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

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

In this episode of 20VC, Harry Stebbings sits down with UiPath CEO Daniel Dines to discuss the evolving landscape of artificial intelligence and

Key takeaways

• Daniel Dines argues that the real enterprise value is found in the 'map of work'—the specific workflows and procedures—rather than just the underlying models.
• UiPath currently operates with approximately 4,000 employees, including a massive engineering force of over 1,000 people.
• A major limitation of current AI is that it does not learn on the job by altering its weights in real-time, unlike human employees who are transformed by their work.
• Dines highlights the 'asymmetry' where AI can be used to create software and automations that provide the 'exactness' required for enterprise execution.
• The displacement of the 'credentialed middle' is a risk, but Dines emphasizes that roles requiring human initiative and cultural connection are vital for enterprise strength.

Main topics

• AI vs. Human Workflows
• The Future of Enterprise Automation
• Open Source vs. Closed Models

Notable quotes

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

Conclusion

The episode provides a deep dive into why workflow orchestration and a

Transcript preview

Speaker 1 (0:00) In my opinion, even the labs in China that are building AI right now, I would classify them as 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 real value is. This Speaker 2 (0:24) is 20VC with me, Harry Stebbings. Now joining me in the hot seat today is one of my dearest friends. I'm 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. But before we dive into the show today, founders face a different set of challenges at every stage of growth. For Sid Shate, co-founder and CEO of D-Matrix, J.P. Morgan delivered the guidance and expertise to help navigate what came next. He credits J.P. Morgan's high-touch approach with supporting D-Matrix as it grew and expanded internationally. Whether you're in the early days or expanding into new markets, J.P. Morgan helps startups navigate complexity with real confidence, offering personalized guidance and deep sector expertise. Find out how J.P. Morgan helps founders at jpmorgan.com forward slash grow without limits. JPMorgan is the bank of the innovation economy. While JPMorgan 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 onboarded in your workflows, ready to deliver. With Asana, your whole company can work on the same plan towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents work flow together. Try it at asana.com. That's A-S-A-N-A dot com. While Asana aligns the roadmap, 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. To win, you just have to be first. Base44 is that edge, the move that skips the troubleshooting and Speaker 2 (3:33) 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. Speaker 1 (3:45) 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. Speaker 2 (3:57) 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? Speaker 1 (4:12) No offense, you're not doing it Speaker 2 (4:15) for the royalties. Why did you decide to write a book? Speaker 1 (4:18) Man, I dreamt to write a book since I was a kid. And I discovered I have no talent. Speaker 1 (4:26) Now I got really a great opportunity. Claude and Chad GPT 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? 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 would like to do because the Einsteins will do the work for us. Can Speaker 2 (5:11) 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? Speaker 1 (5:20) When I heard about this statement that in a couple of years we will have millions of Einstein in the data center, I was really concerned. 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 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 some of the reasoning powers of Einstein, which I agree, but not Einstein's as a person, not Einstein's that are capable of learning on the job. Because do you agree that one of the major expectations 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. Speaker 2 (6:27) 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. If Speaker 1 (6:44) AI can or... as well as a human hurry. But you say that AI learn on the job. AI can create 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 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 food. So it's not that you can write down your enterprise on a sheet of paper. It's much more complex. It's becoming. 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 grandmaster 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 becoming a skier. Speaker 2 (7:48) 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 it's 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. I Speaker 1 (8:17) 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 going to 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. It's a big difference between writing an operating model on a piece of paper and living 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? Yeah, sure. So this is not possible. Speaker 2 (9:04) But I also think you're talking about, and with the greatest respect, today's state of AI. I think at the pace of AI. This is Speaker 1 (9:10) one of the biggest bottlenecks right now because AI doesn't train on the job, doesn't train their own weights. Every time I am doing something, after this talk with you, I am being transformed. I carry with me. this discussion in all my thoughts. This is not true about AI. Well, I mean, I'm not being rude. It is. That's why Speaker 2 (9:32) people remain with open AI, because it has memory and it is able to infer from past queries, prompts, and give you suggestions based on those. Speaker 1 (9:42) So it does have memory. 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 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 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. 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 Speaker 2 (10:41) regardless how many Speaker 1 (10:42) 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 going to answer like that person that live it. To me, really, this is the biggest limitations that the models have right Speaker 2 (11:07) now. Speaker 1 (11:08) So Speaker 2 (11:08) 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? I Speaker 1 (11:27) 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 intrude infinitely. Basically, this is the theory. So they will simulate a world as complex as our own world within it. But that means that we are part of an infinite simulation. So I don't know where it's going to 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 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, put into a self-improvement loop, and this model is going to generate will. I don't kind of believe so. But Speaker 2 (12:59) 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. It 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? Speaker 1 (13:25) I would say if they truly believe that this technology is becoming wrong 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 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 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. 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 others 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. I would take even, in my opinion, even the labs in China that are building AI right now, I would classify them as 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 a way of interpreting, I guess. Speaker 2 (15:26) 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 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? I Speaker 1 (15:48) think so, yes. 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 don't know if I'm manufacturing somehow shrews or whatever. I don't think OpenAI is going to come and compete with me on this thing. But probably some of... Other guys can get indirectly via the models the same intelligence. If only I will train the model. I think that's the real danger. And I think it's a legitimate danger. And I think everyone is trying to protect their IPs. You said about Speaker 2 (16:34) kind of clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process? Speaker 1 (16:43) 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 100, 200 steps by AI, even at each step you have 99 % probability, for instance. It's, you know, 0.99 at power of 100. you will end up with maybe 60 % probability to do the entire step. So AI doesn't have this mechanism to follow steps exactly hundreds, millions of times in the same way. You can see it even if you ask AI to multiply very large numbers millions of times. 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 are you not using a computer that is doing these multiplications millions of times, you know, 100 % 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