I don't disagree with the thesis here, I just don't think costs are coming down quite that quickly.

> The reasons why I believe that ... the highlands were functionally cut off is ...

So maybe the extension is that professional SWEs who work at big corporations will need to use AI to 'keep up', but hobbyists can still get enjoyment out of their own work at hobby scale without using AI (similar to the point in TFA).

IMO knowledge work productivity has gone up a massive amount since 1990, and companies could in fact fire most of their knowledge workers while still getting their most critical work done. (These are the bullshit jobs/email jobs.) But companies mostly haven't done this, which implies other reasons for employing so many knowledge workers.

Even if AI becomes capable of doing everything, I would predict there will be a period of many years/decades where companies still employ a similar number of people to sit around and babysit/watch what the AI does.

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Here’s an example: I had asked Claude for some music recommendations in a certain style. Part of its output was:

—

*Long journey tracks*

Clinic — “The Return of Evil Bill”

Guided by Voices — not really, wrong band

Silver Apples — “Oscillations”. Proto-everything, deeply repetitive, hypnotic.

—

So at some point there, the next token produced was “Guided” or “Guide” or whatever, and then because it can’t go back, it had to correct itself after the fact.

Reasoning/CoT have helped a lot, but I feel like small versions of this still happen all the time.

Human writing is like 90% editing.

Presumably you could use the same reasoning trace, run multiple generations, and get different outputs (if the temperature is >0).

But now I’m interested in playing more with Cowork or Claude Code/Codex for prose writing to see if the set of tools there affects outputs at all. I guess you might need a more custom “writing” harness.

On the other hand, LLMs are forced into picking some likely-ish word, and then have to build the rest of their response to retcon that choice into making sense.

Even good human writers would probably struggle with this constraint. It would be like someone interrupting your writing to tell you the next word MUST be such-and-such, and then you have to try and make it work as best you can first try, without going back to edit. The result would probably be a little clunky. (Maybe it’s impressive LLMs write as well as they do.)

Maybe they did? Or maybe they don't realize they're playing against other agents.

Of course, if agents running different models are competing in these 'games', I wonder how much of the theory of mind would translate.

(N.B. - I don't think they're all defecting from the first turn, although it's not clear. It just says 'they all defect at the same time'. So if they're playing for 10 iterations, they might all decide to defect after turn 6, but since they all do it together they don't get the benefits. I would expect these models know that optimal strategies in repeated prisoner's dilemma start with cooperation.)

The real problem for the hyperscalers would be demand stalling out entirely (or maybe small local models getting good enough that people don't use the cloud).

One interesting thing is that it also removed some "internal CTAs" to engage (like "Start your day with our podcast!"). These aren't technically ads, so something like uBlock doesn't remove them. But honestly, I don't really mind having those filtered out too.

Obviously more detailed data isn't easily available to report on, but I'd like to know:

- What are the labs spending on compute specifically to serve models? Are they really profitable "on inference" of existing models? If so, how long is the payback period to recoup their training costs for those existing models only? If not, how much would prices need to rise to be profitable?

- How much capex have the hyperscalers invested in just the compute being used to serve those existing models? Are they making money "on inference" when accounting for just that amortized capex? If so, what are the margins like?

- What share of hyperscalers' AI revenue is from training vs inference? (Presumably training is more dependent on VC investment and inference is more self-sustaining.)