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

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Summary

At-a-Glance

  • Verdict: Watch fully
  • Core thesis: Daniel Dines argues that enterprise AI value will accrue primarily to the documented map of work, deterministic automation rails, and governed workflows around interchangeable models—not to raw model access itself.
  • Why it matters: This is a directly relevant operating thesis for agent systems: reliable enterprise deployment requires process discovery, explicit exception handling, model portability, and deterministic execution rather than autonomous agents operating from prompts alone.
  • Best use: Use it to pressure-test agent-platform architecture, identify the durable data asset to build for enterprise deployments, and distinguish prototype-level AI gains from production-grade automation.

Executive Summary

Dines’s central argument is that current models can reason and use tools but do not truly learn on the job in the human sense: they retain external memory or context, rather than continuously changing their weights through experience. Enterprises therefore cannot simply hire “AI employees” by giving a model Slack and an account. For AI to operate reliably, the enterprise must explicitly provide a detailed manual of how work happens—including systems, workflows, policies, exceptions, and contextual judgment—which he calls the “map of work.”

His recommended architecture is a division of labor: use probabilistic AI at design time to discover processes, generate code, propose improvements, and repair broken automations; use deterministic software and automation at run time for tasks requiring exactness, repeatability, testing, auditability, permissions, and security. This is his “map and rails” formulation: the map supplies enterprise context, while orchestration and automation rails constrain what agents can do. He regards ungoverned swarms of agents executing critical functions such as finance as unsafe.

This leads to a differentiated view of displacement. AI will reduce headcount in many functions, especially where work is framed and documented, but companies should not conduct blind percentage cuts. Employees produce unmeasured institutional outputs—customer trust, initiative, cultural continuity, informal exception handling—that may be exactly what enables successful AI adoption. Dines proposes creating a workforce ledger that captures both formal role outcomes and these less legible contributions before deciding whom to redeploy or remove.

Commercially, Dines believes models will commoditize enough that most enterprise operational traffic will use cost-efficient models, with open-source or self-hosted alternatives as a strategic fallback. The durable moat lies in owning process context and workflow implementation, not merely selling model calls or generic legal/knowledge answers. He is optimistic about AI’s long-run impact but skeptical that enterprise diffusion will be as immediate as claims of fully autonomous AI workers suggest, because production systems still need documentation, testing, integration, security, governance, and maintenance.

Key Takeaways

  • Claim: The key current limitation of AI is that it does not genuinely learn and transform through work experience; memory and scratchpads are not equivalent to changing the model through on-the-job learning. | Evidence: Dines contrasts a model storing policies in a notepad with a human chef whose 20 years of Japanese versus Italian cooking changes how each executes the same recipe. He also notes UiPath coding agents perform better on technologies already represented in model weights than on UiPath-specific technology supplied through prompts and skills. | Implication: Do not design enterprise agents on the assumption that they will absorb tacit organizational judgment simply by being given access, chat history, or a long context window; externalize and operationalize that knowledge. | Caveat: He treats this as a limitation of present systems, not an immutable barrier; recursive self-improvement or new architectures could eventually change it, but he does not know when or whether they would generate human-like will or learning.
  • Claim: The enterprise’s most valuable AI asset is its map of work: an explicit representation of processes, systems, procedures, exceptions, and contextual decisions required to achieve business outcomes. | Evidence: Dines defines the map of work as all workflows, exceptions, procedures, and enterprise systems used to fulfill a process. He says a company cannot easily train or migrate its own model to a better base model without retaining this underlying manual and data. | Implication: Prioritize process intelligence and structured operational context as proprietary infrastructure; it enables model switching, fine-tuning, agent grounding, and workflow redesign without losing organizational knowledge.
  • Claim: Reliable enterprise AI should use probabilistic models to build and maintain deterministic software, rather than using probabilistic agents as the direct executor of every high-stakes workflow. | Evidence: Dines argues that even 99% success at each step compounds poorly across 100–200 steps, while conventional software can run the same validated operation millions of times exactly. He describes AI-generated automations that are auditable, human-reviewable, and testable, with AI later used to repair them when upstream applications change. | Implication: For agent systems, separate design-time intelligence from run-time control: enforce typed tools, permissions, tests, deterministic transaction paths, rollback/approval mechanisms, and observability for consequential actions. | Caveat: This does not mean agents have no execution role; Dines supports agentic operation within explicitly defined rails, but not unconstrained autonomy over sensitive systems.
  • Claim: Process discovery is a prerequisite and bottleneck for enterprise AI adoption, because the important rules are often undocumented exceptions rather than the nominal workflow. | Evidence: UiPath’s proposed “cartography” discipline uses a cartographer agent to observe desktop work and interview subject-matter experts in real time—for example, asking a finance worker why an invoice with an unusual zip code was routed differently—then consolidates input from multiple workers into process maps. | Implication: Treat discovery as a deliberate change-management and knowledge-capture program, not a passive telemetry project. Capture decision rationale and exceptions, not just clickstreams or nominal SOPs. | Caveat: Monitoring employee behavior creates understandable fear that captured work will be used to replace them; Dines says leadership communication and credible redeployment commitments materially affect adoption.
  • Claim: AI-driven workforce transformation should not be managed through indiscriminate cuts, because formal credentials and measurable output miss the initiative, relationships, and institutional knowledge needed to make AI adoption work. | Evidence: Dines calls narrowly credentialed domain experts the “credentialed middle” and argues that AI can reduce the need for some of their codified expertise. Yet he warns that companies can accidentally cut employees who maintain customer trust, mentor colleagues, carry culture, or exercise initiative—qualities he believes are harder to replace. | Implication: Build a workforce ledger before redesigning teams: assess formal deliverables alongside relationship capital, exception handling, cross-functional initiative, AI literacy, and ability to help codify work. | Caveat: He still expects fewer people will be required in many roles; his disagreement is over how to identify and sequence reductions, not whether productivity change will occur.
  • Claim: Models are likely to be interchangeable enough that enterprise value capture will concentrate in workflow products that embed process context and execute work, rather than in generic model calls. | Evidence: Using legal AI as the example, Dines says a generic legal opinion or model call is unlikely to capture the entire labor pool’s value; a product such as Harvey or Legora becomes more valuable if it maps the legal department’s actual work and creates the workflow around it. He predicts roughly 90% of enterprise operational flow will go to cost-efficient rather than frontier-grade models. | Implication: Avoid businesses whose differentiation is primarily access to a single frontier model. Build portability across models and monetize the operational layer: context, integrations, approval flows, deterministic execution, and accumulated process data. | Caveat: Dines expects enterprises to continue using providers such as OpenAI and Anthropic where efficient, while maintaining an open-source fallback; he does not predict a wholesale replacement of closed providers by self-hosted models.
  • Claim: Vibe coding materially improves prototyping but has not eliminated the difficult work of production software: schema design, integration, permissions, security, audits, testing, and ongoing maintenance. | Evidence: UiPath tried replacing a procurement tool with software initially written only by AI, but did not trust it for production because testing was insufficient and its AI-created database schema was “completely bogus,” requiring human intervention and redesign. | Implication: Evaluate AI-generated systems by production ownership cost and control requirements, not demo velocity. Require architecture review, test coverage, threat modeling, permissioning, migration plans, and maintainers before retiring incumbent systems. | Caveat: Dines views coding agents as one of the largest advances since ChatGPT and chain-of-thought, so this is a production-readiness warning rather than a dismissal of AI-assisted development.

Detailed Brief

Model sovereignty, routing, and infrastructure economics

  • Claims: A responsible enterprise should not be locked into one frontier provider; model optionality and a verifiable open-source fallback are strategic requirements.; Dines expects enterprises to use cost-efficient offerings from frontier labs when they are economically superior, while retaining the ability to move workloads to open models.; The decisive enterprise asset for custom-model efforts is not merely proprietary data storage but a structured understanding of what data is relevant, in what context, and for which process.
  • Evidence: Dines says UiPath uses Fireworks and views open-source exposure as one component of diversified enterprise AI bets.; He argues that simply possessing data is insufficient: a tape can store data, but value requires intelligence that retrieves the right data and constructs the right context for a model.; He sees European demand for on-premises deployment, model sovereignty, and model optionality as a potentially large market, though one that requires suppliers to demonstrate the technology before demand converts.
  • Caveats: Running a proprietary model may not be cheaper than consuming optimized offerings from large providers, whose infrastructure scale may deliver more intelligence per dollar.; Open-model inference providers must secure substantial compute capacity to meet enterprise-scale demand; Dines suggests this could require raising tens of billions.
  • Implications: Architect model routing and fallback before procurement lock-in, with standardized evaluation, prompts/context interfaces, and workload portability.; Differentiate data infrastructure by retrieval quality, contextualization, and workflow linkage rather than raw corpus size alone.

Market timing, enterprise diffusion, and UiPath’s strategic framing

  • Claims: Dines is less convinced than the interviewer that AI will rapidly replace broad categories of enterprise labor, largely because enterprises must first codify how their work actually functions.; He believes infrastructure may be overbuilt on a near-term three-year horizon even if it is not overbuilt over a decade, because capital deployment is premised on a still-uncertain timetable for labor transformation.; UiPath’s bull case is that AI-powered enterprises require a foundational orchestration and automation layer rather than a magical top-level agent that directly runs the company.
  • Evidence: UiPath reports approximately $1.6 billion in revenue and approximately 14% growth, while Dines says investors currently classify public software companies broadly as AI winners or losers based heavily on visible growth.; He cites Gartner moving UiPath from challenger to leader in its business orchestration and automation technologies Magic Quadrant.; His concrete risk example is an unconstrained finance-agent swarm routing $20 million to a rogue invoice or competitor.
  • Caveats: The purported UiPath moat fails in Dines’s own bear case: genuinely autonomous, extremely cheap “Einstein” agents that can independently take over every enterprise job would reduce the need for orchestration and automation infrastructure.; His market and valuation observations are personal opinions in an interview, not substantiated financial analysis.
  • Implications: Treat “agentic transformation” as a phased process-by-process rollout with measurable proof, rather than an enterprise-wide autonomy commitment.; For an orchestration/control-plane investment thesis, the relevant question is whether the platform becomes the system that holds the map, policy, tools, and controls—not whether it merely exposes agent APIs.

Notable Concepts & Terms

  • Map of work: Dines’s term for the enterprise manual: workflows, systems, procedures, policies, exceptions, and contextual decisions needed to execute work and ground AI.
  • Map and rails: His proposed agent architecture: the map gives the agent organizational reality and procedural context; automation/orchestration rails constrain the permitted tools and actions.
  • Cartography: UiPath’s proposed process-discovery discipline for surfacing undocumented work through desktop observation, SME interviews, and process mapping.
  • Cartographer agent: An agent that interviews workers as they record work, eliciting the rationale behind deviations and exceptions that ordinary process mining may miss.
  • Exactness: The requirement to use deterministic software for repeatable, high-consequence operations rather than rely on a probabilistic model over long chains of actions.
  • Credentialed middle: Employees selected largely for formal, codified domain expertise; Dines believes AI may reduce demand for this expertise while increasing the value of initiative and institutional contribution.
  • Model optionality: The ability to switch among frontier, cost-efficient, and open-source models so enterprise systems are not strategically locked into a provider.
  • Design-time AI versus run-time automation: The operating split in which AI generates, tests, improves, and repairs software, while deterministic systems execute approved workflows in production.

Operator Notes / Why Ken Should Care

  • Create a process-intelligence backlog for every target agent workflow: nominal flow, exceptions, decision rationale, systems of record, permissions, escalation paths, and measurable outcomes.
  • Adopt a production gate for agentic systems that separately verifies deterministic execution paths, test coverage, audit logs, authorization boundaries, failure recovery, and human approval thresholds.
  • Require model portability in enterprise architecture: maintain a benchmarked fallback model path and decouple workflow logic/context assets from any one model vendor.
  • Before using workforce telemetry or process capture, establish an explicit employee communication, consent, access-control, retention, and redeployment policy; otherwise discovery efforts may suppress the candid knowledge needed for automation.
  • Score AI opportunities by whether the operating frame is already explicit versus exception-heavy and tacit; start with bounded, well-instrumented processes, then use discovery work to expand coverage.
  • Evaluate vertical AI investments and internal tools on ownership of workflow execution and proprietary process context, not on generic answer quality or nominal labor-market size.

Source/Metadata

  • Title: Daniel Dines on Why Work Processes Not Models Will Be The Most Valuable Asset in AI
  • Transcript words: 15588
  • Duration seconds: 4744
  • Timestamp note: No usable timestamps or chapter markers were present in the supplied transcript; the transcript also contains substantial duplicated passages near the end.
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Full transcript 11278 words · 69 min read
0:00

In my opinion, even the labs in China that are building AI right now, I would classify them as good guys. Joining me in the hot seat today is one of my dearest friends and one of the greatest founders of the last decade, Daniel Dines, founder and CEO of UiPath. And today we discuss the biggest questions. What on earth does pacing the frontier mean? Is it even possible? Will we replace humans with AI? I've never hidden from my employees that there will be a transformation. Jensen is bound by the success of open source.

0:28

This is a truth-telling, myth-busting conversation, and it's just a fantastic discussion between two old friends on what no one is talking about, but everyone needs to know in the world of AI today. Models are interchangeable, but the workflow, the map of work and the workflows around the map of work is where the real value is. Ready to go? Daniel, dude, it is so good to have you in the hot seat. I have been looking forward to this one.

1:05

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 and a lot of people write books. With the greatest of respect, you're my friend and I care deeply about you. Why on earth did you decide to write a book as a public company CEO? No offense, you're not doing it for the royalties. Why do 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.

1:47

Now I got a great opportunity. Claude and ChatGPT 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 almost six months effort doing this. And I started with different threads of thought. One was, 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?

2:45

I think it's nice to take it in 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 a data center. Look, I was really concerned. And Dario is a guy that I highly respect. And he's highly successful in what he did. 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 everything. And I ask, do my job or do whatever job in the enterprise. It seems that reality is maybe different.

3:27

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 learn 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. Right. 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.

4:25

Humans are difficult to manage. I'll take the AI any day of the week, please. If AI can do it 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. Because 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.

5:18

It's becoming. Think about if I give to someone the ability to read all the books about chess. Do you think you 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 it's largely outbound and inbound, whether it's social media.

5:51

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 churn and I can act before 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?

6:51

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 and all my thoughts. This is not true about AI. Well, I mean, I'm not being rude. It is. That's why people remain with OpenAI, because it has memory.

7:50

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. It's memory. It's just a thing that is written down. When I'm talking to you, I don't go back into my memory. I am being transformed. It's like a model. It's like a new version of the model that comes improved. The new version is not the old version plus a piece of paper that has memorized. It's transformed into its own weights. This is why a model becomes much better. Look, I want to give you a simple example on using our own technology.

8:33

We make our UiPath platform available for coding agents. So people are making it 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 weights. It's very different than, and think of all the metaphors in the world when you read something versus when you learn something. You can read the biography. It doesn't mean you live that life.

9:21

It doesn't mean you are transformed and you are going to answer like that person that lived it. So I think to me, really, this is 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 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. Yeah. Because, look, do this thought exercise.

10:08

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-replicating. 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. This is the theory.

10:42

But I think everyone does, and that's why everyone is chasing 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.

10:51

Yeah. Because, look, do this thought exercise. Let's put a model that we have today with the best technology, put it in a spaceship and throw it to the stars. Let's say that we have this technology, like I think von Neumann imagined, that is self-replicating. This spaceship goes to different stars, get energy and they can continue. So compute will be infinite. And models will self-improve where they would end up with. Maybe they will create a simulation of a world like ourselves, right? Because they will improve infinitely. 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 simulations.

10:58

So, look, the answer is, 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. And it's not like that we are certain that the will to do something emerges from reasoning or from even from consciousness. I think will is a kind of a separate part of the fabric of the universe that I don't think we as humanity have a clarity about what will is. I think it's wishful thinking to believe that I can take a big model, put into a self-improvement loop, and this model is going to generate will. I don't believe so.

11:03

I get that. But I think we don't know. And I think that's what's so challenging about trying to predict what happens. It's a world of recursive self-improvement. It is unknown in terms of where it lands. It's a technology you can create can become something you didn't know it could be. And I guess for me, the question then is, we see the news this week. Dario says we need to pace the frontier. You've just written the book and have a lot of clearer thoughts. You run UiPath today. Do you feel we need to pace the frontier?

11:08

Yeah. I would say if they truly believe that this technology is becoming rogue and they cannot control it, and their experiments will create significant loss for, I don't know, internet, other systems, if I were them, I would pace it at any cost because I don't want to risk going to jail. Honestly, I think there are laws that control this type of rogue behavior. So honestly, I don't need an external pressure to control. I would just be a concerned citizen, and I would not build a technology that is causing harm.

11:18

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 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 good a manner as possible. But in the same time, I want to be free of consequences. To me, I think this is how I read this memo.

11:33

And because otherwise, I think it's obvious. I don't think we can 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 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.

11:46

You work with some of the biggest enterprises in the world with UiPath. Alex Karp said from Palantir that the biggest enterprises in the world are scared to work with Frontier Labs because of threat of them coming in to their businesses over time. They have the data, they could build their own and compete against them. Do you see large enterprises scared to work with Frontier Providers?

11:57

I think so, yes. I don't think people are scared that OpenAI will build a competitor to them necessarily. I don't see this coming. I think they are more scared that their IP would leak to other existing competitors somehow. Because if I, I don't know if I'm manufacturing screws or whatever, I don't think OpenAI is going to come and compete with me on this thing. But probably some of other guys can get indirectly via the models the same intelligence if 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.

12:00

You said about clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process?

12:02

It has become very clear to me that another limitation of AI is what I call exactness. And 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 right. 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.

12:04

And also it surfaces another simple idea. Even if you have a tool like AI to multiply that is capable of doing multiplications. Why are you not using a computer that is doing these multiplications millions of times 100% of the 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.

12:09

Isn't it because it's where you are? And that's the importance of being in the harness of the workflow. Which is, 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.

12:13

I think that 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 computer and they will give you the exact number. This is part of the power of the models. So you actually can see on the desktop level, on this type of coworker or ChatGPT work, you see the capability to call tools. And what I am saying, 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.

12:16

And here comes, I think, the most interesting thing. There is an asymmetry 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 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.

12:20

Plus, when an automation breaks, because of 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 really 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, 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.

12:25

Totally get that.

12:27

So this is the asymmetry that is happening right now. Plus, when an automation breaks because of any change in the upstream system, AI comes again into play and fixes the automation itself. So this is really the pattern that we are seeing emerging in an enterprise. And this is that AI is actually creating the software that runs an enterprise. And this software really 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 for a certain input will guarantee that the software will behave in the same way. So that again makes this pattern extremely powerful. You create, you use AI to create software that runs the enterprise in a predictable, governed, auditable way.

12:31

Totally get that. How many engineers do you have today? Maybe more than a thousand. More than a thousand engineers? Yeah. How many people do you have? Around 4,000. You have 4,000 people? Yes, why so, right? I find 18 a nightmare. What? 4,000? Oh my God. No. Do you have too many?

13:42

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, at the same time, 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 at the same time as we successfully adopt AI in an enterprise. And that's actually another point that I discovered while 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 look like.

13:54

One of the things that seems very simple in retrospect is that people don't have very simple jobs that can be defined on a sheet of paper. I think every job has some kind of 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 stick to my technology. This is a different outcome. If I'm going blindly and I cut a number of people and say this is 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 can they look at how the AI transformation looks like? What kind of jobs will be affected? How can I move people maybe from one job to another? Because they still carry some 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 reduction of 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 the credentialed middle. That kind of was the type of people that were prevalent in any enterprises. And if you think of our education system and the way we hire people, we hire based on deep expertise in a particular domain, credential expertise. And this is exactly the type of expertise that might not be needed so much. AI can really help. So you will need fewer of these experts, but you will need more people that have initiative, 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 cut and you will tend to cut those people that are not the biggest experts in the domain. But you will cut exactly what you will need to bring the AI to supplement these experts.

14:00

So one thing that I... But maybe you just need less of them. And so I was speaking to a lawyer today and I said, you know, how big is your trainee program? And they go, well, historically it was 25. And I said, wow, that's 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 there is verifiability, which is finance and accounting. It's quite clear what is right and what is wrong.

14:27

I disagree with you. It's not about verifiability. It's about if the work has been defined in a framework set by other people. If the frame is clear, then the AI can understand the frame. But the frame is clear in finance and accounting. They do your expenses.

14:36

No, it's not. This is one of the domains 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 rules there. I know for this customer, NVIDIA is going to ship with priority to OpenAI. Maybe they have a rule that says yes, my first chips go there. Maybe they don't. If it's not captured in a frame, I think AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful. What do you mean the map of work?

14:55

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. Yes, exactly. When NVIDIA comes back and says, 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.

15:14

Even in finance, you cannot replace everybody. Not everyone, but you've got one or two people. Look, 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 have to go to maybe do different jobs? How do you know?

15:19

Because ideally, you will get the people that will have literacy in AI that can display initiative. 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 cares less about how they treat the customers? Because I want to keep the first because the second is more prone to being displaced by AI. This is the ledger that I think enterprises have to create and understand different outputs of people. They need to judge people by this kind of hard to define output.

15:23

So how does one do that then? For the illegible data that isn't captured within companies, how do we do that? You know, Zuck and Facebook have said about monitoring every single action that's on the screen of employees. I don't think that captures the tone of a call, the warm text afterwards to a customer, the illegible data. How do we think about capturing the data that shows value that we don't capture?

15:28

I think this is the crux of the problem. And here you're a true philosopher. Yeah. And this is where we put a lot of effort as a company. And we are introducing a new technology that we call 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 aspect of cartography is to actually 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.

15:30

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 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 aspect of cartography is to actually have investigated what people are doing on their desktops. And we have a product that we call the cartographer agent that can interview people, real subject matter experts, have them record what they are doing. And the agent is interviewing them in real time.

16:26

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 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 implementing software.

17:38

So you start with a process that at point A is fully manual. Of course, you have enterprise systems like system of records and everything. But people operate the system. I think the goal of any enterprise is to have less people operating the system and more automations and agentic AI operating the systems. 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. So I think it very much depends how you pitch the company. So in a new path, I think it was important to tell people, again, we are not doing anything stupid.

18:12

We are not doing any mass extinction 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. I think 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.

18:49

Look, in the beginning of the year, I think the fear of people across the industry, and not only in my company, but in many companies was off the 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. Can I ask you, one of the companies we've invested in, McCore, data provider, Brandon Foodie, the CEO, tweeted yesterday that they spend 3x the spend of human salaries on inference today.

19:26

What percent or multiple, give or take, would you say you spend human salaries on inference? Look, I personally don't care about it. Let me do something. It's a simple hypothesis. If the work, if the quality that a human can do is 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 picture while the cost of machines will decrease. 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 will be the real question if you replace a person with AI.

20:26

But the real problem today is that AI cannot replace a person. Because if you bring me an Einstein that replaces me and I will happily go on any vacation in the world. But this Einstein doesn't yet exist. And probably I would like to be interviewed and have this podcast with another Einstein. Man, this thing doesn't exist today. That's the reality. So let's call it a reality. Let's call a spade a spade. Maybe this technology will emerge and somehow Einsteins that embody a person, that have will, that get transformed on the job, learn on the job, have the capability of reasoning, imagination of Einsteins exist. Of course, all the jobs will go extinct. I get you.

21:28

I have a show with Jason Lampkin from Sasta, famous SaaS platform. He's cut his team from 25 to 2. If he were in my seat now, he would say, no, no, no, it does. I replaced my VP finance. I replaced my VP marketing. And actually, the AI is better. I want to see this, man. I think I heard companies that replaced hundreds of support people in the past. And now they are rehiring these people. I think until this model is proven and is proven at scale, not in a particular industry for a particular guy, I don't think we can extrapolate from one data point that is going to go across industry. You said about extrapolation and over-exaggeration. The SaaS-pocalypse was very real.

21:54

We're going to vibe code everything. Did you vibe code tools out? Look, it was amazing. Yes, we did. But not an extraordinary success. Initially, it seemed extraordinary. But when we tried to put it in production, we started to see some real bottlenecks with these tools. And you need to have a lot of things that you have to maintain. Connectors, permissions, audit, security. Taking a software from a prototype to production is actually where the work is. It's not necessarily the writing code. Writing code is fun.

23:08

But it's not 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 production. And this is the testing and everything else. Look, we were trying to replace a procurement tool ourselves. And I think we have a lot of success. And it was initially written only by AI. But then it comes to, speaking to this self-improvement loops kind of thing, we don't trust it. We don't have enough tests. We don't have enough trust to put this tool in production.

24:19

100%. And humans, in our experience, humans have to intervene a lot into how this vibe code tool works. For instance, the database schema that the vibe code tool created was completely bogus. So a human has to come and create the structure. So right now, you are not at the point where a business user that understands a problem will vibe code a tool. So you will still need to put engineers and people. You need to maintain it. So it's a long process. So eventually, you will end up paying probably as much, if not more, as the tool you replace. While you keep some of your good and best people's bandwidth occupied. Would you buy Salesforce today?

25:32

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 at the bottom of SaaS-pocalypse. Even our own stock has been doing better. But the markets today are driven so much by sentiment and not value. So it's kind of hard for me to make a judgment of an individual company. But I don't think Salesforce can be replaced by vibe coding, if that's the question. I don't understand why a company would go public today.

26:47

If you think about the two drivers of being public, number one is liquidity for shareholders and employees. 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 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 casinoization of public markets, as you said, with current stock markets being sentiment driven. I don't understand why one would.

27:14

But, Harry, let's not make a confusion between some very exceptional companies and most companies that are out there. There are so many companies that are zombies right now. These 2021 zombies. They would fare better in the public market right now. At least their investors will have a way for exiting. Their employees will have a way to make some money. Nowadays, all of them are sitting on paper money. Okay. Public markets will face them with the reality of what their real valuation is. So I would not say there is no value in the way. Why Anthropic is trying to do IPO in the end.

27:55

But they're a unique company alongside OpenAI, which just have to because they have exhausted all private funding that exists. I mean, they are literally hitting the tap out button on the supply of private capital. They're extraordinary companies because they just need too much money. But, Harry, let's not make a confusion between some very exceptional companies and most companies that are out there. There are so many companies that are zombies right now. These 2021 zombies. They would fare better in the public market right now. At least their investors will have a way for exiting. Their employees will have a way to make some money.

28:25

Nowadays, all of them are sitting on paper money. Okay. Public markets will face them with the reality of what's their real valuation. So I would not say there is no value in the way why Anthropic is trying to do IPO in the end. But they're a unique company alongside OpenAI, which just has to because they have exhausted all private funding that exists. They are literally hitting the tap out button on the supply of private capital. They're extraordinary companies because they just need too much money. Do you believe all the investors in OpenAI and Anthropic will stay in the companies for years to come? No. So maybe... I think some will do. Some will do, of course.

29:29

But I think we will see an exodus of... Honestly, I don't believe at $2 trillion or whatever. Maybe they will reach $5 trillion. Because if I buy at $2 trillion, I need to have a path to $5. 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.

30:41

I didn't invest. You would have made more money on that than Salesforce or ServiceNow or whatever. Yeah. Yeah. A hundred percent. So look, I would not sell... I think it's the same with OpenAI. I don't have... I think OpenAI has caught up quite nicely. And I use them interchangeably right now. I just think we're in a market where the big get bigger and value concentrates more than ever. Actually, more than ever. But the real question is, Harry, would I buy $2 trillion?

31:55

That's my real question. And look, right now, I'm not... I don't... 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 amazing.

32:50

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. I'll take more in the video. I think Jensen is bound by the success of open source. Because if Anthropic and OpenAI, if this is becoming a dual monopoly, and basically their... I think their TAM is in like trillions. It's basically the work. Yeah. They will print their own chips, man. Honestly. It's not such a big deal in the end to print chips.

33:56

Of course. I mean, OpenAI are doing Jalapeno and Anthropic are doing their chips. Exactly. So I don't think Jensen's will be doing so well if they are the single biggest providers and the source of truth and light of God will come from only Anthropic and OpenAI. So therefore, the open source should succeed. 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.

35:01

Why? Because I think it encourages its host all the open source model. It's becoming... It's putting the money where the money is for his company. Totally. It does also make a neutral provider not neutral anymore. Bias. Yeah. Obviously, they have NemoTron and they have their own models now as well. You could say there's a loss of independence now that it's owned by NVIDIA. But yes, I think NemoTron is still a small cog in the picture. Sure.

36:09

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? Everyone talks about NVIDIA investing here, buying here, the circular economy that comes 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.

36:52

So it's clear that now everybody, that it cannot be, there is only 100% of the pie. And people are building right now 200% of the pie. There will be losers. Do you not think that 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 water. We have data centers. We have regulation and policy. We have a significant hindrance to supply side. And we are underbuilt, not overbuilt, which is why every ounce of compute is taken.

37:45

Yes, but are we underbuilt to the extent of the trillions that come into the infrastructure? I don't know to answer this. And 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 can be merciless. Maybe I'm a childish optimist, but I saw Andre Karpathy say that he used coding tools for 20% of the work.

38:15

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? 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. Yeah. Harry, you should read my book, my friend. Because it answers exactly the same questions. I did. When the frame that a person operates is really well defined by someone else,

39:13

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. Oh, you're about to spend two days with a lawyer who is my girlfriend. She will tell you that law is highly ambiguous, subjective in terms of writing styles. She would... So is the human language. And AI understands it perfectly. And it understands every nuance of human sensitivities. As long as it's documented and it's well defined, there is a manual for the 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.

40:11

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. What do you mean? I mean, maybe out of $90 billion, I think 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, I think open source models, frontier models will do just fine.

40:50

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 Lagora are doing this, they map really the work, they create the workflow, and 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%. Maybe it's $10 billion total opportunity in tokens.

41:29

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, I think 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 Lagora are doing this, they map really the work, they create the workflow, and they create a legal department for me.

41:57

Of course, it's a bigger, 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 through the frontier model like Astra or Fable versus a very cost efficient models? And to me, I think for enterprise work, my prediction is that 90% of the flow will go to very cost efficient models. I don't think you need frontier level quality of models for most operational work.

42:26

And so just to be clear then, so we will actually still use the core provider, which is OpenAI and Anthropic. It'll just be deprecated older models. I will still use Anthropic and OpenAI. They are cost efficient models. 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.

43:04

And I think it goes to the statement of owning your own intelligence, not renting it. It's why we invested in Fireworks. And I believe in the open model ecosystem. 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 also, 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.

43:34

But in next two months, there is another better model, base model coming into the picture. How can I do transfer learning from my old model into the new model if I don't have the data and the exact manual? I cannot. Or there are terrible losses when I do this. So, the real investment for an enterprise is to creating this map of work that documents how they actually work. And with this one, they can train their own models. Whether it's in Fireworks or other providers, it doesn't matter. But this is their IP. This is their core data. Make sense? I totally get that. And so you do, sorry, just one sense.

44:01

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. I'm not clear if I can provide the same cost efficiency with my own model versus a cost efficient model from Anthropic or OpenAI. Because I think these guys are in the position to truly optimize large infrastructure. So, I think part of their business model will be to deliver more intelligence per dollar than even I can squeeze from my own models.

44:20

If you have highly specific data that is exact to the request that you have, which is your data, I think you'll get more token efficiency with your own model than you would an optimized frontier model. Only if you are training your models, that might be true. And only if you can deliver, if Fireworks can deliver at a large scale, this 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.

44:41

Because if they don't secure compute, I don't understand how they will, how can they give me the inference of the scale that I want. What do you think? Because you invested in them. I did. I think you're absolutely right that they need to move into the compute layer. And I think Lynn is doing that, I'm sure, very soon. So they will have to raise tens of billions now. 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. It's a great team. And we work with them before the big hype around them.

45:14

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. I think. But what's the difference? And 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.

45:59

With the context. Because if you don't have just data. But you don't have a way to create a really good context. To give the model when they ask something. It's useless. I think you would say that they have more data than anyone else. Across more categories than anyone else. So when the model requests highly specific data. Because of the breadth of their library. They're able to provide it in a way that others aren't. If it's their own data. And it's valuable for models. I'm sure the modelers will buy the data in an instant. Can I ask you, what have you changed your mind on most in the last 12 months?

46:58

I didn't understand this necessity of AI to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. That every time I'm running a query towards AI. And I should have at their disposition the entire way my company work. Or this particular process work. When I realized this, I think I understood also that this thing that... It's the biggest difference between memory and true learning. And I let you think. Because this is how we started the discussion. And I'm not sure I really make a point. But it's a huge difference between just laying down, having a scratch pad. That we're being transformed by an experience.

47:39

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 think 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 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. You cannot just give. Because otherwise, I will just ask AI, read this book.

48:52

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, putting in a laptop. And it might be possible. Who knows? At the pace of technology. Maybe 20 years from now, I can have a 10 trillion model that is my own model and is getting transformed along with me. And that's, but we need to see. I think there might be a few series of innovations to get there. Because I want to give you also an interesting data point.

49:45

AI is solving very interesting math problems that humans didn't solve before right now. But AI still, it's not capable of creating frameworks. Like, 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.

50:09

So to me, this is going to be the biggest breakthrough in AI technology. When I can have models at the size of Mythos being transformed on a job, being putting in a laptop. And it might be possible. Who knows? In the pace of technology, maybe 20 years from now I can have a 10 trillion model that's my own model and is getting transformed along with me. And that's, but we need to see. I think there might be a few series of innovations to get there.

50:14

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 isn't capable of creating frameworks. Relativity is a framework. And I was thinking, why? 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, what happens when you go 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 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 a 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. The one million is very hard to go beyond this one million tokens context window. A frame might require a transformation as you work on that frame. It's a different way of learning than pure memory.

50:15

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. So I don't want to sound like an AI doomer, because I believe that... I don't think you do. I am. No, I don't think you do. I think you sound... 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.

50:39

I'm much more optimistic that we won't have so much because based on my own experience with AI, I don't think the diffusion is as fast as you imagine, particularly because enterprises have to document in much greater detail their processes. 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?

50:55

Yes. It's hard to admit the reality, but from a technology standpoint, I think we are largely irrelevant. But it's so stupid because the biggest producer of machines that make chips is based in Europe. It's ASML. We could have made these chips in Europe. Some of the most brilliant minds that build AI—Dario, Sam, all of them are European origins, Ilya, recent. So we have the talent. We have the technology to build the machines. But somehow we are losing it. 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 5 p.m. they are all in the pub. Why is that? Because money matters more?

51:33

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

51:56

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 middle management can make sizable million dollar bets on new technologies in 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?

52:04

Yes, that's the sad reality. I think that unless they build for a specific market with some specificity in mind, if they build universal technology, they will have a better chance to succeed in US. There will be many successful European companies coming out of this. I think maybe not as frontier labs, but I think for the application of AI. And I think... Do you buy sovereignty as an argument? Yes. I think it's an important one. Energy sovereignty, model sovereignty. Yes. A hundred percent. And all European customers right now would prefer on-prem software, model sovereignty, and model optionality.

52:46

Yes. A hundred percent. Which is a big business that's coming here. Look, I talk to our friends at Fireworks and I actually try to convince them to make their software available on-prem. Right now they are in "show me the money," but I can tell you, 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?

52:53

I think it's public data. We are at 1.6, growing last year, 14 percent this year. So Jason Lemkin taught me that unless you're growing 20 percent plus, you're just done 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. Yeah. Is that right? Yeah, because I think the public markets are very confused right now about 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.

53:07

So if I were to flip it on you, we do a final one for a quick fire. What is the bull case to UiPath being a $50 billion company? Think about even today. Gartner released their new magic quadrant for business orchestration and automation technologies. And we are one of the leaders. We moved from a challenger into a leader in the last year. So it shows that we as a company made this transition from RPA and automation technology into an orchestration and automation technology.

53:11

And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI powered. You cannot have 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 something that people stop believing. So 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, it's Forrester, it's industry analysts being very bullish on us. So that's really the argument right now.

53:17

Now, I think, as I said, this asymmetry that AI is creating right now is more obvious than printing software that runs your processes has become much easier than a year ago. But creating an AI agent that runs your software is as difficult as a year ago. So you make a huge investment into printing this software, capturing the enterprise context that we call the map of work, putting this enterprise context inside these rails that I named this orchestration automation as the map and rails. 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 have control on top of your agents and they cannot go rogue. You cannot have any sane enterprise right now will put a swarm of agents and just ask them to do my finance accounting for me. Because who knows, maybe they will send 20 million bucks to some rogue invoice to your competitor.

53:19

I completely get you. OK, what is the bear case? I think the bear case is AI will somehow become genius. Token costs will be next to zero.

53:32

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 control on top of your agents so they cannot go rogue. You cannot have a sane enterprise right now that will put a swarm of agents and just ask them to do my finance accounting for me, because who knows, maybe they will take your competitor and send 20 million bucks to some rogue invoice. I completely get you. OK, what is the bear case?

53:34

I think the bear case is AI will somehow become genius. Token costs will be next to zero. And we'll have literally millions of Einsteins in a data center. But Einsteins in a true sense, not only reasoning in the sense of replacing a person. And I can assign them to every work in an enterprise and they will just do it. That's the bear case against us. Token cost has gone from $60 to $1 per million. So I think the token cost will go to next to nothing. It's possible. This is why I told you I would not right now stop investment based on token cost.

53:36

Dude, I could talk to you all day. I'd love to do a quick fire with you. So I say a short statement and you give me your immediate thoughts. OK? 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's changed most about how you work as a CEO because of AI?

53:52

I'm spending maybe half of my day right now alone with myself in Visual Studio Code, working with Claude and ChatGPT. I have way more leverage on my company than before because we changed completely the way we operate. Most of the people, when they come with an idea to me, a year ago they would come with a deck and it was very hard even to prepare with this deck. Now everyone is going to come with a markdown file and I can put it into a giant strategy folder that I have with AI agents working with this. I put this document in my folder and then I can ask intelligent questions about it.

53:56

If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing? You know what we do? We create clips, which is like you take little segments and that's like the clip. I'm just thinking of like, it'd be a great first clip. That would be interesting. NVIDIA in three years time, will it be above $7.5 trillion? Where they are today, $5.6, yeah. I can easily imagine a 40% run for NVIDIA. I would bet more on NVIDIA rather than Anthropic being a $7 trillion company. Trillions are nothing today. Yeah.

54:17

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 like 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. What would you advise to founders who feel lonely in their head and struggle with that today?

54:24

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

54:31

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. I'm very excited about longevity. My friend, I have almost twice your age. So my body aches after any kind of gym or anything. What are you doing longevity wise? I'm doing quite a lot. I got into peptides, into supplements. I'm thinking around 60 different supplements a day and three peptides. Seriously. 60? 60. 6-0, yeah. What are you taking? And all of them have been recommended and vetted by AI. That's extraordinary. I mean, you look incredibly young, but 60 supplements? Like in pills?

54:57

No, you take them in, because I do the longevity shape from Brian Johnson, which is like 60 in one. And I just have it every morning. You actually have 60 separate supplements. I have a lot of pills. I also do some in the form of powders. But yeah, I have little bags throughout the day, like AM1, AM2, AM3. 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 run better. But honestly, I feel way better even than 10 years ago. Reducing booze quite a lot helped. I saw pictures of you and I know you are a big fan of booze. You don't still drink, do you? I drink a lot less these days.

55:36

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. And 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 there a 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 I like reading. It's my gift to you, Harry, of course. There we go, dude. Thank you so much. Thank you, man. Thank you. Probably, yes. If this hypothesis of open model is true, which I believe is true, I think they are undervalued.

56:24

I think they will have to get very soon in this big game of securing compute. Because if they don't secure compute, I don't understand how they will, how can they give me the inference of the scale that I want. What do you think? Because you invested in them. I did. I think you're absolutely right that they need to move into the compute layer. And I think Lynn is doing that, I'm sure, very soon. So they will have to raise, you know, tens of billions now. 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. It's a great fucking team.

57:03

And we work with them before the big hype, you know, around them. 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. I think. But what's the difference? And I think, man, 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.

57:45

And, you know, extract the right data at the right time. Feed really the model with the data that is needed. With the context. Because if you don't have just data. But you don't have a way to create a really good context. To give the model when they ask something. It's useless. I think you would say that they have more data than anyone else. Across more categories than anyone else. So when the model requests highly specific data. Because of the breadth of their library. They're able to provide it in a way that others aren't. If it's their own data. And it's valuable for models. I'm sure the modelers will buy the data in an instant.

58:26

Can I ask you, what have you changed your mind on most in the last 12 months? I didn't understand this necessity of AI to have a manual in order to work. That was maybe the biggest breakthrough in my understanding. That every time I'm running a query towards AI. And I should have at their disposition the entire way my company work. Or this particular process work. When I realized this, I think I understood also that this thing that... It's the biggest difference between memory and true learning. And I let you think. Because this is how we started the discussion. And I'm not sure I really make a point.

59:20

But it's a huge difference between just laying down, having a scratch pad. That we're being transformed by an experience. And that's a thing that I realized the most. And I think I realized also what's kind of human for us. Because I think 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.

1:00:17

So you need to be transformed. You cannot just give. Because otherwise, I will just ask AI, read this book. And write in the spirit of this author. And it's not really working. 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? In, you know, the pace of technology. Maybe 20 years from now, I can have a 10 trillion model that it's my own model and is getting transformed along with me. And that's, but we need to see.

1:01:13

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, it's not capable of creating frameworks. Like, 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.

1:02:07

Einstein has been changed by his experience thinking about the speed of light of what happens when you go, you know, behind the light. It's not like Einstein wrote it down. And then every time he thought again, he rewrote a piece of paper. No, he became gradually, you know, a different Einstein than the one that started to think of a problem when he created this thinking, this frame of relativity. Models don't do this way. Even if I put swarm of agents, everything, they have to write down everything. They are not being transformed by the process. So therefore, it's very difficult. In the end, they will have this, you know, context.

1:02:53

The one million is very hard to go beyond this one million tokens context window. A frame might require a transformation as you work on that frame. 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. So 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 think you sound... 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.

1:03:42

I'm much more optimistic that we won't have so much. Because based on my own experience with AI, I don't think the diffusion is as fast as you imagine. Particularly because enterprise have to document 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.

1:04:33

Because, you know, the biggest producer of machines that make chips is based in Europe. It's ASML. 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 kind of, you know, European origins. Ilya, recent. So it's, we have the talent. We have the technology to build the machines. But somehow we are losing it. It's, man, it's very stupid. Do you see a difference in work ethic having a team in the US and the UK? Yes. Yes. Yes. I experience with teams in UK. And at 5 p.m. they are all, you know, in the pub. Why is that? Because money matters more?

1:05:26

I think culture matters more than money. It's a more dynamic culture. This is why 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, you know, in American school of entrepreneurship, even if I started my company. 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. Of course. It's an easier to access revenue machine than in Europe, clearly. Faster. It's easier.

1:06:14

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, you know, proof points as the European companies. And even people in the middle management can make sizable million dollar bets on new technologies in US. I haven't seen this appetite in Europe. I don't want to ask this, but I am interested. If you were to advise a young European entrepreneur today, would you say to go to the US? Yes, that's the said reality.

1:06:57

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. And I think maybe not as a frontier labs, but I think for the application of AI. And I think... Do you buy sovereignty as an argument? Yes. I think it's an important one. The energy sovereignty, model sovereignty. Yes. A hundred percent. And all European customers right now would prefer on-prem software model sovereignty and model optionality. Yes. A hundred percent. Which is a big business that it's coming here.

1:07:47

Look, I talk to our friends of fireworks and I actually try to convince them to make their software available on-prem. Right now they are in show me the money, but I can tell, guys, this is a big business. You need to prove first because this is Europe. Show them the technology and the money will come. How much revenue does UI path do today? I think it's public data. We are at 1.6 growing last year, like 14 percent this year. So Jason Lemkin's taught me that unless you're growing 20 percent 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. Yeah. Is that right?

1:08:35

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, we do a final one for a quick fire. What is the bull case to UI path being a $50 billion company? Think about even today. We, Gardner, released their new boat magic quadrant, business orchestration and automation technologies. And we are one of the leaders.

1:09:28

We moved from a challenger into a leader in the last year. So it shows that we as a company made this transition from an RPA and automation technology into an orchestration and automation technology. And there are all the arguments in the world that this is really required in order to create this new enterprise that is AI powered. You cannot, 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.

1:10:12

So 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 this is what we have. And it's not only me saying, but it's Gardner saying, it's Forrester, it's industry analysts in being very bullish on us. So that's really the argument right now. Now, I think, as I said, this asymmetry that AI is creating right now is more obvious than printing software that run your processes has become much easier than a year ago. Bringing, creating an AI agent that run your software is as difficult as a year ago.

1:11:03

So you make a huge investment into printing this software, capturing the enterprise context that we call the map of work, putting this enterprise context inside this rails that I named this orchestration automation as the map and rails.

1:11:25

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 of your agents cannot go rogue. You cannot have 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 take your competitor to send 20 million bucks to some rogue invoice. I completely get you. OK, what is the bear case? I think the bear case is AI will somehow become genius.

1:12:15

Will tokens cost will be next to zero. And we'll have this literally millions of Einstein's in a data center. But Einstein's in a true sense, not only reasoning in the sense of replacing a person. And I will I can assign them to every work in an enterprise and they will just do it. That's the bear case against us. I mean, token cost has gone from $60 to $1 per million. So, I mean, I think the token cost will go to next nothing. It's possible. Yeah. This is why I told you I would not right now stop on 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.

1:13:06

OK? Mm-hmm. 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's 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.

1:14:00

Now, everyone is going to come with a markdown file. And I can put it into, I have a giant strategy folder that I, you know, I have, you know, AI agents working with this. I put this document in my folder and then I can ask intelligent questions about. If you had unlimited resources and zero retribution from Wall Street, what would you do that you're not doing? Sorry. You know what we do? We create clips, which is like you take little segments and that's like the clip. I'm just thinking of like, it'd be a great first clip. That's it. Maybe I'm like, whoa. Yeah, that would be interesting. NVIDIA in three years time, will it be above $7.5 trillion? Where they are today, $5.

1:14:51

$5.6, yeah. Yeah, I can easily imagine 40% run for NVIDIA. I would bet more on NVIDIA rather than Anthropic being a $7 billion company. Trillion. Trillion, of course. Billions are nothing today. Billions are nothing today. Yeah. 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 like 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. What would you advise to founders who feel lonely in their head and struggle with that today?

1:15:38

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

1:16:31

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. So my body aches after any kind of gym or anything. No. What are you doing longevity wise? I'm doing quite a lot. I got into like peptides, into supplements. I'm thinking around 60 different supplements a day and three-fourth peptides. Seriously. 60? 60. 6-0, 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?

1:17:27

No, you take them in like, because I do the longevity shape from Brian Johnson, which is like 60 in one. And I just have it every morning. You actually have 60 separate supplements. I have a lot of pills. I also do some in the form of powders. But yeah, I have, I'm going to show you tomorrow all my, all, I have, I have little bags, you know, throughout the day, like AM1, AM2, AM3. I know. That's extraordinary. 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 turn better. But honestly, I feel, I feel way better even than 10 years ago. Kind of reducing booze quite a lot helped.

1:18:11

I saw pictures of you. And I know you are a big fan of booze. You don't still drink, do you? 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. And I, I'm printing also a few copies. We have our big fusion event coming in a couple of weeks. And I am, I'm distributing to everybody coming there a 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.

1:18:52

Thank you, man.

1:19:00

Thank you.

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