Pod Insights Decoder UiPath CEO Daniel Dines on AI agents replacing our jobs About this episode Today, I’m talking with Daniel Dines, the co-founder and once again the CEO of UiPath, a software company that specializes in something called robotic process automation. We’ve been featuring a lot of what I like to call full-circle Decoder guests on the show lately, and Daniel is a perfect example.
He was first on the show in 2022, and UiPath has had a lot of changes since then, including a short stint with a different CEO. Daniel is now back at the helm, and the timing is important: the company needs to shift, fast, to a world of agentic AI, which is radically changing the RPA business. We got into all that and more in this episode. It’s a fun one.
Links:
UiPath’s Daniel Dines thinks automation can fight the great resignation | Decoder
Daniel Dines: Why Agents Do Not Mean RPA is Fucked | Harry Stebbings
UiPath to re-appoint Daniel Dines as CEO | UiPath
UiPath shares tank 30% after company announces CEO shakeup | CNBC
UiPath to lay off 10% of workforce in companywide restructuring | CNBC
UiPath looks for a path to growth with Peak agentic AI acquisition | TechCrunch
How RPA vendors aim to remain relevant in a world of AI agents | TechCrunch
UiPath finds firmer footing with pivot to general automation, AI | TechCrunch
Transcript: https://www.theverge.com/e/643562
Credits:
Decoder is a production of The Verge and part of the Vox Media Podcast Network.
Our producers are Kate Cox and Nick Statt. Our editor is Ursa Wright.
The Decoder music is by Breakmaster Cylinder.
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Transcript 10,988 words · 10 speakers
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1:28 Speaker 04 Hello and welcome to Decoder. I'm Neelap Patel, editor in chief of The Verge, and Decoder is my show about big ideas and other problems. Today, I'm talking with Daniel Dines, the co-founder and once again the CEO of UiPath, a software company that specializes in something called robotic process automation. We've been featuring a lot of what I like to call full circle guests on the show lately, and Daniel is a perfect example. He was first on Decoder back in 2022, right before he moved to a co-CEO arrangement with Rob Insland, a Google Cloud executive he brought on to help steer UiPath after it went public. In January of last year, Daniel actually stepped down as co-CEO to become chief innovation officer, and Rob became the sole CEO.
2:08 Speaker 04 Then less than six months later, Rob resigned and Daniel took his job as CEO back. Founder stepping aside for outside CEOs and then returning as CEO later on is a pretty familiar story in the tech world. And Daniel and I spent quite a while pulling his version of that story apart. He made some pretty key decisions, including important personal decisions, along the way to relinquishing control of the company he founded, and then some equally important decisions on the way to coming back. If you're a Decoder listener, you know I'm fascinated by the middle parts of these stories that usually get glossed over. So we really dug in here. But there's way more going on with UiPath than the C-suite shuffles. The company was founded to sell automation software, and that entire market
2:50 Speaker 04 is being upended by AI, particularly Agentic AI, which is supposed to click around on the internet and do things for you. The main technology UiPath has been selling to people for years now is called robotic process automation, which has been around since the early 2000s and aims to solve a pretty big problem that lots of organizations have. Let's say you run a hospital and you depend on some ancient billing software. You could spend millions upgrading all of that software and the computers it runs on at great risk, or you could just hire UiPath to build a robotic process automation system for you that automates that software and presents a much nicer interface to your actual users. This decreases the risk of upgrading all that software, makes your users happier because they're using a nicer interface,
3:32 Speaker 04 and it might provide you some efficiency by developing new automated workflows along the way. Daniel and UiPath built a pretty successful business doing that version of robotic process automation. I encourage you to go listen to our episode from 2022 where we unpack it in detail. But as you'd expect, that's all getting upended by Agentic AI systems that promise to automate things in much more powerful ways with much simpler natural language interfaces. So Daniel has to figure out how UiPath can integrate and deploy AI into its business and its products or risk being made obsolete.
4:03 Speaker 04 Obviously, Daniel and I got deep into that conversation and I really pushed him on the practical economics of the business. The big AI startups like Anthropic and OpenAI don't have to make any profits right now. They're just raising mountains of investment and promising massive returns when all of this AI works. But UiPath is a public company and it's licensing this technology at a cost. So I want to see what Daniel thought about the costs of licensing AI technology, selling it to customers, and trying to have all of that make a profit while the underlying economics of the AI industry itself remain pretty unsettled. Daniel and I also talked about what all of this might mean for our experiences at work and whether a world of robots sending emails to other robots is actually a good idea. This one really goes places. Daniel was game to dig in. I think
4:48 Speaker 04 you're gonna like it. Okay, UiPath's CEO, Daniel Dines.
5:06 Speaker 04 Daniel Dines, you are the founder and once again the CEO of UiPath. Welcome back to Decoder.
5:11 Speaker 05 Thank you so much for having me, Neil.
5:13 Speaker 04 I'm very excited to talk to you. I love a full circle episode of Decoder. You were last on the show, spring of 2022. It's been a little bit of a roller coaster since then. You were just about to have a co-CEO named Rob Ensline. You hired him from Google Cloud. Then you stepped down a little over a year ago to focus on being the chief innovation officer. And then Rob was associate, and then Rob stepped down and now you're CEO again. You've made some changes to the company. Explain what's going on there. That's kind of the big question I have is that's a lot of decisions. Obviously, we're sure about decisions. And then there's a lot of AI stuff I want to talk about. But let's just start with that little bit of history. Why step down and why come back?
5:52 Speaker 05 Well, roller coaster, I think it's a good word. Sometimes people exaggerate with it, but in our case, it's really what happened. Why? I was always trying to do what's best for this company. This company is, in a way, my baby. Twenty twenty five is twenty years since I founded UiPath. So I thought that if we can get the best talent and especially with a background in go to market, this is gonna help us. Rob is a nice guy. We got along pretty well. It's been mostly a good ride and it gave me some time off.
6:38 Speaker 05 So I switched to Chief Innovation Officer. So I I ran our product and engineering teams.
6:46 Speaker 05 I had my own time for reflection, like two thousand twenty three, especially after I moved a lot of my responsibilities to Rob. I I spent that summer in a more of a reflection, honestly. It was a bit of a soul searching and understanding what do I want? Yes, in a way, I I would say that kind of I missed my early twenties craziness of people, you know, having a lot of fun and going on spring breaks. I kind of had to work, so I in a post-communist Romania, it's been a lot of turmoil. So life was not that fun for me at that stage. So I thought, maybe, maybe I get to experience.
7:31 Speaker 05 What what does it mean to take it a little bit easier? It was important for me because I discovered that actually UiPath is kind of an anchor for me. Gives me a framework of mind, a direction. It's very hard for me to wake up every day and. You know, give myself something to do unless I am in this big machine. And this machine is on a trajectory and it force it forced my mind to be there. And I'm surrounded by great people. I talk to smart investors, analysts, customers, partners. It's a living organism.
8:13 Speaker 05 I discovered that this is a gift that I have, you know, being in the position to run this company. And then things in early 2024 didn't go well for us from a overall market perspective. I think the macro was pretty bad for some companies. We had some execution issues. So our go to market, we've over rotated into going only mostly after big deals. Our initial go to market was land and expand. And we over rotated the company to go mostly after big deals.
8:54 Speaker 05 So our float business suffered and paired it with some of the macro challenges. It created a difficult environment and Rob decided to leave the company in May 2024. And at that time, in all fairness, I was ready to take it back. It came faster than I anticipated, but mentally I was prepared after, you know, my summer and my time, you know, a bit, a bit off.
9:28 Speaker 04 Did you go on spring break? Did you, did you take a minute? Did he try? Were you, were you in Palm Beach?
9:33 Speaker 05 No, no, I didn't go to Palm Beach, but I spent a few weeks in the Mediterranean on a boat. So maybe close to.
9:41 Speaker 04 Uh, spring break is not the same in your forties as in your twenties is a thing that I.
9:44 Speaker 05 I know. Yeah, exactly.
9:47 Speaker 04 I always want to drill into the actual moments of change. I, I always joke that I, I watch a lot of music documentaries and there's act one, and everyone's in the garage and there's act three and they're playing Shea Stadium. And act two, where the actual moments of change happen are often glossed over. This is a moment, right? You made a decision to come back as CEO. Rob made a decision to leave. What was that conversation like? Did you initiate it? Did he start it? Was it that he was leaving and you decided that you were already coming back? Walk us through it.
10:18 Speaker 05 It was simple, actually. So we we decided to meet in New York following Q1 2024. And we met and uh he told me that he thinks it's better that I take the company back. And he he considered to resign for personal reasons. He needed to spend some time off, you know, for, you know, some members of his family who were were not well. Look, I told him, look, let's reflect a bit, let's think a bit and but in the end, he was resolute in his decision and I realized
11:03 Speaker 05 after that discussion that there will be many changes in the company. We need to, we need to contract a bit. We've oversized the company for this elephant hunting. You know, there will be a few changes and I realize it's uh it's actually better that I do the changes in the company. It's gonna be anyway, a lot of pain and we've been through some sort of pain. Last three quarters were not easy for us by any metric.
11:35 Speaker 04 Would you have made the change if he hadn't volunteered? Was it obvious to you that you were gonna come back as CEO?
11:40 Speaker 05 It's very difficult to get an external CEO while I am here. It's kind of not possible. I think I would rather right now I would consider growing someone from internally rather than bringing someone externally. Because it's it's it's really hard to know someone after you talk a few hours and you go for a dinner. And how and it affects so much the culture of a company. Even if I have the controlling stake of the company, it's still, it's not like you get someone and you commend it every day. You do this and this and this. No, it's it has a really huge implication. So I I care deeply about the company and the people.
12:24 Speaker 05 And even, you know, Rob had all the best intentions in the world. But e- even seeing, you know, some of the things that
12:34 Speaker 05 Sometimes made me uncomfortable. It's not easy and it's not easy for anyone and it's naturally there there are two camps that are created like Daniel Kemp or Rob Kemp and sometimes they don't talk. So again, without our intention, it was a dynamic that didn't work well. So to me, it was clear that I have to either take back a CEO, drive the company, or next time I will step down completely.
13:06 Speaker 04 This is a pretty common problem with founders. Obviously, the verge is much smaller than UiPath, but I only have a handful of co-founders left. I often tell people that they should be the editor in chief and it's perceived as a threat. Like they're like, no, we wouldn't if you were here. Did you have the power to just do that as the founder and the controlling stakeholder to say, I'm just making this decision, I'm coming back. Was there an approval process? This is one of those moments with founders. It seems like it comes up a bunch.
13:35 Speaker 05 Theoretically, I had the power to do this. But in practical terms, it's something very difficult to do. So because look, we are a public company. It's it's board governance. I have a seat on the board. The board should make the decision. So the board should have make a collective decision to fire Rob on my pressure. They could have mutiny against me. It's not so simple.
14:05 Speaker 04 I mean, that's really the question, right? Some of these decisions, I think we see them from the outside. And the founders coming back as a CEO seems like a very natural course of events. But then inside, it's very complicated and particularly founders who were the CEO and they stepped aside for another CEO and they come back.
14:21 Speaker 05 If there is a battle between the founder and the then CEO, yes, things would be pretty ugly. In our case, really was not Rob really exited in the in the best conditions. He gave me all the time. He assisted me with the transition. He took some time off then to, you know, fix his personal stuff. So in, uh, in from this perspective, it was, uh, it was a smooth transition.
14:51 Speaker 04 You mentioned the company had grown in ways you didn't want it to. Obviously a new CEO, there are cultural implications for how they would like to run the company. And then you're the founder, you come back, you want to change it back. You guys just had financial results. Things seem to be a little bit more stable than they were in the past. What changes have you made either to change in a forward direction or to go back to the way things were when you were the CEO?
15:14 Speaker 05 I wanted to, to bring back some of our mojo of being customer centric, working with customers, do whatever, you know, they require to be successful. And we went back largely to our land and expand motion, being customer centric while still preserving the muscle to do big deals. We need both. And in a company that is depending only on big deals, forecasting is kind of difficult. The lumpiness in revenue can create issues with forecasting. So it's normal to have both sides of the equation. And maybe that's also a thing that I didn't realize.
15:59 Speaker 05 We, we are not a technology that you can go day one and say, okay, I will sell you a hundred million dollars of automation. Things work more into, let's see in a smaller division, let's see how it works and then expand into other division and then expand into a company wide. So regardless, if you have a good Rolodex, you will not go to another CEO and say, okay, man, my friend, give me this big deal because look, I'm here for you. I promise you will do it best. You need to prove it and you need to earn your way. That's why in our DNA, the essence is to stay extremely customer centric, work with them, help them find opportunities.
16:46 Speaker 05 And have them deliver the value, prove the value, have them message internally the benefits of automation. So and we kind of lost a bit this muscle. And now I we segmented different, I created an executive accounting program where we have our top 50 diamonds accounts with, you know, all our executives are attached to accounts and we are taking it very seriously. With this account, we have a coin ovation program where we really built software together. We decentralized our customer success function that was centrally run. And it was a bit disjointed from the sales
17:30 Speaker 05 motion. So we decentralized it into the region. And it's much more aligned with the customer right now. And we we change even the compensation of our sellers and customer success to to be more closer to the adoption of our software. Regional partners were moved also with the sales teams. I simplify streamline the international part of our business into one big region. And it's been, it's been really a lot of changes.
18:04 Speaker 04 Were all those changes in your head while you were the chief innovation officer? You were, you were watching the company change and the results. And you were thinking this is how I would fix it? Or did you come to this plan after you retook the role of CEO?
18:15 Speaker 05 I think some of the pain that we were experiencing was clear, was known at that point. Changes, not really so much. It took me kind of a month until we started to understand who will be the people that, you know, are my team, what kind of changes are we going to make?
18:46 Speaker 04 We have to take a quick break. We'll be right back.
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20:30 Speaker 04 We're back with UiPath CEO Daniel Dines. Before the break, Daniel was telling me the story of how he stepped aside and then came back as CEO after taking some much-needed time away from the top leadership role. So now I wanted to ask him about his decision-making framework and whether it's changed since he was last in Decoder three years ago, given everything that's happened since.
20:51 Speaker 04 I love having people come back on the show because I get to read their old decision-making frameworks back to them. You obviously left, right? You took a break. You got to think about who you wanted to be and how you wanted to spend your time. The last time you were on the show, I said, "How do you make decisions?" You said, "I'm trying to learn more by listening to people. I have no idea how to run a big company at this stage because I've never been in this situation before, but I'm trying to build a close-knit executive team that relies on each other." And then, obviously, you said the thing people say, which is, "You try to make decisions fast so they can be reversed, and do them slowly if they're reversible." Is that still your framework? Have you come to a different approach? Is that still the basics?
21:25 Speaker 05 I think largely, yes. I like to give space to people to delegate. My style is to agree on goals, agree on the plans, and then let the people run. And my style of inspection is to, if I found issues, sometimes even small issues, I start to dig around them to see if they are like signs of potential cancer. Things are completely not working. So you discover interesting things. But largely, yes, I think it's the faith of the company
22:06 Speaker 05 depends extremely on the cohesion of the people in the leadership team. This is maybe a big difference in how I make hiring decisions, where, you know, placing people in different positions compared to 2022, is that I will never trade the chemistry for talent. So bringing a talent that doesn't fit into an organization, for me, never will.
22:41 Speaker 05 And long term, it creates really big issues.
22:44 Speaker 04 I asked you about the structure of the company last time and you had a really interesting answer. You didn't talk about the structure at all. You talked about the culture. And you said, I want the culture of the company to be one word, and the word you picked was humility. And you talked about that for a minute. It's been two years since that time I've come to believe that the structure question is really a proxy for a culture question. That by describing the structure of a company, you're describing a culture. Would you still pick humility if I just asked you to describe the culture of the company?
23:11 Speaker 05 I think at that time humility was the most needed aspect because we we ride it a very successful IPO, stock was, you know, kind of very high, many people made a lot of money. We lost a bit humility at that point. I think right now, you know, we are back into our roots. I think the company has been through pain. It's we understand better. It's I am not smart enough to learn from successes and you IPAF is not smart enough to learn from successes. But I think we are smart enough to learn from pain and from suffering. So
23:55 Speaker 05 we've been through humility. So I think humility is something that was in the genesis of our company and it's an integral part. What we need now more, I think is to be bold and fast. We are making a big push into our agentic automation era and I see great things happening. It's a new energy. Also, we ran RPA for seven, eight years. It was a bit of fatigue at the end. So it was just, you know, perfecting the software, getting into some white spaces, but
24:40 Speaker 05 it was not that exciting. Now the AI, agentic AI brings a lot of excitement to the table and
24:51 Speaker 05 We pivoted in product and engineering overnight, basically, more than half of the organization into building the new Agentic products. And all the teams are energized because this is, they know. We basically put Agentic automation, it's our number one priority as a company. So we go there. So we literally changed the direction. It's not a Titanic, but it's a big boat. I think very few companies have a chance to enact it. And we have this chance. And AI and automation are so synergistic. I think more and more people came to that conclusion.
25:36 Speaker 05 Agentic, Agentic in essence is basically AI plus automation. It's a fusion between AI and automation. We are so well positioned to deliver on this promise. So our product and engineering, it's, it's going at a neck breaking pace. Made really bold decisions with, from a technology standpoint, with re-platform or workflow engine to a much more modern technology. And it's really, they embodied being bold and fast. I cannot say yet this is true for the other parts of the company.
26:16 Speaker 05 This is where I, I really work with, you know, our leaders to, to move, to be, to be completely prepared for our act.
26:27 Speaker 04 I want to ask one more question about structure and then I have a lot of questions about Agentic AI and automation. A lot of questions. But one of the big decisions you made when you took over the role as sole CEO once again, is you, you cut about 400 people, you let off 10% of the company. Did you end up restructuring around that cut? Why make that decision and what was the goal?
26:48 Speaker 05 We looked into our central functions at that point.
26:56 Speaker 05 In all fairness, we over hire people in that central functions. And we had to streamline the organization. So, you know, decisions to fire people are the hardest from an emotional standpoint, from a cultural standpoint, financially, it's very hard to make them. If every time we had to do them, it's been a thorough process behind it. I was never rushing or I was never, and I was always fighting more on to, do we really need to?
27:41 Speaker 05 And it came, it came really on one of the lowest moment on us with the changes, CEO. It's, I think now, as we put it behind, we're more prepared. You know, the world is in a interesting, challenging phase right now. I think nobody knows where it's gonna go. I think we as a company are a bit more prepared, more streamlined, agile. We, we take time to heal the pain of this and the, I think the confidence in the company is restoring.
28:25 Speaker 05 Looking back, I, I think that was the right thing to do for the company.
28:29 Speaker 04 I wanted to ask that question as the lead in to AI, because you're describing making these cuts as a low moment, as something that was very difficult to do, the right decision, but very difficult to do. You pull the thread on AI and what I hear from our audience, what they're worried about. And they say this automation is going to come for our companies and we will all be out of a job. Right? White collar workers will be out of a job. Software engineers might be out of a job. Lawyers terrified of being out of a job. Do you see that connection that if your software is successful, you will reorient the economy and a lot of people might lose their jobs?
29:03 Speaker 05 If we are realistic right now, it's all a matter of the time of change, not the change itself. Because, look, your job, my job, as we are doing right now, has changed over time. Many, jobs change, okay? It's really, it's the matter of the time when it's gonna be, how, how compressed is this change? Right now, I'm not so fearful that it's gonna come so sudden. If you look at AI and the real use cases and we still have to see a widespread adoption of AI.
29:48 Speaker 05 It's kind of a productivity gain right now. It's more like a assistant type of AI. I'm asking something, I get the response, I do my job a bit faster and better. But it's not yet at the point to affect really huge volumes of population. I think a Gentic AI is one of the step forward to deploy AI more into an enterprise context and might accelerate
30:20 Speaker 05 a bit the way jobs are transforming. I mean, what, what do I mean by this? I think a job today, it's not like a simple task, like it's very few people that you can describe one single task when they are doing. So a job, it's, it's a multitude of, you know, like operational things, repetitive things you do and many ad hoc things that, you know, it depends on different the environment, business. I think the many repetitive tasks has been solved. Many of them, we have the technology to, to basically eliminate from one's job. And now we have also the technology to
31:04 Speaker 05 help people with more of these ad hoc tasks, like research tasks. And the
31:11 Speaker 05 I think the jobs will be moved more where people will make decisions mostly. They will analyze what agents are, what information agents are retrieving or putting together. Agents plus automation. People will analyze, will make decisions. And then the actions will be carried on by, you know, enterprise workflows, robots that we already have. So jobs will transform more into decision making, inspections, following, you know, overseeing from, from like command plane.
31:50 Speaker 04 I think about this all the time. I don't know that I'm a great editor in chief. I feel like you could automate me by just walking into rooms and having a soundboard that says make it shorter or make it longer. And you just spin the wheel and pick one and that's my job. But I know when to say those things because I spent years writing blog posts and then stories and now podcasting. I have all this experience executing the decisions so that I have a high level of confidence in the decisions that I'm making when I make them.
32:20 Speaker 04 How do you get that if no one is executing the decisions, if that's all robots? That's your, but I just want to make the comparison to you. You were the founder, you, you spent all this time running this company. How would you make good decisions if you didn't have all of that experience?
32:34 Speaker 05 The execution experience?
32:36 Speaker 04 Yeah.
32:37 Speaker 05 That's a good question. In, in all fairness, eventually many things are a black box. I don't know how my keyboard in front of me, I don't know why if I press, you know, a key, it's, you know, it displays on the screen. But I can make the decision to press. So in, in a way, yeah, operations will be like a black box. For many companies and decisions will be a bit at a higher level. I don't think necessarily will be, we cannot make decisions if we don't know how the things are cooked behind the scenes.
33:15 Speaker 04 I'm curious how that plays out. I I'm just of the school that says the best leaders are the people who are who are the ones who've spent time on the ground. That's not always true. I've talked to a lot of leaders on the show, but particularly when I talk to founders, that experience at every stage of the company is what informs the confidence to make the changes.
33:34 Speaker 05 Hundred percent.
33:36 Speaker 04 Operations are a black box. Like, I I wonder where that confidence comes from.
33:40 Speaker 05 It's I need to reflect more.
33:42 Speaker 04 Yeah.
33:42 Speaker 05 On this thing. Yeah, probably will, you know, yeah, the best, the best people will understand the operations as well. And they even if they are carried by robots and AI, they will understand in order to make better decisions and change the operations. And it's more of analytical type of people. This type of jobs where it's more mechanical, typing, copy pasting, it's they are gonna disappear.
34:14 Speaker 04 So the last time you were on the show, I don't think there was a lot of hype around RPA. I was into it because I am fascinated by the idea of computers using computers. And robotic process automation, at the time you were on the show in 2022, was sort of the height of that, right? You were riding high. This guy said you needed humility. The idea that instead of upgrading a lot of old computer systems, we would abstract them away with UiPath technology, build new interfaces, and that would allow all kinds of flexibility. That was a big idea. I think that has changed, right? In the AI age, we see a lot of companies promising agentic capabilities. We see a lot of companies saying that they'll move even farther up the stack, all the way up to decision making. But when I look back on that conversation
34:57 Speaker 04 and then everything that's happened since, the thing that gets me is robotic process automation, the idea that you have some old hospital billing system and UiPath will build a modern way to use it. Is deterministic, right? You knew where all the buttons in that software were. You could program your way through them. Maybe you needed some machine learning to understand the interfaces better or to make it less brittle. But you knew what the input was, you knew what the output was. RPA knows what the path between those things are. AI is totally not deterministic, right? It's the robot's gonna go do something. Is there a connection between the software you were building, the RPA you are selling and the agentic capabilities you want to build?
35:39 Speaker 04 Because it seems like there's a fundamental technology shift that has to happen there.
35:43 Speaker 05 I think you expressed the essence of what we are building. When you say deterministic and non-deterministic, these are exactly the terms that I am explaining how robots and AI should interact. If you think that L L M's in their essence are not meant to do deterministic tasks. If you ask an L L M to multiply two numbers, they are not, they, they, they cannot figure out how to multiply two numbers because it's not the statistical matching. They would, what they would do best, they would understand, ah, I am required to multiply two number.
36:27 Speaker 05 I have a tool that knows how to multiply two number and I call a tool and I will get the precise answer. This is how they work. They don't have the intelligence inside that. Because it's not, it's a non-deterministic tool. It's not meant to do a series of deterministic steps. In the same way, you can think of many transactional work that produce a side effects in enterprise systems. It should be deterministic. You cannot have 95% chances of succeeding a payment transaction. It has to be 100% or if there is an exception, people should be notified. It cannot be maybe.
37:11 Speaker 05 Our robots offer this fully deterministic way to do transactions across multiple systems, transactions that create, you know, effects on these systems.
37:25 Speaker 05 With L L M's and with the technology like OpenAI Operator or Computer Use from Anthropic, which actually we are users of it and we work closely with both of these companies to integrate their technology. You can complement what R P A is doing on parts of the process that we couldn't automate before. If I have a process that relies on doing research, like if I have a travel, I want to create a travel agent by A I. And this travel agent will have to do research of, you know,
38:05 Speaker 05 available flights and across different multitude of airplanes, right? It's not a big harm if I miss a flight or not, right? So I can have tool, a non-deterministic tool, going and extracting information. Then an agent can make some decisions, present a user look. These are available flights. But then when I book a flight, I have to use something deterministic. When the money transacts, money change hands. So basically, we can have the both of best work. We can extend really the reach of deterministic with non-deterministic while we can accept the risks of non-deterministics.
38:50 Speaker 05 And there are domains like research or like testing an application. We can take more risks. But it makes sense, this framework, it depends on your level of risk that you can accept.
39:03 Speaker 04 It makes sense to me. I think I'm, I see your competitors and your partners like OpenAI and Anthropic. They've made their entire technology bet on Agentic A I. Like that's the company. And I assume that their plan is for that to get good enough to do everything. And your approach is to say, there's some stuff that our traditional R P A needs to do, the traditional deterministic computer needs to do. And that can be layered with an L L M system or an A I system. And I'm just wondering what the intersection point is or if there ever will be an intersection point when OpenAI says, look, operator can do it all. And that presents some kind of paradigm shift for your business.
39:44 Speaker 05 I am absolutely sure that the intersection point is when you can define.
39:53 Speaker 05 A task in a deterministic way. So you know kind of the steps. There is really no point to have an L L M all the time that is doing this task to rediscover it, how to do it, to think of every step. Because you, it's impossible to get to a hundred percent accuracy. We are testing these L L M's even for simple form feeling. They can work very well, but think about you need to run hundreds, thousands of times, hundred percent accurate. It's not, this is not the technology for. What, what I am saying is L L M's will eventually create routines that can work hundred percent accurate.
40:38 Speaker 05 But the idea that L L M's all the time will discover a process like you do first time in your life when you see an application or your book, humans don't work like this. You learn an application and then if you watch yourself, most of the things you do are more on auto, automat, you know, automatic pilot.
41:04 Speaker 04 We need to take another quick break. We'll be right back.
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42:48 Speaker 04 We're back with UiPath CEO Daniel Dines, discussing the complexity of automation and how Agentic AI might change how companies do all kinds of work.
42:57 Speaker 04 So, one of the things that's really interesting to me is we've had other companies come on the show and talk about their Agentic software approaches. And actually, they were facsimiles of the Agentic software they wanted to build, right? So Rabbit came on the show and their first version of the Rabbit R1, it was running testing software in the background. So you would ask for a song on Spotify and it would just click around on the Spotify website in the cloud and then stream the song to you. And, you know, their claim is that they actually did build the agent. They needed to build the first version and have proof of concept. But the deterministic system, in one very real way, can act like the thing people want from the AI system, right?
43:40 Speaker 04 It can do it almost, and then it's brittle. And the AI can make it less brittle by reacting to change or an unexpected outcome. How do you merge those things together? How do you decide which system to use? Because that seems like the technology problem of the moment.
43:58 Speaker 05 So the way we are seeing
44:02 Speaker 05 the adoption of Agentic AI and automation combined is putting a workflow technology on the top of it. So our agents are more like data-in agents, action-out agents, not necessarily conversational agents. We focus on delivering this, you know, enterprise agents that work in the context of an enterprise process.
44:29 Speaker 05 To us, the critical piece is this orchestration part. That, let's say you have a loan agent that have to approve some loans. So you get a workflow is triggered when the loan application is received. So you have an enterprise workflow. And then that workflow first will send the application to a reading agent that is specialized in extracting the information from the, from the application. Then I can send it to a human user to verify. If I'm not confident enough in what I have extracted, to just verify something basic. Can be a low, kind of more junior people that do this verification.
45:14 Speaker 05 Then the workflow will send it to an agent that will make loan recommendations. So, and that agent can start to call tools like get the credit score of this person. So this tool, it's definitely something deterministic. It's either an API to a credit score agency, or you can use an RPA bot for this one. But it's clearly deterministic. You are not gonna use something like, like operator to just figure out what's the credit score of a guy. There is absolutely no point and it's taking too much time and it's not reliable. So already, you see, it's a combination.
45:58 Speaker 05 The workflow that knows how to direct, you know, the fixed path of a process. Agents that are capable of making recommendations, calling tools that will give the context. And then after the agent make a recommendation, okay, approve this loan. We'll go to a human user. The workflow will create a task. A human user will get in their inbox, okay, approve or not, press a button, approve. The workflow will go back maybe to the last agent, say, please compose a nice accepting message particular to this client. It's a simplistic view, but this is how we believe.
46:40 Speaker 05 The work and enterprise customers will adopt agents. It's also they need to have some confidence in the system. You said, we are talking about this black box system. Until you take a black box, a swarm of agents that do their magic, and sometimes they make mistakes. Until you accept it, you need to have confidence and you need to see the world. Everybody is more confident when they see the workflow. They can see, look, if that happens, it goes this. If that happened, goes this. So you can trace it, you can understand, you can reason with it. One of my takes with between the interaction between humans and AI.
47:25 Speaker 05 I think for a long time, we have to speak the same language. Even when you create, when you create an application, when you create an automation, AI actually creates code. AI can eventually work directly with machine code. They don't have to create Python code.
47:45 Speaker 05 But it's important that AI creates Python code because humans can reason and can change and can accept it. It's gonna be the same in automation, application. AI will use existing platforms, will create artifacts on the top of existing platforms and people will validate.
48:03 Speaker 04 And on the consumer side, the value of the existing platforms is, I think, under enormous threat. So I call this the DoorDash problem on the consumer side. We just had Amazon's Panos Panay on, right? They're rolling out Alexa. You're going to be able to say, Alexa, buy me a sandwich. And then we'll just like get DoorDash to send you a sandwich. This is a huge problem for DoorDash, right? Their margins are under significant pressure if their interface gets commoditized in that way. We're gonna have the CEO of DoorDash on the show eventually and I will ask him this question. But I can, I can abstractly see the pressure on some of these systems that are gonna get commoditized by new kinds of interfaces. RPA, the classic RPA, truly depended on those systems existed. You needed the existing loan system that nobody wanted to upgrade.
48:48 Speaker 04 So you could build the RPA interface on top of it. You need the credit score interface that maybe doesn't have a great API, but you can use RPA to go get it from their website or whatever. AI changes that because it's coming to all of those systems as well. Right, there's there's some part of the AI industry that's chasing all of those things at once, not building this orchestration layer. How do you think about the long term longevity of those systems? Because I look on the consumer side and I say, oh, this is a big problem for DoorDash. This is a big problem for Uber. I don't know exactly how it works on the enterprise side.
49:24 Speaker 05 It's a thing that we will see how it evolves. The fact that we still have a lot of mainframes and our RPA touches a lot of mainframes. The changing of enterprise system is much more difficult than in consumer space. And if you look at the enterprise, look at the complex enterprise applications, Workday, SAP. I can see people adding a nice layer of voice on the top of it of AI. You know, change my vacation responder to this word. But in essence,
50:03 Speaker 05 like tablet, you know, a tablet and a mobile phone didn't make keyboard or mouse obsolete. I think they will still have to coexist. Many people can work on user interface faster than with voice. Voice is gonna become a good way to interact with applications. But when you need to absorb a lot of information simultaneously, you need to have the user interface. In many cases, you still need to interact with the it's easier than to tell. Please press the OK button. I will just go and click the button. It's easier, it's faster. So they have to coexist. In a way, you basically say Alexa at some point can build their own DoorDash.
50:47 Speaker 05 If they can, if they can control the interface with the client, in the end, it doesn't matter who delivers.
50:54 Speaker 04 Right, and that it's not that they will build their own DoorDash, it's that DoorDash's opportunities to make additional revenue will go away, right? They won't be able to up sell, they won't be able to do deals, they won't be able to have exclusives, their interface will be commoditized and they will just become a service provider and Amazon will be in control or whoever AI agent will be in control of their business. You see that for a lot of these ideas, right? That you you need an ecosystem of service providers for the agent to go and address.
51:21 Speaker 05 Yeah.
51:21 Speaker 04 And that crushes the margins of the service providers.
51:24 Speaker 05 It's possible.
51:25 Speaker 04 Yeah, I'm very, I think, I think I see it in consumer, right? You see the back and forth, right? There's some amount of, we don't want you here, we're going to block your agents from using our services, that is already happening on the consumer side. There's some amount of deal making. And then on the enterprise side, it seems like there's going to be a lot of deal making, right? Where maybe instead of API access, we're allowing agentic access or we're allowing RPA access. And because the data is what's valuable there.
51:53 Speaker 05 To certain extent, we had the same problem even with RPA. Because think about the most enterprise software, SaaS software, was licensed by user seats. With RPA, you need a lot less user seats. You can, you can have one seat and it can does, you know, the job of hundreds of seats. And they found ways to kind of prevent this and create some kind of special service accounts to deal with it. Some, some vendors do not allow you, you know, to, it required creating some different types of accounts. I'm sure
52:35 Speaker 05 they will find some ways to, because how, how can Alexa order if DoorDash doesn't want to receive the order? In the end, so they have, there has to be something in for both.
52:50 Speaker 04 I think there's an enormous technical challenge. I think the business challenge is even harder. Right? You have to get a lot of people to agree to fundamentally restructure their businesses in order for any of this to work. I, again, on the enterprise side, there's more deal making, right? You, you have some instincts, some history, some moves to say, okay, here's, here's how we're going to structure access to the data. I have no idea how it will play out on the consumer side. You mentioned a thing about LMS not having memory, right? Having to, to rethink the workflow every single time. That's true. I think the AI companies are working on that. But they're also pushing the idea of reasoning, right? That now we're going to layer LLM approaches over and over and over again. Somehow this will become a simulacrum of human reasoning. I don't know if that's correct. They say they can do it.
53:36 Speaker 04 Is that having an impact on what you're doing? That you're able to say, okay, here's the decision. Like, here's the process by which a decision is made.
53:46 Speaker 05 The way we are seeing the reasoning part. It's more helpful in our world into creating automations. We have this co-pilot type of technology that you describe a process and that it can create the artifact to execute the process. The more, the smarter an LLM is.
54:10 Speaker 05 The creation gets closer to reality and the developer have to
54:16 Speaker 05 change it less. So in a way, it's like creating code. If you want, it's the same, it's the same thing. The smarter LLMs will create better code. But that code still gonna be executed in, you know, by hyperscalers. Or who would execute the code? It's not LLMs that think about. You can ask me, is the, well, maybe LLMs will do everything. They, why would they generate code at all?
54:43 Speaker 04 You mentioned hyperscalers. One of the things that I've been thinking about a lot is the amount of investment the hyperscalers are doing just to buy Nvidia chips, to build data centers, to invest in nuclear fusion against the promise that there will be this much demand for AI services. They got to make money doing this somehow. Unclear how the bleeding edge frontier AI companies are going to make money. I don't know how OpenAI will ever make a dollar. I don't know how Anthropic will ever make a dollar, except by raising more money, which they are very good at. But that's on a long-term plan. You're a public company. You have to make the money. You have to buy the tokens. You have to use them. You have to build the products. You have to charge your market price. Is this sustainable at the rates we're at now?
55:24 Speaker 05 I don't know for them if it's sustainable or not. But if I were them, I would do the same. Because what if this is indeed the biggest revolution of our time and all of this? GPUs and AI agents will take over the world and I am not there.
55:43 Speaker 04 But I'm saying you've got to charge your customer some price for the use of an AI tool. You're not running all of your own models, right? You're partnered with some of these companies. You're you're buying some of their capacity. They're in turn buying capacity from Azure or AWS or whoever they're running on.
56:02 Speaker 04 In turn, like all of these companies need a margin and some of their margins are negative. Like OpenAI loses money on inference right now. There's but they're selling that capacity to you. At some point, they're going to turn the knob and say, we got to make money. They're going to raise prices on you and you will have to pass that cost to your actual customers. We're actual businesses trying to automate their companies and raise their own margins. When will it become too expensive? Because that seems like the the correction that's coming. You're going to say, OK, OpenAI raised our prices. Customers, UiPath has raised its prices and some customers are going to say no.
56:38 Speaker 05 I think at this point, really, if we look at through our lenses of the processes we automate, what's the what's the alternative using human labor? It's more, I think, even if OpenAI is increasing the prices, I still don't think that humans can compete with AI plus automation when it is possible. And long term, anyway, the pricing will go down and there's a lot of competition for the business.
57:10 Speaker 04 Yeah.
57:11 Speaker 05 Look, I'm not concerned really about this aspect.
57:15 Speaker 04 Are you have you structured your technology so that you can swap between AI providers? Are you tied to OpenAI? Are you tied to Anthropic or is that easily modular?
57:24 Speaker 05 No, not at all. We even offer to our customers a piece of technology that we call AI trust layer, where they can actually switch between different providers or bring their own on-prem model if they want.
57:39 Speaker 04 You've you just bought a company called Peak, which is another AI provider. Why make that bet? Why bring in technology?
57:45 Speaker 05 We want to get into more vertical agents. I think it's a pricing and inventory agent. And they they have really solid experience in delivering this dedicated solutions based on agentic AI. And we want to extend them. Of course, we'll integrate first in our platform. We want to come with more dedicated agents. It's it makes the entire go to market easier. And we want them to work a bit like a locomotive for the entire platform because they can create more more demand for automation, basically.
58:24 Speaker 04 How does their technology plug into your existing stack? Right? Because I understand, OK, they have markets that you might not have or that you want to get bigger in. But ideally, you know, you buy a company and what you're going to do is sell their existing markets more of your tools.
58:38 Speaker 05 Yeah, yeah, yeah, definitely. That was that was in our mind. I think we have really good synergies in our in our go to market. I think we can reaccelerate their go to market, particularly in the manufacturing industries. We have very solid manufacturing practice in US, Germany, Japan.
59:00 Speaker 04 Do you think that there's an opportunity for you to commoditize the AI tools themselves? This is the thing that gets me. I I just keep thinking about this, right? You have your AI trust layer, you have your own vertical systems that you're buying that you might deploy. At some point, what matters to companies is the business outcome, not that they have an open AI partnership. And it feels like the big AI companies are trying to be everything to everyone. And you're trying to specialize. Do you think at some point you're going to say, look, what we deliver is business outcomes and the technology doesn't actually matter?
59:35 Speaker 05 I think the, you know, general AI is going through this. Phase. Initially, you know, it was like a nice toy. Everybody put budgets to experiment with it. And now I think we are moving to the phase where people really want outcomes. And if initially they all used OpenAI. And our strategy was actually use OpenAI because it's the best. And if you want to make a proof of concept, why would you use something different? But as you go and you specialize it for different type of industries, processes, I think you can you can choose whatever is more appropriate.
1:00:20 Speaker 05 We we look at everything from DeepSeek, Llama, Anthropic. We use all of them in different parts of the business. In the end, many, we are more an AI engineering company. And our job is to build nice products that deliver value for customers. And behind the scene, use whatever L L M's are best, you know, for this particular scenario.
1:00:44 Speaker 04 I actually want to ask you about DeepSeek. Was that as shocking of a moment for you? Like, the industry reacted to the idea that you could run the model much more cheaply. Very harshly, right? Like, very harshly. Did you see that and say, oh, this will bring my cost down? This is also a revolution?
1:01:01 Speaker 05 Selfishly, for UiPath, any any open source capable model, it's a great thing for us. And I think for our customers. Because my belief is that this dedicated agents will require a combination of fine tuning and really good prompting. If you can have a great model and you can fine tune it and you can also and combine it with good prompting, that that will get, you know, the highest value and even the cheapest price. Because we fine tune it, you can actually distill it into a smaller model that works very well for a particular domain.
1:01:46 Speaker 04 Where do you see the biggest growth for traditional RPA, for AI and for the hybrid of AI and RPA?
1:01:54 Speaker 05 RPA is an established industry right now that grows into like low double digits. The demand that we are seeing right now about our agentic technology, I have never saw in the RPA world. It opens really all the doors. We get a seat to a table where we were not used to be in from, from, only from the automation perspective. People are really excited about this idea of agentic automation. They get it. Because the very problem is kind of simple for us.
1:02:37 Speaker 05 I can go to my clients and tell them, guys, look, where did you deploy robots, right? How people interact with robots today? Why we are not reducing the work of people and put agents and create an enterprise workflow that will connect agents with people and robots? It's a, it's a no-brainer proposition. It resonates. It's simple. It creates a lot of excitement.
1:03:01 Speaker 04 I want to tell you about my favorite Slack room at my company Vox Media, and I just want to get your reaction to it. We have a room, it's called Finance Support. And in this room, people just ask a Slack robot to do stuff, file invoices, give receipts, all this stuff. I look at it like once a week. It cracks me up every time. I like, literally fall over and giggles every time. Because the people who are new to this room type full sentences. "Hi, I need help with this receipt. Can you itemize this thing? I've got a flight." And the people who are repeat users of the room have discovered that they just need to scream nouns at a robot. So they just show up and they just say the word expenses. And all of this is in one stack, right? It's people who are very polite and then it's the people who are just yelling nouns at a robot.
1:03:46 Speaker 04 And they have their own, like, you can see this secondary language of human-machine interaction developing. Like, I'm just gonna say keywords at the robot because that's all it needs from me. I look at that and I say, that's a revolution. First of all, it's very funny. This is a revolution in business, right? You're going to have some people who are just saying keywords in Slack to get things done in their business all day long to an agent that might just like go off and do it. Then you have the people who are used to all of the niceties of business, fluffing up their communication. And at some point, you're just going to have robots saying nouns to each other, right? Instead of using an API, right? In many ways, that's what RPA was. You're just using the human interface instead of an API. Do you see all of business changing around this as clearly as I do when I look at the Slack room?
1:04:31 Speaker 05 Yeah, and even for RPA, this is too, because many people are using RPA by creating a Slack channel that connects directly with the robot that does something. AI just extend the same idea. It's, to me, it's kind of fascinating. How do we communicate with, with bots and with, I discover myself that, well, maybe it's just an impression, but if I say please, I think that LLMs will come with the better.
1:05:04 Speaker 04 Would you, here's a thing that I also worry about, and this is my last question. You're the CEO, you get a lot of emails, you send a lot of emails. Do you ever worry about the loop where you're responding to an email that was written by AI with another email that's written by AI? And suddenly, everyone's just pushing the summarize button and no one's actually talking?
1:05:22 Speaker 05 I personally write myself my emails because everybody in the company and clients know my own tone and my broken English. So I cannot use LLMs. But yes, I've seen, I've seen many instances where it looks like, you know, LLMs are talking to each other.
1:05:38 Speaker 04 Yeah, I mean, you're the automation vendor. Do you think LM's talking to each other? There's something hollow there, right? Is that something you want to achieve with your products? Is something you're trying to avoid with your products?
1:05:50 Speaker 05 I think to certain extent, we want to achieve with our product. We want to facilitate agents talking to each other, but in a more controlled environment.
1:05:59 Speaker 04 Daniel, you've given us so much time. You're gonna have to come back. I feel like I could just do the philosophical repercussions of all these systems with you for, for many more hours. But you've given us so much time. Thank you for being on Decoder.
1:06:09 Speaker 05 Thank you so much. Yeah, I'd love to come back. It's, it's a lot to talk.
1:06:18 Speaker 04 I'd like to thank Daniel for taking the time to speak with me and thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode, or really anything else, drop us a line. You can email us at [email protected] . We really do read all the emails. Or you can hit me up directly on Threads or Blue Sky. We also have a TikTok and an Instagram. They're both at DecoderPod. If you like Decoder, please share it with your friends. Subscribe wherever you get your podcasts. If you really like the show, hit us with that five-star review. Decoder is a production of The Verge and part of the Vox Media Podcast Network. Our producers are Kate Cox, Nick Statt. Our editor is Ursula Wright. Decoder music is by Breakmaster Cylinder. We'll see you next time.
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