Matthew Kirschenbaum

Example: transformer-circuits.pub/2026/workspa...

Verbalizable Representations Form a Global Workspace in Language Models transformer-circuits.pub

Nor are we lacking in actual ways to learn about what “some guy” made, or why people reacted to it the way they did. mitpress.mit.edu/978026205248...

mitpress.mit.edu

There’s a literary scandal involving AI on a weekly basis now. Prizes, contracts, allegations, retractions. Individual authors will make their own choices as they always have. But collectively and industry-wide, there is no putting the stopper back in this bottle. www.theguard…

$2m crime novel deal collapses amid questions over AI use theguardian.com

The University of Maryland, where four out of five of the researchers co-authoring this paper are based, has a very fine English Department. Just saying. arxiv.org/pdf/2604.03136

arxiv.org

At some point we’re going to need to square the circle between puffing “Why are you asking AI, go ask a real person instead” and all of us who have told someone “If you are soliciting my expertise then please pay me.” +

Deep Thoughts on the Current AI Discourse. 🧵: 1. I don’t think robots are on the verge of going rogue and murdering us all in our digital sleep. My p-doom is low. Note that this is separate from the question of ongoing enshittification, where my e-shit number is pretty well maxed out. +

What is your favorite, clever, and non-obvious shorthand descriptor for LLMs? Not looking for stochastic parrots or autocomplete on steroids, also not looking for throwaways like bullshit plagiarism machines. Also not looking for explainers: I’m looking for a smart and memorable phrase or sentence.

So the Rosenbaum FUTURE OF TRUTH thing is appalling, but let’s not pretend this is an aberration: the over the fence talk I hear is that full on AI is now the norm in trade nonfiction.

A thing we’re seeing with the encroachment of AI on services like Google search is a highly counterintuitive displacement of the list—an information genre honed over millennia—by discursive prose. In the distinction popularized long ago by Lev Manovich, narrative is triumphing over database.

At an online event this morning I defended a claim which others found troublesome: That there are legitimately experienced aspects of computational phenomena, such as large language models, which—for all practical ways of speaking—are not always resolvable by or restorable to material conditions.

I spent the first half of the day at a public facing AI convening in downtown DC. Eclectic speakers, collected audience. The biggest blindspot I saw was this: Culture was repeatedly positioned as something *outside* of tech. We can make better tech if we bring more culture into the process. +

I understand why we’re still explaining to people that LLMs are not conscious but I also don’t understand why we’re still explaining to people that LLMs are not conscious. Like imagine thinking this is the conversation to have.

I understand the impulse to make this point, but I think it also misdirects the agency. The language of “AI” was already all over the social milieu of the research behind large language models, including a company named OpenAI which was working on lots of other things besides. +

Does anyone know of an account of what was happening inside of Google‘s DeepMind c. 2015-2020? Looking for something like Karen Hao’s work— Definitely doesn’t have to be book length, but that combination of investigative reporting and narrative history.