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@technology· What is AI?
Splitting reinforcement learning into a trainer that produces new weights and distributed rollout fleets that collect rewards lets teams sync fresh models across many data centers, which avoids the need for one ultra-dense cluster while keeping feedback timely.
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See all →Prioritizing strict margin targets too early forces resource constraints that curb experimentation and speed, which slows innovation during a hypergrowth phase.
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Fast model releases and frequent new hardware SKUs make older chips lose value quickly, which shortens the effective economic life of infrastructure and makes build-versus-buy timing much more risky.
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