The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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
00:28:50 · Daniel Dines
[00:28:50] S0: true philosopher. Yeah. Yeah. And this is this is where we put a lot of effort as a company. And we are introducing a new technology that we call it cartography. And cartography is a discipline. So it's a discipline to help companies surface all the information of how the work is done and help them create this map of work. And one big important of cartography is to actually have investigate what people are doing on their desktops. And we have a product that we call the Cartographer Agent that can interview people, real subject matter experts, have them basically record what they are doing. And the agent is interviewing them in real time. They... If if you interview a finance person, it can ask why did you change this invoice when the ZIP code was different? Why did you choose a different path? Tell me. And they can start surfacing all of these exceptions. So that's real agents interviewing real people. It's pretty cool stuff. And then you consolidate data from multiple people, and then we create the process maps. So we show them how the work works in kind of real time. And then after this map of work that show the work as is, you can use our coding agents and you can come up with an idea of how you should transform the process. And you transform the process by printing software. So you start with the process and at point A, it's kind of fully manual. Of course, you have enterprise systems like system of records, but people operate the system. I think the goal of any enterprise is to have less people operating the system and more automations and agentic AI operating the systems.
00:30:37 · Harry Stebbings, Daniel Dines
[00:30:37] S1: Do you not fear pushback from people working in the company aware that you are watching what they do to replace them? [00:30:45] S0: That is what Zuck got. This is inevitable. It very much depends how you pitch the company. So in UiPath, I think it was important to tell people, again, guys, we are not doing anything stupid. We are not doing any mass extinction, you know, for the pretense of AI, But transformation is inevitable. And you guys have to transform, and everybody will get a chance. And the people will... That will become more literate in respect to AI will have a better chance not only here, but in the future in any other job. That's the message that I think everybody should get. Did they respond to it? Like, did you see AI adoption through the roof [00:31:24] S1: after that? [00:31:25] S0: I think the response is good. AI adoption requires more cycles than just discovering the process and people's input in this. But look, in the beginning of the year, the fear of people across the industry, and not only in my company, but in many companies was off charts, the fear of completely being replaced. Now I think people start to get a bit more understanding of the durability of their 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 Einstein's are not hirable yet.
00:31:25 · Daniel Dines, Harry Stebbings
[00:31:25] S0: I think the response is good. AI adoption requires more cycles than just discovering the process and people's input in this. But look, in the beginning of the year, the fear of people across the industry, and not only in my company, but in many companies was off charts, the fear of completely being replaced. Now I think people start to get a bit more understanding of the durability of their 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 Einstein's are not hirable yet. [00:32:04] S1: One of the companies we invest in, McCore, data provider, Brandon Foudy, the CEO, tweeted yesterday that they spend 3x x the spend of human salaries on inference. What percent or multiple or take would you say you spend human salaries on inference?
00:32:04 · Harry Stebbings, Daniel Dines
[00:32:04] S1: One of the companies we invest in, McCore, data provider, Brandon Foudy, the CEO, tweeted yesterday that they spend 3x x the spend of human salaries on inference. What percent or multiple or take would you say you spend human salaries on inference? [00:32:20] S0: I personally don't care about it. Let me let me tell you something. It's a it's a simple hypothesis. If the work, the quality were better of a human can be done by a machine, I will hire today a machine even if it's more expensive man than a human. Because human cost will only increase, and they bring errors into the pictures while the cost of machine will increase. So I will have a competitive advantage compared to people that will stick to humans. I think everyone will do this. So I don't think the cost of tokens, it will be the real question if you replace a person with AI. But the real problem today is that AI cannot replace a person. Because if, again, if bring me an Einstein that replace me, and I will happily go in any vacation in the world. But this Einstein doesn't yet exist. I would like to be interviewed and have this podcast with another Einstein. Man, this thing doesn't exist today. That's the reality. So let's call it a reality. Let's call a spade a spade. Maybe this technology will emerge and somehow Einstein's that embody like a person, that have will, that gets transformed on the job, learn on the job, have the capability of reasoning, imagination of Einstein's exist, of course, all the jobs will go extinct.