This is impressive work. Every time I see hobbyist-scale semiconductor projects, it reminds me how much innovation still happens outside big labs. Curious how far this approach can scale.
The semiconductor device industry and Silicon Valley would have never appeared if the early companies working in this field would have been controlled by people obsessed about secrecy and "IP protection".
During the fifties and the sixties, and even until the early seventies, it was common for everyone to publish research papers very unlike those that are published today, where the concrete information is minimal.
In the early research papers about semiconductor devices and integrated circuits, it was normal to give complete recipes, including quantities of chemicals, temperatures and times for the processing steps and so on. After reading such papers, you could reproduce the recipes and make the device described and you could measure for yourself to see how true are the claims presented in the paper.
That open sharing of information has led to a very quick evolution of the semiconductor technologies during the early years, until more traditional business-oriented management has begun to restrict the information provided to the public.
It is said that such sharing of information still exists in China in many fields, and it is the source of their rapid progress.
> until more traditional business-oriented management has begun to restrict the information provided to the public.
Curious to know why you think this cutthroat approach is 'traditional'. Is there another historical background to it? Every account that I've seen, including the origin story of free software (at MIT) and even the rest of your own explanation, seem to suggest that such institutionalized confiscation and hoarding of knowledge is a recent phenomenon - since about the 70s. Am I missing something?
That's a fair and good interjection. The truth is probably that at society scale, both approaches are traditional.
The open sharing approach is traditional for research and academia, while the information restricting approach is traditional for business-oriented thinking.
So, a young field will typically start out fairly open and then get increasingly closed down. The long-term trajectory differs by field, and the modern open-source landscape shows that there can be a fair bit of oscillation.
We're seeing the same basic shape of story play out in generative AI.
