I've been saying for a long time now that "open source AI" isn't really open source.

BTW, you owe it to yourself to watch this entire interview with Whittaker. It's not every day you get to watch a video and feel much smarter after having watched it than you were before. I learned a lot from this and have much food for thought to chew on.

@jaredwhite

Brian Long in this interview speaks alarmingly several times about the ability of "open source" models to evade controls against these sorts of fakes.

not only do these models lack signature benefits many of us have traditionally associated with "open" in favor merely of running the black boxes locally, "open source" is being associated directly with this sort of exploitation

https://www.wbur.org/hereandnow/2026/02/12/ai-deepfake-online-tools

@idlestate wow, OK I'll bookmark that.

It touches on another but somewhat related problem: people associate "open source" with "good" and that's a fallacy. Just because a technology is open source does not mean it is inherently good.

@jaredwhite

The debian foundation also had a proposal to greatly limit what can be called an open model by their standards to require releasing the actual training data too:

Edit: the proposal was withdrawn though, with the proposer saying that it needed more time to be properly discussed before the vote I hope we see a clear definition like that again soon.

https://www.debian.org/vote/2025/vote_002

Agreed. At most, we can call those "open weights" - but declaring a model open-source means that it must be trained exclusively on data compatible with free software and free culture licenses, and preferably be able to have each token be tracked back to the work it was originally trained on for credit purposes. The closest thing I've seen to this is the Comma LLM model, and even that seems to lack the latter capability. huggingface.co/common-pile/com…

@jaredwhite Calling a model “open weight” is like saying notepad.exe is “open machine code” because you get to look at the bytes. It’s total bullshit.

@jaredwhite IMO it was a mistake for OSI to rush to try to create a label for AI that included the term open source when it is no such thing. It's possible that a model could be fully open source if the training data was also made available, but how many companies are going to do that and expose what went into the training? Not many.

@jaredwhite

Narrative arbitrage is a _hell_ of a phrase.

@jaredwhite "Open source AI" is a scam. Just like Facebook, they tell users to volunteer to improve locations in-their-wall-garden for free. Then booom, Facebook kill this feature and get the big database to make ton of money from target advertising.