Bessemer Venture Partners closes $5.75B fund, with $1.75B for early stage and $4B for growth

Speaker 10: talking Thank to you you.

Speaker 1: Goodbye. Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI, own the data platform that powers it. Coming back on the show, we got Talia Goldberg, partner at Bessemer Venture Partners. We very much enjoyed her last appearance.

Speaker 2: What's going on?

Speaker 1: How you doing?

Speaker 12: Hey. Great to see you guys.

Speaker 1: You had a hot take last time that went very viral and proved true. Everyone thought everyone was like, no, it's about to collapse. And you were like, no, I think like it's okay if there's some, you know, margin compression in the short term. Things will iron out.

Speaker 2: Models are gonna get cheaper.

Speaker 1: And everyone was like, she's not taking finance seriously. And here you are. Vindicated. Vindicated. So congratulations. Victory lap, Patrick.

Speaker 12: Your margin is my opportunity.

Speaker 1: There we go. There we go. How are you processing the current moment? What's exciting to you? Is the is the lab trade over? Or is there more opportunity above the fold at the application layer, below the fold at the at the semiconductor, Neo Cloud build out level? Like, what's exciting to you personally?

Speaker 12: Yes and yes. I'll say a few things I've been thinking about lately. One is physical AI and one is this new concept that a portfolio company might fall Oh, yeah. Coins.

Speaker 1: Love that.

Speaker 12: Called token market fit. Yeah. And I'll talk about like, I love this idea of token market fit. Wish I had invented it, but they Yeah. And the idea of token market fit is like, simply put, like, what are the areas in the categories where we've found the ability to productively use the average end user, like $10,000 a month of tokens. Mhmm. And if you actually look at where a lot of the spend has gone, it's really like there are basically only three categories I can think of right now that have like real token market fit. One is encoding. Like, you know, enormous spend encoding. The second is in is is in video and in media and and in your line of work where you can very productively spend huge amounts of money creating great content. Yep. And then maybe the third is in high frequency trading. But if you look at like on average, you know, legal, customer support, sales, like, we actually haven't really hit like product market fit in a real way in a lot of those areas, and we will. And I think the bottlenecks are moving away from this like code and video.

Speaker 2: Mhmm. Is it possible that you can have product market fit without massive costs? Because, like like like, I would argue that that you have, like, token market fit in something like a legal Mhmm. But it's just not you're not gonna see massive spend in the way due to the nature of the work. Right? So it's like Okay. Possible that you can have I I I'm just this is maybe a question. It's like, can you have token market fit with just like relatively modest per person spend at a company?

Speaker 1: Sort of the argument that like well, like, we need a lot more software but maybe we don't need that many more legal briefs or something? Like, the market size is small? Is

Speaker 2: that Yeah. There's there's in some ways almost like infinite soft like a product is never finished

Speaker 1: Okay.

Speaker 2: But in in legal work, like, do a deal and then it's sort of, like, done and there's not you know, I don't know. Yeah. I do need to Yeah. My question is, like, I believe there's I believe there's gonna be some categories where, like, AI is incredible. It transforms, like, the the task or the job, but you just don't end up with that much. It's just very efficient and you don't end up with, like, you know, exceptional spend. Right? You're saying, like, $10,000 a month Yeah. Per company in a category.

Speaker 12: It's possible. I mean, I think, like, I guess, yeah, there's difference between product market fit and token market fit. But legal is like a really interesting example. Customer's been a long time investor in Legora Okay. Which is like a a great example of this. And I think they're still very early, actually in this transition towards what's possible and what spend even for their customers is possible. So I think legal is like yet to be unlocked in a lot of ways. And in a lot of use cases, Logora is still co pilot. It's not really autopilot. It's not like truly doing the work of of lawyers. And and then you don't see like large law firms like, you know, massively changing their their team compositions yet. I think that is still to come. And that's on the come. And as we can give and figure out how to productively leverage models to give more work to to agents, like, will be spending more on agents and on models in in in fields like legal too.

Speaker 1: Yeah. Yeah. I mean, we saw that transition maybe last year, earlier this year with like the software engineer who says like, I don't read the code anymore. And that can sometimes be a little bit risky, but in a lot

Speaker 2: of

Speaker 1: places

Speaker 2: The lawyer who's like, I don't even read the contract.

Speaker 1: Well, that would be token market fit. Like if I heard a, you know, a star lawyer say, yeah, I don't even read the the filing anymore, document, I'd be like, okay. Yeah. Super intelligence is here. It's working. Like

Speaker 2: Yeah. Well, the the only difference is, like, software, you can ship a feature and it can be it could be broken 1% of the time, where in legal, if you have a 1%, you know, major error rate in contracts, it's Could be bad. A lot of money on the line and

Speaker 1: Yeah. Maybe it'll be a little bit higher up. Yeah. Yeah.

Speaker 12: Look, there are these categories that like I guess the point is like, there's still to come. Like, they're still growing. And so like, what are those next areas with token market fit from an investment perspective are interesting. Because while you're right, there will be some that are maybe a lot more efficient. A lot of the dollars are in the token flow.

Speaker 1: Yeah. Yeah. How how important do you think it is to be the entry point to a per to a certain token flow to be an aggregator in the Strathecari parlance? I was just thinking about Fall and Higgs Field. They, you know, access to incredible models. There's a few other companies in the category. But a lot of people, the workflow is like go to their preferred AI agent, then send an API key over and they've already put some credits in an account and then they're using another tool to sort of access that inference. And there's a world where that billing relationship lives within Cloud Code or lives within Codex and they're taking sort of an app store cut. Is that a nightmare? Is that a death now for the inference provider? Or could that actually be the future way of these companies working together in a positive way that actually grows the market and the distribution so much that it offsets whatever whatever rent the Model Labs and the and like the the front the front doors are taking?

Speaker 12: Yeah. Look, I think it's probably not like black and white in every And single so, it's somewhat different. Like even in the case of fall as an example, like they are the front door. And so, you you can even access a model like in the past models like Nano Banana and Google's models even through fall.

Speaker 1: You don't

Speaker 12: have to go to. So, they have their cake and eat it too. But we do see this playing out. And I think like Instinct and Muse and what's happening with Meta or Amazon and Shopify

Speaker 1: Yep.

Speaker 12: Are other really interesting analogies of this aggregation, disaggregation theory. And at a high level, like, I am a believer. Like, there won't be places for both. And like, both exist. Amazon exists and Shopify exists. And, like, the Sure. The tension between the two is real.

Speaker 2: Yeah. Personal agent predictions?

Speaker 1: Oh, yeah.

Speaker 2: How are you thinking about the category?

Speaker 12: Well, we're very small seed investors in instincts, so we're, you know, very bullish on on their on their I don't know if I should be offended by that sound or

Speaker 2: No. No. No. That's the error.

Speaker 1: No. No. No. It was a small check. But now, it's because you went in the scene, it's big now.

Speaker 12: I wish we were bigger investors. But like it is such a magical experience. Obsessed these products. Like Yeah. Other than when ChatGPT launched, like this is the second, oh my God, moment that I've had. Like my parents are having this moment and my sister's having this moment and they don't even use technology. And so it's kind of crazy like

Speaker 1: I Yeah. It is much broader than what we saw with the Clog Code Codex boom where it was this magical experience for people in tech and for people who knew how to open up a terminal and then were familiar with code and had these tasks that sort of fit neatly in that world, this is something that you can give to a family member who doesn't have a GitHub account and they'll have fun and they'll and they'll get some value out of it and be and and sort of see the progress, which they might not have updated on in three years or something. But And

Speaker 12: so, yeah. Everyone is gonna need to own this and there's gonna be a major war and every large lab, every large company is gonna be out there trying to figure out how do I create like similarly such a delightful experience. And how do you bring this to enterprise? I think it's just like the the interaction modes, the delightful proactivity, the simplicity of the interaction is something that could absolutely be ported over to the enterprise use cases as well. And so, I think this is not a winner take all market. There will be multiple agents and and different angles on it. And I think this is about to be arguably the most important next category in AI.

Speaker 1: Another knife fight too which is fun. Yeah. Give us the update on Bessemer, raise some more funds. But what's the structure of

Speaker 2: the fund? What's the structure of the strategy evolving Yeah. All that good stuff.

Speaker 12: Yeah. Look. So we have exciting news that we just raised $5,750,000,000. 1 point

Speaker 2: That sounds good.

Speaker 12: That's how we feel. We're we're so excited. We have $1,750,000,000 of that is dedicated to early stage companies, which lets us write really meaningful investments in companies at their earliest days when conviction matters a lot, when it's not, you know, very obvious. $200

Speaker 1: seed rounds. Let's do it. Get it done in two deals. Probably not.

Speaker 12: But And looked like 70% of our investments have started early often way before there's even revenue or or sometimes even a company name. Mhmm. And we're gonna continue to do that. And then $4,000,000,000 for growth, which lets us keep backing companies as they're at their inflection points. And not just participate or let others kind of invest in them. We're gonna be leading these rounds. Returns are concentrating, as you all know, in fewer larger winners. And so, the right move for us is to be a meaningful investor and have meaningful positions in the companies that really really matter. And we don't want to spread it thin and be peanut buttering across our growth dollars across a bunch of companies. We want to be super disciplined, but disciplined not by taking small checks in in growth companies, by making big checks, being highly selective, and really committing fully to a smaller subset of companies. So that's the strategy.

Speaker 1: That's exciting. Amazing. Very exciting times. Well, you so much for coming on the show.

Speaker 2: It is so funny to rewind like ten years and and if somebody from the future came and they're like, Talia, like, you're gonna have a $5,750,000,000 fund, and you would be like, so we're the biggest venture investor in the world. Right? It's like, well, you know, this is now like, if you wanna be like a real fund

Speaker 9: I know.

Speaker 1: You gotta have Companies are staying private so much longer. I mean

Speaker 2: Yeah.

Speaker 1: The yeah. The trillion dollar IPOs are, you know, unthinkable years ago. And now, there's

Speaker 2: Here we are.

Speaker 1: It's crazy.

Speaker 2: Wild time.

Speaker 12: Yeah. Lot lot to come. Thank you guys for having

Speaker 2: me. To see you. Thanks. Great update.