Elad Gil: Silicon Valley’s Most Dangerous Startup Advice

There's a bunch of conventional wisdom in Silicon Valley that I either think is always wrong or mostly wrong. An example of that is you always need a co-founder. Michael Dell didn't have a co-founder and Jeff Bezos didn't have a co-founder.

Your thing isn't working and you stick with it too long. There's radical openness to trying things that didn't exist three or four years ago. And that's really important in a way that I think few people really understand.

Wow. They keep inviting me back. I don't know why. So, thanks for keep on raising this big ass fund. So, we know needing to invite you back. So, you know, we've been here for three times.

Yeah, I think there's multiple ways to start a company. So I don't think there's like a single prescriptive approach or like a single right thing. I think fundamentally there's two or three different ways that you can approach things that are still pragmatically true. One is obviously being very customer centric. Sometimes is building for yourself as a customer. That's Braintrust, you know, Anker Goyal's company where it's an eval and prompt and you know etc tool and he built that for himself effectively as a potential user that's something he kept beating over and over. You can also do that for specific customers. So one approach to the world is doing that and being very customer-centric or honestly self-centric. Second approach which I think is really interesting in today's day and age which I think is very unique which is I'm starting to see people do these AI-driven rollups or buyouts where they're effectively buying companies and then expanding margin dramatically using AI. And there you need three skill sets. You need the ability to buy things. You need the ability to operationally change them, which is often the work part, the change in management. And then you need to do the AI stuff on top of that, so you can actually expand margins and make it a better business and make it more software-like. And then, you know, there's a third, which I know most of you is kind of a grab bag, which is you know, you iterate on different things, you invent things, you run it by customers, you know, you try customers. That's kind of almost a traditional approach and it's a little bit more of a winding road in some cases where you come up with an idea and then you try embed it and you know see what people think.

And so I think that part is dramatically faster and better and then I'd also say faster in two ways one is you can build things much faster which makes a big difference but the second way is customers are very interested in trying stuff in a way that I haven't seen in my lifetime and because of AI every CEO has the edict you need to do something in AI what are you going to tell your board about AI and how are you going to do AI and so there's radical openness to trying things that didn't exist three or four years ago and that's really important in a way that I think few people really understand. And so I remember when I was diligencing I invested in Harvey the AI legal company really early in their first round and then I led their series B and for Harvey I remember calling their customers as due diligence and I call these big law firms and I asked them and usually law firms by the way are really bad adopters of technology right they're very slow they're hesitant to change. There's a big security reviews like many legal laptops are completely locked down, right? You can't download things on them without like CIO permission at the law firm. So I call these people running these law firms, you know, the 10 biggest law firms in the world and I asked them what is the biggest change you expect the party and why you adopting it and all the rest. And even two or three years ago the insight was so interesting and unexpected where they said we think this will augment associates to the point where we'll be fewer of them for the same book of business where people can grow our book of business quite a bit and we grow partners from associates. So who are going to be our future partners? Are we going to have enough associates per partner to actually have the right number of partners? Like it's part of the training. It's part of choosing people. It's a selective criteria. It's the filter. It's like whatever you want to call it. And I thought that was fascinating as an insight and this is insight like 3 years ago or whatever it was 2 years ago. And so I do think there's other aspects that are really overlaid on this that are like super fascinating.

100%. And I take it a step further, which is even a couple years ago. So I think there's a bunch of conventional wisdom in Silicon Valley that I either think is always wrong or mostly wrong. And an example of that is you always need a co-founder and you look at the biggest market caps in the world and Michael Dell didn't have a co-founder and Jeff Bezos didn't have a co-founder and you know you go through company by company. Steve Jobs was a dominant founder. There was two founders but he was really the main one although wasn't of course built everything initially but you kind of accompanied my company and either it's unequal in most cases until YC right YC and Google was roughly equal though Larry had a little bit more stock even.

No co-founder right so you look at many of the big Bill Gates co-founded with somebody but then he really became the sole founder when Paul Allen left so over and whoever is either unequal or solo for many of the biggest things. Now there's lots of people who've been incredibly successful in some cases is equal in some cases unequal. If you actually look at cap tables as companies go public you actually realize that most sins were unequal and it was really YC that normalizes equality. So anyhow there's a lot of conventional wisdom that I just think is wrong.

Yeah. No I think winning is important for startups. People often ask me what is the single biggest determinant of culture in a startup and I say winning you know it's not the kombucha it's not the ping pong table it's you know not the number of GPUs you have although that is exciting it's winning but anyhow back to the original question the companies at least that I've been involved with that have tended to work have tended to work early and it's not like we're going to grind for 70 years and then the thing will work and it's usually something starts working really fast.

Right yeah this actually ties into competing schools of thought in Silicon Valley around are there enough founders in the world so the YC school of thought is there should if you just have more founders more magical things will happen and on the alternative viewpoint of the world and I think both are partially correct is there's only so many markets that are open in a given moment in time and therefore there's only so many markets in which you could build a big company and they're open because of a regulatory change they're open because of customer behavior they're open because of technology shift right right now we're going through a simultaneous massive shift in technology and a massive shift in buying behavior so many more markets are open right now than I've ever seen in my life.

There's a few there's also a lot of stuff that took off in ways that people didn't anticipate, right? Lope, but also ChatGPT. It was launched Thanksgiving week. This is true. is a buried launch November 2020. It was like oh it's nobody's gonna pay attention to the sea house launch it during Thanksgiving. So like what does that mean in terms of our ability to predict what's going to be compelling? So today we have three to four players let's call it three players who are kind of dominating the frontier and we'll say four there's obviously you know OpenAI there's Anthropic there's Google and then we'll put all the Chinese open source models into their own kind of bucket right so let's say there are four kind of big categories do you think that this is a stable play or do you anticipate this shifting call it in a five-year frame.

Actually sorry you're right I mean if you want the if you want the beans and what's happened. Yeah. As in Grok is the best of that.

And you know, so I wrote a post about this maybe three or four years ago where I tried to predict the foundation model market and at the time what I thought would happen was that you'd end up with three players each of which would be aligned with a hyperscaler and it'd be kind of a captive hyperscaler model relationship where the hyperscaler would fund it and then the key on that platform. And so I thought it'd be Google for itself with GCP and then OpenAI and Microsoft because it would be deep relationship at the time. Then I thought Anthropic maybe partners with Amazon or something. And then I thought Nvidia would be the primary funder of open source. And if you look at every technology wave visually a big company that funds and monetizes the open source. So during the 90s for Linux sells IBM where they spend billions of dollars effectively on Linux development and really monetized it by selling it through services. And I got it wrong. I got it partially right and partially wrong. I got it right in terms of thinking be a oligopoly market. And the reason I thought it would be a oligopoly because of the scale of capital needed to do it. So eventually you just don't have new entrance for people to compete. But I got it wrong in a few ways. One is Meta ended up being the US-based funder for the time being and China the Chinese government became the other major source of economic funding for open source models and then I got it wrong in terms of the models are partnering more aggressively across right and is partnered with GCP and Amazon Microsoft is working with multiple models is you know Google is working with Anthropic so I it's much more of a overlapping world than what I anticipated and everybody kind of crossed it's actually honestly closer to what happened in China across a few different waves of like the big Chinese companies effectively funding the versions of Uber and you know other sort of next-gen applications. So I do think the natural structure assuming one of the companies doesn't truly lift off in terms of model capabilities ahead of everybody else's oligopoly that seems like the natural state for now. I do think one interesting thing that's emerged is the importance of the harness relative to some of these applications like Claude Code. I think a big shift there that I observed was when o1 came out for OpenAI, there was less switching than I anticipated because some people argued it was a bit of a better model, but people had already been using Claude in a harness and was really optimized for using Claude and so fewer people switched than I expected would. It could be the model was only incrementally better or it could be a variety of thinking. So an open question to my mind is how much does the harness matter for stickiness now versus the core model and if that transition happens and that has really deep implications in terms of how you think about things and then the other piece of it that's the interesting question is when do the certain models asymptote and functionality and if you hit the asymptote then other aspects become more important in terms of switching or not if you don't hit the asymptote and it continues to be what's the best model again with this overlay is it significantly different enough that you'll abandon whatever harness you're And then the question is like what's the enterprise version of the harness?

Yeah. No, I think there's tons of room. I think the question is more what is the closeness between the models and what is the utility you get through things that ancillary and the ancillary things include the harness. I think includes brand actually.

Yeah. Yeah. No, seriously, that matters. So, I think it's a couple different things. You just get used to having things configured a certain way, different types of prompts or you know, everything is kind of set up for you. And I think it's a recent phenomena. I think it's very new and that's why I think it's not discussed very much as like a thing.

At this stage, you might as well just go work for a foundation model company, you know.

Yeah, we all get absorbed when they work on a railroad.

It's very durable. So I think there's a few different types of models, right? So there's the core language foundation model like the LLM, etc. That's mainly what we talk about. And I think two or three years ago, everybody thought, okay, this is going to go agentic over time. And for agentic things, you need some form of persistent memory and you need to have these sort of actions. You need to be able to tie into these different to integrate with these APIs, you know, like I think it was kind of clear what to do and it was just a matter of time to go do it, right? We knew reasoning was coming because we'd have definitely seen reasoning in other contexts and you know so I think a lot of it has been reasonably predictable and we knew like I remember talking to Eric Stein Burer who runs Magic and three years ago he was talking about super long context windows being incredibly important for code because then you could put the whole codebase in and you could work across it and you know so again I think a lot of the smartest people had all these ideas and announce and similarly I think there's a very clear road map in terms of what's to come for other areas I think there's lots of really cool stuff to do. So are you know what are the best models for physics simulation what are the best models for materials what are the you know you kind of go through whole by circle or area by area there's super interesting stuff to do now that may be severable for economic value.

Maybe two questions rolled up into one here. So I mean how I think that over the last 6 weeks there's probably more encroachment from the foundation models into a number of like different domains right like you know thought going out for profit thought for legal thought for finance and obviously of all the coding related things. So I guess the two questions here are like how do you as a startup if you could start companies how do you think about what is it in the effective blast radius of like what these models can do and then secondly I guess related to that is what are the durable modes that an application or a product can have and I think one of the things you said in the past which actually resonated with me a lot was that data is maybe overrated as a mode everybody talks about we have proprietary data and we can you know do better like context window stopping and that feels a little thin to me and an ultimate. So two different questions folded up into the same thing there.

It's great. We'll be all, you know, we'll be all at the edge of the cemetery because I got a little bit distracted.

Yeah, exact. So, I think if you look at durability, there's two ways you can be durable. And by the way, if you look, you know, the whole wrapper around a model thing, I don't really buy because if you look at a SaaS company, is this a wrapper around a SQL database and a lot of things that those things just bite, right? I think usually what you need is you need to build a multi-product company. So there's three theories. One is there's a system of record? You're basically where core data about a thing is stored in all its attributes. And so, you know, that's Workday for all the people working at your company or whatever. And so then you can build a dozen applications around it and it's very durable. That's one hypothesis in terms of what creates durability. I think the single biggest thing is just can you build a dozen different products that say you're cross-selling roughly to the same customer or user. They're deeply integrated and therefore you have multiple workflows and I think then you're very defensible. So for example, if Harvey has two dozen different workflows for different legal applications and use cases, that's defensible. If they're just, you know, bust a child with a legal document and not very defensible.

It's a lot more doable today because you can just crack that stuff out. Yeah. It's what's the surface area of your product? And I think it's very under discussed and it's often discussed as a revenue driver, which it is. And you know you have different wedges in that you can cross-sell against. You can cross-sell to the same account. It's hugely defensible, right? Because usually what happens is a founder will start a company, they'll build one product and they'll win because the product is 10x better and that'll get them distribution and then they keep going and they forget the fact that now that they're the incumbent and they have all these customers, they can build a product that's 80% as good as the best thing and still win because they're just...

They've worried on the security review, they've worried on the purchasing, they've gone through procurement, they've gone through everything legal. And so it's really easy to start cross-selling, in which case it becomes very defensible, right? It's really hard to rip out a dozen things instead of just one thing.

Well, Teams is such an interesting example of that, right? So yeah, it's actually an interesting example of what you said. Yeah.

So the early days of a startup, the primary thing you're competing with is other startups. And then incumbents often used to have five to seven years to react literally because they could just cross-sell to everybody. And so that's Slack and Zoom versus Teams, right? They were both like this and then they flattened out and then Slack had to sell. And the reason is because Microsoft bundled it in Teams, gave it to everybody and they cut off their growth and they won. And so in the olden days, like last decade, you had five to seven years of fighting other startups and then an incumbent would come in and if you could escape that incumbent, you won. Now maybe there's less time for the incumbent to react because you can iterate so quickly on code and the markets are open and everything else. But the incumbent can also build share way faster even if they're getting in their own way. And so there's this interesting dynamic that that timeline should shorten on both sides. So we'll see what happens there.

They're fast moving. They're also good companies. Yeah, look at companies and they have the distribution and traditionally, you know, probably the biggest headache for them is they in that five to seven years, and maybe they have two or three years, right? But there is time.

You know, I think there's two types, there's three types of failure mode. I think one type of failure mode is your thing isn't working and you stick with it too long. And it's back to like you should eventually iterate into something really working. And if it's taking two years and nothing's working, you should rethink. In most cases, not all. Again, there's counter examples. The second thing is there's really bad advice given to things that are working from people who've never had anything work. So what used to happen is you'd get advice from founders who'd seen product market fit to founders without it and they say go hire a sales team and scale really fast and burn a lot of money and do it, and that was terrible advice. But it goes the other way. If something's really working and people tell you to stay as lean as possible, don't hire executives, don't scale, that is awful advice. And that's where a lot of companies break for a while. And other people, competitors who are going to scale, come in and it's a very common pattern for things that are working that founders don't actually build out their company as a product and they don't really go for it in an aggressive enough manner. So, I think that's a big failure mode. And another failure mode is spending money on stuff that just doesn't matter. You know, you start training a crazy model on something instead of just testing something sheer with an existing like SaaS lab and just seeing if people want it, you know.

I actually have not funded many things like that.

And anybody asking, yeah, he ran all his ways. You see at the back, see for at the back, but some salary, yeah. But it's quite hard because your range of what you can pay yourself is pretty bounded, I think, in terms of what is accepted in Silicon Valley. There's only a certain rate in which you can hire and create people. It's not easy to hire well and quick. So often if you raise, you know, 100 at 500, I tell people that you can do it, but it requires everything to go right for 12 to 18 months. And if you had any hiccups along the way, it's just really hard to zig and zag the company, which is actually opposite of what you'd expect because most people are like, boy, I raised a bunch of money now I can, you know, afford to get it wrong. But I actually think it's much harder to get things wrong. You might have the money but you don't actually have room to face my day.

Yeah. I think you're setting different expectations for yourself. I do think there are circumstances where you raise $100 million and you build, you know, like what Physical Intelligence is doing, you know, on the robotic side or building these foundation models for robotics, like that needed a lot of money because they need a lot of computing, did a lot of training data, you know, great. There's a rationale behind it. I just think it's tough depending on what you're doing and so it really has to be tailored against what are you actually trying to accomplish and what proportion of it is GPU versus headcount. And often you see people raise these amounts and, you know, 80% of it's supposed to go to GPU or something and the other 20% is headcount. So you see these differential ratios versus I'm using 100 just to have people, which I think is a tougher thing for a very early company.

You know, I think ultimately there's a lot of APIs for payments and so, you know, anything that's programmatic, an agent can interact with. And that could be a stablecoin or it could be a traditional payment system, but it's more just like what sort of access do you provide to things. And Stripe, I think, has done both, right? Effectively. They've been thinking about agent entity APIs for payments and they've also been doing a lot of really interesting work in stablecoins. And I kind of view Stripe as like the hidden crypto company that nobody's talking about that's doing pretty crazy stuff between their acquisitions and, you know, acquisition and, yeah, they do some interesting stuff.

You know, I'm a long-term crypto bull. But it has cycles and we're obviously in a down cycle right now. And my anticipation, this is not investment advice. My anticipation is that it's going to get worse before it gets better. And if you look at historical cycles, you know, Bitcoin should probably drop to somewhere between say 35 and the 40s. And, you know, maybe it doesn't. It does better than that this cycle and in the 50s or 60s or whatever, but and it's already in the 60s where it has been. But you just kind of look at it, you have a very standard crypto cycle and you have a halving and it runs again. You know, it's the same stuff and maybe it not, people always say it won't apply this cycle and then it applies again and at some point it won't apply anymore. But that kind of drives a lot of the ancillary behavior in crypto because so much wealth in the crypto world is tied up in that that you see people trading sequentially across the different crypto assets with Bitcoin as sort of one of the more stable pools and then you have a bunch of other tokens on top of that and then you have other companies. And so I think we have these boom cycles that also draw founders in and out. One thing that I think is really interesting is there's a certain type of technical founder that depending on when they graduated from school, they either went into crypto or they went into AI and it's like a six-month difference literally. It really is. They've actually done analysis of this in the context of, it's like pre-FTX, post-FTX, you know.

And it really swings people's outcomes in careers. And there's actually really interesting data in general that I remember seeing years ago where if you graduate into a recession versus a boom cycle, you make dramatically less over your entire career. And the reason is you have fewer opportunities, you have jobs, you never manage people early. Like all the things that come with the boom cycle, you miss and all the things and the hardness of a bear cycle you hit. You're just also less optimistic. Like I read that same study which is that you expect the world to be and my life, I think you're doing your cancer.

You know, I think you just expect that like, hey, your expectation of the world is that growth is low and that there are larger systemic forces which are actually preventing you from flourishing, as opposed to kind of like YOLO, it's just like, you know, you go up and up, right?

And so I think that same thing is, I'm not saying financially, I just mean in general, there was that crypto-AI divide. And you had founders who started crypto companies and they stuck with them in many cases and then you had other people who started AI companies and it was a few month difference. It's the exact same type of person. And so I think that's kind of fascinating and, you know, there's all these weird coordinatic, like there's all this stuff we don't talk about that's kind of happening in the background. I think another interesting question, which we can skip if you don't want to talk about it, but I think you also see Silicon Valley kind of moving in these five to seven-year cohorts of. And so there's this interesting question of like how do people maintain relevance across multiple cycles or what does that mean? Or why do two people who seem equally good have very different outcomes? Is it them? Is it probabilistic? Is it, you know, if you run a Monte Carlo simulation of a person's life, you rerun their life a billion times, is the expected outcome of that person and how do these circumstances impact that? So there's all these really interesting things that you can think about in terms of talent. I'll give you another talent question. Jensen Huang, amazing CEO, brilliant, amazing strategist, like so good at like running the company in a controlling way, biggest market cap in the world, like high EQ, very technical, like amazing. For years, he was running a $6 billion company, right?

For 30 years, he ran a company and he was this hidden gem of a brilliant CEO who eventually steered things into AI and, you know, game over. How many other Jensen Huangs are there out there running public companies where it's just in a market that right now isn't booming but they're exceptionally talented? Is it zero, is he just uniquely good and that may be true. Is it 100? Is it like how do you identify these people and how do you unlock that? And so I think there's these really interesting sort of talent questions like what is the aggregate available talent on the planet and how you harness it in different ways that I think is kind of never discussed really.

Most likely there's enough good founders who are just not pointed at the right things. And so it's an ineffective or it's a faulty search function. And if you think about it, entrepreneurship, that's a distributed search around the economic landscape of the world, that's really what's happening, right? You're really running a giant search function across the economy and it's not an efficient process and there's, you know, unequal information, unequal access and all the rest of it. And so it doesn't work well, but I think there's probably enough good people that if you pointed them at the right thing. Now the flip side of it is if you ask people how many great product people exist on the planet, I asked a very well-known public market CEO who runs one of the most interesting product companies that question. I said how many great product people do you think exist in Silicon Valley or in tech and he said at most a few hundred. And so then each company has at most a couple of those and those are the people with high agency who are grinding big things for those very large companies, right? And so there's, now he may be wrong, maybe there's tens of thousands in the wrong starter chess or maybe there's like 50, I don't know. But it is striking if you look at the overlay of, you know, if everything's a bell curve, then you need outliers on multiple aspects of multiple bell curves to do certain things exceptionally. And so then you're compounding small probabilities. So one argument is for any given thing, there's not that many people and one of the parts of that bell curve is almost always agency.

It's about, I think it's probably both. And I think, you know, one of the questions I ask at the end, which you probably just ask now, which is in this incredible age of technological capabilities, what do you want your kids to have? And the first answer is just like agency, like I want all my kids to be like.

I think yeah, Bitcoin. So my wife's brother was visiting us this last week and turns out that I gave all of his children a bunch of Bitcoin six years ago and then I asked him like, you know, where the keys are and he's like, lost it.

You can buy Bitcoin ETFs now by the way, so it makes it.

Yeah, we have iron keys, that's, yeah.

Yeah, it's been a while. Yeah, I don't think, I think a lot of it is still reasonably relevant because a lot of it was about people-related stuff. It's true for the basics of fundraising or how do you fire somebody for the first time or how do you hire executives or how do you do M&A or, you know, so I think there's some aspects that were kind of moment in time like for some of the major funders of startups and stuff like that and there's like a section on that. But I think in general, it reasonably still kind of works. I think I'm actually writing another book for Startups asked me to write this, which is more about the zero-to-one phase of startups.

And so I've been working on that and that's been pretty fun as like a project.

Yeah, I don't have anything prescriptive because I think it's a little bit market segment dependent. And if you're looking at like a defense tech company or something, it's going to be pretty different than if you're looking at an AI vertical company. But in general, you know, everything is growing faster than one would expect. And on the AI side in particular, obviously, we're seeing these massive ramps. You know, I think Cursor is now rumored at 20 something ARR.

And you see multiple companies growing really fast. You know, Harvey's growing really fast, like Decagon and others are growing really fast. So, you just see these things lift off and I think it's back to like these markets are open, the capabilities are massive, the transition is large, the ability to iterate on product is high, everybody wants to try things right now. So, again, it's a very magical moment and a very manic moment. And, you know, I was looking back in history at the '90s, and in '99, 450 companies went public. In the first few months of 2000, another 450 companies went public. And so, say you had 1,500 to 2,000 companies go public over a five-year span. How many of those are still relevant? I don't know the number. It's a dozen, two dozen. It's very, very few. Most of the companies went to zero. Those are the most successful companies. They went public, right? It's not the average company. It's the most successful companies, 90-something percent are just gone. And so then you think about it in the context of AI and you're like, okay, most of these things are not going to exist. A handful of things are going to be Amazon and Google and etc, right? And so then as a founder, how should you think about that? And for every company, there's a handful of companies that'll keep going forever, right? That's probably OpenAI and Anthropic and a few things will just keep going forever. There's a lot of companies that are looking really good right now, they probably sell at some point. And for every company, there's like a 12-month period which is sort of the value-maximizing period that's going to be the most valuable and important it will ever be and then in many cases these things go to zero or close to it. So, one thing that some people do to have good hygiene around this, and by the way, I think there's some companies that should absolutely never sell, right? The companies that really work should never ever ever sell. But how do you know if you're one of those, right? And so good hygiene, I think, for companies, especially if you have a board, is once a year to preschedule a board meeting that's talking about exits. Should we sell? And if so, at what price? And is this a value-maximizing moment? And because you preschedule it and it's annual, it takes the emotion out of it. It doesn't look like you're trying to sell or want to sell. It doesn't look like you're against it. You're just going to have a rational logic-based conversation on the market, on competition, on your position, on your growth rate. Like if your growth is like this, like you see the second derivative changing, now maybe I could tend to sell and you can fix it, right? And so I think it's worth just having that conversation ongoing because again, in most of these cycles, 90-something percent of things end up not working even if they look like they're working.

I don't know. Who knows? That's why Amazon's so successful. [Laughter] Um, I mean I think Jeff Bezos is back to building now I hear.

Yeah, right. He wasn't technical and so he hired some guy to build stuff. If you look at Uber, right? Travis has talked about this publicly. Travis and Garrett set it up as a side project. They found the CEO Ryan on Twitter. They literally tweeted, 'Hey, we have this side project. Who wants to run it?' And somebody put up their hand on Twitter. That was Ryan Graves, right? And then they I think they initially outsourced the app to third-party developers and they brought it in house. Right. That was early Twitter. I mean early on paper I rise because they were CEO.

Yeah. E-point always says he built the first version and maybe he did, quite well. What is, again, I wasn't in the middle of it. I'm just saying like it's interesting to look at historical precedents and I think we have all these myths.

One aside by the way is I do think builder CEOs tend to do better.

So I'm not at all negating that and just saying there's counter examples and it's because they're in the weeds on the product or detailing that they're doing stuff, they don't need another person to enable that so they can ship things and do, they know all sorts of reasons why it's a huge pilot. So not at all, in general I tend to work with technical founders, I'm just saying there are examples where that isn't true.

I think people should have always been hands-on and it's back to where, but I think micromanagement is underrated. I think it is underrated.

Yeah. But there's a lot of things that you should be in the weeds on and there's a lot of things that you personally should micromanage and those are the most successful companies.

You should actually micromanage yourself. That's the highest yield in the most important things. And they're actually good. Yeah. Yeah. Thank you. Your dads are coming.