Before anything else: whether the specific story in the linked post is literally true doesn’t actually matter. The following observation about AI holds either way. If this example were wrong, ten others just like it would still make the same point.
What keeps jumping out at me in these AI threads is how consistently the conversation skips over the real constraint.
We keep hearing that AI will “increase productivity” or “accelerate thinking.” But in most large organizations, thinking is not the scarce resource. Permission to think is. Demand for thought is. The bottleneck was never how fast someone could draft an email or summarize a document. It was whether anyone actually wanted a careful answer in the first place.
A lot of companies mistook faster output for more value. They ran a pilot, saw emails go out quicker, reports get longer, slide decks look more polished, and assumed that meant something important had been solved. But scaling speed only helps if the organization needs more thinking. Most don’t. They already operate at the minimum level of reflection they’re willing to tolerate.
So what AI mostly does in practice is amplify performative cognition. It makes things look smarter without requiring anyone to be smarter. You get confident prose, plausible explanations, and lots of words where a short “yes,” “no,” or “we don’t know yet” would have been more honest and cheaper.
That’s why so many deployments feel disappointing once the novelty wears off. The technology didn’t fail. The assumption did. If an institution doesn’t value judgment, uncertainty, or dissent, no amount of machine assistance will conjure those qualities into existence. You can’t automate curiosity into a system that actively suppresses it.
Which leaves us with a technology in search of a problem that isn’t already constrained elsewhere. It’s very good at accelerating surfaces. It’s much less effective at deepening decisions, because depth was never in demand.
Very well put. This is a dimension to the ongoing AI nonsense that I haven’t seen brought up before, but it certainly rings true. May I say also that “They already operate at the minimum level of reflection that they’re willing to tolerate.” Is a hell of a sentence and I’m a little jealous that I didn’t come up with it.
Thanks, I really appreciate that. I’ve been getting a little grief this weekend because some of my posts are adapted from essays I’ve been working on for Substack, and apparently careful editing now makes you suspect as an actual person.
I’m very real, just flu-ridden and overthinking in public. Glad the line landed for you.
Before anything else: whether the specific story in the linked post is literally true doesn’t actually matter. The following observation about AI holds either way. If this example were wrong, ten others just like it would still make the same point.
What keeps jumping out at me in these AI threads is how consistently the conversation skips over the real constraint.
We keep hearing that AI will “increase productivity” or “accelerate thinking.” But in most large organizations, thinking is not the scarce resource. Permission to think is. Demand for thought is. The bottleneck was never how fast someone could draft an email or summarize a document. It was whether anyone actually wanted a careful answer in the first place.
A lot of companies mistook faster output for more value. They ran a pilot, saw emails go out quicker, reports get longer, slide decks look more polished, and assumed that meant something important had been solved. But scaling speed only helps if the organization needs more thinking. Most don’t. They already operate at the minimum level of reflection they’re willing to tolerate.
So what AI mostly does in practice is amplify performative cognition. It makes things look smarter without requiring anyone to be smarter. You get confident prose, plausible explanations, and lots of words where a short “yes,” “no,” or “we don’t know yet” would have been more honest and cheaper.
That’s why so many deployments feel disappointing once the novelty wears off. The technology didn’t fail. The assumption did. If an institution doesn’t value judgment, uncertainty, or dissent, no amount of machine assistance will conjure those qualities into existence. You can’t automate curiosity into a system that actively suppresses it.
Which leaves us with a technology in search of a problem that isn’t already constrained elsewhere. It’s very good at accelerating surfaces. It’s much less effective at deepening decisions, because depth was never in demand.
If you’re interested, I write more about this here: https://tover153.substack.com/
Not selling anything. Just thinking out loud, slowly, while that’s still allowed.
That is a hell of an astute observation
Very well put. This is a dimension to the ongoing AI nonsense that I haven’t seen brought up before, but it certainly rings true. May I say also that “They already operate at the minimum level of reflection that they’re willing to tolerate.” Is a hell of a sentence and I’m a little jealous that I didn’t come up with it.
Thanks, I really appreciate that. I’ve been getting a little grief this weekend because some of my posts are adapted from essays I’ve been working on for Substack, and apparently careful editing now makes you suspect as an actual person.
I’m very real, just flu-ridden and overthinking in public. Glad the line landed for you.