A solo developer built a distributed GPU network for AI music generation. It's a DAW plugin with 4,200+ downloads. The code is open-source. The financials are public. And GPU providers keep 85% of the revenue they generate.
This is OBSIDIAN Neural, and it's a quiet experiment in what cooperative AI infrastructure might look like.
The model: transparency and alignment
Most GPU networks are opaque. Providers don't know what they're earning relative to what users are paying. Revenue splits are negotiated behind closed doors. Usage data is proprietary. If you're contributing compute, you're trusting the platform to pay you fairly - and you have no way to verify it.
OBSIDIAN Neural does the opposite. The code is on GitHub. The financial model is documented. Providers see exactly what users pay and exactly what they earn. The 85/15 split isn't hidden in a terms-of-service document - it's the headline.
The developer is now recruiting GPU providers. Not with promises of future upside or equity participation, but with a simple proposition: contribute compute, keep most of the revenue, and verify everything yourself because the numbers are public.
Why this matters beyond music generation
The immediate use case is AI music generation in real time. Musicians working in digital audio workstations (DAWs) can generate stems, loops, or full tracks without leaving their production environment. The plugin connects to the distributed GPU network, runs the model, and returns audio in seconds.
But the more interesting thing here is the infrastructure model itself. If you can build a cooperative GPU network for music generation, you can build one for image generation, video processing, code completion, or any other compute-intensive AI task. The architecture is the same. The revenue-sharing model is the same. The transparency is the same.
What's different is the incentive structure. Centralised platforms extract value by controlling access to compute. Cooperative networks distribute value by making the infrastructure itself a shared resource. Providers have a stake in the network's success because they're earning directly from usage, not hoping a platform will eventually share profits.
The challenge: scaling without losing the model
Cooperative models work well at small scale. Everyone knows everyone. Trust is social, not contractual. But as networks grow, coordination costs increase. Governance gets harder. Free-riders show up. The transparency that makes a small cooperative functional can become a liability when bad actors start gaming the system.
OBSIDIAN Neural is still early. 4,200 downloads is proof of concept, not proof of scale. The question is whether the model holds up as the network grows - whether transparency and fair revenue splits are enough to attract serious GPU providers, or whether the usual platform dynamics (marketing budgets, network effects, winner-takes-most) reassert themselves.
What builders can learn from this
The lesson here isn't "build a cooperative GPU network". It's "make your model legible". Open-source code. Public financials. Clear revenue splits. These aren't just ethical choices - they're competitive advantages when trust is scarce.
Developers are tired of platforms that treat them as inputs to be monetised. GPU providers are tired of opaque pricing and invisible margins. Users are tired of not knowing where their money goes. If you can build infrastructure that solves a real problem AND makes the economics transparent, you're competing on a dimension most platforms ignore entirely.
OBSIDIAN Neural might not scale. The music generation use case might not be big enough to sustain a network. But the experiment is worth watching - because if it works, it's a template for what comes next. And if it doesn't, the failure will teach us something about why cooperative models struggle to compete with centralised platforms even when the economics are better.