Latent Space: The AI Engineer Podcast - Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures

Episode Date: November 14, 2025

Deedy Das, Partner at Menlo Ventures, returns to Latent Space to discuss his journey from Glean to venture capital, the explosive rise of Anthropic, and how AI is reshaping enterprise software and coding. From investing in Anthropic early on when they had no revenue to managing the $100M Ontology Fund, Das shares insider perspectives on the fastest-growing software company in history and what’s next for AI infrastructure, research investing, and the future of engineering.We cover Glean’s rise from “boring” enterprise search to a $7B AI-native company, Anthropic’s meteoric rise, the strategic decisions behind products like Claude Code, and why market share in enterprise AI is shifting dramatically. Das explains his investment thesis on research companies like Goodfire, Prime Intellect, and OpenRouter and how the Anthology Fund is quietly seeding the next wave of AI infra, research, and devtools.Full Video EpisodeTimestamps* 00:00:00 Introduction and Deedy’s Return to Latent Space* 00:01:20 Glean’s Journey: From Boring Enterprise Search to Valuation* 00:15:37 Anthropic’s Meteoric Rise and Market Share Dynamics* 00:17:50 Claude Artifacts and Product Innovation* 00:41:20 The Anthology Fund: Investing in the Anthropic Ecosystem* 00:48:01 Goodfire and Mechanistic Interpretability* 00:51:25 Prime Intellect and Distributed AI Training* 00:53:40 OpenRouter: Building the AI Model Gateway* 01:13:36 The Stargate Project and Infrastructure Arms Race* 01:18:14 The Future of Software Engineering and AI Coding This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Hey, everyone. Welcome to the Layden Space podcast. This is Alasio, Founder of Kernel Labs, and I'm joined by Swix, editor of Layden Space. Hello, hello. And today we're finally joined by the epic return of Didi Das. Welcome back. Thank you for having me, guys. Again, I'm so glad to see you. All of us have different jobs now. All different jobs. Classic Bay Area, you know, it's been two years, right? So last time it was April, 2023, you joined us remote, and you were still at Glean back then. And I was actually even also looking at the cloud timeline. So Claude 1 was March 2020. And Cloud 2 was July 2020. It just feels like so long ago.

Man, I remember the time when, I don't know when your first experience using Claude was. But mine was, I remember early glean, there was somebody from the company was like, hey, there's this interesting new LLM that's not OpenAI. And the only way you can talk to it is by tagging Claude in a Slack channel. And I'm like, that's a bizarre interaction model for a whole new product. It's the best model. And now, fast forward to now. And I'm like, okay. Yeah.

It's, we've come quite away. Yeah. I think actually, they only recently introduced Claude in Slack, right? Or, like, publicly. Come back. Yeah, yeah, yeah. It's like how it started. And now Cloud is track and Slack.

Cloud and Slack. And so since then, I wanted to start with Glean, obviously, because, you know, we're going to cover a lot of startups in this episode. So Glean has, Glean was like a billion dollars, I think, based on my research. And now it's at $7 billion. So your options are good. What's your take on how Gleens going and the market in general? I would say that now being on venture side, I have a bit of a different take than I would have had at Glean. But broadly, one of the things that I love about Glean is it's such a boring, unsexy company that became sexy later.

So from 2019, I remember going to parties in the big. Bay Area. And I would say Enterprise Search, and it's shutting down the conversation right there. You're like, nobody would ever ask a counterquestion if you said Enterprise Search. They're like, oh, that sounds boring as hell. Leave me alone. Like, and fast forward to 2022, Enterprise Search gets more, got more conversations. It was like, interesting.

Tell me how you're doing this search. I think what was nice about that observation is in those three years, we did a lot of work and not, didn't take shortcuts on a lot of things. that ended up generating a lot of value for us now. And I can go into what all of those things are, but if you look at Glean from a high-level business, it is top-down enterprise sales. It's very hard to rip and replace.

We expand contracts very easily because the TAM is so large. Every knowledge worker could use a version of enterprise search and then the AI on top, I still call it search, but information retrieval in the enterprise. And we've solved a lot of critical problems, go into that too in order to get there. Then comes, you know, December 2022, the chat GPT moment, everything that's happened since. And now when I look at Glean, you know, it's a different world. We were very quick and correctly prioritized LLMs earlier on. It did a lot of good for

our business and the company. But now there's fire from a lot of angles. Like everyone wants to be a part of the enterprise search story. And it makes sense. I mean, it's a large, unconstrained ham. LLMs are particularly useful for gathering information. Obviously, consumers are interesting and enterprise, therefore are interesting. How do you do this in the enterprise? We'll gather all the knowledge and then put an LM on top. So that being said, I'm still very happy with Glean stock. You know, Gleens also valued at $7 billion, not $100 billion. So I think the company has a lot of growth. I think it's done a lot of the hard work that nobody's willing to do. And I also think, you know, VCs have a tendency, including myself now, to trivialize a problem into a one-sentence

sort of narrative. And with Gleen, that narrative was often, oh, well, you guys built this enterprise search thing, which never worked. And then AI came along and it started becoming a thing, which I think is not the story at all. I really think we did all the hard work to build search. And AI happened to accelerate our go-to-market motion at the right time. And now I see companies trying to tack on search, it's not easy. I know the kind of like last mile stuff we did for some of our customers. And I just know that when I think about other companies, I'm like, would you really go all that distance? It's not a moat. The mode is just we did the hard work. And so I'm pretty happy. I mean, things can go any direction, but I'm pretty happy with the way Glead's going right now.

And just to spell out the two main challenges. So one is obviously Claude, I think today launched enterprise search. I have the screenshot. Did you see like, hey, we're introducing enterprise search. I'm like, yeah. Son of a gun. And then the other side, you have the data providers adding this rate limits, kind of like Salesforce is done with Slack.

It feels like that part is more challenging than like the competition from other companies. Like, yeah, any, how do you think about that? Two questions, I guess, competition and the rate limits. On the rate limiting side, it's happened for several of the SaaS tools. I think one advantage that Glean has is, well, the first thing, let me address the premise of the argument. When I think about why SaaS tools would limit API access, inherently it never made sense to me. I can see why you do it for business reasons. Maybe you want to launch a competing product.

But Glein doesn't eat into your revenue. If you are Slack and you've sold, call it 100 seats at a company, and you have Glein at that company, if anything, Glean is only shows Slack results to the 100 seats that you've sold. So we aren't eating into your business. So from primary first principles business logic, I don't see why you'd do it. If Glein is on Slack and more people are searching through Slack, it actually lets you sell more seats, not less, because we don't reveal permissions to people who don't have access. If we were to do that, then I could see maybe a business case like, oh, you're taking the Slack

data that I've only sold one license for and you're showing it to a thousand people. that's problematic, but we're only showing it to the licenses that you've sold. So firstly, that's my first point. The second thing is we do have thousands of integrations, and in a lot of enterprise customers, Slack is really important, and that's a critical data source, but we also have many, many more. And so, it's just, you know, the law of large numbers. So maybe if everyone decides to shut it down, it could be more problematic, but if one

person does than, you know, less. And the third thing I'll say is if you talk to the customers, they're also super unhappy about this because they're like, look, we bought your product. We own the data. You don't own the data. And so if we want to buy another product to use our data in Slack, why can't we do that? Why are you blocking the API? So those are the three prongs of the argument. I can't, I don't know how this will all end up, but I don't think it's that sensible that it is like this. And I'm still optimistic that we'll clear out some of those issues. Yeah. Anything else you want to say? So, like, you know, obviously we're about to move to Anthropic and the topic just launched Enterprise Search. And so what would you say as a veteran of

enterprise search, Anthropic should like, you know, take a take note? The question of the labs competing with Glein has always been a thing since 2022. Sam Altman, like we were just discussing earlier, like Sam Altman once came out and said, if you're investor in Open AI and one of these five companies, including Glean. We don't want you as an investor. Yeah, it's just fact. And but yet, here's what I see. Look at the revenue of Anthropic and Open AI right now.

These are billion dollar revenue scale businesses. Glein is several hundred million dollar revenue scale business. So the way I think about, and this can even allude to like how I think about startups right to compete and right to win is for Anthropic and Open AI to build a deep enterprise search system, it doesn't make them that much money. They have to put all this effort to make, what, an incremental 100K cell, 200K cell maybe, even a seven-figure cell? Is that moving the needle on your, you know, five-plus billion dollars in revenue

or 10 plus to the end of the year for Open AI? Not really. And the amount of effort it takes to get there is big sales teams, huge FDE teams, tons and tons of customization. And my question is, like, in the long, long run, you could build a semi-reasonable enterprise search tool. If you really want to go deep, I don't think you will ever dedicate the people to do it. And the last thing I'll say is, you think about it from an anthropic engineer's perspective,

you joined a big AI lab to work on models, not to build Google Drive connectors. There's a meme, like, you know, I built the fucking integrations, build integrations. I think I'm still very bullish, but yeah, competition happens. Yeah, yeah. Actually, I wasn't asking about competition. It was just more about what are the hard problems that people don't appreciate. Oh, okay. We can talk about that all day.

That is probably a safer category for you. You know, basically like, you know, and I'm in this boat as well. I've joined an enterprise AI company that has to worry about and build for these issues. And I'll just give you one very example. Until this point, we never had to deal with two slacks. Like, and Enterprise has like, you know, when you acquire another company, you have different systems. And they all duplicate and they all overlap. Yep.

man, I have some great stories about So Devin, you know, like, I'm sure there's like some pro user version of this, but I still haven't figured out how to use Devin properly with two slacks. Wow. Because Devin's off space for one slack. That's funny. That's funny. So Slack workspaces, like, that reminds me that was

the thing that we had to address at Glein. I think like every enterprise company has like the same sort of hurdles. No, no, no. We looked at each other. We're like, oh yeah, we're a real enterprise. And now we have two of everything. That's funny. Okay, Glein, a bunch of interesting problems.

I'll talk about some of them, if you want to prod, feel free. I think number one, most interesting to me when I joined the company was consumer search was largely regarded to be a solved problem. Not really, but largely. The way most consumer search systems work is by aggregating feedback data on how users use search, whether they click, hover, how long they stay on a website. And that's what powers ranking systems to get better over time. Very, very powerful, critical way of how Google being in all the above work. In enterprise, if you take a 10,000 person company, even if every user issues two search queries a day, which is quite a lot, say even five, I don't know, that's just not enough volume to have any meaningful quantity of feedback for this to be relevant.

On top of that, add to the fact that freshness is way more critical in the enterprise in certain ways than it is in there are more freshness seeking queries in enterprise than there are in consumer. and then number two is the distribution of queries in consumer is very head-heavy. It's not an enterprise. In enterprise, maybe the query that everyone wants to search for is benefits or payroll. It's just not that useful. Really, like, it's every person's doing a job and they have different needs and they have different things they want to look up. So given all of that, the techniques behind the hood, under the hood, that work for

consumer, they don't translate to enterprise. You have to invent a whole new set of signals that actually makes enterprise. research work. And evaluation becomes very, very difficult, too. On consumers, you have tons of data to pick and choose how you want to evaluate what's the right result to show for this query. In Enterprise, and I have this story a lot, like, we look at some of our customers' data, and we would look at each other and go, like, we don't really understand what this query

means. We don't really understand what these results are. We don't know what is the right ranking or not. We have actually no idea what we're doing here. And which happens, like, it's so out of domain for even us. Some of our customers are working on very, very specific problems. And so all of those, that's one huge you challenge. How do you make ranking work in enterprise, you know, in a great way?

There's many. I'll touch on the more second interesting one. Second interesting one is selling productivity tools to enterprises are challenging because as no matter what ROI argument you make, people aren't actually buying tools for ROI. People buy productivity tools because their users like using them. So, for example, when people buy Slack, I don't think. any buyer is going like, let's measure how much faster our, how much more productive our team is getting by using Slack. It's probably not even getting that more productive.

That's not what they're looking at. They're kind of saying, everyone uses Slack. It's pretty useful. I'm going to keep Slack. I don't think we're going to share that one. If you take that analogy to search and search systems, the issue is search systems aren't inherently viral or growthy. Slack has a very clear virality moment. Everyone's talking to everybody else. And so that's just how you have to speak. In search, it's kind of a one-player game. You're not really sharing things. You're not really talking to everybody else.

So the challenge for us was like, how do you get, sell a productivity tool by getting everyone to love this on day one for a product like search? It's not easy. If you look at how Google did it, they had Chrome. So great. Like, have a great source of sense of distribution. Get everyone to like query.

And then they'll learn to love it hopefully. So we had to figure out what that meant in the enterprise as well and how to get everyone to like adopt and embrace. and love this new tool. Yeah. So two of the many. The pointers. Yeah, just the question on that.

Yeah. Was there any, because, you know, oh, you have a new search tools. Like, go search. And it's like, what am I searching? You know, like, what was that blank canvas onboarding for people? Anything good? Several different things worked well for us. I can think of two at the moment, but I'm sure there were many, many more.

I'll say one of them was, say, for a handful of companies, like many companies, actually, we would say we want to take over your new tab page. And then the critical part was tell us what we need to do to earn the right to do that. No one wants to give away their new tab page. So we went the last mile. There were companies where like, well, we have a new tab page. We're pretty happy with it. So we'd ask, do you have a search bar on it?

They'd be like, well, yes. I'm like, okay, what is that using? And they'd be like, well, it's using our internal thing. I'm like, do you like it? Clearly not. That's how you're putting to us. So let's just rip and replace that. But doing that extra mile was pretty important.

So that's one, new tab. The second one that we liked was Chrome extension and then doing the, I forget what we call this, but when you were on your native product and you were issuing a search query, we ran a lot of evils, and we thought we were better at every product at their own search. So if you were searching on Google Drive, we will do a glean replace of the search bar and the page pretty natively. And it would teach people to use glean and be like, okay, this is pretty useful. I think these results are great. and it automatically filters to Google Drive anyway.

So functionality is not lost. And we would slowly get people to be into the ecosystem that way. Yeah, super set adoption. Something that open router also does. Okay, so Anthropic, we have to obviously address the elephant in the room. You guys are huge, huge, anthropic investors. I think right after you maybe got promoted or you became a partner, you guys led the D. What was the chronology that?

I think we did part of the C and then the D and then every single round we had. more than Perrata. Yeah. Obviously, one of the greatest companies in AI, I honestly had no idea that it would, like, we would be sitting here. Like, Anthropic is 10xed in the time that you've been at Mendo. And I just,

what's it like being an Anthropic investor? What would you think about what are considerations back then versus now? Anthropic is the fastest growing software company of all time. I think I can say that fairly. I haven't been disproven yet. So I think the- People say that, but like, everyone says, like, you know, we're like, first,

So like $1 billion, first to 100 million. I don't know. It's hard to tell. I do believe the numbers are zero to 100 in one year, 100 to a billion in one year, and this year would be one to the projection that is public is nine. But even to this point, like, I know a lot of people we've seen the graphs on Twitter. I had a lot of some of that is bullshit, some of that's GMV,

but in Anthropics case, I think it's like fairly legit revenue, and I do think it makes it the fastest. Definitely at like the $1 billion plus scale, I can't think of too many examples. So clearly has outdone itself. I would say that when we invested in the company, it had no revenue. I mean, that's just fact. So when we wrote our first investment, it had no revenue, it was a...

$18 billion. Or $4 billion. Right. It's been fascinating to see this company succeed. I couldn't have predicted it. All of us, this was beyond our wildest expectations. I think whether or not it continues to perform at, at this rate, I believe it will, but it is already somewhat of a generational company in many ways.

And so it's kudos to the team to deliver like these, these awesome results. You know, one of the risks, I would say, like kind of taking a tangent, one of the risks with a company like Anthropic is you essentially had a team of extremely idealistic researchers. And very often, you know, the standard deviation of outcomes when you have teams like that or similar to that is quite large. There was a world where maybe they would have not worked at all and would have absolutely fizzled to the ground. But I think it is the same qualities that would make them have a high propensity to fail, made them

them had a high propensity to succeed. And if you look at, there's many other things they did right, but if you just look at a product like Claude Code, there's not many product innovations in AI that I can think of that are so critical as something like that. Because we had the whole chat era of rag systems and chat GPT. That was a critical innovation. But since then, there was a lot of followers, a lot of deep research, which is kind of, I would say, an addendum. A couple of other things happening here and there, agents, cool. But, you know, if you think about agents that actual end consumers use and gain value from, in my mind, at least, Claudecode was the first time I saw that in a terminal,

in a weird interface. It was just weird. Like, it was like every PM's nightmare. No PM would have thought of that. And so it's such a... Except for Cat Woo. Yes, except for Cat Woo. And so, you know, it kind of gives...

It goes to show how Anthropic is able to function at the company to be able to innovate like that, which is quite rare, especially for that scale. There's some extent, I think you just like hire good talent and then like let them loose with a lot of tokens, see what they come up with. They tend to build good stuff. Like, it's interesting to talk about, right?

Like, because take open AI and deep mind as a comparison point. Like, I think we'd all agree. They all have great talent. But they all don't innovate the same way. And it's always been interesting, like, just as an academic exercise, to think about, like, different leadership styles. And maybe from the outside looking in, you'd be surprised how little I actually know from an investor standpoint about how anthropic actually operates. But it seems like it's a company that has, you know, such high retention numbers on employees because they are very free-spirited in how they let the

employees guide the direction of the product versus other companies which are much more either top-down or prescriptive or like, hey, we need to go after this and we need to go after that. It's like, hey, let's see, let's see what happens. Yeah. Yeah, I think at my last conference, Signalfire had some stats. They track all the LinkedIn pages of everyone and like the topic has like the best retention and it's like a net gainer whereas everyone else is like a net donor of employees

to The Topic or something like that. I'm referring to the exact same article where I think their retention, one of your attention on employees is 80%, which in AI world is quite wild. Yeah, I mean, Antrobic does not have image generation. They do not have an IMO goal winning model. I feel like they don't, they just do their own thing.

Yeah. They do it great. You have nice hats? Yeah. Thinking caps. So actually, I really want to discuss this, but I don't know how to, I think I need to get like some like marketing PR agency person because people actually forget 20s 24.

They had out of home advertising campaigns which sucked. Everyone was like dog piling on them. And then this year it's like slightly changed. It's still anthropic but hole. But like slightly like and but they just decided to focus on thinking and like suddenly everyone loves them. And they have like the cafes and all that. Like it's it's a very interesting public image rebrand. And I don't know if it's because the models were just better or it was actually like PR.

Like which one comes first, like chicken or egg, like models or PR? It's a good question. Yeah. It's a good question. I would say, though, like ignoring the model side, like, like, I do think this one is like aesthetically better. Yeah. Like purely on the aesthetics. Yeah.

And the vibes and I don't know. It's hard to. I have sat in those meetings and it's like, like someone's pitching you an idea and you're like, I don't know. Looks good. Okay. And then like it becomes one of the most hated campaigns of all time. And then one year later, someone else. comes with like a slightly different looking idea.

And it's like four, the words are like different in like four ways. Like they chose like slightly different words, but it's not that many words. And suddenly that one is the one at works. Yeah. Well, as somebody who like writes online a lot,

I can relate to like a couple of things different can be the difference between something people care about and not. Yeah, early like in Gleen we had, I had such run-ins with marketing because the first campaign, we actually did this campaign. I was just like really AI for work that work. works.

Okay. Is that a hit? No. I mean, in Enterprise, like, how does one even measure what is a hit? What is not? I mean, no one really cares enough, I feel, one way or the other. But, you know, we've all seen, like, really cringe AI ads. If you've seen the Cisco ad in the airport, I hated that one for a while.

All kind of generic. So I like, anyway, I like the anthropic one. Okay. I'm going to sprinkle in some of your tweets. So you had one ad about the billboard where there was the, the, the, reddest guy was like, my boss really wants you to know that we're an AI company. I thought that was the single most honest billboard I've seen in San Francisco. Absolutely.

I think it's like the testament to all the comments of people going like, yeah, I relate. I mean, we've all heard it. Like everyone, it feels like even on the technical side, people are struggling to catch up, gain a sense of meaning again. I've had developers go like, fuck man, like, is this it? Like, what do I do anymore? And even that's happening on the technical side of people who semi understand what's going on. On the non-technical side, people are like, so there's this new thing. It's AI.

And generally, my boss literally just wants me to do something in it and I don't really understand. Other than chat chip, it is quite helpful. Yeah. I have some charts. I don't know if you like have any of these like in mind, but I'm just going to sort of bring up some of the anthropic charts. I think it's just, I want to just put it on the record for people who are not paying attention to understand. And in 2023, according to these are Mendoa numbers, right? 2030 market share for opening I was 50%.

And when mid-2020-5, you guys have opening eye at 25% market share. Anthropic was at 12, now at 32. It's like API, Enterprise API market share. Correct. So I should clarify that that is Enterprise, LLM API, spend. The market that Anthopic happens to focus on. And critically, it's also spend numbers, not token numbers. So I think those clarifications are important.

important and also the methodology is going and surveying, you know, vast amounts of enterprise users on how they are doing their spend. Yeah. But that being said, yes, the point, the point remains. Market share opening has gone down. It's not a negative. Obviously, opening has done super well. It's just that diversity has gone up. Like, it used to be there was basically only one choice.

And now there's like three or four, like, legit frontier labs. Maybe more than that, if you can't, like, all the open models as well. But I think it's just super interesting. and under-discuss still that you can actually build a sustainable advantage as a frontier lab. You know, I'm sure you guys remember. Like, there was a lot of conversation at some point about the commoditization of models. And to an extent, maybe it's happened. I mean, like, models, a lot of the frontier models are neck and neck on a lot of things.

But in practice, and this data was in that market map of that market survey as well. is that once people like something and they get used to it, they don't really churn off it once it fits their needs. And so we've seen a lot of that. So there's a lot of like churn and hobbyist developer type category. But in terms of enterprises, often what will happen is they'll buy up large chunks of long-term compute and dedicated instances,

in which case you just don't churn, right? Like this is what you use. So I think that's part of the effect. And, you know, to command open AI, like Open AI was just focused on something. else, which is, you know, they have, they've launched the most incredible consumer product that we've seen since God knows when. So, you know, they were probably not focused on enterprise until now again. Yeah. How do you re-underwrite the company internally as you invest? So, I mean, even since we talk

about clock code, right, it's like, I think that was like a pivotal moment and like the trajectory of Anthropic. What are the things that matter to you when you're like looking at a company, like Anthropic? Like, does this market share number matter? Like, how do you evaluate? both the opportunity and like what are the numbers that you really care about versus like sure hire market share but like that's not what we cared about. I don't think the market share number is, the market share number is more critical to understanding the TAM. At that stage, to be very honest with you, at the stage that we invest in Anthropic now,

like the only things that would really move the needle on the decision is here's the revenue, here's the margin and here's the trajectory and here's the other markets. we may be able to underwrite that they want to go into, that they may be early in or planning on going into. I think it's really difficult to underwrite on market share other than knowing what the potential cap of the TAM might look like. So the pie will also expand potentially. But other than that, I don't think it's just like it's a nice vanity metric

more than anything else. Yeah, in your mind is it kind of like, you know, people in crypto are always about the flippening of like Ethereum and Bitcoin. Like, is there something that matters? like, entropic can go to 50% or is it opening eye was only 50% in a moment in time, which was a new market.

Like, yeah, I'm curious how you think about the... I don't want to color like the way anthropic probably or the way all of us think about this, but I just don't think it matters that much. In my view, I'm a very paranoid person with startups and companies and technology. And so in my view,

I'm like, great. Now, let's make it last. Or like, great, but what's next? And so to me, it's like nice to have. It's really not, I mean, look, if we're investing in a round right now, which is like north of 170 billion, sure, it matters. Some of the numbers matter. But the future of the company is all the value is really in what we underwrite as the future. And the future means that I'm more concerned about what's happening next. What are the new models?

How do you gain market share? What has to be done? What are the new products that are going to be built? I'm less concerned about like where it's at right now. in terms of market shirt. But that's just me. I don't want to speak for others. Yeah.

I think the new models are really good. I mean, Opus 4.1, Sonnet 4.5, Haiku 4.5, all released in the last few months. And it's really interesting. I think OpenEI and Gemini are in this sort of price war a little bit with the Pareto frontier that I track in terms of like LMSS versus the pricing. And Claude can still charge a premium, but still like have a lot of market share. share obviously. And I think that's just because they have a better model. And people just naturally

gravitate to it, especially for coding, but also other things. And I just think, like, articulating what makes a model good is just very, very difficult. Obviously, this is benchmarks and evils, and everyone has like, okay, today it's your turn to be best at SweetBench. And then, like, tomorrow is my turn. But

like, like, it's really stupid. Like, we're just talking about, like, you know, point one, two differences in, like, Sweet Bench. But I wonder, you know, if you're talking about like, okay, I am investing $13 billion in Anthropic for Series F to underwrite Cloud 5, right? What does it have to do? What kind of conversation does that look like? I have no idea. I'm not saying that you know, but I'm just like... I would say that despite what you said about the premium, I think everything you said is true.

I still do worry. I think cost is a concern for a lot of people. And so the period of the frontier does still matter. I'm glad Anthropics were it's... where it's at right now, but who knows where that changes. When it comes to Cloud 5 and thinking about the future, one thing I think about actually that's really nice is I think we can take for granted right now that furthering the intelligence of models and chat GPT, a consumer product does not lead to more users or more retention.

It only is really applicable to a thin slice of users who care about very smart type queries. And I would say maybe like under 10 million, right? Maybe that's just a random estimate, but most of the 800 million users on chat GPT are asking, like, how do I fix my dishwasher? How do I, like, rephrase this email that I've sent to somebody. And that's done. Like, we know how to kind of do that. So what's interesting there is now that means we're at a point in consumer where maybe this is too early to say, but opening eyes, kind of won, right?

Like, how do you catch up to something where model quality is not going to be a differentiator? You already have the users, you already have the retention, you already have great product and people are paying. But the interesting about Anthropic is if you look at coding, that's probably never going to be the case. Like there's always an increasing frontier of how good you could be at a task like that. And we're nowhere close to that frontier. So it's more possible to underwrite the quality of the future models versus like an open AI where it wouldn't be as much of a revenue driver on their consumer business than as it would be for Anthropic. Yeah. I was talking about coding.

Let's just like talk about it because I think like this is also a very fun discussion. One, there's like the, what are the margins of cloud code, which there's some numbers. I don't want you to get yourself in trouble. But then there's also like, how do you think about the Claude rappers, right? And we've talked to Bolt and Lovable, but then also like, I'll put cognition and cursor in there as well, right? Like, how do you think about this market of, like, basically there's a whole ecosystem of startups. They have all done really well, built on top of cloud. I think it's great.

I mean, there's... Sustainable, is it? I don't see why not. I mean, I don't... I kind of will allude to the margin question, which is like, can Anthropic continue to do this strategy? Which, you know, I'm not going to comment on the margins, but, like, if you are trying to build out a enterprise-friendly business,

there's, like, two broad approaches, right? Like, high customization and high price, which is usually less scalable. And then you have low customization, low price, which is very, very scalable. So I don't know what I mean? In a SaaS world, I guess it's a Slack Palantir continuum. And so this is kind of different, but generally Anthropic wants to play here, where scale fast, keep it cheap, get everybody on it.

If we trust that most people or a significant number of people will stay on Claude if they continue to build products on top of it, then I think that's a win for the ecosystem and it's a win for Anthropic. I don't see why they would care. I think the interesting thing, and again, I don't know what Anthropics future plans are, but like, you know, Ben Thompson obviously talks about this is classic strategy, which is every time you own the, I guess, the means of production, you will end up getting into the markets that your users use at you for. And so the classic Amazon example, which is like first you are the market where people sell, you find all the places that you can sell things that are commodity at high volume, and then

you start creating batteries and Amazon branded batteries and then you push out a bunch of people who sell batteries. So that's a risk, I think, for those companies that use Claude heavily and rely on Claude to think about. But at this point of time, we're too early. I don't think Anthropic is anywhere near thinking about that because you're still very much competing with other models on that layer. Yeah, playing a different game. Yeah. Yeah. It's interesting. Would you rather be an investor? This is basically model layer versus app layer. So far, model layer has won. And I think there's been there was a there was a kind of an app layer summer and then now it's like very back to models again. I mean, I like the discussion.

I like the discussion because I was at a dinner where we were like somebody was talking about this kind of question. And I was thinking about it more. I just at that dinner. And maybe this is an ill-formed thought. So like feel free to push back. Yeah, we're riffing. Yeah. But when I think about like motes, it's a classic like VC startup banter, in my mind, I think the most.

is what is the hardest to do in any part of the stack. And so when I think about people that tend to dismiss, there's other aspects to it too, but people tend to dismiss like, oh, you know, the app layers will capture all the value. Well, if the app layer is easier to build, I think the model layers is harder and therefore will naturally capture all the value

net of competition from other model providers. So said a different way, it is far easier for Anthropic to try to go into one of the spaces of the apps than an app to try to go into the space of Anthropic, which makes me feel like one is more defensible than the other all else equal. So I think both can thrive and that's ideally what everybody wants. But yeah, yeah. I think very brutally as an investor and as a human with my own limited time on Earth, you know, if Anthropic can go from three. $4 billion to $183 billion in two years, then everything else is a waste of time.

You know what I mean? So like, you kind of like do want to like really get this right. You can't just be like, oh, like everyone's great and like, you know, and sort of hedge your bets. Like sometimes you have to go all in on the right thing and you spend a lot of time and effort identifying the right thing. And so yeah, that's where I'm what I'm trying to do more of these days. I think the means of production thing is interesting because cloud code only makes sense to be built if it's like the best thing, right?

Because if cloud code is like mid, they're better off promoting Devin and cognition to sell more tokens. So I'm curious, I guess the market gets more competitive. On one way, it's like, well, we don't want you to use Devin because Devin supports all the models. And so we end up losing some of the revenue. But I think there's right now, Cloud Code is obviously the best way to use the cloud models. So it drives the most usage. But I'm curious in the future, there's going to be more pressure on like, hey, this product actually needs to be great to make sense for us, again, to invest our resources into building it. Yeah.

So going from model lab to model lab plus products company, right, which is what that opening has done. I would push back on what a, I don't think everyone would agree that Claude code is the best way to use Claude. I've heard multiple people even in the last few months say that I would, I'm a cursor guy. I'm a Devon guy. Like people have their preferences, so I don't think it's set in stone. However, a Claude code is a great way to use Claude also. And there are nice flywheel effects, obviously,

because once you capture the way people are using Claudecode, you also get so much data to then make Claude Code better over time. So I think those are the two main reasons. But at this point of time, maybe this is oversimplifying. But I can't think of too many, apps that have a very meaty layer on top of the model that's like very impressive yet there are somewhat meaty layers and it's getting there it's a time thing as well right most these companies haven't

existed for more than two years so um i think it gets there but i don't think we're at a point where you know we're like holy shit that app has so much stuff interesting things and technology built on top of the model where it becomes so difficult for the model company to go and try to compete i think tomorrow if Anthropic decided to, or Open AI decided to take on another app, given their distribution and their engineering and the fact that these are still not as thick as you'd like them to be technically, they could. Whether they should or not is different, but they could. And that's something I do think about. Thank you for engaging in all this, like very meaty discussions. Yeah, you don't even work at Anthropics. I know we put you on the spot.

Yeah, but like this is what I want to get on the podcast because a lot of people don't get the chance to talk about this. But then this is like a lot of. a normal SF dinner. The last hit on Anthropic, I'll point out, which is more fun, which is, there was a new CTO joining Anthropic from Pesit. And, you know, you're like the king of Indian posting. What's the sending Finzler this for you? You know, last time you're on the podcast, you talked a lot about like the Indian, the university

system and all that, and to see this guy rise up. In India, largely academics holds the same sort of prominence as sport would hold in America. everyone talks about it. It's Asian culture. Everyone talks about it. It is top of everybody's mind. It is something a lot of people want to be good at. And it's an extremely competitive society with a very large population.

The way, and everyone on average, people are quite poor. So education is seen as the means to social mobility by a large amount of people in India. The way it works is similar to countries like China or some other countries where you take a big exam. You get ranked. A million people take the core engineering exam. the top 10,000 get in and the top 200 get into computer science. That's how hard it is. That's pretty hard.

And those top 10,000 get into IIT. Everyone's heard of that. That's like where a lot of the great Silicon Valley people from Sundar to many other people come from from IIT. And in India, often what I've seen, and this is something that I'm generally very curious about is like what is the motivation of humans and what is the dictator of outcomes in their life and their career. And one thing I've noticed a lot is, A, there are some societies that are inherently, I think, less meritocratic, where you get so judged for what you have in the past that you're not allowed to prosper later.

And I think largely many work environments in India and other places in Asia can be like that, number one. So you're not judged on the merits of your work. You're judging on the merits of what you've done. And number two, there's a very strong self-fulfilling prophecy effect of, I've seen people who underrate themselves because they think they couldn't be number one at something. It's like your own mental. It's your own mental block where like I couldn't get into like, I don't know, you know people in the Bay Area also like this. Bay Area is kind of like Asia. In the Bay Area, I know people who grew up who are like, I couldn't get into a good college.

Therefore, I am stupid and therefore I should not work that hard. Right. Like it's inherent that they could be smart. They just believe they're not. And that also has an effect, psychological effect, on your long-term prospects. You look at a guy like Rahul-Badil who's become the CTO of Anthropic, and he's not from a top university in India. Some people obviously debate that.

But in general, I don't think it's a really well-known university in India. And he's come to a society that is quite meritocratic. And he sort of worked his way up to a position of such prominence. I don't know him. I don't know what everything else he's done. But it's testament. to the fact that, you know, I think this is why it resonated with so many people is even though you didn't have the opportunities early and even though you might not believe you could do it, if you work hard enough in certain environments for a long time on things you care about, anything can happen. And I think that's why I wanted to share it. I thought it was. And you choose to work at Stripe and glean and, you know, do well. I think choosing the right company is also a very, like, okay, if you're not going to do the credentials path, you have to be lucky and, you know,

selective and working at good places. And a lot of people make that mistake. And I definitely did. I had good credentials and I worked at bad places. And yeah, it's very interesting that kind of... You work a pretty good place right now. Yeah, but I took a long time to get there. I mean, just that, you know, this is funny.

I have this like automated pockets research. And when it sent me the email about you and it's like, you know, DDS is a strong presence in AI and immigration for the top two topics that it talked about. Yeah, let's talk about. the anthology fund. So it's a $100 million fund and close partnership with Anthropic. Talk a bit about that. I think people are really curious about how close that actually is. Yeah. So, you know, the anthology fund we set up when we invested in Anthropic around the beginning of last year. And the sort of idea was, okay, Anthropic, again, it's so hard to think about.

Anthropic was a very different company back than it was a much smaller company. And they were like, look, there's incentive for us to run our own fund, opening eye runs their own fund. and there's a developer ecosystem that we want to create around this. It's really nice to have great startups that are using Anthropic, close to Anthropic, building around Anthropic. And we said, okay, but we had a discussion about do you want to have it inside Anthropic or do you want to have it outside Anthropic? Because inside Anthropic would mean something would mean a corporate venture fund.

You'd have to hire for that. You have to have a whole role. And typically, if you look at corporate venture funds in history, obviously besides opening as a notable exception, they tend to not be very good because all they prioritize, prioritizes who uses my stuff the most. And that's not a good way to invest in companies. So we thought this would be better. And the incentives on corporate venture funds are a little bit, not misaligned. So we did that. And now we look back at this fund, obviously Anthropics in a very different place. We've funded about 40 companies. The rate, it's kind of a hard thing to calculate, but the rate at which

companies graduate from when we invested in them to the next round is significant. hire on Anthology Fund companies. And we write both small and lead checks. I mean, the two, several notable companies from the anthology program have been open router, good fire. There's a company called Endia, Prime Intellect, Whisperflow. So there's quite a handful of pretty interesting things here. And yeah, I think what the other really nice thing about it is it really allows us to move fast on on companies that you know where we may not feel immediately comfortable or ready to write like the full check so we can like participate in a round and then get closer and hopefully go and build a relationship and lead that in the future lead that the next round in the

company in the future and also lets them get really close to the anthropic ecosystem so we have all these events with like the founders and all execs and things like that and people really enjoy like getting it from from hearing it from the horse's mouth now I think you I would say, like, Anthropic is in such a different place. It's no longer an unknown entity. So the program gets a lot of demand, but, you know, people kind of know what they need to know. And so we're still working on, like, how do we make this program more useful and more beneficial for founders and anthropic-like? Yeah.

Also, you know, congrats on all this. I think it's pretty successful. One thing I'm, one reason I'm trying to highlight this for Lin-Space is also, like, how does AI change venture? right and something that's something that's something that Alessso was exploring as well and that's why like I don't really know how to categorize anthology funds because it looks like a kind of like

what conviction is doing what what YC is doing maybe but like later stage right like some of these already have their Cs some of these already have their A abacus is in there is that is that is that our abac? No no no it's a different abacus but what's the model like what are the predecessors

that you draw inspiration from for for like setting up this fund or do you just not? It's like a corporate venture fund managed by Menlo, somewhat funded by Anthropic. I would say, like, you can think of the companies that go into anthology in three categories. One is strategically important to Anthropic, and those could typically be somewhat later round, somewhat bigger companies. Two are companies that are using Claude heavily and are just great companies to be in. And three is just very, very early stage founders that are very high potential. that may potentially be using cloud models and anthropic and so on.

We don't require people to use a certain model or the other, so we keep it pretty open, and we do everything from like a 100K check to a $20 million check. So I think it's really broad in terms of what we can do, and we want it to intentionally keep it that way. When it comes to where we draw, there's some old, old examples, but I don't think it's really relevant. There was a fund called IFund that the client earned it with Apple way back in the day.

It was kind of similar. How did that turn out? I don't remember. I don't actually have enough data on that, but that's one example. Then you know the answer. No, I'm sure there are some great companies that came out of it. I just don't know the details about what was in it. So, yeah, I mean, I think that's kind of how it's been for us.

And I think it's been a really great program. And we've had, I mean, we were excited about the companies that we could lead the rounds in as well. Yeah. I wanted to get quick hits for people who maybe never heard a Goodfire. And like, I know, I know that because I've been, I've invited Mark to my conference and I've been to a bunch of their events. Actually, I'll just give you, I'll just give you that list, right? Good Fire and Prime Intellect are in your research category. There's others with like diffusion based language generation, novel architecture.

It's all over the place. Research is like the most wild west of this. How do you view like sort of research investing? I can talk about any of those companies for briefly as well. But the way I view research investing is it is extremely hard to pull off. But when you pull it off, the results could be very remarkable. One of the hard parts is the tension between do you keep investing in research, hoping for something that yields a better result that leads to a better product?

Or do you try to monetize and scale what you have already? That's tough. It's a really tough thing to do. It's a really tough decision to make when you're working with those founders. You're on that board. It's like somewhat anxiety-inducing when you're thinking about this even from an investor. standpoint. Like, do I just get to like a couple million a bar? Do I like start doing something? Or do I like keep the research bet strong? The way I think about research investing overall and is honestly

follow where the talented people have the most competence and then have an idea around how this could be useful in what I call a top down way. It's not really top down. But the way I frame it is if I fast forward 10 years from the future, what do I think is very likely to exist? And what are the ways I can get there? If I do believe strongly that there's something like that, and I believe there's this team very strongly headed towards that direction, I can sort of draw a dotted line and go like, okay, maybe we can see something here. So that's how I broadly think about it. So concrete example, Goodfire is like the most, the most interesting one. Mechanistic interpretability. I didn't even think that was a market that was worth investing in, obviously Anthropic does.

and they seem like they have good vibes. What's the, I guess, the summary of your take on the company? The way I think about the company is right now, almost all frontier and some many non-frontier AI models are complete black boxes. You don't understand why they produce the outputs they produce. All of the eval and studies on them are empirical studies, not intrinsic to the model. So it's like, hey, here's the outputs we saw. And therefore, this is the benchmark score or this is how we think it did.

If we believe as a society that five and ten years later in the future, these models are going to be critically important for making pretty heavy decisions, whether it's, I call it, anything from whether somebody should get a loan or insurance or a legal decision, then I don't think that the black box approach is long-term scalable. It's just not how society can function, where you say, you throw your hands. up and say, well, this is what the model said. And then I asked it, explain yourself and it said this other stuff. Great. That's kind of what we have today. That's the best thing that we have. Mechanistic interpretability is really going into the weights of the model and trying to figure out

why did the model do what it did. And one of the more concrete and relatable examples of this that, you know, you guys may be aware of is GPT-40 had this phase of sycophancy that a lot of users really liked. but it's kind of one of those things that's not as easily detectable in an eval. Unless you know you're specifically maybe testing for it, even then it's quite hard. It's very personalized. It's not like any key words might arise, obviously. But it is something that is quite easy to tell in even current interpretability methods.

You can tell one a model is being sycophantic. You can tell one a model is trying to lie. You can tell when a model is trying to steal or persuade you of something. And so I think if we further that research direction two, three years in the future, we will be able to understand why models say what they'd say. It's brain surgery for LLMs, is my catchphrase. But doesn't apply to LMs only, all models. And that is a pretty important insight into deploying AI at scale.

Yeah. And you don't know the business model yet. Don't need to. There are some ideas that we have, but not already to talk about publicly. and some that are working also, it's not right to be publicly. Does it feel worthwhile to do this on such small models? Because I think most of the work is done on the open source releases, like how much of a gap is there between what they're able to do

and then translate that into doing it for. There's no gap for scale. Like, they've shown that even for the biggest open source models, you like, even really deep seeks big models, they can do it. And in general, like scaling is not the bottleneck. Obviously, access to the weights would be a bottleneck. but not. But they're in the anthology fund,

so they can work with Anthropic. They can work with Anthropic, but they don't have cloud access, cloud weight access. For listeners who want to hear more about Meccanterp, we did a podcast with the Mechinturb team, Emmanuel from Anthropics, so that's your one-on-one there.

We'll do something with Goodfire at some point. Prime Intellect, another very hypey company. You don't have to say it, but I know it's very much in the water that they have raised a very large round. So I ignored distributed AI for a long time. It's usually crypto people coming over saying like, hey, we have these GPs all over the place. We will somehow ignore the speed of light.

And it's like you can use our GPUs to trade models. That's why I ignored Prime Intellects. I was wrong. Tell me why I was wrong. You may not be wrong. I mean, look, I could be the kind of person who goes to shills all of their companies and says it's the best thing ever. And if you don't think it's going to be a $10 billion company, you're wrong.

Every company has risks at this stage. And Prime Intellect has their fair share of risks. and whatever went through your mind, went through my mind when I was looking at that company. I do strongly believe in, like, I'm sure you've seen this quote too, is in the quote of pessimists are probably right often, but they rarely change things. And it's an easy thing to say. But when you're investing, it's something to think about, which is there's a lot of things that could be potentially wrong with prime intellect for sure.

But the thing that I really liked that drew me to them is, like, if they were right about a couple of things, what could go fantastically? Distribute training is one of them. Access to talent, I think, is one of the things that I underwrote for them. The ability to hire fairly great people away from other labs is really hard. And so I think they can do that. And the third thing I think is there's a broader vision to prime intellect that is not yet realized yet, where the first step of that was a distributed compute.

And we'll see if they realize that. Yeah. Yeah. Well, you know, Will Brown's been on a podcast multiple times, and they've launched kind of like a verifier's SaaS platform or something or a marketplace. I'm not really sure what exactly. I should probably try it out. But it's very interesting. I mean, the other thing I'll just say out there is like, like, everything in AI changes like every three, four weeks. So I'd be a fool to say like I could tell like what this company is going to do. Yeah. Well, well, you know, all I am trying to do is like trying to capture for people who are like not in the loop on like, you know, that this. these are the companies that people are talking about, right? Okay, so let's at least hit on OpenRouter, maybe one more of your choice that maybe is like less known,

but you want people to know more about it. Open Router, we have to cover. Big deal. I do think, like, this one I was like relatively early on in terms of like, I saw the products. I saw what he was trying to do. And I mean, it clearly has done really well. I did not know he was taking investment or I would have invested.

He wasn't. Okay, say more, say more. Open router was sort of my, like, you know, like, I don't want to make this about me. It's really about them. But in my mind, it was my darling deal. Because I'm just like, man, I entered venture and I'm like, that is the company I would have built. Yeah. And I think we're skipping a bit.

Let's explain who Alex is, what he did before. Right. So let me give you the background to open rudder. Alex is a phenomenal, phenomenal founder. He started a company called OpenC before, which was the NFRA. company, obviously that at its peak was, I think, a $14 billion, but more than $10 billion company did not meet that valuation's expectations, but look, there's many things out of control and in your life. Then Alex started this company called OpenRouter, and what gravitated me

towards it initially was two things. One, it was very clear from my time at Gleen that this is a perfect problem where engineers all think it's easy until it becomes so annoying to keep maintaining this. That's the sweet spot because no other person, no other company will gravitate towards it, yet it is so, it is kind of thorny to be able to maintain a portal that accesses a bunch of models. The nuances are quite tricky and annoying and boring. So that's one thing I like. Second thing I liked is I was pretty convinced that if there was a market for anything like this, it would have to be a PLG motion. I think goes so far as to say for in any SaaS market, if there can be a PLG motion, the PLD motion will win.

What I mean by that for, like, if you're not, people are not familiar with venture words like PLG is all users have to be able to access and self-serve the product and try it in order for that to be successful. Without talking to anyone. Without talking to somebody like the classic, like get on the phone on a SaaS website. So those two things that really drew me to the business. And then, of course, third one is just quality. Like there's these small details at OpenRouter.

Just like beautiful website, beautiful landing page. It's not some like SaaS trash of like, here's what we do and product solutions about us. I am so sick of that. You land on the page. It's a developer page. It's like, here's how many people are using what models. Love it. I'm like, this guy knows what his users really want.

And all of those were compelling. I went out to New York to talk to Alex. He ignored me a bunch of times forever. I'd write him what I call love letters. I'm like, hey, man, love it, dude. Like, it's so cool. I don't even want to invest. You just talk to me.

I don't really care. I just want to meet you. I have so many ideas and interesting things. And it was one of those companies where I generally felt that way. So when I did meet him, we started jamming on things. And I don't know the VC motions of how to sell. So I wasn't really even trying to do that. But when I told him, like, look, if you are ever going to raise, I will make it happen.

I just love everything about this. So that's how we ended up doing the round. I think the company is interesting from a business model perspective. I get this question a lot. How does this business model scale? and I think right now the business is doing fairly well. Volume. He takes like 5% of everything.

There's that business model, but then there is a reasonable threat factor where what if the spend on the net goes down over time as tokens go up. So you do carry some risk of the prices of LLM falling to a point where the business stops working. And I know many other companies take that risk as well. So that's one risk of the business on just pure consumer spend.

Second risk would be, you know, keeping people on a, like, a lot of hobbyists use open router, and they tend to churn. And then a lot of enterprises will use open router to evaluate and then go pick a model that they want to settle with later. So that's a problem to fix. And so those are two of the risks. But overall, I think they've just like been executing phenomenally. Yeah. How do you think about the Bursall AI gateway, for example? I think that's been, I mean, I'm a fan of open-rider.

We'll also do it, Versaelle. Yeah, I'm interested where you already have, like, I use NextJS, right? And it's like, well, I just use AI SDK. AISDK comes with AI Gateway. It's like kind of makes sense to do it. How do you think about this market and like how tied you need to be to like the actual application development versus you're just kind of like this, what's the land, hey, we don't have, you know, open router doesn't have a developer framework, for example.

You know, if we're in a partners meeting, that's maybe what I would ask. My simple answer is I don't think the AI gateways of other products are ever going to be their first priority. And the other simple answer is I think OpenRouter has this mind share and momentum that just doesn't go away overnight. So it would be similar to asking like, hey, I'm Open AI in 2020. What if somebody else does this? Yeah, they could or 2022. They could. But we are so far ahead in some ways already.

I think the last thing is I think that. they have built a lot of smaller things that are non-obviously useful that other people probably won't sweat the details to go out and build. And so when I say that, I'm like, it's everything from, like, here's something that nobody even cares about about OpenRouter, but they have a feature flag where you can only want to go to certain LLMs that do not retain your data. They go to that level of granularity of thinking about what is, what do the users actually want?

And that's one example. Another example is their detail. on the provider level. Almost nobody has provider insights. There was a very interesting side study of how Kimmy K2 did this whole study of different... The verifiers?

The verifiers. Okay. But I think that's interesting. Like the fact that people don't really acknowledge this, but the same open source model or the same host source model can be served by different providers and have different context windows,

different quality, different latency, different throughput. Where would you go to see all that information? Will you see it on open router? and there's some elements of scale where there's enough people using the different providers so you get that data.

So all of those things, I think, are somewhat defensible on OpenRouter and hopefully more over time. Yeah, and I think their leaderboard charts are like one of the best growth hacks. Very good graphics. Yeah, especially people that are into open source AI are always posting these things,

saying, hey, open sources up, we're back. One thing I used to joke about is open router is the only. non-Elon company that Elon has tweeted the most about for obvious reasons. Grog Code Fast 1, number one right now. I'm sure that's M-Code free plan. It was like a good week where I was like, every day it's like, open router, open-router, open-rower. I'm like, yeah.

Yeah. And so for those who don't know, that's because Grog Code Fast is like a top model. Yeah, because it's free. Yeah, yeah. Yeah, there's a lot of gaming, right, of this stuff where it's like, oh, we'll give it to you for free, but then we'll say we're very popular. I'm like, yeah, you're free because you're popular. Right. Yeah. You're popular because you're free.

The other way around. Okay, very cool. And okay, so there's a bunch of others. We're not going to go through all 40. What comes to mind? What do you want to talk about? What do you think maybe is a very interesting company in your portfolio that, like, more people should know about? I'll talk about, whisper and Inception, or the two I want to talk about. Inception. Inception. Inception is not even here. That's why I was. Yeah. So, so. So, you can, we can say, we can talk about the company without saying the name. Yeah, okay. Let's just try that. Let me, let me try that. And then, I mean, also like

inception, it's like if I Google inception, it's not like I'm finding it anyway. Let's talk about these two things. A whisper, I can talk about first. That's a clear one. So Whisper is a company that does, you know, a very, in many people's eyes, something very commodity, which is voice dictation on your phone and laptop. The things that I really liked and that stood out to us about Whisper was in that quote unquote commodity market, they are in my mind, like the fastest and best and most delightful product that kind of in many ways set the frontier of the nuances of how to make this easy. Press your function key on your Mac, talk to it.

It's always on. It has fantastic accuracy. As you're dictating, if you ever stutter and go like, oh, no, I didn't mean that. I actually meant this and knows what you went and it goes and corrects it. I find that they have this metric they use called zero edit rate inside, which is, you know, amount of times you don't need to edit. Correct. And there's zero edit rate, I think, is north of 80%, which is insane for a voice dictation product.

So I, you know, many other risks of that business too. But one thing I think I love is users love it. Users stay on. The retention is great. And it might make voice suddenly work. Because if you think about computing, people type slower than they, talk. And so it could, it is unlocking this new, faster way that people feel comfortable talking to their computers that really didn't happen in voice dictation before. And it's not just a whisper model,

which is a common question I get. So, yeah, for people to know, it's WISPR. Yes. Which, you know, you got to spell it somehow. I mean, the question here is always like, it's the same thing, right? Like, voice is very commodity. I actually happen to use Super Whisper. Right. Right. Mostly, it's by Jeremy, actually. And then granola is very popular. Notion has like this notion speech thing. Like how? What's the what's the plan? This is every. Yeah. This is why I'm not an investor. How do you survive? Trying to reason about why you should be the winner. Even Chad GBT desktop has like the, you know, has some shortcuts for stuff. I don't know if it like does exactly the same thing. But like, you know, it's not that far away. Anyway, you're excited about it. I do see a lot of tweets about Whisper. And it's one of those things where, like, yeah, the PLG is getting me, man.

Like, I'm like, should I switch? I don't know. Like, my thing's fine, but like, what if it feels better on the other side? I don't know. Well, we'll see. We'll see how that plans out. There's some interesting plans to get it to be a cooler product. But we'll see.

The other company, and again, we'll call this StealthCo. StealthCo. One thing I find very interesting about StealthCo is, comes in the purview of research. We talk about different architectures all the time. One of the most compelling alternate architectures for AI is diffusion models. So one thing that I think is really interesting about it is that you talk a lot, Sean, about like the peritone of latency, cost, quality. Diffusion models today are, I would say, 80 to 90 percent of the quality at one-tenth

to cost and latency. So has huge implications on obviously the stock market, which is kind of NVIDIA and many other things. But also, like, there is clear examples that you can show. of use cases where that might be very valuable because there are many applications that work in volume that do not require high quality, but definitely require better latency

and everyone could use some cheaper models. So, you know, there are, I think there's an interesting area of research there. Maybe it gets to frontier, maybe it doesn't. The one thing I want to draw attention to a diffusion that I think is particularly interesting is left to right reasoning for code doesn't actually really make sense because in code, we don't, like, we might,

sometimes write code left to right, but after you write code, you go up and down and figure out, hey, is this variable set? Did I do this? There are many bidirectional dependencies in code. So there's a natural tendency to lend itself to diffusion models where you can imagine, like, as you are denoising, you fix partial issues in different parts of the code at once versus this reasoning paradigm where you kind of have to figure everything out and then go give your final answer. Yeah, yeah, I like that a lot, especially for like, uh, since,

tax structures, like C-like languages when you need to open and close the bracket and all that and hold that state. I think, like, it's, I, the question is always the sort of, quote-unquote, the hardware lottery of transformers. Like, transformers is all you need. And, like, diffusion is kind of, like, a different branch off of that tree of research. They are related. But we might be too far gone down the transformers tech tree to come back and then go down diffusion. Like, being the point where, like, they might never be frontier. because we've just had like four more years extra of like Transformers LLM research. Yeah, it's true.

I think about this all the time. Like, thinking about in the course of history, what are the significant moments where if only something forked off a different way, that maybe there would be a completely different paradigm of outcome? Yeah. And usually the worst tech wins, like Blu-ray DVD, HD DVD or something like that. I think there's like a lot of variations with this. Even like, I think there was a discussion about AC versus DC currents, like back in like Edison's days. Like, there was this like big fight between the Tesla and Edison. I don't know if you.

I mean, I'm aware of the very, very basic details. But like it's so interesting, right? Because like just you take something like this and then the question becomes like, okay, do we bet on it? Or is the timing just off because something took off? And we can't pull this like rocket ship back to Earth. And so we've lost that fight. I don't know. I'm not a purest scientist anymore where I believe like the best ideas and things win. I think in markets, it's very obvious that that's not true. I think a lot of things go into winning and sometimes it's out of your control.

Yeah, yeah. It's very true. Like, you know, speaking of Anthropic and like things that happened this year, MCP happened this year. And I was, when MCP came out, I was sleeping. And then when they came and then did the workshop with me and I think as you see a lot more noise and I was like, okay, There's something to this. And like now it's like basically kind of de facto one as the interrob layer for all the labs and all the, all the models. And there's no reason why this could have won versus anything else apart from like it was well speced out. It was backed by anthropic. It's kind of a similar thing. Like I don't know if it's like the best, but like it was good enough.

Yeah. It happens. It happens so often. It kind of makes it tricky to, not even just investing, but in general to think about ideas. We see this with startups as well. It's very heartbreaking. every once in a while you'll meet a founder where I'm like your idea is fantastic. Your execution is great.

I just don't see it work because the market dynamics are not in your favor. Maybe I'm wrong about some of them. But you know, when you say market dynamics, is it Tam or something else? No, it's sometimes it's like I don't see that like you are a small group of people trying to wedge something into a market. We know how long that takes and we know the other forces at play. And if I don't, like, I just don't see, imagine a single person running in a tunnel with a light at the end but the tunnel's closing in on you,

you could be the fastest runner in the world and you might not make it out of a tunnel. That's kind of the analogy. And so you might be doing everything right. It's just that that window is not there, or at least I might not think that window is there. I do think a lot of companies fall into this bucket of ideas. And so to me in a way,

I almost think of companies like Mosaic-ML in a way, which is like, hey, we got this amazing team. We can help you find two models. And yeah, but nobody, you know, the market dynamic, there's really nobody find tuning models. And part of it is like the open models are not the good. And part of it is like people don't really have good data. They don't have the expertise.

And again, if you go back now, now there's like, you know, RL environments and like RFTs, like the next way of that. And it's like maybe they'll be able to get in the window. But it's just interesting how, you know, now I'll say it's... And yet, the other flip side of that is, and yet they get a quantified. for this amazing price. But yeah, because the market is just so big.

I mean, even if you think about something like, yeah, diffusion models for text, right? It's like, you know, it's a bet. It's like if you sell it for a billion dollars, right? It's like 0.01% of like embedded market cap. And so it's like, okay, well, the amount of money being spent in the space is larger enough to justify betting. Like the same way Instagram was like 1% of Facebook market cap.

It's like, this is similar. Where it's like, man. Data bricks is rich enough thing. Exactly. It's like, you know. They really want you to know that they're an AI company. Exactly. And now they're worth $100 billion.

I mean, you know, like without Mosecamel. Exactly. It's like without Mosaic ML, maybe they're not on the same trajectory. It's like, I don't know. Maybe they are because, you know, all these great and all that. I don't know if you guys have ever talked about like the roll-up companies, which is my favorite, like, little. The P.E. wall-ups?

Yeah. I didn't know that was the topic of yours? It's not really a topic of mine. I just find it quite interesting to see how, speaking of AI companies and markups, it's, there companies, obviously not going to name them, but there are companies who go like, hey, here's like a small company that does a million of ARR completely with humans. I'll buy it for two million and then I'll do some of it with AI, but now I'm an AI company and a million of ARR. An AI company world is a hundred million dollar valuation. And so, you know, it's pure like multiple arbitrage

on the category that you're in. Yeah. But like, yes, that's the like, like, cynically, ha-ha, but then, like, what if they actually works? Because, like, the hard part is getting the customers. The hard part is, like, getting the domain expertise. You drop a bunch of software engineers in there and, like, you know, automate it, make it scalable, make it cheaper. And, like, yeah, maybe it works. No, you're right.

Yeah, absolutely right. I think it's just pricing. He founded a company that bought a tax firm. Yeah. Yeah. No, no. Accounting firm or tax firm. A law firm.

Law firm. Yeah. If it works, it works. I just think of what was interesting. to me is like you can 50x the value of the company before you actually landed anything with AI yet. Yes. But then you use that funding and the equity to hire the people. It's weird.

So there's this concept I always talk about which I'm surprised people don't really understand. It's reflexivity. The belief that something can be true can make it true, even though it's not true at the time that you believed it. Yeah. That's venture capital. Yeah. Just give money and everybody's like, oh, they raise 300 million. It's a great company.

Yeah. I love that company. It's like, yeah, I'm an investor in it, so I love it too. And it's like, all the employees are like, I love this company. My stock is worth a lot of money. There's also that effect that's very clearly in venture capital where not just what you said, which I agree also happens. But imagine there's times where people funnel so much money into a company before it's really like prime time,

which dissuades anybody else from entering that market. And then they become the de facto owner of the market because they cancel the competition with funding. And you can think, and I'm not going to. name the categories, but you can think of numerous categories in this market, in this paradigm, that's already happened. And I feel like even in AI,

it's like maybe two and a half years ago when Chad GPT game out, it's like, this is cool, but like, you know, a lot of enterprises were like maybe skeptical of like, it's the strength going to continue. But then once you start seeing tens of billions of dollars being put in open AI and Anthropic and it's like, it's got to work. Especially you can deploy in the hardware. Yeah.

Which, you know, I think like you're at that point, you're building infrastructure and infrastructure very capital intensive and like you actually can do the math. It's not humans anymore. It's like machines and land. Yeah, exactly. Power. Like Amazon is building all these like training chips and like all this infrastructure

for Anthropic. It's like, do you really think they're dumb? Like, you know what I mean? I think at some point it's like same with Stargate. It's like, do you think all these people are dumped? Yeah. You're saying the models are not that good? It's like, you know? The podcast we released today with Kyle. Like he was still kind of skeptical. that they had 500 billion for Stargate.

And I'm like, not only do they have the 500 billion, they have the next, like, trillion, like, lined up, mostly. Because, like, the projections, I think, like, I've been talking about this a lot, and I'm very out of my death because I'm not Dylan Patel. But, like, I think it's the most big, it's probably the biggest story of the year, like, beyond the models. Like, just the infra build. Like, you know, and I think, like, people don't understand, like,

the roadmap is very, very strong for them, like, the rest of this decade at least for opening I to go from like two gigawatts of compute this year to 30 with everything they've already announced. And then there's a plan to afford the next 125. Like the United States uses 300. It's like crazy ambitious. Do you think like I guess it's a question for you guys also because I don't have a good answer yet. The belief is always obviously bitter or less than billed, right? Like you buy more compute therefore you get the both models. By way, it's an anthropic relevant thing. Right. Right. And so, but But, like, is, I guess, is that necessarily true?

Like, there could also be a world where that's just not true. So, you know, you are kind of betting. This is what makes it bitter. It's like, what if it doesn't apply to me this time? Right. Right. And I think, you know, being in Sam Altman's place, it's absolutely the right chess move to play.

But, you know, I do wonder what happens if, like, all this investment in compute doesn't actually lead to economic gain, flash better models. slash everything else. But I feel that we've reached the point where, like, the models are good enough that even if the next generation is not 10x better, we'll be able to use the compute. I mean, and again, the data centers, like, you know, they're writing it down for like 30 years. So it's like, you know, can you run GPD5 Pro over the next 10, 15 years?

Given the amount they're spending on compute, this is a general question. I'm not criticizing open it at all, is even if everyone was using clot, like whatever, Codex, codcode, whatever, all the time. Like, inference demand is not that big globally. Not yet. Not yet. So what would you have to believe for that to be true? Because they're 800 million weekly active users.

This is what Greg Roman says, like a GPU for every human on Earth. I'm somewhat shipposting. I'm somewhat shi posting, but they actually say this on their official comms. So I'm just repeating him. I don't necessarily disagree. I'm just trying to work backwards to like, what do we need to believe to get there? because chat GPT compute is not that much. Correct.

Right? So they're not doing like agentic stuff. Maybe they will be in the future. Most people are doing basic Q&A type queries. By the way, I put it up on chat. So if people are watching on YouTube, they can see this, which is this year, open-a-spent $7 billion on compute. Only two of that was for all of their inference.

The remaining five was R&D. So all of chat GPD, all 800 million users, all of SORA, all of like, all the other sort of like API volume, 2 billion. And they have two and a half times that for R&D. Right. And so my point being like, if inference is one thing, I don't know how that will scale to that volume,

but then you'd have to believe that the rest of it goes into R&D and therefore produces models that are so much better that therefore have more demand, et cetera. But if in any case that, like, I don't know, the incremental marginal is not that, that big, then, you know, that's the risk of the bet. Yeah. So all you, like, to disrupt opening eye, you need to have more efficient research because right now it's pretty inefficient, you know, spend five to get two.

So, like, what opening I did to Google is what the next opening I has to do to opening eye. Yeah. You know what I mean? Like, Google was spending a lot of money. Facebook was spending a lot of money. And like, they didn't come up anything. OpenI did.

And it was like a small, tiny little, you know, startup. And, you know, they had, you know, GPTs and other. Radford, but like someone else will, it may or may not come up with that. It's like that classic quote, your margin is my opportunity. Like Google was milking those margins and they didn't want to spend the compute for every search query. And so now opening I is willing to.

So we've covered a lot of topics. I think this, thanks for indulging. Like, I think this is like, for me it's like a survey episode of like, here's everything. We're also catching up with the former guest and it's always nice. Maybe we can end it on this like coding interview thing, which literally you tweeted about today. What is the situation that, you know, I guess engineers should be aware of. And I think this like maybe ties into LLM psychosis a little bit. You know, like, so I tweeted, I'll just cover the tweet

first. I tweeted about this, uh, guy who wrote a blog post about, he was in an interview from a, I didn't think it was a legit, a LinkedIn message where he was interviewing for the company. They sent him a coding interview. They said, clone this repo, run this code, make this edit. Kind of not untraditional. So it's pretty, pretty run-of-the-mill type interview. It happens. And in that interview, he claims that he went to cursor and asked whether the code had anything, any vulnerabilities or anything he should be aware of. And it revealed that it had some link, they had a byte array that compiled into a link that would go and take a bunch of private information from you. So that was the TLDR. And I tweeted about that saying, you know, like, the, the, the, the, the,

world, interestingly enough, it was solved by vibe coding, but it could very easily the world of vibe coders who don't really look at code, I imagine are more susceptible to being in attacks like this and in the future. And it got me thinking about a lot of things. I'm like, what do attack vectors even look like if people aren't looking at code? There's so much that can go wrong. And one of the implications on model safety and how models behave in those environments, that's one. But I think the broader thing, and I'm curious what you guys think about this is, What I've been noticing more and more is I was having this conversation yesterday with some of my close friends where some of the joy of coding used to really be, you're stuck on this annoyingly hard problem and you just bang your head against a wall and you want to kill yourself. And then eventually you're like, I've figured it out and then you solve it.

And that's that's the muscle that you build when you improve and get better. And now I find myself even doing this, it's so hard to do if you just have a constant slot machine that might give you the right answer. And who knows if it will, who knows if it doesn't, but you just pull it all day long. Please fix, please fix, please fix. And what does that mean for the craft of engineering or software engineering in the future? I don't know. Like this vibe coding stuff, I mean, great for the rest of the world that was not an engineer.

But I'm now seeing how it's affecting the trained software engineers. And it's kind of like a drug for them. And it stops them from like living their own life, which is doing the engineering. because it turns your brain off because it turns your brain off yeah I think you know self-driving guys people thought about this first

this is why when you drive your Tesla you have to like keep your eyes on the road because they don't want you to turn your brain off and we don't have that equivalent in developer environments yet maybe we should like watch your eyes we remove one

one word in the code which one was it write it back so my answer I mean I happen to have shipped a model today two models and part of that is actually what I've been calling the semi-async value death. And a lot of it, I think, is my reflection on coding agents in terms of like we started with co-pilot, which was tab autocomplete. And then when we went all the way to the clock code, which is like very async, very, you know, like it could take 30 minutes, could take 30 hours.

I don't know. It just runs. And I think like something that cognition is very interested about is fast agents or something I've been writing about more as fast agents. is where like under a certain level you actually want to just be in a mind meld with the human and AI to have like fast responses so that you can get helpful assistance if it helps, you can get out of the way if it doesn't help. And like that is actually where you do your hardest problems. And then the async agent is where you do the commoditized, dumb, boring labor stuff that

you know how to do, you just don't need to do it. But when you are actually very deep work and focus and you're working on a hard problem, you are like, you should be applying your human intelligence. augmented by AI in an unintrusive fashion, which I think is the way that obviously I think it's like, it's a pro-human message, but it's also like a really interesting area of research for us. But that's almost like to play devil's advocate there. That's like telling somebody, well, I'm going to put the cigarettes right here. I know you love smoking, but so please don't do it. It's not a cigarette. It's right here. It kind of is. There's an analogy, right,

to be made here. It's a cigarette for your brain because you do not think anymore when you pull that button. And over time, I feel like, you know, the brain will get weaker if you don't use it for that task. And I like your message. I mean, I would ideally like if I was had a team of engineers, I would also tell them the same thing. But I mean, I worry about the reality, which is that's not what they do in many cases. But I mean, you got to ship the thing, right? Like, I agree. But at some point, you got to close the ticket and merge a PR. So how are you going to get the code done? right it's like they are doing it or they're going to get fired if they're just like generating they're running the enterprise and one way or the other one way and the b-to-be SaaS yeah it's interesting okay

so maybe i'll put it this way and i'll see honestly how you respond okay so um we have the formula the fundamental formula for coding agent performance okay it basically is find the right files and then write to the right files that's it like so read and write read the right files and write the right files. That's it. Right. So actually, what fast agents can do or like what, you know, what, what I just did today was basically the equivalent of a heads up display, like give you more info, but you take, you still take all the actions. So we help you read, read faster, read more efficiently, read with more focus, but you still write. And so I think that's still, that's not a cigarette so much as like we try to be helpful and we're evaluated on the healthfulness

of the reading and the comprehension so that you can hold everything in your head. That'll be the pitch. It's true. I think there, if, I don't know how the product looks, I would love to eventually play with it with the sweet rep and all of that stuff. But there's a world where I think the product decision also goes a long way into how people use it.

So if it is like that, then maybe. And I think when people use, even for example, if someone uses a cursor, A lot of people like the fact that they can see the code and then they kind of have to hit the final accept. So human in the loop. Human in the loop. But, you know, I still, I worry. I still worry. And I worry the most about, like, the younger kids, right?

Like, you think about the people growing up in college, how would you ever get yourself to think if you just had this, like, clearly more intelligent thing than you? At least for, like, I don't want to, like, rate myself too highly. but if I'm working in a domain that I understand, I can at least tell, yeah, yeah, model, you're doing the wrong stuff. Like, you definitely don't do that. Don't write that at all. That's a terrible file. Why are you hitting four files for this?

But if you think about what it looks like to an 18-year-old CS major freshman, they're just probably like, I guess that's how you do things. And like they can't hold it at that. So when they, like their training is just a little bit different. Cool. Yeah. Hi, Dedi. Thanks for indulging and welcome back.

And thanks for coming back. Thank you guys. Always fun. Trying with you guys.