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

25:17Daniel Dines:And he said, yeah, but this year it'll be four. 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? I think you can choose quite simply for where is their verifiability? Finance and accounting. It's quite clear what is right and what is right. I disagree with you. It's not about verifiability. It's about if the work has been defined in a frame set by other people. If the frame is clear, then the AI can understand the frame. But the frame is clear. Finance and accounting, they do your expanses. No, it's not. This is one of the domain where the frame is not clear because when I receive an invoice or I receive an order from a customer, I can treat it differently.

26:06Daniel Dines:There are not always the rules there. I know for this customer, NVIDIA is going to ship with priority to open AI. Maybe they have a rule that says there, yes, my first chips go there. Maybe they don't. That rule, they don't. If it's not captured in a frame, AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful. What do you mean the map of work? Who basically capture how the work works, how the work happens in an enterprise. This is the map of work. It's all the workflows, all the exceptions, all the procedures, all the systems that you use in order to fulfill a goal of a process.

26:53Sure, but then you have a head of finance who sits on top of 30 agents. And exactly, when NVIDIA come back and say, whoa, whoa, whoa, we're your biggest buyer and we have special terms on our payments. They go, yeah, sure, that's right. Don't worry about it.

27:10Daniel Dines:But you come to my point, even in finance, you cannot replace everybody. Not everyone, but you've got one person or two people. You'll need a certain number of people. The thing is, if you have X number of people, how do you understand which of them stay and which of them has to go to maybe do different jobs? How do you know? Because ideally, you will get the people that will have literacy in AI that can display initiatives. Because AI cannot exhibit initiative in the human sense. So out of this number of people that you want to keep, you want to keep the people that display the most initiative.

27:49Daniel Dines:Even a finance person treating an invoice with the 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 cares less about how they treat the customers? Because the second is more prone to being displaced by AI. This is the ledger that I think enterprise have to create and to understand different outputs of people. They need to judge people by this hard to define output. How does one do that then for the illegible data that isn't captured within companies? How do we do that? Zuck and Facebook have said about monitoring every single action that's on the screen of employees.

28:32I 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?

28:45Daniel Dines:I think this is the crux of the problem. And here you're a true philosopher. Yeah, yeah. And this is where we put a lot of effort as a company. And we are introducing a new technology that we call it cartography. And cartography is a discipline. So it's a discipline to help companies surface all the information of how the work is done and help them create this map of work. And one big important of cartography is to actually have investigated 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.

29:32Daniel Dines:And the agent is interviewing them in real time. If you interview a finance person, it can ask, why did you change this invoice when the zip code was different? Why did you choose a different path? Tell me. And they can start surfacing all of these exceptions. So that's real agents interviewing real people. It's pretty cool stuff. And then you consolidate data from multiple people. And then we create the process maps. So we show them how the work works in kind of real time. And then after this map of work that showed the work as is, You can use our coding agents and you can come up with an idea of how you should transform the process.

30:15Daniel Dines:And you transform the process by printing software. So you start with the process and at point A, it's kind of fully manual. Of course, you have enterprise systems like system of records, but people operate the system. I think the goal of any enterprise is to have less people operating the system and more automations and agentic AI operating the systems. 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. This is inevitable. It very much depends how you pitch the company. So in UI path, I think it was important to tell people, again, we are not doing anything stupid.

30:58Daniel Dines: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 that will become more literate in respect to AI will have a better chance, not only here, but in the future in any other job. That's the message that I think everybody should get. Did they respond to it? Did you see AI adoption through the roof after that? I think the response is good. AI adoption requires more cycles than just discovering the process and people's input in this. But look, in the beginning of the year, the fear of people across the industry, and not only in my company, but in many companies was off charts, the fear of completely being replaced.

31:48Daniel Dines:Now, I think people start to get a bit more understanding of the durability of their jobs. This AI diffusion in enterprise that can happen at a slower pace and can happen one process at a time, because these millions of Weinsteins are not hireable yet. One of the companies we've invested in, McCaw, data provider, Brendan Foodie, the CEO, tweeted yesterday that they spend 3x the spend of human salaries on inference. What percent or multiple or take would you say you spend human salaries on inference? I personally don't care about it. Let me tell you something. It's a simple hypothesis. If the work, the quality or better of a human can be done by a machine, I will hire today a machine even if it's more expensive than a human.

32:36Daniel Dines:Because human costs will only increase and they bring errors into the pictures while the cost of machine will increase. So I will have a competitive advantage compared to people that will stick to humans. I think everyone will do this. So I don't think the cost of tokens, it will be the real question if you replace a person with AI. But the real problem today is that AI cannot replace a person. Because if, again, if bring me an Einstein that replaced me and I will happily go in any vacation in the world. But this Einstein doesn't yet exist. I would like to be interviewed and have this podcast with another Einstein.

33:13Daniel Dines: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 Einstein's that embody like a person, that have will, that gets transformed on the job. learn on the job, have the capability of reasoning, imagination of Einstein's exist, of course, all the jobs will go extinct. I have a show with Jason Lemkin from Sasta. He's cut his team from 25 to 2. 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. I want to see this.

33:54Daniel Dines:I heard companies that replace hundreds of support people in the past, and now they are rehiring these people. I think until this model is proven, and it's proven at scale, not in a particular industry for a particular guy, I don't think we can extrapolate for one data point that is going to go across industry. You said about extrapolation and over-exaggeration. The SaaSpocalypse was very real. We're going to vibe code everything. Did you vibe code tools out? Look, it was amazing. Yes, we did. But not an extraordinary success. Initially, it seemed extraordinary. But when we tried to put it in production, we started to see, you know, some kind of real bottlenecks with these tools.

34:41Daniel Dines:And you need to have a lot of things that you have to maintain. Connectors, permissions, audit, security. and always taking a software from a prototype to production, it's actually where the work is. It's not necessarily the writing code. Writing code is fun, but it's not there where you can really make the difference. I think it's much easier today to make a prototype. Prototype is so easy, but then you have to iterate to make the prototype in production. And this is the testing and everything else. We were trying to replace like a procurement tool. writing ourselves. And I think we have a lot of success.

35:21Daniel Dines:And it was initially, it was written only by AI. But then it comes to, you know, speaking to this self-improvement loops, kind of don't trust, we don't have enough 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 vibe coded tool created was completely bogus. 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.

36:04Daniel Dines:So you will still need to put engineers and people, you need to maintain it. So it's a long process. So eventually you will end up paying probably as much, if not more, as the tool you replace while you keep some of your good and best people bandwidth occupied. Would you buy Salesforce today? I would buy Salesforce as a system of record. As a stock? As a stock. Look, I invest in software as a category. And I think I made a good investment in, you know, a few months ago because I bought in the bottom of Saspocalypse. Even our own stock, you know, has been doing better. But the markets, the markets today are driven so much about sentiment and not the value.

36:50Daniel Dines: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 Vive coding, if this is the question. I don't understand why a company would go public today. If you think about the two drivers of being public, number one is liquidity for shareholders, employees and shareholders. Well, Stripe and many companies are able to have liquid stock in private markets. And two is the ability to have M &A, a tradable asset that you can buy with. I mean, many, many private companies are able to buy with private stock. with Stripe, we're going to do PayPal with private stock for 60 billion.

37:26So that's not a barrier. And the casinoization of public markets, as you said, with current stock markets being sentiment driven, I don't understand why one would.

37:35Daniel Dines: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. this 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. But public markets will face them with the reality of what's their real valuation. I will not say there is no value in the way the Anthropic is trying to do IPO in the end.

38:16But they're a unique company alongside OpenAI, which just has to because they have exhausted all private funding that exists. They are literally hitting the tap out button on the supply of private capital. They're extraordinary companies because they just need too much money.

38:33Daniel Dines:Do you believe all the investors in OpenAI and Anthropic will stay in the companies for years to come? No, I think some will do. Some will do, of course, but I think we see an exodus honestly i don't believe a two trillion or whatever maybe they will reach five trillion or because if i buy a two trillion i need to have a path to five but if they went public at two trillion would you sell anthropic in particular 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 you would have made more money on that than the sales force or service now yeah yeah 100 so i would not sell and i think it's same with open air man i don't have uh i think open air has catched up quite nicely and i use interchangeably right now i just think we're in a market where the big get bigger and value concentrates more than ever but the real question is harry would i buy a two trillion that's my real question and right now i'm i need to see the real numbers to understand if i will go if i will put money in their ipo 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 going to win what usage is going to win and if that is the case by 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 going to be better than the next codex.

40:04I don't know if Cursor are going to come out with something fucking amazing. But I do know that Jensen's going to be sitting there going, here's another chip. Here's another chip. Here's another. Great.

40:13Daniel Dines:Yeah, but I think Jensen is bound by the success of open source. Because if Anthropical OpenAI, if this is becoming a dual poly, and basically I think their TAM is in like trillions. It's basically the work. They will print their own chips, man. Honestly, it's not such a big deal. in the end to print chips. Of course. I mean, OpenAI are doing Jalapeno and Anthropica are doing their chips. Exactly. So I don't think Jensen's will be doing so well if they have the single biggest providers and the source of truth and light of God will come from only Anthropica and OpenAI. So therefore, the open source should succeed.

40:51I absolutely agree, which is why I think Jensen is doing the open letter, which everyone signed. Absolutely. Encouraging open source.

40:57Daniel Dines:And I think it was a great move buying... Hugging face. Cogging face, yes. Why? Because I think it encourages its host all the open source model. It's putting the money where the money is for his company. Totally. It does also make a neutral provider not neutral anymore. Bias. Yeah, obviously they have 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. I think Nemotron is still a small cog in the picture. I think it's valuable, but I would not call it, it's not the same chip size as the others. Do you worry about the round tripping revenue?

41:36You know, everyone talks about NVIDIA investing here, buying here, the circular economy that come from Oracle and OpenAI. Do you think that's overblown?

41:44Daniel Dines:It can be because every major infrastructure in history has been overbuilt. And I think it's a simple explanation because I was thinking why every infrastructure is overbuilt. because you have to to make sure you get you know the biggest piece of the opportunity if the opportunity is big it doesn't it doesn't matter you build a little bit more than it's necessary so it's clearly that now everybody that there is only 100 of the pie and people are building right now 200 of the pie there will be losers 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 right, actually, in prior technology cycles, we overbuilt supply side and demand side was lagging behind.

42:33Now we have energy that's being a massive constraint. We have water, we have data centers, we have regulation and policy. We have a significant hindrance to supply side. And we are underbuilt, not overbuilt, which is why every ounce of compute is taken.

42:47Daniel Dines:Yes, but are we underbuilt to the extent of this trillions that comes into the infrastructure? I don't know to answer this. Everything happens on the premise that AI is going to replace human work in a really large scale. We need to see the timing of this replacement and transformation. It's a big difference if it's coming in 10 years versus next two years. So I don't think it's overbuilt for the next decade, but it might be overbuilt for the next three years. And stock markets and capital, you know, can be mercyless. Maybe I'm a childish optimist, but I saw, you know, Andre Kapathy say that he used coding tools for 20 % of the work.

43:30And then six months later, he said that it did 80 % of the work and he helped it with 20%. We're investors in Lagora. I interviewed lawyers when we did that deal. And they all said to me, you're such tech bros. You think you can replace us? Ha ha, we went to law school. And I was like, OK, 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.

43:58Daniel Dines:Harry, you should read my book, my friend, because it answers exactly the same questions. 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. 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. So is the human language, and AI understands it perfectly, and it understands every freaking nuance of human kind of sensitivities.

44:43Daniel Dines:As long as it's documented and it's well-defined, there is a manual for the freaking law, AI is amazing. When there is no manual, AI is not amazing, and it doesn't work. That's the huge difference. But$300 billion is the legal industry in the U.S. It's a lot. If you think about how much labor could be replaced by that, I think 30 % would be reasonable. That would be$90 billion of available revenue. Yeah, but that's not going to convert in token revenue. Maybe out of 90 billion, companies might charge maybe 10%. Maybe it's 10 billion total opportunity in tokens. Am I mistaken to invest in Lagora and other Harvey investors mistaken to invest in Harvey?

45:28if it's 10 billion, not 90 billion?

45:31Daniel Dines:What I can tell you that from a law perspective, open source models, frontier models will do just fine. Maybe they do also the custom workflows around the legal process, which is really valuable. But to me, that's also 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 real value is. If Harvey and Laguerre are doing this, they map really the world, they create the world, they create a legal department for me. Of course, 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 going to be a hundred billion dollar market, a hundred percent.

46:19What percent of token traffic do you think will go through open versus closed models in 12 months? To me, I think the question is different.

46:28Daniel Dines:What percentage of the traffic will go to truly frontier model like Astra or Fable versus a very cost efficient models? For enterprise work, my prediction is that 90 % of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most operational work. 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. I will still use Anthropic and OpenAI. They are cost efficient model, but I will have a verifiable backup on open source all the time. I should be, as a responsible enterprise, I should be able to switch models.

47:18Daniel Dines:I cannot be locked in. Can I ask you, we invested in, I'm just checking my portfolio against your brain. I believe strongly in open models, and I think that every company, not every company, but mid - to large-scale company will have their own model, own their own intelligence, and feed their own data into it. And 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. Yeah, you know, I'm a big fan of fireworks, and we are using them quite a bit. Do you like them? Yes, we like them a lot.

47:48Daniel Dines:And I'm a big believer that an enterprise should distribute their bets. And one of the bets should be on open source. And very importantly, should be on this map of work. Because think about if I 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 menu? I cannot. Or there are terrible losses when I do this.

48:32Daniel Dines:The real investment for an enterprise is to creating this map of work that documents how they actually work. And with this one, they can train their own models. It's in fireworks or other provider, doesn't matter. But this is their IP. This is, and this is their core data. Make sense? 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. I do believe that they will at least have their own models as a backup to frontier models. To me, where I'm not clear, if I can provide the same cost efficiency with my own model versus a cost-efficient model from Anthropic OpenAI.

49:15Daniel Dines:Because I think these guys are in the position to truly optimize large infrastructure. So part of their business model will be to deliver, you know, more intelligence per dollar than even I can squeeze from my own models. 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, then you wouldn't optimize front-time. Only if you are training your models, that might be true. And only if you can deliver, if Fireworks can deliver at a large scale and in a very optimized way. Would you invest in Fireworks at$15 billion?

49:55Daniel Dines:Probably, yes. If this hypothesis of open model is true, which I believe is true, I think they are 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 can they give me the inference of the scale that I want. What do you think? Because you invested in them. I did. I think you're absolutely right that they need to move into the compute layer. And I think Lin is doing that, I'm sure, very soon. So they will have to raise, you know, tens of billions now. And she did that at Facebook. I think that's a unique thing to this team.

50:31They did compute securing. It's a great team. We really like them. And we work with them before the big hype. I also think the data providers are massively underpriced and underappreciated. McCaw and Serge in particular. Everyone's like, oh, they're commodities. You're just buying data. Data is the most important thing to feed model quality.

50:53Daniel Dines:But what's the difference? I think one thing is storage. And one thing is understanding of the data. Because if I have a storage, I can have a tape. and I can put data on the tape. Would you invest in a tape company? I don't think so. You need to invest in the intelligence that understands the data and feed the model and extract the right data at the right time, feed really the model with the data that is needed with the context. Because if you don't 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. I think you'd say that they have more data than anyone else across more categories than anyone else.

51:34So when the model requests highly specific data, because of the breadth of their library, they're able to provide it in a way that others aren't.

51:41Daniel Dines:If it's their own data and is valuable for models, I'm sure the models will buy the data in an instant. Can I ask you, what have you changed your mind on most in the last 12 months? I didn't understand this necessity to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. But every time I'm running a query towards AI, AI should have at their disposition the entire way my company worked or this particular process worked. When I realized this, I understood also that this thing that it's the biggest differentiation between memory and true learning. This is how we started the discussion.

52:25Daniel Dines:And I'm not sure I really make a point, but it's a huge difference between just laying down, having a scratch pad, or being transformed by an experience. And that's the thing that I realized the most. And I think I realized also what's kind of human for us, because I experienced a lot with AI writing, not code, but writing a book. So I've been through different styles. I understood a lot how to prompt them. Now, AI doesn't have a style, and 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 are the choice that we made and the choice that we don't make, in a sense.

53:12Daniel Dines:So you need to be transformed, because otherwise, I will just ask AI, read this book and write in the spirit of this author, and it's not really working because you need to be transformed by the experience. So to me, this is going to be the biggest breakthrough in the AI technology when I can have models at the size of Mythos being transformed on a job, being, you know, putting in a laptop. And it might be possible. Who knows, you know, the pace of technology, maybe 20 years from now, I can have a 10 trillion model that it's my own model and is getting transformed along with me. But we need to see that I think there might be a few series of innovations to get there.

54:00Daniel Dines:Because I want to 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, it's not capable of creating frameworks. I don't know, Relativity is a framework. I was thinking, why so? I think one of the main reasons relates still with this, not being transformed when you are on a job. When I'm writing a book, I am being transformed by the act of writing this book. Every time I'm writing something down, there is something in me that changes that is not necessarily the memory thing. It's me that is changing. Einstein has been changed by his experience thinking about the speed of light of what happens when you go, you know, behind the light.

54:52Daniel Dines:It's not like Einstein wrote it down. And then every time he thought again, he rewrote a piece of paper. No, he became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity. models don't do this way. Even if I put swarm of agents, everything, they have to write down everything. They are not being transformed by the process. So therefore, it's very difficult. In the end, they will have this context that one million is very hard to go beyond this one million tokens context window. A frame might require a transformation as you work on that frame.

55:35Daniel Dines:It's a different way of learning than pure memory. So I want to make this argument as clear as possible. Are you optimistic for your children? I'm extremely optimistic for myself, Ari, therefore for my children. I don't want to sound like an AI doomer, man, because I believe that... I don't think you do. I am. No, I don't think you do. I sound like a doomer in a way. I think we'll have a lot more job loss. I think it will happen a lot quicker. I think we're seeing it in real time. 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 enterprises have to document in much greater detail their processes.

56:18Can I just ask, sorry, we're both Europeans and we're both sitting in London. I don't know how to say this, but we don't matter anymore, just being blunt. Do you think that gets better or worse in the next three to five years?

56:31Daniel Dines:Yes, man. It's hard to admit the reality, but from a technology standpoint, I think we are largely irrelevant, but it's so stupid because the biggest producer of machines that make chips is based in Europe. It's ASML. Yeah, we could have made these chips in Europe. You know, some of the most brilliant minds that build AI, even if you think Dario, Sam, all of them are European origins. Ilya, we had the talent, we have the technology to build the machines, but somehow we are losing it. And it's very stupid. Do you see a difference in work ethic having a team in the US and the UK? Yes, I experience with teams in UK and at 5pm they are all, you know, in the pub.

57:18Why is that? Because money matters more?

57:21Daniel Dines:I think culture matters more than money. It's a more dynamic culture. I don't think I would have succeeded in Europe the way I did in US. So I'm a European, but as an entrepreneur, I'm American. This is what I told to everybody. So my formation is in American school of entrepreneurship, even if I started my company in a year. Listen, 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. It's an easier to access revenue machine than in Europe, clearly.

58:00Faster. It's easier. The teams have scaled GTM functions before. completely agree with you.

58:06Daniel Dines:And American companies are making larger bets on vision without waiting for so many proof points as the European companies. And even people in the middle management can make sizable million dollar bets on new technologies in the US. I haven't seen this appetite in Europe. I don't want to ask this, but I am interested. If you were to advise a young European entrepreneur today, would you say to go to the US? Yes, that's the said reality. I think that unless they build for a specific market with some specificity in mind, if they build a universal technology, they will have a better chance to succeed in US.

58:49Daniel Dines:There will be many successful European companies coming out of this. Maybe not as a frontier labs, but I think for the application of AI. Do you buy sovereignty as an argument? Yes, I think it's an important one. The energy sovereignty, model sovereignty. Yes, 100%. And all European customers right now would prefer on-prem software model sovereignty and model optionality. Yes, 100%. This is a big business that it's coming here. Look, I talked to our friends at Fireworks and I actually tried to convince them to make their software available on-prem. Right now they are in show me the money, but I can tell, guys, this is a big business.

59:33Daniel Dines:You need to prove first because this is Europe. Show them the technology and the money will come. How much revenue does UiPath do today? I think it's public data. We are at 1.6 % growing last year, like 14%. So Jason Lemkin's taught me that unless you're growing 20 % plus, you're just fucked in the public markets. It's grow or die. And it's a horrible reality in that way. I'm not condoning it. It's horrible. Is that right? 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 growth and traction, they automatically put you into AI losers without looking really deep into the business.

1:00:19Daniel Dines:There are so many hundreds of software companies in the public market. So then it's hard to look at each of them. So if I were to flip it on you, and we do a final one for a quick fire, what is the bull case to UiPath being a$50 billion company? Think about even today, Gartner released their new boat magic quadrant, business orchestration and automation technologies. We are one of the leaders. We moved from a challenger into a leader in the last year. So it shows that we as a company made this transition from an RPA and automation technology into an orchestration and automation technology. And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI powered.

1:01:12Daniel Dines:This idea that you 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. You need to have an underpinning orchestration and automation technology and this map of work that I taught in order to power your processes. And this is what we have. And it's not only me saying, but it's Gartner saying, it's Forrester, it's industry analysts in being very bullish on us. So that's really the 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.

1:02:03Daniel Dines:So creating an AI agent that runs your software is as difficult as a year ago. So you make huge investments into printing this software, capturing the enterprise context that we call the map of work, putting this enterprise context inside this Rails that I named this orchestration automation as the map and Rails. Map is the context, Rails is the orchestration automation. You put them in the same platform, and then you can assign an agent to do work. And you tell the agent, this is the reality. These are the rails you can use. This is the map that describes how to use these rails. This is the goal.

1:02:43Daniel Dines:And that's the way you can control. You can have a control on the top. But your agents cannot go wrong. 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 attack your competitors. to send 20 million bucks to some rogue invoice i completely get you what is the bear case i think the bear case is ai will somehow become genius tokens cost will be next to zero we'll have this literally millions of einsteins in a data center but einstein's in a true sense not only reasoning in the sense of replacing a person, and I can assign them to every work in an enterprise, and they will just do it.

1:03:33Daniel Dines:That's the bare case against us. I mean, token cost has gone from$60 to$1 per million. So, I mean, the token cost will go to nothing. It's possible. This is why I told you I would not right now stop an investment based on the token cost. Dude, I could talk to you all day. I'd love to do a quick fire with you. So I say a short statement. and you give me your immediate thoughts, okay? What's the hardest thing about your job today as CEO of UiPath? It's aligning people. It's so different personalities, pride, ego that comes into place. This is the hardest. What has changed most about how you work as a CEO because of AI?

1:04:12Daniel Dines:I'm spending maybe half of my day right now, half of my day alone with myself in Visual Studio Code right now, working with Claude and ChatGPT. I have way more leverage on my company than before because 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 going to come with a markdown file and I can put it into, I have a giant strategy folder that, you know, I have, you know, AI agents working with this. I put this document in my folder and then I can ask intelligent questions.

1:04:58If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing?

1:05:04Daniel Dines:Maybe I will try to build my own frontier model. NVIDIA in three years' time, will it be above$7.5 trillion? Where they are today,$5. $5.6, yeah. I can easily imagine 40 % run for NVIDIA. I would bet more on NVIDIA rather than Anthropic being a 7 billion company. Brilliant, of course. Billions are nothing today. Billions are nothing today. You said something on a show that we did before, and it was like one of the most resonant things I've ever had in a show. And you said, I think a lot of people think they want to be me, but sometimes it's quite lonely, alone in my head. And I always remember this because I often feel the same.

1:05:47Would you advise to founders who feel lonely in their head and struggle with that today?

1:05:54Daniel Dines:I think they should surround themselves with their best friends from maybe childhood, have more frequent chats with them because they are the people that can relate the most to them before and they can see them part of the transformation. and it's a nice thing to do anyway. You'll still be lonely, but you'll have the sense of some kind of continuity in your life. I find one of the most rewarding parts of my life, chatting, being with friends, with family. This is really where you get a lot of the loneliness, in a way, you know, appeal from the loneliness. A final one. What are you most excited for when you look forward?

1:06:34Like my mother's got MS. I'm really excited for some of the breakthroughs that we'll see with chronic conditions and treatment of them.

1:06:41Daniel Dines:Yeah, I'm very excited about longevity. You know, my friend, I have almost twice your age. You're not quite. After any kind of gym or anything. No. What are you doing longevity-wise? I'm doing quite a lot. I got into like peptides, into supplements. I'm thinking around 60 different supplements a day. 60 supplements seriously 60 60 60 yeah what the fuck are you taking and all of them have been recommended and vetted by ai and what that's extraordinary i mean you look incredibly young but 60 supplement like in pills no you take them in like because i do the longevity shape from brian johnson which is like 60 in one and i just have it every morning you actually have 60 separate I have a lot of pills.

1:07:35Daniel Dines:I also do some in form of powders. But yeah, I have. I'm going to show you tomorrow. I have little bags throughout the day, like AM1, AM2, AM3. That's extraordinary. I know. It's very dorky of me. Do peptides, do you feel better? I think it's supposed to feel in the long term better. But honestly, I feel way better even than 10 years ago. kind of reducing booze quite a lot helped. And I know you are a big fan of booze. You don't still drink, do you still drink? I drink a lot less these days. I love that. Dude, this has been so much fun. Thank you so much for putting up with my meandering. When does the book come out?

1:08:18Daniel Dines:It's already available for download. I'm printing also a few copies. We have our big fusion event coming in a couple of weeks. And I'm distributing to everybody coming their copy. Dude, this has been a pleasure. I'm going to get a copy. I'm going to get a physical copy because I'm old too. And so I like reading. It's my gift to you, Harry, of course. There we go, dude. Thank you so much. Thank you, man. But before we leave you today, founders face a different set of challenges at every stage of growth. For Sid Shait, co-founder and CEO of Dematrix, JP Morgan delivered the guidance and expertise to help navigate what came next.

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