The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:27:10 · Daniel Dines, Harry Stebbings
[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. [00:28:20] S1: So how does one do that then for the illegible data that isn't captured within companies? How do we do that? Zuck and Facebook have said about monitoring every single action that's on the screen of employees. I don't think that captures the tone of a cool, 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? [00:28:45] S0: I think this is the crux of the problem.
00:28:45 · Daniel Dines, Harry Stebbings
[00:28:45] S0: I think this is the crux of the problem. [00:28:49] S1: And here you're a