Benchmark's Everett Randle on house money, open-source AI risk, and why app companies building internal research teams is a mistake

cycles um that when they are finally like yes now is the time people are I don't know they're skeptical but we have Ev Randall in the waiting room let's bring him in to the TVP Ultradom Ev how you doing

hey gentlemen How are we doing?

Welcome to the show. Traveling.

We have Tyler Cosgrove

on our team, a guest host today.

Very excited to have you. Um, how are you doing? How is uh how's the year going? I'm interested in just like your general state of the markets. You recently said, I've you said it's an incredibly disorienting time to be investing. What's disorienting? You just put money in every company and they all go up.

That's that's certainly what it feels like. um feels like for the last 18 months, which is which is scary in in and of its own. Yeah, I did um I was on um you know my my partner Jack Alman's podcast Uncapped with with Trey and Delian over at Founders Fund and I I was saying that one of the scariest things of

today and then we can go to the disorienting part, but one just the scariest parts is this feeling of inevitability. like it kind of feels like sometimes often times it does feel like as venture investors we are monkeys throwing darts at a dart board.

Um and you know you never really know when what like what what what number you're going to hit. Um but imagine you're that monkey and you just keep hitting like the triple 20

like every time you throw it and you're like this is weird. Um and and you know your response to that usually is just like throwing a lot more darts. So the last 18 months, last 24 months, um everyone I think is is feeling um feeling uh you know very very confident in in the market or just like how how all their companies are going and all these companies are growing extremely fast

and so everyone is investing a ton of money. It's all getting marked up very very quickly. We obviously had the SpaceX IPO which was huge for several firms.

We're probably going to have the Open AI entropic IPOs that are going to be huge for a bunch of firms.

Yeah. Do you like there's just this sense that like we all know that AI is going to change the world in so many ways. We all know that you know um all these you know space is big, defense is big, nuclear energy is big, all these things are big and therefore um you know more and more money into these things and they just keep getting marked up. And the the last time I felt like investors had this much of a sense of inevitability like yes it's expensive but like it's going to get marked up in six months so therefore we should do it. um was the summer and fall of 2021. Um and we we all kind of know how that that ended up.

Yeah. I want to talk about what's similar to 2021, what's different. Uh but first uh there was something I don't remember exactly who said it on that podcast that you did with Jack, Dell, and Trey. that uh there was this concept of when a firm has one of these huge power law wins like a SpaceX and and an an anderal and anthropic and open AI uh then the liquidity is coming and they're the the phrase that was used on the podcast was playing with house money. Does that mean like literally recycling or just your LPs are more excited to back you write bigger checks like the purse strings are looser? Like what does playing with house money mean? How does it feel? What are the risks? Yeah, but a useful analogy here is maybe like in sports. So like let's say you are, you know, um up to bat in baseball in the World Series and you hit a grand slam

and then the next the next bat you hit like another grand slam. Um and so you're like, "Okay, I've hit two grand slams in a row. Basically, whatever else I do for this game,

like I did my job. Like I'm good. Like I'm great." And um counterintuitively honestly that might make you ba better because you're just swinging free. You're swinging away. You're you're taking a lot of risk. You're like screw it. I've I've already done my job. I've already um you know you know contributed to my team and scored a bunch of um a bunch of runs for my team. So like I'm just going to swing away. I think that very much is the case um around the industry where um if you have a lot of exposure especially in Enthropic but also in OpenAI and a few other companies um a lot of these firms have sprinkled their exposure across several different funds. So, it's not like you invested in Enthropic in one fund. Like, I know funds that have Enthropic in like eight of their funds because they're like, "This is like this is the this is the the golden goose, the the MASA Golden Goose, uh, you know, thank all the golden eggs, so we're going to put it in every fund." And so then now they're like, "Look, all of our funds look great. Um, they're going to look even better when Anthropic IPOs. So, like, let's take some risk. Let's like, you know, let's swing away." And honestly, it might it might do them well. But that's that's the whole concept of playing with house money is like Anthropic's going to boy so many fund returns across their portfolio of funds that you might you know you're like look like we don't need to play super conservatively from here on out

and and that feels like something that is uniquely different than 2021. 2021 felt like much more of a broadbased bubble in the sense that there there were power law winners at the time, but you didn't have the same effect of if you've spread this one company across all of your maybe I'm just not remembering, but I don't remember it that way. I remember it much more like there are there are so many SAS companies that are going to go from a hundred million ARR to two billion, one billion that we can underwrite them all even though they are competing. And then there's this AI wave that's coming. But what do you remember about 2020 2021 that was similar or different? It feels like it was a little bit more driven by spreadsheets and actual growth math as opposed to a major technological shift, but what was that era like for you?

Yeah, it's funny. Yeah, you you still had like obviously you didn't you didn't have the extent of IPOs of like a SpaceX and opening ironic. Um but you know you had like Door Dash, you had um you know Airbnb um you had Palunteer um so you had like you you did have some liquidity you know New Bank um a few others so you had like there was a lot of money that was made but it wasn't it you're right that it wasn't to the extent of anthropic where you're like oh we had New Bank so therefore like we can like go to the beach and just like take a bunch of risk

and Palunteer in particular was I mean what 10 billion IPO and not really sprinkled across many funds very concentrated and sort of a black sheep of venture for a long time. I don't know if that's the right term, but um

yeah, yeah, you're you're totally right. Like the actual IPO like it, you know, one, it didn't do well. Um I think the share price was like stuck at like $8 for for like a year or two or three years. Um it took a while for it to take off. So you didn't you didn't have nearly as much money. It's it's funny. I actually went back I remember in in um I think in in like 2024 20 Yeah, 24. So, a couple years ago, I was like, I want to go back and like read like what were we doing? Like were we like were we all high? Like what like what was going on? Like were we like were we just drunk?

Yeah. There was this guy who wrote this whole piece about like aggressive crossover funds coming in. That was a crazy moment.

That was a crazy moment. But I was like, okay, like this is like what like what was go like if I was to go back and read investment memos.

Sure.

Like and put myself in my mind then um and not me but just like the industry's mind like was this all rational? And I think that the really tough part about 2020 and 2021 was that um from like the last two decades before then all of these trends that we were investing behind were very secular. So if you like look up the e-commerce penetration rate as a percentage of total commerce in the US, it's like the most straight linear line you've ever seen. But then 2020 happens, we all get locked up and there's like this insane acceleration. And so tech investors were always, you know, taught and and trained on the fact that like growth kind of goes one way. Like there's it's not cyclical. It's not like up and down.

And so you look back at a lot of these investment memos and a lot of what people were thinking. And the companies were doing unbelievably well. Like all these SAS companies were growing like you know 200 300%. Um they had really good fundamentals. The cohorts looked good. Like customers were expanding. They were staying. um like the the all the fundamentals were really really good and then of course you had an overlay on top of that which was like the public markets were pricing SAS companies at like 40 to 50 time sales. Um that's the craziest thing to me. It's like you look back at at like you know the Snowflake IPO.

Yeah.

And you're like I I was I was reading the S1 and I was like the thing was at like less than 500 of revenue went out to like $80 billion.

Yeah. And I was like, "Okay, there's some crazy stuff that's happening right now, but we like we have not seen anything anything like the tops of the SAS 2021 bubble when like Snowflake was going out at like 120 times revenue on the public markets.

How much compression that's happened is venture capitalists actually learning their lesson, the market learning their lesson or just purely interest rate effects?" I

I think it's actually just um the the compression in multiples that we see now is people are just more scared of the business models. Like obviously you know everyone's saying like oh SAS is basically dead like terminal value concerns all those things. Um but when you think about all the most popular business models and the most popular businesses that are getting funded right now like there's no there's no precedence on public markets of like an AI app company. Like we don't know what like an AI app company will trade at.

We don't know if it'll trade much better than SAS, moderately better than SAS or the same as SAS. We we don't really know how like a space company besides you know with the exception of SpaceX which is like an exception not the rule we don't know how like a space company we will trade we don't know how um you know wait with that one what about like as rocket lab like there's a couple public comps in like the pure play space area now

that's true I I um and less on launch I more mean like you know satellite companies or or like

and yes you could say the same thing about like yeah with Android there's obviously a lot of a lot of primes but God, I hope it doesn't trade like that, you know? So,

yeah. Yeah. Much more like that.

That's the thing where it's like a lot of the things that people have been investing in either don't have precedence. Sure.

They have something about them like lower gross margins or capital intensity that's scary.

Um or the incumbents trade really poorly. And so you're like, well, like, you know, this is kind of like when a firm went out. Um

I think in 2020, no one knew if it would trade like a bank or like a payments company. Yeah. and it like sort of traded as a hybrid of the two, but no one really knew how to like um how how to how to um think about the company for a few quarters until it's public. So,

I think lower multiples mostly because people um are just discounting because they don't really know how how these things are going to trade.

Sure. Uh, I want to talk about closed source, open source, uh, just token maxing and the economics of AI right now, but I want to go back to your January 31st, 2024 post. You said making a real effort to not take for granted the quote $3 Uber around Uber across town era of AI. And I hope you are too. That feels remarkably preient, extremely true. I didn't realize I think the last time we talked about it was around just the models getting expensive around reasoning and the gross margins changing, but now it feels like this is like directly targeted at CFOs of like large companies who are showing up with I mean we have Eric Lyman coming on the show. uh they had one their AI spent spend hit 1.5 million in a single week and so yeah that that we are out of that era but as you as you reflect on that was that what you were predicting how are you how are you seeing opportunities across new companies uh in the era of like cost control and ROI maxing how are you processing that idea of like the end of the Uber era because in many ways there's still a knockout dragout fight between codeex and cloud code and they're resetting limits every 12 hours, 6 hours. They're fighting it out. Like we're still sort of in the capital fight, but at the same time, we're also in the some some enterprises are really uh controlling cost now.

Yeah, there's God, there's so much to this topic and there's so many different things we could talk about. So, I'll try to hit the the best portions of it. Yeah, the tweet the tweet I think there was like a consumer part of the tweet and then an enterprise part of the tweet and like the consumer part of the tweet was like there's no ads,

you know, the the LLMs don't want anything of us. They're still super raw. It's just this like silly little service that like clearly is going to need to mature um into um an adult, you know, cash flowing product one day, but right now it's not. Or like you could also compare it to like the old days of Instagram, like when Instagram didn't want anything from you. Um it was amazing. It was like the best app ever and now it wants money from you in the form of like you uh you know being an ad unit and so all you see is short form video and it's like you know rotting you know and like brain rot um and TVP videos. So those are the good ones. Those are those are the the ones that say and then on the enterprise side there was all this um you know that there was um reports at sometimes where it was like man cursors it's really hard to compete against something like cloud code because they would compare the basically amount of cloud usage you could get through a subscription plan with with with enthropic versus API via um cursor and you'd get like 20x the usage with enthropic like you see insane subsidization on enthropic and I think both just the incremental adoption of AI by enterprises and that starting to like move down a bit. I still think there's like a fair amount of subsidization. Um but it's like starting to go away a little by little as as these um as just like the token hunginess of the models gets much higher like now that we've done like you know we've moved from like again just chatbt to longunning agents um that are like way token hungrier because they're reasoning models um and all and like that use sub agents and all these things. Yeah, the costs have just like absolutely ballooned. Um, and and so I like on one hand it's it's like very clear that we've like exited the $3 Uber stage. On the other hand, you know, there's folks like Dylan Patel um at semi analysis and there's some of these like really forward-leaning companies that are like not only are token costs reaching our human labor costs, but like we hope they go over. Yeah. you know, and like we hope all of our competitors use open source models and like we hope all of our competitors use, you know, um like dumb models because our employees will be using the frontier intelligence models and like that's our competitive advantage. And so I don't think there's like there's there's a lack of clarity um around all of this that I think is again part of the disorienting thing about even investing in AI. I will say we're seeing an immense amount of app companies. I think if an app company like app companies are either already at 80% plus um open- source usage for like their own apps that they're that they're serving to to their customers or like 90% of them are trying to get to that ratio plus. Um so across our portfolio and beyond a lot of people are working with folks like Fireworks Y um to you know custom train and fine-tune um these increasingly good open source models um because and like Jesse at Decagon actually had a really good post on this where he talked about just the fact that like for mature use cases that can be that are wellknown and can be fine-tuned on you just don't need frontier intelligence anymore and oftent times it's best for these app companies to fine-tune open source models around those specific use cases. Yeah. Are are are you finding with the application layer companies internally they're adopting the semi- analysis approach, they're using the frontier to build their tools, but then if they're vending out tokens, they want those to be very efficient because that scales with their user base, not with their employee count.

Yes, I think that that is probably completely correct. I think it's like a extremely good take. Yeah. Where it's like Yeah. like for and and but the only reason

I think of that is because what are they using it internally for,

you know, like like they're using it for coding

and like they're using it for and like these are these are high velocity startups where like the number one thing that matters is high quality shipping velocity of software. And so, um, if they can get a competitive different, uh, if they can be differentiated competitively by shipping faster by using Fable

and just like spamming, you know, Fable the whole time, they're going to do that.

Um, but then the thing that they're selling their customers is is a very different use case. So, if it's like a support, you know, agent totally. Um, you can use open source for that, but like the frontier intelligence on coding is still you're seeing a lot of incremental gains for that.

Yeah. Yeah. The frontier labs are the spikiest on coding. um like that that thinking machines example from Bridgewater felt like a unique spike in in research and news analysis that might not show up at a frontier lab because it's maybe a smaller market. Uh but uh but thingy machines able to like bring that to bear, fine-tune something that outperforms everything at a lower cost. Um what what else are you sort of retreating to in the age of the application layer? Are you more likely to look at two-sided marketplaces, network effects, uh something with like the other sources of power from zero to one? Um if if just like big pile of code is not defensible in the long term like it was maybe a decade ago. Um yeah, what are you what are you actually looking for? And then are you actually seeing entrepreneurs acknowledge that and then go and build that?

Yeah. Yeah. I mean I I will say like seeing a nice atscale marketplace with clear network effects.

It's like a it's like a drink of water in the desert. just like oh my god you know like I don't have to worry about AI labs or you know business model quality or any of these things and so it is a breath of fresh air because um I feel like you know those are always in style you know um these like super seven powers mod businesses that somehow avoid AI exposure because maybe they're they're consumer marketplaces or something.

Um so so I absolutely think that's the case. I'm also I I I I've always been relatively less um or like not really in the bearish camp around lab risk for a lot of these um app companies. Um you know one one like one one like silly example could be like you know some people are like oh like you know clog for legal is coming out like anthropics going into legal like uh oh legal AI space

and my response has always been like look like anthropic in like 3 months of 2026 probably added the amount of like near like even medium or long-term TAM that exists in AI legal which is still like a ton of revenue like it's still an immense amount of revenue But like you're telling me that they're going to put like their eight like their SWAT team

Yeah.

to like grind out what is a top down sale market over like seven years

to maybe add some portion of the AR that they added like in Q1.

Yeah.

Like what? Like it just doesn't like it just doesn't make any sense at all in terms of like the highest and best use of like the labs time. Yeah. Yeah.

And so I think all of these markets like um people need to think about like well what like what are the theoretical competitive risks and what are like the practical competitive risks and like throughout SAS I'm sure like if you know um you know Microsoft at one point was like we're going to win this extremely niche market for X at any given point and it became like the number one project for the business um then they could go do that but they didn't do that because they had like Microsoft Office you know doing tens of billions of AR and like their security business doing tens of billions of ARR. And so it's all these things around um prioritization that I think are that that I think um people don't think about. And so I'm not that bearish on on the app layer stuff. And then the last thing I'll say is that the the other really interesting thing that I'm seeing is that there are all these like short-term things that I totally get why companies are doing them.

Um and I think they they like kind of have to do them that in the long term we're going to look back and be like this clearly made no sense to do long term. The biggest one um that that I see is like every app or not every app company but like a lot of these app companies now feel like they need to have like a labs team where it's like every single app company um you know they'll hire like you know a few researchers for meta or something and then all of a sudden it's like well we're you know we can defend ourselves from the labs

because like we also have a research team and like we're doing like we're doing like AI research and it's something that I think we'll look back at and like in some cases it will have been valuable but in many cases I think it's just like a way for founders to be like, "Oh, no, look, like we also have researchers. Like, we don't have risk from the labs. Like, we're doing our own research." And I just think that like there's not there's not that many opportunities for like an in-house research team to be doing that much groundbreaking work um you know, relative to like what what the actual best researchers are doing um with within all the places that have the most GPUs, which is the frontier labs.

Yeah, it's interesting because labs can mean a few things. It could mean AI research or it could mean experiments. And if you're an eBay,

this isn't Ramp Labs, by the way, because Ramp Labs is is doing like very different case, but um but uh there's there's a world where you're eBay and you realize that like the labs aren't really going to steamroll you because you have this like liquidity and this market and this network effect. Um but you know, just figuring out the right way to integrate AI and maybe it's not just stuffing a chatbot in the corner. Maybe it's something a little bit more polished and having a team that can go out and look at the full product surface area without needing to be like the top down AI mandate of like every feature needs to be AI enabled is probably the wrong pattern. but letting a team go around and say, "Well, yeah, actually, we don't need to add AI to the checkout flow because we want addresses to be deterministically verified." But in terms of descriptions, if somebody's asking about this product, throwing an AI summary there might make sense and we're going to use an open source model for that because it's going to run on, you know, millions and millions of of product descriptions or something like that. Uh Tyler, I want to give you a chance to ask a question if you have anything.

Yeah. Yeah. I was I was curious. Um, how worried are you about like the massive dependence that open source has on on China? There was this article I think a week ago and it was something to the extent of like Beijing is looking at uh curbing overseas access to Chinese top AI models. Um,

almost all of the the western open source models seem to be like quite reliant on the Chinese models which in some sense seem to be reliant on on American source. Maybe it's just a circle, but it seems like that's like if a lot of these app layers are are just training their own models. They're doing fine tunings on open source models if

China if those go away or

they stop accelerating because China says no.

Yeah.

Yeah. Yeah. 100%. Yeah. It's it's it's funny. I um I on a on a podcast recently um I I just for some reason can't prevent myself from saying spicy takes that get people mad at me. And uh I I we were talking about open source models. Um I think it was on Harry Stevings pod. Uh and I was like where like where are the good western models? Like why like where like all like and some and you know I'm like we don't have any good open source models. Then everyone was like we have so many good open source models. This company and this company and this company is like good. And then I I I didn't because I restrained myself. I just wanted to tweet back the open router token rankings. Yeah. I'm like then where are they? if they're so good, why don't people use them? Yeah. Why do like look at the top 10 on Open Router? Like all of them are Chinese. All of them. Um and so I I think that um I think that the but like a good push back to to what I said on that podcast was like, well, they're coming. And I've always been like, well, where are they? But now we actually are starting to see some American teams like actually really go hardcore with the opportunity. And so like one, Nvidia obviously cares deeply about there being a strong open source ecosystem. have done more than any single company um across the entire stack to fund and support and like bring into existence an an awesome open source ecosystem. And so I think we all owe Nvidia and Jensen a debt of gratitude for pushing so hard for a healthy open source ecosystem. So they have Neotron now. Um Neimotron by by all appearances is like a great start and I I know a lot of people in our portfolio that are actually quite bullish on like the future of Neotron. Um, Thinky just Thinking Machines Labs just came out with Inkling. Um, which again a lot of people are very excited about. Um, they don't claim that it's, you know, at the frontier even of Chinese open source yet. Um, but again it's it's extremely customizable which people love. Enterprises and app companies love. Um, and again it's it's a commit, you know, seemingly a bit of a commitment to continue to develop open source models. Reflection AI has always been, you know, that they they now have have their strategy is they I don't think they've released one yet, but like their strategy is clearly to, you know, have the American open source model. And so I think relative to even three or four months ago, we there there's more that you can point to um around like okay like the west is actually trying to do these things in terms of like the nested contingencies of like who's distilling who and like where are all the model capabilities coming from. I think like I think the whole distilling thing is like I think it's oversimplified. Like I think it's like a little too simplistic. I I think it's also like a little like maybe there's some like xenophobia in there where it's like um in the public where it's like well the only way that the Chinese models are good is if they're just like distilling and they're doing nothing else. Like they clearly cook some stuff up.

Like they clearly are doing some really good creative things and like the deepse paper was like truly groundbreaking. like it was awesome

and you can see it because like open source architectures get ported back into foundation models and closed source labs all the time. Uh and and also you just see that there's a ton of successful AI researchers who come from China and stuff but yeah I think that's all a good point. Um yeah I have I have another question related to the application layer. Uh uh I'm interested like venture's been through these like sort of experiments uh whether it was like biotech for a little bit uh DTOC e-commerce was sort of enabled by Facebook and then eventually uh DTOC didn't die it just sort of returned to the the uh the better fit was like CPG private equity firms know how to do it. They still do it. there's still some amazing outcomes usually in like the 500 million to one billion category. You're not seeing trillion dollar CPG companies uh anytime soon. But uh is there are there any areas in the application layer that you're seeing oh this category is now potentially investable as a venture opportunity because of AI? Uh, I'm thinking like game studios or something else or is there a or is there a pocket of previously venturebackable companies that maybe should be moved more over into hey just bootstrap that get it to scale do some private equity secondary run cash flow positive more of like a lifestyle business.

Yeah. Yeah. I think I think there's a lot of both. I mean even like if you think about it even even in some of the more obvious verticals where we've seen early AI winners like even in legal right like legal

before AI was not seen as like a venture category there was like

Atrium and Clear Spire two like really solid runs at that the tech enabled law firm and you know rough goes on both on both accounts and now it's like oh the money's flowing the business looks like a normal tech company yeah there's still like margins and whatnot they got to pay for tokens but like In general, it looked a lot more like a tech company than a law firm.

For sure. And and even the SAS companies that sell sold into law, it was always just like, oh, super constrained TAM. It's a slog. You can't get law firms to pay a lot.

And now, you know, Lor and Harvey have just like absolutely eye watering numbers um that that um like you know, for the last three years now. Um and clearly are among like the very

And Harvey just acquired Benchmark, right?

Just kidding.

That was a jump scare. I was like jump scare.

I was like, we did what?

Wait, what? Yeah, that was that was quite

probably a good strategic move, but also just a a hilarious troll in the timeline on the PE like on the on the P on the um on the other side. Um there there's like obviously we're we're in raor and they've become this like huge awesome platform for the task economy and kind of like you know finding the data that actually now moves these models forward. There's so many if you think about like at the limit if the limit to like getting AI agents to be able to do everything is to like find all the data in the world and like feed it to them in a really high quality way. There's so many people I know that are finding like going out in the world and finding like extremely niche or just like data that no one would think of and then like you know making it you know like high quality servable to um to labs or anyone that wants to buy that data. So like there's one for example that's like basically instrumenting a medical clinic. So like every conversation is recorded. Every you know thing that they're doing is video recorded. They're like you know almost doing like a yeah like like you know putting telemetry throughout every portion of a medical clinic and then like making that a data set. And so I feel like like again like is that venture scalable trillion dollar? Probably not doing it if you're like just like the medical clinic data company. Yeah.

Um, but I think there's going to be a lot of like entrepreneurial people that make a lot of money just bootstrapping these things and building them to, you know, 50 to$100 million revenue businesses for like 8 to 10 years. And like that's that's sort of all you need to do. You can kind of like entrepreneurship broadly. Like it's just it's just an exciting time to be building. Uh, well, thank you so much for taking the time to come chat with us. Have a great rest of your day. Have a great weekend and we'll talk to you soon.

Thanks, guys.

Goodbye.

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