# UiPath CEO on AI Workflow Value

**Podcast:** The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
**Published:** 2026-09-21

## Transcript

In my opinion, even the labs in China that are building AI right now, I would classify them as good guys.
I've never hidden from my employees that there will be a transformation.
Jensen is bound by the success of open source.
Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the real value is.
This is 20VC with me, Harry Stebbings.
Now joining me in the hot seat today is one of my dearest friends.
I'm one of the greatest founders of the last decade, Daniel Dynes, founder and CEO of UiPath, and today we discuss the biggest questions.
What on earth does pacing the frontier mean?
Is it even possible?
Will we replace humans with AI?
What happens to the pipeline of talent if it narrows down and we have fewer and fewer juniors?
What about human consciousness if we have recursive super intelligence really being as powerful as front-end model providers say?
This is a truth-telling myth-busting conversation if I can get myth-busting out and it's just a fantastic discussion between two old friends on what no one is talking about but everyone needs to know in the world of AI today.
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Daniel, dude, it is so good to have you in the hot seat.
I have been looking forward to this one.
So thank you so much for joining me, dude.
Likewise, dude.
It's always a pleasure to be here.
And it's the, I think the hottest moment in technology.
So I'm very excited to talk to you and get your perspective also on a lot of topics.
It really is the most wild time right now.
And so I want to start.
You've written a book.
A lot of people write books.
With the greatest of respect, you're my friend and I care deeply about you.
Why on earth did you decide to write a book as a public company CEO?
No offense.
You're not doing it for the royalties.
Why did you decide to write a book?
Man, I dreamt to write a book since I was a kid.
And I discovered I have no talent.
I got really a great opportunity.
Claude and Chad GPT helped me a lot.
They were my ghostwriters.
And it was a good moment, actually, to put my own ideas in order.
Because really, when you write something, you get much more clear perspectives on what you are doing.
So it was maybe almost six months effort.
And I started with different threads of thought.
One was, you know, what are the limitations of AI?
Is there any durable limitation of AI?
Where in a couple of years there will be millions of Einsteins in a data center and we can all go to play or do whatever we would like to do because the Einsteins will do the work for us.
Can we just start on that then?
I think it's nice to take it in kind of segments.
Limitations of AI, Einsteins in data centers.
How should we be thinking about that moving forwards?
When I heard about this statement that in a couple of years we will have millions of Einstein in the data center, look, I was really concerned.
Dario is a guy that I highly respect and he's highly successful.
So I was thinking, what does it mean for us?
What does it mean for, can I hire one of these Einstein, put it in a laptop somehow, assign an enterprise account, a Slack account, and I ask, do my job.
or do whatever job in the enterprise.
It seems that reality is maybe different and probably Dario wanted to say that we'll have millions of entities that will have some of the reasoning powers of Einstein, which I agree, but not Einstein's as a person, not Einstein's that are capable of learning on the job.
Because do you agree that one of the major expectations when you hire someone is that they will hire on the job?
There is no manual that a company has to give a new employee.
This is exactly how you do your job from end to end.
So we expect that...
No, I think humans do learn on the job and they do improve on the job, as do models.
So similar there, except humans get tired.
Humans want more money.
Humans want culture.
Humans can be toxic.
Humans are difficult to manage.
I'll take the AI any day of the week, please.
If AI can work as well as a human, Harry, but you say that AI learn on the job.
AI can create a notepad on the job, a scratchpad, where they can memorize some of the policies on the job.
But AI doesn't alter its weights.
on the job, in the way humans are transformed by a job.
This is a huge difference.
Let me give you an example.
You have two chefs.
One chef, 20 years, has done only Japanese food, the other only Italian food.
And you give them one recipe.
They will create different foods.
So it's not that you can write down your enterprise on a sheet of paper.
It's much more complex.
It's becoming.
It's like, think about...
If I give to someone the ability to read all the books about chess, do you think he will become a grandmaster without playing, without losing, without going through all of this process?
Probably not.
If I have you watch all the videos about skiing, are you becoming a skier?
No, you are not becoming a skier.
But I think it depends what task and workflow you're doing within the enterprise.
If we look at the majority of actually what the people within UiPath and every company do, whether it's accounting and finance, whether it's marketing and sales, whether largely outbound and inbound, but whether it's social media and...
Dude, most of this is execution oriented, where yes, judgment and ambiguity and taste is important at the top.
But most of what people do is execution.
I disagree with you.
I think most of the people will display some sort of micro initiatives during the job.
Maybe I have a hunch this customer is going to chirp.
And I can act before even any data is coming.
How do I develop this hunch?
It's through my years of transformation.
It's not written on a piece of paper.
It's a big difference between writing an operating model on a piece of paper and leaving it.
It's almost impossible for an enterprise, for AI.
do you admit that everything has to be written down documented every time i'm asking a question to the model the model will read my entire enterprise will have to read it okay yeah sure so this is not possible but i also think you're talking about and with the greatest respect today's state of ai I think, at the pace of AI.
This is one of the biggest bottlenecks right now because AI doesn't train on the job, doesn't train their own weights.
Every time I'm doing something, after this...
talk with you i am being transformed i carry with me this discussion in all my thoughts this is not true about ai well i mean i'm not being rude it is like that's why people remain with open ai because it has memory and it is able to infer from past queries prompts and give you suggestions based on those so it does have memory it has memory but memory it's not necessarily learning it's not the same thing Memory is just a thing that is written down.
When I'm talking to you, I don't go back into my memory.
I am just being transformed.
It's like a model.
It's like a new version of the model that comes improved.
The new version is not the old version plus a piece of paper that has memorized.
It's transformed into its own ways.
This is why a model becomes much better.
Look, I want to give you a simple example using our own technology.
We make our UiPath platform available for coding agents.
So people are making, it's much easier to create automations on UiPath right now.
But what we discover is that a model that has read open source technology, and they have a lot of examples, and they already have in its weights.
a certain technology will be much better than to create on our own technology.
Because regardless how many prompts and skills we create, the model has it in its own ways.
It's very different.
And think of all the metaphors in the world when you read something versus when you leave something.
You can read the biography.
It doesn't mean you live that life.
It doesn't mean you are transformed and you are going to answer like that person that lived it.
To me, really, this is the the biggest limitations that the models have right now.
So I actually do agree with you, but I think everyone does.
And that's why everyone is chasing, you know, recursive self-improvement so much, where it's models that can continuously learn from themselves and improve themselves over time without need for human intervention.
Does that not remove the limitation that we just said?
I don't know, Ben.
Maybe we are the result of a self-improvement loop.
Look, do this thought exercise.
Let's put a model that we have today with the best technology, put it in a spaceship and throw it to the stars.
Let's say that we have this technology, like I think von Neumann imagined that is self-replicated.
This spaceship goes to different stars, get energy and they can continue.
So compute will be infinite and models will self-improve where they would end up with.
Maybe they will create a simulation of a world like ourselves, right?
Because they will improve infinitely.
Basically, this is the theory.
So they will simulate a world as complex as our own world within it.
But that means that we are part of an infinite simulation.
So I don't know where it's going to lead.
But I know that there is a big distinction that I made in the book between will and reasoning.
It's not...
Like that we are certain that the will to do something, you know, emerges from reasoning or from even from consciousness.
I think will is a kind of a separate, you know, part of the fabric of the universe.
I don't think we as humanity have clarity about what will is.
I think it's wishful thinking to believe that I can take a big model, put into self-improvement loop, and this model is going to generate will.
I don't kind of believe so.
But I think we don't know.
And I think that's what's so kind of challenging about trying to predict what happens.
It's like a world of, as I said, recursive self-improvement.
It is unknown in terms of where it lands.
It's like a technology you can create can become something you didn't know it could be.
And I guess for me, the question then is like, we see the news this week.
Dario says we need to pace the frontier.
You run UI path today.
Do you feel we need to pace the frontier?
I would say if they truly believe that this technology is becoming wrong and they cannot control it and their experiments will create significant loss for, I don't know, internet, other systems.
If I were them, I would I would pace it, you know, at any cost because I don't want to risk going to jail.
Honestly, I think there are laws that, you know, kind of control.
this type of rogue behavior.
So honestly, I don't need an external pressure to control.
I would be just a concerned citizen and I would not build a technology that, you know, is causing harm.
But now, of course, they think that this is the only way to protect against the bad boys.
So we are the good guys, but there will be some bad boys that probably in other parts of the world that will build the technology regardless.
So I think that probably they ask more like a pass.
I want to build this technology at any risk and I'm willing to open my gates for other to see how I'm doing because I want to do it in as of, you know, like a good manner as possible.
But in the same time, I want to be free of consequences.
To me, I think this is a bit how I read this memo because otherwise I think it's...
kind of obvious.
I don't think we will reason with the bad guys and we can make a coalition with the bad guys to stop the frontier.
So we can make a coalition only with the good guys regardless.
I would take even, in my opinion, even the labs in China that are building AI right now, I would classify them as good guys.
But someone...
To me, I think the indirect, the attack is probably on open source because basically they say even if the good guys are building open source, but that open source will get into the hands of the bad guys.
And this is unknown bad guys.
This is the real danger.
So the danger is in open source.
So indirectly, it's also an attack to open source in this way.
It's a way of interpreting, I guess.
You work with some of the biggest enterprises in the world with UiPath.
Alex Karp said from Palantir that the biggest enterprise in the world is scared to work with Frontier Labs because of threat of them coming in to their businesses over time.
They have the data, they could build their own and compete against them.
Do you see large enterprises scared to work with Frontier providers?
I think so, yes.
I don't think people are scared that...
open ai will build a competitor to them necessarily i don't see this coming they are more scared that their ip would leak to other existing competitors somehow because i i don't know if i'm manufacturing somehow shrews or whatever I don't think OpenAI is going to come and compete with me on this thing.
But probably some of other guys can get indirectly the other models, the same intelligence, if OpenAI will train the model.
I think that's the real danger.
And I think it's a legitimate danger.
And I think everyone is trying to protect their IPs.
You said about kind of...
clearer articulation of thoughts that come from writing.
What was another thought that you clearly articulated through the writing process?
It has become very clear to me that another limitation of AI is what I call exactness.
AI by its nature being probabilistic at every step, in a way it can lose it.
While you do 100, 200 steps by AI, even at each step you have 99% probability, for instance.
It's 0.99 at power of 100.
You will end up with maybe 60% probability to do the entire step.
So AI doesn't have this mechanism to follow steps exactly hundreds, millions of times in the same way.
You can see it even if you ask AI to multiply very large numbers millions of times.
At some point, they will make a mistake.
And also, it's surface kind of another simple idea.
Even if you have a tool like AI that is capable of doing multiplications, why you are not using a computer that is doing these multiplications millions of times, you know, 100% of time exact.
The fact that the tool can do a job doesn't mean you have to use that tool.
to do that type of job.
Isn't it because it's where you are?
And that's the importance of being in the harness of the workflow, which is like, I get you completely.
You could use something else.
I'm asking here, I'm not saying.
But because you're in ChatGPT continuously every day, instead of switching to a computer or calculator or whatever, you ask what you're already in, the importance of the environment.
ChatGPT acts like an interface to convert my questions in natural language into exactness.
But the exactness is not run by ChatGPT.
Exactness is run by a computer.
Because even today, if you ask ChatGPT, please multiply these two big numbers, they are using behind the scene a tool, a computer, and they will give you the exact number.
This is part of the power of the models.
You actually can see on the desktop level, on this type of co-work or chat GPT work, you see the capability to call tools.
And what I am saying, you extend this capability to the enterprise level.
When everything that should be exact should run on exact technologies.
There is no point to run it on probabilistic technologies.
And here comes, I think, the most interesting thing.
There isn't a symmetry in the deployment of AI and automation in an enterprise.
Deploying AI agents is not getting easier today than it was two years ago, in my opinion.
But deploying automation has become much easier because I can create these automations with AI, with coding agents.
Coding agents has been the most major giant leap.
that we were seeing in the past year.
I would say since the invention of ChatGPT, then chain of thoughts, and then coding agents were the major milestones.
So with coding agents that act in kind of a design time, when I build the systems, I can create automations that work with exactness every time during the execution time.
So this is the asymmetry.
that is happening right now.
Plus, when an automation breaks because any change in the upstream system, AI comes again into play and fix the automation itself.
So this is really the pattern that we are seeing emerging in an enterprise.
And this is that AI.
It's actually creating the software that runs an enterprise.
And this software cannot behave wrong because this software cannot change its behavior in real time.
It's not a probabilistic technology.
Even if the software is created by AI, we can really audit.
We can have humans that read it, validate it.
I can have tests that, you know, for a certain input will guarantee that the software will behave in the same way.
So that's...
again, makes this pattern extremely powerful.
You create, you use AI to create software that runs the enterprise in a predictable, governed, auditable way.
How many engineers do you have today?
Maybe more than 1,000.
More than 1,000 engineers?
Yeah.
How many people do you have?
Around 4,000.
You have 4,000 people?
Yes, why so, right?
I find 18 a fucking nightmare.
What?
4,000.
Oh my God.
Do you have too many?
It's a complicated question because I think I would answer more with the thing that is in my book.
I think the more we transform our companies using AI, we have to, in the same time, to transform our workforce.
I've never hidden from my employees that there will be a transformation in the company.
But I told them upfront, guys, we are not doing...
anything stupid.
We are not just using AI as a pretext to cut a part of the company.
We need to do the transformation in the same time as we successfully adopt AI in an enterprise.
That's actually another point that I discovered writing this book.
If you look, I was looking deeply at jobs and what jobs can be affected by AI, what jobs can be enhanced, and how this transformation is going to is going to look like.
One of the things that seems very simple in retrospect is that people don't have very simple jobs that again can be defined on a sheet of paper.
Every job has some kind of an outcome that is measurable and this is the outcome that people are hired for but there is another outcome that is part of the institutional strength.
Think about my deep relationship with the customer.
It's not necessarily part of the numbers that I'm producing, but it's maybe what makes this customer sticking to my technology.
This is a different outcome.
If I'm going blindly and I cut on a number of A's and say, because AI is going to replace them, AI can call the customers and write emails, I don't think AI can supplement the human connections and the trust.
So this is a different outcome of the job.
And it reflects on every employee, on every type of role in the company.
So to me, an enterprise should have a ledger where they actually understand what people are doing besides their main definition of the role.
And only after they have this ledger and understanding, they can look at how does the AI transformation look like.
What kind of jobs will be affected?
How can I move people maybe from one job to the other?
Because they still carry some kind of the cultural aspect of the enterprise.
So I don't think it's a simple problem as...
AI is going to cut 20% of the company.
Let's do a riff 20% out, and then we increase the AI adoption.
I think on the contrary, when you do this blindly, you reach to hell of the enterprise of exactly the same talent that thrives during AI.
Because let me give you an example.
In the book, I call this credentialed middle that was...
the type of people that were prevalent in any enterprises.
And if you think of our education system and the way we hire people, we hire based on deep expertise in a particular domain, a credential expertise.
And this is exactly the type of expertise that might not be needed so much.
AI can really help.
So you need fewer of these experts, but you will need more people that have initiatives that are capable of maintaining a relationship with the customer, that can be mentors for new employees that bear the cultural aspect of the enterprise.
So it's counterintuitive because you will tend to cut those people that are not the biggest experts in the domain, but you will cut exactly what you will need.
to bring the AI, to supplement these experts.
But maybe you just need less of them.
And so, you know, I was speaking to a lawyer today and I said, you know, how big is your trainee program?
And he goes, well, historically it was 25.
And I said, wow, that's a lot of trainees.
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 did.
They do your expenses.
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.
There are not always the rules there.
I know.
For this customer, NVIDIA is gonna 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.
Sure, 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.
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.
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 care less about how they treat the customers?
Because the second is more prone to being displaced by AI.
This is the ledger that I think enterprise have to create and to understand different outputs of people.
They need to judge people by this hard to define output.
So 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.
I don't think that captures the tone of a call, the warm text afterwards to a customer, the illegible data.
How do we think about capturing the data that shows value that we don't capture?
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 investigate what people are doing on their desktops.
And we have a product that we call the cartographer agent that can interview people, real subject matter experts, have them basically record what they are doing.
And the agent is interviewing them in real time.
If you interview a finance person, it can ask, why did you change?
this invoice when the zip code was different.
Why did you choose a different path?
Tell me.
And they can start surfacing all of these exceptions.
So that's real agents interviewing real people.
It's pretty cool stuff.
And then you consolidate data from multiple people.
And then we create the process maps.
So we show them how the work works in kind of real time.
And then after this map of work that showed the work as is, you can use our coding agents, and you can come up with an idea of how you should transform the process.
And you transform the process by printing software.
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 yes we are not doing anything Stupid.
We are not doing any mass extinction, you know, for the pretense of AI.
But transformation is inevitable.
And you guys have to transform and everybody will get a chance.
And the people 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.
Now, I think people start to get a bit more understanding.
of the durability of their jobs.
And this AI diffusion in enterprise that can happen at a slower pace and can happen one process at a time.
Because these millions of Einsteins are not hireable yet.
One of the companies we've invested in, Macaw, 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.
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.
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 two.
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.
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 Vibecode everything.
Did you Vibecode 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 some kind of real bottlenecks with these tools.
And you need to have a lot of things that you have to maintain, connectors, permissions, audit, security, and always taking a software from a prototype to production, it's actually where the work is.
It's not necessarily the writing code.
Writing code is fun, but it's not there where you can really make the difference.
I think it's much easier today to make a prototype.
Prototype is so easy, but then you have to iterate to make the prototype in production.
And this is the testing and everything else.
We were trying to replace like a procurement tool, writing ourselves.
And I think we have a lot of success.
And it was initially, it was written only by AI.
But then it comes to, you know, speaking to this self-improvement loops, kind of don't...
trust.
We don't have enough 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 code the tool.
For instance, the database schema that Vibe code the tool created was completely bogus.
A human has to come and create the structure.
So right now...
you are not at the point where you know you will have like a business user that understands a problem and they will vibe code a tool so you you will still 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 a few months ago because I bought in the bottom of Saspocalypse.
Even our own stock has been doing better.
But the markets today are driven so much about sentiment and not the value.
So it's kind of hard for me to make a judgment of an individual company.
But I don't think Salesforce can be replaced by 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.
Stripe, we're going to do PayPal with private stock for 60 billion.
So that's not a barrier.
And the casino-ization of public markets, as you said, with...
current stock markets being sentiment driven, they don't understand why one would.
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 okay 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 why why the anthropic is trying to do ipo in the end but they are unique company alongside OpenAir, 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.
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 will see an exodus.
Honestly, I don't believe that $2 trillion or whatever, maybe they will reach $5 trillion, because if I buy $2 trillion, I need to have a path to $5 trillion.
But if they went public at $2 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 the same with OpenAI.
I don't have...
I think OpenAI 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 $2 trillion?
That's my real question.
And right now, I need to see the real numbers.
to understand if I will go, if I will put money in their IPO.
I'm going to 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, buy the underlying infrastructure.
that you know is going to be there.
And so for me, I agree with you.
I don't know if Claude is going to be better than the next Codex.
I 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.
Yeah, but I think Jensen is bound by the success of open source.
Because if Anthropy can open AI, if this is...
becoming a dual poly.
And basically, I think their time 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 Anthropical are doing their own chips.
Exactly.
So I don't think...
Jensen 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 Anthropical OpenAI.
So therefore, the open source should succeed.
I absolutely agree, which is why I think Jensen is doing the open letter, which everyone signed.
Absolutely.
Encouraging open source.
And I think it was a great move buying...
Hugging Face.
Hugging 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 Nemetron 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 Nemetron 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?
You know, everyone talks about NVIDIA investing here, buying here, the circular economy that come from Oracle and OpenAI.
Do you think that's overblown?
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 make sure you get the biggest piece of the opportunity.
If the opportunity is big, 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 now we have energy that's being a massive constraint we have 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 computer is taken.
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 next decade, but it might be overbuilt for the next three years.
And stock markets and capital, you know, can be merciless.
Maybe I'm a childish optimist, but I saw, you know, Andre Kapathy say that he used...
coding tools for 20% of the work.
And then six months later, he said that it did 80% of the work and he helped it with 20%.
We're investors in Lagora.
I interviewed lawyers when we did that deal.
And they all said to me, you're such tech bros.
You think you can replace us?
Ha ha, we went to law school.
And I was like, okay, cool.
I interviewed them two weeks ago.
Every single one of 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.
Harry, you should read my book, my friend, because it answers exactly the same question.
When the frame that a person operates is really well defined by someone else, like in law, AI can be devastating 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.
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 US.
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 if it's 10 billion, not 90 billion?
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 $100 billion market, 100%.
What 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.
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 open ai and anthropic it'll just be deprecated older models i will still use anthropic and open ai with their cost efficient model but i will have a verifiable backup on open source all the time i should be as a responsible enterprise i should be able to switch models, I cannot be locked in.
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 it 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.
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 the 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.
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.
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 Anthropicon.
open ai 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 then 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 than you would an optimized...
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?
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 at 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 computer and i think lynn is doing that i'm sure very soon they will have to raise you know And I will be there.
And she did that at Facebook.
I think that's a unique thing to this team.
They 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 Surge in particular.
Everyone's like, oh, they're commodities.
You're just buying data.
Data is the most important thing to feed model quality.
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 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.
So when the model requests highly specific data, because of the breadth of their library, they're able to provide it in a way that others aren't.
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 This is how we started the discussion.
And I'm not sure I really make a point, but it's a huge difference between just laying down, having a scratch pad, 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.
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.
So you need to be transformed.
Because otherwise, I will just ask...
AI reads 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.
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 is not capable of creating frameworks.
I don't know.
Relativity is a framework.
Okay.
And I was thinking, why so?
I think one of the main reasons relates still with this not being transformed when you are on a job.
When I'm writing a book, I am being transformed.
by the act of writing this book.
Every time I'm writing something down, there is something in me that changes that is not necessarily the memory thing.
It's me that is changing.
Einstein has been changed by his experience thinking about the speed of light of what happens when you go, you know, behind the light.
It's not like Einstein wrote it down and then every time he...
thought again, he rewrote a piece of paper.
No, he became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity.
Models don't do this way.
Even if I put swarm of agents, everything, they have to write down everything.
They are not being transformed by the process.
So therefore, it's very difficult.
In the end, they will have this context that one million is very hard to go beyond this.
1 million tokens, context window.
A frame might require a transformation as you work on that frame.
It's a 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 it, I don't think the diffusion is as fast as you imagine, particularly because enterprises have to document in much greater detail their processes.
Can I just ask, sorry, we're both Europeans and we're both sitting in London.
I don't know how to say this, but we don't matter anymore, just being blunt.
Do you think that gets better or worse in the next three to five years?
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.
It's 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 have 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.
Why is that?
Because money matters more?
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 am American.
This is what I told to everybody.
So my formation is in American school of entrepreneurship, even if I started.
my company in the 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.
Faster, it's easier.
The teams have scaled GTM functions before.
I completely agree with you.
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 sad reality.
I think that unless they build for a specific market with some specificity in mind, if they build a universal technology, they will have a better chance to succeed in US.
There will be many successful European companies coming out of this.
Maybe not as 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.
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 of fireworks and I actually tried to convince them to make their software available on-prem.
Right now they are in show me the money, but I can tell, guys, this is a big business.
You need to prove first.
Because this is Europe.
Show them the technology and the money will come.
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 told 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.
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 the uipath being a 50 billion dollar company think about even today gardner released their new boat magic quadrant business orchestration and automation technologies we are one of the leaders we move from a challengers into a leaders in the last year so it shows that we as a company made this transition from an rpa and automation technology into an orchestration and automation technology.
And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI powered.
This idea that you can have an AI agent that runs everything for you 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.
Creating an AI agent that run your software is as difficult as a year ago.
So you make a huge investment into printing the 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.
And that's the way you can control.
You can have a control on the top.
Your agents cannot go.
rogue no sane enterprise right now will put the swarm of agents and just ask them do my finance accounting for me because who knows maybe they will 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.
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, 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?
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 the markdown file and I can put it into, I have a giant strategy folder that, you know, I have, you know, AI agents working with this.
I put this document in my folder and then I can ask intelligent questions.
If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing?
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 trillion.
$5.6 trillion, yeah.
I can easily imagine a 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.
Would you advise to founders who feel lonely in their head and struggle with that today?
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, 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?
Like my mother's got MS.
I'm really excited for some of the breakthroughs that we'll see with chronic conditions and treatment of them.
Yeah, I'm very excited about longevity.
You know, my friend, I have almost twice your age.
You're not quite.
Dude, are you kidding me?
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 and three, four peptides.
60 supplements?
Seriously, yeah.
60?
60, six zero, yeah.
What the fuck are you taking?
And all of them have been recommended and vetted by AI.
What?
That's extraordinary.
I mean, you look incredibly young, but 60 supplements?
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 separates up there.
I have a lot of pills.
I also do some in form of powders.
But yeah, I have...
I'm going to show you tomorrow all my...
I have little bags throughout the day, like AM1, AM2, AM3.
That's extraordinary.
I know.
That's extraordinary.
It's very dorky of me, yes.
Do peptides, like, 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?
It's already available for download.
I'm printing also a few copies.
We have our big Fusion event coming in a couple of weeks.
And I am 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.
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You describe it?
Yeah, Base 44 builds it.
apps, websites, AI agents, real working products built in minutes using nothing but plain language.
And it's all batteries included.
The backend, the database, the authentication, the hosting, the heavy lifting is handled.
So you just really stay in the flow.
This doesn't just take the busy work off your plate, but it gives you an advantage and pushes you past what you thought you could build alone.
So in this market, fast is the baseline.
To win, you just have to be first.
Base 44 is that edge, the move that skips the troubleshooting and gets you straight to the breakthrough.
Build your next thing at base44.com.
That's base44.com.