Speaker 190% or greater of use cases can now be fully handled by many, many different models, including open source models. I think like for almost all other AI companies that are not doing frontier model training, they should see the model companies as a huge asset. So once you move towards consumption, there's no inherent bundling advantage. You have to do 10 times the work to get the same amount of revenue from your customers.
Speaker 2This is 20VC with me, Harry Stebbings. Now, I have to admit, I started fasting. And the trouble with fasting is you can get a little bit hangry. Now, I did this show late in the afternoon. And Arvind Jain, the incredible founder of Glean, is one of the technology luminaries of the last decade. He founded Rubrik before, which obviously IPO'd very successfully and is a brilliant public company now. He's gone on to found Glean, an incredible business today that's raised money from Kleiner Perkins and many other great investors. And it's... I was, I would say, divisive in this show. I'm almost slightly nervous to listen back because I really pushed him in a way that I probably don't push other guests. But it actually led to one of the most phenomenal discussions that we've had in recent times on the show, which makes me think I should probably be hangry a little bit more. But it was a great show. I'd love to hear your thoughts. Do you like happy Harry or hangry Harry more? But before we dive into the show today, most companies have tried AI. Most aren't seeing results. Not because AI doesn't work. It's because AI hasn't reached the workflows yet. That's the gap Asana is built to close. Asana is the operating system for human agent teams. Your easy button for AI productivity across every team. Ready-to-go AI teammates, pre-built for marketing, ops and IT. No prompt engineering, no setup. They show up where the work is happening, already onboarded in your workflows, ready to deliver. With Asana, your whole company can work. On the same plan, towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents work flow together. Try it at asana.com. That's A-S-A-N-A dot com. While Asana aligns the roadmap, MongoDB powers the build. MongoDB has always been the database developer's love. Well, now it's the data platform AI agents need. Agents need accurate context, fast. MongoDB stores, searches, and reasons over your data in real time. JSON native database, vector search, and Voyage AI embeddings all in one place. One system instead of 10. No data pipelines to maintain. Oh, this sounds too good to be true. Build and scale from your first user to billions of vectors. Run on any cloud, on-prem, or your laptop even. That's why 75% of the Fortune 100 run their most critical apps on MongoDB, moving trillions of dollars every single day. And it's why AI-native companies like Eleven Labs run 40 million agents on MongoDB. So if you're building an AI, MongoDB for Startups helps you move faster with Atlas and Voyage AI credits and dedicated support. Don't build agents that answer once and forget. Build agents that remember and learn from your real-time data. Go to MongoDB dot com slash agents. That's M-O-N-G-O-D-B dot com slash agents. While MongoDB scales the product, AlphaSense sharpens the strategy. We used AlphaSense on an investment that helped us close an $8 million deal. $8 million, baby, that's a lot of money. That's why I'm genuinely excited to have them as a partner on 20 VC. AlphaSense combines AI with one of the world's deepest libraries of market intelligence, including expert interviews, broker research, earnings calls, company filings, and real-time news. Every answer is grounded in this incredibly trusted evidence and fully traceable to the original source, which is so important. So you can make really high conviction decisions, with confidence. But the best part, they're building super analyst and always on AI analyst. So instead of starting your day with another search, you'll start with work you already done, your coverage monitored, the important developments surfaced, and your investment brief already waiting for you. See for yourself, head to AlphaSense dot com slash 20 VC. That's Alpha-Sense dot com slash two zero VC. You have now arrived at your destination. Arvind, I'm so excited for this. We have a mutual friend in Mamoun who says many, many wonderful things about you, and I think he's one of the greatest investors of our time. And so I'm really excited for this. So thank you for joining me. Thank you for having me. Now, I think with entrepreneurs, you're either thrilled by winning and it's that chase to win or you're terrified of losing. And it's that fear of losing that inspires you.
Speaker 1Which one are you? That's a good question. I think I would probably say the latter. I'm always worried about what can go wrong and that keeps me up at night.
Speaker 2I love that it's only the paranoid survive. Has it always been that way? Yeah, mostly. Even with the success you've had, I'm sorry, it's so interesting. Like Rubrik was a phenomenal success. Public Companies Day and you're one of the co-founders. Yeah. It doesn't change with time.
Speaker 1No, because I think, number one, like every time you do a new company or start a new project, it's sort of like starting from scratch, in my opinion. Like you have some good lessons from before, but it's a new world. It's a new environment. And when you think about Glean, like, you know, it's fundamentally different from Rubrik in all ways possible and especially in the world of AI. I think you have to think that way because like there is a disruption every single day and if you start to focus more on sort of keep building on what you've already built, like the underestimating mindset that, you know, you're winning with something, you want to sort of double down on it. I think that's not sufficient in this new AI world.
Speaker 2For those that don't know, can you provide a 60 second summary on what Glean is and how you work?
Speaker 1So Glean is an enterprise AI company. We started as a search company for businesses. So help an employee quickly find information that they need. You know, that's sort of built across one of 100 or 1000 different systems inside their company. So that was sort of like how we started the Google. It's a Google for your work life. But then over time as AI models got better, so it evolved into an AI platform. So today the way to think about Glean is that first, it's a superset of ChatGPT, Cloud, Gemini, all of those combined into one product experience. It's a coworker for your employees. And it's connected to all of your company's context, how work happens inside your company. So Mr.
Speaker 2Alex Karp from Palantir went on CNBC last week, and he said that the largest enterprises in the world were more skeptical than ever of frontier model providers. You work with some of the largest, you have incredible customers. Do you agree with him? Are they more skeptical than ever?
Speaker 1Two things, you know, one, they're terrified of them, like in the sense that, I mean, just like every software company is worried about that, hey, we'll be in business, will the models eat it all? Similarly, enterprises leaders are also worried that is their sort of core IP, their data, their information, as well as their way of learning, their way of doing things, like would it all be, will they be subject to too much of technology dependence on these model providers? So that feeling is there for sure. But I think what he said was that AI is not working in the enterprises, then everybody's afraid to actually say so because, you know, it's not a cool thing to say.
Speaker 2Before we get to AI not working, because I think it's probably one of the most important questions. There's a whole separate segment. Do you think they're right to be afraid of the frontier model providers eating their lunch or not?
Speaker 1Well, yeah, I mean, depending on the enterprise, yes. Well, look, like, you know, if we are talking about fundamentally changing how people work and if we are saying that majority of the work that we do today is going to be done by an agent which is fully powered by one of these frontier model companies, then in some sense, like you've now transferred a lot of your operations to these technology providers, this is more than technology dependence. This is actually real sort of operational dependence on the on the companies that are actually running those agents for you. It's actually interesting. If you think about how work happens over time, like, you know, there is, you know, like when you initially do a task for the first time, maybe you'll document a process like, you know, what are the 10 steps you need to take to actually complete some piece of work and then over time people start to sort of optimize and tweak that process. A lot of it never gets documented and you just you sort of based on doing this work one over again, you now built all these learnings that you apply in real time to do this work. In the future, all of that institutional learning is actually going to accumulate in that agent that is doing that work. So if you don't have any control on running that agent yourself, you don't own the learning that it actually gains over the years, then you're basically fully dependent on these AI companies, you know, to do, you know, to get your work done. So like it's absolutely, I think there's a fundamental question in front of enterprises today, how do they actually use these AI technologies but still retain control? And all the compounding learnings that happen with AI, they belong to the enterprises.
Speaker 2Are you seeing enterprise customers route away from frontier model providers towards open source?
Speaker 1That's something that's happening now. So I think we are at a real inflection point with open source. Part of it, you know, like you're waiting on the open source models to actually get better. You know, the desire has been there for many, many years. Nobody, there's no enterprise, you know, that we talk to which is OK with saying that, hey, look, I can get my work done with OpenAI or with Anthropic and I'm good. Everybody wants to make sure that they are in control of their destiny, that they get to use many of these models. And now, given that AI has become so expensive, people hear stories all the time about companies, you know, coming up with a annual budget for AI and they run past that, like, you know, within a month or two.
Speaker 2Poor CFOs.
Speaker 1So that sort of, that has really accelerated that, that desire, you know, for open source. Because, you know, and that coupled with the fact that We now have really good models in open source. What do they care about?
Speaker 2Do they care about cost? Do they care about ownership in terms of their data staying on-prem in models that they actually can have visibility on? What is it?
Speaker 1I think right now the open source drive is coming from the cost point of view. There are certain businesses, of course, that have the requirements to actually keep all the inferencing workload within their own private data centers. When AI just came, companies were a lot more afraid of getting their data outside of their own control and model companies training with their data. But that sort of is a fear that it's no longer there. People believe that the model companies are going to be responsible and not train their models on enterprise data so long as I've signed up for the right kind of contract. So right now the drive is coming from cost.
Speaker 2In terms of where you sit in the landscape, every one of your ambassadors that I spoke to, you said that I have. I had to ask this question, which is the obvious question. Do you worry that Anthropic will do what they did to Figma, say, or what they've done with legal or what they're doing with health and move into your space and cannibalize your business?
Speaker 1First of all, I think we should be careful in terms of what they've actually done for Figma or legal space or finance space. They are launching these sort of vertical packs, but I think they're quite shallow in my opinion. And I don't actually know of people who are sort of moving their workload entirely from... from Figma or for that matter, from any other tool to Anthropic. It's actually sort of net new always, like, you know, or like, I think it's expanding the market. Like, for example, now in design, the designers still use Figma, but the non-designers, you know, are using, you know, cloud design, right? I mean, so that's sort of what we're seeing is that AI is making things simpler. So if people are not experts, you know, on the primary users of that particular tool, they can actually start to do some of that work with cloud.
Speaker 2So you don't worry that they all put emphasis on moving into enterprise and being that context?
Speaker 1Well, they're already doing it. Or whether they're doing it or not, we actually face that competition every day with enterprise customers. People often ask us, well, I mean, cloud can also connect with enterprise systems through MCP. So what's different? Like, you know, what can Glean do, which, you know, cloud cannot. So we have to go and explain, like, what context really is and why it is actually complicated to actually build it. So we are, we are competing. In fact, actually, I would say that they probably started to compete with us before others. Because, you know, if you think about cloud, co-work as an application. Or cloud desktop, the primary use case for that has always been question answering, right? That's like the largest application or use case for AI in the world today is, in fact, information seeking and question answering.
Speaker 2How important do you think being first to market is?
Speaker 1It's actually very advantageous. It's the thing that it helps you, but it's the thing that's not going to carry you. So for us, you know, we actually get a lot of credit for being the first enterprise AI company in the world. The first ones to actually bring RAG into the enterprise. The first ones to build conceptual. Yeah. Semantic search. And so that actually gives us, that brand, the right to compete in this market. Even though now, like, you know, we're much smaller compared to the giants that OpenAI and Anthropica have become. So it's a huge, it's a huge sort of asset, but neither is it a requirement, nor is it a savior.
Speaker 2How do you advise founders who are losing sleep at night, worried that the frontier model providers will come into their space?
Speaker 1Oh, I would say, like, absolutely don't worry about that. I think as a founder, you have to, you have to actually. Solve problems, not worry, number one. Yes, you know, like, you have to always, you know, anticipate, you know, what they're going to do. You have to see their current capabilities. But I think, like, for almost all other AI companies that are not doing frontier model training, they should see the model companies as a, as a huge asset, not a competition, in my opinion. You know, we actually believe that everything that Anthropica is doing, everything that OpenAI and Google is doing, as well as all the innovation that's happening in open source, there's great news for us. Like, we don't worry about that as, we don't think of that as competition. In fact, like, you know, they've allowed us to actually deliver a product that we could never, you know, without that help.
Speaker 2Do you not think we're seeing the ultimate commoditization of the model layer, when you speak about Anthropica OpenAI and the rise of the model layer and the speed with which new models are coming out, especially from open source Chinese providers, are we not seeing the ultimate commoditization of the model layer?
Speaker 1So, one thing is clear, let's, let's talk about enterprise use cases, 90% or greater of use cases can now be fully handled by companies. So, there is definitely commoditization from that perspective. In fact, like, you know, we at Lean, that's actually one of our core value-adds to our customers, you know, which is cost control. We will actually tell them that, "Hey, look, you know, as people actually complete their tasks on our platform, we actually picked the right model for you." And if you're okay with using open source models, we'll use them, you know, when we think it's appropriate, when it's going to generate a high quality answer.
Speaker 2What percent of customers are not okay with open source models?
Speaker 1This is actually so new. Like, I would say that open source truly coming to within three months of frontier capabilities, that has just happened literally like a month back or not even a month, right? You know, I would say a GLM 5.2 is the very first time where our own team, for example, feels comfortable that now we can run majority of our workloads on that model. So, we are yet to find what people are going to tell us. Like, from a point of view of open source and using the model, everybody's going to be fine. The question is going to be, are they okay with the Chinese model or not? That's the only question here. It's not open source versus closed source.
Speaker 2Why would they not be okay with the Chinese model when you look at the ownership that you have, the ability to have it on-prem, you're not sharing anything back to China? Why would you not be?
Speaker 1I think it's just, what if something goes wrong? There's always paranoia and fear. What if there's a backdoor, you know, some backdoor that we don't even understand, like, you know, then that could be a backdoor. So, there are some concerns. There's also, if you use these models. If it becomes a known thing, then, you know, it could be used against you in some ways, you know, by your competitors and things like that. So, like, you know, a variety of factors that is, but ultimately, like, it again boils down to who's willing to be bold, because this is a new thing. Like, you know, in large enterprises, I have to make this move, and the early movers will make the move first, and then it'll become a more normal thing.
Speaker 2I'm always doing this show to learn, if 90% of enterprise workflows can be done with open models, have we completely mispriced the front end?
Speaker 1frontier model landscape. It's a very different time. I do feel like, you know, the model business on its own, regardless of like, forget open source for a minute, there's plenty of competition, even within the labs, and more and more companies are coming into that space. So, you know, in that fierce competition, even in a three way race, I think you can actually get, you know, good amount of pricing pressure. And now, of course, you know, with open source, like, you know, it actually is an order of magnitude cheaper prices. So like, I actually heard rumors that OpenAI was going to drastically reduce, you know, their model prices in response to like these developments, like, you know, competition as an open source. The model business, you know, on its own, is actually probably not as lucrative as everybody believes. But these companies now have a lot more things. It's not just that they're no longer model companies only.
Speaker 2Totally get that. But if they're doing shallow things in those adjacencies, they're not exactly going to generate a trillion dollars of revenue, like Dario said.
Speaker 1Yeah, well, I mean, if you think about like, you know, first of all, like, you know, these two labs, they're very fundamentally different businesses. OpenAI, of course, has amazing consumer product. And Anthropic, actually, the interesting thing that is happening is people are actually building on top of their platform. And so when you think about Anthropic, right now, there are a lot of folks who are actually developing automations and skills, and everybody's sort of, they're creating these MCP servers to their internal systems getting connected all to cloud. So there's an ecosystem that actually that's being developed. So they very much, you should consider them an application level company, not just a model company.
Speaker 2If you were to make a guess in three years time, what percent of your workflows do you think are through open source?
Speaker 1Well, we've been telling customers, I believe that majority of enterprise workloads will actually be on open source models in three years, for sure. Yeah.
Speaker 2Another competitive element that you face for getting the model providers, it's like, actually, you have Microsoft, you know, Microsoft have made a phenomenal business on the back of creating a 70% as good product, but bundling it into a bundle for enterprises, and then selling it with a nice sticker on it. How do you think about the bundling pressure from a Microsoft co-pilot as a competitive threat?
Speaker 1Well, for us, they are one of our most significant competitors. And the bundling strategy actually works. And you have to fight, you know, against that. I mean, there's luckily, like, you know, there's always been room for best of breed, you know, software and, you know, like, all customers think of us exactly like that. If you are trying to actually bring a great search product, if you're trying to build a horizontal comprehensive AI platform, they know that, you know, we do do it better. So companies are willing to invest on top of that, like, you know, as part of the bundled product suite from Microsoft. But the other thing, like, you know, is actually, is maybe making bundling not as effective of a strategy anymore, is the fact that AI is moving towards consumption based models. So once you move towards consumption, there's no inherent bundling advantage, because, you know, like, and as a business, I can get six tools, and I let the users choose where they want to do their work, wherever they do their work, I have to pay for it for that particular unit of work. So consumption can ultimately break that bundling strategy.
Speaker 2Respectfully, I don't know if it does, if you're working with enterprise, because they will make you compliant as an enterprise bundle. And so you'll go through approvals processes, sign off processes internally, for the largest enterprise in the world, your VWs, or your... Fords, or your GEs, or Tyson chickens, I always use them as like, you know, I know random companies, but they'll approve Microsoft as one vendor. Yeah. If they're suddenly having to approve 15 vendors, forgetting the pricing and the transactions, it It creates a vendor management problem that they didn't have before.
Speaker 1That is true. But I think the, I would say that if you go and talk to companies that have been on the other side of the Microsoft, you know, onslaught, most of them will talk about pricing as the main killer, because I think it's hard to compete with free.
Speaker 2Who's a fiercer competitor, Microsoft or the frontier models?
Speaker 1Good question. I think it's early to tell that, but Microsoft is formidable. So if you look at our experience with Asvigo and Prospect, I think we hear this answer more often that, well, like, you know, we are a Microsoft customer and we already have, you know, we're getting co-pilot. And so therefore, like, it doesn't make sense for them to consider us. Like, we do hear that. And we hear that more often than we don't hear from somebody that, oh, like, you know, I've embraced, you know, one of the lab products and therefore there's nothing else that I'm going to do.
Speaker 2We mentioned Alex Copson at the beginning and I interrupted you and said, let's, before we dive to like, we're not getting value, I think 2026 H2 and 2027 is the year where everyone goes, oh, hang on a minute. Is this spend generating output or return of ROI? How do we think about the return on investment that enterprises are getting? And is Alex Cop right in saying everyone's going, what the fuck? Where's, where's my return?
Speaker 1I would say that there is pockets of value realisation today. For example, take customer support as a vertical. I think there it was easy to measure productivity. You could actually say that. Like, you know, your company is support agent resolves 10 cases a day and now they're able to do 12 because of AI. So you could see that in, you know, like a very concrete measure of productivity increase. And that's it. That's the use case, you know, where AI is actually pretty good because a lot of like, you know, that time that is spent by the support teams is about reading knowledge and then summarising it to your customers. So, so there are definitely areas where there is clear value realisation and, and, you know, and enterprises are feeling good. Some other ones are more complex. For example, I think the majority of the AI spend right now is on coding. And you know that the coding as a practice has changed. Most developers now actually use AI to write code. They're not writing it by hand anymore. In some ways, you can say that, yes, AI made a big impact, but are they shipping the products faster or not? And that's where we hear most of the companies saying that, no, that the actual shipping speed of products has not increased, even though coding speed increased significantly. Because I know that's only a small part of overall shipping a product. Has your shipping speed increased? I would say it's hard to actually measure. That's the challenge. Because engineering productivity is one of the most difficult things to measure. It's the fuzziest of the jobs out there. If you look at some of the metrics like lines of code written, of course, we're writing way more lines of code now. But if you look at, are we shipping features at a greater pace? Yes, we are. But it's a result also of we have a larger team, we have a team that is more tenured than it was before. So sometimes it's hard to tease it apart. But with that, what do we do as a company? We are right now saying that, look,
Speaker 2we are just going to keep investing. What percent of Glean code, say, do you think is written by AI?
Speaker 1Now it's probably about almost 100%. Nobody's actually writing the initial code by hand anymore. Yeah, so almost all the code is being written with AI, but we actually enforce human reviews. So you cannot actually generate tons of AI code and then just check it in the repos. We're probably more conservative than most other companies. There was, in fact, a discussion inside the company that, well, now AI can write so much code, and the real bottleneck has shifted from the person who writes the code to person who has to review. And so there was a proposal to actually eliminate code reviews. And many companies are doing that. They just let AI write the code and directly get submitted into the repos. So there was a discussion inside the company that
Speaker 2said, well, if you have to have a stringent code review process, it almost removes the point of having a fastened code development process. Yeah, it's true. But I think what it is doing
Speaker 1right now is, we're still in the learning phase of using AI effectively, thinking about long-term ramifications of it. Because when you write code, for example, with AI, you can write a million lines of code, but it becomes incredibly hard to actually maintain it and understand it and manage it over time. Is that not what you're saying?
Speaker 2Is that not what AI does, though? You have AI that does refactoring, and AI that does security, and AI that does...
Speaker 1Yeah, the only thing is that it's not that perfect right now. I think that right now, we're willing to pay the cost of reviewing the code. So we're still faster than before, because the writing part is actually much faster now. And the person who writes is the one who actually does the first review.
Speaker 2So when you say AI ROI is really a throughput problem, what does that mean?
Speaker 1The first thing that we have to do is make sure that you are able to bring the right context to these AI agents. If you think about most enterprises today, the way they're rolling out AI is that they actually just throw it into the system and connect AI with all of enterprise systems in a rudimentary manner using MCP servers. And now you're letting any piece of work that you are trying to do with AI, you're letting the models sort of brute force their way into trying to figure out and assemble the right raw materials that they need to complete the task and then do it. And in this sort of in this mode, AI is super slow. It takes a lot of time to actually just assemble the basic information it needs to do the work. It also becomes very, very costly because most of the tokens are being burned just trying to assemble the right context for that given task. And you're trying to sort of use AI for things where it's not even good at or needed. So instead, what we talk about is to make AI really perform and deliver, you have to sort of invest around it. You have to make sure that you provided the right context so that it can actually work faster at a lower cost. What does it mean to invest
Speaker 2around it? And are we wrong as CEOs to be urging all of our team members to be trying to replace themselves with AI even if it means that we're wasting tokens?
Speaker 1It's a wrong goal in my opinion to say that, "Hey, replace yourself with AI." First of all, I think we've been giving too much credit to AI when you say that. It's just not ready right now. You give me a name of one job that you can replace with AI. For example, do you think it can replace your EA? Mine, no, but I'm a fucking diva.
Speaker 2For most people, I think it can do the majority. Yeah, I do.
Speaker 1And that's the thing. It can actually take care of a lot of things for any given role, but it cannot replace the final intangible. No, but that can be a tipping point where actually
Speaker 2for a lot of people, if it does 90%, fine. You know what? You'll do that birthday present for your wife because it's once a year. It's not very often. And Claude isn't quite personal enough to know your wife's preferences of perfume, but that role will get cannibalized.
Speaker 1I'm not sure. And I'll tell you why. You want to be performing the best in whatever you do, and I don't think you're going to take a 90% solution.
Speaker 2I know, but I'm not cost-constrained being a dick.
Speaker 1Well, I mean, look, it's not about you not being cost-constrained. It's about you have to be competitive in your work with others. Remember, they also have all the AI tools that you have, but if they also have a human on top, how are you going to compete with them?
Speaker 2So do you not think team... How many people would you have now?
Speaker 1We're over a thousand people now.
Speaker 2Over a thousand people. How many do you think you'll have in five years' time?
Speaker 1Well, hopefully 5,000, 10,000. Wow. So you don't...
Speaker 2We're going to grow. But that is very atypical. I sit with the biggest CEOs in the world, and every single one of them is shrinking teams. And every single one of them is saying to me...
Speaker 1I absolutely don't believe in it. Why? Well, I mean, I think like first thing logically, take two companies, take Coca-Cola and Pepsi, or two companies that compete with each other. One company decides to shrink, and the other one still has a lot more people. Both of them have full access to the same AI tools and technology. And so now the question is, if you were trying to do the same amount of work, and you believe you can do it with fewer people, and therefore you shrink, your competition can also do the same, but they chose actually not to do the same amount of work. They chose to actually elevate and build a 10X better product, or build 10 times more, produce 10 times more goods, because they have more people. Yeah. They're going to be larger. They're going to beat you.
Speaker 2But I don't think more people makes for better products. That's a different thing. If I can cut headcount, and then afford the best frontier models, the best technology for my 100X engineers, because I've reduced headcount, the best engineers will want to come to my company, and actually I'll be creating better products faster with my smaller team.
Speaker 1Yeah. That's a good point, but that's not an AI argument. That argument has always been true.
Speaker 2Sure. But combined with the AI element of you're able to ship more, if you're able to ship more, I promise you when you have, and you know this, when you have more people, they'll just put up the barriers to
Speaker 1get in the way of that product going out. Yeah. Well, look, even in the... We have these AI discussions right now, but before that, just post-COVID, many companies felt they were bloated. They cut down 15%, 20% of their staff, and every CEO came out and said they're actually, as a result of that, they're actually moving 20% faster. So a lot of companies, companies came and talked about that. So that's the argument that's always there. At some point, teams get large, they start to slow each other down. Humans do that. I also believe in that. But ultimately, people are also your asset, and you have to be able to deploy them correctly in the right set of projects. I don't think the world's greatest companies are going to be companies with 100 people. And look at the model companies, same for them. Why are they
Speaker 2hiring so aggressively? Do you not think they're the best people who want to work with the best technology? And we'll see an increase in technology spend by the biggest companies in the world from 8% to 12% where it is today. day to maybe 16 to 20. Yeah. And then actually you'll see a reduction in headcount, but an increase in technology spend and the best people will want to go where they have the best tools
Speaker 1and equipment. I'm not sure about that either, because I think technologies is actually not supposed to increase in cost. First of all, I think, do you admit that currently this technology is priced absurdly for what it delivers? I think it totally depends on what it's doing.
Speaker 2So no, I don't at all for Cursor or for any of the dev tools. I think it's still dramatically underpriced. When you look at Mark Benioff spending 300 million on Anthropic, it's 3.7% of developer salaries. I think that's relatively small. I would say it's absurdly expensive.
Speaker 1I'll give an example. We had this cool triage agent for engineering. We have 15 people team on call team that their work was to actually triage every single production issue that happens, like any system alerts, things that are going. And we built this agent that actually now is taking care of like 95% of those issues automatically for them. But even there is actually doing it a cost, which is actually questionable. Like, you know, is it actually more efficient than humans? We were spending a million dollars a month on that particular agent. And that was actually more than the cost of a million a month. Yeah. Are you buying Cristiano Ronaldo? What are you doing? Well, I mean, the air costs are like that. I mean, it is quite expensive.
Speaker 2But sorry, can I just go back? You said, because- Because I discuss this a lot on the show, so you're making me much smarter. You think that spending 3.8% of developer salaries on these tools is a lot. If you think that's a lot, then these model providers are absolutely screwed.
Speaker 1Well, I mean, I think the point that I'm making is, well, the 3.8% number is actually doesn't seem high at all, when you look at it that way. But I also know that already you see with open source that you can do the same work for a tenth of the cost. That's number one. Number two, historically, for as far as I can remember, we've not put technology costs and labor costs in the same sort of sentence ever before. This is the first time we're actually hearing that, that, hey, I would rather have fewer humans and more tokens. The first time. This is not how technology works. The models are supposed to get cheaper and cheaper.
Speaker 2The tech is going to be more and more affordable. I'm so sorry. This is so funny for me because the co-founder of Clean and Rubric and so who the fuck am I but a podcaster. But this is exactly what technology is for. This is agents. This is what I'm doing. This is what I'm doing. This is what I'm doing. This is what I'm doing. This is what I'm doing. This is what I'm doing. This is being proactive, having an opinion, making a decision. They should absolutely be included or put in the same sentence as labor because they are replacing the labor that we used to spend money
Speaker 1on. I think good technologies figure out how to make technology really, really cheap. And it's going to happen here too. That's my belief. You're going to see inferencing costs come down by orders of magnitude. I think we saw something bizarre actually. In the last six to nine months, every dollar will actually increase their per token price. And if you go back 15 months, everybody thought that the per token price is going to just keep falling like it was before. So we don't know what happened here. This is also sort of unique. Well, they needed to prove that they were good
Speaker 2businesses before they went public. I can say things that you can't.
Speaker 1Yeah. Yeah. But my bet is on AI getting much, much cheaper than what it is today.
Speaker 2If AI gets much, much cheaper than it is today, he's already lost making businesses, which prop up our entire global economy pretty much at this point are very threatened.
Speaker 1Yeah. I mean, like, you know, my take remains the same.
Speaker 2So it's really interesting. So you don't expect like an engineering team to get smaller in the
Speaker 1future? I think per person productivity is going to shoot up, but so will the demands. To make the same amount of revenue, you have to produce a 10x better product in the future,
Speaker 2unfortunately. When you think about token spend internally, how did you sit down and think about it as a team and sitting with your CFO? How did you go through the decision of how to think about token budgeting? Well, I think we did probably what most
Speaker 1companies did, which is we didn't do anything. I think like, you know, because we were in this phase of let people figure out what they can do with this tech.
Speaker 2And what did you see? People went crazy. People didn't adopt it. What happened?
Speaker 1There's a power law, like, you know, in our company and also at all of our customers, you will see some people who spend $10,000 or $15,000 in tokens every month. And then you have others who are spending $20. One thing is interesting though, that everybody has embraced AI to some degree. Like everybody is using the basic, you know, as I mentioned before, the number one application of our use case for AI today in the world is information seeking, question answering, and everybody's doing that. Like everybody on the team, in our team, as well as our customers, they're all doing that. Everybody's asking questions. Everybody's getting some basic summarization information synthesis going. But the advanced use cases are limited to like 5% of the employee base.
Speaker 2Is there anything you do as a leader to try and infuse AI as aggressively as possible? We had Nikesh from Palo Alto on. Every week he has a leadership meeting where he's like, show and tell, and everyone needs to stand up and show something that they've done with AI that week that it replaces what they do, improves their job, whatever it is. Is there anything that you can do?
Speaker 1Yeah, that's actually a really good idea. Like, you know, I've thought about doing that. We never did the token maxing dashboards. And I always thought that was not the right idea to just sort of reward people who are consuming more tokens. I felt like, you know, we didn't need to do that. You know, we are a native AI company ourselves and people are already kind of educated enough and they will use AI when they need to. But executives like, you know, sharing a success story, we haven't sort of demanded it from every single exec every single week. But we have the showcase, like in our town hall, for example, we'll always ask people to share those wins. Like every, every town hall, like, you know, there's a section dedicated to these are the new AI agents that teams are using to work differently.
Speaker 2Can I ask, in terms of the execs and the people that you have, I think recruiting has never been harder. How hard is recruiting today with some of the largest model providers, as we said, paying
Speaker 1just enormous salaries we haven't seen before? I would actually say maybe like last two or three months. Let's put that aside for a minute. I would say that recruiting was actually getting easier for me compared to the SaaS peak time. Wow. Why? Because I think companies have been, they haven't been growing their headcount. Like if you look at the big, the largest employers of tech talent, many of them actually haven't been growing. Many of them have been laying off continuously. I think about Meta, for example, right? Like in every year, there's significant layoffs. I don't know the overall headcount. My guess is that it's probably down from the peak of like 2021 or 2022, right? So actually there was more talent available in the market as such than before. But, but if you're not starting to get more talent in the market, you have to talk about, okay, AI talent, ML talent, top people are sought after way more than ever before. And also the pay scales have completely changed and not just from the model companies, but even from startups, because startups are also like, you are giving them too much money to compete for talent. So like even startups actually these days pay a lot.
Speaker 2We have to. Yeah, we have to, because their alternatives are so large too. If it costs three, four, 500 grand for a great- Oh shit, the $2 million seed round just doesn't go anywhere. For the founder building the team, even if they don't take a salary, if I'm going to hire four people, I need 6 million bucks. That's right. Do you think founders should raise large seed rounds?
Speaker 1I think it's better. Like I always prefer to raise as much, you know, of a round as you can, you know, from the get go.
Speaker 2What round felt the most highly priced?
Speaker 1So first, actually, we never actually went out to raise, you know, except for our first round of the company. You know, we always had somebody come in. It was a relationship. It was, you know, we had, you know, we always had somebody come in who was a relationship that got built over some time and kind of became the de facto, like, you know, that, you know, they are going to be the ones putting money in. I would say like our, our, our series C probably felt the most, I guess you could say the most expensive because we barely had any business, like definitely like, you know, sub two or $3 million, maybe $5 million. I don't remember exactly, but then the valuation was north of a billion. So that was, that was extreme, but I guess we, you know, we, you know, we take what we get.
Speaker 2I mean, that's incredible. Do you worry about scaling into that when you're doing it? Or do you just head down and think, this is great, a low dilution for a high price?
Speaker 1The way we thought about it more was that there was a statement to be made to the prospective employees more than anything else. If we wanted to make the market understand that we're building something special and that kind of gives us that validation.
Speaker 2Do employees give a shit who your investors are?
Speaker 1Absolutely. Yeah. I mean, like, you know, the-
Speaker 2A lot of founders are like, you know what, the best people don't care. They're there for the mission. They're there for, and I'm always like, I promise you, if you have Kleiner or you have DST or you have Sequoia, great candidates suddenly want to talk to you a lot more.
Speaker 1Yeah. I mean, like investor reputation directly impacts your reputation.
Speaker 2When you look today, what have you changed your mind on most in the last 12 months?
Speaker 1Personally, like my style has been a little bit too disciplined to be the right strategy anymore. I get that feedback from my team that, you know, we are trying to be conservative. We're trying to make sure that our capital goes a long way. And in that mindset, we may lose the land grab. I'm sort of feeling the pressure to change it myself. Like, you know, just change how I think about like how we should be spending, how we should be investing. But at the same time, like, you know, I have this fundamental belief that a business is always built on discipline. Like you have to charge for the product. It has to generate value for the customers. You know, for every dollar that you invest in marketing, there has to be some good return back from it. You cannot assume that you just keep raising the money to make up for all those things, you know, that were not, that were not there. Do you agree with that when you have
Speaker 2examples like Uber, which proves that a bad business model can turn good with scale?
Speaker 1Yeah, I mean, that's, that's what I'm saying. saying that like you know that's that's the one where i feel that pressure that like you know perhaps my way of thinking is incorrect do you think it is a land grab we are absolutely in a land grab like you know no question like every single company in the world wants a product like ours today either we get in today or it's going to be like 10 times harder to actually get in the
Speaker 2future we spoke about kind of job displacement we had an interesting uh conversation around that what job does not exist today that you think will be incredibly common in three to five years time
Speaker 1well the composite roles will be will be very common so like as for example you know somebody who can build a product i don't know what to call them but they they act like engineers product managers designers similarly in go-to-market somebody who can sell the product and they are capable of not only doing the business negotiations but they can actually demo the product they can actually talk about use cases and instead of having that segregation between account executives and you know solution engineers and then both sales solution architects i think we will see more and more generalization of roles like away from specialization and in fact i was trying to drive that very very hard even in our company
Speaker 2but i'm sorry i mean this in a nice way the composite roles goes exactly against the idea of maintaining team size because if you have composite roles where you bring in four different
Speaker 1specialities into one that is smaller teams it is yes but as i said like you know you have to do 10 times the work to get the same amount of revenue from your customers in the future you have a much smaller team to deliver the same amount of work that you used to
Speaker 2deliver before we just are forced to do more got you okay and then what role do we have today will we not have what do we look at and go oh my gosh i can't believe we used to do that a lot of analyst
Speaker 1roles the data analyst roles which are not business thinkers you know they were given a task that hey like i need to see this data and then they produce like they sort of go and build those specific dashboards configure back-end systems i think like that's the thing that's the thing that's the thing that's the thing that's the thing that's the thing that's the thing that's the thing that's that kind of work definitely goes away i think business intelligence is going to be very different business owners will directly be able to get answers to their questions so so business and business analysts like you know data analysts you know that's sort of one many hr roles sources for examples so sort of like in recruiting that's the role that i think is going to definitely get consumed in you know into like a full cycle recruiting role i do have to ask one final one
Speaker 2which is we're sitting here in europe and it brings about a question of sovereignty the u.s. and europe bluntly have not come up to muster so to speak on open source yeah do you think we will have a world of sovereign models and do you think given what we've seen in the last month or so
Speaker 1that we need to have sovereignty over our models so i think the the desire for sovereign models like is strong and it's actually i would say like it was probably stronger a year back compared to now at least like you know i feel like i'm hearing less of it to someday you know like there was a period where every nation thought that they could build one when ai was still in its early stages but then like a lot of those nations actually figured out that you know you know that's not going to be the way and so they're okay with letting you know their enterprises within their own countries you know use open ai or anthropic or all the other models so so i don't i'm not an expert i don't know like you know whether this trend is on the on the rise or or sort of like you know on the fall
Speaker 2a little bit um i think i am first but the greatest i think it's unequivocally on the rise given what we saw with the trump administration banning anthropic's latest models and it's understanding from a lot of especially europeans that we cannot rely on a u.s individual who could ban our access to intelligence but but but where are the results from it i mean that was a month ago so i think to expect to stand up model within three weeks
Speaker 1would be tough yeah yeah yeah but but like you know even before that i think the it just hasn't happened right like you know the only country in the world you know that has produced models outside of u.s is china yeah and then of course like maybe a little bit in
Speaker 2like you know france with mistral is that simply an incentive problem the lack of open source community in the u.s and why we don't have any u.s open source to real degree and substantiveness
Speaker 1no i think the there is good good open source community in the u.s in many other areas well i mean what open model from the u.s no there's no yeah you're right that we don't have open models but it's not because open source has as a movement as a dollar you know as a concept is weak in the u.s it's actually quite strong it's probably if you think about models they require a lot of upfront investment which is not open source friendly in many ways a lot of open source software has been skunkworks developers just getting no funding associated with them and they still get something built they couldn't build models that way and so that's why like you know naturally this thing didn't work out and you you know you need these techniques you know where like super high investment is not needed
Speaker 2do you worry then when you look at the state you know i i spent a lot of time on open router and i see the model usage and traffic and like you know anthropic today was first u.s model of seventh the first six were chinese do we just like you have bucket who cares that the ccp are funding the
Speaker 1top six models the fact that you know you can actually run inferencing on those like you know in that contained environment makes people feel comfortable and but i don't think you know that's as u.s you know like you know u.s won't feel i don't think it's going to be like you know like absolutely won't feel okay with you know that trend there is good work that's happening now to actually promote open source and model development in the u.s there's some models
Speaker 2coming out the alternative is that sam and open ai give five percent to trump and then he puts regulatory capture on anthropic and open ai and puts the tax on open source well i hope not i doubt that's that's going to happen yeah i'm so sorry i'm only learning why else would sam give them five percent it's a quid pro quo i need you you need me well i mean i guess i just believe more
Speaker 1in the u.s system and i don't think right now by the way you need to curb open source like you know
Speaker 2it's too far behind in the u.s you don't think sam and dario are sitting going oh wow we underestimated this and this is a core threat to our business they probably are thinking that but
Speaker 1i don't think they can fix that by through regulation you don't think that sam will be
Speaker 2calling up trump who he has a direct line to saying hey the ccp are funding your biggest our biggest competitors and we cannot promise that there isn't a backdoor to xi jinping you need to stop this and i'll give you five percent for your troubles well isn't the argument the other way
Speaker 1around like you know that right now there are all these open source models which are very good and they're all built in china and u.s needs to build its own like you know we u.s can't be seen as a country that doesn't innovate on technology so it's actually paramount for the u.s to build
Speaker 2what i think sam will be saying it takes billions of dollars and years of time trump defend america and support open ai and anthropic and put barriers up to prevent chinese which are
Speaker 1which is open models from getting adoption taxes bans those maybe yes but the u.s open source models they are going to have a lot of tailwinds and they have to like you know this is a known accepted like issue that every technologist in bay area you know talks about there's a lot of a lot of motivated parties that actually want to promote like including nvidia for example you know they're putting a lot of investment in promoting like you know development of great open source models in the
Speaker 2u.s and i hope they succeed absolutely a multi-model world is important for all of us uh listen i'm going to do a quick fire round with you so i say a short statement you give me your immediate thoughts does that sound okay okay yeah what's your biggest advice to someone studying computer science today it's fine to study it
Speaker 1don't don't get too worried because what other people are telling you which legacy company has adopted ai the best do you think well are you willing to call google a legacy company yeah yeah so google probably rates higher than anybody else in terms of not only embracing ai internally but also uh in their like launching products but there i guess they are ai companies it's kind of hard it's unfair to to put them in that category you start a new company and you can only take one investor who do you take with you well i i think i'll take one of one of our existing ones we have great relationships with all of them which one would you take i don't know i won't answer that question i actually don't have the answer really i get to think about i think it's probably circumstantial depending on like you know what what i'm doing you know different people
Speaker 2bring different strengths what would you most like to change about the startup ecosystem that
Speaker 1we see today i actually do think that you know there is too much capital available today for startups and it's actually sometimes creating failure paths for people i think they're not getting what it takes to build a great company i'll give you an example like a startup that has raised a seed round decides to pay half a million dollars to an engineer like you were saying before and this is happening today and the startup founder is okay with it the investors are okay with it but it's just surely not a sustainable path to actually win and they're paying it while google is not and google knows that they don't need to actually buy talent like that so so i think that is one thing that i feel this or abundance of capital is getting startups to sort of create structures which are not going to be sustainable for them
Speaker 2do you worry about the lack of exit options that are now becoming more and more real what i mean by that is like honestly if you don't have a billion in revenue today it's hard to go public tech acquirers your big companies very specific about what they want to buy p licking its wounds from having a portfolio that's full of medallias it's a tough landscape
Speaker 1startups have never been easy like i think in fact you know for i would say in the last 25 years that i've seen i would say it's easier to build a startup and get a good exit from it these days than it used to be in the past like start is a brutal it's brutal game what does
Speaker 2no one know about being a founder and CEO? from the outside that they should know?
Speaker 1That it's not a sexy job. Like it's actually one of the most stressful things and you really have to be crazy.
Speaker 2I think they know that now. I think one for me is that you have to consistently be unhappy. You should never be happy, I think, as a CEO because there's always something that needs doing could be done better. Telling someone you will never be happy is something they're like jarred by.
Speaker 1That's a good one. This is a tough job all around. And I think oftentimes people who have not done it, they feel that there's a lot of glamour. They feel that this is going to make a lot of money and their life will be fantastic. They're going to have a lot of respect. And I think almost all of those things are irrelevant. You have to be truly mission-oriented to survive as a founder.
Speaker 2Did your style change with money? You've been successful before. I think founders are better and investors are better when they... They are already rich, if I'm being blunt. I think you make more rational, sound decisions that are not made with economic impatience.
Speaker 1I think for me, maybe not. But at the same time, I'm a man with minimal needs and my needs were already met a long time back. So I guess I've definitely built these startups without that worry of, can I feed my family? So yeah, maybe that has helped me. But as I've seen more success, it hasn't changed me fundamentally. In terms of, you have to have that drive. You have to work continuously. You have to work more than every other person in your company. Lead by example and keep pushing. And you have to have this rational need to make something big happen.
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