- The author debated with Richard Stallman in the 1980s about open source software, initially holding the conventional view that proprietary control was necessary for software advancement, but eventually was convinced by Stallman that software as a body of knowledge grows stronger when shared.
- Stallman's free software movement evolved into open source, leading to key projects like GCC and GNU/Linux, which now power much of the internet, demonstrating that transparency and community collaboration enhance security and innovation rather than hinder it.
- Open source software has accelerated technological progress by creating a shared knowledge base that educates engineers and supports a delicate ecosystem of private companies, universities, and volunteers, with many successful companies built on open core models.
- AI is increasingly becoming closed, with frontier models being completely proprietary, risking the locking down of scientific progress and creating a controlled 'library' of knowledge that limits access and understanding, unlike the open principles that drove past software advancements.
- Closed AI models pose deeper problems, as dependence on them without transparency means users cannot audit or fully comprehend results, leading to trust in an unexamined 'oracle,' and while security concerns exist, open models with proper monitoring and rules can manage risks without stifling innovation.
- The distinction between code that runs a model and code that builds it is crucial; current 'open' AI models often only release runnable code while withholding building code and training data, making them opaque and reliant on corporate goodwill rather than true openness.
- To support open source AI, concerted effort from government, private sector, and nonprofits is needed, including public compute grants, support for universities, and default openness for publicly funded AI, ensuring that open alternatives remain viable and beneficial for societal advancement.