Modal CEO Explains Non-Rectangular AI Compute Demand
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Modal CEO Erik Bernhardsson explains that AI compute demand is non-rectangular, meaning it is bursty and unpredictable rather than steady. Modal addresses this by aggregating demand from thousands of customers and offering elastic sandboxes. Sandboxes now account for nearly one-third of Modal's revenue, driven by the 2023 upgrade to language model code generation capabilities. The company achieves fast sandbox startup through its custom file system, process checkpointing, and GPU memory snapshots, with plans to expand these capabilities beyond code generation into training and post-training workloads.
Written with AI from the reporting · updated 10/22/2026
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New at this step ·Modal CEO Erik Bernhardsson explains why AI compute demand is non-rectangular and how sandboxes drive growth
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Modal CEO Erik Bernhardsson explains why AI compute demand is non-rectangular and how sandboxes drive growthLatent Space
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Modal CEO 解释 AI 算力为何呈矩形：需求弹性与沙箱增长
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- Latent Space · Oct 23, 01:40Modal CEO Erik Bernhardsson explains why AI compute demand is non-rectangular and how sandboxes drive growth
Modal CEO Erik Bernhardsson argues that AI compute demand rarely follows fixed-capacity contracts, instead fluctuating throughout the day. He describes the company's strategy of pooling demand across thousands of customers to provide elastic compute slices, noting that sandboxes now comprise roughly a third of Modal's revenue following unexpected growth spurred by coding agents and reinforcement learning.
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- Latent SpaceSelectedModal CEO Erik Bernhardsson explains why AI compute demand is non-rectangular and how sandboxes drive growth
Modal CEO Erik Bernhardsson argues that AI compute demand rarely follows fixed-capacity contracts, instead fluctuating throughout the day. He describes the company's strategy of pooling demand across thousands of customers to provide elastic compute slices, noting that sandboxes now comprise roughly a third of Modal's revenue following unexpected growth spurred by coding agents and reinforcement learning.
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