20VC: Why AI Cannot Replace Humans in Enterprise | Why Work Processes Not Models Will Be The Most Valuable Asset in AI | Why Europe Has Lost and Building in the US vs EU with Daniel Dines, UiPath

Daniel Dines is one of the greatest European founders of the last decade. As the Co-Founder of UiPath, he has scaled the business to a market cap high of $ 44BN in 2021, with the company now generating $1.72BN in revenue, growing 15% year-on-year. The company raised $2BN before its IPO, backed by Sequoia, Accel, CapitalG, Coatue and Kleiner Perkins. AGENDA: 05:00 Why Dario is Wrong About Millions of AI Einsteins? 13:00 Is AI Safety Becoming an Excuse to Kill Open Source? 21:00 Does UiPath Really Need 4,000 Employees? 28:00 Would You Help Train the AI That Could Replace You? 32:00 What Percent of Salary Spend Does Daniel Spend on Inference? 34:00 Can You Really Vibe Code Your Way Out of Paying for Software? 37:00 Why Would Anyone Take Their Company Public Today? 39:00 Could an OpenAI–Anthropic Duopoly Break Nvidia's Business? 45:00 Will AI Models Capture the Value—or Will the Apps? 49:00 Is Fireworks Still Undervalued at $15 Billion? 56:00 Has Europe Already Lost—and Should Founders Leave? 1:03:00 What Could Kill UiPath—and How Is AI Changing the CEO's Job? 1:06:00 60 Supplements a Day: How Far Would You Go to Live Longer?

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

• 0:00Speaker 00

  In my opinion, even the labs in China that are building AI right now, I would classify them as a good guys. I've never hidden from my employees that there will be a transformation. Jensen is bound by the success of open source. Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the the real value is.

• 0:24Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. Now, joining me in the hot seat today is one of my dearest friends and one of the greatest founders of the last decade, Daniel Dines, founder and CEO of UiPath. And today we discuss the biggest questions. What on earth does pacing the frontier mean? Is it even possible? Will we replace humans with AI? What happens to the pipeline of talent if it narrows down and we have fewer and fewer juniors? What about human consciousness? If we have recursive superintelligence really being as powerful as frontier model providers say? This is a truth-telling, myth-busting conversation. If I can get myth-busting out, and it's just a fantastic discussion between two old friends on what no one is talking about, but everyone needs to know in the world of AI today.

• 1:08Harry Stebbings?

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• 1:53Harry Stebbings?

  While JP Morgan powers your finances, Asana keeps the work moving. 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 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.

• 2:37Harry Stebbings?

  Try it at asana.com. That's A-S-A-N-A dot com. While Asana aligns the roadmap, Base44 helps you build faster. You have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pictured and what you actually ship. Well, Base44 is where that wall disappears. You describe it, yeah, Base44 builds it. Apps, websites, AI agents, real working products, built in minutes, using nothing but plain language. And it's all batteries included, the backend, the database, the authentication, the hosting, the heavy lifting is handled. So you just really stay in the flow. This doesn't just take the busy work off your plate, but it gives you an advantage and pushes you past what you thought you could build alone. So in this market, fast is the baseline.

• 3:22Harry Stebbings?

  To win, you just have to be first. Base44 is that edge, the move that skips the troubleshooting and gets you straight to the breakthrough. Build your next thing at base44.com. That's base44.com. You have now arrived at your destination. Daniel, dude, it is so good to have you in the hot seat. I have been looking forward to this one, so thank you so much for joining me, dude.

• 3:45Speaker 00

  Likewise, dude. It's always a pleasure to be here and it's the, I think, the hottest moment in technology. So I'm very excited to talk to you and get your perspective also on a lot of topics.

• 3:57Harry Stebbings?

  It really is the most wild time right now. And so I want to start, you've written a book. A lot of people write books. With the greatest of respects, you're my friend and I care deeply about you. Why on earth did you decide to write a book as a public company CEO? No offence, you're not doing it for the royalties. Why did you decide to write a book?

• 4:18Speaker 00

  Man, I dreamt to write a book since I was a kid. And I discovered I have no talent. And now I got really a great opportunity. Claude and ChatGPT helped me a lot. They were my ghostwriters. And it was a good moment actually to put my own ideas in order. Because really, when you write something, you get much more clear perspectives on what you are doing. So it was maybe almost six months effort. And I started with different threads of thought. One was, you know, what are the limitations of AI? Is there any durable limitation of AI?

• 5:00Speaker 00

  Where in a couple of years, there will be millions of Einsteins in a data center and we can all go to play or do whatever we would like to do because the Einsteins will do the work for us.

• 5:11Harry Stebbings?

  Can we just start on that then? I think it's nice to take it in kind of segments. Limitations of AI, Einsteins in data centers. How should we be thinking about that moving forwards?

• 5:20Speaker 00

  When I heard about this statement that in a couple of years, we will have millions of Einstein in a data center. Look, I was really concerned. And Dario is a guy that I highly respect and he's highly successful. So I was thinking, what does it mean for us? What does it mean for, can I hire one of these Einstein, put it in a laptop somehow? Assign, you know, an enterprise account, a Slack account and I ask, do my job? Or do whatever job in the enterprise? It seems that reality is maybe different and probably Dario wanted to say that we'll have millions of entities that will have the, some of the reasoning powers of Einstein,

• 6:03Speaker 00

  which I agree, but not Einsteins as a person, not Einsteins that are capable of learning on the job. Because do you agree that one of the major expectation when you hire someone is that they will hire on the job? There is no manual that a company has to give a new employee. This is exactly how you do your job from end to end. So we expect that.

• 6:27Harry Stebbings?

  No, I think humans do learn on the job and they do improve on the job, as do models. So similar there, except humans get tired, humans want more money, humans want culture, humans can be toxic, humans are difficult to manage. I'll take the AI any day of the week, please.

• 6:44Speaker 00

  If AI can work as well as a human, Harry, but you say that AI learn on the job. AI can create a notepad on the job, a scratch pad, where they can memorize some of the policies on the job. But AI doesn't alter its weights on the job in the way humans are transformed by a job. This is a huge difference. Let me give you an example. You have two chefs. One chef, 20 years has done only Japanese food, the other only Italian food. And you give them one recipe, they will create different foods. So it's not that you can write down your enterprise on a sheet of paper. It's much more complex, it's a becoming.

• 7:26Speaker 00

  It's like, think about, if I give to someone the ability to read all the books about chess, do you think he will become a grand master without playing, without losing, without going through all of this process? Probably not. If I have you watch all the videos about skiing, are you becoming a skier? No, you are not become a skier.

• 7:48Harry Stebbings?

  But I think it depends what task and workflow you're doing within the enterprise. If we look at the majority of actually what the people within UiPath and every company do, whether it's accounting and finance, whether it's marketing and sales, whether largely outbound and inbound, but whether it's social media. And dude, most of this is execution oriented, where yes, judgment and ambiguity and taste is important at the top. But most of what people do is execution.

• 8:17Speaker 00

  I disagree with you. I think most of the people will display some sort of micro-initiatives during the job. Maybe I have a hunch this customer is gonna churn and I can act before even any data is coming. How do I develop this hunch? It's through my years of transformation, it's not written on a piece of paper. I mean, it's a big difference between writing an operating model on a piece of paper and leaving it. It's almost impossible for an enterprise. For AI, do you admit that everything has to be written down, documented? Every time I'm asking a question to the model, the model will read my entire enterprise, will have to read it, okay?

• 9:02Harry Stebbings?

  Yep, sure.

• 9:03Speaker 00

  So this is not possible.

• 9:04Harry Stebbings?

  But I also think you're talking about, and with the greatest of respects, today's state of AI. I think at the pace of AI.

• 9:09Speaker 00

  This is one of the biggest bottlenecks right now because AI doesn't train on the job, doesn't train their own weights. Every time I'm doing something, after this talk with you, I am being transformed. I carry with me this discussion and all my thoughts. This is not true about AI.

• 9:29Harry Stebbings?

  Well, I mean, I'm not being rude, it is. Like, that's why people remain with OpenAI, because it has memory and it is able to infer from past queries, prompts and give you suggestions based on those. So it does have memory.

• 9:43Speaker 00

  It has memory, but memory, it's not necessarily learning. It's not the same thing. Memory, it's just a thing that is written down. When I'm talking to you, I don't go back into my memory. I am just being transformed. It's like a model, it's like a new version of the model. Model that comes improved, the new version is not the old version plus a piece of paper that has memorized. It's transforming to its own ways. This is why a model becomes much better. Look, I want to give you a simple example using our own technology. We make our UiPath platform available for coding agents. So people are making, it's much easier to create automations on UiPath right now.

• 10:24Speaker 00

  But what we discover is that a model that has read open source technology, and they have a lot of examples, and they already have in its weights, a certain technology will be much better than to create on our own technology. Because regardless how many prompts and skills we create, the model has it in its own weight. It's very different. And think of all the metaphors in the world. When you read something versus when you live something, you can read the biography. It doesn't mean you live that life. It doesn't mean you are transforming. You are gonna answer like that person that live it. To me, really, this is the the biggest limitations that the models have right now.

• 11:08Harry Stebbings?

  So I I actually do agree with you, but I think everyone does. And that's why everyone is chasing, you know, recursive self-improvement so much, where it's models that can continuously learn from themselves and improve themselves over time without need for human intervention. Does that not remove the limitation that we just said?

• 11:27Speaker 00

  I don't know, Ben. Maybe we are the result of a self-improvement loop. Look, do this thought exercise. Let's put a model that we have today with the best technology, put it in a spaceship and throw it to the stars. Let's say that we have this technology, like I think von Neumann imagined, that is self-replicated, this spaceship goes to different stars, get energy, and they can continue, so compute will be infinite. And models will self-improve. Where they would end up with? Maybe they will create a simulation of a world like ourselves, right? Because they will improve infinitely. Basically, this is the theory. So they will simulate a world as complex as our own world within it.

• 12:11Speaker 00

  But that means that we are a part of an infinite simulations. So I don't know where it's gonna lead. But I know that there is a big distinction that I made in the book between will and reasoning. It's not like that we are certain that the will to do something, you know, emerges from reasoning or from even from consciousness. I think will is a kind of a separate, you know, part of the fabric of the universe. I don't think we as humanity have clarity about what will is. I think it's wishful thinking to believe that I can take a big model,

• 12:52Speaker 00

  put into self-improvement loop, and this model is gonna generate will. I don't kind of believe so.

• 12:59Harry Stebbings?

  But I think we don't know. And I think that's what's so kind of challenging about trying to predict what happens. It's like a world of, as I said, recursive self-improvement is unknown in terms of where it lands. It's like a technology you can create can become something you didn't know it could be. And I guess for me, the question then is like, we see the news this week. Dario says we need to pace the frontier. You run UiPath today. Do you feel we need to pace the frontier?

• 13:25Speaker 00

  I would say if they truly believe that this technology is becoming rogue and they cannot control it, and their experiments will create significant loss for, I don't know, internet, other systems. If I were them, I would I would pace it, you know, at any cost. Because I don't want to risk going to jail, honestly. I think there are laws that, you know, kind of control this type of rogue behavior. So honestly, I I don't need an external pressure to control. I would be just concerned, citizen, and I would not build a technology that, you know, is causing harm. But now, of course, they think that this is the only way to protect against the bad boys.

• 14:09Speaker 00

  So we are the good guys, but there will be some bad boys that probably in other parts of the world that will build the technology regardless. So I think that probably they ask more like a boss. I want to build this technology at any risk. And I'm willing to open my gates for other to to see how I'm doing. Because I want to do it in as of, you know, like a good manner as possible. But in the same time, I want to be free of consequences. To me, I think this is a bit how I read this memo. Because otherwise, I think it's kind of obvious. I don't think we will reason with the bad guys. And we can make a coalition with the bad guys to stop the frontier. So we can make a coalition only with the good guys regardless.

• 14:54Speaker 00

  I would take even, in my opinion, even the labs in China that are building AI right now. I would classify them as a good guys. But someone, to me, I think the indirect, the attack is probably on open source. Because basically, they say even if the good guys are building open source, but that open source will get into the hands of the bad guys. And this is unknown bad guys. This is the real danger. So the danger is in open source. So indirectly, it's also an attack to open source in this way. It's it's a way of interpreting, I guess.

• 15:26Harry Stebbings?

  You work with some of the biggest enterprises in the world with UiPath. Alex Kopp said from Palantir that the biggest enterprise in the world is scared to work with frontier labs because of threat of them coming in to their businesses over time. They have the data, they could build their own and compete against them. Do you see large enterprises scared to work with frontier providers?

• 15:48Speaker 00

  I think so, yes. I I don't think people are scared that OpenAI will build a competitor to them necessarily. I don't see this coming. They are more scared that their IP would leak to other existing competitors somehow. Because I I don't know if I'm manufacturing somehow shoes or whatever. I don't think OpenAI is gonna come and compete with me on this thing. But probably some of other guys can get indirectly via the models the same intelligence. If OpenAI will train the model, I think that's the real danger. And I think it's it's a legitimate danger.

• 16:29Speaker 00

  And I think everyone is trying to protect their IPs.

• 16:34Harry Stebbings?

  You you said about kind of clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process?

• 16:43Speaker 00

  It has become very clear to me that another limitation of AI is what I call exactness. AI by its nature being probabilistic, at every step, in a way it can lose it. While you do a hundred, two hundred steps by AI, even at each step, you have ninety nine percent probability, for instance. It's, you know, zero point ninety nine at power of one hundred. You will end up with maybe sixty percent probability to do the entire step. So AI doesn't have this mechanism to follow steps exactly hundreds, millions of time in the same way. You can see it even if you ask AI to multiply very large numbers millions of times.

• 17:27Speaker 00

  At that, at some point, they will make a mistake. And also, it's surface kind of another simple idea. Even if you have a tool like AI that is capable of doing multiplications, why you are not using a computer that is doing these multiplications millions of times? You know, hundred percent of time, exact. The fact that the tool can do a job doesn't mean you have to use that tool to do that type of job.

• 17:53Harry Stebbings?

  Isn't it because it's where you are? And that's the importance of being in the harness of the workflow, which is like, I get you completely. You could use something else. I'm asking here, I'm not saying. But because you're in ChatGPT continuously every day, instead of switching to a computer or calculator or whatever, you ask what you're already in, the importance of the environment.

• 18:16Speaker 00

  ChatGPT acts like an interface to convert my questions in natural language into exactness. But the exactness is not run by ChatGPT. Exactness is run by a computer. Because even today, if you ask ChatGPT, please multiply these two big numbers, they are using behind the scene a tool, a computer, and they will give you the exact number. This is part of the power of the models. So you actually can see on on the desktop level, on this type of cowork or ChatGPT work, you see the capability to call tools. And what I am saying, you extend this capability to the enterprise level.

• 18:56Speaker 00

  When everything that should be exact should run on exact technologies, there is no point to run it on probabilistic technologies. And here comes, I think, the most interesting thing. There isn't asymmetry in the deployment of AI and automation in an enterprise. Deploying AI agents is not getting easier today than it was two years ago, in my opinion. But deploying automation has become much easier. Because I can create these automations with AI, with coding agents. Coding agents been the most major giant leap that we were seeing in in the past year. I I would say since the invention of ChatGPT, then Chain of Thoughts,

• 19:41Speaker 00

  and then coding agents were, you know, the major milestones. So with coding agents that act in kind of a design time, when I build the systems, I can create automations that work with exactness every time during the execution time. So this is the asymmetry that is happening right now. Plus, when an automation breaks, because any change in the upstream system, AI comes again into play and fixes the automation itself. So this is really the pattern that we are seeing emerging in an enterprise. And this is that AI, it's actually creating the software that runs an enterprise. And this software cannot behave wrong.

• 20:26Speaker 00

  Because it can, this software cannot change its behavior in real time. It's not a probabilistic technology. Even if the software is created by AI, we can really audit. We can have humans that read it, validate it. I can have tests that, you know, for a certain input will guarantee that the software will behave in the same way. So that's, again, makes this pattern extremely powerful. You create, you use AI to create software that runs the enterprise in a predictable, governed, auditable way.

• 20:57Harry Stebbings?

  How many engineers do you have today?

• 20:59Speaker 00

  Maybe more than a thousand.

• 21:01Harry Stebbings?

  More than a thousand engineers?

• 21:02Speaker 00

  Yeah.

• 21:03Harry Stebbings?

  How many people do you have?

• 21:04Speaker 00

  Around four thousand.

• 21:05Harry Stebbings?

  You have four thousand people?

• 21:07Speaker 00

  Yes, why so, right?

• 21:09Harry Stebbings?

  I find eighteen a fucking nightmare. What? Four thousand? Oh, my God, no. Do you have too many?

• 21:18Speaker 00

  It's a complicated question because I think I would answer more with the thing that is in my book. I think the more we transform our companies using AI, we have to, in the same time, to transform our workforce. I've never hidden from my employees that there will be a transformation in the company. But I told them upfront, guys, we are not doing anything stupid. We are not just using AI as a pretext to cut a part of the company. We need to do the transformation in the same time as we successfully adopt AI in an enterprise. That's actually another point that I discovered writing this book.

• 22:02Speaker 00

  If you look, I was looking deeply at jobs and what jobs can be affected by AI, what jobs can be enhanced and how this transformation is gonna is gonna look like. And then one of the things that seems very simple in retrospect is that people don't have very simple jobs that, again, can be defined on a sheet of paper. Every job has some kind of an outcome that is measurable. And this is the outcome that people are hired for. But there is another outcome that is part of the institutional strength. Think about my deep relationship with the customer. It's not necessarily part of the numbers that I'm producing,

• 22:45Speaker 00

  but it's all is maybe what makes this customer sticking to my technology. This is a different outcome. If I'm going blindly and I cut on a number of A's and say, because AI is gonna replace them, AI can call the customers and write emails. I don't think AI can supplement the human connections and the trust. So this is a different outcome of the job. And it reflects on every employee, on every type of role in the company. So to me, an enterprise should have a ledger where they actually understand what people are doing besides their main definition of the role. And only after they have this ledger and understanding,

• 23:29Speaker 00

  they can look at how does the AI transformation look like? What kind of jobs will be affected? How can I move people maybe from one job to the other? Because they still carry some kind of the cultural aspect of the enterprise. So I don't think it's a simple problem as AI is gonna cut twenty percent of the company. Let's do a riff twenty percent out and then we increase the AI adoption. I think on the contrary, when you do this blindly, you risk to hallow the enterprise of exactly the same talent that thrived during AI. Because let me give you an example. In the book, I call this credentialed middle

• 24:11Speaker 00

  that was the type of people that were prevalent in any enterprises. And if you think of our education system and the way we hire people, we hire based on deep expertise in a particular domain, a credential expertise. And this is exactly the type of expertise that might not be needed so much. AI can really help. So you'll need a few of these experts, but you will need more people that have initiatives, that are capable of maintaining a relationship with the customer, that can be mentors for new employees, that bear the cultural aspect of the enterprise. So it's counterintuitive because you will tend to cut those people

• 24:55Speaker 00

  that are not the biggest experts in the domain, but you will cut exactly what you will need to bring the AI, to supplement these experts.

• 25:05Harry Stebbings?

  But maybe you just need less of them. And so, you know, I was speaking to a lawyer today and I said, you know, how big's your trainee program? And they go, well, historically it was twenty five. And I said, wow, that's a lot of trainees. And he said, yeah, but this year it'll be four.

• 25:19Speaker 00

  I hundred percent agree. We will need less of the people in probably most of the roles. But the main question is, which ones? How do you choose?

• 25:29Harry Stebbings?

  Well, I think you can choose quite simply for where is their verifiability? Finance and accounting is quite clear what is right and what is wrong.

• 25:37Speaker 00

  I disagree with you. It's not about verifiability. It's about if the work has been defined in a frame set by other people. If the frame is clear, then the AI can understand the frame.

• 25:51Harry Stebbings?

  But the frame is clear. Finance and accounting did. They do your expenses.

• 25:55Speaker 00

  No, it's not. It's not. This is one of the domain where the frame is not clear. Because, man, I receive an invoice or I receive an order from a customer. I can treat it differently. There are not always the rules there. I know for this customer, Nvidia is gonna ship with priority to OpenAI. Maybe they have a rule that says there, yes, my first chips go there. Maybe they don't. That rule they don't, if it's not captured in a frame, AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful.

• 26:33Harry Stebbings?

  What do you mean the map of work?

• 26:35Speaker 00

  Who basically capture how the work works? How the work happens in an enterprise? This is the map of work. It's all the workflows, all the exceptions, all the procedures, all the systems that you use in order to fulfill a goal of a process.

• 26:54Harry Stebbings?

  Sure, but then you have a head of finance who sits on top of thirty agents. And exactly, when Nvidia come back and say, whoa, whoa, whoa, we're your biggest buyer and we have special terms on our payments. They go, yeah, sure, that's right. Don't worry about it.

• 27:10Speaker 00

  But you come to my point, even in finance, you cannot replace everybody.

• 27:13Harry Stebbings?

  Not everyone, but you got one person or two people.

• 27:16Speaker 00

  You'll need you'll need a certain number of people. The thing is, if you have X number of people, how do you understand which of them stay and which of them has to go to maybe do different jobs? How do you know? Because ideally, you will get the people that will have literacy in AI, that can display initiatives. Because AI cannot exhibit initiative in the human sense. So out of this number of people that you wanna keep, you wanna keep the people that, you know, display the most initiative. Even a finance person treating an invoice with a customer contributes to the culture and how my enterprise is regarded.

• 27:58Speaker 00

  How can I make a distinction between that person and the other person that care less about how they treat the customers? Because the second is more prone to being displaced by AI. This is the ledger that I think enterprise have to create and to understand different outputs of people. They need to judge people by this hard to define output.

• 28:20Harry Stebbings?

  So how does one do that then? For the illegible data that isn't captured within companies? How do we do that? You know, Zuck and Facebook have said about monitoring every single action that's on the screen of employees. I don't think that captures the tone of a cool, the warm text afterwards to a customer, the illegible data. How do we think about capturing the data that shows value that we don't capture?

• 28:45Speaker 00

  I think this is the crux of the problem.

• 28:49Harry Stebbings?

  And here you're a true philosopher.

• 28:50Speaker 00

  Yeah, yeah. And this is this is where we put a lot of effort as a company. And we are introducing a new technology that we call it cartography. And cartography is a discipline. So it's a discipline to help companies surface all the information of how the work is done and help them create this map of work. And one big important of cartography is to actually have investigate what people are doing on their desktops. And we have a product that we call the Cartographer Agent that can interview people, real subject matter experts, have them basically record what they are doing. And the agent is interviewing them in real time.

• 29:35Speaker 00

  Like if you interview a finance person, it can ask, why did you change this invoice when the zip code was different? Why did you choose a different path? Tell me. And they can start surfacing all of these exceptions. So that's real agents interviewing real people. It's pretty cool stuff. And then you consolidate data from multiple people. And then we create the process maps. So we show them how the work works in kind of real time. And then after this map of work that showed the work as is, you can use our coding agents and you can come up with an idea of how you should transform the process. And you transform the process by printing software.

• 30:18Speaker 00

  So you start with the process and at point A, it's kind of fully manual. Of course, you have enterprise systems like system of records, but people operate the system. I think the goal of any enterprise is to have less people operating the system and more automations and agentic AI operating the systems.

• 30:37Harry Stebbings?

  Do you not fear pushback from people working in the company aware that you are watching what they do to replace them? That is what Zuck got.

• 30:46Speaker 00

  This is inevitable. It very much depends how you pitch the company. So in UI path, I think it was important to tell people again, guys, we are not doing anything stupid. We are not doing any mass extinction, you know, for the presence of AI. But transformation is inevitable. And you guys have to transform and everybody will get a chance. And the people will that will become more literate in respect to AI will have a better chance, not only here, but in the future in any other job. That's the message that I think everybody should get.

• 31:21Harry Stebbings?

  Did they respond to it? Like, did you see AI adoption through the roof after that?

• 31:25Speaker 00

  I think the response is good. AI adoption requires more cycles than just discovering the process and people's input in this. But look, in the beginning of the year, the fear of people across the industry, and not only in my company, but in many companies, was off charts. The fear of completely being replaced. Now, I think people start to get a bit more understanding of the durability of their of their jobs. And this AI diffusion in enterprise that can happen at a slower pace and can happen one process at a time. Because these millions of Einsteins are not hireable yet.

• 32:04Harry Stebbings?

  One of the companies we've invested in, McCoor, data provider, Brandon Foodie, the CEO, tweeted yesterday that they spend three X the spend of human salaries on inference. What percent or multiple or take would you say you spend human salaries on inference?

• 32:20Speaker 00

  I personally don't care about it. And let me, let me tell you something. It's a, it's a simple hypothesis. If the work, the quality were better of a human can be done by a machine, I will hire today a machine, even if it's more expensive than a human. Because human cost will all increase and they bring errors into the pictures, while the cost of machine will increase. So I will have a competitive advantage compared to people that will stick to humans. I think everyone will do this. So I don't think the cost of tokens, it will be the real question if you replace a person with AI. But the real problem today is that AI cannot replace a person. Because if, again, if bring me an Einstein that replace me

• 33:03Speaker 00

  and I will happily go in any vacation in the world. But this Einstein doesn't yet exist. I would like to be interviewed and have this podcast with another Einstein. Man, this thing doesn't exist today. That's the reality. So let's call it a reality. Let's call a spade, a spade. Maybe this technology will emerge in somehow Einstein's that embody like a person that have will, that gets transformed on the job, learn on the job, have the capability of reasoning, imagination of Einstein's exist. Of course, all the jobs will go extinct.

• 33:37Harry Stebbings?

  I have a show with Jason Lemkin from Sasta. He's cut his team from 25 to two. If he were in my seat now, he would say, no, no, no, it does. I replace my VP Finance, I replace my VP Marketing, and actually the AI is better.

• 33:53Speaker 00

  I wanna see this. I heard companies that replace hundreds of support people in the past and now they are rehiring these people. I think until this model is proven and is proven at scale, not in a particular industry for a particular guy, I don't think we can, we can extrapolate for one data point that is gonna go across industry.

• 34:15Harry Stebbings?

  You said about extrapolation and overexaggeration. The SaaS apocalypse was very real. We're gonna vibe code everything. Did you vibe code tools out?

• 34:24Speaker 00

  Look, it was amazing, yes, we did. But not to an extraordinary success. Initially, it seemed extraordinary. But when we tried to put it in production, we started to see, you know, some kind of real bottlenecks with these tools. And you need to have a lot of things that you have to maintain. Connectors, permissions, audit, security. And always taking a software from a prototype to production, it's actually where the work is. It's not necessarily the writing code. Writing code is fun, but it's not there where you can really make the difference. I think it's much easier today to make a prototype. Prototype is so easy, but then you have to iterate

• 35:09Speaker 00

  to make the prototype in production. And this is the testing and everything else. We were trying to replace like a procurement tool, writing ourselves. And I think we have a lot of success. And it was initially, it was written only by AI. But then it comes to, you know, speaking to this self-improvement loops. Kind of don't trust, we don't have enough test, we don't have enough trust to put this tool in production, 100%. And humans, in our experience, humans have to intervene a lot into how this vibe coded tool. For instance, the database schema that vibe coded tool created was completely bogus. A human has to come and, you know, create the structure.

• 35:53Speaker 00

  So right now, you are not at the point where, you know, you will have like a business user that understands a problem and they will vibe code a tool. So you, you will still need to put engineers and people, you need to maintain it. So it's a long process. So eventually you will end up paying probably as much, if not more, as the tool you replace, while you keep some of your good and best people bandwidth occupied.

• 36:22Harry Stebbings?

  Would you buy Salesforce today?

• 36:24Speaker 00

  I would buy Salesforce as a system of record.

• 36:28Harry Stebbings?

  As a stock?

• 36:29Speaker 00

  As a stock. Look, I invest in software as a category. And I think I made a good investment in, you know, a few months ago because I, I bought in the bottom of SaaS apocalypse. Even our own stock, you know, has been doing better. But the markets, the markets today are driven so much about sentiment and not the value. So it's kind of hard for me to make a judgment of an individual company. But I don't think Salesforce can be replaced by vibe coding, if this is the question.

• 37:00Harry Stebbings?

  I don't understand why a company would go public today. If you think about the two drivers of being public. Number one is liquidity for shareholders, employees and shareholders. Well, Stripe and many companies are able to have liquid stock in private markets. And two is the ability to have M and A, a tradable asset that you can buy with. I mean, many, many private companies are able to buy with private stock. With Stripe, we're gonna do PayPal with private stock for 60 billion. So that's not a barrier. And the casinoization of public markets, as you said, with current stock markets being sentiment driven. I don't understand why one would.

• 37:35Speaker 00

  But hey, let's don't make a confusion between, you know, some very exceptional companies and the most companies that are out there. There are so many companies that are kind of zombies right now. These 2021 zombies, they would fare better in the public market right now. At least their investors will have a way for exiting. Their employees will have a way to make some money. Nowadays, all of them are sitting on paper money, okay? But public markets will face them with the reality of what's their real valuation. I would not say there is no value in the, why, why the Anthropic is trying to do IPO in the end.

• 38:15Harry Stebbings?

  Well, but they're a unique company alongside OpenAI, which just has to because they have exhausted all private funding that exists. They are literally hitting the tap out button on the supply of private capital. They're extraordinary companies because they just need too much money.

• 38:33Speaker 00

  Do you believe all the investors in OpenAI and Anthropic will stay in the companies for years to come?

• 38:38Harry Stebbings?

  No, I think some will do, but some.

• 38:40Speaker 00

  Some will do, of course, but I think we will see an exodus. Honestly, I don't believe a 2 trillion or whatever. Maybe they will reach 5 trillion or, because if I buy a 2 trillion, I need to have a path to 5.

• 38:51Harry Stebbings?

  But if they went public at 2 trillion, would you sell?

• 38:53Speaker 00

  Anthropic in particular, I wouldn't. Because do you remember on our last podcast? I think you asked me, what's the company that I bet on? And I said Anthropic. And Anthropic was worth 60 billion market cap. Maybe I was stupid, I didn't invest.

• 39:09Harry Stebbings?

  You would have made more money on that than Salesforce or something.

• 39:13Speaker 00

  Yeah, yeah, 100%. So I would not sell, and I think it's same with OpenAI. And I don't have, I think OpenAI has catch up quite nicely. And I use interchangeably right now.

• 39:25Harry Stebbings?

  I just think we're in a market where the big get bigger and value concentrates more than ever.

• 39:30Speaker 00

  But the, the real question is, Harry, would I buy a 2 trillion? That's my real question. And right now, I'm, I need to see the real numbers to understand if I will go, if I will put money in the IPO.

• 39:42Harry Stebbings?

  I'm gonna get in so much trouble for this. I think AI is quite like Bitcoin in just the way that it's very difficult to determine what application is going to win, what wallet's gonna win, what usage is gonna win. And if that is the case, buy the underlying infrastructure that you know is going to be there. And so for me, I agree with you. I don't know if Claude is gonna be better than the next Codex. I don't know if Curse are gonna come out with something fucking amazing. But I do know that Jensen's gonna be sitting there going, here's another chip, here's another chip, here's another, great.

• 40:13Speaker 00

  Yeah, but I think Jensen is bound by the success of open source. Because if Anthropic and OpenAI, if this is becoming a dual poly, and basically, I, I think their TAM is in like trillions. It's, it's basically the work. They will print their own chips, man, honestly. It's not such a big deal in the end to print chips.

• 40:33Harry Stebbings?

  Of course, I mean, OpenAI doing jalapeno and Anthropic are doing their own chips.

• 40:36Speaker 00

  Exactly. So I don't think Jensen's will be doing so well if they have the single biggest providers and the source of truth and light of God will come from only Anthropic and OpenAI. So therefore, the open source should succeed.

• 40:51Harry Stebbings?

  I absolutely agree, which is why I think Jensen is doing the open letter, which everyone signs.

• 40:55Speaker 00

  Absolutely.

• 40:56Harry Stebbings?

  Encouraging open source.

• 40:57Speaker 00

  And I think it was a great move buying.

• 41:01Harry Stebbings?

  Hugging face.

• 41:01Speaker 00

  Hugging face, yes.

• 41:02Harry Stebbings?

  Why?

• 41:03Speaker 00

  Because I think it's, it encourages, it hosts all the open source model. It's putting the money where the money is for his company.

• 41:10Harry Stebbings?

  Totally. It does also make a neutral provider not neutral anymore, bias. Yeah, obviously, they have Nematron and they have their own models now as well. You could say there's a loss of independence now that it's owned by Nvidia.

• 41:23Speaker 00

  I think Nimotron is still a small cog in the picture. I think it's valuable, but I would not call it, it's not the same cheap size as the others.

• 41:34Harry Stebbings?

  Do you worry about the round tripping revenue? You know, everyone talks about Nvidia investing here, buying here, the circular economy that come from Oracle and OpenAI. Do you think that's overblown?

• 41:45Speaker 00

  It can be because every, every major infrastructure in history has been overbuilt. And I, I think it's a simple explanation because I was thinking why every infrastructure is overbuilt. Because you have to, to make sure you get, you know, the biggest piece of the opportunity. If the opportunity is big, it doesn't, it doesn't matter. You build a little bit more than it's necessary. So it's clearly that now everybody, that there is only hundred percent of the pie. And people are building right now two hundred percent of the pie. There will be losers.

• 42:19Harry Stebbings?

  Do you not think though, this is the first innovation where we are significantly underbuilt? And actually, if you look at the constraints now, you're, you're right, actually, in prior technology cycles, we overbuilt supply side. And demand side was lagging behind. Now we have energy that's being a massive constraint. We have water, we have data centers, we have regulation and policy. We have a significant hindrance to supply side. And we are underbuilt, not overbuilt, which is why every ounce of compute is taken.

• 42:47Speaker 00

  Yes, but are we underbuilt to the extent of these trillions that comes into the infrastructure? I don't know to answer this. Everything happens on the premise that AI is gonna replace human work in a really large scale. We need to see the timing of this replacement and transformation. It's a big difference if it's coming in ten years versus next two years. So I don't think it's overbuilt for next decade, but it might be overbuilt for the next three years. And stock markets and capital, you know, can be mercy lesson.

• 43:23Harry Stebbings?

  Maybe I'm a childish optimist, but I saw, you know, Andre Capathie say that he used coding tools for twenty percent of the work. And then six months later, he said that it did eighty percent of the work and he helped it with twenty percent. We're investors in Lagora. I interviewed lawyers when we did that deal. And they all said to me, you're such tech bros, you think you can replace us. Ha ha, we went to law school. And I was like, okay, cool. I interviewed them two weeks ago. Every single one of fifteen said they would be severely unhappy if it was taken away. With most of them saying they hadn't written a document in six months.

• 43:57Speaker 00

  Harry, you should, you should read my book, my friend, because it answers exactly the same question. When the frame that a person operates is really well defined by someone else, like in law, AI can be devastating. And the impact, when the frame is not as clear and there are so many exceptions that are custom made for an enterprise.

• 44:22Harry Stebbings?

  You're about to spend two days with a lawyer who is my girlfriend. She will tell you that law is highly ambiguous, subjective in terms of writing styles. She would.

• 44:32Speaker 00

  So is the human language and AI understands it perfectly. And it understands every freaking nuance of human kind of sensitivities. As long as it's documented and it's well defined, there is a manual for the freaking law. AI is amazing. When there is no manual, AI is not amazing and it doesn't work. That's the huge difference.

• 44:55Harry Stebbings?

  But three hundred billion dollars is the legal industry in the US. It's a lot. If you think about how much labor could be replaced by that, I think thirty percent would be reasonable. That would be ninety billion dollars of available revenue.

• 45:10Speaker 00

  Yeah, but that's not gonna convert in token revenue. Maybe out of ninety billion, companies might charge maybe ten percent. Maybe it's ten billion total opportunity in tokens.

• 45:22Harry Stebbings?

  Am I mistaken to invest in Lagora? And are the Harvey investors mistaken to invest in Harvey? If it's ten billion, not ninety billion.

• 45:31Speaker 00

  What I can tell you that from a law perspective, open source models, frontier models will do just fine. Maybe they do also the custom workflows around the legal process, which is really valuable. But to me, that's also, you know, a big part of my thesis. Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the, the real value is. If Harvey and Lagora are doing this, they map really the work, they create the workflow and they, they create a legal department for me. Of course, it's a much bigger value that they capture.

• 46:10Speaker 00

  But if it's only to get a legal opinion, a call to a model, that's not gonna be a hundred billion dollar market, hundred percent.

• 46:19Harry Stebbings?

  What percent of token traffic do you think will go through open versus closed models in twelve months?

• 46:26Speaker 00

  To me, I think the question is different. What percentage of the traffic will go to truly frontier model like Astra or Fable? Versus a very cost efficient models. For enterprise work, my prediction is that ninety percent of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most operational work.

• 46:53Harry Stebbings?

  Just to be clear then, so we will actually still use the core provider, which is OpenAI and Anthropic. It'll just be deprecated older models.

• 47:01Speaker 00

  I will still use Anthropic and OpenAI. They are cost efficient model, but I will have a verifiable backup on open source all the time. I should be, as a responsible enterprise, I should be able to switch models. I cannot be locked in.

• 47:19Harry Stebbings?

  Can I ask you, we invested in, I'm just checking my portfolio against your brain. I believe strongly in open models and I think that every company, not every company, but mid to large scale company will have their own model. Own their own intelligence and feed their own data into it. And, you know, I think that goes to the statement of kind of owning your own intelligence, not renting it. It's why we invested in fireworks and I believe in the open model ecosystem.

• 47:41Speaker 00

  You know, I'm a big fan of fireworks and we are using them quite a bit.

• 47:46Harry Stebbings?

  Do you like them?

• 47:46Speaker 00

  Yes, we like them a lot. And I'm, I'm a big believer that an enterprise should distribute their bets. And one of the bets should be on open source. And very importantly, should be on this map of work. Because think about, if I wanna train a model, my own model with who am I? I need to have this who am I very well documented. I need to create this manual because I'm training one model today. But in next two months, there is another better model, base model coming into the picture. How can I do transfer learning from my old model into the new model if I don't have the data and the exact menu? I cannot, or there are, you know, terrible losses when I do this.

• 48:31Speaker 00

  The real investment for an enterprise is to creating this map of work that documents how they actually work. And with this one, they can train their own models. It's in fireworks or other provider, doesn't matter. But this is their IP, this is, and this is their core data. Make sense?

• 48:48Harry Stebbings?

  And so you do, sorry, just to understand, so you do believe that companies and a lot of them will have their own models with their own data?

• 48:55Speaker 00

  I do believe that they will at least have their own models as a backup to frontier models. To me, where I'm not clear, if I can provide the same cost efficiency with my own model versus a cost efficient model from Anthropic and OpenAI. Because I think these guys are in the position to truly optimize large infrastructure. So part of their business model will be to deliver, you know, more intelligence per dollar than even I can squeeze from my own models.

• 49:28Harry Stebbings?

  If you have highly specific data that is exact to the request that you have, which is your data. I think you'll get more token efficiency with your own model than you would in optimized.

• 49:41Speaker 00

  Only if you are training your models, that might be true. And only if you can deliver, if fireworks can deliver at a large scale and in a very optimized way.

• 49:52Harry Stebbings?

  Would you invest in fireworks at fifteen billion dollars?

• 49:55Speaker 00

  Probably yes, if this hypothesis of open model is true, which I believe is true. I think they are under, under valued. I think they will have to get very soon in this big game of securing compute. Because if they don't secure compute, I don't understand how can they give me the inference of the scale that I want. What do you think? Because you invested in them.

• 50:18Harry Stebbings?

  I did. I think you're absolutely right that they need to move into the computer era and I think Lynn is doing that, I'm sure very soon.

• 50:24Speaker 00

  They will have to raise, you know, tens of billions now, yeah.

• 50:28Harry Stebbings?

  And she did that at Facebook. I think that's a unique thing to this team. They did compute securing.

• 50:34Speaker 00

  Yeah, it's a great team. We really like them.

• 50:36Harry Stebbings?

  Great fucking team.

• 50:36Speaker 00

  And we work with them before the big hype.

• 50:39Harry Stebbings?

  I also think the data providers are massively underpriced and underappreciated. Macor and Surge in particular. Everyone's like, oh, they're commodities, you're just buying data. Data is the most important thing to feed model quality.

• 50:54Speaker 00

  But what's the difference? I think one thing is storage and one thing is understanding of the data. Because if I have a storage, I can have a tape and I can put data on the tape. Would you invest in a tape company? I don't think so. You need to invest in the intelligence that understands the data and feed the model and, you know, extract the right data, the right time. Feed, feed really the model with the data that is needed with the context. Because if you don't have, if you have just data, but you don't have a way to create a really good context to give the model when they ask something, it's useless.

• 51:28Harry Stebbings?

  I, I think you'd say that they have more data than anyone else across more categories than anyone else. So when the model requests highly specific data, because of the breadth of their library, they're able to provide it in a way that others aren't.

• 51:41Speaker 00

  If it's their own data and is valuable for models, I'm sure the mothers will buy, will buy that data in an instant.

• 51:48Harry Stebbings?

  Can I ask you, what have you changed your mind on most in the last 12 months?

• 51:52Speaker 00

  I didn't understand this necessity to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. But every time I'm running a query towards AI, AI should have at their disposition the entire way, you know, my company work or this particular process work. When I realized this, I understood also that this thing that it's the biggest differentiation between memory and true learning. This is how we started the discussion and I'm not sure I really make a point, but it's a huge difference between just laying down, having a scratch pad, where being transformed by an experience. And that's the thing that I realized the most.

• 52:37Speaker 00

  And I think I realized also what's kind of human for us. Because I experienced a lot with AI writing, not code, but writing a book. So I've been through different styles. I understood a lot how to prompt them. AI doesn't have a style. And you realize why they don't have a style. Because they are an averageer of anything. In order to have a style, you need to have kind of a body. You need to have individuality. Because we are the choice that we made and the choice that we don't make, in a sense. So you need to be transformed. Because otherwise, I will just ask AI, read this book and write in the spirit of this author.

• 53:22Speaker 00

  And it's not really working. Because you need to be transformed by the experience. So to me, this is gonna be the biggest breakthrough in the AI technology. When I can have models at the size of Mythos being transformed on a job, being, you know, putting in a laptop. And it might be possible, who knows? You know, the pace of technology, maybe 20 years from now, I can have a 10 trillion model that it's my own model and is getting transformed along with me. But we need to see. I think there might be a few series of innovations to get there. Because I want to give you also an interesting data point.

• 54:03Speaker 00

  AI is solving very interesting math problems that humans didn't solve before, right now. But AI still is not capable of creating frameworks. I don't know, relativity is a framework, okay? And I was thinking, why so? I think one of the main reasons relates still with this not being transformed when you are on a job. When I'm writing a book, I am being transformed by the act of writing this book. Every time I'm writing something down, there is something in me that changes that is not necessarily the memory thing. It's me that is changing.

• 54:43Speaker 00

  Einstein has been changed by his experience, thinking about the speed of light, of what happens when you go, you know, behind the light. It's not like Einstein wrote it down and then every time he thought again, he rewrote a piece of paper. No, he became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity. Models don't do this way. Even if I put swarm of agents, everything, they have to write down everything. They are not being transformed by the process. So therefore, it's very difficult. In the end, they will have this context, the 1 million.

• 55:25Speaker 00

  It's very hard to go beyond this 1 million tokens context window. A frame might require a transformation as you work on that frame. It's a, it's a different way of learning than pure memory. So that's, I want to make this argument as clear as possible.

• 55:42Harry Stebbings?

  Are you optimistic for your children?

• 55:44Speaker 00

  I'm extremely optimistic for myself, Ari, therefore for my children. I don't want to sound like an AI doomer, man. Because I, I believe that-

• 55:53Harry Stebbings?

  I don't think you do. No, I don't think you do. I sound like a doomer, in a way. I think we'll have a lot more job loss. I think it will happen a lot quicker. I think we're seeing it in real time.

• 56:03Speaker 00

  I'm much more optimistic that we won't have so much. Because based on my own experience with AI, I don't think the diffusion is as fast as you imagine. Particularly because enterprise have to document in much greater detail their processes.

• 56:18Harry Stebbings?

  Can I just ask, sorry, we're both Europeans and we're both sitting in London. I don't know how to say this, but we don't matter anymore, just being blunt. Do you think that gets better or worse in the next three to five years?

• 56:31Speaker 00

  Yes, man, it's hard to admit the reality. But from a technology standpoint, I think we are largely irrelevant. But it's so stupid. Because the biggest producer of machines that make chips is based in Europe.

• 56:46Harry Stebbings?

  It's ASML.

• 56:47Speaker 00

  Yeah, we could have made these chips in Europe. You know, some of the most brilliant minds that build AI. Even if you think, Dario, Sam, all of them are European origins. Ilya, we had the talent, we have the technology to build the machines, but somehow we are losing it. And it's very stupid.

• 57:07Harry Stebbings?

  Do you see a difference in work ethic, having a team in the US and the UK?

• 57:11Speaker 00

  Yes, I experienced with teams in UK and at 5pm, they are all, you know, in the pub.

• 57:18Harry Stebbings?

  Why is that? Because money matters more?

• 57:21Speaker 00

  I think culture matters more than money. It's a more dynamic culture. I don't think I would have succeeded in Europe the way I did in US. So I, I'm a European, but as an entrepreneur, I am, I'm American. This is what I told to everybody. So I, my formation is in American School of Entrepreneurship, even if I started my company in Europe.

• 57:44Harry Stebbings?

  Listen, I get it. And you look at Legora and you look at Eleven Labs and some of the best companies to come out of Europe in the last few years. And if you think the revenue machine is anywhere but in America, you're lying to yourself.

• 57:55Speaker 00

  It's an easier to access revenue machine than in Europe, clearly.

• 58:00Harry Stebbings?

  Faster, it's easier, the teams have scaled GTM functions before. Completely agree with you.

• 58:06Speaker 00

  And American companies are making larger bets on vision without waiting for so many proof points as the European companies. And even people in the middle management can make sizable million dollar bets on new technologies in US. I haven't seen this appetite in Europe.

• 58:28Harry Stebbings?

  I don't want to ask this, but I am interested. If you were to advise a young European entrepreneur today, would you say to go to the US?

• 58:36Speaker 00

  Yes, that's the, that's the sad reality. I think that unless they build for a specific market with some specificity in mind, if they build a universal technology, they will have a better chance to succeed in US. There will be many successful European companies coming out of this. Maybe not as frontier labs. But I think for the application of AI-

• 59:01Harry Stebbings?

  Do you buy sovereignty as an argument?

• 59:03Speaker 00

  Yes, I think it's an important one.

• 59:05Harry Stebbings?

  The energy sovereignty, model sovereignty?

• 59:07Speaker 00

  Hundred percent. And all European customers right now would prefer on-prem software model sovereignty and model optionality. Yes, hundred percent. This is a big business that it's coming here. Look, I talked to our friends at Fireworks and I actually tried to convince them to make their software available on-prem. Right now, they are in, show me the money. But I can tell, guys, this is a big business. You need to prove first, because this is Europe. Show them the technology and the money will come.

• 59:38Harry Stebbings?

  How much revenue does UiPath do today?

• 59:41Speaker 00

  I think it's public data. We are at 1.6, growing last year like 14%.

• 59:49Harry Stebbings?

  So Jason Lemkin's taught me that like, unless you're growing 20% plus, you're just fucked in the public markets. Like, it's grow or die. And it's a horrible reality in that way. I'm not condoning it, it's horrible.

• 1:00:00Speaker 00

  Yeah.

• 1:00:00Harry Stebbings?

  Is that right?

• 1:00:02Speaker 00

  Yeah, because I think the public markets are very confused right now with who are the AI winners or losers. And if you don't show serious growth and traction, they automatically put you into AI losers without looking really deep into the business. There are so many hundreds of software companies in the public market. So then it's hard to look at each of them.

• 1:00:26Harry Stebbings?

  So if I were to flip it on you, and we do a final one for a quick fire, what is the bull case to UiPath being a $50 billion company?

• 1:00:36Speaker 00

  Think about even today, Gartner released their new boat, Magic Quadrant, Business Orchestration and Automation Technologies. We are one of the leaders. We moved from a challenger into a leader in the last year. So it shows that we, as a company, made this transition from an RPA and automation technology into an orchestration and automation technology. And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI-powered. This idea that you can have an AI agent that runs everything for you

• 1:01:17Speaker 00

  from top level processes, orchestrate and automate everything by magic, I think it's kind of a thing that people stop believing. You need to have an underpinning orchestration and automation technology and this map of work that I taught in order to power your processes. And this is what we have. And it's not only me saying, but it's Gartner saying, it's Forrester, it's industry analysts in being very bullish on us. So that's really the, the argument right now. I think, as I said, this asymmetry that AI is creating right now is more obvious than printing software that run your processes

• 1:02:00Speaker 00

  has become much easier than a year ago. Bringing, creating an AI agent that run your software is as difficult as a year ago. So you make a huge investment into printing this software, capturing the enterprise context that we call the map of work, putting this enterprise context inside these rails that I named this orchestration automation as the map and rails. Map is the context, rails is the orchestration automation. You put them in the same platform and then you can assign an agent to do work. And you tell the agent, this is the reality. This, these are the rails you can use. This is the map that describe how to use these rails. This is the goal.

• 1:02:44Speaker 00

  And that's the way you can control. You can have a control on the top and your agents cannot go rogue. No sane enterprise right now will put a swarm of agents and just ask them do my finance accounting for me. Because who knows, maybe they will, they will attack your competitors.

• 1:03:01Harry Stebbings?

  Send 20 million bucks to some rogue invoice, I completely get you. What is the bear case?

• 1:03:07Speaker 00

  I think the bare case is AI will somehow become genius. Tokens cost will be next to zero. We'll have this literally millions of Einsteins in a data center. But Einstein's in a true sense, not only reasoning, in the sense of replacing a person. And I can assign them to every work in an enterprise and they will just do it. That's the bare case against us.

• 1:03:35Harry Stebbings?

  I mean, token cost has gone from sixty dollars to one dollar per million. So, I mean, the token cost will go to nice, nothing.

• 1:03:43Speaker 00

  It's possible.

• 1:03:43Harry Stebbings?

  Yeah.

• 1:03:44Speaker 00

  This is why I told you I would not right now stop an investment based on the token cost.

• 1:03:49Harry Stebbings?

  Dude, I want, I could talk to you all day. I'd love to do a quick fire with you. So I say a short statement, you give me your immediate thoughts, okay?

• 1:03:55Speaker 00

  Hmm.

• 1:03:56Harry Stebbings?

  What's the hardest thing about your job today as CEO of UiPath?

• 1:03:59Speaker 00

  It's aligning people. It's so different personalities, pride, ego that comes into place. This is the hardest.

• 1:04:07Harry Stebbings?

  What's changed most about how you work as a CEO because of AI?

• 1:04:12Speaker 00

  I'm spending maybe half of my day right now, half of my day alone with myself in visual studio code right now, working with Claude and ChatGPT. I have way more leverage on my company than than before. Because we we change completely the way we operate. Most of the people, when they come with an idea to me, a year ago, they would come with the deck. And it was very hard even to prepare with this deck. Now everyone is gonna come with the markdown file. And I can put it into, I have a giant strategy folder that, you know, I have, you know, AI agents working with this.

• 1:04:53Speaker 00

  I put this document in my folder and then I can ask intelligent questions.

• 1:04:58Harry Stebbings?

  If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing?

• 1:05:05Speaker 00

  Maybe I will try to build my own frontier model.

• 1:05:08Harry Stebbings?

  Nvidia in three years' time, will it be above seven and a half trillion dollars?

• 1:05:14Speaker 00

  Where they are today, five.

• 1:05:15Harry Stebbings?

  Five, six, five point six, yeah.

• 1:05:17Speaker 00

  I can easily imagine forty percent run for Nvidia. I I would bet more on Nvidia rather than Anthropic being a seven billion company. Really, of course. Billions are nothing today.

• 1:05:29Harry Stebbings?

  Billions are nothing today, yeah, yeah. You said something on a show that we did before, and it was like one of the most resonant things I've ever had in a show. And you said, I think a lot of people think they want to be me, but sometimes it's quite lonely alone in my head. And I always remember this because I I often feel the same. Would you advise to founders who feel lonely in their head and struggle with that today?

• 1:05:54Speaker 00

  I think they should surround themselves with their best friends from maybe childhood. Have more frequent chats with them because they are the people that can relate the most to them before. And they can see them part of the transformation. And it's a nice thing to do anyway. You'll still be lonely. But you'll have the sense of some kind of continuity in your life. I I find one of the most rewarding part of my life chatting, being with friends, with family. This is really where you get a lot of the loneliness in a way, you know, appeal from the loneliness.

• 1:06:31Harry Stebbings?

  A final one, what are you most excited for when you look forward? Like, my mother's got MS, I'm really excited for some of the breakthroughs that we'll see with chronic conditions and treatment of them.

• 1:06:41Speaker 00

  Yeah, I'm I'm very excited about longevity, you know, my friend, I have almost twice your age.

• 1:06:48Harry Stebbings?

  You're not quite, dude, are you kidding me?

• 1:06:51Speaker 00

  Kind of gym or anything.

• 1:06:53Harry Stebbings?

  No, what are you doing longevity wise?

• 1:06:55Speaker 00

  I I'm doing quite a lot. I I got into like peptides, into supplements. Man, I think I I think in around sixty different supplements a day and three, four peptides.

• 1:07:07Harry Stebbings?

  Supplements?

• 1:07:07Speaker 00

  Seriously, yeah.

• 1:07:08Harry Stebbings?

  Sixty?

• 1:07:09Speaker 00

  Sixty, six, zero, yeah.

• 1:07:11Harry Stebbings?

  What the fuck are you taking?

• 1:07:13Speaker 00

  And all of them have been recommended and vetted by AI and.

• 1:07:18Harry Stebbings?

  What? That's extraordinary. I mean, you look incredibly young, but sixty supplements, like in pills? No, you take them in like, because I do the longevity shape from Brian Johnson, which is like sixty in one. And I just have it every morning. You actually have sixty separate supplements?

• 1:07:33Speaker 00

  I I I have a lot of pills. I also do some in form of powders. But yeah, I have, I I'm gonna show you tomorrow all my, I have little bags throughout the day, like AM one, AM two, AM three.

• 1:07:48Harry Stebbings?

  That's extraordinary, that's extraordinary.

• 1:07:50Speaker 00

  It's very dorky of me, yes.

• 1:07:51Harry Stebbings?

  Peptides, like, do you feel better?

• 1:07:53Speaker 00

  I think it's supposed to feel in the long term better. But honestly, I feel I feel way better even than ten years ago. Kind of reducing booze quite a quite a lot helped. And I know you are a big fan of booze.

• 1:08:07Harry Stebbings?

  You don't still drink, do you? Do you still drink?

• 1:08:09Speaker 00

  I I drink a lot less these days.

• 1:08:13Harry Stebbings?

  I love that. Dude, this has been so much fun. Thank you so much for putting up with my meandering. When does the book come out?

• 1:08:18Speaker 00

  It's already available for download. I'm printing also a few copies. We have our big fusion event coming in a couple of weeks. And I am I'm distributing to everybody coming with a copy.

• 1:08:31Harry Stebbings?

  Dude, this has been a pleasure. I'm gonna get a copy. I'm gonna get a physical copy because I'm old too, and so I like reading.

• 1:08:36Speaker 00

  It's my gift to you, of course.

• 1:08:38Harry Stebbings?

  There we go, dude. Thank you so much.

• 1:08:40Speaker 00

  Thank you, man.

• 1:08:42Harry Stebbings?

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• 1:09:27Harry Stebbings?

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• 1:10:11Harry Stebbings?

  Try it at asana.com, that's A S A N A dot com. While Asana aligns the roadmap, Base44 helps you build faster. You have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pictured and what you actually ship. Well, Base44 is where that wall disappears. You describe it, yeah, Base44 builds it. Apps, websites, AI agents, real working products, built in minutes, using nothing but plain language. And it's all batteries included, the backend, the database, the authentication, the hosting. The heavy lifting is handled, so you just really stay in the flow. This doesn't just take the busy work off your plate, but it gives you an advantage and pushes you past what you thought you could build alone. So in this market, fast is the baseline.

• 1:10:56Harry Stebbings?

  To win, you just have to be first. Base44 is that edge, the move that skips the troubleshooting and gets you straight to the breakthrough. Build your next thing at base44.com, that's base44.com.