Investor Elad Gil’s next moves

Basically, everything software is AI. It's going to put the power of building things in the hands of millions of people who couldn't do it before. It's already doing that.

Yeah, let's see. Definitely Andreessen, Airbnb, Coinbase, Instacart, Stripe, Square, a little bit of SpaceX, OpenAI, Perplexity, a couple other things, Cognition. Yeah.

I hadn't felt like I made it yet. So, you know, why not?

It's a couple things. One is, it depends on if I made it, right? Like I feel like I'm in a point in my career where I can finally start doing interesting things because there's enough leverage to do it. But I think fundamentally 'made it' could mean what's the impact you're having on the world. It could mean what have you built or done that's important or relevant. It could mean financial success. It could mean familial success. It could mean all sorts of things. And I think part of it is I feel like there's a lot of stuff to do and there's so much ahead that has to happen. So I definitely don't feel like I've made it from all those perspectives. And then, you know, it's interesting. You look at somebody like Elon Musk and you're like, 'What an inspiring figure and look at all the stuff he's done.' And I know founders who are very successful who look at him and they're like, 'I've done nothing compared to this guy. I have to keep going.' And so I think it's all kind of relative, right? Like a lot of my friends have done extremely well. They've built major companies. And because you're surrounded by people who've done so much, you feel like I haven't done enough.

We haven't announced anything on the funds or anything like that.

Well, I don't have anything that interesting. I think, you know, there's some things that we're moving towards, like our hope is to announce our first sort of statue or monument soon. We've been working on a project to do that for like societal beauty, like art and society at large scale. And so, there's some stuff getting pretty close there. There's a few things that we funded that haven't announced their rounds yet. So, you know, there'll be things like that. So that, you know, there's a bunch of stuff going on, but I don't think there's anything I can really talk about yet. But I'm happy to chat again if you'll ever have me.

I think there's two ways to interpret what he said. One way is to say look at the people who are tinkering with interesting things and those things often become really big. And that was the early microcomputer revolution that things turned into PCs was just hobbyists in the 70s. You could argue that GPT-1 and 2 kind of felt toy-like even though there were these clear scaling laws behind it, right? But they couldn't do that much, but it's super interesting, right? So there's all sorts of toys or people working at the frontier. There's a second way to interpret work on toys, which is basically if you look at the consumer internet wave, those things truly felt like toys. You know, Twitter was like text your friends, like literally you could only text. Or, you know, Airbnb was like crash on somebody's couch and pay money for it. You know, originally was literally like it was called the air bed and breakfast because it was literally, you know, mattresses that the founders would inflate so they could have people stay with them during design conferences and monetize it, right? So, I think that era of consumer internet has moved on and you don't show up with to when you're selling like a SaaS product with something that's a toy, right? You show up with something that's fully baked or that works well or that solves some use case. Often the best things people will use even because they're broken, right? Because they need the product so badly. So I think that that's separate. But I think that toy phase at least in the context of products that most people are building today doesn't quite exist anymore in part because the consumer stuff doesn't exist anymore. But I could make an argument that almost any really early model before you really scale it feels like a toy, right? Like a material sciences model, a physics model, whatever it may be, isn't going to do like amazing stuff, but it'll give you a hint or glimpse of the future. That's very different from like are hobbyists working on interesting things, you know, or are you building something that people just use because like Twitter, it felt such a need despite being so simple at the time, right? And I think that era has shifted for a lot of things. We're building things for space like SpaceX and rockets, drones for defense, you know, nuclear reactors, like those things don't tend to feel like toys early on.

I think some of those things definitely grow and blow up and change and etc. And so again it depends on what you mean by toy, right? That I would consider a toy. I've like I talked to a real estate broker who hated being a real estate broker because a lot of his time had to be spent making phone calls and he goes, 'I don't want to do that, but you know what? I can actually create an automation that will make phone calls for me.' Phenomenal. He did that. And now he's creating software that does that for realtors, which is amazing. Yeah. And I don't view that as a toy. I just view that as like experimenting, building for yourself, etc. Toy-like to me means, hey, it's silly or people kind of denigrate it or they think it's dumb. But I'm sure when he showed it to his friends...

Yeah, even back to Twitter, it was like here's a really sloppy looking site and here's a short code and you can, you know, the 140 character limit is because that's all that could fit into an SMS. It wasn't because they were thinking of like the gravity or something. And then it kind of stuck as a feature for a really long time, much longer than it should have, right? Because it was an accident of just SMSes having character limits. So I think there's almost like this, I think it depends on then what you mean by toy if it's like hey I'm gonna build something really quick and simple and dirty and honestly that was all the vibe coding stuff like there used to be these really bad demos on GPT-3 of hey look I'm going to type in some stuff and make a really shitty UI for me and now we have Lovable and Replit and Figma and K and all these companies like providing vibe coding and so you know that felt toy-like back then but everybody I knew was like oh wow if these models got much better. This is going to be amazing.

I think those small things sometimes become giant things. And I think people often underestimate total addressable market or the number of people want something or and so, you know, many great things have very humble origins and I think that continues to be the case and so I don't I actually think through things like all the different coding models and you know Claude Code or OpenAI or Cognition or like it's going to put the power of building things in the hands of millions of people who couldn't do it before. It's already doing that and I think a subset of that will turn into really amazing huge things or huge companies and even 15 years ago or whatever Minecraft originally was like three people, right? It was tiny. People always say oh when's the first billion dollar single person company? That was Minecraft, right? And that was over a decade ago so even then individuals could do really big things but you had somebody who's a, you know, very obsessed with building this one thing or the technical capabilities to do it. And now those technical capabilities are expanding dramatically, which means dramatically more people could build the next Minecraft in the future.

I think the biggest things in the world are only getting bigger, right? And there's more aggregation than less and there's a long tail of stuff that can get much bigger than it used to be able to get and part of that is we've moved to global liquidity on the internet where we went from, you know, 10 million users to 100 million users to billions and billions of users and so suddenly you have this global market that can really accelerate adoption, right? OpenAI, Anthropic and others are the fastest companies to $10 billion in revenue basically ever, right? And the market caps if you go back 15 years The biggest market cap in the world I think was Exxon and it was like 400 billion, right? And now we have market caps 10 times that size, which nobody ever expected. There's eight companies now worth over a trillion dollars each, which back then nobody would have thought was even possible. I remember talking to like, oh yeah, maybe something will someday be a $5 billion company. What a giant outcome, right? But the biggest things have gotten way bigger, right? And so Microsoft is way bigger than we thought. Google's way bigger than we thought it would be. Meta, etc., Nvidia. So if anything, value is aggregated up much more than anybody expected. And the question is, does that continue or not? And I don't see why it doesn't continue. But it also means that a thousand flowers can bloom and instead of being a $100 million company, they could be a $500 million company. Like I think everything grows behind it.

I'm not seeing a lot of them, but that's also one could argue that in Silicon Valley, which is still a lot of the activity or in tech or whatever you want to call it, Silicon Valley is a place, but I was thinking of it as a concept. People tend to get on the venture train too much or on the let's raise money train, right? And so definitionally, they raise money, they hire people, they don't really bootstrap much. And one could argue outside of Silicon Valley, people raise too little money. There's too little access to capital. And so often the ideas end up smaller than they should be because people try to get profitable right away. They aren't very aggressive. And so to some extent, one could argue Silicon Valley is arbing builders into environments where they're fueled with capital and hopefully they get much bigger than they would in other domains or areas, regions. But the flip side also happens where companies will grow faster than they should in many cases or could and or they'll misprice things because they don't really think about their pricing power, right? When you know if you charge a lot up front. I remember talking to I think it was Olivier from Datadog or something like that. They basically said we always charged a lot for our products because we worried about profitability. And so they ended up as these cash gushers, right? And that's because very early on they had price discipline. They wanted to make money off of what they were doing, etc. And too few people do that in Silicon Valley. But too many people do that outside of Silicon Valley. So, I don't know that there's a right answer there.

Yeah. So our respective firms helped set up this company called Brinkco which is a company which basically builds it has both a common platform that is like data infra and evals and a bunch of other stuff for people to use and then on top of that it builds vertical specific apps for extremely large enterprise or other very large institutions to basically use and adopt AI rapidly against a subset of specific vertical use cases. And so, you know, that's been a very fun thing to, you know, kind of help build over time and to incubate and to have me and my team involved with.

It's building stuff. Yeah, it's basically asking what are things that would be very high ROI to implement. What would create good returns for these customers and what can be done in an expedient manner based on what we've built to date so that that way we have a set of modules that we're using that we've already built there's a platform that we've already built and so how do we rapidly accelerate something so that we can show very fast success and then once we show success we can add the next thing within a vertical and build against that may take a little bit longer but now they trust us they know us, you know, we may have access to data or other things that can be used to sort of implement these other things with the idea that that basic fundamental module tool can be customized and cross sold to other customers in the same vertical over time.

Yeah, an example would be and I'll have to double check if we can run this because I don't remember if it's public or not. But I'll give you an example. An example would be we built a permitting approval flow for a government. So basically anytime you as a builder would show up, you'd submit plans, you'd submit documentation, etc. and you'd be waiting for a permit to get approval and as you were sitting waiting for the permit, you were basically losing money on the project, right? Because you've already bought the land, you've already designed it, you're ready to go. And so if it takes three to six months to get approval, that's just three to six months of lost time and money. And we basically took a few months of manual review and we condensed it into like an hour and then it still goes for manual review, but we've prepackaged everything. We point out the risks, I should say. By we I mean the company has done this and then that's something that can be sold to other governments around permitting approval for the specific use case because it understands architectural diagrams and other things but the same type of workflow could also work for other types of permits in a government context. So that'd be an example.

Yeah, I think there's a lot coming in terms of building on top of existing systems of record like a CRM or things like that. And the big question is at what point do some of these sorts of systems displace CRM or displace ERP or displace a lot of these kind of systems? And I don't know the answer to that. I mean to some extent these things are kind of fancy databases, right? With some UI built on them. But all of SaaS is, you know, people always talk about AI as or AI apps as oh you're just building a wrapper around GPT or whatever. And SaaS is just building a wrapper around a SQL database if you want to trivialize it, right? But it really isn't, right? It's a workflow and it's how do you actually build the product and what do you integrate against and all the rest of it as the cost of building new software drops or the speed to building it drops dramatically we should assume that there's going to be dramatically more customization but that customization again is probably around a common set of platforms because the platforms bootstrap you into the ability to do things you don't rewrite a database from scratch you use SQL right or Postgres or whatever you want to use so I think it's going to be the same for some of these things where eventually there'll be some commonalities and the question is are some of those platforms the existing CRM the existing ERPs etc and the fact that they actually become less relevant with software with this new type of AI development could be argued either way you could say they're going to become way less relevant because it's really easy to add a database and rebuild all the CRM customization you care about or the other argument is to say well it's a common store of data we already know how it works it already integrates a bunch of stuff we'll just ignore it maybe we negotiate pricing way down, but we'll build a bunch of apps on top of it because it's already our system of record and always has it already has a bunch of crap in it and so we'll just keep using it. And I don't know which way that goes.

I think it's going to persist or some subset of stuff will persist. Obviously, some will get eaten away by AI. But if something works and it's already been battle tested for security and a bunch of other stuff and it integrates with a bunch of things. If it's cheap enough, you'll just keep using it. Like why even spend time on it or worry about it, right? So part of it also comes down to like what's your relative cost of something and how much do they charge you and how onerous is it to replace it? And if you replace it, are there other dependencies? And eventually, you know, your coding agent will be able to deal with some of those dependencies. But is that the highest value thing for your coding agent to do? This is why internal tools often don't get giant teams of companies, right? It's because they want to deploy those engineers against product development, right?

Okay. And the question is, is it rebuilding the landing page software or is it doing something else? And it's going to depend on the customer and the context and what it's worth and all the rest of it.

Yeah. I mean, he may create the next Mailchimp, right? I don't know. So, that's the question is where does innovation come from and where does scale come from? And in general, what you find I mean this happens all the time, right? Where say you do have an internal tools team building something for you? This happened with a company that I helped out with quite early called Braintrust where they basically provide like an eval suite for enterprise amongst a bunch of other stuff, right? They have sort of a series of products now. So what they kind of started with was can we help make eval really easy as you adopt LLMs so that you can change the model and you can see how do those changes propagate in terms of performance relative to some test set that you have and a lot of companies are using them now in terms of sort of the leading companies that have adopted AI and I think that was a good example where I helped them with early customer calls and we'd call a customer and those customers would often say things like oh yeah I already have an internal team working on this we don't need it and then they'd call back three months later and they'd say actually we really need this our internal team the resources should go to something else this is important to us but it's not our secret sauce and not just that you're now I'm making up the number 50 people and all of them are working on this one problem and you're covering the whole surface area in ways we never will and our three engineers should just be deployed somewhere else and we should just start using this software instead right and so I think there's a lot of examples like that back to is it really worth your time to go and figure it out and all the nuances and all the use cases and all the lines of code and all the customization and all the...

Performance and the cross-platform nature of it, and cross-cloud nature, and how you interrogate different data sources, you know, all that stuff, like why go do that yourself? So Braintrust, I think, is a good example of this future.

Oh yeah, 100%.

Specifically people. Well, specifically certain types of highly repetitive, language-centric jobs.