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Google paid $2.7B to bring one of its top AI researchers back. Then cut his team’s compute allocation. He left for OpenAI. If compute can’t be guaranteed to a $2.7B hire, startups have no chance. H100 rental prices: up 60% in a year. Cloud providers now demand multiyear contracts and up to 30% upfront. The bottleneck isn’t talent or capital. It’s access. And that’s the problem @ionet is building against.
Artificial Intelligence usage is increasing, and token consumption is expected to increase 24x by 2030. Agents, @OpenAI's "dots," and new lower-cost models could further amplify demand for compute. But the physical infrastructure powering compute remains a bottleneck. Read more on The Stack: https://www.grayscale.com/the-stack/cheaper-agentic-ai-means-greater-demand-for-compute
Somewhere, a GPU is sitting idle. Somewhere else, a startup is stuck on a waitlist. The race to build more data centres gets the headlines. Making better use of the compute we already have deserves more attention. @ionet connects the two. Your runway should fund your product, not your place in the queue. https://io.net/blog/solving-the-ai-compute-crisis-why-decentralization-is-the-only-way
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