OKX Ventures: AI Computing Power Financialization, Open Source Models Are Pushing Computing Power Toward Capital Markets
- OKX Ventures argues that compute is shifting from an IT operating cost to a capital asset with financial risk exposure.
- The report says GPU purchases, data center buildouts, and long-term contracts are increasingly financed with debt, while standardized GPU-hour forward prices and hedging tools are still lacking.
- It highlights Take-or-Pay contracts as a key structure because they lock in some future revenue and make cash flows more predictable for lenders.
- The report says GPU value remains exposed to technology iteration, future rents, residual value, refinancing, and default disposal risk, especially as leverage rises.
OKX Ventures says AI infrastructure is entering a more capital-intensive phase, and compute is increasingly being treated like a capital asset rather than a simple on-demand IT expense. In its view, GPUs, servers, data center capacity, and multi-year purchase commitments are moving onto balance sheets and creating new financial risk exposure.
The report argues that debt financing is becoming more important as AI capital spending scales up. It cites Take-or-Pay contracts as the main mechanism supporting this structure, since they lock in some future customer payments and give lenders more predictable cash flow. CoreWeave is presented as the most typical example of this model, with more than 98% of revenue coming from such contracts.
At the same time, the report says compute assets still face meaningful price and residual-value risk. GPU rents and resale values can change as new chip generations arrive, and there are still no public, standardized hedging instruments for GPU-hour prices. The overall market impact described is neutral for crypto and wider risk assets, but potentially mixed for AI infrastructure and related equities because easier financing supports expansion while leverage increases refinancing and valuation risk.
OKX Ventures：AI算力金融化，开源模型正推动算力走向资本市场
OKX Ventures认为，AI基础设施正在进入更重资本投入的阶段，算力也逐渐从单纯的按需IT支出，变成类似资本资产来管理。报告指出，GPU、服务器、数据中心容量和多年采购承诺正在进入资产负债表，并带来新的金融风险敞口。
报告称，随着AI资本开支持续放大，债务融资的重要性正在上升。Take-or-Pay合同被视为支撑这种融资结构的关键，因为它能提前锁定部分客户付款，让贷款方更容易判断现金流。报告把CoreWeave视为最典型案例，称其超过98%的收入来自这类合同。
与此同时，报告强调算力资产仍面临明显的价格和残值风险。随着新一代芯片推出，GPU租金和二手价值都会变化，而市场目前仍缺乏标准化的GPU小时对冲工具。就市场影响而言，这一判断对加密货币和大类风险资产偏中性，但对AI基础设施和相关股票可能是偏中性到略偏利好，因为融资更容易支持扩张，但杠杆也会抬高再融资和估值风险。