This week, Mistral announced it raised €3 billion in a Series D funding round, pushing its post-money valuation past €21 billion. With the influx of cash — $3.5 billion in US dollars — it plans to expand its frontier research, scale compute capacity for model training, and grow its infrastructure. 

Where Mistral’s allocating new funds suggests what the French AI company is betting on for the future of AI power: open-weight models can only do so much if the compute and infrastructure underneath remain concentrated among a few key players. 

So far, model superiority has been a major factor in who gets to rule the AI roost. Some open-weight advocates have been touting open-weight models as a way to combat this concentration by giving developers more choice over the models they use — and a way to escape dependence on proprietary APIs. This way, rather than relying exclusively on one provider’s model, developers can adapt open-weight models for their own use.

The catch? Running powerful models takes enormous amounts of compute. Training frontier models — and serving them at high volume — requires compute capacity concentrated among a relatively small number of labs, chip suppliers, and infrastructure providers.

For his part, Dario Amodei, CEO and co-founder of Anthropic, challenged that vision for open-weight models last month in an exchange on X, where he wrote that open weights “are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips.” 

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