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The open-source AI revolution hasn’t happened yet! Yes we have impressive open-weights models, and thank you to those publishing weights, but if you can’t reproduce the model then it’s not truly open-source. Imagine if Linux published only a binary without the codebase. Or published the codebase without the compiler used to make the binary. This is where we are today. This has a bunch of drawbacks: - you cannot contribute back to the project - the project does not benefit from the OSS feedback loop - it’s hard to verify that the model has no backdoors (eg sleeper agents) - impossible to verify the data and content filter and whether they match your company policy - you are dependent on the company to refresh the model And many more issues. A true open-source LLM project — where everything is open from the codebase to the data pipeline — could unlock a lot of value, creativity, and improve security. Now it’s not straightforward because reproducing the weights is not a easy as compiling code. You need to have the compute and the knowhow. And reviewing contributions is hard because you wouldn’t know how it effects performance until the next training run. But someone or a group motivated enough can figure out these details, and maybe it looks significantly different than traditional OSS, but these novel challenges is why this space is fun.

Anniversary of my PhD defense and leaving academia. Leaving academia has let me enjoy math more than I did when it was my job. If you're on the fence about industry / academia and want to learn more about that transition, my DM's are open.