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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,
by plutodev 9mo ago
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.
- m_Anachronism 9mo ago"Yeah that's exactly the point - when you're actually working with these models on the infrastructure side, the whole 'intelligence' narrative falls away pretty fast. It's just tensor operations at scale. Curious about your experience with decentralized GPU networks though - do you find the reliability trade-off worth it for most workloads, or are there specific use cases where you wouldn't go that route?"
- deleted 9mo ago[deleted]