I think tokenized compute is becoming a distinct digital asset class. Most AI investing still focuses on models and applications. The market is becoming more interested in the resource every one of them must continuously pay for = compute. The reason is a basic supply-demand imbalance. Digital demand can scale almost immediately. A new AI agent can launch in hours, operate continuously and execute multi-step tasks that consume significantly more compute than a normal chatbot request. Physical supply cannot expand at the same speed. New capacity requires electricity, grid connections, permits, data centers, cooling systems, GPUs, high-bandwidth memory, networking equipment and specialized labor. Grid connection alone can take years. This creates a financial setup I find increasingly difficult to ignore: Demand is software-scaled. Supply is physically constrained. If agent activity continues growing, more value should accrue to the companies and protocols that own, finance or efficiently allocate active compute capacity. Crypto is starting to make that exposure investable in several forms. @Compute_Labs and @gaib_ai focus on the ownership and financing side. They turn GPUs, infrastructure financing and the resulting cash flows into onchain assets. This is the closest category to an RWA investment in productive AI infrastructure. @AethirCloud, @ionet and @akashnet_ operate compute marketplaces. Their networks aggregate hardware from different providers and let users rent capacity for AI workloads. Here, the main questions are utilization, customer revenue and how much economic activity reaches the token. @orbiodotso and @Cerebro_RH are testing another model: tokenized inference credits. Instead of tokenizing the GPU itself, they make access to AI inference transferable and programmable. Trading fees can fund credits that agents later consume. @opentensor approaches the market differently. Its subnets produce digital commodities such as compute, inference, storage and prediction. While TAO coordinates incentives between miners, validators and capital. @virtuals_io belongs mainly on the demand side. Its agents need inference, memory and runtime to operate. As agents begin paying these costs directly from their wallets and earned revenue, compute becomes a recurring operating expense within the agent economy. This is why I would not value every “compute token” in the same way. One may represent ownership of hardware cash flow. Another may provide access to future inference. Another coordinates a marketplace. Another captures demand generated by productive agents. I do not think every project in this sector will benefit equally. Compute is only scarce when it has the right chips, available power, suitable location and real customers. But the broader direction is becoming clear to me. Tokenization can turn GPU ownership, inference rights and compute-generated cash flows into liquid onchain assets. AI gave compute economic demand. Crypto may give it an open capital market.