Exclusive: OnlyFans Investor James Sagan Sits Down with The Information’s CEO Jessica Lessin

27:17It's like a real call to adventure at the firm. We have a very spiritual mission. And you're based in San Francisco. In San Francisco. And so, you know, we've typically succeeded by creating, you know, by identifying market structure problems, structural inefficiencies in private markets, and solving them either by creating new financial instruments, solving them by, you know, raising capital for things that are misunderstood, where we can attach at a, you know, low price that's very downside protected and help build, you know, a way in which we generate meaningful convexity. And so what made me tick is I'm sort of a value investor at my core.

27:58And today that looks like dealing with really complicated things in private markets. Yeah. Not many of you around these days, right? Are there? Or it just seems like everything has swung in the other direction. There's really not. And we are kind of unusual. And I think that's why we've been able to attract amazing talent to the firm. Because it's really exciting. And we're having a lot of fun. Well, James, thank you for joining. Thank you for sharing more about your plans and, you know, addressing the controversy. We will be watching and have you back on TI TV to see how it's gotten. Thanks so much, Jessica.

28:35I really appreciate it. Thank you.

28:40That was Jessica Lesson with Architect Capital's James Sagan here on TI TV. NVIDIA this morning released a new version of its open source models, and my colleague Phoebe Liu, Our NVIDIA reporter has some new reporting on the open source strategy at NVIDIA at large. I want to bring on Phoebe to talk about her reporting. Phoebe, welcome back to the show. It's great to have you here. Thanks for having me. Okay, so NVIDIA made some releases this morning. You have a ton of reporting on the broader ambitions behind those releases, but let's get to the news first. What was it that they announced here?

29:17Nemotron 3.5 Lightning, what is that? So the Pneumotron 3.5 Lightning is a smaller, so relatively 30 billion parameters compared to the 3 trillion that the biggest models today have. Yeah, relatively tiny, even though still a lot. And it's trained on NVIDIA's current largest model, Pneumotron 3 Ultra. And it's meant to be a kind of a lightweight version of that to run AI agents for a long time, faster and more efficiently. And the other thing that NVIDIA released today was model routing software. So this is software that would help a company kind of probabilistically route an AI task to the best and cheapest model for that specific task.

30:05So Open Router is probably the most famous company that does that. So NVIDIA is releasing free software to help anyone do that, to kind of lower the barrier of entry to making routers, which is very interesting. And just so I understand this, the routing software here, this is an open source software that routes models, whether or not the model is open source or closed source. It's basically just the software altogether that is accessible for anyone to see the code, right? Yes, that's correct. And the example that NVIDIA gave alongside the release was kind of routing tasks between Pneumotron 3.5 Lightning and Opus 4.8, which is a closed source model.

30:48Right. Now, I want to talk about the size of the model briefly here because size in model land has become so important. We obviously know that Meta's latest open source model release, Mark Zuckerberg, really prioritized the fact that it is a small model. And we've talked about small models being a focus. So the 30 billion parameters that you talked about, who is the audience here? What is the customer target here that NVIDIA is trying to access with this new model? Yeah, so I think NVIDIA's biggest customers by far has quite a bit of customer concentration between kind of OpenAI, Microsoft, SpaceX.

31:26So the target of something like this is kind of everyone else who's either trying to save money running agentic AI tasks that don't need kind of full horsepower, three trillion parameter model. And also kind of people running this on kind of their local clusters. A 30 billion model can run on NVIDIA's new kind of supercomputer PC, DGX Spark, which is interesting. And also some of its Jetson hardware aimed at robotics. Right. So let's talk about the story that you published then this morning. Put this release in context of what you found NVIDIA is going for in terms of its broader open source strategy and how it's actually investing in it and growing that team.

32:09So I think for a long time, NVIDIA has shied away from saying that it does want to develop frontier leading edge open source models because a lot of its biggest customers also make advanced AI models. But from talking to a lot of people involved in this effort, the directive really is to make the best open source AI model in the world, which is hard. We don't know for sure if it's going to be able to do that. But the fact that that is the directive is very interesting. And alongside that effort, which is targeted at Nemo Tron 4, which is their next family of models. And there's going to be one that's kind of bigger than the rest, probably labeled Ultra.

32:46um alongside that effort nvidia has been scaling up its cloud computing spending quite a bit um i think the outstanding kind of multi-year cloud services bill for uh from the nvidia regulatory filing from april 2026 was about 30 billion and that's pretty much triple that of a year ago, which is a very marked increase and kind of a proxy for just how much NVIDIA is doubling down to invest in trying to create the best open source AI model it can. And so Nematron 4, is the ambition here ultimately to challenge the open source models that are best in class here in America? Are they going to try to target the open source models in China?

33:39Because Those are obviously the ones that are leading the pack here. How good do they think they can get with it? Yeah, so I think the goal is to be the best open source model in the world, which would mean trying to challenge those Chinese leading edge open models. Obviously, that's going to be hard. I think some of the people who are working on it are saying the parameter size is going to have to be much bigger, which is part of the reason NVIDIA is scaling up spending there as well. I think NVIDIA doesn't like to think about this as competing with its customers, but I think inherently there is a little bit of that tension at play.

34:16I think the general takeaway from all of the people I was speaking to is that NVIDIA is doing this because they want to kind of spur competition and encourage more people to be able to develop more Western or American open source AI models more easily. And then the idea here is what? That as these smaller customers can use more of these models, they can inevitably buy more cloud services, which then in turn can help the cloud providers buy more chips? I mean, how does it all get back to the chips? That's exactly it. So, yeah, obviously NVIDIA is not just doing this out of the goodness of their heart.

34:56Like, this is a business decision. One of the VPs that's kind of the face of this effort, Brian Catanzaro has said that the new mode run effort is essential to the future of NVIDIA as a company because it's one of the key things they're investing in to kind of diversify their customer base because they need more people than that big that small handful of big labs that's kind of buying a lot of NVIDIA GPUs sometimes through cloud contracts they need like regular businesses and more labs and other people to want to consume that many GPUs as well to kind of unlock the next phase of growth for its business because all the software is free.

35:38Like, they need to sell hardware at the end of the day. The thing that I'm sort of thinking about, and, you know, maybe you can put this into context for us, is inevitably you have to be a certain size of a company to buy chips directly from NVIDIA, right? Most of these companies are buying, again, through the cloud providers or accessing through these cloud compute platforms or these infrastructure providers. And so I sort of feel like even if you get more customers on the ground using NVIDIA's open source models, does it actually help diversify the NVIDIA customers at the end of the day? Because, I mean, look, even if you're a company with, I don't know, 1 ,000 employees, I don't know that you're buying chips directly from NVIDIA.

36:21You're still going through AWS, GCP, Oracle even. Isn't that the reality on the ground? Yeah, for sure. I mean, NVIDIA definitely is trying to kind of stimulate a broader ecosystem of small up-and-coming clouds as well. But I think it just depends on what you count as a customer. I think what I'm referring to here is kind of diversifying the base of end users, like people who need to compute, if that makes sense. Great. Well, Phoebe, I want to thank you for coming on. It was a great story. That is Phoebe Liu, our NVIDIA reporter, here at The Information. As Anthropic marches towards its IPO, the company's early investors stand to make some big gains.

37:02Menlo Ventures is one of those firms. The company also recently raised new capital of its own, announcing$3 billion in fresh funding this summer. I want to bring on Matt Murphy, a partner at the firm, for his view on some of these topics. Matt, welcome to the show. It's great to have you here. Thanks. Great to be here. so i want to start about the chatter around open source we saw this morning nvidia put out their latest version of their open source models meta of course is diving deeper into the open source game yet again i guess after a brief little uh closed source diversion there they're they're now targeting things on both fronts uh you know who do you think is the most likely company to to offer the most competition to the open source companies in China that are really leading the pack here.

37:50Is it Meta? Is it NVIDIA? Are you looking at the TMLs of the world? I mean, who comes to mind right now? Yeah, I mean, I think the exciting thing about it is there's such a wide open landscape right now. There's no one who's really emerged in the US or rest of the world that's kind of like, you know, cutting edge, or you'd say they've really pulled ahead. So I think a lot of this chapter is going to be written going forward. And just the proliferation of open source models makes an exciting time for developers and our whole ecosystem as that leads to a lot of derivative investments around the kind of more open model, open weight stack.

38:26But if you were to pick one, who would you pick? I don't know. You know, I mean, there's obviously extremely well-funded teams. There's now 60 going on, 200 neo labs who many of them are trying to do things very specifically like, let's say, skilled in robotics or Axiom math, doing more formal verification things. So there's these very specific models. And then there's ones that are trying to be very much like the next foundation model. And that kind of research takes a long time. Reflection is a name that's thrown around a lot. You've got maybe Mistral trying to make some kind of a comeback. but underneath that, you've got tens of Neo Labs, you know, Jeff Dean's company, and I'm not sure if you know exactly what their approach is, but you know, there's like continuous learning techniques, you know, there's the kind of more vertical technology around models like robotics or whatever.

39:19So I think we're just gonna see so much. So I can't really give you a perfect, clean answer to that, but - Right. Otherwise, maybe you'd have invested in that. I'm sure you're invested in some of these Neo Labs players as well. So look, I mean, core to this open weight story is some of the pushback right now that companies developing closed source models have gotten. And you, of course, are one of the best known investors in Anthropic. When you hear this pushback that big tech CEOs put out there into the world, we see the discourse on X as well. I mean, look, there's both sides to all these arguments.

39:57what's your reaction to it broadly speaking i mean you're saying what's what's my reaction to this kind of like and i should open wave models be allowed well i'm more talking about you know you've got these big ai labs there's are they training on your data or not there's something you don't know obviously there's zero data retention is certainly what people say but you don't know what's going on under the hood i mean i i'm just wondering as someone who you know is very involved in the ecosystem. What's your reaction to that dialogue and discourse? Well, first of all, I don't like distillation.

40:35I think that is some form of kind of like stealing IP. So, you know, it takes a lot of really amazing researchers and capital to build some of what's been built. You know, I don't buy into, you know, necessarily that any of the main foundation models are stealing somebody's data or things like that. I think that, when you partner closely with customers and you help them solve their problems, your model will inherently get smarter. But I think that a number of the foundation models have kind of had a little bit of a more than a Chinese wall between what they're training on and what their customers data is.

41:14But I think it's just kind of a sign of the times that everything's kind of exploding right now around the growth of the foundation models, the open weight models, and everybody's just trying to figure things out. And so that leads to these questions. And there's kind of like a stage of fear in the unknown. And then we all learn more and get smarter as we go. And we're kind of in that transition now. Because for a while, as you know, it was really just OpenAI on Anthropic that really mattered in this category. And now we've got a whole wave of other potential models that customers can use to complement those main models.

41:49Right. When you look at Anthropic's expansion strategy, I mean, they've made big strides, you know, in biotech, in design, you know, certainly the coding realm as well. I mean, it's really spread its wings into so many different vectors of the AI puzzle. I wonder, since you're so close with the management team there, I mean, what is one area you think they wouldn't expand into? Where won't they go in the near future, do you think? Yeah, I mean, you know, it's sort of saying, you know, I'm just sitting there, you know, the quiet periods. I can't talk about two things, any forward-looking things.

42:26But I think generally speaking, my view is the power of the model keeps getting greater and it kind of ends up emerging into different categories. The fact that they've got things like co-work, anthropic as co-work, where you can build skills. So there's a lot of, one of the great things about this, it leads to an explosion of DIY, do it yourself, right? So you have the model, but then you've got all these capabilities surrounding it and the quote, you know, hardest where you can develop a lot of your own capabilities. So I think that, you know, the DIY market probably expands by 10x, but then also you've got a set of like leading companies that really focus on, you know, building an end-to-end application that crosses organizational boundaries, that deeply understands, you know, customer workflows through FDEs and the learning of their systems.

43:14So I think the application market is going to be just fine. and you look at companies like Lagora and Lovable who are absolutely flourishing and 10 other legal companies, like we're in a company called Manifest and Eve, and it used to be the worst place for the venture capitalists to invest. And now it's created maybe 10 companies that are hyper-scaling. So that's one of the exciting things about this is that the models have enabled that versus taking those opportunities away. It's freeing that. So you mentioned some of your portfolio companies, and Menlo obviously raised$3 billion in new capital for investing in these types of companies.

43:55AI companies, I should say, is very much the focus of the new capital. Right. You know, a year ago when we were talking to VCs on this show, there was a lot of conversation around DPI being the big concern for LPs and liquidity being something that LPs were obviously looking for. Have the times changed now? Is the dialogue different? Where do we stand on the DPI discussion for LPs? Yeah, I think there's kind of like your old funds and your new funds. And your old funds, the kind of SaaS era or the era before AI, I think people are kind of curious about like, hey, look, there's a bunch of TVPI, but how's that translating into DPI, especially as some of these companies are getting a little bit longer in the tooth, or maybe they're transitioning their plans to AI, adding AI products, et cetera.

44:44And some of those companies are really going to flourish and be massive around the AI wave, and some will be a little bit left behind. And that's what happens in new technology waves like this. So I would say that, yeah, I mean, I think it's normal for our LPs to want to know how venture firms and the companies in those portfolios are kind of dealing with this environmental change. I think on the new investment side, right now, people are just looking, meaning LPs and funds, It's more about, you know, leaning into the current environment and making new investments and helping, you know, companies scale up things like that rather than, you know, focused on VPI.

45:24Although I would say that, you know, we are going to be in a period of consolidation. It's already happened. You're talking about consolidation in terms of the venture fund climate or where exactly? Yeah, well, like we had a company called Graphite bought by Cursor. So, you know, if you kind of take a look at the stack, there's a lot of pieces, you know, we sold a company to called Clarify to Nebius. It's kind of, you know, the companies that are more at the infrastructure layer saying, hey, we need some more, you know, differentiated software capabilities. And, you know, I mentioned a cursor acquisition that we're part of.

46:00So it's just, it's going to be a very inquisitive environment because there's too many companies to begin with, you know, probably pursuing too much, especially like the AI infrastructure stack. and those will kind of come together and be added on to some of the leaders. And I think you'll see a lot more of that over the course of the next year. What about this trend here? You know, we hear a lot about venture is a tale of two cities right now where the big funds have obviously done very well. They're raising new capital. Smaller funds, you know, it's tougher for them. I mean, this is a story that's been going on for many years now.

46:35It's nothing new. But I just wonder, as people talk about consolidation in the venture sector, the big getting bigger, what's the net impact that you think that has on startup investing, broadly speaking? Yeah, I mean, historically, there were swim lanes to venture investing, more seed funds, venture, and growth, and then the public markets. And now a lot of those and a lot of firms kind of consolidate into one, especially given the public markets are taking so much longer. So we have a dedicated seed effort with a few partners focused on that. We have our normal kind of Series A motion. We have a growth vehicle that now is able to double down on companies and invest well over a couple of hundred million dollars as they kind of scale up.

47:19So in some ways that's good for, in many ways that's good for the venture ecosystem because you've got a number of firms that are more full stack that can kind of, you know, scale with the company. Overall, there is more capital. Entrepreneurs have more choices. I think maybe what you're talking about is, so what happens to the seed funds relative to these big sources of capital? I would say we have a seed motion, but I think if you're a firm and that's all you do, there's still plenty of opportunity out there. Many of those firms are complementary to what we and a number of the other big firms with a lot of capital are doing.

48:01But certainly we don't want to seed the seed opportunity. So again, we have dedicated partners to go after that. I think one of the things that can happen in an environment like this is you can get everyone just chasing heat and high valuations, the kind of known outlier companies. And certainly we want to be in the best of the best outlier companies, but you have to have the disciplined team, the focus to go after the early stage and catch them before they kind of blow up. That's just a key part of the job that always has to happen as a VC. Great. Well, Matt, I want to thank you for coming on.

48:34That is Matt Murphy, partner at Menlo Ventures here on TI-TV. Software companies' quarterly results so far have been a bit of a mixed bag, but the questions around the future of these businesses still loom large. For an update on that conversation, I want to bring on Jackson Ader, Managing Director of software equity research at KeyBank Capital Markets. He joins us from the company's annual tech conference. Jackson, welcome back to the show. It's great to have you here. Thanks, Ikash. Yeah, it's good to see you again. Thanks for having me. So I was looking at a basket of about 100 software companies that I regularly track, as the coolest among us regularly do.

49:10I know you do the same thing every morning. This particular basket I was looking at, it's the Meritech Capital Benchmark Index. It's up 38 % over the past three months. These are all software companies. What is driving that growth right now? Well, a couple of things. I would say without knowing the all 100 constituents of that basket. The companies you follow. It's the same companies we're talking about. I mean, what's driving it is, I think, a couple of things. One is some better fundamental expectation on infrastructure software. So that's security. that's observability, that's monitoring. I'm sure results from Palantir the other week didn't hurt that index, but the broader, I would say, application software cohort has also traded up significantly in the last few months.

50:04You could argue that that's been more driven by market technicals and some momentum unwind because let's face it, most of software has not exactly been in that top quartile or decile for momentum longs in the last year. And when you get an unwind where some money comes out of the long momentum and into maybe some of the relative losers of late, sadly for me and the rest of the sector, the relative losers have been software companies. And so we've seen a really nice bid over the last couple of months. And now we have some earnings where, depending on, I think you characterized them as mixed, that would be fair.

50:42Some earnings have been better than expected, particularly for some of the larger caps. Right. So I want to look at some other activity. Bending Spoon buying Airtable was a deal that I'm sure you paid close attention to. I mean, you cover a whole host of companies that could find themselves one day being another Airtable. Among the companies that you cover, who is the next Airtable, do you think? Yeah, we put out, every year we put out a note that we kind of call the sharks and minnows, right? So sharks eat the minnows and it's an M &A report, right? Kind of a state of the M &A landscape. And, you know, what you find is a lot of the minnows, you know, it's been some of the minnows that have been gobbled up.

51:32Sure, they've been taken maybe by strategics, but it's also been some private equity activity that has taken some of, that have kind of gobbled up those minnows. if I keep the metaphor going. I mean, as far as who's next, I mean, Akash, who could say? I don't know. Give me three of the names in the report. I mean, did you name some candidates in the report? Yeah, of course. So, I mean, one that comes to mind would be, especially maybe at this valuation, might be something like HubSpot, specifically as it relates to Airtable. I mean, you know, their competitors, maybe not a perfect overlap, but that's monday.com and asana yeah i don't think that monday is necessarily in a position you know with their founders where they would be interested in selling but you know those are the types of minnows that we're talking about still recurring revenue um still very high margin upside from where we are today and that's probably what people are going to be looking for whether it's private equity or strategics okay strategic wise do we see a big ai lab making an acquisition of one of these companies at all?

52:38It could be an AI lab, except that, and the same is true really with hyperscalers, you know, because for the longest time, the short list of who could acquire these companies would have been, you know, Microsoft and Oracle. And, you know, now these AI labs, they certainly have the equity power to do it. But they are, I don't know if the right word is preoccupied, but a lot of these companies, their cash is spoken for. And so the traditional strategic acquirers are spending their cash elsewhere. And so it might be more consolidation a little bit further down market. We've seen ServiceNow get more active in acquisitions, a company obviously that we cover.

53:24We've seen Adobe get more in strategic acquisitions of late. And so I think maybe a little bit further down, Salesforce is always in the market, right? Like a little further down the cap structure from the absolute mega caps or even the AI labs might make some sense to consolidate these companies. You mentioned Salesforce very quickly before I let you go out. I do want to ask you about the leadership shakeup in the top engineering execs for Salesforce. They have their earnings coming up in a couple of weeks. What should we be watching for and what was your reaction to that shakeup? I mean, you know, Salesforce and it's not just Salesforce.

54:04It's a lot of these companies, you know, many of the top, you know, SaaS market dominators, they, you know, they lose executives because those executives for one reason or another, maybe they want to go, you know, trade up and, you know, be a CEO somewhere else or go to even maybe a younger company and try and try and hit the growth accelerator. So I don't necessarily I'm not thinking to myself, oh, my goodness, the the the engineering leader is now out of there. and so AgentForce is in trouble. I'm not thinking that. What I'm thinking is that just what we hear from CIOs and from partners and from customers is that the hope that AgentForce would be this wonderful engine of acceleration, the reason that we were overweight the stock for about two years after they introduced AgentForce is just not coming to fruition.

54:54And so that was the reason for our recent downgrade. But what we'll be listening for is what we've been listening for for the last year. When is Agent Force going to materially or materially accelerate the overall growth rate of the business rather than showing some nice KPI metrics? Right, because if I recall correctly, Salesforce has a habit of posting some big AI-related growth numbers, but you never really know what goes into there. And so to your point, until that actually meaningfully raises the total top line, it's kind of hard to say that it's making a dent for the company. That's exactly right.

55:33That's exactly right. Great. Well, when that quarterly result comes out, we will be sure to bring you back on. I'll let you go back to your conference, Jackson. I want to thank you for coming on. That is Jackson Ader, Managing Director of Software Equity Research at KeyBank Capital Markets here on TI TV. That does it for today's show. A reminder, we are on this stream Monday through Friday at 10 a.m. Pacific, 1 p.m. Eastern. If you can't make it then, episodes are available on theinformation.com, on our YouTube channel, or wherever you get your podcasts. Make sure to follow us on social media, on X, on Instagram, on TikTok, and on LinkedIn.

56:07I'm already excited for our next show tomorrow. Have a great rest of your Tuesday. Bye-bye for now.