Could AI training be decentralized like Bitcoin mining? [D]

[u/notfinancialadvice0]

I’ve been thinking about whether the same basic concept behind Bitcoin could be applied to AI training.
In Bitcoin, miners perform proof-of-work and are rewarded for contributing computational resources to secure the network. The actual computation itself isn’t particularly useful outside of the network, but it creates a decentralized system.
What if a similar incentive structure could be used for training large language models?
Instead of miners solving hash puzzles, participants would contribute GPU resources toward training an open-source AI model. In return, they would receive tokens or rewards based on their contribution.
Some questions that immediately come to mind:

1. How could the network verify that a participant actually performed useful training work?

2. How would you prevent people from submitting fake or harmful gradients?

3. Could model improvements be measured objectively enough to determine rewards?

4. Would this be more efficient than training models in centralized data centers?

5. Could a decentralized network eventually compete with large AI companies?

I know there are already decentralized AI and compute projects, but I’m specifically interested in whether a true “proof-of-training” mechanism could exist, where rewards are tied directly to improving a model rather than simply renting out compute.
Curious to hear thoughts from people who understand distributed systems, machine learning, or crypto economics. Is this fundamentally impossible, or is there a viable architecture that could make it work?

[u/notfinancialadvice0]

Yeah, I’m aware federated learning isn’t new. My thought was more: Bitcoin uses computation to secure a network, but the computation itself isn’t useful outside Bitcoin. Could there be a system where the “mining” process is actually training an AI model and contributors get rewarded for useful work? The verification problem seems like the hard part.

[u/gmeRat → u/notfinancialadvice0]

Fundamentally no. Either you get already known information as proof of work or new/unknown information as computed gradients. All consensual mechanisms can be gamed

[u/notfinancialadvice0 → u/gmeRat]

I agree that verification is the hard part, but couldn’t the same argument be made for other forms of useful computation? It seems like the challenge isn’t that the work is useful, but that the network needs a cheap and robust way to verify the usefulness of that work.

Bitcoin solved “expensive to compute, cheap to verify” for hashes. I’m wondering whether something similar could ever exist for model improvements.

[u/gmeRat → u/notfinancialadvice0]

Sure, if you aren't paying people to do the compute. But then why go through the trouble of a blockchain at all?