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.
This framing makes sense. What we call “AI thinking” is really large-scale, non-sentient computation—matrix ops and inference, not cognition. Once you see that, progress is less about “intelligence” and more about access to compute. I’ve run training and batch inference on decentralized GPU aggregators (io.net, Akash) precisely because they treat models as workloads, not minds. You trade polished orchestration and SLAs for cheaper, permissionless access to H100s/A100s, which works well for fault-tolerant jobs. Full disclosure: I’m part of io.net’s astronaut program.
You’re not alone a lot of founders and builders experience this ADHD-like pattern: intense curiosity, deep focus, then idea overload. Your brain is optimized for exploration, not maintenance, which is why vision comes easy and follow-through feels hard. What helps is having one place to dump ideas, time-boxing deep work, offloading admin, and keeping tools minimal. Switching off is still tough, but physical activity, hard stop routines, and accepting rest as part of productivity make a real difference.