AI Wants to Be Open
Every centralized AI dependency now has a decentralized alternative. We mapped 35 of them.
In 2023, decentralized AI was more concept than product. The ideas were compelling, but there was very little you could actually pick up and use.
We started paying attention anyway because of the talent density. Elite researchers and infrastructure veterans who could have shipped at any centralized lab were choosing to build here instead. They saw decentralization as a force multiplier, not a constraint.
At our AGM last year, we told our LPs that AI was concentrating power in a handful of labs, and that the answer was open, verifiable intelligence built on crypto rails. “Not your weights, not your brain.”
In the time since, four things changed. Governments started requiring AI to run inside their own borders, on infrastructure they control. The GPU shortage priced most startups out of compute. People grew wary of handing every prompt and document to a few opaque providers. And as AI agents began managing real money, they needed a way to prove the right model ran on the right data. Each of those is a reason to build outside the centralized stack.
And the results are no longer hypothetical. A network of 70-plus strangers trained Bittensor’s Covenant-72B on commodity hardware and beat LLaMA-2-70B on MMLU. x402, the HTTP-native agent payment rail, has settled over 161 million machine payments across roughly 69,000 active agents. Aethir booked more than $128 million in real revenue last year from 150-plus enterprise clients. Venice runs private inference for over 2 million users with zero data retention.
What makes decentralized AI different is structural. Three things stand out.
Openness is built in, not bolted on. Pluralis splits a model across many machines so no single party ever holds the full weights. You can use the model, but you can’t take it private and walk away with it.
Cryptography does the work that trust used to. You don’t have to take these systems at their word. EigenCloud ran the same inference 10,000 times and got an identical result every time, which is what lets a smart contract actually rely on the output. Venice encrypts your prompt on your own device, so neither Venice nor the GPU provider ever sees it.
And the people building this are not who you’d expect. NEAR’s founder co-authored the Transformer paper. Sentient’s co-founder invented the core technology behind 4G. This is serious technical talent choosing the harder path.
The obvious objection: OpenAI and Anthropic are running away with this, so why does any of it matter? Because the question was never who builds the smartest model. It is whether AI infrastructure ends up winner-take-all or multi-vendor. The centralized labs win on raw capability, and they will keep that lead. But capability is not the whole market. Governments that won’t run on American clouds, enterprises that can’t expose their data, and agents that need provable execution: none of that is a capability problem, and none of it is something a closed API solves well. Centralized AI compounds through data moats and switching costs. Decentralized AI compounds through networks and adoption. The first wins the short term. We are betting the second matters more over time, and regulation, GPU scarcity, and sovereign mandates are all pushing that way.
The bear case is real:
The centralized labs are extremely well capitalized, backed by the best investors and run by exceptional operators. That is an enormous head start.
Most decentralized AI projects are yet to find real organic demand. Many are propped up by token emissions rather than customers.
“Decentralized” often isn’t. Look closely, and many of the nodes for these networks are fairly centralized.
Several layers depend on trusted execution environments, which have been broken before.
A lot of this rests on research breakthroughs that have not landed yet, like privacy-preserving computation. By the time they arrive, the window of opportunity may have closed.
We don’t wave any of that away. Our job is to separate the projects solving a real bottleneck, where the centralized option genuinely can’t go, from the ones solving a problem that centralized AI already handles fine.
So we built the map we wanted to read. 35 projects across 9 layers, with the team, the traction, and our view on each. It is a companion to our Semiconductor Stack and Physical AI maps, and it is fully interactive: filter by layer, search by project, click into any one.
We said it a year ago and we will say it again. Still early, but not too early.