20VC: Why OpenAI and Anthropic Won't Win the App Layer | Why Teams Will Get Bigger Not Smaller in a World of AI | Why AI Removes Incumbents Advantage of Bundling | China vs America: Who Wins the AI War with Arvind Jain, Co-Founder @ Glean

Arvind Jain is the Founder & CEO of Glean, the enterprise AI leader valued at $7.2 billion after raising more than $770 million from investors including Kleiner Perkins, DST Global, and more. Before Glean, Arvind co-founded Rubrik, helping build it into one of the world's leading cloud infrastructure companies before its successful IPO. Prior to that, he spent over a decade at Google as a Distinguished Engineer, working across Search, Maps, and YouTube. AGENDA: 00:00 – The Shocking Truth About Frontier AI: 90% Is Already a Commodity 02:04 – Can OpenAI & Anthropic Own Enterprise AI? The Battle for the Workplace Begins 10:18 – Will OpenAI and Anthropic Win the App Layer 18:03 – Microsoft Is the Real Enemy… Not OpenAI? 20:53 – "Where's the ROI?" Why Enterprises Are Starting to Question the AI Hype 26:00 – Will AI Replace Your Job? Harry & Arvind's Heated Clash Over the Future of Work 33:43 – The Billion-Dollar Mistake Every AI Company Is Making on Token Spend 39:20 – The AI Land Grab Is On: Why Founders Must Move Now or Lose Forever 42:20 – China vs America: Who Really Wins the AI Race? 47:20 – Rapid Fire: The Future of Computer Science, Hiring, Fundraising & AI's Biggest Winners

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Ninety percent 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 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.

• 0:24Harry Stebbings?

  This is 20 VC 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 IPOed 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 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.

• 1:06Harry Stebbings?

  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.

• 1:48Harry Stebbings?

  They show up where the work is happening, already on boarded 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 workflow 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 developers 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.

• 2:28Harry Stebbings?

  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 11 Labs run 40 million agents on MongoDB. So if you're building in 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.

• 3:10Harry Stebbings?

  Build agents that remember and learn from your real time data. Go to MongoDB.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.

• 3:53Harry Stebbings?

  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 alphacense.com slash 20 VC. That's alpha hyphen sense dot com slash 20 VC. You have now arrived at your destination. Arvind, I'm so excited for this. We have a mutual friend in Mamun who says many, many wonderful things about you. And I think he's one of the greatest ambassadors of our time. And so I'm really excited for this. So thank you for joining me.

• 4:36Speaker 00

  Thank you for having me.

• 4:37Harry Stebbings?

  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. Which one are you?

• 4:51Speaker 00

  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.

• 4:58Harry Stebbings?

  I love that. It's only the paranoid survive. Has it always been that way?

• 5:02Speaker 00

  Yeah, mostly.

• 5:03Harry Stebbings?

  Even with the success you've had, I'm sorry, it's so interesting, like Rubrik was a phenomenal success, public company today, and you're one of the co-founders.

• 5:11Speaker 00

  Yeah.

• 5:11Harry Stebbings?

  It doesn't change with time.

• 5:13Speaker 00

  No, 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. Like think about Glean, like it's fundamentally different from Rubrik and always 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 that's the winning 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.

• 5:48Harry Stebbings?

  For those that don't know, can you provide a 60 second summary on what Glean is and how you work?

• 5:53Speaker 00

  So 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 buried 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, Claude, 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.

• 6:30Harry Stebbings?

  So Mr. Alex Karp from Palantir went on CNBC last week.

• 6:34Speaker 00

  Yeah.

• 6:35Harry Stebbings?

  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.

• 6:44Speaker 00

  Yeah.

• 6:45Harry Stebbings?

  Do you agree with him? Are they more skeptical than ever?

• 6:47Speaker 00

  Two 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, will we be in business? Will the models eat it all? Similarly, enterprises leaders 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 and everybody's afraid to actually say so. Because, you know, it's not a cool thing to say.

• 7:21Harry Stebbings?

  Before we get to AI not working, because I think it's probably one of the most important questions, but it's a whole separate segment.

• 7:27Speaker 00

  Yeah.

• 7:27Harry Stebbings?

  Do you think they're right to be afraid of the frontier model providers eating their lunch or not?

• 7:32Speaker 00

  Well, yeah, depending on depending on the enterprise, yes. Well, it looks 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. And 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 that are actually running those agents for you. It's actually interesting, the 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, you maybe you'll document a process, like, you know, how what are the 10 steps you need to take to actually complete some piece of work?

• 8:16Speaker 00

  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 over and over again, you now built all these learnings that you apply in real time to 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, if you don't own the learning that it actually like 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 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?

• 8:59Speaker 00

  And all the compounding learnings that happen with AI, they belong to the enterprises.

• 9:03Harry Stebbings?

  Are you seeing enterprise customers route away from frontier model providers towards open source?

• 9:08Speaker 00

  That'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 actually get better.

• 9:17Harry Stebbings?

  Yeah.

• 9:17Speaker 00

  Like, you know, the desire has been there for many, many years. Nobody, there's no enterprise, you know, that we talk to, which is okay 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 run past that like, you know, within a month or two.

• 9:46Harry Stebbings?

  Poor CFOs.

• 9:47Speaker 00

  Yeah. So that's sort of, that has really accelerated that that desire, you know, for open source. Cause you know, and that coupled with the fact that we now have really good models in open source.

• 9:57Harry Stebbings?

  What do they care about? Do 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?

• 10:08Speaker 00

  I think right now the open source drive is coming from the cost point of view. There are certain businesses, of course, you know, that have the requirements to actually keep, you know, all the inferencing workload within their own private data centers. When AI just came, there was a companies were a lot more afraid of getting their data outside of, you know, their own control and and model companies training with, you know, with their data. But that sort of is a fear that's no longer there. Like people believe that the model companies are going to be responsible and not train their models on on enterprise data, so long as, you know, I've signed up for the right kind of contract. So right now, the the drive is coming from cost.

• 10:45Harry Stebbings?

  In terms of where you sit in the landscape, every one of your ambassadors that I spoke to, he said that 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?

• 11:03Speaker 00

  First of all, I think we should be careful like 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

• 11:13Harry Stebbings?

  shallow.

• 11:13Speaker 00

  Quite shallow, in my opinion.

• 11:14Harry Stebbings?

  Yeah.

• 11:15Speaker 00

  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 cloud design, right? I mean, so that's 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 Claude.

• 11:45Harry Stebbings?

  So you don't worry that they all put emphasis on moving into enterprise and being that context?

• 11:49Speaker 00

  Well, 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, Claude can also connect with enterprise systems through MCP, so what's different? Like, you know, what can Lean do which, you know, Claude cannot? So we had 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 Claude, CoWork as an application or Claude 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.

• 12:31Harry Stebbings?

  How important do you think being first market is?

• 12:34Speaker 00

  It's actually very advantageous. It's a thing that helps you, but it's a 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 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 Anthropic have become. So it's a, it's a huge, it's a huge sort of asset, but neither is it a requirement, nor is it a savior.

• 13:08Harry Stebbings?

  How do you advise founders who are losing sleep at night, worried that the frontier model providers will come into their space?

• 13:14Speaker 00

  Oh, right, 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 Anthropic is doing, everything that OpenAI and Google is doing, as well as all the innovation that's happening in open source, is great news for us. Like, we don't worry about that as, and 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.

• 13:56Harry Stebbings?

  Do you not think we're seeing the ultimate commoditization of the model layer? When you speak about Anthropic Open Air and the rise of the model layer and the speed with which new models are coming out, especially from open source Chinese providers? Have we not seen the ultimate commoditization of the model layer?

• 14:11Speaker 00

  So one thing is clear, let's, let's talk about enterprise use cases. Ninety percent or greater of use cases can now be fully handled by many, many different models, including open source models. 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 pick 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 higher quality answer.

• 14:45Harry Stebbings?

  What percent of customers are not okay with open source models?

• 14:48Speaker 00

  This, this is actually so new, like, I would say that open source truly coming to within three months of frontier capabilities has just happened, literally like a month back, or not even a month, right? You know, I would say GLM 5.2 is a 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, 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, it's 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.

• 15:23Harry Stebbings?

  Why would they not be okay with a 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?

• 15:33Speaker 00

  I 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, so there are some concerns. There's also, if you use these models and 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's come, it again boils down to who's willing to be bold, because this is a new thing, like, you know, in large enterprises are, have to make this move and the early movers will make the move first and then it'll become a more normal thing.

• 16:10Harry Stebbings?

  I'm always doing this show to learn, if ninety percent of enterprise workflows can be done with open models, have we completely mispriced the frontier model landscape? It's a very different town.

• 16:22Speaker 00

  I do feel like, you know, the, the model business on its own, regardless of like, forget open source for a minute, there is 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, 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, that OpenAI was going to drastically reduce, you know, their model prices in response to like these, these developments, like, you know, competition as, and, and 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.

• 17:07Speaker 00

  It's not just, they're not no longer model companies only.

• 17:10Harry Stebbings?

  Totally 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.

• 17:18Speaker 00

  Yeah, well, I mean, if, 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, and everybody is sort of, they're creating these MCP servers to their internal systems, getting connected all to Claude. So there's, there's an ecosystem that actually that's being developed. So they, they very much, you should consider them an application level company, not just a model company.

• 17:55Harry Stebbings?

  If you were to make a guess in three years' time, what percent of your workflows do you think is through open source?

• 18:02Speaker 00

  Well, 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.

• 18:09Harry Stebbings?

  Another competitive element that you face, forgetting kind of the model providers, is like, actually, Microsoft, you know, Microsoft have made a phenomenal business on the back of creating a seventy percent as good product, but bundling it into a bundle for enterprises and then selling it with a nice sticker on it.

• 18:25Speaker 00

  Yeah.

• 18:25Harry Stebbings?

  How do you think about the bundling pressure from a Microsoft copilot as a competitive threat?

• 18:31Speaker 00

  Well, for us, they are one of our most significant competitors and the bundling strategy actually works.

• 18:37Harry Stebbings?

  Yeah.

• 18:37Speaker 00

  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, and, you know, like, people and all customers think of us exactly like that. Like, 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

• 19:18Speaker 00

  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.

• 19:31Harry Stebbings?

  I respectfully, 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 enterprises in the world, your VWs or your Fords or your Gs or Tyson Chickens, I always use them as like, you know, I know random companies, but they'll approve, approve Microsoft as one vendor.

• 19:54Speaker 00

  Yeah.

• 19:54Harry Stebbings?

  If they're suddenly having to approve fifteen vendors, forgetting the pricing and the transactions, it creates a vendor management problem that they didn't have before.

• 20:03Speaker 00

  That 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.

• 20:16Harry Stebbings?

  Who's a fiercer competitor, Microsoft or the frontier models?

• 20:19Speaker 00

  Good question. I think it's early to tell that, but Microsoft is formidable. So like, if you, if you look at our, our experience with, as we go 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 Copilot. 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.

• 20:50Harry Stebbings?

  We mentioned Alex Kopp since you at the beginning and I interrupted you and said, let's, before we dive to like, we're not getting value. I think twenty twenty six H two and twenty twenty seven is the year where everyone goes, oh, hang on a minute.

• 21:01Speaker 00

  Yeah.

• 21:01Harry Stebbings?

  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 Kopp right in saying everyone's going, what the fuck? Where's, where's my return?

• 21:17Speaker 00

  I would say that there is pockets of value realization today. For example, take customer support as a vertical. I think there it was easy to measure productivity. You could actually say that, like in your company, your support agent resolves ten cases a day. And now they're able to do twelve because of AI. So you could see that, you know, like a very concrete measure of productivity increase. And, and that's, that's a 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 summarizing it to your customers. So, so there are definitely areas where there is clear value realization and, and enterprises are feeling good.

• 22:00Speaker 00

  Some other ones are more complex, like for example, I think, I think the majority of the AI spend right now is on coding. And you know that the coding as a practice has changed, like most developers now actually use AI to write code. They're not writing it by hand anymore. In some ways you can, you can say that, yes, like 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 like overall shipping a product.

• 22:30Harry Stebbings?

  Has your shipping speed increased?

• 22:32Speaker 00

  I would say like, you know, it's, it's hard to actually measure. That's, that's the challenge, because engineering productivity is one of the most difficult things to measure. It's the most, 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, you know, if you look at, you know, are we shipping features at a greater pace? Yes, we are, but it's a result also of we have a larger team. You know, we have a team that is more tenured than it was before. So sometimes it's hard to tease, tease it apart. But with, with that, what do we do as a company? Like, we are right now saying that, like, look, you know, we, we are just going to keep investing.

• 23:09Harry Stebbings?

  What percent of gleaned code say do you think is written by AI?

• 23:13Speaker 00

  Now it's probably about almost hundred percent. Like, nobody's actually writing the initial code, you know, by hand anymore. Yeah, so almost all the code is being written with AI, but, but we actually enforce hu- human, human reviews. So you cannot actually generate tons of AI code and, and then just take it in the, in the repos. We're probably more conservative than most other companies. There was in fact a discussion inside the company that, well, like, you know, now AI can write so much code and the real bottleneck has shifted from the person who writes the code to the person who has to review. And so there was a proposal to actually eliminate code reviews and many companies are doing that. You know, it's like, let AI write the code and it gets submitted into the, into the repos.

• 23:56Harry Stebbings?

  Well, if you have to have a stringent code review process, it almost removes the point of having a fastened code development process.

• 24:04Speaker 00

  Yeah, that is true, but I think like, you know, what it, what it's doing right now is, you know, 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 like a million lines of code or it becomes incredibly hard to actually maintain it and understand it and manage it over time.

• 24:25Harry Stebbings?

  Is that not what AI does, though? You have AI that does refactoring and AI that does security and AI that does.

• 24:31Speaker 00

  Yeah, the only thing is that, you know, it's not, it's not that perfect right now. I think that right now we're willing to pay the cost of, of reviewing the code. So we're still faster than before, because the writing part is actually much faster now. And, and the person who writes is the one who actually does the first review.

• 24:45Harry Stebbings?

  So when you say AI ROI is really a throughput problem, what does that mean?

• 24:51Speaker 00

  The first thing that we have to do is like make sure that you are able to bring the right context to these AI agents. If you think about most, most enterprises today, the way they're rolling out AI is that they actually just throw it in, into the system and connect AI with all of enterprise systems in a rudimentary manner using, you know, MCP servers. And now you're letting like 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, like, you know, the basic information it needs to do the work.

• 25:32Speaker 00

  It also becomes very, very costly, because most of the tokens are being burnt just trying to assemble the right context for that given task. And, and you're trying to sort of use AI for things, you know, where it's not even good at or, or needed. So instead, like, you know, 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 provide it the right context, so that it can actually work faster, you know, at, at lower cost.

• 25:57Harry Stebbings?

  What does it mean invest around 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?

• 26:07Speaker 00

  It's a, it's a wrong goal, in my opinion, to say that, hey, like, replace yourself with AI. First of all, I think you're giving too much credit to AI, you know, when you say that. It's just not ready right now. You give me a name of one job that you can replace, you know, with AI. For example, do you think it can replace your EA?

• 26:23Harry Stebbings?

  Mine, no, but I'm a fucking diva.

• 26:27Harry Stebbings?

  For most people, I, I think it can do the majority. Yeah, I, I do.

• 26:31Speaker 00

  And that's, that's the thing. It can actually take care of a lot of things, you know, for any given role, but it cannot replace the final intangible.

• 26:40Harry Stebbings?

  No, but that can be a tipping point where actually for a lot of people, if it does ninety percent, 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.

• 26:58Speaker 00

  I'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 ninety percent solution.

• 27:07Harry Stebbings?

  Well, no, but I'm not cost constrained, being a dick.

• 27:11Speaker 00

  Well, I mean, like, look, it's not about you not being cost constrained. It's about you have to be competitive in your, in your work with others. You remember, they also have all the AI tools that you have, but if they also have a human on top, you know, how are you going to compete with them?

• 27:23Harry Stebbings?

  So do you not think, how, how many people would you have now?

• 27:26Speaker 00

  We're over a thousand people now.

• 27:27Harry Stebbings?

  Over a thousand people. How many do you think you'll have in five years' time?

• 27:31Speaker 00

  Well, hopefully five thousand, ten thousand. Yeah, we're going to grow.

• 27:36Harry Stebbings?

  But, but that is very atypical. You, 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.

• 27:43Speaker 00

  I absolutely don't believe in it.

• 27:45Harry Stebbings?

  Why?

• 27:46Speaker 00

  Well, I mean, I, I think like, first thing logically, take two companies, take CoCo and Pepsi or like, you know, two, two companies that compete with each other. One, one company decides to shrink and the other one is 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, you know, elevate and build a ten X better product or build ten times more, produce ten times more goods, because they have, they have more people. They're going to be larger, they're going to be, they're going to beat you.

• 28:26Harry Stebbings?

  But I don't think more people makes for better products.

• 28:29Speaker 00

  That's, that's a different thing.

• 28:31Harry Stebbings?

  If I can cut headcount.

• 28:33Speaker 00

  Yeah.

• 28:33Harry Stebbings?

  And then afford the best frontier models, the best technology for my hundred X 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.

• 28:48Speaker 00

  Yeah, that's a good point, but that's not an AI argument. That argument has always been true.

• 28:52Harry Stebbings?

  Sure, 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 get in the way of that product going out.

• 29:04Speaker 00

  Well, well, look, the, like, even in the, we have, we have these AI discussions right now. But before that, just post-COVID, like many companies felt they were bloated. They cut down fifteen percent, twenty percent of their staff. And every CEO came out and said they're actually, as a result of that, they're actually moving twenty percent faster. So a lot of companies came and talked about that. So, so, so that's a, that's a, that's an argument that's always there. Like, you know, at some point, you know, teams get large, they start to slow each other down. You know, 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, the world's greatest companies are going to be companies with hundred people.

• 29:47Speaker 00

  And look at the model companies, you know, like same, same for them, like why are they hiring so aggressively?

• 29:51Harry Stebbings?

  Do you not think that the best people will want to work with the best technology? And we'll see an increase in technology spend by the biggest companies in the world from eight to twelve percent, where it is today, to maybe sixteen to twenty.

• 30:02Speaker 00

  Yeah.

• 30:02Harry Stebbings?

  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 and equipment.

• 30:10Speaker 00

  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 the, currently this technology is priced in, you know, absurdly for what it delivers?

• 30:23Harry Stebbings?

  I think it totally depends on what it's doing. So 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.

• 30:39Speaker 00

  I would say it's absurdly expensive. I'll give you an example. We had this cool triage agent for engineering. You know, and 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, you know, things that are going bad. 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 at 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.

• 31:14Harry Stebbings?

  A million a month?

• 31:15Speaker 00

  Yeah.

• 31:16Harry Stebbings?

  Are you buying Cristiano Ronaldo? What are you doing?

• 31:18Speaker 00

  No, like, well, I mean, yeah, costs are like that. I mean, it is, it is quite expensive.

• 31:23Harry Stebbings?

  But sorry, can I just go back? You said, 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.

• 31:39Speaker 00

  Well, I mean, I think, I think the point that I'm making is, well, the 3.8% number is actually, doesn't seem high at all. Like, when you, when you look at it that way.

• 31:47Harry Stebbings?

  Yeah.

• 31:48Speaker 00

  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, like historically, for as far as I can remember, we've not put technology costs and labor cost 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. Like, the models are supposed to get cheaper and cheaper. The tech is going to be more and more affordable.

• 32:16Harry Stebbings?

  I'm so sorry. This is so funny for me because you're, you know, the co-founder of Bean and Rubrik. And so who the fuck am I but a podcaster? But this is exactly what technology is for. This is agents 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 on.

• 32:38Speaker 00

  I think good technologies figure out how to make technology really, really cheap and it's gonna, it's gonna happen here too. That's, that's my belief. Like, you know, you're, you're gonna see influencing costs come down by orders of magnitude. I think we saw, we saw something bizarre, actually, like in the last six to nine months, like every model actually increased their per token price. And like, if you, if you go back, you know, 15 months, everybody thought that the per token price is gonna just keep falling like it was before. So, so we don't know, like, you know, what, what happened here. Like, you know, this is also sort of unique.

• 33:09Harry Stebbings?

  Well, they needed to prove that they were good businesses before they went public. I can say things that you can't.

• 33:15Speaker 00

  Yeah, yeah. But my, my bet is on AI getting much, much cheaper than what it is today.

• 33:20Harry Stebbings?

  If AI gets much, much cheaper than it is today, these already loss-making businesses, which prop up our entire global economy, pretty much at this point, are very threatened.

• 33:31Speaker 00

  Yeah, I mean, like, you know, my take remains the same.

• 33:36Harry Stebbings?

  So it's really interesting. So you don't expect like an engineering team to get smaller in the future?

• 33:39Speaker 00

  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, unfortunately.

• 33:51Harry Stebbings?

  When you think about token spend internally, how did you sit down and think about it as a team, sitting with your CFO? How did you go through the decision of how to think about token budgeting?

• 34:03Speaker 00

  Well, I think we did probably what most companies did, which is we didn't do anything.

• 34:08Speaker 00

  I think like, you know, because we were in this phase of let people figure out what they can do with this tech.

• 34:13Harry Stebbings?

  And what did you see? People went crazy? People didn't adopt it? What happened?

• 34:17Speaker 00

  There'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 your others are spending $20. One thing is interesting though, that everybody has embraced AI to some degree. Like, everybody is using the, the basic, you know, as, 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, advanced use cases are limited to like 5% of the employee base.

• 34:58Harry Stebbings?

  Is there anything you do as a leader to try and infuse AI as aggressively as possible? We had Nikesh and 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?

• 35:18Speaker 00

  Yeah, that's, that's actually a really good idea. Like, you know, I've, I've thought about doing that. We never did the token maxing dashboards. And I, 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 do, to work differently.

• 36:01Harry Stebbings?

  Can 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 just enormous salaries we haven't seen before?

• 36:15Speaker 00

  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, compared to the, the SaaS peak.

• 36:26Harry Stebbings?

  Wow, why?

• 36:28Speaker 00

  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 now start to talk about, okay, AI talent, ML talent. Top people are sought after, you know, way more than ever before. And also the, the pay scales have completely changed and not just from the model companies, but even from startups because, because, you know, startups are also like, you know,

• 37:10Speaker 00

  you are giving them too much money to compete for talent. So like, even, even startups actually these days pay a lot.

• 37:16Harry Stebbings?

  We have to. Yeah, we have to because their alternatives are so large too. If it costs three, four, 500 grand for a great dev.

• 37:24Speaker 00

  Yeah.

• 37:25Harry Stebbings?

  Well, 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 gonna hire four people, I need 6 million bucks.

• 37:33Speaker 00

  That's right.

• 37:33Harry Stebbings?

  Do you think founders should raise large seed rounds?

• 37:36Speaker 00

  I think it's better. Like, I always prefer to raise as much, you know, for round as you can, you know, from the get go.

• 37:42Harry Stebbings?

  What round felt the most highly priced?

• 37:45Speaker 00

  So 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 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 C3C probably felt the most, I guess, you could say the most expensive because we barely had any business. Like, definitely like, you know, sub $2 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.

• 38:19Harry Stebbings?

  I 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?

• 38:26Speaker 00

  The 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.

• 38:40Harry Stebbings?

  Do employees give a shit who your investors are?

• 38:43Speaker 00

  Absolutely. Yeah, I mean, like, you know, the.

• 38:45Harry Stebbings?

  A 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. Ah, great candidates suddenly want to torture you a lot more.

• 38:57Speaker 00

  Yeah, I mean, like investor reputation directly impacts your reputation.

• 39:02Harry Stebbings?

  When you look today, what have you changed your mind on most in the last 12 months?

• 39:07Speaker 00

  Personally, 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 sort of, 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, 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, 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.

• 39:52Harry Stebbings?

  Do you agree with that when you have examples like Uber, which prove that a bad business model can turn good with scale?

• 39:59Speaker 00

  Yeah, I mean, that's, that's what I'm 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.

• 40:06Harry Stebbings?

  Do you think it is a land grab?

• 40:07Speaker 00

  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 future.

• 40:18Harry Stebbings?

  We spoke about kind of job displacement, we had an interesting conversation around that. What job does not exist today that you think will be incredibly common in three to five years' time?

• 40:29Speaker 00

  Well, 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 say the 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.

• 41:09Harry Stebbings?

  But I'm sorry, I mean this in a nice way, the composite roles goes exactly against the idea of maintaining team size. Cause if you have composite roles where you bring in four different specialities into one, that is smaller teams.

• 41:22Speaker 00

  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 deliver before, you just are forced to do more.

• 41:35Harry Stebbings?

  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?

• 41:43Speaker 00

  A lot of analyst roles, 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 backend systems. I think like that, 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, sourcer, for example, sourcer like in recruiting, that's a role that I think is going to definitely get consumed in, you know, into like a full cycle recruiting role.

• 42:21Harry Stebbings?

  I do have to ask one final one, which is we're sitting here in Europe and it brings about a question of sovereignty. The US and Europe bluntly have not come up to muster, so to speak, on open source.

• 42:33Speaker 00

  Yeah.

• 42:34Harry Stebbings?

  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, that we need to have sovereignty over our models?

• 42:43Speaker 00

  So I, I think the, the desire for sovereign models like is, 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 some, 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, this, 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 OpenAI 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 a little bit.

• 43:26Harry Stebbings?

  I think, I think it's unequivocally on the rise, given what we saw with the Trump administration banning Anthropic's latest models. And this understanding from a lot of, especially Europeans, that we cannot rely on a US individual who could ban our access to intelligence.

• 43:43Speaker 00

  But, but, but where are the results from it?

• 43:46Harry Stebbings?

  I mean, that was a month ago. So I think to expect a stand up model within three weeks would be tough.

• 43:51Speaker 00

  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 US is China.

• 44:01Harry Stebbings?

  Yeah.

• 44:01Speaker 00

  And then of course, maybe a little bit in, like, you know, France with Mr. Al.

• 44:05Harry Stebbings?

  Is that simply an incentive problem? The lack of open source community in the US and why we don't have any US open source to real degree in substantiveness?

• 44:14Speaker 00

  No, I, I think the, there is good, good open source community in the US, in many other areas.

• 44:20Harry Stebbings?

  Well, I mean, what open model from the US?

• 44:23Speaker 00

  No, there's no, yeah, you're right that we don't have open models, but it's not because open source as, as a movement, as a, you know, as a concept is weak in the US. It actually work quite strong. It's probably because 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 skunk works, Dara Pers is 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.

• 44:58Harry Stebbings?

  Do you worry then when you look at the state, you know, I, I spend a lot of time on OpenRouter and I see the model, the usage and traffic. And like, you know, Anthropic today was first US model, it was seventh. The first six were Chinese. Do, do we just like, yeah, fuck it, who cares that the CCP are funding the top six models?

• 45:16Speaker 00

  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 US, you know, like, you know, US won't feel, 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 US. There's some models coming out.

• 45:36Harry Stebbings?

  The alternative is that Sam and OpenAI give 5% to Trump. And then he puts regulatory capture on Anthropic and OpenAI and puts a tax on open source.

• 45:48Speaker 00

  Well, I hope not, I doubt that's, that's going to happen, yeah.

• 45:50Harry Stebbings?

  I'm so sorry, I'm only learning, why else would Sam give them 5%? It's a quid pro quo, I need you, you need me.

• 45:56Speaker 00

  Well, I mean, I guess I just believe more in the US system and I don't think right now, by the way, you need to curb open source, like, you know, it's too far behind in the US.

• 46:05Harry Stebbings?

  You don't think Sam and Dario are sitting going, oh, wow, we underestimated this and this is a core threat to our business?

• 46:11Speaker 00

  They probably are thinking that, but I don't think they can fix that by, through regulation.

• 46:16Harry Stebbings?

  You don't think that Sam will be calling 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 5% for your troubles.

• 46:31Speaker 00

  Well, isn't the argument the other way around? 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 US needs to build its own, like, you know, we, US can't be seen as a country that doesn't innovate on technology. So it's actually paramount for the US to build.

• 46:46Harry Stebbings?

  What I think Sam will be saying, it takes billions of dollars and years of time. Trump, defend America and support OpenAI and Anthropic and put barriers up to prevent Chinese, which is open, models from getting adoption, taxes, bans.

• 47:02Speaker 00

  Those, those maybe, yes, but the US 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 B area, you know, talks about. There's a lot of, 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 US.

• 47:26Harry Stebbings?

  And I hope they succeed.

• 47:27Speaker 00

  Absolutely, yeah.

• 47:30Harry Stebbings?

  A multimodal world is important for all of us. 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?

• 47:37Speaker 00

  Okay, yeah.

• 47:38Harry Stebbings?

  What's your biggest advice to someone studying computer science today?

• 47:42Speaker 00

  It's fine to study it, don't, don't get too worried because what other people are telling you.

• 47:46Harry Stebbings?

  Which legacy company has adopted AI the best, do you think?

• 47:50Speaker 00

  Well, are you willing to call Google a legacy company?

• 47:52Harry Stebbings?

  Yeah.

• 47:53Speaker 00

  Yeah, so Google probably rates higher than anybody else in terms of not only embracing AI internally, but also in their like launching products. But there, I guess they are AI companies, so it's kind of hard, it's unfair to, to put them in that category.

• 48:08Harry Stebbings?

  You start a new company and you can only take one investor, who do you take with you?

• 48:12Speaker 00

  Well, I, I think I'll take one of, one of our existing ones, we've created relationships with all of them.

• 48:17Harry Stebbings?

  Which one would you take?

• 48:18Speaker 00

  I don't know, I won't answer that question. I actually don't have the answer really, I get to think about it, I think it's probably circumstantial, depending on like, you know, what, what I'm doing. You know, different people bring different strengths.

• 48:29Harry Stebbings?

  What would you most like to change about the startup ecosystem that we see today?

• 48:34Speaker 00

  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 it's, it's 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 ore abundance of capital is getting startups to sort of create structures which are not going to be sustainable for them.

• 49:18Harry Stebbings?

  Do you worry about the lack of exit options that are now becoming more and more real? And 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. PE licking its wounds from having a portfolio that's full of medallions, it's a tough landscape.

• 49:38Speaker 00

  Startups have never been easy. Like, I think in fact, for, I would say, in the last twenty five 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. Startups are brutal, it's a brutal game.

• 49:57Harry Stebbings?

  What does no one know about being a founder and CEO from the outside that they should know?

• 50:02Speaker 00

  That it's not a sexy job. Like, it's actually one of the most stressful things and you really have to be crazy.

• 50:09Harry Stebbings?

  I 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.

• 50:26Speaker 00

  That's a good one, Dag. This is, this is a, this is a tough job all around and I think oftentimes, you know, people like who have not done it, they feel that there's a lot of glamour, they feel that, you know, 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, you know, are irrelevant. You have to be truly mission oriented to survive, you know, as a founder.

• 50:49Harry Stebbings?

  Did your style change with money? You've been successful before. I think founders are better and investors are better when they are already rich, if I'm being blunt. I think you make more rational, sound decisions that are not made with economic impatience.

• 51:06Speaker 00

  I think for me, maybe not, but at the same time, you know, I'm a man with minimal needs and my needs were already met like a long time back. So, so I guess I've, I've definitely built these startups without like, you know, that worry of, you know, can I feed my family? So, yeah, like maybe, maybe that has helped me, but like as I've seen more success, it doesn't change me fundamentally in terms of, you still have the, you know, you have to have that drive, you have to work continuously, you have to work, you know, more than every other person in your company, lead by example and keep pushing and you have to have this rational, you know, need to make something big happen.

• 51:42Harry Stebbings?

  I so appreciate your time. I apologize for being robust in my discussion back. I think it was a different interview to a lot of interviews that you do. It was more discursive, but I so appreciate the time and you've been fantastic, dude.

• 51:57Speaker 00

  Yeah, thank you.

• 52:00Harry Stebbings?

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• 52:42Harry Stebbings?

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• 53:24Harry Stebbings?

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• 54:05Harry Stebbings?

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