Polymath LLMs: One model that does it all

The big AI labs are pivoting. Instead of training dozens of narrow models — one for code, another for math, a third for reasoning — they're building polymath LLMs that can handle many tasks at once.

The idea is straightforward: rather than having a different model for every job, train one model to be good at many things. The result is less fragmentation and easier integration. You don't need to juggle a dozen models anymore. You just swap which one you're using when.

This matters because the model wars are about to get real. The companies that figure out how to train and serve these polymath models efficiently will have a serious edge. The ones that don't will end up with a mess of specialized models that don't talk to each other.

Why this matters for us: if the next wave of AI tools is built on polymath models, they'll be cheaper, faster, and easier to use — and that means more of us can actually afford to use them.