Compute Finance: The Financial Layer Being Built by the AI Economy, 0G is Constructing a New Paradigm for Computing Assets

Source: 0G

Almost all scarce assets go through a similar path: first produced, then priced, and finally financialized. Oil has benchmark crude and futures markets, electricity has forward contracts, and now computing power is starting to follow the same path.

Today, users can rent a GPU in just a few minutes, and the rental prices for mainstream GPUs have formed a daily updated benchmark. Major global exchanges are also preparing for computing power futures. However, compared to mature commodities like oil and electricity, computing power still lacks a true financial layer of its own: people can buy and rent computing power, but it is still difficult to hold it as an asset for the long term, earn returns, or have tradable rights to future computing power.

This is precisely the market that "Compute Finance" aims to describe. Its core is to turn computing power itself into a fundamental asset. While DeFi financializes capital, compute finance hopes to financialize one of the most critical production materials of the AI era—computing resources.

Mining Has Already Verified This Path

Compute finance is not without precedent. The development of the Bitcoin mining market has already provided a relatively complete reference.

In the early days, hash power existed only in mining machines and data centers: invest electricity, run equipment, and receive block rewards. Later, platforms like NiceHash began to establish a hash power rental market, allowing miners to sell computing power, with buyers determining prices through public bidding. But having only a trading market is not enough, as the industry still lacks a unified price benchmark.

The real turning point was the launch of Hashprice by Luxor. It abstracted the computing power from different mining machines, different electricity costs, and different operating conditions into a standardized yield metric. Subsequently, forward contracts emerged around this metric, eventually leading to the development of hash power futures listed on exchanges. Today, miners can hedge their future mining income just like farmers hedge agricultural product prices.

This path can be summarized as: first a spot market, then a price benchmark, and finally financial derivatives. The computing power market is now repeating this process, only on a larger scale.

Computing Power Has a Price, But Not a True Capital Market

Currently, the spot market for computing power is actually quite mature. Major cloud service providers like AWS and Google Cloud offer on-demand and reserved computing power, while platforms like Vast.ai and RunPod rent GPUs by the hour, and decentralized networks like Akash and Render bring similar models on-chain.

In other words, computing power does not lack trading venues. What is truly lacking is the financial infrastructure built on the spot market.

This change has already begun to appear. There are already daily rental price benchmarks for mainstream GPUs, and some data has even entered financial terminals; forward price curves are beginning to form, and major exchanges have announced plans to launch cash-settled computing power futures. Meanwhile, major buyers and sellers have started locking in future computing power prices through over-the-counter agreements.

Traditional finance is gradually viewing computing power as a tradable commodity.

However, if we shift our perspective back to the end users who actually consume computing power, the situation is completely different. The most common forms currently are still cloud service credits or prepaid points. These credits are usually non-transferable, lack property rights attributes, and may even expire. Even if a user rents a GPU, there will be no asset left to hold after the rental period ends.

This is the biggest gap in the computing power market right now: the price layer has begun to establish, but the capital layer has not.

What Components Might Compute Finance Include?

First of all, compute finance is not the stock of chip companies, nor is it a data center REIT, and certainly not a simple GPU rental market. It emphasizes that investors directly hold rights linked to computing power itself, rather than indirectly gaining exposure through a company that sells computing power.

The first important concept is Compute Yield, which refers to computing power yield. The returns from traditional staking are usually tokens or interest, while the logic of computing power yield is: users invest capital, and what they ultimately receive is usable computing resources. This way, computing power is no longer just a cost but can become a source of sustainable revenue on the balance sheet.

This is especially important for AI Agents. In the future, an Agent could completely own a certain asset and continuously obtain the reasoning resources needed to maintain its operation. Once computing power can become a stable cash flow, it can be valued; and once it can be valued, it has the foundation for further trading and financialization.

The second concept is Compute Claims, which represent a transferable future right to use computing power. Users can buy in when computing power prices are low and sell when demand surges, or use it as collateral for borrowing. At this stage, what the market truly begins to price is no longer just Nvidia, cloud vendors, or data centers, but computing power itself.

The third direction is the self-financing AI economy. Today, the reasoning costs of most AI products are ultimately borne by corporate budgets or venture capital, but in the future, an AI service could directly use a portion of its income to purchase the next phase of computing power. It no longer relies on external continuous funding but pays its reasoning costs with business income.

For Agents, this change is particularly crucial. An Agent that cannot bear its own reasoning costs is more like an experimental product, while an Agent that can earn money and pay its own operating costs is beginning to truly approach an independent economic entity.

However, the premise for this model to work is that there must be a real income loop. Any "future computing power rights" imply that someone bears the delivery obligation, and the delivery of computing power itself incurs costs every day. Therefore, the real questions that need to be answered include: who is responsible for providing computing power? Who pays for the actual reasoning? If income is insufficient to cover costs, how can computing power rights be fulfilled?

If these questions do not have reliable answers, the so-called "computing power assets" may ultimately just be another form of packaged IOU.

Can Computing Power Be Standardized Like Oil?

One of the biggest challenges facing compute finance is the high degree of non-standardization of computing power itself.

While there are quality differences between one barrel of crude oil and another, they can still be traded through a benchmark pricing system. Computing power is much more complex. H100 and B200 are not equivalent, and the performance of the same chip varies under different computational precisions, while actual AI workloads are also affected by factors such as memory, network, and communication efficiency.

What’s more troublesome is the rapid iteration speed of chips. Today’s flagship GPU may just be a mid-range product in a few years. If the underlying assets are constantly changing, what should be used as the settlement standard for financial contracts?

However, the mining market has actually answered similar questions. Bitcoin mining machines also come from different generations, with different energy efficiencies and cost structures. Hashprice did not attempt to make all mining machines identical but established a unified reference metric, allowing different hardware to form discounts and premiums around this benchmark.

Financial markets have long dealt with non-standardized commodities in similar ways. For example, different grades of crude oil and electricity from different regions can ultimately form a pricing system around a certain benchmark.

Computing power is likely to move towards a similar structure: select a reference GPU or standardized computing unit, establish a settlement benchmark, and allow different types of hardware to trade around it. The truly valuable infrastructure may not be proving that all GPUs are the same, but rather establishing a set of acceptable discount, premium, and basis systems that the market can accept.

Why Now?

The most direct reason is that AI is making computing power increasingly resemble a scarce commodity. Demand is growing rapidly, supply expansion requires substantial capital expenditure, and prices exhibit significant volatility. Historically, such assets often develop their own financial markets.

Moreover, the crypto industry excels at providing the infrastructure that this system needs: programmable rights, transparent collateral, and open markets.

More importantly, AI Agents may become the most natural users of compute finance. Agents do not need offices or employ traditional staff; their core cost is reasoning. For Agents, computing power is equivalent to wages, rent, and raw materials.

Therefore, an economic system truly composed of Agents is unlikely to rely long-term on corporate credit cards and monthly cloud service bills. It needs a type of computing power asset that can be directly held, earned, and consumed by software.

From this perspective, the computing power market has likely reached the midpoint of its financialization journey. The spot market already exists, price benchmarks are forming, and exchanges are exploring.

How Does 0G Understand This System?

0G positions itself at the bottom of this compute finance system and has launched two related products.

Ascend is a liquidity staking product of the 0G Ecosystem. After users stake 0G, they can obtain a0G, allowing them to continue participating in DeFi while maintaining staking rewards; a0G is also the basis for minting computing-related assets in the future.

The Infinite AI (iAI), set to launch on September 29, is positioned as a digital asset linked to computing power. Users can mint iAI using a0G and obtain computing quotas by staking eligible iAI for use in 0G Private Computer, 0G App, and other ecosystem products. According to the initial design, eligible iAI stakers can receive a certain amount of computing quota with daily usage value, which still depends on the product terms.

This process can be summarized as: stake 0G → obtain a0G → mint iAI → stake iAI → obtain computing power → use AI.

In the framework of 0G, Ascend takes on the staking and liquidity layer, iAI corresponds to computing rights, while the actual consumed computing quotas are used to connect financial assets with real AI usage needs.

Computing Power May Be Becoming a New Financial Primitive

In the past, concepts like "tokenized hardware" and "GPU-backed credit" have emerged in the market, but these describe more localized products rather than a complete market.

What Compute Finance attempts to propose is a broader category: computing power is no longer just a cost item for AI companies, but begins to possess the potential to become an independent asset class.

If spot trading, price benchmarks, computing rights, yield products, and derivatives can ultimately connect, the computing power market may gradually form a financial system similar to energy and commodities.

This market is still very early. Many key issues, including standardization, delivery responsibilities, credit risks, liquidity, and regulation, are far from resolved.

But at least one thing is becoming increasingly clear: as AI expands from the software industry to a new economic infrastructure, computing power itself is gradually transforming from a technical resource into an asset that needs to be priced, financed, and managed for risk.

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