Trillion Token Context. No, Really — Anima Anandkumar & Benedikt Jenik, Accelerated Understanding

Key takeaways

• Legacy chip design freezes digital logic before running physics simulations as a passive sanity check, which locks designers out of thermal and electromagnetic headroom. Read more →
• Large language models struggle with sparse feedback like binary thumbs-up ratings, while physical AI models receive dense, continuous error signals across every point in space and time. Read more →
• Neural operators bypass the fixed-resolution limit of standard vision models, letting teams train on coarse simulations and infer directly on dense physical meshes. Read more →
• Accelerated Understanding trains physics models on 1-trillion-token inputs and outputs, scaling up to 5-trillion-token inference runs. Read more →
• Training a single model across multiple physical regimes beats giving dedicated parameter budgets to narrow, domain-specific surrogate models. Read more →