AI has already transformed the internet, the way we write, code, generate images, and solve problems. However, there's another component of the AI economy that could prove to be just as significant: inference.

Each time you ask an AI model a question, create an image, summarize a document, or instruct an AI agent to perform a task, the model must process the request. This is known as an inference.

Now, something interesting is going around it. Inferring is no longer a behind-the-scenes, technical process. It is slowly making its way to being priced, financed, owned, and even traded.

This new financial layer is being referred to as Inference Capital Markets.

So, What Exactly Is Inference?

Imagine teaching a student.Imagine teaching a student like AI.

Training is the process of making the AI learn from a huge amount of data and eventually able to answer questions or even perform some tasks.

When that trained AI actually does the work, this is called inference.

When you send a prompt for an AI chatbot to create an article, the AI is carrying out the inference task. If an AI coding agent generates hundreds of lines of code during a night's sleep, that's also inference.

All requests use resources like:

GPUs
Electricity
Memory
Data-center capacity
Network bandwidth

The key difference is that of scale.

Training a model can be a periodic, huge expense. Inference continues over and over for every user, every AI agent.

The level of inference needed might increase significantly, as AI agents become more independent.

Why Is This Becoming a Market?

In the past, AI inference worked pretty much as it is described and seemed to be a one-way street that was restricted to certain companies who had the servers and charged customers for access, while the provider did the rest.

However, the AI economy is getting more complex.

But powerful AI models aren't just the domain of the large tech giants anymore, with open-source models like Llama, Qwen, DeepSeek and Mistral making their way into the world. Meanwhile, decentralized networks are testing means of supplying computing power and AI services.

This opens the door to develop a financial system based on inference itself.

Galaxy Research reports that onchain inference capital markets are taking three broad approaches – tokenized access to inference, tokenized inference production, and financing of the hardware that enables inference.

In simple words:

Financial markets can be built on the top of economic value, which is created by inference, which is created by computing power, which is needed by AI.

Venice AI: An Interesting Example

A project under discussion is Venice AI.

Venice emphasizes access to multiple models and privacy in the provision of AI inference. It doesn't create everything around one proprietary model, but it can allow access to various different models.

The Venice token model is particularly interesting for the purposes of inference capital markets.

Venice has two tokens, VVV, DIEM, which is a tokenized claim on Venice inference credits. Galaxy says this is an effort to make the access to inference a consumable, rather than a possessed asset.

This sounds like an odd thing to begin with.

Suppose you had to pay for a "streaming subscription". Access costs money, use it and it's gone when your subscription runs out.

Suppose a portion of that access could be embodied in an asset you could own or pass on.

The fundamental concept behind tokenized inference is that.It is the fundamental concept behind tokenized inference.

From Renting AI to Owning AI Access

This is the most intriguing idea in the whole narrative.

In most cases, access to AI is traditionally available on a subscription or pay-as-you-go basis.Typically, access to AI is available on a subscription or pay-as-you-go basis.

You pay → you use the AI → the transaction ends.

Inference capital markets try to add another variable to the equation:

You pay: you get a claim on future inference: that claim may have value beyond the original purchase.

It alters the dynamic of users using AI infrastructures.

It also raises a whole new question:

What if the power of AI computing could be seen as a resource?

That remains to be seen and it's not guaranteed. The tokenized inference market is in its infancy and crucially hinges on the actual demand for the underlying AI services.

It Is Not Just About Tokens

The term "Inference Capital Markets" could be mistaken for another crypto trend, were it not for the fact that it has been in use for quite a while.

It's more than that.

One part involves tokenized access.

The other is to fund the GPUs needed for providing inferences.

AI infrastructure companies require a massive amount of expensive equipment. Traditional banks will be able to fund larger data-center operators, while smaller providers of GPUs may have a more difficult time obtaining capital.

This could be a means to bridge the gap between those operators and investors more efficiently through onchain financing.

Galaxy Research notes this is one of the more practical applications as the underlying asset and cash flow are more transparent – money is used to finance computing hardware, the hardware produce revenue and the cash flow can repay the money.

This is a far cry from a token being created just because a project wants a token.

The Rise of AI Agents Makes This Even More Important

Now the fun begins.

An AI might ask a human 10 questions in an afternoon.

An autonomous AI agent might potentially make thousands of model calls when tackling a complex task.An autonomous AI agent may be able to make thousands of model calls for a complex task.

It may look for information, write code, test the code, analyze what it has found, make changes and retest it and so on.

Inference is necessary for each step.

Demand for inference may grow significantly as AI progresses from answering questions to continuously working tasks.

That's one of the reasons why analysts are looking at inference as a possible economic layer and not just a technical cost.

But There Is a Catch

It may sound like the future, but it's crucial not to mistake an intriguing idea for a business model.

Inference capital markets are in an early stage of development.

There are a number of problems.

One – there is such thing as real demand.
While a token may have some value, it's not of much value if no one's actually using the underlying AI service.

Second, it is very competitive.
There's already massive cloud provider infrastructure and distribution and AI companies already have huge amounts of them.

Third, tokens can be risky in terms of price.
Useful AI services with highly volatile tokens can make the financial product less appealing to regular users.

Last but not least, the economics must be sound.
The difference between token incentives for using AI and businesses that are actually profitable as their customers pay for the computing expenses is quite significant.

These are challenges not to be taken lightly. However, Galaxy Research states that onchain inference is still a “fairly small portion” of the total AI market and that many projects are still yet to prove their sustainable, usage-based demand.

So, Is This the Future?

Maybe.

But the more interesting is that something more fundamental is happening.

The era of companies investing mainly in training models is transitioning to an era where millions of models and agents can constantly utilize computing resources.

That's an enormous question of economy:

Who owns the computing power, who provides the inference, who provides the financing of the hardware and who captures the value?

An attempt to answer these questions with financial and blockchain infrastructure is called inference capital markets.

The city of Venice is trying token-based access. Other projects are investigating means to incentivize the creation of inferences, and financing systems are considering how to finance the industry's GPUs.

As of yet, whether these experiments will become commonplace or a small corner of crypto remains to be seen.

However, there is one thing that is becoming obvious:

Model-based AI is not the only way to create the next big AI economy. It's possible it's constructed around the giant computing power it takes to make those models useful.

And when that does happen, inference may transform from a quiet server room affair to an industry unto itself.

That's the true concept behind Inference Capital Markets.