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- [00:00]
Ev
We used to have golden rules or, like, North Star metrics in investing. When you think about software, there was, like, sort of five or six things that mattered, and it was very legible, and it was very spreadsheetable. And now all of the golden rules of the past that defined, like, the spreadsheet investing era are all gone. In the new AI paradigm, you can have businesses that are well over a billion dollars in revenue that haven't proven out their unit economics. They haven't proven out, you know, durable product differentiation. You know, we have developers that are spending $3,000 per month themselves, like, each on Claude Co. So it's like, wow, okay, so that's $36,000 per developer. When you think about the anthropic $380 billion round if anthropic was to go public and get liquid at a $1.5 trillion valuation, I just don't know if people really understand and are ready for the impact that this much liquidity could have on the ecosystem.
- [00:59]
Molly
Ev, welcome to Sorcery.
- [01:01]
Ev
Thank you, Molly. It's great to be here.
- [01:02]
Molly
I'm so happy. This is, like, the first interview I've ever done with someone from Benchmark. You're one of the newer partners. I did an interview with Jack before he joined, but now we have you.
- [01:14]
Ev
That's great. I'm excited to be the first.
- [01:15]
Molly
So what's going on? I know you guys are just coming off the heels of your agm, you had a mass, kind of collective reflection on the market today. How are the vibes?
- [01:24]
Ev
Yeah, vibes. I mean, the vibes within Benchmark are definitely high. I think, as a market, I think the whole venture growth ecosystem is going through this sort of phase of disorientation. Like, everyone sort of feels like, down is up, up is down. And I think that we were trying to, as we have kind of our annual meeting with all of our partners. Oftentimes that's like when we try to introspect and figure out how we've been feeling about the market and things we've been looking at. And the one visualization or like, the one framework that I came up with that I think explains a lot of it, is that in the previous paradigm, especially in software, you had almost like this inverse relationship between scale and risk or like, you know, scale and risk of, like, mega Impairment or like, a company going out of business. And that's because, you know, historically, as you're scaling as a startup, you're sequentially de risking different parts of your business. And so the first thing you de Risk typically is like, can you get product market fit? Like, do people want to buy your product? And then you de risk unit economics, like, are you selling a product for what will end up being unit economic positive over time? And then you de risk things like the tam of your market. Are you able to grow not to just, you know, 10 million of revenue, but to 100, maybe multiple billions of revenue and then eventually market leadership. And if you don't de risk those things while you're on the journey, you stop scaling. Like, you just stop growing. And so there's just like this nice clean relationship between those two variables where if you're continuing to scale, it usually means you're becoming a less and less risky company. And I think that the thing that's changed massively in the new AI paradigm is that you can have businesses that are well over a billion dollars in revenue that haven't proven out their unit economics. They haven't proven out durable product differentiation. In many ways it feels like the risk of impairment or a company either going to zero is the worst case scenario. But even just being devalued from its last round in a significant way, it feels like the risk of that over time is actually sort of flat or even like, maybe there's like a weird positive correlated relationship with scale and risk. And it's a very disorienting thing because you're just very used to the fact that the bigger you are, kind of the safer the company is and it doesn't feel like that anymore. And there's a lot of reasons for that that we can go into. But I think that's, that's the reason why folks in the, in the market and investors and our peers and even us feel, feel like something is like the risk reward paradigm is very different these days.
- [04:06]
Molly
How do you evaluate that as an investor? We were kind of talking about this beforehand, but you say this is the death of spreadsheet investing. So is that kind of what you mean? Is it categorical or what's the premise there?
- [04:18]
Ev
Yeah, you know, we used to have all of these like golden rules or like North Star metrics in investing. And there's some exceptions to this in again, like, you know, hard tech's always been different or even some of the capital intensive consumer Internet businesses of the past. But when you think about software, there was like sort of five or six things that mattered. It was like, you know, high gross margins are better than low gross margins. So, you know, at least 70%, 80% is good, 90% is amazing. You want to Be selling typically pure software, which means you don't have a ton of services load and implementation load on your software because that limits your ability to scale and it also brings down your gross margins. Typically these businesses were capital light, there wasn't a lot of capex and you had a lot of operating leverage on your R and D spend. Like R and D was relatively efficient. Over time you had a really high gross retention, which means that your customers, once they implemented your product, typically stayed around. And a lot of these software companies had 90% plus gross retention rates, which means that over a year long period, if you had 100 customers, less than 10 of them would leave a year. So there's a lot of things like that that basically when you took all of them, the output you got was a capitalite business that essentially at maturity would spit out really high rates of free cash flow in a way that secularly grew at multiples of gdp. So it's sort of a mouthful. But you can think about the whole reason why, you know, software companies traded at these high revenue or high multiples of NTM revenue. And this is really important for an asset class whose foundation is paying really, really high multiples of ARR. Like, the whole reason why we're sort of allowed to do that or have been allowed to do that is because eventually the companies, you know, are extremely profitable. They're very durable. Like those profits are very durable and the profits grow at a multiple of GDP for a very, very long time. So that was sort of the setup of software and it was very legible and it was very spreadsheetable. Like all those things I said fit really, really nicely in a spreadsheet. And there was metrics like rule of 40, which was your growth rate plus your free cash flow margin that were like sometimes you could literally abstract the quality of a software company into a single metric. And some investors would invest just on like a rule of 40 score. And many times. And now if you think about all the most popular companies, all the most popular categories, they're sort of like the inverse of every single one of those golden rules that I just said. It's like, you know, there was a tweet that said like FD is the new plg. Like it's the most popular distribution strategy.
- [07:04]
Molly
And thank you to Palantir.
- [07:05]
Ev
That's right, yeah, the, yeah, yeah, the, the Palantir ification of everything means that now you have all these, these folks that are sort of like sparkling implementation consultants that used to be like, they used to be bad. Like you weren't supposed to have that you were supposed to have selling pure software, but that draws down gross margins and then now gross margins. If your gross margins are high, that's actually a bad thing because AI inference costs a lot of money. And if you have an AI product with high gross margins, that means that no one's using your AI features. And so, you know, it's like there's this bizarro world where and then, you know, now like another popular thing right now is like, well, if you're going to be safe from the labs you need to have, you need to be training your own models and you need to or at least be post training on the data that you're getting from your users. And so now it's like, okay, now you want people to set up their own research labs within a software app company in order to train their own models. That's unbelievably capital, you know, Capex intensive or capital intensive relative to the old era of software where you didn't have any capex or any kind of capital outlays on GPUs or anything like that. And so it's like all these bizarro things where it's like all of the golden rules of the past that defined like the spreadsheet investing era are all gone. And actually the most popular companies and categories are almost the inverse of all these golden rules. And I think the reason why that's so confusing for people is, is because these golden rules in a vacuum, they are the first principles building blocks of a high quality company. Because at the end of the day you need to have a company that's producing a lot of free cash flow for a very long period of time in order for it to be valued highly. And when the most popular companies, you kind of look at them and you say, well, from a first principles basis they just seem less attractive than SaaS. You have to kind of rethink a lot of how you invest.
- [08:58]
Molly
I have so many secondary questions to this. The first one is who do you think has the best instinct on managing these AI scale and economics in the companies? Is it the founders? Are you finding it like within different firms or other kinds of researchers or economists is new to everyone. How are you making sense of it and who do you think has the best handle?
- [09:22]
Ev
It's a great question. It's really interesting because these companies are also extremely different from each other. Like if you take a company in our portfolio like Fireworks, which we think is becoming sort of the AI inference cloud, and you compare it to Another incredible company that ostensibly is in the same category like a Crusoe. They're actually completely different companies with completely different business models, with completely different economic models. What I mean by that is that Crusoe is actually going and constructing data centers. They're going and acquiring power, land permits, and they're building data centers oftentimes for, for hyperscaler counterparties who become their customers. And then sometimes they, you know, they also have their own cloud product where they can sell to, you know, inference to startups or whatever, whereas Fireworks does not have their own data centers. Like, they're actually leasing, you know, inference capacity and GPUs from partners themselves. And then the thing that they're getting paid for is both the inference, but then also what their software is doing on top of the inference to make it more cost effective and, you know, reduce latency and just like make it a better inference product altogether. And so from the outside in, you could say, like, oh, it's just like two inference companies, but they have completely different business models, they completely different capital intensities, they have completely different margin profiles. Like, they're actually more different than they're alike. And I started my career at actually a software PE firm called Vista Equity Partners. And the CEO Robert Smith would always tell us software tastes like chicken. And that's why it's beautiful. Every software company, when you look, you know, under, you know, at the, at the balance sheet, at the P and L, they're all the same, like, they all have the same line items. They all essentially have the same, like the PNLs look the same at maturity. They're more similar than they're like, even if they're selling the completely different markets or they're completely different products. And like, that is also not case. Now, like, there's so many different business models that exist in AI. If you're like an AI app creator, if you're a foundation model lab, if you're an inference platform, if you're a data center construction company, they're just all over the place. And so it's sort of hard to tell like the answer to that would actually be on a case by case basis, depending on the category and the company. But I think that there's a definitive advantage to investors and founders that have gone through the thinking of like, what is the new taxonomy for an AI company? Because, like, what it comes down to in my mind is this idea of like P times Q times M. You know, this is like econ 101. What's price times quantity times Margin. And you know in SaaS land you had like price was your ACV, like what is the annual contract value of, of your contracts that you sell to customers? Your queue is like how many customers either do you have or that are in your tam? And and then your M was again gross margin which was 70 to 90%. Well now in AI, if we take an AI app company, the Q is probably the same like you're selling to the same people that would buy SaaS. The M is almost definitively lower for I think 99% of AI app companies. It's lower than 70% but the P can be immensely high. You have these inference platforms that have nine figure contracts with, with startups there's very rare SaaS companies that have nine figure contracts with anyone, much less a startup. And now you have a lot of these AI companies that have just these unbelievably large, these unbelievably large contracts. And I think that understanding that taxonomy and understanding what it means to the evolution in company quality and how these things will look when they mature I think is a very important dynamic that's still being figured out by everyone on the field.
- [12:57]
Molly
So are you saying it's disorienting for making net new investments or also understanding current portfolio companies and how they're growing and scaling in different ways marginally and like business model wise?
- [13:11]
Ev
Definitely both. I think thankfully again at Benchmark, the main thing we do well is partner with entrepreneurs extremely early and by doing that you don't have to worry quite as much about a lot of these questions because again a lot of these questions have to do with like, well, what is the multiple that the company is going to be worth when it, you know, IPOs or is sold to a company, you know, how are they going to use capital effectively at scale? But when you're backing an entrepreneur at inception at 50 post, you, you like if you're having to answer those questions, you sort of already done your job. You know, like the company has already gotten to a scale and like a level of, yeah, just a level of raw scale and maturity that you're probably looking pretty good. And I think that's the case for a lot of the companies in our portfolio that are relevant for this conversation. But I think as they mature and as we just think about, well, what categories now going forward are there going to be really big profit pools to go after? It is something that we care about, but I think the benefit that we have is that like great founders are always in style, whereas like these business models can go in and out of style and things become popular and less popular. You know, you weren't supposed to touch hard tech. Now hard tech is apparently the only thing that's safe from the foundation labs and consumer. And consumer. Because, yeah, sports. Yeah. Buying, you know, buying a baseball team is, you know, insulated. Insulated from. From open eye and anthropic, thankfully. But the nice thing is that what is at the center and what is at the core of all these companies, no matter what the category, they all have amazing entrepreneurs that are driving them. And so I think that the benchmark model, as it's been since our founding, has been very entrepreneur out versus like, theme in. It would kind of suck to be like the SaaS fund right now. And I know a lot of people that are, that have kind of been the SaaS fund have reinvented themselves, like, oh, we're now like the SaaS AI fund. But that's just a much tougher position to be in, I think, strategically than being the fund that's always been around. Let's back the greatest entrepreneurs and then we'll figure out everything else along the way because those are the people that figure out those challenges and come out the other side even stronger.
- [15:31]
Molly
Since it's not a thematic fund, but we are entering a new, like, kind of stage state of the AI era where things are maturing more. Brad Gerstner thinks it's, you know, he says it's the age of inference and the companies that are going to be rewarded most now are downstream of that. You know, I mean, think about like the AI agent economy, which, you know, well, with GUM loop, everything that's downstream of that, whether it's, you know, hyperscalers, clouds, power, all the different kinds of connectors, routers are now big. Yeah, huge. People are going after those categories. So how do you think about, I guess, inference too, and then maybe tie in GUM loop a bit. But this whole, like, economy that's booming because of the proliferation of agents versus chats.
- [16:23]
Ev
Yep, a hundred percent. Yeah. I think the framework I have around inference right now. So I was working with one of our portfolio companies that has this amazing mass of developer users and an amazing product, but they just hadn't figured out their business model yet. And I sat down with them and I was working with them around, kind of how are we figuring out kind of what the revenue model is going to be? And the thing I said is like, it's sort of like you're on this riverbank and you have this bucket and you want to Fill the bucket up. Because like, it's like the revenue bucket, whatever, but you're just sitting on the riverbank and over there there's a fucking waterfall. And what you should go do is instead of just sitting there on the riverbank wondering what to do, you should just go walk under the waterfall. And like, you don't know until you're under the waterfall whether there's like holes in your bucket or how big the bucket is or anything like that. But the first thing you should do is get under the waterfall. And the waterfall is inference. And just the absolute, just unbelievable wave of revenue and demand for so many different businesses and business models that has come out of inference, I think to your point cannot be ignored. If you think about, again, we're huge investors in fireworks. But if you think about the other inference platforms like FAL or base 10 modal, a lot of app companies that are using a usage based platform model or an outcome based model, all of these are different derivations of monetizing inference. And so when you hear about all these companies and you hear about all of these businesses that are going not like, you know, 1 to 3 to 9 to 20 like they used to, but are going 1 to 20 to 100 or 1 to 30 to 300, all of that can be traced back to the enablement of a business model via inference. Because the business model used to be, you know, we're going to charge some raw dollar amount times some amount of heads within your company and now it is, hey, we're going to charge some calculation of making a margin essentially on the inference that we're reselling you. And some of those business models are a little bit more thin and are just like actually just kind of like reselling inference as a broker. And some are really abstracting it away. If you think about like a Sierra, that is, you know, outcome based pricing for actual completed deflections of customer support queries and things like that. That's largely abstracted. But you're still monetizing this thing which is based on inference. And what that allows is it removes any sort of rate limiter that you have on your potential to grow. And it's the enabler to these unbelievable revenue growth rates that we're seeing time and time again with all these new AI companies.
- [19:08]
Molly
What do you think is going to happen with the proliferation of agents?
- [19:11]
Ev
I think so. Obviously it's interesting because on one hand I think that I always cringe a little bit whenever a word or a term becomes over product marketed. So FDES is certainly A version of this. And I think agents is absolutely a version of this.
- [19:32]
Molly
What about AI?
- [19:33]
Ev
Yeah, well, I mean, AI, that's at least this umbrella term. But, you know, it's like when I think, like when private equity firms are telling all of their portfolio companies to say that they sell agents, that's when, you know, it's like, okay, the term is cooked. Like, we need to move on from this term onto something new or something different. I think that, like all of that said, it is still fundamentally the greatest both product innovation and business model innovation that I think we've seen since the start of SaaS and like, you know, since the cloud business model. And the reason for that is because it is the underpinning of how we move to this idea of selling work or like actually replicating what a human being is doing when they're trying to complete a task. And on the business model side, as I mentioned, it's this, you know, it allows a buyer of software to move their mental framing from like, oh, I buy a license to, oh, I buy, you know, intelligence on tap, or like a white collar activity on tap or some activity that created some economic output for my business on tap via an API or via software product. And so it unlocks this, like, new, you know, price for value equation for buyers that allow them to think about software and the budget that they have for software in a whole different way. I think one of the things that we got insanely excited about when we were first tracking the rise of coding agents was when cloud code, even when it was still cloud cli, but when it became cloud code, we were talking with developers and companies in our portfolio, and they're like, yeah, like, you know, we have developers that are spending, you know, $3,000 per month themselves, like each on cloud code. So it's like, wow, okay, so that's $36,000 per developer.
- [21:17]
Molly
Yeah.
- [21:18]
Ev
And. And you know, oftentimes in this, in the SaaS era, if you had a $50,000 ACV, that was like, that was okay, but instead of having a $50,000 overall contract value with this customer, you were now getting that per developer. And it was still growing. And so it was just this idea that, that like, wow, this could actually become, you know, not a, you know, 200k line item for the average company, but a $20 million line item for the average customer and average company or for some. Or for some 500 million a month. A month. It's like that, that's like a. That's a different era of technology. And so I Think like even though I'm so annoyed at the whole agent thing because now it's just the only thing that you can see when you go online.
- [22:05]
Molly
Yeah.
- [22:05]
Ev
And it's been marketed to death. It represents the most important both product like product slash, technology technology and then also just like raw revenue and business model shift we've ever seen probably in like maybe the history of technology. And I think that was one of the reasons and we can, you know, go into Gumloop if that's interesting. That was one of the reasons we also got really excited about Gumloop.
- [22:26]
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- [23:55]
Molly
R yeah, explain Gumloop.
- [23:57]
Ev
Yeah. So Gumloop. Gumloop is a independent third party software platform that does a collaborative AI agent and AI automation canvas for enterprises. What that basically means at a very simple level is that all of like we first saw encoding and coding agents where developers like with Opus 4.5 coding agents got to a place where developers felt like they could do a vast majority of their day to day job by offloading the work not just like tab to complete like we saw with Cursor, but like actually offloading a bulk of their work to coding agents in cloud code, Codex or Cognition or any of these other products. And the thesis was you Know what we've seen in code is going to happen in most white collar job functions and eventually probably most blue collar job functions as well. Like the average white collar worker is going to be doing an immense amount to automate the rote redundant work in their day to day via agents and via AI automations. So that's what Gumloop provides. It's a platform for enterprises and every single employee within an organization, not just developers, not just salespeople, not just marketers, every single employee within an organization can create, can iterate on and can collaborate on both just like very simple automations like you do with like a zapier back in the day but then also full on AI agents that they can build and use cross functionally across the organization to do all sorts of things both triggered by someone talking to them in Slack or Microsoft Teams or running in the background without anyone having to trigger them at all. And obviously there's going to be and there already are great products like Cloud Cowork and the news around OpenAI combining Codex and ChatGPT into a productivity product that they're going to have big businesses in this general category as well. But we think that there's an immense amount of and this goes to you talk about how routers are becoming a popular term as well in the agent space. We think it's really, really important that there is a third party independent vendor for two reasons. One is which the models are actually pretty jagged. Like the models at any given point are good at different things. And so Gemini is like the best multimodal model. Claude typically has the best coding model, although now a lot of people think GPT 5.5 is actually better than the latest Opus model at coding. And then you have open source models which typically have immense value for the cost that you're paying for the inference of those models. And so there's great models for all these different tasks and you do need to have a third party vendor that's sort of watching out for the customer and not just saying you know, like these. In some of these enterprises you have employees checking the weather with Opus 4.8
- [26:41]
Molly
I know, I, well I was talking, I've talked to a couple of companies about this already and like how do you control spend on your tokens and the token maxing debate and all that kind of stuff. Because like I know when I'm like prompting Claude or whatever I'm using it's usually the dumb it like it's not. It's a small fix. I could probably just go in Myself, but look, the tool is magic, so I want to do it myself and make that small fix. But you do that over huge pieces of content or whatever and thousands of employees. And thousands of employees. And it clicks clearly aggregates very large bills and great revenue sources for some
- [27:19]
Ev
people, like asking Opus 48 what I should have for lunch. You know, it's like, it's probably not, that's probably not the best use of,
- [27:25]
Molly
of our inference budget, but it's like, it's interesting. So I know you guys are investors in Lagora. I was at Harvey the other week and, and they were talking about the same thing. It was like, it's kind of similar to, to how like text messaging was before you would pay per text.
- [27:38]
Ev
That's right.
- [27:38]
Molly
And like, so now it's, it's happening within our chat bots and like within the different kind of prompts that we have. Like, how are you seeing? Like, do you, how do you think the business model of that will play out? And like, will it get more efficient?
- [27:52]
Ev
Yeah, yeah, I think, I think it all depends on, it sort of depends on two different things. So there's, I have something that I call and there's, there's many other mom tests out there. And so like it might even be trademarked, I don't know a mom test. But my, so my personal AI mom test. Okay, is. And you know, I, again, I. Not every mom is like this, but my mom is, you know, from, you know, you know, lives in like this rural suburban town in Colorado. And she's not exactly an AI native or, you know, she like doesn't know how to reset her password on her iPhone, for example. She's not like the most tech savvy person. And so the thing I always ask is like, okay, for my mom, what is the amount of queries or the amount of things that she needs out of AI the, that can't be done by a really, really, really cost effective open source model. And like, you know, this was, you know, I started asking this two years ago and there was actually like, oh, well, I don't know. Now it's like 100%. Like there's, there's nothing that my mom actually asks of her AI products that needs to be done by the frontier or even a nearer frontier model. And you can start like layering that out. It's like, well, okay, well what about like a high school student? Or like, what about like this, this like this person or this person or this professional in this area and depending on your use case and who you are and what you're trying to get out of AI, you either you might need the frontier. Like again, like, I think what we're seeing in the market right now is like people are willing to pay a huge premium to access frontier intelligence to this day, but there's a growing portion of tasks in the economy and within these enterprises where you just don't need a frontier model. And oftentimes you don't even need anything close to a frontier model. And so I think what you're seeing and Cognition published some work on this as well, where they actually trained, I don't know if they, I think they post trained an open source model on tasks that they saw done within cognition where there was low task complexity. Like it didn't need frontier intelligence, but they were extremely popular things that were happening within cognition. So it was like people were maybe just defaulting to using a frontier model for this thing and they're like, wow, you'd actually save an immense amount of money if you took it away from the frontier because it's a very simple kind of gated task that doesn't need frontier intelligence and you're going to save 95% on that action or that query. So I think you're seeing like more and more as the models get better and better. There's a growing proportion of queries or actions or whatever you want to call the use of inference that's going on that doesn't need the frontier. That said, again, I think what's also growing is usage for frontier intelligence because again, I don't think we had unbelievable quality coding models until opus 4.5 until last winter. Which is why you saw Anthropic's revenue and why you saw Claude code go so parabolic, because there was a genuine breakthrough in the usability of those models. And so it's not that frontier intelligence isn't growing and the demand for that and the ability and willingness to pay a premium for that isn't growing, just that there's a lot of non frontier tasks as well and the demand for those is also growing immensely. And so I think the, like my partner Eric Vishra had this really great tweet where, you know, a lot of people try to make this like this very zero sum thing. It's like, okay, well like is open source going to win or are Anthropic and OpenAI going to win? And I forget all the things that he went down, but it was basically like on device inference. Yes. Open source inference, yes. Proprietary models, yes. It seems like there's a lot of demand for all of this stuff and the demand is all going parabolic and so it's not this zero sum game at least yet in terms of like, well either it's all going to be open source or all going to be frontier model from one of the three frontier providers. It turns out that like there's use for all of it and it's all growing very, very quickly.
- [31:34]
Molly
So I guess to push on that a little bit. So to your point, earlier this year and even to today OpenAI has crazy amounts of revenue. Anthropic was like last rumored and reported around 45 billion and now people are speculating around 60 billion. Both are trying to go public with the same as SpaceX and Xai. What happens once those models become efficient? Right, like what do you think's going to happen to the scale? Do you think it's going to continue? Do you think what do you think is going to happen?
- [32:04]
Ev
Yeah, yeah. I mean it's, it's, it's the one trillion dollar question or the multi trillion dollar question. If you kind of combine SpaceX, AI, OpenAI and anthropic onto that equation and I think it's left like it depends on what the next 12 to many years look like. And what I mean by that is do you believe that we're on a path to recursive self improvement and this future of a bunch of geniuses in a data center? Whereas if you do have a bunch of geniuses in a data center, if the frontier goes that high, then you legitimately do have probably a lot of pricing power and a lot of ability to continue to grow and continue to monetize and probably even re accelerate growth if you're a Frontier Lab. On the other hand, if at any point it seems like, you know, capabilities are actually hitting an absolute ceiling and distillation continues as it has historically and the open source actually gets, you know, 95% as good as wherever the ceiling of capabilities tops out. That's a really scary situation for the, for the Frontier labs. I don't think it's like a death knell for them because you know, it's like again like most of the users of ChatGPT would use ChatGPT, whether or not it was a GPT model in there or not. Like they like the product. Yeah, most of them don't even know. They wouldn't be able to tell you if the model is different. In our little bubble in San Francisco, people will be able to tell you. But like the vast majority of the 900 million weekly active users.
- [33:32]
Molly
They have no idea what 5.5 is.
- [33:33]
Ev
No idea. And like, they wouldn't be able to tell you if you swapped it out for like five, two or something. They wouldn't be able to tell you. And so I think, like they're. It wouldn't. Like, they're not. Like that scenario wouldn't kill them because again, people like the products and they've done both. OpenAI anthropic have done a tremendous job of building amazing products on top of their models, but it would certainly be a very, very different situation because you'd have a much, much greater impact from open source, depressing their ability to have pricing power and charge a premium for the tokens that they're producing. And it's like, I mean, open source inference also costs money. It's not like open source means free. Someone still has to run the GPUs, someone still has to build the data center, someone has to still operate the data centers. It's more just about like how much premium margin that the Frontier Labs can create. I don't think we'll know until it's very clear whether or not we actually have RSI and there's going to be, you know, the, you know, geniuses or God in a data center or whatever you want to call it, at which case we'll have different problems to be thinking about or if we do end up, you know, at some point hopping out on capabilities sometime in the next few years and distillation continues. I think it's much harder to garner a premium margin if you're a Frontier model company.
- [34:43]
Molly
It becomes much harder given the scale and how fast things are running. Another common question is how do we fund this? And there's different types of funding that's happening. I mean, I think we're like literally across from General Catalyst and they have their CVF fund and like there's people who are going after debt for tokens and trying to create the right mix. But the kind of like, like the undercurrent of all this is companies are raising money faster than ever before in. One of the questions in our conversation before is like, do we talk about the bubble? I don't think the bubble is very interesting. I think most interested in like, what are the biggest concerns right now with that? Because reading through the cracks of all of it, right? It's like, what I think we all know when companies are running off the tracks and when they're overfunded to an extent. But then again, like there are. It's a Weird time. Like there are total Hail Mary's where these companies are outperforming what you thought they were doing. And so they raise another round and then six months after that they raise another round and then they just keep on getting more and more capital under them. But, okay, so to this point, the funding ecosystem has changed dramatically. The playbook is out the door. Yeah. And so with a firm like Benchmark, who is just fundamentally on the early stages, like, how do you capture that value at the early stages and continue that?
- [36:04]
Ev
Yeah, I think, I mean, it's something that we talk about a lot internally, because you're right, that even the funding market, it's not just like, I think the one that everyone talks about, like the change in the funding market and like the venture growth asset class. The trend that everyone talks about is like companies staying private for longer. That's been like the prevailing trend that everyone talks about is that, you know, you have, you know, I mean, name your company. I mean, obviously some of these companies are now going public, like SpaceX, but before it was like, you know, SpaceX is never going public and Stripe is never going public and databricks is never, you know, it's like these would be these. If this was 2005, all these companies would have been public for years. And now there's just so much capital available for them in the private markets via these venture growth platforms that they just stay private, you know, essentially indefinitely. And I think a more recent change is that, as you're mentioning, AI is just so, so different because depending on what you want to do, oftentimes there are massive day one costs. Like, if you want to create a Neolab and you have some incredible research direction that you want to pursue, but you need $2 billion of computer to see whether it's going to work or not. That's very, very different than airbnb raising the 500k or whatever they raised at YC in their seed to see if Airbnb would have any product market fit. It's just a completely different equation and type of funding mechanism and just it's a completely different capital market. Also, I think what you're seeing is now because of the staying private longer thing, you also have these crazy situations where oftentimes you almost like see a rebirth of a company, where oftentimes people think like, oh, well, when it's a growth stage, there's, you know, it's like early stage you get high multiples on money, but you know, it's higher risk. And then late stage, you get like, you know, you can get like a 2 to 3x, but it's much lower risk. And I think the crazy thing is that because these markets are so big in AI and elsewhere and because these companies are staying private for much longer, you actually have these situations where a late stage company can have much higher upside than like a Series C, which is super weird. It's so weird. It's like, like my, my first investment when I was at KLEINER Perkins was SpaceX and it was at an over $100 billion valuation. And you know, talking with peers and friends at the time, it's like, man, like, are you gunning for like a 2 1/2 x? Like, like how much upside could there possibly be if you're getting in at a triple digit billion entry valuation? And I think the thing that ended up being true for SpaceX was like, well actually you had this thing called Starlink that had started, you know, a couple of years before that and had really started to scale and like, that was like a rebirth of the company. And if you look at like the piano today and in the S1, the vast majority of the business is their consumer broadband business and B2B broadband business via Starlink, it's not even the launch business. And so I think the two things that we grapple with and think about a lot is like, how do we continue to be the best venture capital firm that partners with entrepreneurs very early in their journeys and is their most meaningful partner when you have a much, much more capital intensive early life cycle for some of these AI businesses and when you have these businesses that actually sort of go through these transformational moments at later stages where it feels like actually Even though they're 15 years in, they're still only 1% done with their journey. So I don't have a great answer for that. I think we're extremely flexible in what we do. Like, we don't say that we just do seed or we don't say we just do Series A. Like we partner with the very best entrepreneurs in the world when we think that we can deliver unbelievable upside for our LPs and be the most meaningful partner to them. Like those are the kind of the constraints that we think about and I think the trends that we're seeing both in AI and in capital markets writ large definitely expand our thinking in terms of like, what actual rounds and what type of situations are applicable to a firm like benchmark.
- [40:31]
Molly
There are definitely new types of funding vehicles too. Like, yeah, private credit has really taken over where banks left off and different kinds of funding to add into it. So how has venture capital in the grand scheme of the alternative asset class changed and how has the rule changed?
- [40:49]
Ev
Yeah, I think, I mean the, I think this is where, where, where things get tricky when people are having these discussions is that these firms can completely change what they are and yet we still just use the term venture capital. And so whenever I like, you know, write like an occasional blog post or something, I always say like venture dash growth because I'm like, venture capital is something very particular. And so even when I'm discussing like, you know, something like a databricks or something, I'm like, like that's, that should be called something different. And so I'll call it like venture growth. And that's like one way to do it. But I think a lot of these firms, they have just become alternative asset managers and like that is what they are. And they have venture products, but they are not venture capital firms. And that's not me being like, oh, they're not VC firms because they're bad. I think a lot of these firms are wonderful, but like General Catalyst and Andreessen Horowitz, those are alternative asset managers because they have so many products. You know, many of these places have like, if you think about like an iconic, like yeah, like iconic has like a great growth stage venture practice, but they also have like, they started as a high net worth wealth management practice as well. That's a very different thing than just being a venture capital firm. So I think as these firms have evolved, one of the things that I think as an industry we haven't done a good job of is just evolving our nomenclature and the way the terminology of how we talk about these things. And so yes, General Catalyst still has a venture product and they also have a growth product and they also have a debt product and they also have a health assurance product and they have a wealth management product because they're an alternative asset manager. And so I think that like venture in many ways is still the same, but it's a product now for many of these firms. It's not the firms themselves.
- [42:35]
Molly
We like definitely alluded to this a bunch during the conversation, but I'm really curious to get your perspective. The public markets are seeing the largest IPOs of all time and it's all apparently going to be happening this year. And so like, how do you, how do you think the markets are going to take that? Will it be absorbed? Will it be kind of like aftershocks? How that will, how will that actually then affect this Asset class?
- [43:02]
Ev
Yeah, yeah, it's, I think in a few ways. The first is like I did this, I did this analysis, like this very quick analysis because I was just like, I know that like just using a single example with anthropic after the $380 billion round, I was like, man, I know this is so much bigger of a deal than we've ever seen in a growth round ever. And I also, and I also know that we're not talking about it enough currently, just how different it is. And so I did this analysis where I basically charted out over the last 10 years, I took like four of the best pre IPO investments ever over the last decade in terms of like both just the raw scale, like they were big rounds. And then also the investors did really well in the rounds. And so I think it was Slack, Doordash, Snowflake and Nubank. And like typically in those rounds they were like, you know, the total round size for the pre IPO round was like 500 million to 2 billion. And the investors over a 4 year period made from like a 2 to 5x and like everyone went home happy with that. That was like a great outcome for all involved. So I think like The Snowflake pre IPO round over a four year period ended up, you know, it was like 500 million turned into like two and a half billion. Like that's insane. That's amazing. When you think about the anthropic $380 billion round. If anthropic was to go public and get liquid at a $1.5 trillion valuation, which they just raised it about a trillion dollar valuation, I don't think people would think that's that ambitious of an estimate. And I think a lot of people would have their estimates higher than that. The gross return of their $380 billion round, the 30 billion that they raised at 380, it would return 35 times that of the snowflake pre IPO round. So it's 35 snowflake pre IPO rounds in a single round. And like I have friends, like I know several people that have like three to four billion dollars invested into Anthropic. And like, like it's like so hard to even talk about. Like these numbers are so big that it's hard to even comprehend. Because five years ago a growth fund was like a billion dollars.
- [45:21]
Molly
Yeah.
- [45:22]
Ev
Like until Covid brought all these growth fund sizes up, a normal growth fund was like billion dollars. And now there's an individual company that these people have $4 billion invested in and they might return five times their money in less than five years on $4 billion. Like it's unbelievable. And at least SpaceX took, you know, 20 plus years to like, you know, to make everyone rich and put all this money in the ecosystem. But I just don't know if people really understand and are ready for the impact that this much liquidity could have on the ecosystem. I think the one place that people are definitely predicting it and are already seeing it is in the SF housing market.
- [46:04]
Molly
Oh my God.
- [46:05]
Ev
Where it's like, you know, everything's going for 2x asking price at this point. It's all cash or in equity or in lab equity. But I think there's so many knock on effects in terms of like what do those employees do, what do they invest in? What, what causes or companies do they then invest in? Do they start new companies? Do they stay at these labs? Like there's just, it's a, it's going to be a shock that's going to impact every single aspect of life, both practical life in Silicon Valley, but then also the lives of employees, investors and founders in the ecosystem as well.
- [46:40]
Molly
Oh, absolutely. I've done now two episodes with Michelle Del Buono who to your point, Andreessen Horowitz has Multi products. He runs their multi family office of Mark and Ben and the principal's there, so it's the wealth management division for them. And each time I talk to him I'm asking because there's going to be a huge wealth event, like the, the most amount of wealth creation probably ever because of all these things happening simultaneously. And so we've gone through a couple of the questions. I'm obviously not going to really reiterate them now, but like one of the biggest questions is what do you do when 90% of your net worth, probably more at this rate, is in one position?
- [47:20]
Ev
Right.
- [47:20]
Molly
And so he has some really great answers to that. Today's episode is sponsored by VCX by
- [47:26]
Sponsor/Announcer
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- [49:09]
Molly
so as we wrap up, I have like a couple more questions, but one of the things when I was doing research on you all was super interesting was like how diverse your portfolio is, especially in the AI mix. Like you have StarCloud data centers in space named in the SpaceX S1. Then you have Cerebrus that just went public Sierra LangChain, Lagora Fireworks, which we talked about a bit.
- [49:34]
Ev
Mercour.
- [49:35]
Molly
Manus.
- [49:35]
Ev
Manus.
- [49:36]
Molly
Merkor. Yeah, Merkor. Interesting. I just listened to his podcast with Harry Stevens and I was really surprised about the positive things that are going on there.
- [49:44]
Ev
After the data breach, he cleared a lot of air.
- [49:46]
Molly
Good for him, honestly. Hey Gen Gum loop that we talked about in exa. So because of this whole mix and like there has been, you know, you guys have a concentrated strategy. Like how do you, how do you come up with a portfolio of this type? They're all very mature. They're all doing really well. Like what is, I guess, like what is the magic between all of that?
- [50:07]
Ev
Yeah, I think so. When I was coming in and I was kind of in the, in the like before, before I joined Benchmark, I was like, wow, they've done such an amazing job thematically. Like they have like the vertical AI winner in Lagora, they have like the horizontal AI winner in Sierra, they have like a data infrastructure play in Lagora, they have like a developer play in LangChain. You know they have like a prosumer play in hey gen and you can kind of keep going and keep going, you know, overall data centers in Star Cloud. And then I got in, you know, then I joined and I realized, like, oh, wow. That like, there was literally no thought of. Of. Of, like, oh, this is the category that we're going into. It was truly kind of what we talked about before it was all founder out. It was all based on the founder. And yes. Like, did they resonate with the idea that the founder was pitching? Yes, but there was zero thought to, oh, this is like, this is going to be big category. It was just, this is an unbelievable entrepreneur that is going to put their energy to an idea that they've convinced us is really, really interesting. But even in some of those cases, some of these companies were pivots and, you know, so there was no way that we were, you know, super smart to know, you know, that some of these companies were going to be in big categories because they weren't even the categories that we invested in. And again, I think that's sort of the benefit of benchmark is like, we don't invest in categories, we invest in people. When chathan invested in StarCloud, it was like, weeks. Like, we closed the investment weeks before Elon started, like, you know, professing his love and bullishness on Orbital Data Center.
- [51:45]
Molly
You guys have the partner meeting. You're like, wow, look at that.
- [51:48]
Ev
We were definitely, I think Elon, I forget which which platform it was on, but he did like a long interview with somebody and, you know, like, half the interview was was on Orbital Data Centers. And we started like, sending it around our group chat. We're like, oh, man, this is about. This space is about to get really hot. And it was so ironic because it was like, you know, again, there's so many things, you know, there's like, it's such a heated debate of, like, you know, oh, like, are Orbital Data Centers of the future? Are they going to be too hard to pull off and that, like, we obviously discussed both sides when we were talking about the investment. But, like, the investment was about the people. It was about Philip and the team that he had built. And it's like, wow, like, these are like one. They already had, you know, a GPU working in space. And so, like, they had proven out their metal and they were the only company that had a GPU in space. So there was. There was already some things that we saw in their traction, but it was based on the people. I think when you focus and you have your strategy around that Again, great entrepreneurs are always in style and they always pull rabbits out of hats, and they end up being magnetized and drawn towards where these really massive categories, and they often end up pioneering them.
- [52:54]
Molly
Wow. Okay, so as we close out, I have to ask our Brax question.
- [52:58]
Ev
Of course.
- [53:00]
Molly
So Brexit is all about performance. If you didn't know that, now you know, spending smarter, moving faster. But an element of performance that I think is very valuable is who you surround yourself with. You've been at so many legendary funds at this point. You've been surrounded by the top investors over and over again. But I'm really curious, from your standpoint, like, who has been kind of like a mentor to you? Like, who do you keep, like, as a guiding figure for you as you continue to evolve and grow in your career?
- [53:28]
Ev
Yeah, I mean, I wake up every day and I, like, pinch myself because I've just been so unbelievably lucky and fortunate to, as you said, work with a lot of the people that have, like, defined the history of, you know, venture capital and growth investing. And not only have they been extremely good at their job, but a lot, like, basically every single one of them has been a wonderful person. I feel like I learned something different from every single one of them. And so, like, like, if you take like, a Napoleon Ta, who is a GP of Founders Fund, who runs the growth investing practice at Founders Fund, I mean, I. I learned a ton about investing with him, but the most important thing that I learned from Napoleon was his focus on family and work. And he had a very simple life. He doesn't, you know, he doesn't do, you know, he doesn't do podcasts. He doesn't, you know, he doesn't network. He doesn't, you know, try to spend a bunch of time with other investors. He goes to work, he works really, really hard, and then he spends time with his family and. And it's like this beautiful, simple life. And he loves both spending time with his family and working with the Founders Fund team. And it was something that made me realize, like, wow. And just helped me prioritize just my time and how I think about the important things in my life. I think another one now is. I mean, currently, Eric Vishri, I think, is just like an incredible role model and leader within Benchmark. I think he is someone who, obviously, when you look at his number three on the Midas list this year, if you look at his results, he's best of the best. Like, he is a Mount Rushmore venture capitalist at this point, but you Spend an hour or two hours, however long with him. And he is just a extremely kind, hardworking, completely normal person where it's like, it's clear that he's unbelievably smart, but he's almost unassuming in like, he's not trying to prove anything to you, he's not trying to make you feel smaller, make you feel like he's the smartest person in the room. He's worked really hard, he works really smart. And he just brings like a kindness and like a normalcy to his conversations and his workings with his entrepreneurs and his partners in this room. That, that, that motivates me and inspires me to do more of the same. Where it's like there's not, you know, there's not some alchemy to doing this job really, really well. If you work hard and you kind of know the direction that you need to go and you're a great partner to entrepreneurs and you work really hard for them, that ultimately is the job. And if you just do that and then get some lucky breaks over your career, you can end up being one of the best VCs of all time. So those are the two that come to mind mostly because I think they've taught me more about lessons outside of the day to day work than inside it. But I think every single person I've worked with, I've been very fortunate to do so because they've all taught me so much.
- [56:08]
Molly
Amazing. That's a sophisticated answer compared to the typical growth investor that chooses Charlie Munger.
- [56:17]
Ev
Yeah, unfortunately, unlike David Sinner, I've never had dinner with Charlie Munger. Otherwise maybe it'd be Charlie instead.
- [56:23]
Molly
Oh, amazing. This is a great place to end. Thank you so much. I had so much fun talking everything about AI and who knows what actually happened.
- [56:30]
Ev
Exactly. That's the best part. We can look back at this in a year and I've probably been wrong on everything.
- [56:33]
Molly
What, what isn't the agent anymore?
- [56:36]
Ev
I don't know, it'll be some new term.
- [56:38]
Molly
Well, thank you so much, Ev.
- [56:39]
Ev
Yeah, thank you, Molly. It's been great.
- [56:41]
Molly
Hey, it's Molly.
- [56:42]
Sponsor/Announcer
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- [57:00]
Molly
Link in description to sign up.