Compute is already the most important economic input in the world. We just haven't built the financial layer around it yet. The Big Four are expected to spend roughly $725B on capex in 2026, up 77% from around $410B last year, while Goldman sees $5.3T of cumulative spend through 2030. You don’t throw that much capital into one resource without eventually building benchmarks, forward curves, credit markets and clearing around it. And now those pieces are appearing almost all at once. An H100 costs $25K–$40K. At $2.50/hr, break-even is ~10K–12K operating hours. near-perfect utilization required. At $1.65/hr, you never make the hardware cost back. For a 1,024-GPU cluster, moving from 55% utilization to 85% can flip monthly economics from a $330K loss to a $340K profit. Neoclouds are running leveraged commodity books where inventory depreciates every 12–18 months and the rental price moves underneath them in real time. That's how commodity markets mature: physical supply creates hedging demand, which creates benchmarks, then futures, lending and clearing. CME and ICE both have compute futures waiting on regulatory approval, while Architect already moved first with offshore GPU perps. It's one of the first signs compute is becoming a financeable asset instead of just infrastructure. Compute has a forward curve now. The physical market and the capital market are being built almost simultaneously. Here's the stack forming around it: ▫️ Index providers @OrnnExchange: the CME + Platts for AI compute by building transaction-based benchmarks, derivatives and capital markets around GPU infrastructure. @squaretower_: turn messy GPU pricing into on-chain benchmarks, derivatives and prime brokerage so compute can finally be priced, hedged and financed like a real asset class. ▫️ Physical capacity marketplaces @akashnet: the physical marketplace layer of the compute capital stack, matching unused GPU capacity with AI demand through an open on-chain market. @ionet: decentralized AI compute with an Incentive Dynamic Engine that keeps GPU suppliers paid in USD while routing excess network revenue into $IO buybacks and burns @AethirCloud: the financing layer for compute, using RWA products and GPU tokenization to turn future compute cash flows into liquid, financeable assets. ▫️ Exchanges / clearinghouses @ICE_Markets: extending its commodity market playbook into AI, launching regulated GPU compute futures and clearing infrastructure for institutional hedging. @Kalshi: brought one of the first public market-implied forward curves for GPU compute, letting traders price future rental costs through regulated event contracts instead of traditional futures. ▫️ Custodians @gensynai: the trust layer for decentralized AI compute. It verifies that ML work actually happened, letting anyone buy or sell compute without trusting a central party. @hyperbolic_labs: the liquidity layer for physical GPU supply, pooling underutilized hardware into an open AI cloud so buyers get cheaper compute and owners earn yield on idle GPUs. ▫️ Lenders @USDai_Official: the credit rails behind AI infra, using GPU-backed loans to connect DeFi liquidity with real-world hardware financing. @gaib_ai: the financing layer for AI infra, turning GPU-backed loans and compute cash flows into tokenized yield assets so anyone can get exposure to the AI capex cycle. Compute has already become an essential economic input, yet the capital market around it is still half-built. Billions are already being lent against the hardware. What comes next is standardization, clearing, insurance, custody and machine-native settlement. Capital markets usually become bigger than the assets underneath them.