The term “inflection point” is overused, but it certainly applies to the current state of artificial intelligence. Technology providers—and the companies that depend on them—can choose one of two roads to AI development: proprietary or open source. This dichotomy has existed for decades, with both sides achieving great levels of success. However, I would argue that the stakes for AI are higher than we’ve ever seen, and that the open source model is critical for the productive, economically feasible, and safe productization and consumption of AI.
And, in terms of open source, the Kubernetes project should serve as the blueprint for the way in which we develop, govern, fund, and support AI projects, large language models (LLMs), training paradigms, and more.
