One of the main reasons why companies start new open source projects is because having a good open source option in a given category will usually push the market value of software in that category to $0, and this can be strategically valuable. For example, Google released Android as an open source operating system because they make their money from ads and data collection, not from selling operating system licenses. All the cell phone companies switched from Windows Mobile and Symbian to Android, which gave Google a ton of user data to sell.

For AI, the most profitable part of the value chain is selling inference. None of the big American companies want to release a leading edge model as open source because this would drive the price of inference to $0. Meanwhile, open source AI models are a huge strategic initiative for China. Having commodity Chinese models that are as good as the leading edge American models from 6 months ago forces the American companies to keep paying more and more money to train better and better models since the amount of time they can collect rent on a model they've previously trained is limited to 6 months.

The Chinese approach is reminiscent of the US spending so much on 'defense' in the 1980s that the USSR bankrupted itself trying to keep up.

In business strategy terms this is known as "commoditize your complements".

https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

> None of the big American companies want to release a leading edge model as open source because this would drive the price of inference to $0

Meta/Llama: "What am I, chopped liver?"

I thought the thing keeping inference above $0 was the hardware, and even if that were free there's still the tyranny of the Landauer Limit.

Google had to release at least the core packages in Android regardless because it is based on top of Linux and the GPL license would require it.