Decentralized AI Marketplaces: Fetch.ai and Bittensor Capture 40% of ML Model Trading

Right now, centralized AI is still controlled by a handful of tech giants who were the first movers into this sector. It remains locked behind paywalls, and it is mostly closed to outside innovation. However, Decentralized AI marketplaces have the potential to change this.

Instead of relying on central servers or API, they allow anyone — be they a developer, researcher, or a business — to trade machine learning models and data-processing tasks directly, using Peer-to-Peer (P2P) networks.

This represents the beginning of a major, highly important shift in the current AI narrative, and it is led by projects like Bittensor and Fetch.ai. Together, these two account for around 40% of the decentralized AI model trading market, and they process roughly $12 billion in tasks every month, according to estimates.

The tasks themselves range from image generating to agent-based automation, from predictive analytics to language generation. The important part is that there is growing demand for open and permissionless AI infrastructure. As Web3 evolves and merges with machine learning and other emerging technologies, decentralized AI marketplaces are becoming the foundation of a new type of global intelligence economy.

What are Decentralized AI Marketplaces?

Decentralized AI (DeAI) marketplaces are platforms based on blockchain technology that allow anyone to buy, sell or somehow contribute to the development of machine learning models. Best of all, they can do it without having to ask centralized authorities for permission to do so. So, instead of relying on a single provider, like Google, Meta, or OpenAI, DeAI splits the roles across a distributed network of participants, similarly to how crypto users do not depend on centralized authorities to process their transactions.

At their core, DeAI marketplaces use permissionless infrastructure to operate. Their contributors, known as miners or validators, provide compute power, datasets, and models. In return for their contributions, they earn the network’s native crypto, which is granted to them in exchange for completing useful AI tasks. This results in a market-driven ecosystem that is resistant to any form of censorship, and it provides incentives that are based on performance.

DeAI marketplaces come with an entire list of benefits, such as:

• Open participation
• Token incentives
• Resistance to censorship
• Reduced cost of usage
• Performance transparency

Take Bittensor as an example. The project runs a network of subnets where contributors train language models and offer them to others. Those who can provide high-performing outputs earn TAO tokens. Meanwhile, Fetch.ai focuses on autonomous economic agents that can complete various tasks (data retrieval, routing, predictions) tied to a decentralized validator system, which allows them to earn FET tokens in return.

Market Leaders: Bittensor and Fetch.ai

Speaking of Bittensor and Fetch.ai, the two are dominant names in decentralized AI space today. As mentioned, they are estimated to hold 40% of all decentralized machine learning model trading markets right now. The reason is simple - they created scalable, tokenized infrastructure tailored for the AI economy. Here is a Bittensor (TAO) vs Fetch.ai (FET) comparison:

Bittensor

Bittensor operates a network that revolves around subnets — independent neural networks that are competing to provide the best answers to any queries. Each of these subnets is evaluated using the Yuma Consensus, which is a trustless mechanism that rewards contributors based on how useful their output to the network is.

The network itself is powered by the TAO token, which is used to pay for transactions, provide rewards, and engage in staking. Unlike centralized LLM services that tend to lock users into using APIs, Bittensor allows anyone to contribute computing power and models, and get paid for those contributions.

Fetch.ai

Fetch.ai runs on a different idea - that of Autonomous Economic Agents. These are lightweight pieces of code that act on behalf of the user, whether an individual or a business. The agents can retrieve data, create and negotiate contracts, make predictions, and even interact across chains without needing human intervention or input.

The project has a modular and fast infrastructure which is optimized for completing tasks instead of hosting major models. It also uses ASI-1 Mini, which is a compact agent framework that integrates with Web3 dApps. It allows developers to embed AI decision-making into smart contracts, logistics systems, as well as data marketplaces, all with minimal monitoring.

These two protocols succeeded where others failed because they tackle different ends of the AI stack. Specifically, Fetch.ai was created to automate tasks and decision making, while Bittensor aims to tackle model training and inference. They don’t stand in each other’s way, but rather, they complement each other, and both offer real-world utility.

$12B/Month in AI Tasks

The decentralized AI sector is currently estimated to process around $12 billion worth of AI tasks per month, primarily through networks like Fetch.ai and Bittensor.

This means that they offer a variety of services that are in high demand, such as LLM-based tasks, Image generation, and analytics. LLM-based tasks typically cover anything from text summaries to generating code. Image generating and enhancing can allow users to produce and refine images. Meanwhile, analytics tasks, specifically predictive analytics, allows businesses to process market signals, analyze supply chain data, and predict customer behavior.

Enterprises, developers, and even DAOs are starting to prefer decentralized AI marketplaces for their flexibility, neutrality, and speed at which new models or services can be deployed. In all of these areas, DeAI can beat centralized AI, which suffers from issues like vendor lock-ins and odd update cycles.

In fact, several DAOs have started using Bittensor’s subnets for governance tooling and automated research. Meanwhile, Web3 projects like Fetch.ai’s CoLearn and Mobix already run entirely on autonomous agent logic. In other words, the $12 billion per month is only a start. It does not reflect hype, but real transactions, which prove that DeAI is not an experiment, but a practical application of decentralized AI, which can save money while removing red tape at the same time.

Why Enterprises and Developers are Moving to DeAI

As mentioned, DeAI networks are winning over more than just crypto projects. Developers, enterprises, and data scientists are shifting their workloads to these platforms more and more, not because of ideology, but because DeAI is more practical, economic, and strategic.

In the end, it is about convenience and cost. DeAI is far more cost-efficient as it allows these new clients to only pay for what they actually use, rather than charge based on usage tiers or tokens processed, like centralized AI APIs tend to do. With DeAI, users pay per output, agents compete to fulfill tasks, and there are no vendor lock-ins, allowing users to switch to a better model at any time.

Then, there is data sovereignty and trustless validation. Basically, centralized AI providers store, log, and even use customer data to continuously train models. In comparison, DeAI uses local agent execution or trustless task validation. With the former, the data stays in the user’s environment, whereas the latter uses miners who compete to deliver accurate outputs, which are validated by an open consensus.

Finally, another good reason to use DeAI marketplaces is regulatory neutrality and open-source collaboration. These days, AI regulation is getting tighter and righter around the world, and major providers often suffer for it, as new laws hurt their business models. Decentralized networks walk a different path, using protocols rather than platforms, and being based on open-source models that can be easily audited, forked, or even governed through DAO mechanisms.

Forecast for DeAI Marketplaces

So, what is next for decentralized AI marketplaces? Slowly but surely, they are evolving from a niche trend into a foundational layer for Web3 and enterprise automation. This is expected to continue as demand grows for transparent, secure, and scalable AI infrastructure. The projected market value is expected to grow with it, and even some more conservative estimates say that DeAI platforms could hit a market value of $4.3 billion by 2034.

Moving forward, experts also expect an increase in interoperability, combining DeAI with DePIN, DAOs, and ZK technology, decentralized AI marketplaces start to grow closer to the Web3 sector. Together, these integrations could enable the creation of a new form of autonomous digital infrastructure, which would be open, and not owned or controlled by a single company, government, or other kind of centralized entity.

Finally, DeAI is likely to grow in importance when it comes to enterprise AI sourcing. Right now, most of them still use centralized AI solutions, but experts expect them to soon tire of black-box APIs and pricing models that lack transparency. Once they do, decentralized AI marketplaces will be there, offering an alternative, allowing developers to tap into public model pools, freely customize agents, and contribute their own data or logic for incentives.

Conclusion

Decentralized AI marketplaces are no longer just experiments or theoretical, futuristic solutions — they are out there, and they work. Right now, they process more than $12 billion a month in machine learning tasks, and are carving a place for themselves, emerging as a serious and competitive alternative to traditional AI platforms.

Fetch.ai and Bittensor are prime examples of this, and they are leading the charge, offering more than just ideological alternatives or throughput. They offer new models, based on trust, privacy, and scalability, where developers and contributors are both paid fairly, and innovation is transparent and happens in the open.

So, as enterprises become more dependent on AI, but also want to cut costs and avoid regulatory issues, open-source, decentralized AI marketplaces will be there to welcome them.

Glossary

• Decentralized AI (DeAI) — A model of artificial intelligence where data, computation, and model ownership are distributed across a permissionless network, away from centralized control.
• AI Marketplace — A digital platform where AI models, datasets, and services can be traded.
• Bittensor (TAO) — A decentralized machine learning network built on blockchain technology, which allows participants to contribute and evaluate AI models across different subnets.
• Fetch.ai (FET) — A decentralized AI network built around autonomous software agents that perform various tasks, including data sharing, prediction, and optimization.