The Fastest Path To Super Intelligence

**Ian Fischer** (0:00)
The world is changing so quickly. This is probably a little bit obvious, but you should just try things. And like every day, do something with AI. Last summer, I took a weekend and used GPT-5 to help me build an iPhone app. I hadn't done that in a decade. So fast. Yeah, it's so fast and so easy. And that was an age ago, that was like eight months ago. Now it's even faster and easier. Don't limit yourself. Anything that you imagine, you should just try to use AI and see how far you can get with it. And you'll be making the world better.

**SPEAKER_2** (0:40)
Welcome to another episode of The Lightcone. Ian Fischer is the co-founder and co-CEO of Poetiq, which is building recursively self-improving AI reasoning harnesses for LLMs. Previously, he spent a decade as a researcher at Google DeepMind and founded a mobile dev tools company through YC years ago. Welcome, Ian.

**Ian Fischer** (1:00)
Thank you, I'm so happy to be here.

**SPEAKER_2** (1:01)
What is Poetiq? How is it different than RL? How is it different than context engineering?

**Ian Fischer** (1:07)
At Poetiq, what we're building is a recursively self-improving system.
Recursive self-improvement is this holy grail of AI where the AI is making itself smarter. The core insight that we had is that we could do recursive self-improvement far faster and cheaper than all of the other ways that people had been proposing to do this. Obviously, I can't go into details about what that is, what our particular approach is. But most of the approaches out there involve, they require you to train a new LLM from scratch. And training LLMs from scratch costs hundreds of millions of dollars and takes months of effort.

**SPEAKER_2** (1:49)
And then Anthropic or OpenAI will come along and just eat your lunch in the next model release.

**Ian Fischer** (1:53)
Right, right. And of course, Anthropic and OpenAI and Google, they're exploring recursive self-improvement, but typically at that level of having to train a new model for every step of self-improvement that they do.

**SPEAKER_2** (2:07)
I mean, that seems like actually the defining thing that a startup really, really wants. I know that I want to take advantage of whatever the next model is, but the second year in fine-tuning land, I'm spending millions to hundreds of millions of dollars. And then guess what? I just lit it on fire because the next version of the frontier model comes out, and I'll never catch up. Whereas working with your systems means that I will always have the thing that is better than the thing that's out of box. And that's sort of like the Holy Grail.

**Ian Fischer** (2:38)
Yeah, we think that this is incredibly valuable to anybody who's building on top of large language models. We don't view the frontier models as competitors. They're the ones that were building stilts to stand on top of. But if we didn't have that foundational layer, then Poetiq couldn't exist.

**SPEAKER_2** (2:59)
I mean, being the smartest model, it's a game of inches, actually. And so those inches matter a lot. How do we actually get started? I mean, you've built something that basically any startup could use that it's sort of like stilts, really.

**Ian Fischer** (3:14)
We have built a system that can automatically generate systems for your particular problem that will always outperform the underlying language models. And without kind of the massive expense, as you're saying about the bitter lesson, where what would you have done without Poetiq? You probably would have said, okay, we're going to first collect a large data set, like tens of thousands of examples for a particular problem that we're working on. And we're going to fine tune the best model we can get our hands on. Maybe that's one of the frontier models, or maybe it's an open weights model. It doesn't particularly matter. You're going to spend a lot of money on that fine tuning, the compute is so expensive. And then at the end of it, you have something that works better than the thing that you fine tuned on top of. But by then, a new model has come out. And it's better than the thing that you fine tuned. You know, you fine tuned, you know, like three years ago on top of GPT 3.5 or whatever, and then GPT 4.0, 4 comes out and it just blows you out of the water. And so are you going to do that again, or are you going to go out of business? And like, in some cases, the latter. With Poetiq, what we end up giving you is a, you know, people are calling these things harnesses now, but, you know, we're an agentic system or whatever you want to call it, that sits on top of one or more language models. And it just performs better than them. And when the new model comes out, that same harness is perfectly compatible with it. And you don't need to change anything to get the, you know, an even bigger performance bump. Additionally, we can, you know, continue to optimize for this new model, whatever the new model is that you want to use, and, you know, make it even better. But you don't lose out on, you know, hundreds of millions of dollars. In fact, we do this so much more cheaply than fine-tuning would cost as well.