USDT, Computing Power, and Agent: Tether's AI Financial System Experiment

Written by: Yokiiiya

A few days ago, a friend sent me a website—an SDK for wallet development aimed at developers.

If you only see Tether as a stablecoin company, this page might seem a bit “cross-disciplinary.” So I followed WDK further down the rabbit hole and found QVAC (a local AI runtime) and their released training datasets. Expanding outward, I discovered they also linked a compute chain through Northern Data/Rumble and have invested in cross-chain interoperability and embodied intelligence. The information is scattered across various websites, press releases, and announcements, so I first organized it into a panoramic view, then will break down the underlying structure layer by layer.

Tether AI Layout Panorama

If we decompose the panoramic view into layers, we see a hierarchical structure:

This six-layer structure represents a bottom-up build process: USDT provides the asset foundation → WDK embeds assets into applications and agents → QVAC enables local agent operation → Genesis supports model training with data → GPU networks supply computing power → cross-chain and embodied intelligence expansion boundaries. This is a layered experiment, not just a single product innovation.

These six layers may not yet form a closed loop. But they at least constitute a clear architecture diagram. The question is—are these dispersed technical layouts, or an ongoing infrastructure experiment?

1. USDT, Computing Power, and Agents—How a Network Is Built

Earlier, we saw a layered structure. Asset layer, settlement interface, runtime, data layer, and compute layer coexist, but layering alone doesn’t guarantee a functioning system.

The real question is: Are USDT, compute power, and agents beginning to form interdependent relationships? If they are just running in parallel—stablecoins keep issuing, compute investments continue, AI projects develop independently—that’s just horizontal expansion. But if these three are mutually dependent, a network will emerge.

First, assets. USDT itself doesn’t create productivity; it provides settlement capability. In traditional systems, economic entities are humans, and assets rely on bank accounts. But if future production shifts to machines and agents, assets must meet new criteria: programmable, embedded in systems, no bank accounts needed, globally mobile, and technically stable. Stablecoins technically satisfy these conditions. But assets only become part of the network when they are frequently used. This introduces a second variable.

Next, compute power. It’s not a financial instrument but a source of productivity. Running, inference, and training of models all depend on computational resources. Without compute, agents can’t operate continuously. Without continuous operation, no economic activity occurs. Compute itself isn’t part of the financial system, but when value creation comes from algorithms, compute becomes the physical foundation of economic activity. If the asset layer and productivity layer aren’t connected, they’re just parallel worlds. The link is the behavioral subject.

Finally, agents. Agents are nodes in this network. They consume compute, generate actions, and trigger settlements. When an agent calls a model, completes a task, and triggers payment, assets and compute power form a closed loop. Without agents, compute is just a technical resource. Without assets, actions can’t be settled. Without compute, agents can’t operate. The relationship isn’t parallel but dependent. If we abstract this network into a path, it simplifies to:

Compute → supports model operation
Model → drives agent behavior
Agent → triggers asset settlement
Asset → feeds back into the system

When this path occurs frequently, a machine economy structure emerges. If it’s only occasional, the structure doesn’t truly form. This means the question isn’t whether Tether is deploying AI, but whether productivity, production entities, and production relations are beginning to reconnect around agents.

If we push this further, we find it exceeds corporate strategic choices. It involves re-dividing labor between productivity and production relations.

2. AI and Web3: Division of Labor Between Productivity and Production Relations

In recent years, a common summary when discussing AI and Web3 is: “AI releases productivity, Web3 reconstructs production relations.” This isn’t a strict theoretical statement, but as a structural observation, it’s insightful. If we abstract the network from the first section, a clear division of roles emerges.

AI enhances productivity. Its core function is efficiency. Models reduce marginal costs of content creation, coding, and decision analysis. Combining compute and algorithms greatly expands automation scope. From an economics perspective, this is a productivity increase—the ability to generate value per unit time rises. Repetitive tasks are replaced by machines. High-frequency decisions are made by algorithms. In this sense: compute is the new production equipment. Models are the new tool systems. Agents are the new executors.

When agents can operate continuously, make decisions, and act persistently, they begin to resemble economic participants. But productivity gains alone don’t automatically change economic structures. Efficiency can improve without rules changing. The real issue is: when the production subject changes, do the production relations still fit?

Web3 offers a new framework for production relations. These relations determine participation rules, not efficiency. Who can own assets? Who can join the network? Who can settle transactions? Traditional finance is built on human identities and bank accounts. Accounts depend on national identity; assets depend on legal entities. But machines have no nationality. Agents lack natural person identities. Models can’t sign contracts.

When productivity extends to machines, but relations remain tied to human accounts, structural dislocation occurs. Web3 offers not faster payments but programmable assets and embedded settlement rules.

Stablecoins enable assets to exist outside bank accounts. On-chain settlement allows rules to be executed as code. Embedded wallets make assets part of internal system logic rather than external interfaces. In this framework: compute represents productivity. USDT represents production relations. Agents represent production entities. When all three appear simultaneously, the question isn’t “whether to do AI,” but whether productivity and relations are beginning to realign around machine entities.

This division isn’t fixed. It depends on whether agents become genuine economic participants. If AI remains just a human tool, traditional relations suffice. But if machines start independently engaging in high-frequency economic activities, asset and settlement structures must adapt. This is key to understanding Tether’s experiment. It may not be building the strongest model, but testing whether such a structure can exist.

3. What Is Tether’s AI Financial Experiment Really Doing?

Tether’s layout isn’t concentrated on a single track. It’s not trying to build the largest model company nor directly competing in consumer AI applications. Instead, it’s testing an infrastructure hypothesis: if machines become economic entities, does the financial structure need rewriting?

From current arrangements, at least three levels of validation are involved:

1. Can machines become asset holders?
Traditional finance assumes economic entities are humans or legal persons. Stablecoins and embedded wallets suggest an alternative: assets can exist outside bank accounts, accounts can be embedded within systems, and settlement can be triggered by code. If agents can directly hold, call, and settle stablecoins, machines gain their first capability to participate economically. This doesn’t mean machines have legal personhood, but they can act as execution nodes in economic transactions. This is a production relations experiment.

2. Will compute power become part of the financial structure?
Traditional infrastructure revolves around capital, banks, and clearing systems. Compute isn’t a financial variable. But when value creation depends on model inference and algorithm execution, compute becomes the physical basis of production. Through Northern Data and GPU networks, Tether is attempting vertical integration—bringing productivity and settlement into one structure. If AI economy scales, compute may no longer be just a technical resource but part of the financial fabric. This is a productivity layer experiment.

3. Can agents generate high-frequency economic behaviors?
The core variable isn’t compute scale or stablecoin market cap, but whether autonomous agents can operate at high frequency and produce settled economic activities. The network truly forms when: agents run continuously, trigger real value exchanges, and settle on-chain at scale and high frequency.

If agents are just auxiliary tools, or all economic actions are still human-triggered, the system won’t form a true closed loop. This is the most uncertain part of the experiment. It’s a structural test. From an external view, these layouts are scattered across fields: stablecoins, compute, AI runtime, data, cross-chain. But structurally, they point to the same question: will machine economy become a real economic form? If no, it’s just a diversified layout. If yes, it’s paving the way for future financial infrastructure for the machine era. The experiment has no definitive result yet, but it raises an important question: when production entities change, will finance change too?

Conclusion: An Ongoing, Unfinished Experiment

Tether’s real challenge isn’t whether to “enter AI,” but whether to participate in a future financial structure experiment. Compute represents productivity; USDT provides assets and settlement interfaces; agents could become new production entities. Whether these form a stable closed loop remains unknown.

Stablecoins are mature. Compute is expanding. Large models are embedding into more systems and devices. The real uncertainty lies in whether the production subject will change.

If AI remains just a human tool, traditional finance can continue to support it. But if agents begin to participate in high-frequency, autonomous economic activities, financial structures must adapt. As models become infrastructure, human-system interactions are changing. More actions are no longer triggered manually but automatically by algorithms. This doesn’t mean machines will replace humans, but that they will take on part of the economic execution authority. In this context, Tether’s layout is more like early preparation. It may not be building a complete AI financial system, but it’s testing whether the interface needs rewriting when production entities evolve.

This experiment has no final answer yet.