Stop Treating Physics as a Sanity Check in Chip Design
Benedikt Jenik explains how multi-physics foundation models transform chip layout co-design and solve inverse problems in geothermal exploration.
Trillion Token Context. No, Really — Anima Anandkumar & Benedikt Jenik, Accelerated Understanding
With Anima Anandkumar, Benedikt Jenik · Sunday, September 6, 2026
Accelerated Understanding co-founders Anima Anandkumar and Benedikt Jenik discuss building a foundational, multi-physics model that learns physical dynamics across fluid mechanics, semiconductors, and energy systems. They detail how neural operators allow 4D rollouts with up to 5-trillion-token context windows, explain why dense physical laws enable superior self-improvement compared to language models, and reveal early commercial traction in chip design and geothermal exploration.
Why Physics AI Beats LLMs at Self-Improvement
Benedikt Jenik and Anima Anandkumar explain how dense physics equations and curriculum engineering break the AI data wall.
Why Neural Operators Beat Transformers for Physical AI
Anima Anandkumar explains why neural operators, not transformers, power 5-trillion-token physics simulations and resolution invariance.
Why Physics AI Needs 5-Trillion-Token Context Windows
Accelerated Understanding co-founder Benedikt Jenik explains how 4D physics models train on 1-trillion-token contexts and output 22 terabytes of state.
Why Foundation Models Beat Narrow Physics Surrogates
Accelerated Understanding shows how multi-physics foundation models beat narrow surrogates in chip design and geothermal simulation.