The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:24:07 · Daniel Dines, Harry Stebbings
In the book, I call this credentialed needle that was the type of people that were prevalent in any enterprises. And if you think of our education system and the way we hire people, we hire based on deep expertise in a particular domain, a credential expertise. And this is exactly the type of expertise that might not be needed so much. AI can really help. So you'll need a fewer of these experts, but you will need more people that have initiatives that are capable of maintaining a relationship with the customer, that can be mentors for new employees, that bear the cultural aspect of the enterprise. So it's counterintuitive because you will tend to cut those people that are not the biggest experts in the domain, but you will cut exactly what you will need to bring the AI to supplement these experts. But maybe you just need less [00:25:06] S1: of them. And so, you know, I was speaking to a lawyer today, and I said, you know, how big is your trainee program? And he go, well, historically, was 25. And I said, wow. That's that's a lot of trainees. And he said, yeah, but this year, it'll be four. [00:25:19] S0: 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? [00:25:29] S1: Well, I I think you can choose quite simply for where is their verifiability, finance and accounting. It's quite clear what is right and what is wrong.
00:25:29 · Harry Stebbings, Daniel Dines
[00:25:29] S1: Well, I I think you can choose quite simply for where is their verifiability, finance and accounting. It's quite clear what is right and what is wrong. [00:25:37] S0: I disagree with you. It's not about verifiability. It's about if the work has been defined in a frame set by other people. If the frame is clear, then the AI can understand the frame. [00:25:51] S1: But the frame is clear. Finance and accounting did... They do your expansion No. [00:25:55] S0: It's not it's not... This is one of the domain where the frame is not clear because when I receive an invoice or I receive an order from a customer, I can treat it differently. There are not always the rules there. I know for this customer, NVIDIA is gonna ship with priority to OpenAI. Maybe they have a rule that says there, yes, my first chips go there, maybe they don't. That rule they don't. If it's not captured in a frame, AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful. What do you mean the map of work? Who basically capture how the work works, how the work happens in an enterprise. This is the map of work. It's all the workflows, all the exceptions, all the procedures, all the systems that you use in order to fulfill a goal of a process.
00:25:55 · Daniel Dines, Harry Stebbings
[00:25:55] S0: It's not it's not... This is one of the domain where the frame is not clear because when I receive an invoice or I receive an order from a customer, I can treat it differently. There are not always the rules there. I know for this customer, NVIDIA is gonna ship with priority to OpenAI. Maybe they have a rule that says there, yes, my first chips go there, maybe they don't. That rule they don't. If it's not captured in a frame, AI cannot learn it. This is why you need to create this manual. We call this manual the map of work. You need to hand the map of work to AI in order to be successful. What do you mean the map of work? Who basically capture how the work works, how the work happens in an enterprise. This is the map of work. It's all the workflows, all the exceptions, all the procedures, all the systems that you use in order to fulfill a goal of a process. [00:26:54] S1: Sure, but then you have a head of finance who sits on top of 30 agents. And exactly, when Nvidia come back and say, Woah, woah, woah, we're your biggest buyer and we have special terms on our payments. They go, yep, sure, that's right. Don't worry about it.
00:26:54 · Harry Stebbings, Daniel Dines
[00:26:54] S1: Sure, but then you have a head of finance who sits on top of 30 agents. And exactly, when Nvidia come back and say, Woah, woah, woah, we're your biggest buyer and we have special terms on our payments. They go, yep, sure, that's right. Don't worry about it. [00:27:10] S0: But you come to my point. Even in finance, you cannot replace everybody. You Not everyone, but you got one person or two You you only need you only need a certain number of people. The thing is, if you have x number of people, how do you understand which of them stay and which of them has to go to maybe do different jobs? How do you know? Because ideally, you will get the people that will have literacy in AI that can display initiatives. Because AI cannot exhibit initiative in the human sense. So out of this number of people that you wanna keep, you wanna keep the people that, you know, display the most initiative. Even a finance person treating an invoice with the customer contributes to the culture and how my enterprise is regarded. How can I make a distinction between that person and the other person that care less about how they treat the customers? Because the second is more prone to being displaced by AI. This is the ledger that I think enterprise have to create. And to understand different outputs of people, they need to judge people by this hard to define output.