Open it up
It’s not clear how we resolve this looming problem. We’re still in the “wow, this is cool!” phase of AI coding assistants, and rightly so. But at some point, the tax we’re paying will become evident, and we’ll need to figure out how to extricate ourselves from the hole we’re digging.
One thing seems clear: As much as closed-source options may have worked in the past, it’s hard to see how they can survive in the future. As Gergely Orosz posits, “LLMs will be better in languages they have more training on,” and almost by definition, they’ll have more access to open source technologies. “Open source code is high-quality training,” he argues, and starving the LLMs of training data by locking up one’s code, documentation, etc., is a terrible strategy.
So that’s one good outcome of this seemingly inescapable LLM feedback loop: more open code. It doesn’t solve the problem of LLMs being biased toward older, established code and thereby inhibiting innovation, but it at least pushes us in the right direction for software, generally.