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Daniel Dines on Why Work Processes Not Models Will Be The Most Valuable Asset in AI

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. ----------------------------------------------- Timestamps: 00:00 - Intro 02:47 - Why AI Still Cannot Replace Humans in the Enterprise 07:21 - AI Memory Is Not the Same as Learning 12:25 - Should Frontier AI Labs Actually Slow Down? 15:05 - Why Enterprises Are Scared of Frontier Model Providers 16:37 - The Hidden Problem With Probabilistic AI in Enterprise 19:09 - Why AI Will Create the Software That Runs Enterprises 21:36 - How AI Will Reshape the Enterprise Workforce 28:24 - Why Every Company Needs a “Map of Work” 35:01 - Would You Pay More for AI Than a Human Employee? 37:39 - Why Vibe Coding Cannot Replace Enterprise Software 40:14 - Can Salesforce Survive the AI Era? 41:00 - Why Should Any Great Company Go Public Today? 43:12 - Would You Buy Anthropic at a $2TRN Valuation? 44:44 - Why NVIDIA’s Success Depends on Open Source 46:22 - Is the AI Infrastructure Boom Being Overbuilt? 48:26 - How Much Human Work Can AI Actually Replace? 50:55 - Why Workflows, Not Models, Will Capture the Value in AI 52:04 - Will 90% of Enterprise AI Run on Cheaper Models? 53:11 - Should Every Enterprise Own Its Own AI Model? 56:11 - Would You Invest in Fireworks at $15BN? 01:04:01 - Has Europe Already Lost the Technology Race? 01:08:20 - Why Public SaaS Companies Must Grow or Die 01:09:14 - The Bull Case for UiPath at $50BN 01:12:08 - The Bear Case for UiPath: Millions of Einsteins in a Data Center ---------------------------------------------------------------------------------------------- Subscribe on Spotify: https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466 Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465 Follow Harry Stebbings on X: https://twitter.com/HarryStebbings Follow Daniel Dines on X: https://twitter.com/danieldines Follow 20VC on Instagram: https://www.instagram.com/20vchq Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Visit our Website: https://www.20vc.com Subscribe to our Newsletter: https://www.thetwentyminutevc.com/contact ----------------------------------------------- #20vc #harrystebbings #ai #сeo #danieldines #uipath

Daniel DinesguestHarry Stebbingshost
Sep 21, 20261h 19mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 2:47

    Intro

    1. DD

      In my opinion, even the labs in China that are building AI right now, I would classify them as the good guys.

    2. HS

      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?

    3. DD

      I've never hidden from my employees that there will be a transformation. Jensen is bound by the success of open source.

    4. HS

      This is a truth-telling, myth-busting conversation, 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.

    5. DD

      Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the real value is.

    6. HS

      Ready to go? [upbeat music] 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.

    7. DD

      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.

    8. HS

      I, uh, it really is the most wild time right now. And so I wanna start, you've written a book, and a lot of people write books. With the greatest of respect, 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? [chuckles]

    9. DD

      [chuckles]

    10. HS

      No offense. You're not doing it for the royalties. Why, why did you decide to write a book?

    11. DD

      Man, I dreamt to write a book since I was a kid.

    12. HS

      [chuckles]

    13. DD

      And I discovered I have no talent. [chuckles]

    14. HS

      [chuckles]

    15. DD

      Now, I got really a great opportunity. Claude and ChatGPT helped me a lot.

    16. HS

      [chuckles]

    17. DD

      They were my ghostwriters. And, uh, 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 a maybe almost six-month effort doing this, and I started with different threads of thought.

    18. HS

      Mm-hmm.

    19. DD

      One was, uh, you know, what are the limitations of AI? Is there any durable limitation of AI? Or 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, uh, we would like to do because the Einsteins will do the work

  2. 2:47 – 7:21

    Why AI Still Cannot Replace Humans in the Enterprise

    1. DD

      for us.

    2. HS

      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 think about that moving forwards?

    3. DD

      Look, 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 in, uh, in what he did. So I was thinking, "Man, 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 a, a, you know, an enterprise account, a Slack account, and everything, and I ask do my job or do whatever job in the enterprise? It seems the realities may be different, and probably Dario wanted to say that we'll have millions of, uh, entities that will have the, some of the reasoning powers of Einstein, which I agree, but not Einsteins as a persons. 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-

    4. HS

      Uh, 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.

    5. DD

      If AI can, uh, can work as well as a, as a human, Harry, but, uh, you say, you say that AI learn on the job. AI can create a, a notepad on the job, a scratchpad 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, this is a, this is a huge difference because... Man, let me give you, 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, okay? They will create different food. So it's not that you can write down your enterprise on a sheet of paper. It's, it's much more complex. It's a becoming. Think about if I give to someone the, the ability to read all the books about chess. Do you think he will become a grandmaster without playing, without losing, without going through all of this process? Probably not. If I give, if I have you watch all the videos about skiing, are you becoming a skier? No, you are not become a skier.

    6. HS

      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, um, largely outbound and inbound, but, um, whether it's social media and, uh- 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.

    7. DD

      I dis- I disagree with you. I think most of the people will display, will, will display some sort of micro initiatives during the job. Maybe I have a hunch this customer is, uh, is gonna churn, and I can act before even any data is coming. How, 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 living it. It's almost impossible for, 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, I... the model will read my entire enterprise, will have to read it. Okay?

    8. HS

      Yep, sure.

    9. DD

      So this is not possible.

  3. 7:21 – 12:25

    AI Memory Is Not the Same as Learning

    1. HS

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

    2. DD

      This is one of the, this is one of the biggest, uh, 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, uh, talk with you, I am being transformed and I carry with me this discussion in all my thoughts. This is not true about AI.

    3. HS

      Well, I mean, I'm not being rude. It, 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. That's-

    4. DD

      It has memory, but memory, it's not necessarily learning. It's not the same thing. It's memory, it's just a thing that is written down. When I'm talking to you, I don't, I don't go back into my memory. I, I ju- I am just being transformed. It's like a model, it's like a new version of the model that comes improved. The new version is not the old version plus a piece of paper that has memorized. It's transformed into its own weights. This is why a model becomes much better. Look, I wanna give you a simple example on, uh, on using our own technology. We, uh, we make our UiPath platform available for coding agents. So people are making... It's much easier to create automations on UiPath right now. But what we discover is that a model that has read open source technology will-- and they have a lot of examples, and they already have in its weights, you know, 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 weights. It's very, it's very different then. 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 transformed and you are gonna answer like that person that live it. So I think to me, really, this is the, the biggest limitations that the models have right now.

    5. HS

      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-

    6. DD

      Mm-hmm

    7. HS

      ... without need for human intervention. Does that not remove the limitation that we just said?

    8. DD

      I don't know, man. Maybe we are the result of a self-improvement loop.

    9. HS

      Yeah.

    10. DD

      Because 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, uh, this, uh, technology, like I think Von Neumann imagined, that is self-replicating. This spaceship goes to different stars, get energy, and they can continue. So compute will be infinite, and models will self-improve. W- 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, our own world within, within it, but that means that we are in part of an infinite simulations. So look, the answer is, and I, 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, and 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, uh, you know, part of the fabric of the universe that 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, put into a self-improvement loop, and this model is gonna generate will. I, I, I don't kind of believe so.

    11. HS

      I get that, 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, you know, w- we see the news this week,

  4. 12:25 – 15:05

    Should Frontier AI Labs Actually Slow Down?

    1. HS

      Dario says we need to pace the frontier. You've just written the book and have a lot of clearer thoughts. You run UiPath today. Do you feel we need to pace the frontier?

    2. DD

      I would say if they truly believe that, uh, this technology is, uh, is, is becoming rogue and they cannot control it, and their experiments will, uh, will create significant loss for, I don't know, internet, other systems. If I were them, I would, I would pace it, uh, you know, at, uh, at any cost because I don't want to risk going to jail, honestly. I think there are laws that, uh, 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, you know, concerned citizen and I would not build a technology that, you know, is causing harm. But now, of course, they think that, uh, this is the only way to protect against the bad boys. So we are the good guys, but there will be some bad boys that, you know, probably in other parts of the world that will build the technology regardless.

    3. HS

      Mm-hmm.

    4. DD

      So I think that probably they ask more like a pause. I want to build this technology at any risk, and I'm willing to open, you know, my, uh, 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, uh, this memo. And because otherwise, it's-- I think it's kind of obvious. I don't think we can... 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. I, I would take even, in my opinion, even the labs in China that are building AI right now, I would classify them as the 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 and, 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, uh, in this way. It's, it's, it's a way of interpreting, I guess.

  5. 15:05 – 16:37

    Why Enterprises Are Scared of Frontier Model Providers

    1. HS

      You work with some of the biggest enterprises in the world with UiPath. Alex Karp said from Palantir that the biggest enterprise in the world are 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?

    2. DD

      I think so, yes. I, I don't think people are scared, scared that, uh, OpenAI will build a competitor to them necessarily. I, I, I don't see this coming. I think they are more scared that their IP would leak to other existing competitors-

    3. HS

      Mm-hmm

    4. DD

      ... somehow. Because I, if I-- I don't know if I'm manufacturing somehow screws 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-

    5. HS

      Mm-hmm

    6. DD

      ... if, uh, if OpenAI will train the model. I think that's the real danger, and I think it's, it's a legitimate danger, and I think everyone is trying to protect their IPs.

    7. HS

      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

  6. 16:37 – 19:09

    The Hidden Problem With Probabilistic AI in Enterprise

    1. HS

      process?

    2. DD

      It has become very clear to me that, um, another limitation of AI is what I call exactness. In, uh, AI by its nature being probabilistic at every step, in a way it, it can lose it. You know, 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 mult- at power of one hundred. You will end up with maybe sixty percent probability to do the entire step right. 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. At that-- At some point they will make a mistake. And also it surface kind of another simple idea. Even if you have a tool like AI to multi-- 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, to do that type of job.

    3. HS

      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.

    4. DD

      I think that ChatGPT acts like an interface to To convert my, uh, my, uh, 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 computer, and they will give you the exact number. This is part of the power of the models. So you actually can see on, uh, on the desktop level, on this type of co-work or ChatGPT work, you see the capability to call tools.

  7. 19:09 – 21:36

    Why AI Will Create the Software That Runs Enterprises

    1. DD

      And what I am saying, you extend this capability to the enterprise level, when everything that should be exact should run on exact technologies. There is no point to run it on, uh, probabilistic technologies. And here, here comes, I think, the most interesting thing. There isn't a symmetry 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, 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, that is happening right now. Plus, when an automation breaks, because any change in the upstream system, AI comes again into play and fix the automation itself. So this is the... this is really the pattern that we are seeing emerging in an enterprise. And this is that, uh, AI, it's actually creating the software that runs an enterprise. And this software is, is really cannot behave wrong, because it can't-- 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.

  8. 21:36 – 28:24

    How AI Will Reshape the Enterprise Workforce

    1. HS

      Totally get that. How many engineers do you have today?

    2. DD

      Maybe more than a thousand.

    3. HS

      More than a thousand engineers?

    4. DD

      Yeah.

    5. HS

      How many people do you have?

    6. DD

      Around four thousand.

    7. HS

      You have four thousand people?

    8. DD

      Yes. Why so, right?

    9. HS

      I find 18 a fucking nightmare.

    10. DD

      [laughs]

    11. HS

      And what? Four thousand. Oh my God. No, do you have too many?

    12. DD

      It's, uh, it's a complicated question, because I think I would, I would an- I would answer more with the thing that is in, is in my book. I think the more we transform our companies using AI, we have to, in the same time, to transform our, uh, 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. And that's, that's actually another point that I discovered, you know, writing this book about, uh... If you look... I, 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, one of the thing 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. I think 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, uh, 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, but it's all-- it's maybe what makes this customer sticking to my technology. This is a different outcome. If I'm going blindly and I cut, you know, on a number of As and say, "This is 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 that... So this is a different, this, 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, you know, their main definition of the role. And only after they have this ledger and understanding, 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, uh, you know, the cul- the cultural, uh, aspect of the enterprise. So I, I, I don't think it's a, it's, it's a simple problem as AI is gonna cut 20% of the company, let's do a RIF 20% out, and then we increase the AI adoption. I think on the contrary, when you do this blindly, you risk to hollow the enterprise of exactly the same talent that thrived during AI. Because let me give you an example. In, um, in, in the book, I call this like the credentialed middle that kind of was the type of people that were prevalent in any enterprises. And if you think of our education system and we hi- 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-

    13. HS

      Oh

    14. DD

      ... that AI can really help. So you'll need, you'll need a fewer of these experts, but you'll 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, you know, the cultural aspect of the enterprise. So it's, it's counterintuitive because you, you cut and you, and you will tend to cut those people 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. So one, uh, one thing that I-

    15. HS

      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 is your trainee program?" And they go, "Well, historically it was 25." And I said, "Wow, that's, that's a lot of trainees." And he said, "Yeah, but this year it'll be four."

    16. DD

      I 100% 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?

    17. HS

      I think you can choose quite simply for where is their verifiability, which is, you know, f- finance and accounting. It's quite clear what is right and what is wrong.

    18. DD

      I disagree with you. It's not about verifiability. It's about if the, 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, and the AI can work-

    19. HS

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

    20. DD

      No, it's not, it's not... Th- this is one of the domain where the frame is not clear because when I, uh, I receive an invoice or I, I receive an order from, uh, 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, I think 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.

  9. 28:24 – 35:01

    Why Every Company Needs a “Map of Work”

    1. HS

      What do you mean the Map of Work?

    2. DD

      To 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.

    3. HS

      Sure, but then you have a, a head of finance who sits on top of 30 agents and a- exactly when NVIDIA come back and say, "Whoa, whoa, whoa. We're your biggest, you know, buyer, and we have special terms on our payments." They go, "Yeah, sure. That's right. Don't worry about it."

    4. DD

      But, but you-

    5. HS

      And so-

    6. DD

      ... you come, but you come to my point. Even in finance, you cannot replace everybody, so you-

    7. HS

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

    8. DD

      You, you don't need-

    9. HS

      Yeah.

    10. DD

      Look, you, you will need, you will need a certain number of people. The thing is, if you have X number of people, how, how do you understand which of them stay and which of them has to be... 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 when 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, you know, the most initiative. Even a finance person treating an invoice with a customer contributes to the culture and how my enterprise is regarded. How can I make a distinction between that person and the other person that care less about how they treat the customers? Because I wanna keep the first 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 their, by the- by this kind of hard to define output.

    11. HS

      So h- how do you, 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 call, 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?

    12. DD

      I think this is the crux of the problem.

    13. HS

      [laughs]

    14. DD

      And this is-

    15. HS

      And here you're a true philosopher. [laughs]

    16. DD

      Yeah, yeah. And this is, this is where we, uh, we put a lot of effort as a company, and we are introducing a new technology that, uh, we call it like 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, uh, is interviewing them in real time. Like, if, if you interview a finance person, it can ask, "Why did you put, uh, why did you change, you know, this, uh, 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, in, in, in kind of real time. And then after this Map of Work that show the work as is, you can use our coding agents and you can come up with, uh, an idea of how you should transform the process, and you transform the process by printing software. So you start with a process that... And at point A, it's kind of fully manual. Of course, you have enterprise systems like system of records and everything, 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.

    17. HS

      And 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.

    18. DD

      This is inevitable. And, uh, so I think it, it very much depends how you pitch the company. So in, uh, in UiPath, 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 pretense 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." I think that's the message that I think everybody should get, so-

    19. HS

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

    20. DD

      I think the, I think the response is good. AI adoption requires more cycles than just discovering the process and people's, you know, input in this. Look, in the beginning of the year, I think 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.

  10. 35:01 – 37:39

    Would You Pay More for AI Than a Human Employee?

    1. HS

      Can, can I ask you, one of, uh, the companies we invest in, Macaw, data provider, Brendan Foody, the CEO, uh, tweeted yesterday that they spend three X the spend of human salaries on inference today. What percent or multiple, give or take, would you say you spend human salaries on inference?

    2. DD

      Look, I personally don't care about it.

    3. HS

      [laughs]

    4. DD

      And let me, let me tell you something.

    5. HS

      I love it.

    6. DD

      It's a, it's a simple hypothesis. If the work at 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 man- than a human. Because human, human cost will only increase, and they bring errors into the pictures while the cost of machine will decrease. So I will have a competitive advantage compared to people that will stick to humans. If that's... And I think everyone will do this. So I, I don't think the cost of tokens, it will be the real question if you replace a person with, uh, with AI. But, uh, the real problem today is that AI cannot replace a person. Because if, again, if, if bring me an Einstein that replace me, and I will happily go in any vacation in the world, but this Einstein doesn't yet exist. And probably I will, 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 and somehow Einsteins 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 Einsteins exist. Of course, all the jobs will go extinct.

    7. HS

      I get you. Uh, I, uh, have a show with Jason Lamkin from SaaStr, a famous SaaS platform. He's r- You know, cut his team from 25 to two. He would al-- He, if he were in my seat now, he would say, "No, no, no, it does. I replaced my VP finance, I replaced my VP marketing, and actually, the AI is better."

    8. DD

      I, I wanna see this man. I think I, I, I heard, you know, companies that replace hundreds of support people in the past, and now they are rehiring these people. I think until, 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.

  11. 37:39 – 40:14

    Why Vibe Coding Cannot Replace Enterprise Software

    1. HS

      You said about extrapolation and over-exaggeration, the SaaSpocalypse was very real.

    2. DD

      Yeah.

    3. HS

      We're gonna vibe code everything. Did you vibe code tools out?

    4. DD

      Look, it was, uh, it was amazing, yes, we did. But not to an extraordinary success. Initially, it was-- 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, uh... And you need to have, you know, a lot of things that you have to maintain, connectors, permissions, audit, security. 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 makes the difference. I think it's much easier today to make a prototype. Prototype is so easy, but then you have to iterate to, to make the prototype in production, and this is the testing and everything else is, uh... Look, we, we were trying to, to replace like a procurement tool, writing ourselves, and I think we have a lot of success. And it was in-- initially, it was written only by AI. But then it comes to, you know, speaking to these self-improvement loops, kind of don't trust. We don't have enough tests, 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 the vibe coded tool created was completely bogus, so it ha-- a human has to come and, you know, create the structure. 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, you will still need to put engineers and people, you need to maintain it. So it's, 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 in build-

    5. HS

      Would you buy, would you buy Salesforce

  12. 40:14 – 41:00

    Can Salesforce Survive the AI Era?

    1. HS

      today?

    2. DD

      I would buy Salesforce as a, as a system of record. But I-

    3. HS

      As a stock?

    4. DD

      As a stock. Look, I invest in, uh, I invest in, uh, in software as a category, and I think I made a good investment in, uh, you know, a few months ago because I, I bought in the, in the, in the bottom of a SaaSpocalypse. Even our own stock, you know, has been, uh, has been doing better. And, but the markets, the markets today are driven so much about sentiment and not the, 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.

  13. 41:00 – 43:12

    Why Should Any Great Company Go Public Today?

    1. HS

      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, um, shareholders, um, employees and shareholders. Well, Stripe and many companies are able to have liquid stock in private markets. And, and two is the ability to have M&A, a tradable asset that you can buy with. I mean, many, many private companies are able to buy with, you know, private stock. Stripe were gonna do PayPal with private stock for $60 billion, so that's not a barrier. And the casinoization of private markets as, uh, public markets, as you said, with current stock markets being sentiment driven, I, I don't understand why one would.

    2. DD

      But Harry, 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.

    3. HS

      Mm-hmm.

    4. DD

      Okay? Public markets will face them with the reality of, you know, what's their real valuation. So I would not say there is no value in the... Why, why the Anthropic is trying to do IPO in the end?

    5. HS

      Well, well, but they're a unique company alongside OpenAI, which just has to because they have exhausted all private funding that exists. I mean, they, 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. But like-

    6. DD

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

    7. HS

      No.

    8. DD

      So maybe-

    9. HS

      I think some will do, but some-

    10. DD

      Some will do, of course, but I think we will see an exodus of... And I 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, I need to have a path to 5. Probably-

    11. HS

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

  14. 43:12 – 44:44

    Would You Buy Anthropic at a $2TRN Valuation?

    1. DD

      Anthropic in particular, I, I wouldn't. Because do you remember on our last podcast, I think you asked me which is 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.

    2. HS

      You would have made more money on that than-

    3. DD

      [laughs]

    4. HS

      ... than Salesforce or ServiceNow or whatever.

    5. DD

      Yeah, yeah. Yeah, 100%. So l- look, I, I would not sell, I think it's same with, uh, with OpenAI, man. I don't have, uh... I think OpenAI is, uh, has catched up quite nicely, and I use interchangeably right now.

    6. HS

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

    7. DD

      But the, the real question is, Harry, would I buy a two, a 2 trillion? That's, uh, that's my real question. And look, right now I'm, I'm not s- I, I don't, I, I need to see the real numbers, you know, to understand if I will go, if I will put money in their IPO.

    8. HS

      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 Cursor 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.

  15. 44:44 – 46:22

    Why NVIDIA’s Success Depends on Open Source

    1. HS

      I'll take more NVIDIA.

    2. DD

      Yeah, but, uh, I think Jensen is bound by the success of open source. Because if Anthropic, Anthropic and OpenAI, if this is becoming a duopoly and basically their, their, uh... I, I think their TAM is in, like, trillions. It's, it's basically the work.

    3. HS

      Yeah.

    4. DD

      They will print their own chips, man. Honestly, it's not such a big deal t- in the end to print chips.

    5. HS

      Of course. I mean, OpenAI are doing Halapina-

    6. DD

      Yeah

    7. HS

      ... and Anthropic are doing their own chips.

    8. DD

      Exactly.

    9. HS

      Yeah.

    10. DD

      So I don't, 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.

    11. HS

      I absolutely agree, which is why I think Jensen is doing the open letter, which everyone signed-

    12. DD

      Absolutely

    13. HS

      ... encouraging open source.

    14. DD

      And I think it was a great move buggy, uh, buying, uh-

    15. HS

      Hugging Face

    16. DD

      ... Hugging, Hugging Face, yes.

    17. HS

      Why?

    18. DD

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

    19. HS

      Totally. It does also make a n- neutral provider not neutral anymore. Bias. Yeah, obviously they have, uh, Nemotron, 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. But yeah, so.

    20. DD

      I think Nemotron is still a small cog in, in the picture.

    21. HS

      Sure.

    22. DD

      I think it's valuable, but I would not call it... It's not at the same chip size as the others.

  16. 46:22 – 48:26

    Is the AI Infrastructure Boom Being Overbuilt?

    1. HS

      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?

    2. DD

      It can be because every, man, every major infrastructure in history has been, uh, overbuilt. And I, I think it's a simple explanation because I was thinking why every infrastructure is, is, uh, 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 it cannot be there is only 100% of the pie, and people are building right now 200% of the pie. There will be losers in this build.

    3. HS

      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.

    4. DD

      Yes, but are we underbuilt to the extent of these trillions that comes into the infrastructure? I don't know to answer this. And everything happens on the premise that AI is gonna replace human, 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 10 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-

    5. HS

      Maybe-

    6. DD

      ... stock markets and capital, you know, can be merciless

  17. 48:26 – 50:55

    How Much Human Work Can AI Actually Replace?

    1. DD

      and-

    2. HS

      Maybe I'm a childish optimist, but I saw, you know, Andrej Karpathy say that he used coding tools for 20% of the work, and then six months later he said that it did 80% of the work and he helped it with 20%.

    3. DD

      Yeah.

    4. HS

      I... We're investors in Lawvora. 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 15 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

    5. DD

      Yeah. Harry, you should, you should read my book, my friend, because it answers-

    6. HS

      [laughs] I did

    7. DD

      ... exactly the same questions.

    8. HS

      I loved it. [laughs] Uh, so I-

    9. DD

      When, when the, when the frame that a person operates is really well defined by someone else, like in law, AI can be devastating in the impact. When the frame is not as clear and there are so many exceptions that are custom-made for an enterprise, well-

    10. HS

      Oh. Dude, 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, uh, and it's a really-

    11. DD

      So is the human language, and AI understands it perfectly, and it understands every s- freaking nuance of, you know, human, uh, 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, that's the huge difference.

    12. HS

      But $300 billion is the legal industry in the US. It's a lot. Um, if you think about how much r- labor could be replaced by that, I think 30% would be reasonable. That would be $90 billion of available revenue.

    13. DD

      Yeah, but that's not, it's not gonna convert in token revenue.

    14. HS

      What do you mean?

    15. DD

      I mean, maybe out of 90 billion, I, I think companies might charge maybe 10%. Maybe it's 10 billion total opportunity in tokens.

    16. HS

      Am I mistaken, you know, to invest in Lagora and are the Harvey investors mistaken to invest in Harvey if it's n- 10 billion, not 90

  18. 50:55 – 52:04

    Why Workflows, Not Models, Will Capture the Value in AI

    1. HS

      billion?

    2. DD

      What I can tell you that from a f- from a law perspective, I think open source models, frontier models will do just fine. Maybe they do all the c- they do also the custom workflows around the legal process, which is really valuable. But to me, that- 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 bigger, it's, it's, it's a much bigger value that they capture. But if it's only to get a legal opinion, a call to a model, that's not gonna, that's not gonna be a hundred billion dollar market. 100%, man.

    3. HS

      What percent of token traffic do you think will go through open versus closed models in 12

  19. 52:04 – 53:11

    Will 90% of Enterprise AI Run on Cheaper Models?

    1. HS

      months?

    2. DD

      To me, I, I think the, 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? And to me, I think for enterprise work, my prediction is that 90% of the, of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most, you know, operational work.

    3. HS

      And so 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.

    4. DD

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

    5. HS

      Locked in

    6. DD

      ... I cannot put locked in. I cannot

  20. 53:11 – 56:11

    Should Every Enterprise Own Its Own AI Model?

    1. DD

      be locked in.

    2. HS

      Can I ask you, we invested in Fire- [laughs] I'm just, I'm just checking my portfolio against your brain. Um, w- 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.

    3. DD

      Yeah, you know, I'm a big fan of Fireworks, and we are using them, uh, quite a bit.

    4. HS

      Do you like them?

    5. DD

      Yes, we like them a lot. And I, I, I also, I'm, I'm a big believer that, uh, an, 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 want to 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 manual? I cannot. Or there are, you know, terrible losses when I do this. So 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. You know, 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?

    6. HS

      Mm-hmm. I totally get that. And so you do... Sorry, just so I understand. So you do believe that companies, and a lot of them, will have their own models with their own data?

    7. DD

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

    8. HS

      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 an optimized frontier.

    9. DD

      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 this and in a very

  21. 56:11 – 1:04:01

    Would You Invest in Fireworks at $15BN?

    1. DD

      optimized way.

    2. HS

      Would you invest in Fireworks at $15 billion?

    3. DD

      Probably yes. If this hypothesis of open model is true, which I believe is true, I think they are under, undervalued. 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 they will-- how can they give me the inference at the scale that I want. What do you think? Because you invested in them.

    4. HS

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

    5. DD

      So they will have to race, you know, tens of billions now, yeah.

    6. HS

      And I will be there. [laughs] Uh, no, and she did that at Facebook. I think that's a unique thing to this team. They, they did compute secure and-

    7. DD

      And it's a great team. We really like them.

    8. HS

      It's a great fucking team.

    9. DD

      And we work with them before the big hype, you know, around them.

    10. HS

      I do-- I also think the data providers are massively underpriced and underappreciated, Macaw 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. I think-

    11. DD

      But what's the difference, I think? But one thing, it's storage, and one thing, it's understanding of the data. Because if I have a storage, I can have a tape, and I can put data on the tape. Would we invest in a tape company? I don't think so. You'll-- You, you, you need to invest in the intelligence that understands the data and feed the model and, you know, extract the, extract the right data at 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.

    12. HS

      I, I think you would 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.

    13. DD

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

    14. HS

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

    15. DD

      I didn't understand this necessity of AI to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. That 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 think I understood also that this thing that-- it's the, the biggest differentiation between memory and true learning. And I let you think because 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 scratchpad, or being transformed by an experience.

    16. HS

      Mm-hmm.

    17. DD

      And that's a, that's, that's a thing that I realized the most. And I think I realized also what, what's kind of human for us because I, I, I think I experienced a lot with AI writing, not code, but writing a book. So I've been through different styles. I, I, I understood a lot how to prompt them. AI doesn't have a style, and you, you realize why they don't have a style because they are an averager of anything. In order to have a style, you need to have kind of a body. You need to have individuality because we, we are the choice that we made and the choice that we don't make in, uh, in a sense. So you need to be transformed. You cannot just give-- Because otherwise, I will just ask AI, "Read this book and write in the spirit of this author," and it's not really working, and because you need to be, you need to be transformed by the experience. So to me, this is gonna be the biggest breakthrough in, uh, 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, in, 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. That's, uh-- But we need to see. I think there might be a few series of innovations to get there. Because I wanna give you also an interesting data point. AI is solving very interesting math problems that humans didn't solve before right now. But AI still is not capable of creating frameworks. Like, I don't know, relativity is a framework, okay? And I was thinking, "Why so?" And I think one of the main reason relates still with this not being transformed when you are on a job. When I'm, 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. 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, you know, context. The one million is very hard to go be- you know, beyond this one million tokens context window. A frame might require a transformation as you work on that, on that frame. It's a, it's a, it's a different, it's a different way of learning than pure memory. So that's-- I wanna make this argument as clear as possible.

    18. HS

      Are you optimistic for your children?

    19. DD

      I'm extremely optimistic for myself, Harry, therefore, for my children. So I, I don't wanna sound like, uh, an AI doomer, man, because I, I believe that-

    20. HS

      I don't think you do.

    21. DD

      [laughs] I am.

    22. HS

      No, I don't think you do. I think, I think you sound-- I sound like a doomer in a way. I, 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. Um-

    23. DD

      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 much, in much greater detail their processes.

  22. 1:04:01 – 1:08:20

    Has Europe Already Lost the Technology Race?

    1. HS

      Can I just ask, sorry. W-we're both Europeans, um, and we're both sitting in London.

    2. DD

      [laughs]

    3. HS

      Um, 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?

    4. DD

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

    5. HS

      It's ASML.

    6. DD

      We could've m-- Yeah, we could've 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 kind of, you know, European origins. Ilya Sutskever. So it's, we have the talent, we have the technology to build the machines, but somehow we are losing it. It's, uh, man, it's very stupid.

    7. HS

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

    8. DD

      Yes. Yes. I, I experience with teams in UK, and at 5:00 PM, they are all, uh, you know, in the pub.

    9. HS

      Why is that? Because money matters more?

    10. DD

      I think culture matters more than money. It's a more dynamic culture. This is why I, I don't think I would've succeed in Europe the way I did in US. So I, I'm a European, but as an entrepreneur, I am, I am an, I'm, I'm American. This is what I told to everybody. So I, I... My formation is at, you know, it-- in American school of entrepreneurship, even if I started my company in, uh, Vienna.

    11. HS

      Listen, I, I get it, and you look at Lagora 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.

    12. DD

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

    13. HS

      Faster, it's easier. The teams have scaled GTM functions before. I completely agree with you.

    14. DD

      And American companies are making, uh, larger bets on vision without waiting for so many, you know, 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.

    15. HS

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

    16. DD

      Yes. That's the, that's the sad reality. I would-- I think that, uh, unless they build for a specific market with, 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, and I think maybe not, uh, as, uh, frontier labs. But I think for the application of AI, and I think most-

    17. HS

      You, but do you buy sovereignty as an argument?

    18. DD

      Yes. I think it's an important one.

    19. HS

      The energy sovereignty, model sovereignty.

    20. DD

      Yes, 100%. And all European customers right now would prefer on-prem software model sovereignty and model optionality. Yes, 100%.

    21. HS

      Wh-

    22. DD

      Which is a big business that it's, uh, it's coming here. Look, I talk to our friends at Fireworks, and I actually try 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."

    23. HS

      How much revenue does UiPath do today?

    24. DD

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

  23. 1:08:20 – 1:09:14

    Why Public SaaS Companies Must Grow or Die

    1. DD

      this year.

    2. HS

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

    3. DD

      Yeah.

    4. HS

      Is that right?

    5. DD

      Yeah, because I think the public markets are very confused right now of who are the AI winners or losers. And if you don't show serious, uh, 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.

    6. HS

      So if I were to flip it on you, um, we do a final one for a quick fire. What is the bull case to O- to UiPath being a $50 billion

  24. 1:09:14 – 1:12:08

    The Bull Case for UiPath at $50BN

    1. HS

      company?

    2. DD

      Think, think about even today, we, um... Gartner released their new BOAT Magic Quadrant, business orchestration and automation technologies, and we, we are one of the leaders. We moved from a challengers into a leaders in the last year or 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. You cannot-- This idea that you c- can have an AI agent that runs everything for you from top-level processes, orchestrate and automate everything by magic, I think it's kind of a thing that people stop believing. So you, you need to have an underpinning orchestration and automation technology and this Map of Work that I talked 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, uh, 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 has become much easier than a year ago. Bringing, creating an AI agent that run your software is as difficult as an 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, I, I name 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." And that's the way you can control. You can have a control o- on the top, but your agents y- cannot go rogue. You cannot have-- 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 competitor.

    3. HS

      Send, send $20 million to some-

    4. DD

      Yeah

    5. HS

      ... rogue, uh, invoice. I completely get you. Okay. What is the bear

  25. 1:12:08 – 1:18:53

    The Bear Case for UiPath: Millions of Einsteins in a Data Center

    1. HS

      case?

    2. DD

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

    3. HS

      I mean, token cost has gone from $60 to $1 per million. So I mean, I think the token cost will go to ne- nothing.

    4. DD

      It's possible.

    5. HS

      Yeah.

    6. DD

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

    7. HS

      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?

    8. DD

      Mm-hmm.

    9. HS

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

    10. DD

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

    11. HS

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

    12. DD

      I'm spending, uh- 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. And, uh, I, I have way more leverage on my company than, uh, 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 a deck, and it was very hard even to prepare with this deck. Now everyone is gonna come with a markdown file, and I can put it into... I have a giant strategy folder that I, you know, I have, you know, AI agents working with this. I put this document in my folder and then I can ask, uh, intelligent questions about

    13. HS

      If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing? [laughs] Sorry. You know what? You know what we do? We create clips, which is like you take s- little segments [laughs] and that's like the clip.

    14. DD

      [laughs]

    15. HS

      I'm just thinking of, like, it'd be a great first clip. [laughs] That's it. Maybe I'm like, "Whoa." Um, yeah, that would, that would be interesting. NVIDIA in three years' time, will it be above $7.5 trillion? It's at five-

    16. DD

      We're there today. Five.

    17. HS

      Five six. 5.6, yeah.

    18. DD

      Yeah, I can easily imagine a 40% run for NVIDIA. I, I would bet more on NVIDIA rather than Anthropic being a 7 billion company.

    19. HS

      Brilliant.

    20. DD

      Brilliant, of course.

    21. HS

      [laughs]

    22. DD

      Billions are nothing today, so. [laughs]

    23. HS

      Billions are nothing today. Yeah. You said something on a, 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, like, think they want to be me, but sometimes it's quite lonely alone in my head." And I always remember this 'cause I, I often feel the same. Um, what would you advise to founders who feel lonely in their head and struggle with that today?

    24. DD

      I think, uh, they should surround themselves with, uh, with their best friends from maybe childhood and, uh, you know, have more frequent chats with them because I think they are the people that can relate the most to them before, and they can see them as part of the transformation. And it's a nice thing to do anyway. You'll still be lonely, but, uh, you will have the sense of some kind of continuity in, uh, 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, uh, you know, appealed from the loneliness.

    25. HS

      A final one. What are you most excited for when you look forward? It's 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.

    26. DD

      Yeah, I'm, I'm very excited about, uh, longevity. You know, my friend, I have, uh, almost twice your age. So-

    27. HS

      [laughs]

    28. DD

      ... my, my body aches after-

    29. HS

      You're not quite

    30. DD

      ... any [laughs]

Episode duration: 1:19:03

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