brettworks

• An essay about AI and thinking.

“It can seem unnatural, even repulsive, to imagine that a computer program actually understands, actually thinks. We usually conceptualize thinking as something conscious, like a Joycean inner monologue or the flow of sense memories in a Proustian daydream. Or we might mean reasoning: working through a problem step by step. In our conversations about A.I., we often conflate these different kinds of thinking, and it makes our judgments pat. ChatGPT is obviously not thinking, goes one argument, because it is obviously not having a Proustian reverie; ChatGPT clearly is thinking, goes another, because it can work through logic puzzles better than you can.”

• An essay about the work of film editor and sound designer Walter Murch.

“What you do as an editor is search for patterns at both the superficial and ever deeper levels.”

“If I go out to record a door-slam, I don’t think I’m recording a door-slam. I think I am recording the space in which the door-slam happens.”

• An essay by Kwame Anthony Appiah about AI and de-skilling.

“De-skilling is a catchall term for losses of very different kinds: some costly, some trivial, some oddly generative. To grasp what’s at stake, we have to look closely at the ways that skill frays, fades, or mutates when new technologies arrive.”

“The cultural realm, certainly, has had a long retreat from touch. In the middle-class homes of 19th-century Europe, to love music usually meant to play it. Symphonies reached the parlor not by stereo but by piano reduction—four hands, one keyboard, Brahms’s Symphony No. 1 conjured as best the household could manage. It took skill: reading notation, mastering technique, evoking an orchestra through your fingers. To hear the music you wanted, you had to practice.”

“Once a machine enters the workflow, mastery may shift from production to appraisal […]

That, more and more, is what ‘humans in the loop’ has to mean. Expertise shifts from producing the first draft to editing it, from speed to judgment. Generative AI is a probabilistic system, not a deterministic one; it returns likelihoods, not truth. When the stakes are real, skilled human agents have to remain accountable for the call—noticing when the model has drifted from reality, and treating its output as a hypothesis to test, not an answer to obey. It’s an emergent skill, and a critical one. The future of expertise will depend not just on how good our tools are but on how well we think alongside them.”

“Erosive de-skilling remains a prospect that can’t be wished away: the steady atrophy of basic cognitive or perceptual capacities through overreliance on tools, with no compensating gain. Such deficits can deplete a system’s reserves—abilities you seldom need but must have when things go wrong. Without them, resilience falters and fragility creeps in.”

“Working with large language models, my younger colleagues insist, is already teaching a new kind of craftsmanship—prompting, probing, catching bias and hallucination, and, yes, learning to think in tandem with the machine. These are emergent skills, born of entanglement with a digital architecture that isn’t going anywhere. Important technologies, by their nature, will usher forth crafts and callings we don’t yet have names for.”

“Each generation has had to learn how to work with its newly acquired cognitive prostheses, whether stylus, scroll, or smartphone. What’s new is the speed and intimacy of the exchange: tools that learn from us as we learn from them.”