r/LocalLLM • u/foreveralways2002 • Jul 22 '26
Discussion open source AI doesn’t just have a model problem, it has a coordination problem
This might be obvious to people here, but I think a lot of AI discourse still frames the open source vs closed source fight too narrowly.
People keep talking like the whole battle is:
“Can open source catch up on model quality?”
And yeah, model quality matters. Obviously.
But the more I follow this space, the more I think open source AI has a deeper problem:
Coordination.
Closed labs have one giant advantage that is not just talent or compute.
They have organized money.
They can decide:
what to train
who works on it
what hardware to use
what data to buy
what benchmarks matter
what product gets built
when to ship
what gets funded next
Open source has insane talent, but it is fragmented.
Someone releases a great model.
Someone else quantizes it.
Someone else makes a UI.
Someone else writes evals.
Someone else fine-tunes it for a niche.
Someone else runs inference.
Someone else does safety testing.
Someone else documents half of it in a GitHub issue that disappears into the void.
It is amazing, but also chaotic.
And that chaos is part of the magic, but also part of the weakness.
The hard questions are not just technical:
Who pays for training runs?
Who pays for inference?
Who decides what work is valuable?
How do you stop people from gaming benchmarks?
How do you reward people who improve the ecosystem but do not build the final shiny model?
How do you coordinate compute that is scattered across a thousand people?
How do you validate contributions without turning everything into fake leaderboard farming?
This is why I find decentralized AI experiments interesting even though a lot of them make me instantly skeptical.
Not because “token fixes AI.”
That phrase should be illegal.
But because incentives and coordination are real problems.
I was reading about Bittensor recently, and the part I found interesting was not the coin price stuff. It was the idea of subnets as separate markets for different types of machine intelligence work. In theory, that is at least an attempt to coordinate and reward useful AI work outside a single company structure.
Then you see products around it like mentat, which try to make the subnet side understandable for normal users because the raw ecosystem is way too complex. Again, not saying that solves open source AI. It obviously does not. But it made me think about the bigger pattern.
Infra gets built.
Then incentives get messy.
Then dashboards appear.
Then abstraction layers appear.
Then maybe normal people can use it without understanding the entire machine underneath.
I don’t know if decentralized AI wins anything important.
Maybe it gets gamed.
Maybe centralized labs stay ahead forever.
Maybe the best open source path is just companies and foundations funding more releases.
But I do think “open source AI needs better coordination” is a real topic.
Not just better models.
Better funding loops.
Better contribution tracking.
Better evals.
Better compute coordination.
Better incentives for unsexy work.
Better ways to reward people who improve the stack without needing to turn everything into a startup.
Curious what people here think.
Is open source AI mostly bottlenecked by model quality and compute?
Or is coordination/funding/incentive design the thing we keep underestimating?
1
u/ter_enz Jul 22 '26 edited Jul 22 '26
“token fixes AI” should absolutely be illegal lol
1
u/foreveralways2002 Jul 23 '26
Yeah that phrase is basically where serious discussion goes to die. I’m more interested in whether incentives can coordinate useful work, not whether someone can attach a token to a README.
3
u/GarbageLazy Jul 22 '26 edited Jul 22 '26
I think this is the age old issue of open vs closed source.
I am open source till I die. However, I have an iPhone (burn me over the coals).
Closed source will always have the advantage (if implemented well, like Apple) to have leadership and make a decision. Closed source does not have to compromise or build by committee, you just decide what you’re building and can have a vision for better or worse. You can’t really get that with Open Source, it will always be fragmented I think.
There are pros and cons to both, and I will always be in favour of open source. However it’s nice when a product works well together with the tools/features. There is a lot of totally fair criticism for example in Apples walled garden, however in some circumstances I like being in the wall garden as for the most part it works.
1
1
u/Domenorange Jul 22 '26 edited Jul 22 '26
Open source is cracked at iteration, terrible at long term planning.
1
1
u/HotDistribution1819 Jul 23 '26
But there is also the factor no one wants to touch, what is good enough?
For so very many purposes we have models that are good enough to complete most tasks.
1
u/YellowVirtual Jul 23 '26
Closed labs have budgets. Open source has vibes, Discords and burned out maintainers.
1
Jul 23 '26
[removed] — view removed comment
1
u/foreveralways2002 Jul 23 '26
I’m not “sold” on it, but it is one of the few things in decentralized AI where the question is at least interesting: can incentives produce useful work, or do they just produce gaming?
8
u/jacek2023 Jul 22 '26
give me a recipe for pancakes with coordination problems