What if AI wasn’t something we accessed, but something we collectively owned?

Over the last few decades, computing has moved from something accessible mainly to governments, universities and large corporations into something billions of people personally own. Computers entered our homes, then our pockets, and the Internet connected billions of independently owned devices into a global network.

Artificial intelligence seems to be developing along a different path. While access to AI is becoming widespread, the most capable systems themselves remain concentrated inside enormous data centers operated by a relatively small number of corporations. We can use these intelligences, but we don't own them.

The most capable AI systems increasingly live inside enormous data centers operated by a small number of corporations. We don't own these intelligences. We access them. We pay subscriptions or API calls. Our applications depend on their infrastructure, their models, their policies, their pricing and ultimately their permission.

There are very good technical reasons why this happened. Frontier models are extraordinarily expensive to train and operate.

But I keep wondering whether we are confusing how AI developed with how AI must ultimately exist.

Because another technological path is already being researched. And parts of it already work.

A 2025 systematic review in ICT Express examined 71 studies and projects around what researchers are now explicitly calling Decentralized Artificial Intelligence (DeAI). The motivations identified by the researchers include privacy, ownership, control and the concentration inherent in centralized AI. More importantly, the review finds that the field already contains many of the necessary building blocks — but no broadly adopted decentralized AI network yet combines them into a complete general-purpose system. [1]

So this isn't really a question of whether decentralized AI is scientifically imaginable anymore. The pieces are appearing.

We already know that data doesn't necessarily have to leave your device.

Federated Learning allows many devices to collaboratively improve machine-learning systems while keeping their underlying training data local. [2] The Flower research framework demonstrated federated-learning experiments involving simulations of up to 15 million clients, specifically exploring heterogeneous edge devices rather than assuming everything lives in a homogeneous data center. [2]

Researchers have gone further.

Decentralized Federated Learning removes the central coordinating server itself. Instead of every participant reporting to one authority, nodes can communicate directly with one another. A 2024 IEEE survey describes precisely this architecture and the substantial research now surrounding its network topology, communication and coordination problems. [3]

And in medicine, researchers published something even more interesting in Nature.

Swarm Learning combined edge computing, peer-to-peer networking and decentralized coordination so that institutions could collaboratively train machine-learning systems while keeping medical data locally with its owner and without requiring a central coordinator. The researchers demonstrated it using more than 16,400 blood transcriptomes and more than 95,000 chest X-rays. [4]

Think about what that means. Independent institutions can already collaborate computationally without first putting all of their private data into one company's database. [4]

We also know that computation itself can be distributed.

This idea is much older than modern AI.

Volunteer-computing systems such as BOINC have spent decades turning ordinary desktops, laptops and other consumer devices into distributed scientific-computing infrastructure. BOINC today explicitly describes consumer desktops, laptops, tablets and smartphones as potential computing resources, and its developers are now investigating how this model could provide AI researchers access to large numbers of otherwise unused GPUs. [5][6]

There are serious limitations: consumer hardware is heterogeneous, nodes disappear, Internet connections are dramatically slower than data-center interconnects, and machines sit behind firewalls. BOINC itself identifies these problems. [6]

But again: the question isn't whether distributed consumer computing exists.

It does.

And we already know that large AI models can be distributed across machines belonging to different people.

Petals is perhaps my favorite example.

Researchers demonstrated collaborative inference and fine-tuning of BLOOM-176B by joining computational resources from multiple parties. Their system ran a 176-billion-parameter language model across consumer GPUs distributed over a network rather than requiring every participant to own a machine capable of holding the entire model. The work was published at ACL 2023, with subsequent work studying distributed LLM inference over the Internet. [7]

Again, this doesn't mean that connecting ten million laptops magically creates the equivalent of a frontier supercomputer. It doesn't.

Bandwidth matters. Latency matters. Memory matters. Synchronization matters. Reliability matters. Security matters. Heterogeneous hardware matters.

Research on collaborative edge inference explicitly identifies many of these trade-offs, while recent peer-to-peer federated-learning research continues to identify unresolved problems involving topology, incentives, communication efficiency, malicious participants, trust and node churn. [8][9]

So no: your laptop + my laptop ≠ an OpenAI data center.

But that may be the wrong comparison. Because perhaps we don't need to build another centralized supercomputer.

Perhaps we need to build something else.

Imagine this.

Your computer runs your AI. Your phone runs your AI. Your home can run your AI.

Its memory belongs to you. Your documents belong to you. Your history belongs to you. Your personal context belongs to you.

They don't need to be uploaded into somebody else's intelligence simply so that it can understand who you are.

And your machine possesses something else: capabilities.

Maybe your computer can run a vision model. Mine has a GPU sitting idle tonight. A university has a specialized scientific model. Someone else has enormous storage. A laboratory has instruments. A city has sensors. A researcher has a dataset they cannot legally or ethically distribute, but they may be able to perform computation against it locally. And a human being can possess a capability that no machine has at all.

Now imagine that instead of exchanging all of the underlying private information, these nodes can advertise what they are capable of doing.

Not: "Here is everything I know."

But: "I can perform this task."

A problem enters the network. It gets decomposed. Different nodes perform different parts.

Some compute. Some retrieve. Some simulate. Some verify. Some contribute specialized models. Some contribute human knowledge.

The results are composed. The nodes separate again. Nobody had to own the entire intelligence.

That is the part I find fascinating.

Maybe the fundamental unit of decentralized AI shouldn't be the model. Maybe it should be the capability.

And then the idea becomes larger than technology.

Because if intelligence becomes one of the primary productive resources of the 21st century, there is a political question hiding underneath all of this:

Who owns it?

We hear extraordinary predictions about AI producing abundance. AI will perform our jobs. AI will dramatically increase productivity. AI will create scientific discoveries. AI will operate companies. Eventually, perhaps, humans won't even need to work.

Fine.

But there is a missing question:

Who owns the AI doing the work?

If an AI produces the economic output of 10,000 people but that AI belongs to a corporation, we haven't automatically democratized productivity.

We may simply have automated capital.

The machine works. But it doesn't necessarily work for you.

Now change one variable.

What if individuals owned their intelligence? What if communities owned computational infrastructure? What if universities federated theirs? What if cities did? What if millions of independently owned AI systems could temporarily cooperate when they needed capabilities beyond what any individual node possessed?

Then "AI working for humanity" begins to mean something structurally different.

Not because a corporation promises that its AI will benefit humanity.

Because humans actually own parts of the computational system producing that benefit.

This is where the idea becomes utopian.

There are enormous unsolved problems.

How do you prevent malicious nodes? How do you verify computation performed by strangers? How do you prevent poisoning? How do you establish reputation? How do you compensate someone who contributes computation? How do you compensate someone who contributes a model? Who decides which software updates are accepted? How does governance work? How do millions of heterogeneous devices coordinate efficiently? How do you prevent the decentralized system itself from gradually recentralizing around whoever accumulates the most compute?

These aren't trivial details. Current research on decentralized and peer-to-peer learning identifies security, incentives, topology, communication efficiency, heterogeneity, realistic network conditions and adversarial behavior as major unresolved problems. [3][9]

And the broader DeAI literature reaches essentially the same conclusion: we have many pieces, but not yet the complete architecture. [1]

Which is precisely why I find this interesting.

Twenty years ago this discussion would have been science fiction.

Today we can point to working systems and peer-reviewed research demonstrating: data remaining local while machines learn collaboratively; [2][4] peer-to-peer machine learning without a permanent central coordinator; [3][4] collaborative inference between edge devices; [8] large models distributed across consumer hardware; [7] and enormous pools of volunteer consumer computing already being used for scientific computation. [5]

The individual pieces are real.

What is missing is the system that connects them.

The protocols. The trust layer. The capability layer. The incentives. The governance. The user-owned intelligence sitting at the center of it.

Maybe it fails. Maybe centralized infrastructure remains overwhelmingly more efficient. Maybe the coordination problem turns out to be harder than the computation problem.

But I think this is now a legitimate question to ask:

The Internet gave billions of computers the ability to communicate without belonging to one computer.