The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:13:25 · Daniel Dines
[00:13:25] S0: 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 I would pace it, you know, at any cost because I don't wanna risk going to jail, honestly. I think there are laws that, you know, kind of control this type of rogue behavior. So, honestly, I I don't need an external pressure to control. I would be just concerned Citizen and I would not build a technology that, you know, is causing harm. But now, of course, they think that this is the only way to protect against the bad boys. So we are the good guys, but there will be some bad boys that probably in other parts of the world that will build the technology regardless. So I think that probably they ask more like a boss. I wanna build this technology at any risk, and I'm willing to open my gates for other to to see how I'm doing because I wanna do it in as of, you know, like, a good manner as possible. But in the same time, I wanna be free of consequences. To me, I think this is a bit how I read this memo. Because otherwise, I think it's kind of obvious. I don't think we will reason with the bad guys, and we can make a coalition with the bad guys to stop the frontier. So we can make a coalition only with the good guys regardless. I would take even, in my opinion, even the labs in China that are building AI right now, I would classify them as the good guys. But someone... To me, I think the indirect, the attack is probably on open source. Because, basically, they say even if the good guys are building open source, but that open source will get into the hands of the bad guys. And this is unknown bad guys. This is the real danger. So the danger is in open source. So indirectly, it's also an attack to open source in this way. It's it's it's a way of interpreting, I guess.
00:15:26 · Harry Stebbings, Daniel Dines
[00:15:26] S1: You work with some of the biggest enterprises in the world with UiPath. Alex Karpman said from Palantir that the biggest enterprise 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? [00:15:48] S0: I think so. Yes. I I don't think people are scared that OpenAI will build a competitor to them necessarily. I don't see this coming. They are more scared that their IP would leak to other existing competitors somehow. Because I I don't know if I'm manufacturing somehow shrews or whatever. I don't think OpenAI is gonna 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 it's a legitimate danger, and I think everyone is trying to protect their IPs. You you said about kind of [00:16:35] S1: clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process?
00:16:35 · Harry Stebbings, Daniel Dines
[00:16:35] S1: clear articulation of thoughts that come from writing. What was another thought that you clearly articulated through the writing process? [00:16:43] S0: It has become very clear to me that another limitation of AI is what I call exactness. AI, by its nature, being probabilistic at every step, in a way it can lose it. While you do a 100, 200 steps by AI, even at each step, you have 99% probability, for instance. It's, you know, 0.99 at... At power of 100. You will end up with maybe 60% probability to do the entire step. So AI doesn't have this mechanism to follow steps exactly hundreds, millions of time in the same way. You can see it even if you ask AI to multiply very large numbers millions of times. At some point, they will make a mistake. And also, it surface kind of another simple idea. Even if you have a tool like AI that is capable of doing multiplications, why you are [00:17:39] S1: not using a computer that is doing these multiplications multiplications millions of times, you know, 100% of time exact. The fact that the tool can do a job doesn't mean you have to use that tool to do that type of job. Isn't it because it's where you are? And that's the importance of being in the harness of the workflow, which is like, I get you completely. You could use something else. I'm asking here. I'm not saying. But because you're in ChatGPT continuously every day, instead of switching to a computer or calculator or whatever, you ask what you're already in, the importance of the environment.
00:18:16 · Daniel Dines
[00:18:16] S0: ChatGPT acts like an interface to convert my questions in natural language into exactness. But the exactness is not run by ChadGPT. Exactness is run by a computer. Because even today, if you ask ChadGPT, please multiply these two big numbers, they are using behind the scene tool, 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 desktop level, on this type of co work or JADGPT work, you see the capability to call tools. And what I am saying, you extend this capability to the enterprise level when everything that should be exact should run on exact technologies. There is no point to run it on probabilistic technologies. And here comes, I think, the most interesting thing. There is an asymmetry in the deployment of AI and automation in an enterprise. Deploying 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 been the most major giant leap that we were seeing in in the past year. I I would say since the invention of chat GPT, then chain of thoughts, and then coding agents were, you know, the major milestones. So with coding agents that act in kind of a design time when I build the systems, I can create automations that work with exactness every time during the execution time. So this is the asymmetry that is happening right now. Plus, when an automation breaks because any change in the upstream system, AI comes again into play and fix the automation itself. So this is really the pattern that we are seeing emerging in an enterprise. And this is that AI, it's actually creating the software that runs an enterprise. And this software cannot behave wrong because it can... This software cannot change its behavior in real time. It's not a probabilistic technology. Even if the software is created by AI, we can really audit. We can have humans that read it, validate it. I can have tests that, you know, for a certain input will guarantee that the software will behave in the same way. So that's, again, makes this pattern extremely powerful. You create... You use AI to create software that runs the enterprise in a predictable, governed, auditable way.
00:20:57 · Harry Stebbings, Daniel Dines
[00:20:57] S1: How many engineers do you have today? [00:20:59] S0: Maybe more than a thousand. [00:21:01] S1: More than a thousand engineers? Yeah. How many people do you have? [00:21:04] S0: Around 4,000. You have 4,000 people? Yes. Why so? Right? [00:21:09] S1: I find 18 a fucking nightmare. And what? 4,000. Oh my god. No. Do you have too many?
00:21:18 · Daniel Dines
[00:21:18] S0: It's a complicated question because I think I would answer more with the thing that is in my book. I think the more we transform our companies using AI, we have to, in the same time, to transform our workforce. I've never hidden from my employees that there will be a transformation in the company. But I told them upfront, guys, we are not doing anything stupid. We are not just using AI as a pretext to cut a part of the company. We need to do the transformation in the same time as we successfully adopt AI in an enterprise. That's actually another point that I discovered writing this book. If you look... I I was looking deeply at jobs and what jobs can be affected by AI, what jobs can be enhanced, and how this transformation is gonna is gonna look like. And then one of the thing that seems very simple in retrospect is that people don't have very simple jobs that, again, can be defined on a sheet of paper. Every job has some kind of an outcome that is measurable, and this is the outcome that people are hired for. But there is another outcome that is part of the institutional strength. Think about my deep relationship with the customer. It's not necessarily part of the numbers that I'm producing, but it's all... It's maybe what makes this customer sticking to my technology. This is a different outcome. If I'm going blindly and I cut on a number of AEs and say, because AI is gonna 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 on understanding, they can look at how does the AI transformation look like. What kind of jobs will be affected? How can I move people maybe from one job to the other? Because they still carry some kind of the cultural aspect of the enterprise. So I don't think it's a simple problem as AI is gonna cut 20% of the company. Let's do a riff 20% out, and then we increase the AI adoption. I think on the contrary, when you do this blindly, you risk to hallow the enterprise of exactly the same talent that thrives during AI. Because let me give you an example.