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The Powerful Alternative To Fine-Tuning

The Powerful Alternative To Fine-Tuning

15 segments available

Poetiq is a new startup founded by former DeepMind researchers that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of Lightcone, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. Chapters: 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

Segments Timeline

1
0:00 - 0:40
0:40 duration114 words

Intro

"The world is changing so quickly. This is probably a little bit obvious, but you should just try things and and like every day do something with AI. Last summer, I took a weekend and used um GPT5 to h..."

2
0:40 - 1:07
0:26 duration76 words

What Is Poetiq?

"Welcome to another episode of the Light Cone. Ian Fischer is the co-founder and co-CEO of Poetic, which is building recursively self-improving AI reasoning harnesses for LLMs. Previously, he spent a d..."

3
1:07 - 2:07
1:00 duration194 words

Recursive Self-Improvement Explained

">> At Poetic, what we're building is a recursively self-improving system. And so, recursive self-improvement is this uh, you know, kind of the holy grail of AI where the AI is making itself smarter. T..."

4
2:07 - 2:59
0:51 duration183 words

The Fine-Tuning Trap

">> I mean that seems like actually the like defining thing that a startup really really wants. Like I know that I want to take advantage of whatever the next model is, but the second you're in fine-tu..."

5
2:59 - 3:15
0:15 duration56 words

“Stilts” for LLMs

">> Yeah. I mean, being the smartest model, uh you know, it's a game of inches actually and like so those inches matter a lot, >> right? Right. >> How do we actually get started? I mean, you've built s..."

6
3:15 - 5:05
1:49 duration383 words

Recursive Self-Improvement vs. Fine-Tuning

"have built a system that um uh 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'..."

7
5:05 - 6:37
1:32 duration307 words

Taking the Top Spot on ARC-AGI

"as well. >> And you've done this actually a bunch of times, right? Like I remember when you first came out with your paper in December of last year, uh you shot to the top of ARC AGI V2 and then you'v..."

8
6:37 - 8:40
2:02 duration349 words

Beating Claude on Humanity’s Last Exam

">> So recently you guys just announced some incredible results for humanity's last exam. Can you tell us more about those? >> Humanity's last exam is a a set of 2500 really really hard questions writt..."

9
8:40 - 10:26
1:46 duration357 words

How the Meta-System Works

">> Right. You're you're So, you're getting at a core a really core thing. You know, these harnesses, they are um code, prompts, data, you know, built on top of one or more language models, right? And ..."

10
10:26 - 11:33
1:06 duration219 words

Beyond RL: A New S-Curve

">> It sounds like this is a complete different paradigm than RL because we went through the scurve of regular pre-training RL with when OpenAI released 01 and now this feels like a new one. It sounds ..."

11
11:33 - 13:37
2:04 duration402 words

Automating Prompt Engineering

"with um and then in my spare time I you know do a bunch of context engineering and then the thing is we're sort of like tuning it tuning evals tuning like we're context stuffing ourselves. What does t..."

12
13:37 - 14:50
1:12 duration228 words

From 5% to 95% Performance

">> Yeah. And so that definitely varies per problem. But uh what we've seen, in fact, our our last paper at DeepMind was not doing this recursive self-improving stuff, but we were um we were showing th..."

13
14:50 - 16:18
1:27 duration286 words

Early Access & Putting Your Agent on Stilts

">> So if startups want to use poetic to put their agent on stilts, what should they do? >> Yeah, so right now uh we haven't released anything yet, but uh if you go to poetic.ai, AI. Uh there is a butt..."

14
16:18 - 18:30
2:12 duration384 words

From YC Founder to DeepMind Researcher

">> Slight sort of change change of topic, but something I was curious about. Uh so you arrived at Google over a decade ago when they acquired your first YC startup at portable. A portable was it's por..."

15
18:30 - 19:35
1:05 duration216 words

Advice for Engineers in the AI Era

"quickly. This is probably a little bit obvious, but you should just try things and and like every day uh do something uh do something with AI. Always try to push yourself to find the boundaries of wha..."