- cross-posted to:
- technology@beehaw.org
- cross-posted to:
- technology@beehaw.org
Executives working on AI at Microsoft and OpenAI admitted what its critics have been saying all along: Large language models are predatory pieces of technology that have been built on what a Microsoft executive called “an astonishing theft of unprecedented proportions,” and the “largest theft of labor in human history.” An internal Microsoft document said generative AI products have created a “doom loop” that is killing “the entire web.”
Those statements and a series of other mask-off moments feature heavily in an unredacted court filing that was unsealed Thursday in the behemoth New York Times vs OpenAI copyright lawsuit that has been winding its way through the court system for years. In a filing asking for summary judgment (basically, a filing with the court asking it to rule), lawyers for the New York Times laid out a series of admissions made by Microsoft and OpenAI executives in documents and depositions that until now had remained either sealed or redacted at the request of Microsoft and OpenAI.
It’s easy to see why the AI companies wanted to hide this from the public. The statements, taken together, are some of the most damning indictments of the ways LLMs were trained, how they worked, and the immediate threat they pose to human labor. It is a reminder that even as AI becomes more powerful and companies try to shift the narrative to the supposed existential risk of “superintelligent” AI, the tools they have already built were created by stealing from human creativity and labor and are by definition existential threats to the human labor market.



model collapse is the endgame. that’s the whole point of LLM.
What do you mean? Why would collapse be the endgame?
It’s either agi or model collapse. For every AI system actually. If you have a high enough adoption you start to muss original data so if your AI is not self sufficient in time it will collapse, because it will be trained on its own data, which just is an incentive loop
in a manner of speaking - you always end up there. not by design though. models operate via continuous refinement and you can only optimize a model so much until it is a mess and you need to figure out where to roll back. so you either get shit like semantic drift or variance decay and you can whack a mole it to an extent but then you hit the rlhf wall when the model starts gaming its reinforcement framework and the fat lady sings.