Is there anything to the notion that in Go, success and failure are concrete, objective, and more or less easy to measure (or at least measured along the same kind of rules)? While it is computationally intractable to iterate through future moves to an end state, it’s still relatively easy to understand how well you’re doing at any point, and you measure that in basically the same way every game.

For some parts of language, that’s true: there’s grammar, there’s syntax, there’s patois, there’s argot—all these things seem accountable to words’ collective frequency within articulable groups of speakers, more-or-less-fully knowable on their own, and with success metrics that evolve but that do so through collective processes that models can measure and calibrate to. And indeed the models are great at those aspects of language.

“Succeeding” at writing is more than just “saying it well,” it’s also “having something worth saying” and “being worth listening to.” The second point is where things seem to get hazier for computable models. For sure there’s a set of facts that are more or less constant about the world, and well-reported. Science, repackaging history that’s already been done, lurid tales of crime—the stuff podcasts are made of! Not to mention the vast sea of data that sensor networks and automated research can produce—vast reservoirs of subtle truth that humans struggle to begin to mine for insight! It makes complete sense that this is computable stuff, and that computed writing might well be worth learning from.

But important writing—classically, anyway—seems to involve communicating new or idiosyncratic knowledge, and often reveals some of the process of developing it. The podcast Serial, for example, was a smash hit specifically because it didn’t rely on things that were part of the record—and because it reminded people how contingent memory and “truth” are. Bob Woodward writes things that are shamelessly tinted with Bob-Woodward-worldview, but people reveal important and true things only to Bob Woodward because they trust who he is and how he’s behaved for a lifetime (prominent longtime investigative journalist in the US, on the national security beat). Nassim Taleb seems to come up around here: in something like Antifragile his project wasn’t necessarily about new facts but about interpreting them in contrarian fashion and grouping those contrarian insights to synthesize a new theory.

Which brings us to the third component: “being worth listening to.” Writing is an act of communication: the writer matters. A parent hangs its child’s crayon drawing on the fridge not because it’s “authentic to the style of the kids’-crayon-drawing mode of visual art,” not because it’s novel or informative or even true-to-life, but because it came from a person they love. A “Dear John” letter devastates a soldier because it comes from a person with outsized part in their life and identity. Chinese publishers’ booths at trade shows are wall-to-wall translations of The Governance of China because it’s politically unwise not to. My favorite writers feel fresh: you feel elements of their personality come through. People have a special fetish for true crime—not that there’s any lack of fictitious crime to read about, but the fact that it happened to real humans potentiates the drama for these readers. It’s this aspect that I have a hard time understanding as computable (or commoditizable, I guess… are those similar phenomena?).

Already we seem to be drawing these distinctions in our collective reaction to LLM-stuff. We can’t wait to get hallucinations under control so we can chuck in gigantic boring contracts and internal wikis and financial reports, and get out comprehensible insight—but we roll our eyes at the tsunami of empty slop that’s overtaken Google results. We giggle at AI ventriloquism like this Neuro character [0], but die a little inside every time we read anodyne LLM-ish promotional copy and sameish AI art. First-level customer support seems like a perfect role for a chatbot—“turn it off and on again,” but nicely!—but people on the receiving end hate it [1] even for that task well-suited to it.

I’m only a layperson of course, but I wonder if any of those distinctions might be fruitful? Some of it I guess sums up to the old writing advice “show, don’t tell”—are there examples of machine writing showing promise in that way?

[0] https://m.youtube.com/@neurochron_fan_channel (video; brain rot)

[1] https://www.theregister.com/2024/07/09/gartner_simply_replac...