This looks less like an AI failure and more like a compute economics problem. Frontier labs are chasing marginal model gains that require exponentially more GPUs, power, and capex, so burn rates explode even if demand grows. Centralized hyperscale data centers concentrate that risk on a few balance sheets. An alternative is treating AI as a distributed workload problem—using spot or decentralized GPU markets (io.net, Akash, etc.) to tap existing idle capacity instead of financing trillion-dollar builds. You trade enterprise SLAs for lower capex exposure, but structurally it changes the cost curve.
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This looks less like an AI failure and more like a compute economics problem. Frontier labs are chasing marginal model gains that require exponentially more GPUs, power, and capex, so burn rates explode even if demand grows. Centralized hyperscale data centers concentrate that risk on a few balance sheets. An alternative is treating AI as a distributed workload problem—using spot or decentralized GPU markets (io.net, Akash, etc.) to tap existing idle capacity instead of financing trillion-dollar builds. You trade enterprise SLAs for lower capex exposure, but structurally it changes the cost curve.