High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / prediction

Published · transcript-backed

Edwin Chen: prediction

7 Dec 2025 Lenny's Podcast The 100-person AI lab that became Anthropic and Google's secret weapon | Edwin Chen (Surge AI)

“I think one of the things that's going to happen in the next few years is that the models are actually going to become increasingly differentiated because of the personalities and behaviors that the different labs have and the kind of objective functions that they are optimizing their models for.”

— Edwin Chen

Source trail

Everything needed to verify it.

Speaker
Edwin Chen
Attribution
Verified speaker
Claim type
prediction
Recorded
7 Dec 2025
Publisher
Lenny's Podcast

Transcript context

…Awesome. I want to ask a couple broad AI kind of market questions. What else do you think is coming in the next couple of years that people are maybe not thinking enough about or not expecting in terms of where AI is heading? What's going to matter? I think one of the things that's going to happen in the next few years is that the models are actually going to become increasingly differentiated because of the personalities and behaviors that the different labs have and the kind of objective functions that they are optimizing their models for. I think it's one thing I didn't appreciate a year or so ago. A year or so ago, I thought that all of the AI models would essentially become very commoditized. They would all behave like each other, and sure, one of them might be slightly more intelligent in one way today, but sure, the other ones would catch up in the next few months. But I think over the past year, I've realized that the values that the companies have will shape the model. So let me give you an example. So I was asking Claude to help me draft an email the other day, and it went through 30 different versions. And after 30 minutes, yeah, I think it really crafted me the perfect email, and I sent it. But then I realized that I spent 30 minutes doing something that didn't matter at all. Sure, now I got the perfect email, but I spent 30 minutes doing something I wouldn't have worried at all before, and this email probably didn't even move the needle on anything anyways. So I think there's a deep question here, which is, if you could choose the perfect model behavior, which model would you want? Do you want a model that says, "You're absolutely right. There are definitely 20 more ways to improve this email," and it continues for 50 more iterations. And it sucks up all your time and engagement. Or do you want a model that's optimizing for your time and productivity and just says, "No, you need to stop. Your email's great. Just send it and move on with your day"? And again, just because... In the same way that there's like a kind of a fork in a road between how you could choose how your model behaves for this question, it's like for every other question that models have, the kind of behavior that you want will fundamentally affect it. It's almost like in the same way that when Google builds a search engine, it's very different from how Facebook would build a search engine, which is very different from how Apple would build a search engine. They all have their own principles and values and things that they're trying to achieve in the world that shape all the products that they're going to build. And in the same way, I think all the [inaudible 00:50:40] will start behaving very differently too. That is incredibly interesting. You already see that with Grok. It's got a very different personality and a very different approach to answering questions. And so what I'm hearing is you're going to see more of this differentiation.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence