, you just got to make so many decisions every day. Um, you just have to go by gut a lot of the time, right? I I I feel like, you know, building

a startup feels more like playing tennis than solving calculus problems. Like, you just don't have time to think, you just make a decision. And I feel like um so this is why people that obsess day and night with the customer with the company think really deeply and have that conceptual knowledge that when when someone says do I ship product feature A or feature B like you just got to know a lot of the time not always and and it turns out there are so many um to use Jeff Bas's term like two-way doors in startups

because frankly you know you have you have very little to lose so just make a decision if it's wrong change it a week later it's fine. So I find but but to be really decisive and move really fast you need to obsess usually about the customer maybe the technology to have that state of 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 uh one

of the best known sort of tech public company CEOs and his view was that in all of Silicon Valley or in 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 swath of 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 I I feel it's got to be more than a few hundred great product people maybe just as I think there are way more than a few hundred great AI people I think there are but but I think one thing I find is very difficult is um that user empathy or that customer empathy because you know to form model of the user or the customer. There's so many sources of data. You know, you run surveys, you talk to a handful of people, you market reports, uh you look at people's behavior on

other parallel or computing apps or whatever, but there's so many sources of data, but to take all this data and then to you get out of your own head to form a mental model for what your right maybe ideal customer profile or some some user you want to serve uh would think and act so you can very quickly make decisions, serve them better. that human empathy. One of my failures, one one of the things I did not do well in the early phase of my career um for for some dumb reason, I tried to make

a bunch of engineers product managers. I gave them product manager 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 but but I found that one coralate for whether someone you know would have good product instincts is that very high human empathy where you can synthesize lots of signals to really put yourself in the other person's shoes to then very rapidly make product decisions and how to serve them. you know, going back to um coding assistants. It's really interest. I think

it is like reasonably well known that the um cursor team like they make their decisions actually very uh instinctively uh 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 I think uh you know these things change all the time but uh I don't think cloud code incorporates despite you know scale of usage feedback data today um from like a training 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 turns out one advantage that startups have is um uh while you're early you can serve kind of one user profile. uh today you know if if you're I don't know like Google right Google serves such a diverse set of user personas you really have to think about a lot of different user personas and that adds complexity to the product changes but when you're a startup trying to get your initial wage

in the market you know if you pick even one human uh that is representative enough of a broad set of users and you just build a product for one user that you have or one ideal customer profile one you know hypothetical person then you should actually go quite far and I think that uh for some of these businesses be it cursor or cloud code or something if they have internally a mental picture of a user that's close enough it's a very large prospective users uh that you can actually go really far that way >> the

other thing that I've observed and I'm curious if 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 companies a generation ago um are not necessarily that effective when recruited in to these companies as they're scaling like because the pace of the 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 know if you're an engineering leader or a 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. I know many great leaders in lots of different functions still doing things the way they were in 2022. And I think it it it's just got to change when when new

technology comes. I mean, you know, once upon a time there was no such thing as web search today. Who would you hire anyone for any road that doesn't know how to search the web, right? And I think we're well past the point that for a lot of job roles, if you can't use OM in an effective way, you're just much less effective than someone that can. And it turns out um everyone in my team AI fund knows how to code. Everyone is a good account. And I see for a lot of my team members, you

know, when my I don't know um uh assistant general counsel or my CFO or my front desk 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 the computer do it for them and this makes them more effective at job their job function. I think the rapid pace of change is uh disconcerting to a lot of people. uh but I guess >> I don't know I

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 that uh to your point show up in um hires particularly around 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 they selected for people who really understood how to use some of the vibe coding AI assisted coding tools >> because there they 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 up 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 completely yeah know you should you should have a prototype in some cases actually just give you an example recent interviewing engineers for a row and hire there interview someone with about 10 years of experience you know full stack very good resume also interviewed a fresh college grad um but the difference was the person with 10 years of experience had not used AI too as much at fresh college grad had and my assessment was the fresh college grad that new AI would be much more productive and I decide to hire them instead. Turn

out a great decision. Now the flip side of this is the best engineers I work with today are not fresh college grads. 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 a cost of their own. So I feel like I I actually think software engineering is a harbinger of what will happen in other disciplines because the tools are most 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 their customers and the thing that was most interesting to me about some of those customer calls was because legal is 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 raises 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 I 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 ing but as industry by industry people are starting to rethink aspects of their business in really interesting ways and it'll

take a decade two decades for this transformation to happen but as it's compelling to kind of see how people like the earliest adopting verticals and something that the people were thinking deepest about it should be really interesting I I think um yeah I feel about legal startup kos is AI the AI fund help build is doing very well as well um I think I think the n the nature of work in the future would be very interesting so I feel like a lot of teams um wound up you know outsourcing a lot of work, right?

Partly because of uh uh costs. Um but with AI and AI assistance, part of me wonders is a really small, really skilled team um with lots of AI tools, is that going to outperform a much larger, you know, and maybe lower cost team that that may or may not be >> and they have less coordination cost. >> Yeah. So actually so some of the most productive teams I'm on you know that 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 enablement

um and very low coordination costs all together in person. So see we'll see how the world evolves. It's too early to make a call but you can see where I'm maybe thinking the world may or may not be headed. >> I uh 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 >> I've 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 but gross wasn't as strong as it could be. So I actually feel like that's a trap part about this. >> Yeah. >> Um it's basically really it's it's almost are you

being um laxidasical 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 that's amazing, right? Capital efficiency is great but 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 you're way ahead it feels great but eventually if there are incumbents in your market they come in and the faster you capture the market and move up market the less time you give them to 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 miss the mark and you could argue that was kind of Slack versus teams

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 Bohemian coding? Yeah. >> You know, they they were based in the Netherlands. 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? >> Do you think your teams stay this small? >> Do you think my team stay this small? What do

you mean >> in terms of just efficiency of like can can you actually get to you know affect millions and billions of people with 10 50 100 person teams? >> I think teams can definitely be smaller now than they used to be. But uh are we overinvesting or underinvesting? And then also I think to your point the the the analysis of market dynamics right if if it's a if there was like a win take all market then the incentives just >> got to go. >> Yeah it's got move. >> Minecraft I think when it sold

to 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 lean. 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. You didn't hire 30 extra crusty 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% and be more effective right and so I do think people also forget the fact that a there's AI efficiency b there's sort of um high value capital being arbitrageed into markets that normally wouldn't have them legal is a good example great engineers didn't want to work in legal now they

do because of things like Harvey >> and then or healthare >> or healthcare 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 performant which I think is kind of under discussed. >> I feel like one of the this is another reason why that AI instinct is so important. I remember one week um had two conversations with two different team members. Uh one person came

to me to say hey Andrew I want to do this can you give me some more head count to do this? I said no. Later that week I think independently someone else uh very similar said hey Andrew can you give me some budget to hire AI to do this? Said yes. And then so that realization you hire AI not you know a lot more humans for this. You just got to have those instincts. >> Yeah, that's very interesting. >> If you think of um what's happening happening in software engineering as the harbinger for like the

next industry transformations uh you spend a lot of time investing at the application level or like building things there. What what do you think is next or what do you want to be next? >> I feel like there's a lot of uh at the tooling level I feel like >> I'd actually prefer a ranked list, you know, for all investing in this stuff. You know that's actually one that's actually one thing I find really interesting which is a web economist doing a lot of studies on what are the jobs you know at highest risk of

AI disruption. I think I think you're skeptical. I actually look at them sometimes for inspiration for where we should find ideas to build projects. One of my friends Eric Brennelson right his his he he and his company work helix which we're involved in is very insightful in the nature. I like him. >> Yeah. Good. Good. So so I I I find talking to that sometimes useful. Although actually one of the lessons I've learned though is uh in view of top down market analysis I think AI when a target rich environment there's so many ideas that

no one's working on yet because the tech is so new. So, one thing I've learned is um AI fun, we have an obsession with speed. All my life I've always had an obsession with 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, if someone says I did a market analysis, AI would transform healthcare, that's true, but I don't know what to do with that. >> But if someone a subject matter expert or an engineer comes and

says, I have an idea look at this part of healthcare operations and drive all this. They go, okay, great. That's a concrete idea. I don't know if it's a good idea or a bad idea but it's concrete at least we could you know very efficiently figure out do customers want this is this technically feasible and get going. So I find that AI fun um when we're trying to decide what to build we spin a long list of ideas to try to select you know small number that we want to go forward on um we 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 of some dozens of companies so do we need to fully automate that probably not because we're very look at very hard to automate um I feel like doing deep

research on individual companies is in competitive research that seems right for automation. Uh so I I might I don't know I I personally use whether open D researcher and other D researcher types of tools a lot uh to just do at least the cursory marker research things. Um LP reporting that is a massive amount of paperwork that maybe we could simplify. >> Yeah, I'm taking the strategy of general avoidance >> besides you know basic compliance. you know, one of my partners, uh, Bella, she worked at Bridgewwater before, uh, where they had like an internal effort

to take a chunk of capital and then try to disrupt what Bridgewwater was doing with AI. Um, and it's like, you know, macro investing. It's a very different style but I think uh but I think it probably gives us some indications where the human judgment piece of our business I think is not obvious like does an entrepreneur have the qualities that we're looking for when you know your resume on paper or you're a 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 uh 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 like ranking you know um quality of of teams overall. There's actually one thing I feel like um our AI models are getting really intelligent but there's a set of places where humans still have a huge advantage of AI

as often if the human has has additional context that for whatever reason the AI model can't get at and it could be things like meeting the founder and sussing out their you know just how they are as a person and the leadership qualities of communication or whatever um and those things maybe reviewing video maybe eventually we can get that contest AI model but I find that all these things like as humans yeah we do a background reference check and someone makes an off-hand comment that we catch that that affects a decision then how does a

AI model get this information 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've 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 um things that rely on 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 going to 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 um fully automate. >> Yeah. Yeah. Yeah. No. Yeah. Yeah. I think I think like trust because um people know and you know people do trust you

I trust you right because you come say so many things it's very easy to lose trust you know so that that makes sense >> actually one thing I'm curious to get your take on is um you know we increasingly see um highly technical people try to be uh first-time founders you know set up the 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 I spent a lot of 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 view 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 compliment or try to build out the skills that they don't have. In late in careers, they lean into what they're really good at and then they hire people to do the rest. Is 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 learned the most off of and then once he had a certain level scale, he'd hire his next COO. >> I see. Yeah. >> And so I must view it through that lens for founders. >> Yeah.

Competition 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 of the business, who cares? >> So I tend to be very laxidasical. >> 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? >> The most Yeah. Are you building a product that users love, right? And then of course go to market is important and all that

is important, but you solve for the product first then usually sometimes you can figure out the rest too. >> I agree with that most of the time, but not always. Yeah, I think there's lots of there's some counter examples, but yeah, I generally agree with you. >> No. Yeah. Some sometimes you can build a sucky product and have a you know sales channel you can force it through but I I rather not that's not my default it does work >> there's a lot of really bad technology that is big companies right now >> okay if

you have these uh you know firsttime very technical founders with gaps in their knowledge or skill set uh 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. It's actually one thing I realized at you know at at venture firms venture studios 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

um when my firm sits alongside the founders and shares our instincts on you know when do we get customer p faster? Are you really on top of the latest technology trends? How do you just speed things up? Or how do you fund raise? You mo most people don't fund raise that much in their lives, right? Most founders just do it a handful of times. That helps even very good founders with things that because of what we do, we've had more reps at. And then I think um hiring others around them, peer group, I know these

are things that you guys do. Uh I think there's a lot we could do. It turns out even the best founders need help. Um, so hopefully, you know, VCs, venture studios who can provide that to great founders. >> A lot's wiser about this than I am. I I mean, I I can't help myself, but like want to specifically try to upskill founders on a few things they have to be able to do like recruiting, right? But I uh I would agree that the higher leverage path is absolutely like you can put people around yourself to

do this and to learn it on the job. Last question for you. What do you what do you uh believe about broad impact of AI over the next five years? 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 of those that embrace AI will probably be far greater than most people realize. um 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 of all sorts of job functions and also for personal task I think people and braces will just be so much more powerful and so much more capable than they probably even imagined. >> Yeah. >> Thanks Andrew. >> Thanks for thanks thanks sir. >> Find us on Twitter at no prior pod. 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

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