Instead of training models to generalize across many problems, this approach focuses on finding the single best solution for one specific task, like a new material or algorithm. The model itself can be discarded; the value is in the single, world-changing artifact it produces.
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OpenAI co-founder Ilya Sutskever suggests the path to AGI is not creating a pre-trained, all-knowing model, but an AI that can learn any task as effectively as a human. This reframes the challenge from knowledge transfer to creating a universal learning algorithm, impacting how such systems would be deployed.
The AI industry is hitting data limits for training massive, general-purpose models. The next wave of progress will likely come from creating highly specialized models for specific domains, similar to DeepMind's AlphaFold, which can achieve superhuman performance on narrow tasks.
When AI models achieve superhuman performance on specific benchmarks like coding challenges, it doesn't solve real-world problems. This is because we implicitly optimize for the benchmark itself, creating "peaky" performance rather than broad, generalizable intelligence.
![[State of RL/Reasoning] IMO/IOI Gold, OpenAI o3/GPT-5, and Cursor Composer — Ashvin Nair, Cursor thumbnail](https://assets.flightcast.com/V2Uploads/nvaja2542wefzb8rjg5f519m/01K4D8FB4MNA071BM5ZDSMH34N/square.jpg)
The path to a general-purpose AI model is not to tackle the entire problem at once. A more effective strategy is to start with a highly constrained domain, like generating only Minecraft videos. Once the model works reliably in that narrow distribution, incrementally expand the training data and complexity, using each step as a foundation for the next.
The path to robust AI applications isn't a single, all-powerful model. It's a system of specialized "sub-agents," each handling a narrow task like context retrieval or debugging. This architecture allows for using smaller, faster, fine-tuned models for each task, improving overall system performance and efficiency.
Rather than achieving general intelligence through abstract reasoning, AI models improve by repeatedly identifying specific failures (like trick questions) and adding those scenarios into new training rounds. This "patching" approach, though seemingly inefficient, proved successful for self-driving cars and may be a viable path for language models.
Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.
The ultimate goal isn't just modeling specific systems (like protein folding), but automating the entire scientific method. This involves AI generating hypotheses, choosing experiments, analyzing results, and updating a 'world model' of a domain, creating a continuous loop of discovery.
As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.
The central challenge for current AI is not merely sample efficiency but a more profound failure to generalize. Models generalize 'dramatically worse than people,' which is the root cause of their brittleness, inability to learn from nuanced instruction, and unreliability compared to human intelligence. Solving this is the key to the next paradigm.