Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures

Man, I remember the time when don't know when what your first experience using Claude was, but but mine was, I remember early Glean, there was a 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 and now fast forward to now, and I'm like, okay. Yeah.

It's we've come we've come quite a way. Yeah. I think actually they only recently introduced Claude in Slack.

Right?

now being on venture side, I have a bit of a 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 Bay Area, and I would say enterprise search, and it's shutting down the conversation right there.

Know, like, nobody would ever ask a counter question if you said enterprise search. They're like, that sounds boring as hell. Leave me alone.

Like and and fast forward to 2022, enterprise search gets more got more conversations. I was like, interesting. Tell me how you're doing this this search.

I think what 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 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 have we expand contracts very easily because the TAM is so large. It's 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 we've solved a lot of critical problems, I can go into that too, in order to get there. Then comes, you know, 12/02/2022, the chatty pity 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 that there's fire from a lot of angles, like, wants to be a part of the enterprise search story, and it makes sense.

I mean, it's a large unconstrained TAM. LLMs are particularly useful for gathering information. Obviously, consumers are interesting and enterprises, therefore, interesting.

How do you do this in the enterprise? Well, gather all the knowledge and then put an LM on top. So that being said, I'm still very happy with the Glean stock.

You know, Glean's also valued at 7,000,000,000, not a 100,000,000,000. So I'm I 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 to trivialize a problem into a one sentence sort of narrative. And with Glean, 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 the story at all.

I 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 moat 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 the way Glean'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 was gonna say I have the screenshot.

On the rate limiting side, it's happened for for several of the 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 wanna launch a competing product, but Glean doesn't eat into your revenue. If you are Slack and you sold, call it, a 100 seats at a company and you have Glean 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 principle is business logic, I don't see why you do it. If Glean 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, 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 is is really important and that's that's that's a critical data source, but we also have many many more.

And so team, I feel 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, then, 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 wanna 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 and I'm still optimistic that we'll clear out some of those issues.

Sam Altman, like we were just discussing earlier, like Sam Altman once came out and said, if you're an investor in OpenAI and one of these five companies, including Glean, we don't want you as an investor or something to That's fact. Yeah. And but yet, here's what I see.

Look at the revenue of Anthropic and OpenAI right now. These are billion dollar revenue scale businesses. Glean is several $100,000,000 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 OpenAI 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 100 k cell, 200 k cell maybe? Even a 7 figure cell?

Is that moving the needle on your, you know, 5 plus billion dollars in revenue or 10 plus in to the end the end of the year for OpenAI? Not really. And the amount of effort it takes to get there is big sales teams, huge FTE 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 wanna 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.

That's funny. That's funny. So Slack Workspace is, like that reminds me, that was the thing that we had to address at Glean.

I think, like, every enterprise company has, like, the same sort of hurdles. No. No.

No.

That's funny. Okay. Glean, bunch of interesting problems.

I'll I'll talk about some of them. If you wanna 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, like, Google, Bing, and 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 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. This is not that useful.

Really, like, it's every person that's doing a job, they have different needs, and they have different things they wanna look up. So given all of that, the techniques behind the hood under the hood that work for a consumer, they don't translate to enterprise. You have to invent a whole new set of signals that actually makes enterprises work.

And evaluation becomes very, very difficult too. On consumers who have tons of data to pick and choose how you wanna 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. So and which happens. Like, it's so out of domain for for even us.

Some of our customers are working on very, very specific problems. And so all of those, that's one huge huge challenge. How do you make ranking work in enterprise, you know, in a 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 our team is getting by using Slack. It's probably not even getting that much more productive.

That's not what they're looking at. They're kind of saying, everyone uses Slack. It's pretty useful.

I wanna keep Slack. I don't think we're gonna churn that one. If you take that analogy to search and and search systems, the issue is search systems aren't inherently viral or growthy.

Slack has a very clear virality moment. Like, 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 Makes sense. Two of the many. The pointers.

Yeah. Just a question on that. Yeah.

Was there any because, know, oh, you have a new search tool. It's like, go search. It's like, what am I searching?

You know? Like, what was that blank canvas onboarding for people? I mean, 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 for a handful of companies, like many companies actually, we would say, we wanna 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 so so we went the last mile.

There were companies who were 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 what is that using?

And they'll be like, well, it's using our internal thing. I'm like, do you like it? Clearly not.

That's how you're referring So to 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'd ran a lot of evals, 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 it would teach people to use Glean and be like, okay.

That's pretty useful. I think these results are great.

is the fastest growing software company of all time. I think I can say that fairly. I'm I haven't been disproven yet.

So I think the People say that, but, like, everyone says, like, you know, we're, like, first to, like, 1,000,000,000, first to a 100,000,000. I don't know. It's it's it's hard to tell.

But I do believe the the numbers are zero to a 100 in one year, 100 to a billion in one year, and this year, it would be one to the projection that is public is nine. But even 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 are bullshit.

Some of that is GMV. So all this other stuff. But in the Anthropix case, I think it's, like, fairly legit revenue, and I do think it makes it the fastest.

Definitely at, like, the 1,000,000,000 plus scale, I can't think of too many examples. So clearly, it has outdone itself. I would say that when we invested in the company, it had no revenue.

I mean, that that that's just fact. So when we drew our first investment, it had no revenue.

4,000,000,000. Right. It's been fascinating to see this company succeed.

I I couldn't have predicted it. We all of us, this was beyond our wildest expectations. I think whether or not it continues to perform at at at this rate, I believe it will, but it is already somewhat of a generational company in in in many ways.

And so it's kudos to the team to deliver, like, these these these these awesome results. You know, one of the risks I would say, like, kinda 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 the standard deviation of outcomes when you have teams like that or similar to that are is 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 had a high propensity to 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 ChatGPT. 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. 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 in my mind, least, ClawdCode 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 catwoo.

Yes. Except for catwoo.

Well, I like it's interesting to talk about. Right? Like, take OpenAI and DeepMind 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 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. Try.

Yeah.

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.

Well, as as somebody who, 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. The early, like, in Glean, we had I had such run ins with marketing because the first campaign we we actually did this campaign. I was just like, really?

AI for work that works. Okay. Was that a hit?

No. I mean Oh, okay. Yeah.

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, you 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.

But Okay. I'm gonna sprinkle in some of your tweets.

I think though it's like the the testament to all the comments 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 nontechnical side, people are like, so there's this new thing.

It's AI.

enterprise API market share. Correct. So I I should clarify that that is enterprise LLM API Right.

Spend The market that Anthropic happens to focus on. Yeah. And and critically, it's also spend numbers, not token numbers.

So I think those clarifications are are important, and also the methodology is going and surveying, you know, vast amounts of enterprise users on how they are are doing their spend. Yeah. But that being said, yes, the point the point The point is remains.

advantage as a as a Frontier Lab. You know, I'm 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 this data was in that market map of the 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'll 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 this is what you use.

So I think that's part of the effect. And and, you know, to commend OpenAI, like, OpenAI 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, so they were probably not focused on enterprise until now again.

Yeah.

I don't think the market share number is the market share number is more important is is more critical to understanding the TAM. At that stage, be very honest with you, at the stage that we we invested 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 wanna go into, that they may be early in or planning on on going into. I I think it's really difficult to underwrite on on market share other than knowing what, like, the potential cap of the TAM might look like.

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 $170,000,000,000, sure, it matters. Some of the numbers matter. But the future of the company is 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 that are going to be built? I'm less concerned about, like, where it's at right now in terms of market share.

But that's just me. I don't wanna speak for others. Yeah.

I think the new models are are really good.

despite what you said about the premium, I think it's everything you said is true. I still do worry. I think cost is is a concern for a lot of people and so the Pareto the Pareto frontier does still matter.

I'm glad Anthropics where it's where it's at right now, but who knows where that changes. When it comes to, like, Cloud five 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 ChatGPT, a consumer product does not lead to more users or more retention. It only is really applicable to us to thin slice of users who care about very smart type queries.

Right? And I would say maybe, like, under 10,000,000. Right?

Maybe that's just a random estimate. But most of the 800,000,000 users on ChatGPT are asking, like, how do I fix my dishwasher? How do I, like, like, rephrase this email that I've ever sent to somebody?

And that's done. Like, we know how to kinda do that. So what's interesting there is now that means we're at a point in consumer where maybe it's too early to say, but OpenAI is kind of one.

Right? Like, how do you catch up to something where model quality is not gonna be differentiated? You already have the users.

You already have the retention. You already have great product and people are paying. But the the interesting about Anthropic is if you look at coding, that's probably never gonna be the case.

Like, there's always an increasing frontier of how you 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 OpenAI where it wouldn't be as much of a revenue driver on their consumer business than as it would be for Anthropic.

I think it's great. I mean, there's Is it sustainable? Is it I I don't see why not.

I mean, I don't I kind of will allude to the margin question, which is, like, can can Anthropic continue to do this strategy, which, you know, I'm not gonna 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 know what I mean. A SaaS world, I guess, it's a Slack Palantir continuum.

And so this is kinda 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 again, I don't know what Anthropic's future plans are, but, like, you know, Ben Thompson obviously talks about this as classic strategy, is every time you own the, I guess, the means of production, you will end up getting into the markets that your users use it 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 that's a risk, I think, for those companies that use Clot heavily and rely on Clot to think about. But at this point of time, we're too early. Like, I don't think Anthropic is anywhere near thinking about that because you're still very much competing with other models on on that layer.

Yeah. Playing a different game. Yeah.

Yeah. It's interesting, like, 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 a app layer summer, and then now now it's, like, very back to models again. I mean, I I like I like the discussion. I like the discussion because I I was I had was at a dinner where we where, like, somebody was talking about this kind of question, and I was thinking about it more just at that dinner.

And maybe this is this is an ill formed thought, so, like, feel free to push back. Yeah. We're riffing.

Yeah. But when I think about, like, moats, it's a classic, like, VC startup banter. In my mind, I think the moat is what is the hardest to do in any part of the stack.

And so when I think about people that, like, tend to dismiss there's other cons like, 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.

I would push back on, well, 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 I'm a cursor guy. Like, I'm a Devon guy.

Like, people have their 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 Claude code, 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 the this is 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 of these companies haven't existed for more than two years.

So 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 OpenAI 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 and and that's something I I do think about. Thank you for engaging in all this, like, very meaty discussions.

India, largely academics holds the the same sort of prominence as sport would hold in America. Everyone talks about it. It's Asian culture.

Right? Everyone talks about it. It is top of everybody's mind.

It is something a lot of people wanna be good at, and it's extremely competitive society with a very large population. The way and and 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, and 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, you know, Silicon Valley people from Sundar to 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, 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 and and other places in Asia can be like that, number one. So you're not judged on the merits of your work or judged 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 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 kinda like Asia.

In the Bay Area, I know people who grew up who are like, I couldn't get into a a good college. Therefore, I am stupid, and therefore, I should not work that hard. Right?

Like, it's 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 would obviously debate that. But in in in general, I don't think it's it's a really well known university in in India.

And 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.

top two topics that it talked about. Yeah. Let's talk about the Anthology Fund.

So it's a $100,000,000 fund in close partnership with Anthropic. Like, 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 then. Was a much smaller company.

And they were like, look. We there's incentive for us to run our own fund. OpenAI runs their own fund.

There's a developer ecosystem that we wanna 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 wanna have it inside Anthropic or do you wanna 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 OpenAI as a notable exception, they tend to not be very good because all they prioritize is who uses my stuff the most. And 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 we did that, and now we look back at this fund. Obviously, Anthropic is in a very different place.

We've 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 significantly higher on anthology fund companies. And we write both small and and lead checks.

I mean, the thing the two several notable companies from the anthology program have been OpenRouter, Goodfire. There's a company called Endea, Prime Intellect, Whisper Flow. So there's a 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 checks. We can, like, participate in a round and then get closer and hopefully go and build a relationship and and and lead that in the future lead that the next round in the company in the future. It also lets them get really close to the Anthropic ecosystem.

So we have all these events with, like, the founders and execs and things like that. And people really enjoy, like I've to some of them. Yeah.

Getting it from from hearing it from the horse's mouth. Now I think, you know, I would say, like, Anthropic is in such a different place. It's no longer an unknown entity.

So, the program is is gets gets a lot of demand, but, you know, people kinda 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 alike. Yeah.

Well, so, you know, I I congrats on all this. I think it's it's pretty successful.

I would say, like, you can think of the company that go into Anthology in three categories. One is strategically important to Anthropic, and those could typically be somewhat later around, somewhat bigger companies. Two are companies that are using Claude heavily and are just great companies to be to be in.

And three is just very, very early stage founders with that are very high potential that may potentially be using Claude models and anthropic and so on. We don't require people to use a certain model or the other, so we we keep it pretty open and we do everything from like a 100 K check to a $20,000,000 check. So like, I think the it's it's really broad in terms of what we can do and we wanted to intentionally keep it that way.

When it comes to where we draw so there's some old old examples, but I don't think it's really relevant. There was a fund called the iFund that that Kleiner did with Apple way back in the day. It was kinda similar.

How did that turn out? I don't remember. I I I don't actually have enough data on that, but that's one example.

Know the answer? No. I I'm I'm sure there are some great companies that came out of it.

I just don't know who like, the details about who what was in it. So yeah. I mean, I think so that that's kind of how it's been for us, and I think it's been a really great program.

I can talk about any of those companies for Yeah. 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 it's a really tough thing to do. It's a really tough decision to make when you're, you know, working with those founders.

You're on that board. It's like someone anxiety inducing when you're thinking about this even from an investor standpoint. Like, I just get to, like, a couple million ARR?

Do I, like, start do 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 if I fast forward ten 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 that 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.

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, 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, you know, 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 it's 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. Like, 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 may guys may be aware of is g p t four o 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 keywords might arise, obviously, but it is something that is quite easy to tell in even current interpretability methods.

You can tell when a model is being sycophantic. You can tell when 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 that 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 catch catchphrase. Yeah.

But doesn't apply to LLMs only. All 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 don't need to.

As long as we There are some ideas that we have. Yeah. But not ready to talk about publicly and some that are working also.

It's not right to be public.

I was wrong. Tell me why I was wrong. You may not be wrong.

I mean, look, like, I could be the kind of person who go to shills all of their companies and says this is the best thing ever, and if you don't think it's gonna be a $10,000,000,000 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, wait, if they were right about a couple of things, what could go fantastically? Is distributed Distributed 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 people like other labs is is is really hard. And so they I think they can do that. And the third thing I think is there's a broader vision to PrimeIntellect that is not yet realized yet where the first step of that was a distributed compute, and and we'll see if they realize that.

Yeah.

OpenRouter was sort of my, like, you know, like, I mean, I don't wanna make this about me. It's really about them. But to in my mind, it was my my my darling deal.

You're proud of it. You're I'm just like, man, I entered venture and I'm like, that is the company I would have built. Yeah.

I think it then for I I think we're skipping a bit. Let's explain who Alex is, what he did before, like Right. So I'll give you let me give you the background on OpenRodder.

Alex is a phenomenal phenomenal founder. He started a company called OpenSea before, which was the NFT company. Obviously, that, at its peak, was, I think, a $14,000,000,000 more than $10,000,000,000 company.

It did not meet that valuation's expectations, but, look, there are many things out of 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 Glean 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 kinda 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 go so far as to say for in any SaaS market, if there can be a PLG motion, the PLG 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 Without talking to anyone. Without talking to somebody else, like, classic, like, get on the phone on a SaaS website. So those two things 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 the product solutions about us.

Like, 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 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. Just talk to me.

I don't really care. I just wanna 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, you know, 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 on 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.

There's that business model, but then there is a a reasonable threat factor where, you know, what if the spend on the net goes down over time as tokens go up? So you do take you do carry some risk of the prices of LLM falling to a point where the business does 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 OpenRouter and they tend to churn. And then a lot of enterprises will use OpenRouter to evaluate and then go pick a model that they wanna settle with later.

So that's a problem to fix. And so those are two of risks. But overall, I think they've just like been executing phenomenally.

the AI gateways of other products are ever gonna 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 OpenAI in 2020.

What if somebody else does this? Yeah. I mean, they could or 2022.

Like, they could, but, like, 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 I'm like, it's everything from like, here's something that nobody ever, like, 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 Kimi k two 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 whole 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? Well, you see it on OpenRouter.

And one one thing I I used to joke about is OpenRouter is the only non Elon company that Elon has tweeted the most about for obvious reasons. But Last one. Number one right now.

Yeah. I'm sure that's Amcode free plan. A good week where I was like, every day is like, open router, open router, open.

I'm like, yeah. Yeah.

I'll talk about

Yeah. Okay. Let's just try that.

Let's talk about these two things. So 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 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 their 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 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 like, 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.

Well, we'll see. We'll see how that plans out. There's some interesting plans to get it to be a a cooler product, but we'll see.

The other company and I again, we're 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 do talk a lot, Sean, about, like, the the proto frontier of of Yeah.

Latency cost quality. Yeah. Diffusion models today are, I would say, 80 to 90% of the quality at one tenth of the cost in latency.

So has huge implications on, obviously, the stock market, which is kinda NVIDIA and and 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 wanna draw attention to with 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 it 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.

about in the course of history,

I don't know if you I mean, I'm I'm aware of the very, very basic details, but 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 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 purist 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.

We see this with startups as well. It's it's very heartbreaking. Every every once in a while, you'll 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. And 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 the 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 with the tunnels closing in on you. You could be the fastest runner in the world, and you might not make it out of the tunnel. That's kind of the analogy.

And 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 I mean, you know, like, without most It works. ACML exactly. It's like without most ACML, maybe they're not on the same trajectory.

It's like, I don't know. Maybe they are because, you know, Ali's train and all have ever talked about like the roll up companies, which is my favorite like little The key roll ups? Yeah.

Yeah. Well I didn't know that was a topic of yours. It's not really a topic of mine.

Just find it quite interesting to see how speaking of AI companies and markups, it's there are companies, obviously, I'm gonna 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 2,000,000, and then I'll do some of it with AI. But now I'm an AI company in a million of ARR.

And AI company world is a $100,000,000 valuation. And so, you know, it's it's it's pure, like, multiple arbitrage on the category that you're in. Yeah.

he just founded a company that bought a tax a tax firm. So Yeah. Yeah.

A law firm. No. I I Accounting firm or tax A law firm.

Law firm. Yeah. If it works, it works.

like, primetime, which dissuades anybody else from entering that market. And then they become the de facto owner of the market because they canceled the competition with funding. Yeah.

And you can think I'm not gonna name the categories, but you can think of numerable categories in this market, in in this paradigm that's that's already happened.

Do you think, like I I guess, it's a question for you guys also because I I don't have a good answer yet. Is the the belief is always obviously bitter less than pilled. Right?

Like, you buy more compute, therefore, you get the Bose models. And is it by the is it an anthropic relevant thing? Mhmm.

Right? And so but, like, is I guess, is that necessarily true? Like, there could also be a world where that's just not not true.

Right. And I think, you know, being in Sam Altman's place, it's absolutely the right chess move to play.

over the next ten, fifteen years? Given the amount they're spending on compute I think this is a general question. I'm not criticizing the whole manual.

Is even if everyone was using claw like, whatever. Oh, codex, Cloud Code, whatever, all the time, like, inference demand is not that big globally. Right?

So you you would have to believe so what would you have to believe for that to be true? Because there are 800,000,000 weekly active users. This is what Greg Robin says, like, a GPU for every human on Earth.

I I'm I'm somewhat shipwrecking. I'm somewhat shipwrecking, but they actually say this on their official comms, so I'm just repeating him. I I I don't I don't necessarily disagree.

I'm just trying to work backwards to, like, what do we need to believe to get there? Because ChatGPT compute is not that much. Correct.

Right? So they're not doing it, like, agentic stuff. Maybe they will be in the future.

Most people are doing basic q and a type queries.

Right. And so my my my my point being, yeah, 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 and d and therefore produces models that are so much better that therefore have more demand, etcetera. But if in any case that, like, I don't know, the incremental marginal is not that big, then, you know, that's the risk of of of the of the bet.

Yeah.

Like Google was milking those margins and they didn't wanna spend

LLM psychosis a little bit. You know, like, I so I tweeted I'll just cover the tweet first. I tweeted about this guy who wrote a blog post about he was in an interview from a I I didn't think it was a legit account.

He thought it was a legit 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. It 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 and I tweeted about that saying, you know, like, 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 and it got me thinking about a lot of things. Like, what is what do attack vectors even look like if people aren't looking at code?

There's so much that can go wrong. And what are implications on model safety and how models behave in those environments? That's one.

But I think the broader thing, and 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 close friends where, you know, 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 wanna 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 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 and what does that mean for the craft of engineering or software engineering in the future? I 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 I'm now seeing how it's affecting the the trained software engineers, and it's kind of like a drug for them.

Mhmm.

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 gonna put the cigarettes right here.

I know you love smoking, but So please don't do this. It's not a cigarette. It's right here.

It kind of is. It's in there there's an analogy, right, to be made here. It's it's a cigarette for your brain because you do not think anymore when you pull that button.

Mhmm. And and over time, I feel like, you know, the brain will get weaker if you don't use it for that task. And and and I like your your 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 I I worry about the reality, which is that's not what they do in many cases. But but, I mean, you gotta ship the thing.

Right? Like, I I agree. But at some point, you gotta close the ticket and merge a PR.

Mhmm. So how are you gonna get that code done? Right?

That'll be the pitch. It's it's true. I I I think there if I don't know how the product looks.

I would love to eventually play play with it with the and all of that stuff. And but there's a world where I think the the product decision also get goes a long way into how people use it. So if it is like that, then then maybe.

And I 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. Yeah. So Human in the loop.

Human in the loop. But, you know, I still I worry. I still worry.

But And I worry the most about, like, the younger kids. Right? Like, the you 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? Yeah. At least for, like, I I don't wanna, like, rate myself too highly, but if I'm working in a domain that I understand, I can at least tell, hey.

Yeah. Yeah. Model, you're you're doing the wrong stuff.

Like, you don't definitely don't do that. That don't write that at all. That's a terrible file.

Why are you creating four files for this? But if you think about what it looks like to a 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 Yeah. When they like, their their training is just a little bit different.