everyone is racing to build better models. bigger. faster. smarter. but models sit on top of something fragile: massive, long-term compute commitments. @DarioAmodei explained it clearly: if you commit to buying $5t worth of compute because you project a $1t revenue run rate… and you land at $800b instead, you don’t “slow down.” you go bankrupt. that’s the asymmetry. compute is committed upfront. revenue is probabilistic. that gap is the real bottleneck. it’s not about GPUs. it’s about balance sheet exposure. this is the structural risk companies like @OpenAI @AnthropicAI and more are navigating. and this is exactly the layer @primisprotocol is abstracting. separating compute consumption from price and capital risk. so builders scale on demand curves, not on debt curves....