The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:03:57 · Harry Stebbings, Daniel Dines
[00:03:57] S1: I... It really is the most wild time right now. And so I wanna start. You've written a book. 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 did you decide to write a book? [00:04:18] S0: Man, I dreamt to write a book since I was a kid, and I discovered I have no talent. Now I got really a great opportunity. Claude and Chad GPT 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 maybe almost six months effort. And I started with different threads of thought. One was, you know, what are the limitations of AI? Is there any durable limitation of AI? Or in a couple of years, there will be millions of Einstein's in a data center, and we can all go to play or do whatever we would like to do because the Einstein's will do the work for us. [00:05:11] S1: Can we just start on that then? I think it's nice to take it in kind of segments. Limitations of AI, Einstein's in data centers. How should we be thinking about that moving forwards?
00:05:11 · Harry Stebbings, Daniel Dines
[00:05:11] S1: Can we just start on that then? I think it's nice to take it in kind of segments. Limitations of AI, Einstein's in data centers. How should we be thinking about that moving forwards? [00:05:20] S0: 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. So I was thinking, man, 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, you know, an enterprise account, a Slack account, and and I ask, do my job or do whatever job in the enterprise? It seems that reality is maybe different. And probably Dario wanted to say that we'll have millions of entities that will have the... Some of the reasoning powers of Einstein, which I agree, but not Einstein's as a person's, not Einstein's that are capable of learning on the job. Because do you agree that one of the major expectation when you hire someone is that they will hire 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. So we expect that [00:06:27] S1: 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. Humans are difficult to manage. I'll take the AI any day of the week, please.
00:06:27 · Harry Stebbings, Daniel Dines
[00:06:27] S1: 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. Humans are difficult to manage. I'll take the AI any day of the week, please. [00:06:44] S0: If AI can work as well as a human hurry, but you say that AI learn on the job. AI can create a notepad on the job, a scratch pad 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. Let me give you an example. You have two chefs. One chef twenty 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. It's becoming. It's like, 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.
00:06:44 · Daniel Dines, Harry Stebbings
[00:06:44] S0: If AI can work as well as a human hurry, but you say that AI learn on the job. AI can create a notepad on the job, a scratch pad 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. Let me give you an example. You have two chefs. One chef twenty 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. It's becoming. It's like, 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. [00:07:48] S1: 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 largely outbound and inbound, but whether it's social media. 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.
00:07:48 · Harry Stebbings, Daniel Dines
[00:07:48] S1: 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 largely outbound and inbound, but whether it's social media. 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. [00:08:17] S0: 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 gonna churn, and I can act before even any data is coming. How how do I develop this hunch? It's through my years of transformation. It's not written on a piece of paper. I mean, 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? Every time I'm asking a question to the model, the model will read my entire enterprise, will have to read it. Okay? Yep. Sure. So this is not possible. [00:09:04] S1: 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
00:09:04 · Harry Stebbings, Daniel Dines
[00:09:04] S1: 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 [00:09:09] S0: 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 in all my thoughts. This is not true about AI. [00:09:29] S1: Well, I mean, I'm not being rude. It it is. Like, that's why people remain with OpenAI because it has memory, and it is able to infer from past queries, prompts, and give you suggestions based on those. So it does have memory.