I was disappointed to read Cory Doctorow's post where he got weirdly defensive about his LLM use and started arguing with an imaginary foe.

@tante has a very thoughtful reply here:

https://tante.cc/2026/02/20/acting-ethical-in-an-imperfect-world/
A few further comments, 🧵>>

Acting ethically in an imperfect world

Life is complicated. Regardless of what your beliefs or politics or ethics are, the way that we set up our society and economy will often force you to act against them: You might not want to fly somewhere but your employer will not accept another mode of transportation, you want to eat vegan but are […]

Smashing Frames
It was particularly disappointing to see Doctorow misconstrue (and thus, if he is believed) undermine the work that many of us are doing to shine a light on the ways in which the ideology of "AI" and the specific ways in which LLMs and other "AI" products are created do real harm.
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I also want to point out (again) the ways in which lumping together all uses of LMs (like the lumping of technologies into "AI") obscures the issues at hand.

Language modeling is a useful component of many technologies that can be built without extractive, exploitative means. Take the automatic transcription built by and for the Māori people -- there's te reo Māori language model that's part of that.
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And the transformer architecture represented an important step forward in language modeling, that brought improvements to things like spell checking (Doctorow's use case).
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What we argued in Stochastic Parrots, however, was that you can get those benefits of the transformer architecture without ammassing datasets too large to collect with care (meaning consentfully, intentionally, and with the ability to document what's in the data).
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@emilymbender

> without ammassing datasets too large to collect with care

I appreciate your post, and I'm a huge proponent for moving past the transformer.

However, how is worrying about this not choosing "cooperate" every single time in the Prisoner's Dilemma when you know Meta & Co will always choose "defect"?

This is worrying to me, because nefarious actors have no incentive to care about these issues (and actually have plenty of incentives to be hostile to these ideas). Also, they have institutional backing to do so.

@budududuroiu In what sense is this choosing 'cooperate' and also where do you see a prisoner's dilemma here?

@emilymbender

The framing is that there is a Commons of human knowledge that is accessible to virtually everyone (based on how internet protocols operate currently). There's a sort of social contract within these Commons which makes participants feel ok to publish permissively, be ok with bearing the costs of compute so that everyone can access the Commons freely.

To 'cooperate' is to abide by this unwritten social contract, respecting /robots.txt, respecting CC.

To 'defect' is to take advantage of this permissiveness to massively scrape the Commons, ignoring the social contract, offloading the costs to the providers in the Commons.

The current SOTA architecture is transformer-based, which requires massive data for training effectively. By cooperating and not engaging in training a 'free as in freedom' LLM, we're 1) losing the benefits of the Commons (as they either get sloppified or people take more information private because of increased compute costs), and 2) we also don't get to build an artifact based on the knowledge of the Commons that can be contributed to the Commons (an open LLM).

If the DeepSeek moment managed to wipe $600bn off Nvidia's market cap, commoditising LLM training would be the death knell of the AI slop hype race, as who would pay thousands in tokens to OAI and Anthropic when you can use a GPL-licensed LLM (or whatever permissive license we come up with)