Fast learning loops can become a startup’s moat

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 1:09:25

   Eric, in the next few years, what moats actually exist for startups as AI is coming in and disrupting? Do you have a list? Yes, I'll give you a simple answer. And what do you look for in the companies that you're investing in? So first, in the deep tech hardware stuff, there's going to be patents, patents, filings, inventions, you know, the hard stuff. Those things are much slower than the software industry in terms of growth, and they're just as important.

2. 1:09:58

   You know, power systems, all those robotic systems we've been waiting for a long time, it's just slower. Hardware is hard. Hardware is hard for those reasons. In software, it's pretty clear to me it's going to be really simple. these software is typically a network effect business where the fastest mover wins the fastest mover is the fastest learner in an ai system so what i look for is a is a company where they have a loop ideally they have a couple of learning loops i'll give you simple learning loop that as you get more people the more people click and you learn from their click they they express their preferences so let's say i invent a whole new consumer thing which i don't have an idea right now for it but imagine i did and furthermore i said that i don't know anything about how consumers behave but i'm going to launch this thing the moment people start

3. 1:10:52

   using it i'm going to learn from them and i'll have instantaneous learning to get smarter about what they want. So, I start from nothing. If my learning slope is this, I'm essentially unstoppable. I'm unstoppable because my learning advantage by the time my competitor figures out what I've done is too great. Now, how close can my competitor be and still lose? The answer is a few months because the slopes are exponential. And so, it's likely to me that there will be another 10 fantastic Google scale, metascale companies, they'll all be founded on this principle of learning loops. And when I say learning loops, I mean in the core product solving the current problem as fast as you can. If you cannot define the learning loop, you're going to be beaten by a company that can define it.

4. 1:11:45

   And you said 10 meta Google size companies. Do you think there'll also be a thousand, Like if you look at the enterprise software business, Oracle on down, PeopleSoft, whatever, thousands of those? Or will they all consolidate into those 10 that are domain-dominant learning loop companies? I think I'm largely speaking about consumer scale because that's where the real growth is. The problem with learning loops is if your customer is not ready for you, you can only learn at a certain rate.

5. 1:12:17

   So it's probably the case that the government is not interested in learning, and therefore there's no growth in learning loop serving the government. I'm sorry to say. That needs to get fixed. Educational systems are largely regulated and run by the unions. They're not interested in innovation. They're not going to be doing any learning. I'm sorry to say. That has to get fixed. So the ones where there's a very fast feedback signal are the ones to watch.

6. 1:12:43

   Another example, it's pretty obvious that you can build a whole new stock trading company where you learn, if you get the algorithms right, you learn faster than everyone else and scale matters. So in the presence of scale and fast learning loops, that's the moat. Now, I don't know that there's many others. You do have – Do you think brand would be a moat? Brand matters, but less so. What's interesting is people seem to be perfectly willing now to move from one thing to the other, at least in the digital world.

7. 1:13:14

   And there's a whole new set of brands that have emerged that everyone is using that are the next generations that I haven't even heard of. Within those learning loops, do you think domain-specific synthetic data is a big advantage? Well, the answer is whatever it causes faster learning. There are applications where you have enough training data from humans. There are applications where you have to generate the training data from what the humans are doing, right?

8. 1:13:40

   So you could imagine a situation where you had a learning loop where there's no humans involved, where it's monitoring something, some sensors. But because you learn faster on those sensors, you get so smart, you can't be replaced by another sensor management company. That's the way to think about it. So what about the capital for the learning loop?