Sergey Diakov
⬤ METR, a research group tracking advanced AI capabilities, has spotted something striking: AI models are getting better at sustained reasoning faster than anyone expected. Time horizons—basically how long an AI can think through problems without losing the thread—aren't just climbing steadily anymore. Since models like Opus-3 and o1-preview hit the scene, that growth curve has shot past the exponential trend line everyone was watching.
⬤ Why does this matter? Time horizon is becoming the litmus test for whether AI systems can actually act like agents rather than just fancy autocomplete. It's about whether a model can hold it together through multi-step tasks without falling apart halfway through. Early AI tools were mostly one-trick ponies—good for narrow jobs but not much else. What METR's seeing now suggests newer models can actually stick with complex work over longer stretches, which is a genuine leap from what came before.
⬤ This shift isn't just academic. Longer time horizons unlock real applications: deep research projects, coding that spans multiple files, structured analysis that takes hours not minutes, decision-making that requires context from start to finish. Companies are noticing. AI is moving from isolated experiments to tools people actually rely on across departments, and that's only possible when systems can operate coherently for extended periods.
⬤ If this acceleration holds, we're heading toward a different conversation entirely—one focused less on "can AI do this?" and more on "how do we manage AI that can?" Governance frameworks, safety protocols, deployment standards—those become the urgent questions when you're dealing with systems that can genuinely sustain complex reasoning over time.
Sergey Diakov
