From 'cost center' to 'revenue-generating asset,' computing power is entering the financial asset system

As generative AI enters the scaling stage, computing power is undergoing a new transformation from 'technical cost' to 'revenue asset.' GPU clusters and data center expansions have long been viewed as fixed investments within enterprises, but the sustained expansion of model scale and inference demand has caused computing power to gradually exhibit predictable capacity and clear revenue expectations.

In fact, the capital market was the first to capture the aforementioned changes. CoreWeave's asset structure in 2023–2024 has proven that GPU clusters can serve as collateral for large financing, with models not fundamentally different from traditional equipment leasing or commercial real estate. Meanwhile, the over 60% price fluctuations of high-end GPUs like H100/H200 and GB200 in the primary market give computing power trading characteristics similar to futures commodities, and it is being repriced by the market as an asset.

The operational models of tech giants are also changing in tandem. Microsoft, AWS, and OpenAI secure GPU resources in advance to exchange for future training and inference capabilities, forming a new structure of 'computing liabilities' that shifts GPUs from mere device procurement to productive capital with clear return cycles.

As AI inference demand continues to rise, the similarity between computing power and real-world assets becomes increasingly evident: inference capacity has predictable outputs; computing power can be rented and generate income; future capacity can be used for mortgage financing; tasks can be sliced and scheduled, naturally supporting fragmentation; the flow of multi-network computing power also gives it cross-platform configurability. Therefore, computing power has the foundations to become a global income-generating asset, but the industry still lacks a unified financial layer to undertake the roles of pricing, liquidity, and supply-demand organization.

Compute-Fi: Organizing computing power into a 'liquid and composable' financial asset layer

The emergence of DePIN solves the distributed problem of computing power supply but does not address the dilemma of 'how computing power is priced and how it enters the asset market.' Computing power gradually possesses asset attributes but remains on corporate balance sheets or within independent networks, unable to form a unified market structure.

In this context, the concept of Compute-Fi is gradually being pushed to the market. It attempts to abstract computing power from hardware form into a type of revenue right that can be priced, composed, and liquid. It absorbs the yield structure of Liquid Staking, combines it with the supply model of DePIN, and conforms to the trend of 'financialization of production materials,' allowing computing contributions to have the elements of financial assets: measurable, tradable, configurable, and yield-generating.

Current structural issues in the computing power market further highlight the necessity of Compute-Fi: there is a lack of a unified pricing system between enterprise GPUs, DePIN networks, and edge nodes, high-end GPU price fluctuations lack hedging tools, GPUs remain fixed assets in corporate finances and cannot flow, while computing power earnings are almost entirely internalized by cloud service providers. Computing power already possesses asset attributes but lacks a true asset market.

The emergence of Compute-Fi provides a systematic solution. It captures task loads and model demands through dynamic pricing mechanisms, allows computing contributors to obtain stable returns through revenue distribution mechanisms, routes tasks to optimal computing pools through scheduling networks, and enables contributions to enter the open market as tokens or NFT shares through computing tokenization. In this structure, computing power for the first time possesses the capacity to participate in financial markets, not just as a technical resource.

This relationship has a natural contrast with Ethereum's LST/LSDFi: staking ETH generates income from static assets; Compute-Fi allows computing contributions to have revenue-generating and liquidity attributes. As inference demand rises and model costs increase, a global computing financial market is becoming an inevitable demand in the new stage.

Melos' 'computing neural network': the automated market-making layer of computing power

Melos is essentially a computing economic network built around 'computing contributions and value return,' with its core being the organization, scheduling, and financialization of computing power and data resources. In this system, home devices, edge nodes, and Web3 GPU networks can enter the system as computing suppliers, while the network's ultimate task is to unify and orchestrate these multi-source computing powers into a freely flowing computing market.

Within the framework of Compute-Fi, Melos' unique value is more reflected in the liquidity layer it constructs rather than a single hardware form. It integrates decentralized computing power resources into callable, priceable, and participatory computing liquidity through a 'computing neural network,' transforming computing power from static supply to a dynamic asset capable of participating in market mechanisms.

The design logic of this network has structural similarities with the liquidity foundational layer of DeFi. Just as Uniswap provides liquidity for assets through an automated market-making mechanism, Melos attempts to establish a similar automated market-making framework for computing power. Computing power from home devices, Web3 GPU networks (such as Render, Akash, IONet), and enterprise edge nodes will be uniformly organized in Melos' aggregation layer, forming a scalable computing power pool. Once tasks enter, the network automatically routes based on load demand and execution costs, achieving a supply and demand matching process similar to AMM.

In this mechanism, the value flow of computing power becomes measurable and possesses financial expression capabilities. User devices and computing tokens form clear 'computing rights,' nodes integrate these contributions into schedulable resources, the demand side submits tasks, the network completes identification and distribution, and the returns after execution are then distributed back to participants according to their contribution ratios. Each execution of a task is a release of computing liquidity, while prices automatically form within the network according to supply and demand relationships.

The key to Melos lies in its ability to simultaneously accommodate low-cost computing power from home devices and high-performance resources from GPU networks, giving it a dual capability of 'breadth + depth.' Edge inference and high-intensity tasks are unified in the same protocol layer, enabling computing power to possess a market form of cross-device and cross-network collaboration for the first time.

Through this 'computing neural network,' computing power transforms from a closed supply structure to an economic unit that is composable, tradable, and sustainably revenue-generating. Tasks, resources, pricing, and returns are all automatically completed within the same system, enabling computing power to truly possess liquidity similar to financial assets. As participation scales up, the value of computing power is also more easily redirected from centralized computing enterprise systems back to users, allowing 'computing contributions' to become a measurable economic behavior.

The ultimate goal of Compute-Fi: to give users ownership of 'computing equity' in the AI economy

The significant meaning of Compute-Fi lies in changing the value distribution method of the AI economy. In the current landscape, training and inference computing power of AI is almost entirely controlled by AWS, Azure, Google Cloud, and OpenAI, with over 80% of global computing power monopolized by these cloud giants, leaving users as passive consumers with no direct connection to value creation.

Compute-Fi opens a new path for value flow: computing power gradually transforms from internal resources of enterprises into an economic element that can be contributed, shared, and monetized by all participants. User devices become production materials, computing contributors earn income from task execution, data providers receive value returns from model optimization, and nodes executing tasks receive earnings, all actions measured within the same economic closed loop. This marks the first time computing resources possess true attributes of universal participation.

In this new structure, Melos takes on the role of underlying organization for value flowing to users. Users obtain 'computing rights' by staking devices, contributing computing power, or participating in node collaboration, and executing tasks brings actual returns, while local data transforms into model value inputs under privacy conditions. For the first time, home devices shift from consumer goods to revenue-generating assets; this change not only alters the role of devices but also changes the value distribution structure of the AI economy.

As the scale of home nodes expands, a globally distributed computing layer will gradually take shape, providing real resources for task scheduling, inference execution, and model personalization. Computing power will possess characteristics of composability, configurability, and tradability, like assets in DeFi. The 'computing rights' obtained by users essentially constitute 'equity fragments' in the AI economy, representing their value share in the entire system.

Against the backdrop of continuously rising AI cost structures and growing inference demand, Compute-Fi can become the core outlet for the next generation of AI value distribution. It redirects value from cloud giants back to users who provide real contributions to the network, making every device, every computation, and every piece of data a participant that can be valued.

What Melos is promoting is a new structure of computing ownership—an 'equity system' of computing power that belongs to users, is tradable, and can be priced.

Conclusion

The financialization of computing power has already become an industry consensus. Compute-Fi provides a new organizational method that allows computing power to gain unified financial expression across different devices and networks. The computing neural network built by Melos enables this expression to have a practical path that is schedulable, distributable, and sustainable.

Based on the narrative of the Melos ecosystem, as more home devices, edge nodes, and computing networks are included in the same system, a computing layer involving user participation, market pricing, and network coordination will gradually take shape. It is neither as centralized as traditional data centers nor reliant on single-point supply, but rather provides the resources needed for AI's long-term growth in a way that is closer to application scenarios.

In this new structure, computing power will be able to become an economic element that can be recorded, distributed, and continuously circulated. Melos' exploration showcases the early forms of this path and provides a verifiable foundation for the future maturity of Compute-Fi, allowing computing power to truly enter the financial system for the first time outside of the technical system.