20VC: Benchmark's Eric Vishria on Where is the Value in AI: Chips, Models or Apps | Why Nvidia Will Not Be The Only Game in Town | The Commoditisation of Foundation Models | Which AI Apps Have Sustaining Value vs Hype and Short Term Revenue

Foundational models are the fastest depreciating asset in human history. I don't believe Nvidia is going to be the only game in town on infrastructure. We have a major, major shift in AI, which could be bigger than any of these other shifts, maybe combined. It's simultaneously the most exciting and most disorienting time in my 25 years in technology. There's a lot of uncertainty, but we've been more active than we've been since 2010 and 2011.

This is 20VC with me, Hari Stebbins. And today we have Eric Vishria, general partner at Benchmark, joining me in the hot seat. Now, what is amazing about Eric is the breadth of his investing success, from Benchling to Amplitude to Cerebras to Confluent. These are incredible companies, but in totally uncorrelated and different industries.

A once-in-a-generation investor, a true picker, and one of the greats.

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You have now arrived at your destination. Eric, I am so excited for this, man. We've been waiting so— I think it was like 5 or 6 years since our last one, at least.

Wow.

Been a while.

So thank you so much for joining me today.

You look older, Harry.

The Botox isn't working. But I was just listening to you on another show, actually, not nearly as good as mine, by the way. You said that as a CEO, you felt you fell short, and it didn't really go anywhere from there in that conversation. And I wanted to understand why, as a CEO, you think you fell short as specifically as possible.

When I reflect on RockMelt, which was, which is a startup I founded and was CEO of, my reflection is that we fell short, like far short of my hopes and dreams for the company, like my expectations for the company and my hopes and what I, what I thought we could accomplish. We fell really, really far short of it.

You know, and I think there's a few ways to think about it.

One of the reflections, and it's been really useful now as a venture capitalist, is good teams with an interesting idea, it's not necessarily enough.

The, the, the real lesson from it for me is like, Hey, you can put together a great team, and I think we did. You can have an interesting or provocative idea.

We were rethinking the browser, and this is circa 2010. So I think we had some of the right ideas, we had some of the right execution even, and a great team, but distribution for a browser, brutal. But the big takeaway, the big lesson from that actually, I think, is just that startups are really hard, and I think it makes you really empathetic. It makes me really empathetic as a venture capitalist now when I meet entrepreneurs and you kind of understand like, well, you know what, this stuff is really hard and there's a whole bunch of stuff you can do and there's a whole bunch of stuff you can't.

But something is wrong there. If you think about that as an investor today, you're like, it's either wrong team, wrong market, wrong product, wrong time. Something is the inhibitor there.

Yeah, I think the question is whether it's deterministic or not. Like, it's just like, is all of that knowable? Like, we're all taking calculated—

whether you're an entrepreneur you're an investor, you're taking a calculated risk, right, of certain probabilities.

And, and so I just, I don't think it's deterministic.

If you kind of like perfectly evaluate the team, the timing, the product, and everything at the beginning, you can actually determine with certainty whether it's going to work or not. And that's just like, that's part of it, because there's too many things that change along the way. As those things change, the probability of potential outcomes changes a lot. And so like, I think that's the part of it that, that's why startups Is there anything you would have done differently about your CEOship?

Now you've worked with some of the best CEOs, the best founders.

Oh, a million. I do a million things differently. Like, I, and I say this to the CEOs I work with now, some of the big mistakes that I made were definitely not thinking about distribution enough.

That's always a big thing. 2, you really, there were a couple times or 2 people specifically that I think about where What they wanted in comp and what I was like willing to give were separate, like they were just off and they were so far out of market in terms of what their ask was. At least for one of them, I would have just broken all the rules and thrown it out and hired them. Those are 2 examples.

But I think those things, you know, you can get into real trouble if you kind of keep doing something like that.

And so you kind of, it's always a balance.

Can I ask on one, in terms of the distribution element, How do you dig into that as an investor today, evaluating whether a founder has thought that through comprehensively enough for you to get comfortable on that?

I'm not looking for an answer or a right answer. What I'm really looking for and trying to figure out is, has the person thought about it deeply and is constantly learning or constantly applying and adding new mental models to their framework to figure out what the right path is and they're navigating it? It isn't like, hey, I'm a boat captain and I'm looking and like, this is where we're going to go. You start going And then conditions change and you get more information and you have to kind of constantly change.

And so what you're actually trying to evaluate isn't—

they're not going to have all the answers and that's okay. You can have theories and hypotheses and then evaluate and change as time goes on and you get more information. And I think that's really what you're looking for versus this is how it's going to work. And that could be bad in its own way.

Speaking of changing tides, being that kind of captain of the ship and moving with the tides, you then become an investor, very different role than being a CEO. And you said something that I very much agreed with before, but I loved it and I wanted to dig into why. You said career investors, ah, they're all better. They're always better.

They're better investors.

I agree.

Well, also not necessarily better board members, not necessarily better advisors, not necessarily a better bunch of things, but definitely better investors.

Why do you think they are better investors?

Practice and reps matter.

And so if you're kind of—

if you take somebody who's 15 years into their career as an example, and they were an operator on that time, they may have seen 3 to 5 companies that they worked on and know them really deeply and know the ins and outs of them. If you're a career investor, over 15 years you'll have seen 30 or 35 companies And hundreds and hundreds, thousands of pitches. And then of those pitches, you'll be thinking about, well, like, I saw this one and this is what it looked like then. And 15 years from now, like, or 10 years later or 5 years later, like, you've run this like very long-term longitudinal experiment and you have data and mental models around that.

And so I think that that's really helpful.

It can actually hold people back too, but I think that's really helpful. For investors. And so that's why I think the career investors tend to be really better investors.

Why are they not the best board members?

They haven't actually done these things at depth. They haven't done them themselves. They often lack empathy and understanding. I think they often think that things are more deterministic than, than I think they actually are. Like, all of those things I think actually really can get in the way. And I was talking to my partners yesterday About it, kind of, we were talking about a board member and who's at a different firm who is on a couple boards with us. And it's like the challenge with that career investor is they think like if a company has a plan and they miss the plan, that's because the company messed up. And if the company had a plan and they exceeded the plan, it's because the company did great. And like, you know, or executed, or management did great, or management messed up. And it's just like There's about 47 other ways that could go wrong. The plan could just be gobbledygook from the beginning. It could be totally wrong and messed up. There could be external reasons for it. And, the whole job that we have is to try to figure out root cause on those things and help them figure out root cause on them and then fix it. You're not helping your 3-year-old fall or not fall or teach them a lesson. That isn't the job. It's quite a different actual mental model. in terms of what the engagement with entrepreneur is.

My question to you is, you mentioned some amazing companies there that are often completely detached in terms of sector. There are very different companies there from Confluent to Contentful to Cerebras. And I spoke to Bruce Dunlevy before the show, and he said that bluntly, your breadth of sector mastering is completely unparalleled as an investor. And he asked a great question, I thought, which was, what is your learning process for entirely new categories and how do you break it down and learn so fast?

I am not a sector specialist and nobody at Benchmark is. And I think the fundamental idea with Benchmark is there's a small group of people, a small group of partners who are all equal. And right now it's 5 of us, but sometimes it's 4, sometimes it's 6, but it's basically 4 to 6 who cover technology. And the challenge with that, if you kind of think about it, is we can't be sector specialists. The sectors that have the most disruption and things are changing the fastest, like, are changing. That part's constantly changing.

So you have to be, as, as a group of investors, you have to be, you have to be moving, you have to be looking at new stuff because that's where the disruption is happening.

And so, so then the question is, so like, I can't be a sector specialist, I can't be a semiconductor specialist or I can't be an open-source specialist. Of course, we each have preferences and things that we like and don't and lessons that you can apply, but it's not a specialty model. And I think about this a lot and we should talk about it in the context of AI, but you can't have that.

So then the question is, well, what can you evaluate on?

And I think this is it for me, which is you can say, hey, this is an extraordinary entrepreneur. That's a, that's a, that's an evaluation that, that you can try to make. There's a second thing, which is this entrepreneur has a very interesting and unique insight. Like, that's a really important thing for me.

And then you can say this market can sustain a big company.

Like, those are 3 things which I think even coming without sector specialist, without being a sector specialist, you can try to come to a belief or a probability distribution for each of those things. And I think if you have those things and then you add on top of all of it, I have chemistry or want to work with this company or want to work on this mission or whatever. Then I think you have like the basis for an investment. And I think one of the maybe contrarian things around that is I think that a lot of venture capital in the SaaS era, and certainly a lot of growth venture capital, got to be like a very spreadsheet-y investor-bankery type of approach, which is like, it's kind of the model's figured out. We plug these things in and we kind of know and we figured it out. That way. And I think that doesn't work. I think there was a period of time where it might've worked for a bit, but I think it largely doesn't work. I think the AI stuff is going to wipe out that breed of venture capitalists. It's going to be really challenging because you really have to make these other fundamental assessments.

Can I pose an alternative to you there, which is actually those are generally vertical SaaS companies with deep data reserves, which will then be able to leverage foundation models that are relatively commoditized to build much better vertically specific apps and become stronger.

Could be. That's possible. But I would tell you this, we were talking about a company yesterday and it has every possibility that you just said.

The entrepreneur in that case is— one of the challenges with these platform shifts is some form, some variant of the innovator's dilemma. It's not quite the innovator's dilemma as Christensen articulated it, But it's some form of the innovator's dilemma, which is like you have to make the platform shift. And in order to make the platform shift for a company like what you're articulating, you have to kind of be willing to burn down the existing business, and at least in short order.

There's a whole bunch of entrepreneurs who don't do that.

And so like this, this is why I just go back to, if you have a learning entrepreneur who is constantly rethinking how they're navigating the ship, then that makes a really, really big difference in the probability of outcome as you're working through a platform shift.

I've got to take this one by one because otherwise, I'm just going to go all over the place. You said the insight development. I agree, and I love that. I actually ask it. Mike Maples always taught me to ask, what is your insight development? How do you view the world in a way that others don't agree? I asked Pat Grady on the show, who says hi, by the way. But I asked Pat Grady on the show, and I said, do you have to be contrarian and right? What if a founder says, I think that actually, the world is moving to cloud, duh, and I'm gonna facilitate that much quicker.

There's not really an insight.

Does that matter? Do you have to be contrarian and right in your insight?

You know, there are levels to it. Like, yeah, you can say, like, hey, we're going to cloud, and we're doing this. But normally, there's an insight. I would say, like, if you look at most of the successful companies, there's an insight, like, there can be an insight underneath that, right? Which is like, yeah, it's going to cloud, and this is the right way to attack it.

Or it's going to cloud and the core differentiation is going to be here, not here.

Normally, there's an insight there. I think there's a couple of cases where, and you really have to think about it, there's a couple of cases where just violent execution by a determined team in a hypercompetitive market where nobody has an insight has worked.

But even now, actually, you could argue that, say, in an Uber-Lyft case, which is exactly what I was thinking, The insight I think would be that broad is better than narrow in terms of market expansion and being everywhere is more important than honing one.

Yeah. I mean, I think you could say that, and that's a good case of violent execution by a determined team. And so I think there are insights there. As you said, there's little execution detail insights in your point.

And so it doesn't have to be some pie-in-the-sky insight that's like, oh my God, AGI is coming in Q3 of 2025.

That's not the kind of insight that we're talking about.

We're talking about actual things that are nuances that help build a company.

And so, yeah, I think they come at different levels.

I would also say, and this is a difference, and I've been fortunate to work with Pat on a board and work with the larger Sequoia team on a number of companies, and there is a difference also in stage, right? They're growth investors, or at least Pat and his team are growth investors, and we're early stage. And so, there's also a stage difference in terms of how we evaluate You said about markets being large enough to support massive companies.

How do you think about market creation?

I think you have to say, and this goes back to the insight thing, if this thing, whatever the product is, accomplished what it was supposed to and what the entrepreneur said it could do, would there be market creation there? You mentioned Uber. Uber is a good example of this. And this goes back to the whole professor The like taxi cab analysis that was done like in the early days of Uber where it was like, oh, the entire market's this big. And it's like, well, if you can imagine everybody has a smartphone in their pocket, and if you could actually request a ride instantly and pay for it easily with not— with shorter wait time and much more accuracy and much less cumbersome communication, could a market be created?

Yeah.

And I think that was a huge part of Gurley and the rest of the Benchmark teams at that time, which predates me. It was a huge part of their insight in terms of the investment thesis. And so when you kind of think about a situation like that, okay, that's market creation. That's a good example of market creation.

And we have those all over.

I'll give you another one that's kind of really interesting right now, which is AI medical scribes. And so you're like, you go to the doctor and you talk to your doctor, and then they have to do a bunch of notes and everything else. And so there's a bunch of these AI medical scribes.

You kind of like look at it and you look at the proposition.

And I've looked at it now, I've met several of these companies over the years and we haven't invested in any. But going back, I don't know, 5 years when I met the first one, it's like, if this exists and works, like, yeah, it's good. Like, that's a better way.

Like, it's just, it is a better way. So there will be market created there.

It's going to take away from the actual human scribe business, but it is a new market in a way.

And you can imagine, it's not hard to imagine that exists.

And we often say this when we're talking about companies, which is like, if you fast forward 3 years, is this a thing? Is it a thing?

It's often a good sign when you're like, yeah, that's a thing. It's going to be a thing. We believe that.

I totally agree with you. But what on earth do you do in this? And we're totally switching tacks to AI companies, but in this sea of AI companies, We're both in 11x together. Great. Love Hassan. Fantastic. But there's a lot of other competitors to 11x. I'm with you. I've met 5 medical scribe companies. I thought Nabla was the best, but there were like 20 and I don't have a freaking clue who's going to win. So how do you filter when everyone does the same, says the same?

I think this is where we go back to where we started.

Do we have an extraordinary entrepreneur that you believe in? Do you think they have an insight that is cogent?

And I would say this, maybe this is a more useful way to say it.

The more competitive the market is, the higher the bar you have to hold on those 2 things, I think. And so in a hypercompetitive market, you have to really believe in the entrepreneur and really believe in the heart of the insight and their execution ability. And in a less competitive market, It's just misery in life to work as a venture capitalist and serve on the board if you don't like really love the entrepreneur and, and really like love the area. So that's a thing for me, which is I think about a lot when, when I'm kind of looking at a company is like, am I going to be able to authentically help this entrepreneur close great talent? Like, am I going to be able to talk to them and tell them why this should be Like, this should be an amazing— like, this is an amazing person that I can tell somebody that, like, this should be their life's work, like, and help convince them to join a company, right? Like, that's, you know, just— which is a big part of what we do.

And, like, and so when you look at that, I ask myself that question.

And if I— if my answer is like, you know what, I think it's a cool business, it'll probably work, but like, I just don't understand how I can make that pitch, then I don't do it.

Like, that's my—

that, like, I just, I don't do it.

Um, and so, um, and so like, there's this balance there, but like, I think that's a big important part of it. And the more competitive the market, the higher the bar has to be.

I totally agree. My worry is in a lot of these cases, the quality of product does not matter as much as the Existing distribution moats that incumbents have. Could be like AI medical scribes, great example. Microsoft has such large enterprise.

Yeah, Nuance could just crush everybody.

Just crush everyone. You're a 10% better product, agreed, better product, but they just crush everyone with bundled packaging.

Yeah.

Today's incumbents are paranoid and on top of things. And like, is Microsoft and Nuance going to move slower and whatever than your average AI medical scribe company? Of course. But they're not dopes and they don't have their head in the sand. And so like, Yeah, I mean, I think that is like, you do have to factor that in for sure.

Eric, does AI allow companies to charge more price per seat and have better revenues, or does it merely denigrate their margins because of the increased cost of implementing AI?

The answer is for sure both.

I would maybe tweak your question, which is to say, I don't know that it's going to be like the price per seat model, but I'll give you a simple way to look at it.

Let's take the The AI coding area, right? We've seen like tremendous amount of capital go into AI coding and copilots and AI software engineers and whatever. Let's say your fully loaded IT or software person is $200 grand for just for argument's sake. And if you think about that $200 grand, there is, call it $10,000 for every one of these software engineers or IT people. There's $10,000 of tool spend. In that I would put like Jira, which is Atlassian, GitHub, ServiceNow, You know, a computer, whatever development environment, Git, whatever. So all that's called 10 grand a year, roughly. On that 10 grand a year of spend, how many hundreds of billions of market cap has been created? Like 300, 400, 500, some big number of market cap has been created on $10,000 worth of spend for each of these like software engineers. So if the AI, if you can get AI good enough, to eat much more of the $200,000 of value attributed to your software engineer, IT person. It just follows that there's like 20x more value creation and there will be giant, giant outcomes.

So in this way, and this is the challenge with it, which is it's simultaneously the most exciting and most disorienting time in my 25 years in technology. Because that is a really challenging situation where in some ways the prize is so big that you could very easily say you can justify really ridiculous prices on any fundamentals basis. You can justify really ridiculous investment, again, on any fundamentals basis because it's like, well, the prize is so big.

So that's one part of it.

And then the other part of it's like, wait a minute, we're a little ways away from from replacing the software engineer right now. We're a bit away from that. We haven't actually captured that much market value yet. Really, no one's demonstrated anything close to that level quite yet, although the trajectory is really quite good. That's the tension and challenge with it. This is the kind of thing that we have to navigate through every day.

Those companies in that trajectory are scaling revenues faster than ever. I remember when it was like, oh my gosh, they got to $10 million ARR. are in such a— now it's, it's just completely different in terms of revenue scaling. How do you determine and think about analyzing revenue quality? And I've heard you say it before, sugar high revenue versus sustainable revenue.

I was talking to a team that had gone like a 4-person team, $0 to $4 million in 4 months.

Amazing.

Like just amazing kind of revenue trajectory. But I put like almost no value on that, like going $0 to $4 million. And I think one of their things was like, well, like, why are you not giving me credit for that?

And I was like, well, the thing that I take away from that is that whatever you're selling, and this is true for a lot of these AI companies, customers want to buy.

Customers want to buy it.

And so, and I think part of it is just like the products to a lot of these customers, the products are magic. Like they feel like magic to the customer. And so the customer and the ROI on those products It's just tremendous.

And they know that they have to experiment with it, or they have to try it, or they want to try it because they see so much potential value in it and they feel it. So the demand side is very clear and it's just pulling. And I think that's probably the biggest thing that we can take away with these early-stage companies and their traction, which is like, okay, there is demand. And then you have to kind of evaluate and figure out like, okay, do we think that whatever the product the company's building The entrepreneur, I mean, go back to the same things, like has sustainable advantage over time.

And that's a really, you know, that's a really difficult judgment right now.

But I think it's like one of these things that we have to do.

But I totally agree with you.

The quickness of the scale is unlike— it's like 3, 4, 5 years of what SaaS companies, like your traditional SaaS companies were doing, we're seeing in under a year.

And we have a whole portfolio of companies that are, that are like this.

Like, it's amazing.

Yeah.

But I find it paradoxical because you've got 2 questions here, which is the $600 billion AI question. Thanks, David Kahn. The CapEx spend is so much and the revenues are trailing so far behind. And then you also have the speed of revenue scaling is faster than ever. Oh my gosh, they almost seem at odds.

I don't worry about the $600 billion thing.

Do you think that's the right question to ask? That was really my question.

No, I don't. I go back to my software engineer example. The prize is like, forget about AGI for a second. The prize is so big even without AGI. Like, the prize is so big. And so like, yeah, the revenue will materialize. There's a lot to figure out.

I was talking, we had a dinner guest yesterday.

We do these dinners as a partnership with a guest on Mondays. And one of the kind of conversations around it was we were kind of unpacking is like, if you think about search. So search, we started first seeing the search, the first search engines Call it 1995 is when you started to see the first search engines.

And then Google Series A was 1998, I believe.

And, you know, and like immediately was like a better search engine and a better trap and everything else. What I think is lost in time is like Google didn't figure out the monetization. Of course they did it through an acquisition. They didn't actually figure it out themselves. Like they didn't figure out the monetization of search until like '08.

2001, I think, and so like, or late 2000. And so we had like 5 or 6 years of these search engines, which anybody at that time was using them every day. There were crappy display ads all over them and like all kinds of stuff that was just totally like people that had them. I think sure there were like paid search engines, like people tried to do all kinds of things to figure out the monetization model. And so I don't know that we figured out the monetization model, but I think what we can say very clearly Customers perceive a lot of these products, not all of them, but a lot of these products as magic, which they are basically magic.

And like they want them.

There's a bunch of monetization that needs to be figured out, but I kind of don't worry about it. Like it's like we will figure it out.

That's just the delay. And so I feel like, you know, and the reason the parallel to search is interesting is because it's like, hey, there was a new technology that was really powerful, search, web search. And it took a while to figure out monetization. And we have new technology LLMs that are really, really powerful. And we've got to figure out monetization on them. And it's not going to be a $20 a month subscription. That's not the right way. And it's not going to be just bundled API calls.

There's going to be much, much more sophistication and interesting models on that.

How do you think about value in the stack? When we think about where value in the stack is today, it's obviously in compute. NVIDIA has seen that. It's also in models with OpenAI. Everett, your partner Sarah wrote that models are the fastest commoditizing technology ever, which is a great statement. I've used it a couple of times in shows. How do you think about where value accrues in the stack and where you need to spend most time aligned to that?

Foundational models are the fastest depreciating asset in human history. I think it's turned out to be largely true.

Does that mean there's still great value in OpenAI?

I think that's a good question that is really interesting because if you think about OpenAI and Anthropic Meta and Google, and then there's a whole bunch of others coming, xAI and Mistral and so forth, SSI now. I think the foundational model war benefits us all in a way. It's really, really good for consumers and people around it because it's just like they're pushing the state of the art so much.

In terms of value accrual, for Benchmark, we have no foundational model investments. And then 2, we have a set of infrastructure investments, which I think are really interesting.

We have Cerebras, which is a semiconductor and systems company for AI that we invested in and led their Series A in 2016. So we've been working on it for 8 years. We have companies like Fireworks, which are an inference service, and others at that infrastructure software layer. And I think those, again, they're growing very, very quickly, astoundingly quickly, and doing really cool things. But at the same time, you have to ask, okay, what are the foundational models going to do and how are they going to move up the stack. And so this is again where I go back to, you need great entrepreneurs who are constantly updating their mental models and re-navigating. And when it works—

Do you not think we just see all the foundation model companies just get acquired by the big players? We've seen Character, Inflection, Adept.

I don't think all the foundational model companies will get acquired by the big players.

Some may, but—

If you are not, how can you fund survival?

Well, I think this is a question, right? They're going to do it. But I think there's a set of people who certainly believe in the size of the prize, and so they've continued to be able to raise really tremendous amounts of capital to train bigger and bigger models. Just to put it in perspective, like $100 billion, even for the oil state companies of technology, doing a $100 billion acquisition is unprecedented.

But doing a $30 billion is not, and you only need to acquire the LIC pref.

Yes.

I mean, I think there's 2 things, which is like, if $30 billion acquisitions are not unprecedented, and maybe you could say like in this world, therefore $100 billion also is not that big of a stretch. That isn't that huge a multiple, but it does feel like a big number to me.

It would also never get through antitrust.

Well, I think the antitrust thing is a big question.

Does AI today, do AI rounds break the benchmark model? And I mean that slightly deliberately provocatively, but your fund sizes are very disciplined. You are hailed as the boutique provider of venture, very tailored, very artisan. But these rounds are often, you mentioned some of the software creation AI companies, these rounds are like $50 million starting price.

I don't think so. And I don't think so for 2 reasons.

One is through 30 years of performance, I think we have unprecedented flexibility in what we do. And so if we want to write a $50 million check, we can write million check and we have. And if we want to write a $150 million check, we can write a $150 million check. If we deploy a fund in 18 months or a year, like it's fine.

We can do whatever we want.

So like, don't— the fund thing is almost like irrelevant artifact of history and accounting. And so I don't worry about it there. So that's one part of it.

The second part of it is, um, does it not just, you know, I think if I look at it, you mentioned 11x.

Does it not just impact your decision-making? I totally get you. Of course you could. Like every LP wants to be in venture It's the one fund that every LP is like, oh, I want Benchmark. I totally get it. Of course they do. But if you have a 500 fund, you're just not as likely to write a $50 or a $75 million check.

I think one of the things that we maybe think about almost not at all is we almost never think about fund cycle or fund timing or anything else. And we almost never think about or talk about portfolio construction, or anything else. Like, we— it does not come up.

It's really interesting because when I talk to other venture capitalists, they're like, well, how do you think about the portfolio construction? And how do you think about check diversity and company? And just like, you never ever talk about it.

And so it isn't a thing, genuinely isn't a thing. That's a bunch of inherited goodness and flexibility. I think there's this amazing Munger quote, and he said, you know what, finding good investment ideas is hard enough. Finding great companies is hard enough. Let's not over-constrain it, basically. Let's not over-constrain it. Let's not add a bunch of things to it. So I—

what I say back is like, in the Benchmark view and approach, what we're looking for is these exceptional opportunities led by these exceptional people that can turn into something extraordinary if things work.

Like, that combination is hard enough.

Do you not think it helps provide a of focus to narrow your examination of where to spend time?

We very, very openly and regularly talk about things that are just, hey, that's way off. That's a $50 million check for 10% ownership.

It's not something—

that's not the core model, obviously. But the flip side is I look at, you mentioned 11X, that's an amazing company we're super lucky to be part of. I think about Brett Taylor's Sierra, I think about Lens, Fireworks. I go through and I look at these companies and I'm like, I like that AI portfolio. It's a bunch of infrastructure software companies. It's a semiconductor company in Cerebras. It's a few application companies as well. And the foundational model rounds and some of those things have gotten really, really large, but you kind of look at some of the things that are happening on the ground in the early stage in AI and it's like, yeah, it's totally doable, totally manageable.

Do you think that you always need to play the game on the field? Bill Gurley, your partner, said that once, and I oscillate.

I mean, to some extent, you always have to play the game on the field, or you always have to— maybe a different way to say it is, you always have to be aware and cognizant of the game on the field.

So, the game is the game.

You can always choose to play more or less. You can choose to play more or less. So, I'll give you a really concrete example.

2021 was like SaaS craziness, everything crazy. Right?

Like everything was running and everything else. In 2021, we made like 3 new investments as a firm.

3.

That was the game on the field. And just saying, that's okay.

I'm okay not playing that game. And that's great. And I have no regrets on that at all. I think that is fabulous. This year, 2024, the game on the field is we have a major, major shift in AI, which is like, could be bigger than any of these other shifts, maybe combined. It's really big. There's a lot of interesting work happening.

There's a lot of uncertainty without a doubt, but we've been more active than we've been since 2010 and 2011.

What was happening in 2010, 2011? Mobile shift.

To what extent with AI do you think we are overestimating what we can do in one year and we're all getting ahead of our skis?

One of the beauties of this in our model, like I think about if I go back to 2010 and 2011, for In that timeline, that's when Snapchat, Uber, Twitter, Instagram, that's when we did the Series As in Instagram, Snapchat, Uber, whatever, a weird round in Twitter. The round that Peter led in Twitter at that time was technically a Series C or Series D at $200 pre because the company had its history with Odeo and everything else.

And so, So it was a rule-breaking round.

It was a good example of exactly what we were talking about earlier.

Yeah, you kind of have your norms, and then every once in a while you just have to be like, throw it all out and just do it.

And that was a good example. But you think about that body of work, which was obviously tremendous in terms of returns perspective. And fast forward to today, you're looking at the game on the field here. We have to kind of ask ourselves, hey, are there extraordinary opportunities and extraordinary companies getting built here? And if so, You just gotta do it.

I heard you say once, when it came to Cerebras, that Peter's role as your partner was to help enhance your instincts.

Yeah, he did.

He's amazing. I have such a man crush on Peter. I haven't told him, and so it's lucky that this isn't a podcast. Uh, my question to you there is, is that not dangerous? Should a partner not be the counterbalance, not the Duracell battery to your energy?

Well, I think both are true.

My partners have kept me out of countless companies. It's amazing. You asked the sector question earlier, we were talking about it is like, I spend a lot of time trying to understand chemistry, my chemistry with an entrepreneur and, and try to figure out like, am I going to love working with this person? Do I believe this person is a learning machine or not? So I spend a lot of time on that. I spend a lot of time trying to believe like, do I think that insight is cogent? Or not cogent? Does it hold together? And I spend a lot less time on like the sector specifics because I just feel like I if I'm an F on a sector with best effort I can get to a D plus like that's not good enough like and so I'd just rather like not and I actually think this is this is maybe contrarian and total aside I think this is why the memo writing culture at a lot of firms gets you in trouble because you put a lot of information. It encourages putting a lot of information that is third and fourth order stuff into document as if that is impacting your investment decision, where most of these investments, there's really one or two questions that really matter. All energy should go to those and everything else is unknowable, non-deterministic, or irrelevant.

So what Peter did in the case of Cerebras was, and I remember it really distinctly, I met the company, First on a Wednesday.

And I was like, why am I meeting a semiconductor company? Like, this is so stupid. Like, I shouldn't be doing this. Like, we don't make semiconductor investments.

This is maybe February, March 2016.

And I came out of the meeting and was like, wow. And so like the team was amazing. And then the insight was really keen and really sharp. And it's now totally accepted. But at the time it was like so sharp and so contrarian.

Or so not contrary—

contrarian's not the right word, actually.

What it was is unique and novel.

Like, that's what it was.

It was unique and novel.

And so I came out and I was like, this is interesting.

We met again.

So I called in a bunch of partners to meet on Thursday. And so a whole bunch of us, including some of the founders, including Bruce, you mentioned earlier, like he came in because like, you know, like what do I know about semiconductors? Almost nothing. Well, really probably nothing. Colin Bruce, who had actually done semiconductor investments, and discussion. So that was on Thursday. I spent time one-on-one with Andrew on Friday. Bill and I had lunch with Andrew on Sunday. I'm talking to Peter about it on Sunday night. He's just totally discouraging me from doing the investment. He's like, this goes against everything I've learned in the industry. Totally discouraged. He hasn't met the company yet, just based on my articulation. Monday, so I bring the company in. Peter was like, I was like, just have an open mind on it. Just have an open mind. And so comes in on Monday, Andrew pitches, and he had a term sheet already. And so we were running obviously, and he pitches. And at the end of the pitch, we're like debriefing and we have a system where we're like, we talk and then like you can call, like the sponsoring partner basically can call for a vote. And Peter said, call for the vote. He told me, he was like, call for the vote.

And he pushed me.

So he pushed me to call for the vote and to push it over the line after spending literally 16 hours before trying to talk me out of it. And so the answer is, yes, my partners have kept me out of a lot of stuff, but what he was doing in that moment is he had updated his own evaluation of the opportunity and the idea and was like, yeah, it makes sense.

Also, Eric clearly really wants to do it and sees something here.

And so, you know, I'm 18 months into being a venture capitalist and I have one of the greats of all time 16 hours before telling me this goes against everything, like, don't do it, like blah, blah, blah.

And, you know, and so I think that, that encouragement of like, hey, there, you saw something there.

And then like the rest of the group saw it and was like, yeah, there's something there. And like, and so I go back and think about that a lot because each of us in a good partnership Each of us brings our own points of view and our own biases and baggage, but our own insights as well. And so you gotta put all of that together.

And if you do that well, that's like, that's these partnerships at its best.

And I've seen that a whole bunch of times.

In the deals where your partners have saved you, as you mentioned many times, what did they see that you did not see most often?

I'll give you a great example where Sarah saved me. We were looking at a company And she's like, Eric, and this goes back to your sector thing. It's just like a perfect example that ties this thing together. She's like, Eric, I'm telling you, you're used to looking at software companies. At this company, gross margin and these unit economics really, really matter and they suck. There isn't a path to get better and the entrepreneur is not engaged on the topic.

It was just like a great insight because for us, as your kind of traditional software investor and doing things, It really doesn't matter. It's just like all of these things end up, your SaaS companies are going to end up in between 75% and 83% gross margin. They're just going to end up there.

It's fine. It works itself out.

And so a company that starts there way less than that, it's just like, whatever. And you'll fix that. But I think it was a great insight that kept me out of it because there were a ton of things that I loved about the entrepreneur.

It was really like a compelling individual, but I think her point on the nature of the business and the fit between the entrepreneur and the nature of that business in specific was spot on. And so Sarah saved my bacon.

So I was talking to my my partner the other day who comes out of DST, and so he's like trained on like gross margin and like real numbers guy. And he's like, "Oh, the gross margin three million in ARR." And I'm like, dude, no one gives a fuck. No one gives a fuck. It's $3 million in ARR. The gross margin in 10 years, I've got no idea what it'll be like. And even then, if it's still a shit gross margin and it's go-go times, we can still get a great multiple. And if it's amazing in shit times, the IPO market, there's so many fucking variables. I don't have a clue.

I totally agree. I think this goes back to the spreadsheet conversation and why I think spreadsheet investors are going to get wiped out or have a really hard time in this era. And I think SaaS was such a boon and gift to the investor bankery spreadsheet investors. Plug your stuff in and you figure it out at scale, right?

At the early stage though, like, I totally agree with you.

Like, people come in and they have like a million and a half and they're like talking about their net dollar retention or whatever.

It doesn't matter. It's in none of that stuff. And not a single company.

I think I've worked on 5 companies that have gone from zero to more than More than 200 in revenue. And like, in not a single case did the economics at the very early stage extrapolate all the way.

Like, it's just, it's just not a thing. Not even, not even the economics from when they were at 30 or 40 or 50 extrapolated to 200. Like, it just, it isn't how it works.

There's so much change that happens at these companies.

And so like, it just—

false precision around that is just dumb.

And I think you can say, like, let's say, let me take the flip side of it, which is the flip side is there are things that you can see at those stages which would tell you that this thing is like going into a wall or is going to have to undergo a major transformation.

Or like, I think there are problems that you can see, but I think the positives are not really knowable that way.

Can I ask you, in terms of like how you spend your time, I spoke to Victor on your team and he said Eric is so unlike most other VCs. He spends like 70% of his time with his portfolio.

Yeah, at least.

I would love to understand how you think about the makeup of your time between sourcing, picking, and doing diligence and doing references and then servicing, helping portfolio. What does that look like?

I'm probably at this point, I'm probably 80 or 85% on working on the portfolio. I mean, it's a lot.

And part of that's because whatever, I'm on 12 or 13 boards.

I can't remember, but a huge part of it is that is our model.

That is the Benchmark model in a sense. The Benchmark model is a concentrated portfolio of very high conviction commitments. We're making commitments to entrepreneurs.

It's very high concentration and very high conviction.

That is the nature of our model. And I think that is—

Are you on too many boards? 12, 13 is nuts.

It is a lot, but I think they're all at different stages. It isn't as nuts as it kind of seems on the surface because 4 or 5 of them are really young companies that are in their very early days.

And like you said, I spend 80, 85% of my time on them. So it's not like I'm—

if I spent 60% of my time looking for new companies, then of course you wouldn't be able to spend that much time on your portfolio.

Final one for you. You said about call the vote. What does the vote look like?

Our vote system, which is somewhat irrelevant, but it's a way to kind of quantify people's feedback.

So a company comes in, we all talk about it.

At this point in time, most of the time, like there's only 5 of us, right? So at this point in time, we would've chatted about the company and at least 2 or 3 partners would've met typically. And so there's a decent amount of institutional knowledge about the company.

And then at the end, we You kind of quantify your feedback. And so it's a way for partners to quantify their feedback to others.

And so our voting system is you vote 1 to 10, you can't vote 5, 6 and above is yes, 4 and below is no. And it's kind of strength of conviction, right? If you get a bunch of 10s, amazing. I've never seen that happen. If you get a 4, partner's telling you they didn't really like it, but it's not whatever.

If you get a 2, your partner's telling you they're really discouraging you.

Have you ever had a 1?

I don't know.

If your next question is what happens if you don't have the vote and you still want to do it, I have no idea. I don't know what happens.

Does that not go against being non-consensus? Seeing the beauty which others don't?

Not necessarily, dude. I think they're—

Because you have to get a quorum, so you need to get 3 out of the 5.

I don't know that you have to get 3 out of 5. Like I said, I don't know what happens.

Benchmarking is a very very high trust, like high confidence in each other model and structure, right?

Is it always 5 people saying yes?

No, I'm just saying nobody knows what the votes are except the sponsoring partner.

And if a partner wants to do something, I think they can do it. You're getting feedback from your partners who you trust and have confidence in.

Why do the voter tool?

I think it is actually useful to quantify things.

You get all this, you get all of this And anybody who votes a 6 on an investment does it apologetically. They have to be truly conflicting. There's nothing strategic.

That's managing politics. That's not managing, making the best investment decisions.

Do you not think it's about kind of being all in or like, hell no?

Well, I think the sponsor, your sponsoring partner, the kind of advocate probably needs to be that, but your other partners who are looking at it and trying to help you make a decision, And I don't know that they have to be that way. They, you know, they have less information necessarily. And so having their strength of conviction doesn't need to match yours.

Okay, we're gonna do 2 types of quickfire. One's a short quickfire, one's a slightly longer one. Quickfire on people. You've got one takeaway from working with Gurley, Fantin, and Matt Kohler. Okay, I just chose them because they're my favorites. So what's the biggest takeaway from working with Gurley?

Even great companies can be overvalued. One of the things that Bill is really good at is thinking about fundamentals, right? He came from public market investing way, way back when at the beginning of his career. He has that mindset, that analytical mindset. And so he thinks through that and says like, hey, on a fundamentals basis, you will trade sometime at under 30 times free cash flow. And so that's a thing, right? You and I were talking about What's the ARR number and the revenue and all this stuff? And so like, but like ultimately you talked about the 4 areas, which is sourcing, picking, winning, helping build, right? There's a really important 5th stage that nobody talks about and not every venture capitalist gets to, which is exiting. And ultimately our job is to return money back to our investment partners, right? And so when you kind of like think through that, you do have to ultimately, hopefully everyone gets to a place where they're thinking about this like, 5th step. Not everyone does. And in that place, you do have to kind of think about these fundamentals. And so occasionally you have an amazing company that you really believe in, but it can be overvalued too.

Okay. Fantom, what's the takeaway from Peter?

The insights around people and motivations that Peter has are unparalleled. And I've described this before, but Peter and Bill, one of the things that's amazing about the two of them is they are very They're very, very different style investors, like almost diametrically opposite in a bunch of ways. And obviously they have a lot of common ground, which is how their partnership was so effective for so long. Peter is very much like people first. Bill is very much, I would say, like market first. It's a different mental model in looking at these things.

Totally agree with you. I remember Peter once told me price is a mental trap.

He told me a version of that on one of the first investments I looked at at Benchmark in 2014, summer of 2014. The discussion was like, well, could you do it at 40 or 60 or whatever? It's like some price. I was like, well, I'd do it at 40 and not 60. And he's like, nope, nope, that doesn't work. No, I can't do that. I've said this now subsequently to new partners who've joined in my own way where like, yeah, that's not— you can't— not allowed to make that claim.

I remember also when I was debating whether I should get more operating experience before becoming an investor, He was like, "Do you want to be an investor?" And I said, "Yeah." He's like, "Then invest and be an investor." Yeah.

Yeah.

Final one, Cola. What have you learned from Matt?

Matt is the best at understanding the insight and the depth of an entrepreneur's insight. That is Matt's superpower: understanding the depth of the insight. Is the insight bullshit? Is it authentic? Is it really deep? He's six sigma on that. Matt says very little. And it is always insightful.

The normal quickfire.

What do you believe that most around you disbelieve, Eric?

I don't think Nvidia is going to be the only game in town. I don't believe Nvidia is going to be the only game in town on the infrastructure layer. And I think the entire setup right now is an assumption that if AI is real and here to stay and there's real ROI, then Nvidia just continues to run at this level. And I don't believe that.

That's a good one. Credit to you, Eric. That's fantastic. Which venture investor outside of Benchmark do you most respect?

There's so, so many.

Good. You've got one.

I've got one. I think you'd have to say, I'd have to say, I'd have to say Goetz and Jim Goetz. And the reason I would say Jim Goetz, well, one, Jim is He was the one who first told me I should get into venture, and so I'm eternally grateful to him for that. He said that to me in '08. It took me 6 years to figure out that he was right, but I'm super grateful for him for that. But 2, we talked about this, there's almost nobody who's had wins in consumer land at the scale of WhatsApp and wins in enterprise land, and he's done it over and over. So to have Palo Alto Networks, And WhatsApp is just insane. And obviously he's had many, many other wins. And so he's been a spectacular, spectacular investor and I'm eternally grateful to him.

Why did you not join Sequoia?

Just like at that time. So in '08, when I talked to them, I really had it in my head that I wanted to be a founder and I'm super glad I did. I had said conversations but came up with the idea for RockMelt. And so Tim Howes and I, who was my co-founder, pursued RockMelt. And then 5 years later, you know, RockMelt went through our ups and downs. We were bought by Yahoo in 2013. And then, you know, a year later I joined Benchmark.

And you didn't go back to them?

I did not. No. Different time. I think, you know, Sequoia, like a lot of firms, hires people quite early in their careers and like grows their own.

Are you ready for my hardest one?

Okay.

This is off schedule. This is like for true pros, but this has been so good. The heaviest things in life are not iron or gold, but unmade decisions. What unmade decision do you have that weighs on you most?

I don't know. I can think of several little things that I would've done, but I don't know. We had early acquisition interest in RockMelt. That was probably something that I should've leaned into more in retrospect.

That's an example, and that would've been a very different But in the scheme of things, like looking back now, it's 2024.

Would it have changed anything? Probably not. This is the whole— what's that children's story about the horse rider and the soldier and the conscription? Have you heard the story?

No.

It's a really amazing story. So like the summary of it is, is like there's a—

it's like ancient China and there's a draft. And the first part of it is there's a family in rural China. And they get a horse. This kid finds a horse. The villagers are like, oh my God, he's so lucky.

It's so lucky.

It's so lucky. And the wise man is, is like, we'll see. And the kid's riding the horse and breaks his leg. And then the villagers are like, oh my God, he's so unlucky.

It's so unlucky.

It's so unlucky. And the wise man is like, we'll see. And then there's a draft, a military draft, and the draft people come to this rural village. And they draft everyone except the kid with the broken leg because he has a broken leg. And the villagers are like, we're so lucky.

He's so lucky.

And the wise man's like, we'll see. And so I say that because, and it relates, it relates to my RockMelt experience and kind of where we started, which is founding and building companies really, really hard. And you have to have a lot of determination in order to do it. And you go on these ups and downs, and I certainly did. And ultimately we didn't realize the potential of what we wanted, but But did it work out or did it not? And in the fullness of time, I'm like, I couldn't be happier where I'm at. And thankfully those team members, they're at great places and great companies. A whole bunch of them work inside of the portfolio that I work on, which I'm super grateful for. And so we'll see.

Reminds me of the businessman and the fisherman. I don't know if you've heard that one.

I haven't heard it.

Oh yeah, yeah. I have heard this one where it's just like, well, what would you do with all that money?

Well, I'd retire in a small fishing village. Yeah, totally.

Totally.

I've heard that one.

Drink beer and chill.

Yep, pretty much.

Penultimate one, you can call yourself up before the birth of your first child and say, Eric, you should know this. What would you say?

Man, that's hard.

What's the hardest?

I think the big thing in the first few years, and we went from 0 to 2 So, it was particularly challenging but not as challenging as people who went 0 to 3 or 0 to 4. But, I think the hardest thing is your time goes away, it inverts. It used to be on a typical, for you or for me now, my kids are a little bit older, is Friday is the day you're the most tired and then you have Saturday and Sunday to recover. This totally inverts, right? For me, when we had kids, at the end of the work week, was that was the most rested I was going to be, and Monday morning was the most tired I was going to be. And like, it just like completely inverted because the weekend, because it's just so hard with little babies.

I'm so well trained then. In the week I have venture, which is the easy part, and on the weekend I have just back-to-back-to-back media stuff on Saturday and Sunday. And so I'm knackered coming into Monday.

Okay, okay.

Yeah, you're, you're in good shape then.

I had kids and I'm totally Final one for you. When I ask you for a memorable moment from your time at Benchmark, what's the one that you tell grandchildren?

That one for me would be when the founders of Confluent, Jay Nahonjun, called me and said they were going to go with Benchmark for their Series A. That was my first investment. It was 3 founders, you know, and the company's gone on to wild success. That was a huge, huge moment for me. And really memorable. I remember where I was, and it was a big deal.

Eric, dude, I've absolutely loved doing this. Thank you so much for agreeing to do it. This has been amazing.

So fun.

What a man! I absolutely love doing that show. If you want to check it out on YouTube, you can by searching for 20VC. There you'll find all of our episodes in video. I'd love to hear your thoughts and feedback. But before we leave you today, all of you listening use tons of software every day. Sometimes it fills us with rage. You can't figure something out. The chatbot in the bottom right is useless. You keep getting bombarded with these useless popups. And for those of you who build products, no one wants their product to feel like this. Thankfully, a company exists to help users without annoying them. CommandBar. It does a couple of very helpful things. First, it's a chatbot that uses AI to give users extremely personalized responses and deflect tickets. But it can be beyond just text. It can also co-browse with the user and show them how to do things inside the UI. Magic. But it can also detect when users would benefit CommandBar is already used by world-class companies like Gusto, HashiCorp, Yotpo, and AngelList. If you're a product CX or marketing leader, check them out at commandbar.com/harry. And talking about incredible companies with CommandBar, I want to talk to you about a new venture fund making waves by taking a very different approach. It's a public venture fund anyone can invest in. Not just institutions and accredited investors. The Fundrise Innovation Fund is democratizing venture capital, which could have big consequences for the industry. The fund is already off to a good start with $100 million into some of the largest, most in-demand AI and data infrastructure companies. Companies like OpenAI, Anthropic, and Databricks. Check out the Innovation Fund's impressive list of investments for yourself by visiting fundrise.com/20vc. Carefully consider the investment material before investing, This and other information can be found in the Innovation Fund's prospectus at fundrise.com/innovation. This is a paid sponsorship. And finally, let's talk about Squarespace. Squarespace is the all-in-one website platform for entrepreneurs to stand out and succeed online. Whether you're just starting out or managing a growing brand, Squarespace makes it easy to create a beautiful website, engage with your audience, and sell anything from products to content, all in one place, all on your terms. What's blown me away about Squarespace is how easy it is to use. Away is the Squarespace Blueprint AI and SEO tools. It's like crafting your site with a guided system, ensuring it not only reflects your unique style but also ranks well on search engines. Plus, their flexible payment options cater to every customer's needs, making transactions smooth and hassle-free. And the Squarespace AI? It's a content wizard, helping you whip up text that truly resonates with your brand voice. So if you're ready to get started, head to squarespace.com for a free trial. And when you're ready to launch, go to squarespace.com/20vc and use the 20VC to save 10% off your first purchase of a website or domain. As always, I so appreciate all your support, and stay tuned for an incredible episode coming this Friday with the OG of sales and customer success at HubSpot on 20 Sales.