The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:09:29 · Harry Stebbings, Daniel Dines
[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. [00:09:43] S0: It has memory, but memory, it's not necessarily learning. It's not the same thing. Memory, it's just a thing that is written down. When I'm talking to you, I don't go back into my memory. I am just being transformed. It's like a model, it's like a new version of the model that comes improved. That new version is not the old version plus a piece of paper that has memorized. It's transformed into its own weights. This is why a model becomes much better. Look, I want to give you a simple example using our own technology. We make our UiPath platform available for coding agents. So people are making, it much easier to create automations on UiPath right now. But what we discover is that a model that has read open source technology, and they have a lot of examples, and they already have in its weights a certain technology will be much better than to create on our own technology. Because regardless how many prompts and skills we create, the model has it in its own weight. It's very different. And think of all the metaphors in the world When you read something versus when you leave something. You can read the biography. It doesn't mean you live that life. It doesn't mean you are transforming. You are gonna answer like that person that live it. To me, really, is the biggest limitations that the models have right now.
00:11:08 · Harry Stebbings
[00:11:08] S1: So I I actually do agree with you, but I think everyone does, and that's why everyone is chasing, you know, recursive self improvement so much, where it's models that can continuously learn from themselves and improve themselves over time without need for human intervention. Does that not remove the limitation that we just said? I don't know, Ben.
00:11:28 · Daniel Dines
[00:11:28] S0: Maybe we are the result of a self improvement loop. Look. Do this thought exercise. Let's put a model that we have today with the best technology. Put it in a spaceship and throw it to the stars. Let's say that we have this technology, like I think von Neumann imagined that is self replicated, this spaceship, goes to different stars, get energy, and they can continue... So compute will be infinite. And models will self improve. Where they would end up with? Maybe they will create a simulation of a world like ourselves. Right? Because they will intrude infinitely. Basically, this is the theory. So they will simulate a world as complex as our own world within it. But that means that we are part of an infinite simulation. So I don't know where it's gonna lead, but I know that there is a big distinction that I made in the book between will and reasoning. It's not like that we are certain that the will to do something, you know, emerges from reasoning or from... Even from consciousness. I think will is a kind of a separate, you know, part of the fabric of the universe. I don't think we as humanity have a clarity about what will is. I think it's wishful thinking to believe that I can take a big model, put into a self improvement loop, and this model is gonna generate will. I don't kind of believe so.