Entrepreneurs
1 Curated Dialogues
Erik Bernhardsson
Key Views & Dialogues
20VC: Who Wins the Inference Market | Has Open-Source LLM Inference Peaked? | What Happens to the Cost of Compute | The Real Limits of Post-Training and RL with Erik Bernhardsson, Modal CEO
Modal has reached $1 billion ARR after 15X growth in 12 months, yet capacity spending keeps cash flow negative. Its 25-neocloud pool, per-GPU-second pricing and adjacent products aim to smooth demand, retain customers and lift gross margins toward Bernhardsson’s 40–60% range. Watch whether 30–40% monthly growth can support buying roughly two to three times projected capacity as hardware-price timing remains unknowable.
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- Modal has reached $1 billion ARR, but scaling compute keeps cash flow negative. Stebbings reports a $750 million Accel-led raise at $15.75 billion; Bernhardsson says Modal is profitable on a P&L basis while funding GPU capacity. After 15X growth in 12 months and continuing 30–40% monthly growth, he hopes revenue will be “way beyond” $4 billion in another year.
- Inference’s margin upside comes from selling a platform of products. Selling “tokens for dollars” creates easy switching and a “race to the bottom”; sandboxes, training, reinforcement learning and workflows give customers more reasons to stay. Bernhardsson thinks Modal could eventually reach 40%, 50% or 60% gross margins, while rejecting a return to 90% software margins because customer spending must support multiple infrastructure layers.
- Modal’s capacity aggregation turns unpredictable customer workloads into more manageable aggregate demand. It sources from 25 neoclouds, reserves capacity ahead and sells usage per GPU second, letting customers avoid multiyear commitments. With procurement lead times of three to four months, growth requires buying roughly two to three times projected capacity; hardware prices eventually fall “like gravity,” but Bernhardsson cannot say whether today’s increases last another year or five.
- Nvidia’s ecosystem matters more than CUDA itself. Bernhardsson calls CUDA’s moat “a little overstated,” pointing instead to Nvidia-first support across frameworks, kernels and inference engines. He expects specialized chipmakers and Nvidia both to grow, while admitting that 15 years of predicting the arrival of non-Nvidia accelerators have repeatedly proved him wrong.
- Bernhardsson rejects the claim that open-source inference has peaked. He thinks open and frontier-model usage can each grow “5X, 10X, 20X” over the next 12 months, supported by different requirements for customization, control and performance. He also sees an opportunity for a $100 billion specialized-model inference company and says he would have invested in the specialist discussed at $8.5 billion.
- Sandboxes demonstrate how infrastructure demand can arrive well after a product launches. Modal launched them in 2023, but demand accelerated in summer 2025 as models improved at generating code and whole applications; the business now runs at a $300 million annualized rate, roughly one-third of revenue. Bernhardsson hopes the newly launched training product could reach a $100 million run rate within six to 12 months.
- Reinforcement learning’s clearest advantage lies in tasks with verifiable, relatively short feedback loops. Coding and math fit; engineering decisions whose consequences emerge months later may expose limitations, although Bernhardsson repeatedly allows that he could be wrong. He remains very bullish on software engineering demand because greater productivity expands the need for software and shifts work toward understanding human requirements.
- He is almost certain there will be a 20–30% stock-market pullback. That does not change his view that the long-term opportunity is “so fucking big.” Separately, he says he would be nervous about companies dependent on continual equity financing; his operating rule is to raise every round “as if it’s your last round,” with a real path to commercialization and revenue.