The Powerful Alternative To Fine-Tuning
Ian Fischer, co-founder of Poetic, discusses a paradigm shift beyond fine-tuning: building recursively self-improving AI reasoning harnesses that automatically optimize prompts, code, and reasoning strategies on top of existing LLMs. Rather than spending hundreds of millions retraining models from scratch, startups can use Poetic's system to outperform frontier models at a fraction of the cost, achieving state-of-the-art results on benchmarks like ARC-AGI v2 and Humanity's Last Exam while remaining compatible with future model releases.
Key takeaways
- • Fine-tuning is a losing strategy because massive investment becomes obsolete when new frontier models release; recursive self-improvement harnesses solve this by staying compatible across model versions.
- • Poetic's system generates AI reasoning harnesses (code, prompts, data, and reasoning strategies) automatically, reducing optimization costs to under $100k—dramatically cheaper than fine-tuning's hundreds of millions.
- • Reasoning strategies matter far more than prompt engineering alone: in one example, manual prompt optimization achieved 5% performance, but adding structured reasoning strategies jumped results to 95%.
- • Poetic achieved 54% on ARC-AGI v2 at half the cost of Google's Gemini Deep Think (45%), and 55% on Humanity's Last Exam compared to Anthropic Claude's 53.1%, demonstrating the "stilts" approach.
- • The meta-system learns what works for a specific problem without human intervention—it decides when to add context, generate examples, or modify reasoning—inverting the traditional ML requirement to deeply understand your dataset.
- • Startups should experiment daily with AI tools, push capability boundaries, and focus on unsolved hard problems rather than waiting for perfect conditions; the pace of change makes execution more important than planning.
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"We got like to 5% performance with Gemini 1.5 flash this was a while ago and then when we added on the the reasoning strategies we went from 5% to 95%"
Ian Fischer · ▶ 14:09
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