Linux is an interesting example of how open source scales. One small-ish core can become enormous because thousands of people can build on top of it. But AI feels different. The hardest part — training a frontier model — requires enormous compute and data that community developers generally don’t have. So I’m wondering if open source AI needs a different strategy. Instead of trying to build one open model that matches the frontier, could we build a team of smaller specialists? coding + vision + reasoning + search + tools ↓ composition ↓ verifier And perhaps even humans/expert feedback become part of that layer. MoA is one version of this, but I’m thinking more broadly: could composition become the way open AI scales? Is this actually plausible, or does a single large frontier model have capabilities that can’t be recreated through composition? What would be the equivalent of Linux’s “kernel” in this world?   submitted by   /u/WebAssemblyMan [link]   [comments]