I had a thought today that kept bouncing around in my head: the future of AI isn’t necessarily in the hands of a few tech giants, but rather in how everyday people choose to organize around it. Not as consumers of a service, but as participants in something more collaborative. Let me explain.
The current model has a problem
Right now, most people interact with AI through subscriptions. You pay a monthly fee to OpenAI, Anthropic, Google, or whoever else, and you get access to a model with usage limits. The more you use it, the more it costs. If you’re a power user, you might find yourself juggling multiple subscriptions, watching your token count, and constantly evaluating whether the value justifies the expense.
There’s also a deeper issue. When you rely on cloud AI, you’re trusting that the model will remain available, that pricing won’t change overnight, and that your data is being handled in a way you’re comfortable with. In my experience running my family assistant on local AI, one of the biggest reliefs was getting rid of that dependency entirely. Once I moved to a self-hosted setup, I stopped worrying about API bills, rate limits, or the model suddenly getting worse with an update.
But self-hosting isn’t for everyone. It requires technical knowledge, hardware resources, and a willingness to deal with the complexity. Not everyone wants to spend a weekend configuring quantization settings or troubleshooting KV cache inefficiencies. That’s where the cooperative idea comes in.
What if we went cooperative?
Here’s the thought experiment: imagine a small group of people with similar AI needs. Say, a team of developers who all use AI for code review, documentation, and debugging. Or a group of translators who use it for first-pass translations and terminology research. Or even a few families who want a smart home assistant. These people don’t need a different model each. They need access to the same good open-source model, and they need it to be reliable, private, and cheap.
Instead of each person paying a separate subscription, they pool their resources. They pick the best open-weight model available for their use case, rent a GPU server together, and split the cost. Everyone gets unlimited usage. No token counting, no rate limits, no worrying about going over a monthly quota. Just a shared resource that they collectively own and manage.
The economics work out surprisingly well. A decent GPU instance that can run something like Qwen 3.5 or Gemma 4 might cost a couple hundred euros per month. Split among 10 or 15 people, that’s well under what any of them would pay individually for comparable API access. And if more people join, the cost per person goes down even further. It’s the classic economy of scale, but applied at a grassroots level.
Why now?
A few years ago, this wouldn’t have made sense. Open-source models weren’t good enough, and the gap between them and proprietary models was enormous. But that gap has been closing rapidly. Models like Qwen, GLM, Gemma, and others are now genuinely competitive with the best proprietary options for a wide range of tasks. For most everyday use cases, the difference is negligible.
At the same time, the infrastructure for self-hosting has matured significantly. Tools like Llama.cpp, vLLM, and text-generation-webui like OpenWebUI have made it easier than ever to deploy and manage AI models. You don’t need to be a machine learning engineer to set up a working inference server anymore. The barrier to entry has dropped considerably, even if it hasn’t disappeared entirely.
And then there’s the privacy argument. When you send your data to an API, you’re trusting the provider with that information. When you run your own model, your data never leaves your server. For a team of developers working on proprietary code, or a family sharing personal information with an assistant, this matters a lot. In my own case, the decision to go local was largely driven by not wanting to send family data to third-party servers. I’d imagine many other people feel the same way.
The practical bits
Obviously, this isn’t without challenges. Someone needs to manage the server, handle ocasional updates, and keep things running smoothly. There needs to be a fair system for contributing and potentially some basic governance around how the shared resource is used. If one person hogs all the GPU time with massive batch jobs, that creates friction.
But these aren’t insurmountable problems. They’re logistics problems, not technical ones. A simple queue system, some basic usage monitoring, and a shared understanding among the group would handle most of the friction. And the beauty of open-source is that you’re not locked into anything. If a better model comes along, you switch. If the group outgrows the hardware, you scale up and the cost distributes across more people.
There’s also something to be said about model stability. When you self-host an open model, it doesn’t change unless you decide to update it. There’s no risk of the model getting a silent update that degrades performance on your specific use case. You have control, and control in the AI space is increasingly valuable.
A thought, not a manifesto
I’m not saying this is going to replace cloud AI services overnight. There will always be a market for managed AI products, especially for enterprises that need guarantees, support, and compliance features. And there are plenty of people who just want something that works out of the box without any hassle.
But I do think there’s a growing space for cooperative AI, especially as open-source models continue to improve and the cost of compute continues to drop. It’s a model that aligns well with the open-source ethos: shared resources, collective benefit, and freedom from vendor dependency. It’s the same reason I moved my family assistant to local AI, just scaled up to a group level.
If you’re reading this and thinking “I’d be up for something like that,” you’re probably not alone. The tools are ready. The models are good enough. The economics make sense. The only missing piece is the coordination, and that’s something humans have been doing for a very long time.