Full Transcript for Inside the Anthropic Bet That Made Menlo a Top AI Firm | Matt Murphy

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Matt, welcome to the show. Thank you. Appreciate you having me. So Anthropix S1 just dropped. Everyone has probably read it or reading it right now. A lot of people are commentating on the numbers. so what do you think about all the numbers behind anthropic yeah well uh i guess they're still in in their quiet period so i can't comment a lot on the numbers but you know i think there's things that you read in s1 and you're like well what is operating cash flow what is gross margin was growth rate i think those are the things that most people really look at as a barometer indicator of like how the company's performing And then you have these kind of gap charges like non-cash

charges for warrants or convertible notes and things like that. So, you know, there's been this headline around negative 42 million burned or something like that. And that's not really, you know, it's not a burn number. It's certainly not a cash number. It was kind of a... a non-cash issue. So I think that, you know, there's just a lot of people don't know how to read an S1.

So hopefully there's some good reporting coming on like metrics that really one should look at to kind of measure the business and understand, you know, what Gap tries to do, which just gives you a bunch of different dimensions of accounting charges and things like that that are non-cash and I'd say not really leading indicators of how the business is performing.

So if somebody says all these AI companies don't make any money and it's just like a pyramid collapse or whatever like is that not true then well i mean if you if you look at the business again like a lot of uh people you know analysts people who um mutual funds etc you look at operating cash flow because essentially you know and they're they showed that they're been they've been operating cash flow positive and expect to you know stay that way so i think that's you know that's a more interesting indicator of the health of business.

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That's why I mentioned gross margin too. There's kind of things that are a little bit more indicative of the fundamentals of a business than accounting charges and things like that. Not that they're not important, but you've got to look at the whole picture and understand, you know, what really are the true kind of leading indicators. But...

Aside from all that, there's so much exciting about finally having this flip because there's been so much speculation in terms of the business, what it's all about, what its growth rate is, etc. If you look at when I was earlier in my career, I got to go to all the board meetings from the time I was at Kleiner Perkins when we invested in Google to the time it went public.

Still today, probably people think it was the fastest growing business ever. I think it was like... zero to a billion in five years, zero to 10 billion in 11 years, or eight years, and then 10 billion to 100 billion in 11 years after that. So you're talking 19 years to get to 100 billion. Which that's, even in today's standards, that's still pretty high.

Oh, yeah, it's tremendous. But then you have a company that comes along and can do that in about a third or less of the time. So that just kind of puts in perspective how quickly this company is growing and what just a different phenomenon we're in in the growth of AI. was there any other similarities or maybe differences between the two companies, just kind of getting to see them both up close? I mean, you're possibly the only person that's seen both of them that close.

Yeah, I mean, a similarity is, I think when Google started, Larry and Sergey did not want an advertising business at all. So you didn't really go into it knowing what the business model was. They were going to charge per search query or things like that. And they got kind of thrust into the advertising business. And I'd say in the case of Anthropic, they knew what the business was going to be, but they didn't know they were really going to be an enterprise business.

They started off just kind of like, hey, we've got an API. We've got a great model. We'll figure it out. you know, where it's best to take it to market. But at the time, OpenAI was kind of dominating the consumer, you know, interface or consumer interaction, and they partnered up with Amazon and Google both for compute, but also for distribution and technology partnerships.

And that kind of took them down the enterprise path, which ended up being, you know, the even faster more lucrative market to go after, which is how they catapulted themselves to number one. So that's kind of like just some different, some interesting similarities in terms of how these companies, oftentimes they might be, there's a great underlying foundation or technology, but it's not entirely clear exactly, you know, how you're going to monetize or what the business path is forward.

And I'd say that's true of a lot of technical founders when they know when they start a business and certainly both companies were founded by elite and uh special technologists in dario and larry and sergey so when you first invested in anthropic they were pre-revenue right they had no no revenue generating products. Pre-revenue, pre-launch.

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At least in the case of Google, when we made the investment, you could see the usage was very, very high. They just weren't monetizing yet. So how did that go? I mean, how did that even come about? Because I think everyone's heard these stories about Anthropic couldn't raise that round. Like it just, no one wanted to do it. Yeah. So how did that come about where you guys ended up leading the round? yeah well i think um so we participated in the series c and then led the series d the series c is when we participated we could have led but you know again like you know the the company at the time like you said pre-revenue pre-launch um four billion four plus billion dollar

valuation and i know to a lot of listeners today it's like oh four billion like isn't that isn't that kind of common but if you roll the clocks back to 22 23

there, there was no, there were no companies that was like a spectacular exit for a company, like literally spectacular in the history of venture. If you exited for a $4 billion valuation and now that was kind of the entry point. And then you've got issues like, well, what fund does that go in? How does a venture fund do that? And then because it's pre-revenue and pre-launch, like how does a growth fund do that? So it was kind of caught in the middle.

Um, but you know, uh, I'm glad that we had a partnership that was all in on AI and aligned around, hey, this is at least the second best company in the category, the best technical team, and we just have to be in this category. We bent the rules a little bit to say, all right, it's imperfect for either fund, but this is special team, team that created ChatGPT.

The models were benchmarking really well and massive market and we bent the rules a little bit to say, hey, let's participate in this round with a goal to lead the next round. Obviously, that worked out fabulously, where as soon as we invested, we just had a different type of relationship with the company. We're working with them on a bunch of stuff, be it

recruiting, talking to customers, and just a lot of strategic issues that come up early in a company. And then we really saw them put together the deals with AWS and Google and the revenue of Flywheel just starting to spin. Both kind of like big meaty deals where they were kind of saying, hey, we'll come in and help you do kind of an FD project for deployed engineer to come in and help you build something special with like a large telecom.

but also this kind of drip of just a long tail of developers. And then so we saw that happen where the company went from like zero when they launched in April to it's been reported something like 85 million at the end of the year. And we saw that happening in September, October, and just jumped on it to be able to lead the round. And I think unless we had made that small initial investment, we wouldn't have had the relationship to have seen all of this stuff going on, as well as to have kind of earned the trust with the team to be able to lead that round.

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Now it's not a very happy time and fun time to be raising money. So

In terms of all those internal discussions you had, where I'm sure it was probably, of all times, a pretty

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difficult time to say, hey, we're going to plant our flag in the ground right here. So how did those discussions go of taking a really big bet? Well, I think it started off, as you say, it was just really difficult because it didn't fit in either fund. And it was kind of like, what are we doing at $4.5 billion? Even if this thing doubled, it's not going to be meaningful to the fund.

So these are all the kind of conversations you get into with VCs. Like, hey, it's not going to move the needle unless it's a 10x. And who's going to bet this company is going to be worth less than $45 billion? That sounds crazy, right? Yeah. But we're in new times here. There have been massive exits like Cursor, etc.

That came after our investment. But I think the point was, as this AI market was taking off, it was taking off at growth rates that no one had ever seen before. So like the other technology ways have always been constrained by something, whether it was devices or access to, you know, high speed internet or comfort of your data being in the cloud.

This was immediately for everyone. So you just saw all the trends kind of showing that this was going to be a much, much bigger trend than any that we'd seen before. So it was controversial, but at the same time, it's like, hey, We want to be a leading AI firm. How can we be a leading AI firm unless we find the number one or number two category that is a model company? Because the model is basically the heart and soul of this whole AI economy.

It's the brain and the intelligence that helps all the other applications build around it. So I think from that standpoint, I initially was like, oh, my partners are going to think I'm kind of crazy and And do I really want to be out over my skis? And fortunately, when you have a very aligned partnership that's willing to dream the dream with you, they're like, come on, Matt, let's do that.

So that was the first investment. And then, like I said, I think the second investment where we did the $500 million plus SPV, that was a little bit more like, hey, this is much more obvious. Now, the valuation then jumped up into the $18 billion range. So now we're talking a whole different quantum. But at that point in time, you're like, all right, this company is extremely legit they're they're scaling quickly amazon and google have chosen them as their partner of choice so now you had kind of the open ai microsoft axis versus you know anthropic and and google and amazon you know that's all kind of changed but at the time that's the way it was playing out so you kind

of saw these really important pieces come into place you saw the the model just being frankly better and then executing really well. I think Dario has just always been an amazing technical visionary and some of the best researchers in the world were all flocking to join the company. All these advantages were starting to compound. uh and and it's kind of crazy that you know you know the cycle of raising capital these days used to be every like you know two years maybe two and a half years that all happened in under six months all the things i talked about you know amazon google that revenue going from zero to you know roughly 100 all that so you know you saw those kind of

data points and just felt like this was our one shot to be aligned with this really really special company and again

It was great to have a partnership that said, hey, we need to do this because we want to back one of the two main model companies. And this is the one that we think is over time will be by far the best. So anyway. that's kind of that's kind of story and um and then you know off to the races since then you know not no none of these things are a straight line and i think how the enterprise versus consumer market played out finding an absolute you know hit mega market like coding and the way it just kind of like came out of the anthropic labs it just shows you not only the level of technical talent they had there, but also the creativity and understanding how to take a great

technology and meet the market. Some of those things were starting to become obvious early on. Maybe it's hard to tell, Do you know what it was that made all the best technical talent want to go work with Dario at Anthropic? Did he say a specific line or was there just some kind of a culture there that attracted people? Do you know what it was? i mean i certainly authentic um a true you know technologist i think a lot of the best researchers in the world want to work for somebody who's kind of like them and you know no offense but sam's more of like a business person i always have seen him like a deal guy almost like him as like a good deal maker which he does he's done

it many times exactly and and you know that's his superpower and Dario's is building amazing technology and recruiting amazing researchers. So I think that kind of felt like he was more like them and the vision he had and wanting to be part of that vision. I also think Anthropics always had a tremendous culture and a low drama culture, and they have certainly stood out there relative to some of their peers.

So then in terms of how you did this SPV, because I think it's probably interesting to people. I think your fund... Our first SPV ever, by the way. And I think your fund was like 600 million bucks. Am I remembering that right? 750. 750. And so you raised a SPV that was almost the same size as the fund. Yeah. So what was that process like for...

Because I've heard actually many people say, we don't do SPVs. We've never done them before. Did you guys just... You just had never done it. You didn't see a need for it. Like, how did this kind of come about and what made you kind of flip over? I think historically, you know, if you look at Menlo going from an early stage firm, seed series A, and then we started off our inflection fund, which this...

investment eventually came out of which was considered early growth which we define as kind of like one to ten million of sorry three to ten million of ar that was early growth right that's like a customer right right well that's and and so by the way that's why our strategies have evolved a little bit because now you know that used to be like a year to 18 months and now that's like a week for a lot of companies so that yeah three to ten range uh is is harder to uh pursue I feel like some of them are adding $10 million a day now.

They'll publicly say, we just had a $10 million day. Last day of the quarter can be $10 million days now. Because we had this opportunity to lead, what had actually happened was we had Anthropic at our LP meeting, which was in early November. Was this 23? Yeah. We had Anthropic at our LP meeting, and this executive came and said, kind of blew everybody away.

It was Nirav, I think. Yeah, it was Nirav, exactly. Yeah, kind of a stealth warrior within the company who's been kind of a jack of all trades, but has really worked on important initiatives. And he was kind of working on the financing and just really the big customers they had, a number of things like that early on when a company's lean and everybody's doing a lot.

And so he came to the LP meeting and absolutely blew everyone away. And our LPs after were like, hey, how do we get in this company? What did he say? Like, what did he say that blew people away? It was really just like, I don't feel like at that point in time, people really understood the power of AI and how it was being used in the wild and what it was capable of.

And, you know, especially from people from all over the country. And now, yeah, it feels like, oh, we're all using it. We all understand. But just like valid, just like, you know, just like four years ago, $4 billion valuation felt like hard to fathom. You know, so much has changed so quickly that, you know, lots of things that we take for granted today, you know, just were new to us back then.

So I think it was just kind of understanding all that. And he was incredibly articulate and just painted this amazing vision for where AI was going and what it was going to sell. So out of that meeting, we had a bunch of our LPs saying, this is fabulous that you're in this company. You know, can we co-invest with you? And before you know it, there was like, you know a couple 300 million of kind of interest and we're like oh we've never done an spv before but like this is a special company we want to we want to lead the round but the round's going to be pretty big we can't do it out of our 750 million dollar fund i mean we can do 50 or 100 out of that fund but we can't do

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the rest so we said hey how about we take capital from our fund and our lps put this whole thing together and make a proposal to to lead the round Um, and you know, uh, as history would have it, we were, we were definitely a little over our skis at the time because, you know, you get a lot of people interested and said, but you know, when you go and shake the trees, uh, you know, uh, some things kind of fall out of the trees and some, and some don't.

So there was a kind of a, a scary period there for about 30, 45 days where I was like, oh my God, I think we've kind of over committed here and we, we may not pull this off. And then are you just like texting everybody? Just like, Hey,

We did a couple of webinars, but it's funny. What I didn't realize when we raised the SPV, we were very collaborative with Anthropic. They were supportive of it. But they said, you can't share any financial information. so i'm like okay fine people are just going to believe this dream the dream as much and so we could say we could take like publicly released things that give us some product slides but you're asking people to invest in an 18 billion dollar valuation right and you can't show any financial information uh you i think we could say like what last year's revenue was But we couldn't do any forward projections.

So that's kind of a tall ask when you've got big state pension funds and things like that. So we did a couple of webinars, and then we do a bunch of kind of one-on-one follow-ups. But there were a bunch of people like, look, we just can't responsibly do that as a state pension fund. But I can tell you that three did, and they've all made– uh well over a billion dollars at the last round valuation so uh you know all in um this is a pretty amazing return so you know fortune favors the brave we're glad that we had some lps that came along the road for us but it was not uh yeah it wasn't it wasn't what maybe you would expect in in hindsight because you know just things

things have always been unclear and then they get clear as the company's um grown and shown what it's capable of but uh we got it done i think we closed the you know kind of in two tranches with the last one in april so you did you did a little bit of time to kind of land the plane it wasn't like a one week and no we signed the term sheet in like mid-december and then we didn't even really start the process until january and let's say we kind of had everything firmed up by march and then it all closed like last close was early April or something like that.

But it gave me a lot of empathy for entrepreneurs raising capital because essentially what I and one of my partners became was basically the capital raisers for Anthropic. Not that they really needed because if Dario got in front of anybody or that team, Tom Brown, All of them, they would have had a much easier time than I did, but we committed to raise the capital.

And so we were out there talking to LPs and saving them the time of talking to a bunch of institutions so they could focus on the thing they're really good at, which is building amazing technology. And I'm glad it worked out. It definitely makes you feel like when you're in there raising the money yourself and just people are just like, yeah, I'm not that interested.

Like this doesn't seem that good, right? Like it just hits you in the face over and over again. You're like... you're putting your name on the door, you know, putting yourself out there. Oh yeah. I mean, some of our biggest LPs who, like I said, have made well, well over a billion dollars. So, uh, it's great that we were right, but it did feel at the time, you know, like a potential existential moment for the firm that if we were wrong and we had, you know, um, losing state pension money or, you know, uh, are not leaving our best LPs in good graces with us would be a really bad thing.

But yeah, That's like anything that's a big swing always has those attributes or those dynamics about it. I'm just thrilled that we were able to do it. We knew this company was going to be big and special. We just didn't know quite the quantum of what that would be and how fast. This episode is brought to you by Clerk. When I had Clerk co-founder Colin Sidoti on the show, we talked about why authentication keeps changing.

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talking more about menlo yeah more broadly so are you guys 50 or 51 years old turned 50 in november okay yeah well then you're almost 51 yeah yeah it's already almost november again no no no no this this year we turned 50 oh you're turning 50 turning 50 20 26 yes okay so and you joined about 11 years ago yeah so of all all the places you could have gone why did you pick menlo yeah um many reasons but like uh one one easy one was one of my closest friends from business school mark siegel had kind of been handed the keys to take over the firm from the founders and generational transition is really really hard it blows up a number of firms it slows them down they miss

technology waves it's one of the hardest things in venture and menlo did it beautifully now beautifully in the standpoint that they handed the keys to mark and said all right mark you go do this and then And then Mark did an amazing job to first get Fanky and then me. And then Sean Carlin was off running a company. And then we recruited him back after about a year.

So that was kind of the band that came together. And then Mark retired about three years after that. I guess he figured his best work was done to get us all together. But I think... So anyway, it just felt like after being in the business for 15 years to come in and really have the opportunity with an almost 40-year-old firm, great brand, great longstanding firm, reputation for, hey, Menlo's been in a lot of amazing companies.

They're a group that always... does the right thing, those reputational things that you want. But I think at the time we were also missing a bit of edge, like how hungry is Menlo? Is Menlo everywhere? Do you feel Menlo in the market? Are they early stage? Are they getting there before you? Were people saying that you weren't doing any of those things? Well, I think that was just our internal, you know, critical observation is that, you know, the firm had gotten much bigger to, you know, billion dollar plus funds.

And as part of kind of bringing it down to the studs with me, Mark, Sean, and Venky was to say, hey, we've got a single $400 million fund. We're going to go back early stage and just rebuild from there with a smaller team. Then all of what you see today has really been brick by brick since then, adding an early growth fund because we felt like at that point in time, there was this wedge in the market where you had growth, it was like 10 and up, and then you had early venture but the kind of momentum companies in the middle weren't really that covered now this was like the 3 to 10 million yeah right yeah and so now i'm talking back in 2016 to 2018 the the the venture and

growth markets were a little bit more bifurcated and now everything's kind of blurred a lot more but anyway that kind of got us into the early growth the growth business It allowed us to get into a number of high-velocity winners. It allowed us to double down on a company like Anthropic and continue to do that round by round. Then it came to saying, I'll give you another example.

When I joined the firm, we didn't really have any services team. The firm I came from, Kleiner Perkins, we had a pretty big services team. But it was like we pulled our entrepreneurs and we asked our entrepreneurs and a network of people in the industry, what are the two or three things you really care about from your venture capital firm, your partner? It was like, well, I want a really great partner who's kind of there and does the right thing and up and down times, super solid, trustworthy, doesn't whipsaw around, just a great kind of advisor on the things that really matter.

So Chad, okay, I think we're all kind of Wired that way. And then after that, it was, hey, I want people who can help with talent and deals. And so we didn't have any of that. So I brought over a guy named Jordan Ormont, who I worked with at Kleiner Perkins for eight years to kind of build out our talent function, which now has three people.

We hired a person named Humon out of Qualcomm to come over and run our kind of biz dev function. We run a lot of EBCs, any customer you want to get to, talk to Humon or Brian, who's on his team. So how do we kind of look at the market and say, what is Menlo doing today? What is the market expecting? How do we fill in that void? So that was kind of like the kind of tactically and strategically, like we need that to be a more competitive firm.

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Then there was like, hey, there's a lot of high... velocity kind of companies in that three to ten range we can't play in that market because the fund size isn't big enough and we're doing our stage venture so you know let's let's add that um and then you start getting into things like hey now now i'll kind of roll the clocks forward a little bit but you get into things like uh hey this new technology wave is coming we sat out crypto We sat out VR, thankfully.

Those are probably some good moves. Yeah. You know, crypto money was made. But, you know, in terms of real businesses and so forth, there's a lot of kind of trading companies, things like that. Anyway, it just wasn't for us. We can't be all things to all people. But as soon as the AI wave started to come along, we're like, this is our moment.

We have incredibly high conviction inside the whole firm. We're just going to put all our chips on the table on that. So what period was that? Like what? You know, it was kind of shortly after launch of ChatGPT. So what are we talking about? 2020 to 20, you know, roughly that timeframe. So, you know, maybe like six months before we funded, you know, Anthropic.

So, you know, but so... We were the first firm, I think, to put out something on like the AI infrastructure stack. Like every time a technology wave comes along, there's some new infrastructure stack that comes out. So we kind of put out this market map and we did it for our own internal thesis work to kind of guide where we thought we should be investing.

And that's kind of the way we are, very thesis-oriented thematic firm. And so did that. And then you start saying to yourself, hey, like, know this wave of even applications like if you look at sas applications there wasn't really a lot of technology under the hood right i mean you had to build some things efficiently but you used amazon and and you know you're building more really cool workloads workflows and interfaces and things like that now if you look at all of these companies you know whether it's uh you know something like legora we're an investor in or lovable etc like there's huge amounts of technology under the hood so So we felt like the investors who thrived

in the SaaS era were not as technical as those that were needed to thrive in this environment. And that I gave you an example was even on the application front. But if you look at the infrastructure front, same thing. I mean, so we hired Tim Tully, who was the CTO of Splunk for a long time. Cisco bought him for $20 billion. So obviously a very successful company there.

Geoff Redfern, who is the chief product officer at Atlassian. Matt Cranning, a PhD in machine learning from Stanford, who sold his company to Palo Alto Networks. You know, Didi Das, who was the founding PM and engineer at Glean. Amy Wu, who kind of had been in venture at FTX, interestingly. So all this team kind of came together over a period of three, four years, much more technically oriented, understood developer communities, things like that, that are really flourishing right now.

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So I think like, If you think about all the bricks that had to come together, we had to have this fund that could invest in a company like Anthropic at their stage. We had to have a prepared mind in the thesis work to say, hey, here's what we're looking for and why. We had to have the services team to help add value. Companies would say, hey, why do I pick Menlo?

Then the thesis work that I described. All of that is compounded to be a real advantage. I don't think if we just anthropic had shown up and we're like, hey, this company is great. They created ChatGPT, let's do this at four billion. It took all of this. It took a team that was surrounding us to give us the courage to do it, the technical work that we'd done.

the ability to win because they were like, look, Menlo seems like a great firm. You have all these things behind you. So, you know, every journey has, you know, is not a straight line. And ours was kind of very intentional, putting all the bricks together, really proud of how it's come together and even more so about what's ahead. Yeah. You were telling me that that's been like the most fun you've been having is actually the team building side of it.

Yeah. Yeah. You know, I think this, this, you know, venture, can oftentimes feel like an individual sport. And I think we've always tried to practice it more as a team sport because it's more fun to do it that way. I think it's more helpful to entrepreneurs because you get the whole team who are just like, you know, different people who can provide different things.

Jack Redfern, former CPO, Tim Tully, you know, former CTO. So there's got a lot of people on our team that can help people in any situation, but not just help the portfolio companies, but help me. I mean, the reality is, even on the Anthropic investment, I felt highly, highly convicted about it. But it was Tim who went and spent a lot of time with Tom Brown to say, okay, I understand this compute multiplier thing, what they're doing, why they've been able to build these really powerful models at a much lower cost, and why we think that can compound in its advantage over time.

There were things like that. So I would have been making more of an intuitive people decision. Like, I just feel it. I feel like this is going to work. Yeah. Yeah. and then when you can back that up with somebody who has been the former cto of a you know 20 billion dollar company saying now this is special technology that that made my life a lot easier um and you know made sure i'd done my homework as much as one could so no i mean i think Besides, like I was saying, it can feel like an individual sport at times.

We play as a team sport. But also, I think at the stage of my career that I'm at, having been in the business for over 25 years, it's just created a new challenge and opportunity for me. To feel like we were building more than just an investment track record, but a set of people that were really proud and happy to work together and we're more productive working together.

Seeing the firm rise from where we started with all the things I mentioned, our challenger mentality and getting up into one of the top handful of firms in this wave of AI, which I would say, that we're at, um, you know, there's, there's, that's, that's more gratifying than the next incremental investment by far. And I think that that gives us the opportunity to work with even better entrepreneurs, to, uh, have a bigger impact, to have a higher win rate, have, have a higher access, all those things that matter.

It's just all compounding. And so it's energizing, right? It just feels like you've been, you were grinding away for 11 years to, you know, kind of get to this point and anything along the way of those pieces I mentioned that didn't happen, maybe we never would have gotten here, especially in including, uh, you know, that, that, that initial decision on Anthropic spent so seminal to, you know, our success is affirmed, but then, you know, you wake up every day, not with a, you know, a paranoia, but more like a, wow, we've worked so hard and done so many things to get to where we are now.

And how do we build on that? How do we take even more advantage of that? No one is kind of happy to be, you know, whatever, resting on our laurels or feeling like we've arrived. It's like, now we're here. And how do we make this an even bigger deal and do more with that opportunity? That's what's completely exciting and exhilarating. So when you say the challenger mentality, it feels like you're trying to continue to carry that forward.

So what is it? And maybe another way to think about it is what is not having a challenger mentality? Yeah, I mean, I would say the challenger mentality when I started 11 years ago was like, hey, there's a big gap between where we are now and where we think we want to be. not in like you know necessarily reputation or principles or who we are as people but kind of like you know where we are how we're perceived in the in the market uh the kind of things we're in the mix on and and again like our ability to to win our ability to have an impact with entrepreneurs all those things there was a gap so that that challenger mentality is we're going to fix all that you can't flip a

switch and so you know that's kind of the long journey so I think now the challenger mentality is a little bit different. And so it's not kind of going from here to here. It's like, all right, now that we're in this cluster of really great leading firms, how do we continue to compound that as an advantage instead of kind of like reaching to get there? Now that we're there, that part of it's like, how do you not lose that position? Because there's no telling when you're going to find your next anthropic.

It's like, well, what else are we doing to continue to build in the team, compound the reputation, show why entrepreneurs should work with us. And, you know, that's just kind of the mission every day. So I'd say like, I'm just saying it's a bit of a different challenge when you feel like you're climbing up the hill. I don't feel like we're at the top of the hill at all, but I feel like we've come such a long way.

And now it's more about like making sure that we really just continue to capitalize on where we've arrived. And that, that often starts, you know, in our business starts with just convincing entrepreneurs that you're the firm that they want to work with because you're, you're different, you know, you're, you're more fun to work with. You're better to work with you, you, you add more value, you know, you, uh, you, you ask the right questions.

You don't, you don't bother them. You know, you do the right thing and in hard times. And I think those are the kind of values that we want to take forward that will compound for a long period of time. So I'm interested, I'm asking this because you've been doing this for about 25 years. And there's definitely, if you look back, there's always these firms that kind of fall off.

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I mean, it's always happening. You guys displaced someone when you went on. Believe me, we've all seen it. Yeah, so... And I think you first got into this in 1999. Am I right? Yeah. So have you seen any common patterns of like what tends to lead to this kind of decay? Yeah. I mean, I think it's poor handling of generational transition. So how does that usually go, you know, in a bad way or a good way? Bad way, people hang around too long and younger talented partners leave.

So the guy who would have brought in the new founder, he starts his own firm, goes somewhere else, and you don't even see him. Right. You kind of lose your younger generation, or let's just say you don't have as much alignment in the firm. I mean, I'll keep saying this. I feel it sounds like a broken record, but some of the best decisions we've made is because we've had incredibly high alignment within the firm to do something that at the time felt like Why are we doing that? It's a little bit off-spec for us, but because we were so aligned, a high-trust organization.

And if you don't have a high-trust organization, if people are fractured, they're doing their own thing. When somebody comes in with an exception like that, it's like, that's not what we do. So you start creating false guardrails. A lot of things can start to break down like that when you don't have alignment with the firm. But I'm just giving you One example, I think a lack of generational transition can lead to that.

But frankly, a lot of those dynamics can happen in a firm even if it's not a generational issue. It's just people are practicing the business too much as individuals and you're not getting that collective strength that's making you better as a firm. I think also a massive one is what we already talked about. It's like missing a technology wave.

you know if you're if you were late to the ai game and you weren't a top firm like i don't know how you recover right and now if you're top firm you can recover because of your reputation but you're playing kind of catch up so i think those are all the big you know some of the many stumbling blocks that can happen within firms and um you know they're they're always out there kind of uh facing firms it's not it's not an easy job for for many reasons Well, I think, I mean, one hard one was sitting out of crypto or Web3.

Like, I had many people that told me, like, you got to get into Web3. Like, you're missing out. There's a lot of fast money made and a lot of money lost. Yeah. So then what do you think you have to do in order to have a culture of being able to do that? Like, how did you guys think through and how do you kind of define that? We have this culture that allows us to make these decisions that are maybe hard at the time? Yeah, I mean, I think some of it was just started a little bit serendipitously where we just did it like, hey, Matt just really felt passionate that we had to be in this company, so let's make an exception.

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And then you see some of the exceptions that you look back on, you're like, oh, well, we didn't have... know we didn't have ownership why why you know that that's a waste of time it's like well actually we didn't but we leveraged that into a follow-on investment i'll give you another example you know we started this thing called the anthology fund in partnership with anthropic after we led the round and it was like 100 million dollars for you know carved out for you know startups where we do between 100k and a million roughly Just to give us a broader aperture, because it was hard to tell at that time what was going to be big and what was not, like which application

areas, who knows, which areas of infrastructure. So it really gave us a much wider aperture on a much broader set of companies to see which ones started working. That strategy led to OpenRouter. Our first investment was $100K in OpenRouter, and you read about the... rumored $8 billion acquisition by Stripe that led to Whisper, which has been leading dictation.

Just the voice to text. Yeah. Multi-billion-dollar valuation, Goodfire, you know four or five really you know valuable companies came out of that strategy so i think it was kind of allowing you know creating just some flexibility to kind of open up the aperture around a new technology way of not be kind of narrow and thinking around old thoughts like a venture firm doesn't invest at this valuation if you don't have a certain ownership it's not going to move the needle i can go down the list and i think So many of those exceptions led to where we've been able to get to as a firm.

And now we've more institutionalized it where, you know, you say, hey, as part of the fund, you know, we've got like full slots and then call them like, you know, we call them kind of exception slots or kind of like stub investments where you kind of start with a smaller investment, hoping that it gets bigger later instead of being very religious or dogmatic about going in that we need to own a certain percent at that time.

Because sometimes, you know, frankly, Even if you're a great firm, you may lose. And let's say you lose and you're like, well, okay, but would I rather have something in to build a relationship or just say I lost and I'm not going to do anything because if I can't own 10, 15, 20%, I'm not doing it. So I think you have to really be thoughtful about being behind the best entrepreneurs, the best companies.

You can't go wrong in this business if you're behind the best entrepreneurs and the best companies. So find a way to do that. A lot of the discourse gets caught up on the math and the ownership, and it's right. Like, they're 100% right that you do want to own more versus less. Again, it's like...

do you want to own more of something that's not worth anything or own a little bit of something that's worth a lot? Yeah. I mean, obviously, you know the answer to that one. So I guess it's like finding the happy medium. Yeah. You can't do all exceptions. Then you're going to have a fund of just, you know, a scattered set of things where, you know, you don't have any board seats.

You're not building that reputation as that investor that people really want you on their board. And those are times where you get more ownership. And unless you've proven yourself to be worthy of that, you're not going to be able to lead the next round. So you have to have clear examples of how the firm operates in both kind of situations.

So super important. And I know one thing you were telling me that you've changed your mind a little bit on sort of the size of the outcome. So I don't know, 25 years ago, a billion dollar outcome was, that was like what Anthropic is today. Like it was like, that was the max. You don't get any bigger than that. How have you kind of evolved your thinking around how big companies can be and how fast they can get there? Yeah.

Well, I mean, it helps when you've got companies growing from 1 to 100, you know, a billion to 10 billion, all these kind of numbers. So that just tells you that something's different. You know, sometimes we have to, like, speculatively say, like, is there even a market here? How big it is? The great thing about this is there's enough existence proof here around...

how quickly some of these companies are growing, the outcomes we talked about earlier, like whether it's Cursor or OpenRouter. I mean, look, I've been in this business for over 25 years, I'd say that again too, and never have I invested in a company, not even close, where 14 months after investing, The company's being acquired for a rumored $8 billion or whatever.

That just doesn't happen. So times are different. And I was around in 99 when it all collapsed. And I can tell you this feels dramatically different given the scale and quality of the business models and who they're selling to. Sticky enterprise customers, applications that would be really hard to reach out, et cetera, et cetera. And yeah, some companies need to work a little bit on their gross margins, but that's a longer conversation about open weight models and all those kind of things.

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Yeah. Maybe we should talk about that. But was there anything in terms of the parallels between 99? Was there anything that was happening in 99 that was...

maybe um maybe it wasn't obvious at the time but obvious now that's not happening today well i think with 99 you had kind of a much narrower set of people doing new things that were just like making things like take off and explode but on a relatively low percentage of the population And so I think, you know, that's one of the reasons why things hit a wall, meaning what I mean by that.

So, you know, internet browser, how many people had great broadband, how many people had PCs, things like that. So you had like a, it was highly uncomfortable to use your credit card. So you had some small set of the population that had those things that could get over that could help these companies get off to a fast start. But then you didn't get the next 98% of the population.

So we kind of plateaued in a sense in a lot of ways. Yeah. It's not that it was a false positive, but I think if that next wave, if you could have more easily gotten to the next 98% or even the next 50%, then the scale of the businesses would have kind of caught up and helped them fix their gross margin and things like that. But it took too long to get to that.

And the beauty, as we said earlier about AI, is it's kind of immediately available to every consumer, every enterprise, you know, every everybody. And so it's just a totally different dynamic. Now, that said, I mean, I think it's more than that. I think like, you know, this wave has proven to have good durable business models, like I said, selling to enterprise customers.

There was a lot of ad swapping and, you know, just really poor business models back in 99. But if I really look back on why things hit a hit a wall yeah it was the combination of not having durable business models around too small of a population and not enough time to catch up to that at scale maybe interesting segue you mentioned the open model so the uh you know there's a discourse now it's like nobody's going to use these frontier models anymore you should use open source models for everything yeah I see people saying that.

It seems like that's not necessarily happening. Maybe it is a little bit, but where do you think that kind of settles out? Like, is everyone going to continue to use the state of the art frontier models? People are going to use open source? I mean, and then, you know, you have the Jeb wave and that's like, you know, an alternative to models.

So I think what basically is happening is as companies mature, is they've built, you know, everybody's built their harness of some kind and they're figuring out within their harness, like what the best model is to serve that application, that part of the application, that query, that agent. you know, et cetera, et cetera. And that just makes sense.

You know, at scale, when you get so big, you start to kind of turn the knobs. The first part is just like, do I have an application anybody cares about? Let me just make sure it's awesome. And I don't care what it costs because I just want it to be awesome. And then you're like, all right, now it's definitely awesome. But my gross margin is not great.

Right, so how do I kind of fix that part? And then so you go down there and then you kind of do a lot of testing and say, well, If I use frontier intelligence for 70% of the application, what's the outcome? If I use it for 20, what's the outcome? And so I think the narrative kind of overcorrected on the, hey, it's all about open weight models.

And now you kind of see people kind of walking that back to open weight models are great for some set of tasks, but... Frontier intelligence just really, really matters. And when you have that, it can make so many applications so much better. It's worth paying for it because it's like, what do consumers want? A great product. And if you give them a bad product, they're going to go look for a really good product from somebody.

So you can't compete by providing an inferior quality of product or inferior intelligence. You just have to figure out where it matters and where it doesn't. I think that all that turning the knobs underneath the harness is where there's a lot of expertise going on right now. What do I think it looks like? I don't know. I think it's probably over 50% the main foundation models, like Anthropic.

It's probably somewhere in the 20-30% for open weight models, depending upon your application. But there's other things happening too, like caching is becoming... a lot more predominant. So now the harnesses can catch a lot of the requests and feed it from cash versus from the model. So there's a bunch of other techniques that can bring costs down.

And then you've got something like Jev, and we'll see how big that can become. But, you know, it's just natural stuff. People are always optimizing their infrastructure. I mean, that's, you know, we're talking at the model layer, like there's a bunch of optimization going on underneath the models with things like a company that where my partner Tim's on the board of Gimlet, where you try to figure out what's the right underlying GPU to serve that request.

It's not even about which model, it's like which GPU, is it Nvidia, is it AMD, or is it a TPU that's best to serve that? Which stack? Sometimes it's based on availability, but oftentimes it's based on what's the per token cost for that type of query and that intelligence. You know, same thing that OpenRouter was helping people work on is how do you figure out the right model for your right query? So then where do you invest now today? Like, how do you, I know you talked a little bit about how you do smaller checks at seed and then you kind of deploy maybe a little bit later.

Are there certain areas you think that make a lot of sense? And or, I know you've mentioned before, you don't think Series A is a good spot. Maybe you can explain. No, I wouldn't say that. We're definitely doing Series A's for sure. I mean, I think what I meant is we've taken a little bit more of a barbell approach right now. This is never static.

Because I remember two years ago, we were doing more early growth, the 3 to 10, and then that segment kind of shrunk. And we basically said... On the growth side, then I'll come back to early. On the growth side, whenever you have a big technology wave like this, there are a set of early leaders that continue to compound and be the leaders down the road.

We talked earlier about Menlo's investment in Uber. So that was kind of one of the formative applications around mobile and Airbnb and Spotify and Square and things like that. And you roll the clocks forward and Those companies that were kind of that first wave that cracked a significant use case are the ones that have stood the test of time, AWS, et cetera.

And so what we've thought about was how do we identify those of this technology wave, which is why you see us in Lovable, Agora, Suno, Higgs Field,

OpenRouter, and Anthropic, obviously, and on and on. So it's kind of like, hey, let's make sure this is a unique point in time these winners are likely to compound in an outsized way for a long, long period of time. And we want to make sure we're in every one of them. So every one of the ones that we think has a great entrepreneur, etc. And so that's kind of one strategy.

And then the earlier strategy is really more like, let's go earlier, let's do more seed, not just the anthology fund, but real seeds, you know, ownership seeds, spend more time at places like Stanford, in the technology labs, things like that, where a lot of these researchers are coming out. And, and as an aside on that, like, you know, nobody's really spent a lot of time tracking down researchers 10, 15 years ago, because basically they're just generally not great entrepreneurs.

And so now they're the researchers kind of King is entrepreneur for our queen for a lot of these, uh, you know, for a lot of these companies. And it's been a great wave to see that whole pool of talent really, you know, lead this, this, this next wave of, um, you know, being, being entrepreneurs, but yeah. You know, what I'd say, the thing that we've done less of, there's kind of a messy middle right now between seed series A and later stage growth.

And why it's messy is because Companies can start so quickly now. There will be many of them. They're all gonna grow initially really quick. You might have a false positive around a company. There were tons of competitors going after lovable segment. And then lovable kind of pulled ahead, became that kind of household name in its category.

And you could have easily made a mistake and been not in lovable and in something else. And you can go through different categories where that's true. Or maybe there's a category, uh, like, like legal where there's Lagora and Harvey, and there's kind of two leaders and a bunch of other companies that don't matter, or just, you know, they're, they've taken all the oxygen out of the category.

So, you know, basically it's kind of like getting early and believe big in, in, in a market and, you know, hope you can build a company, hope you're right. Uh, but that messy middle where you're going to pay a very high valuation for a company that it's entirely unclear whether it's the leader or it will be the leader. It's just kind of a tricky spot right now.

And I'm not saying we won't do it. It's just that used to be kind of our sweet spot for growth. And now we've said, Hey, let's do more barbell. We want, early technology projects and later leaders. So what do you look for then when you're when it's flipping to, oh, they're a leader now? Like, is there something that or some things that typically are a tell for that? Yeah, I mean,

I mean, is it just the growth rate or is it like... I mean, certainly growth rate's part of it. I think there's kind of validation that there's a really significant market there. So how do you gauge that? Probably by their kind of customer diversity. They don't have some huge customer concentration that they've sold to the two labs or two Fortune 500 companies where they've gotten like two $10 million deals, but they've got like a long tail of significant revenue.

But, you know, certainly growth rate is a validator of market as well. But anyway, you just kind of feel like, hey, look, this product is clearly very, very different than the last wave of product that was serving this market or this market didn't have a product before. And like the time to close a deal is very short. Something's just going on there like it.

you know, to go and legal is a good one because legal was a terrible area to sell software. And then you kind of start selling something like Lagor and you're like, well, sales cycles are like ridiculously short. Deals are fast and usage rates are crazy high. You're like, well, you know, that's probably telling you, you know, you've got a big market, but I think even earlier on, you just start to see like, yeah, there's just some dynamics like, wow, like sales quotas per sales person is not like the normal, hey, it's 1.2 to 1.5 or 1.8 for an enterprise sales rep.

Like enterprise sales reps are closing like three to $5 million of business or this thing's just being pulled, more PLG. So you start to see things like that happen early on where you can extrapolate from that versus saying, well, yeah, we had to add one salesperson with a one million dollar quota to sell this and then we you know added three more just the physics of that are very slow and the kind of really outlier standout companies now are growing either through most often through plg but also through ultra efficient enterprise sales Yeah, I mean, I think Anthropic is, like, the

kind of king of that insanely surprises you how big the enterprise relationship is. Like, I think there was a number in the S1 that said, like, two customers were 25% of the revenue or something. Like, just an insane, like, you can charge a billion dollars to one customer. It's like, you used to only be able to maybe do that to the government.

Right. And now we've got, I mean... when i think of like what those customers are paying anthrop for probably a lot more customers out there they're going to be paying similar amount of money yeah i mean it just shows you the value of the technology and if that happens from you know some large company you're like well why isn't that happening for many more large companies probably just because they haven't tried it out enough they haven't enabled their organization to do it they haven't re-platformed all their existing applications.

And that's why, you know, just not specific to Anthropic, but just if you think about this market and how big these companies have become in such a short period of time, then you look and you're like, wait a minute, how penetrated is the technology in the Fortune 5000? Like, almost none. You know, like, okay, so they're coding some productivity.

How many of them have really replatformed their whole application stack? Nobody. You know, we're just at the very tip of the spear, you know, on kind of all the replatforming, all the new internal productivity apps that all these companies are going to build out, and just their comfort level and understanding of AI. So, like, the whole next leg of this wave is spectacular as well.

Yeah. It's always surprising to me when somebody says that, you know, AI is kind of like overrated or like they don't really get it. And like, oh, so what do you use? Oh, we use like Copilot or something like in Word. Like, well, that's why. Like, come on. Exactly. Yeah. And so I think they're sort of the anthropic revenue chart. You look at that and you say, okay, let's invest in the next Anthropic.

There's a bunch of these kind of neo-labs is what people call them. It's basically like, we're going to do exactly what OpenAI did, we're going to do what Anthropic did. I think there's something like 70, over 70 of them. I think there's like 77 was the last number, but it's probably... I think we're in 10, by the way. Oh, you are in 10? Yeah, we've been pretty active.

Okay, so why do that? Yeah. What's the thinking on investing in more of these? Yeah, so... I think there's kind of two strategies. One is there's a kind of alternative universe of where the model is super strong. So we did skilled for robotics, right? Just robotics is kind of how you train the model, what it does. It's dealing with things like gravity, et cetera.

They're just different physics, right? Chai for drug discovery. Not to say that, obviously, that's a big thrust for philanthropic, but Chai has their own models to develop antibodies and things like that. So just like alternative models that are complementary to the main foundation models. Axiom math, which is basically a math-oriented model.

Historically, foundation models haven't been... They've been language-oriented. or if it was on words. Or look at something like Suno. Mikey and team there have built their own audio models to make that experience magical. So for them, they built the model to enable the service. Same with Whisper. They built their own models There's a bunch of that going on.

There's companies that have built their own model to deliver consumer service, but then there's a bunch of other models that are just highly complementary to existing foundation models. We have this one called Inception that does diffusion for text, which makes it super fast for things like coding and things like that. It's an alternative.

approach. We've got another one that does continual learning, which is a different approach. So I think anything we're doing, we're not saying, hey, this is going to go right at Anthropic and take revenue from them. It's more like, where are the adjacent opportunities or use cases that can benefit from a complementary model? So that's how we're pursuing the strategy.

Now, 70 of these... and many of them raising a billion as an A, and then two or three billion as a B, that's clearly not going to end well. But right now, the desire for the technology and the talent, there's going to be some amazing outcomes, like Feifei's company. I have no idea what they had actually built, their revenue was but that's a spectacular outcome to be acquired for eight billion before i think you really have any you know revenue attraction so i think right now like those assets you know if uh you know if uh if frontier intelligence is the product you know that product is incredibly valuable right now if you as a company don't have it so i think we will see

some spectacular um acquisitions and outcomes and i think we're also going to see uh a

lot of big creators in the ground as well So I think a lot of people would wonder why do you have to raise so much money? So do you know what are people usually spending all that money on when they raise a billion dollars to start the company? For the model companies, it's much clearer. For the application companies, maybe it's a more nuanced answer.

But for the model companies, really training's expensive.

The caliber of people you have to hire to compete with

all the labs that want this talent, A, you have to pay people insane amounts of money, You have to either buy or rent a bunch of GPUs just for training before you can even start generating revenue. And before you start generating revenue, you have to take a long time to figure out like, well, where's my competitive advantage relative to the models that are dominating the market? And then how do I take this to market in a way that's consumable for entrepreneurs and for other companies who want to use it? I mean, one of the beauties of of Anthropic is the way they built out their harness earlier, very quickly moved away from it's just the model to it's all kinds of

surrounding surface areas like cloud code, cloud code for life sciences, co-work, et cetera, that just breadcrumb people into the experience. A model is like half the battle, it's table stakes. You have to have a great model, but then you have to figure out how to meet the market. I think that's a highly underestimated part of the equation here.

So we talked about the labs companies. Then it's the application companies. So why do application companies consume so much? Well, because growth is expensive. I mean, the reality is you may need a lot of FDEs. You still need a lot of salespeople. You need to support customers. So I think the capital raised by these high-growth companies is really more of a function of their growth than, hey, they're just spending a lot of money.

If they were growing at 100%, 150%, which historically would have been amazing, these companies would be wildly efficient. Also, we talked earlier about some of them needing to fix their gross margins. Some of that is a bigger capital burn, etc. I just wanted to cover both the sides. There's the model company burn, there's the application burn.

And I know you kind of think about it in sort of two phases. There's kind of phase one and phase two. What were those two phases? I think that phase one was really dominated by, hey, I just need a model. I'm going to use OpenAI or Cloud. And if I'm doing stuff in the enterprise, Cloud obviously became the default. And that was wave one. I just want to use this technology, figure out how to use it.

have some success with an application and great. Now I have something that's working, scaling, you know, perplexity, you know, Logora, OpenEvidence, all these companies that we talked about before. So that was phase one. You know, phase two is really now thinking about, okay, so now I've scaled up a company. I've proven that there are a number of, you know, consumer and enterprise hit use cases so now i really need to think about optimizing my infrastructure stacks that's probably the way i really think about the second phase is there's so much broader around you know not just open weight models but some of the alternative models that we talked about the whole sdlc

software development life cycle is being rewritten because suddenly like the great thing about all this code being written with cloud code is it creates a ginormous amount of new potential applications and potential functionality, but the SDLC becomes a big bottleneck because you have to figure out, okay, well, how do I merge all this? How do I test it? How do I get into production? How do I make sure it doesn't break anything? There's a whole set of surrounding tooling security.

How do I make sure that in light of all these potential vulnerabilities were at an all-time high in terms of risk. How do I make sure the code is secure? There's a whole set of surrounding products that are really taking off around the fact that so much more code is being written and applications are being rewritten. Then we already talked about some of all the you know, infrastructure stack optimization, which, you know, if you can get, if you can even shave a few cents off cost per token, it's a ginormous difference.

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And so there's a big race for companies who can help people do that better, like, like fireworks, where you kind of train and optimize your inference together. How do you think, um, Anthropic IPO, SpaceX just IPO-ed. I actually don't know when OpenAI has an IPO. Next year. Next year. How do you think that's going to change kind of the venture market? Any big difference or do you think it's just kind of going to keep cruising along? I mean, I don't know that we need a lot of changes right now.

Meaning, there's been enough really strong M&A to

validate that some of these valuations and companies we're backing are going to have great outcomes. I think the issue could be, if there wasn't a vibrant M&A environment right now, it's like, well, everybody's got all their chips back. on the table and there have been no exits, that's kind of scary. Should we slow down? And maybe we should slow down anyway.

But I think because there's been some pressure off the release valve of kind of acquisitions, capital back to LPs, returns, that's good. I do think that one of the benefits of the labs, the major labs going public, is just it'll turn some attention to the next wave of companies after that. Because for obvious reasons right now, I think people just are kind of all centered on those two top companies and you know understandably why they're the biggest and they're kind of the again kind of the heart and soul of what built this whole industry um but you know once they're public i think people start to look at like more and more like okay so what are the 10 companies after

that and right now uh attention's very focused uh on the on the two yeah now that you mentioned that i feel like i've heard less about spacex since it went public yeah which I didn't even notice until just reflecting on it the past couple seconds. Well, I think partially. I don't think you're going to hear any less about Anthropic. Okay, that's good.

I think that that's a function of that being kind of a multi-asset business. And so, you know,

are you talking every day about rocket launches, you know, in our echo chamber here in Silicon Valley? Absolutely. this ai wave it's everything about what's going on in ai world and i think you know xai um you know a lot of good technology there etc but you know they're not really in the headlines of competing for you know the core of this market like some other companies so i think it's you know i think it's brilliant what they did with colossus and the partnership for capacity with anthropic and things like that but i think that's probably why i don't think it's that oh, they went public and people don't care anymore.

It's just kind of like there was a big buildup. It's Elon, it's SpaceX, all this. But when you look at the thing that people are really, really talking about right now, it's the XAI piece. And that piece of the business just isn't quite as exciting as Anthropic, let's say. Yeah, that's fair. So you've been doing this 25 years. Is there anything that you've done differently recently versus earlier on? Like anything you've changed about how you kind of operate as an investor? Yeah, I mean, some of what we've talked about a lot was just being a little bit more flexible.

You're saying like you bend on the rules a little bit if it feels... Yeah, I kind of grew up like I'm kind of a rule follower and I grew up with rules and those principles and how you do this business. And I think that there's some really good things about that that you want to adhere to. And then there's others where you're like, yeah, times are a little bit different right now.

We need to make more exceptions and be more flexible and listen to my younger partners more and learn from them and not feel like I'm kind of all knowing. It goes back to our our team approach. Like really, I think like the energy that it brings by having younger partners or more technical partners or partners with more operating experience, it just adds so much to the way we make decisions.

It makes me better. Things like that. So yeah, I think it's partly just the way we're prosecuting this opportunity as a team. I think it's kind of bending the rules a bit when it makes sense. Not always. You can't live by exception. And then maybe I said this before, like more courage. you know dreaming the dream more you know i think earlier in your career certainly me you know you're kind of very worried about like oh you can't make a mistake and i think yeah you know the more it gets in your head that you can't make a mistake like um alex smith you know famous football player number one draft pick talks about this about how early in his career his coaches were just on

him all the time about don't make a mistake and it was only when he got Harbaugh as a coach, and he was like, you know, just go out there and don't worry about making mistakes. We've got the play for you. And if the play didn't go right, that's on us. It's not on you. And I think it took a while for me to kind of get out of that mode of worrying about making mistakes and really allowing myself to be bolder, think bigger.

And, you know, your partners can help lift you up on that. like we did with Anthropic. And then I can help lift other people up too. And that's been a really exciting chapter. Play more to win. Don't worry about losing. Win as a team. Accept the fact that this business is really hard and there's going to be a lot of losses and do that in a very classy, empathetic way.

Well, there's been a lot of fun. Thanks for coming on the show. Yeah. I really appreciate it too. Good to talk to you. Yeah. And good to have you listening as well. Thanks again to this episode's sponsors, Flex, Monaco, Numeral, and Amplitude. If you liked the conversation, share it with a friend who's still caught up in ownership targets or anyone who thought Anthropic was too expensive at $4 billion.

Make sure to check out the back catalog of over 150 episodes with investors like Gary Tan from YC, Naveen Chadha at Mayfield, and Chetan and Eric at Benchmark. If you want to miss future episodes, subscribe to my newsletter, The Split, linked in the description to get each episode plus the transcript emailed to your inbox every week. Thanks again for listening.

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