AI Weekly Malaysia

Latent Space interviews Modal CEO Erik Bernhardsson after Modal reported crossing $1B annualized run rate, raising a $750M Series D at a $16B valuation, and spinning up nearly 3B sandboxes. The core argument is that fixed GPU capacity contracts look like rectangles, while real AI workloads spike and fall, so Modal pools demand across thousands of customers and sells elastic slices; the risk is locking in supply at today’s prices if GPU prices fall. Modal’s sandbox product was nearly shut down before coding agents, background agents, and reinforcement learning created new demand.

Why: If you build coding agents or background agents, this gives a concrete signal that code-execution sandboxes can become a major workload: Modal says it has run nearly 3B sandboxes, and the product survived only after agent workloads arrived. That argues for designing agent execution around per-use elastic sandboxes rather than buying peak GPU capacity up front, and for checking whether a provider’s pooled-capacity model exposes you to repricing if GPU prices fall.

At Nvidia’s GTC conference in Berlin, Jensen Huang told CNBC that demand for neoclouds is “off the charts,” but the missing piece is working capital to fuel growth. He said capital demand exceeds availability and that power is a major constraint, especially in Europe where electricity prices are higher. Huang said allocating scarce energy to companies like Nebius and Mistral is crucial for their growth.

Why: Huang frames the bottleneck as working capital and, in Europe, power allocation—not demand. If you are choosing GPU providers for AI or agent workloads, treat neocloud capacity and pricing as supply-constrained; for European deployments, factor in higher electricity costs and energy-allocation risk instead of assuming demand alone will produce cheaper capacity.