Given the likelihood that AI will increasingly permeate the software and systems we depend on, it’s fair but unrealistic to want those AI models to be open source. Vaughan-Nichols blames “top AI vendors [that] are unwilling to commit to open sourcing their programs and data sets,” suggesting that “businesses hope to gild their programs with open source’s positive connotations of transparency, collaboration, and innovation.” Maybe? Or maybe they don’t have the luxury of giving away all their code because that turns out to be really bad business. I know some like to lazily gesture at Red Hat as some classic example of what business success looks like, but it’s actually a terrible example when compared to Meta, AWS, etc. As Hugging Face’s Sasha Luccioni said at the United Nations OSPOs for Good Conference, “You can’t really expect all companies to be 100% open source as the open source license defines it. You can’t expect companies just to give up everything that they’re making money off of and do so in a way they’re comfortable with.”
Maybe we’d like reality to be different, but after decades of open source and proprietary software living comfortably together, why would we expect AI to be any different?
Just as with cloud and with on-premises software before that, most AI software will not be open source. Now, as then, most developers simply won’t care, because most developers care more about going to their kids’ soccer games after work than existential open source issues. For years we’ve fixated open source conversations on the wrong things, and younger developers have mostly tuned it out. But whether young or old, developers care about getting stuff done. They care about the cost, speed, and performance gains of Mistral’s latest model, and not so much about its non-open source license. Ditto OpenAI, Meta’s Llama, etc.
