What is fascinating is how you can witness it at so many levels of organization. One example: Employer executive get enamored with moving from labor to capital. They believe that by using LLMs, they can replace a lot of workers. At my place of employment, we have people that are surprised they can't file a Jira ticket describing a product ask, and have it kick off an implementation. You can build the skill to attempt that, but invariably you'll get back questions like "what do you mean by " and "what do you want to do in this case, a, b, or c?"; questions that a product person or an exec are not well suited to answer.

In the past, programmers did that kind of interpretation and judgment call. So then you're in a quandary; who should do that work? Work that previously, you never imagined was an inherent part of what the replaceable code monkeys do at your beck and call?

And then, how do you hire for that? How do you find the training for the people that are experienced enough with... something... to know what a cohesive error response is, or what kind of telemetry strategy is best for that particular product and organization, what collection of product asks are incredibly complicated for what they're asking and can deliver 95% of the benefits at 5% of the work if we just do this instead, and whether you want to aim more towards thick or thin clients?

Who are those people? Wait, those are programmers? Wait, there's this whole collection of inherently human skills that we devalued, by not appreciating they were always quietly doing that for us in the past?

That's just one example. There's a repeating pattern of discovering where the work truly is, work that was embedded in manual patterns we might not have to involve ourselves with anymore, but is yet still essential. So the nature of our jobs changes massively, but the overall level of employment does not.

At least, not in the medium to long term. There is a lot of painful churn we have to suffer through first.

What's also cool is the more advanced llms know lilypond and music theory too, so they can do things like... I don't know, check for counterpoint errors. I've used it with limited success to expand my jazz lead sheets into two-hand piano arrangements just for practice exercises.

https://concludia.org/graph/g_2ecb8083-52ec-3448-8c30-2f9bc7...

So maybe our CEOs are responding with a lot of foresight and inside information and know that that level of quality is going to be cheap really soon. But barring that, they're going to experience either sticker shock or a slowdown.

I think the real endgame is probably more accurate "models of models" (model routers) that know exactly how to split prompts between expensive frontier and cheap/free local models.

I haven't found anything that requires running all night. I could tell it to one-shot a big plan but given how often I realize I want an intermediary thing to be slightly different it seems like a waste of effort.

I'm guessing the next thing I should probably look into is some sort of machine vm I can tunnel my codex-gui requests to so I don't have to deal with the sandbox approvals (I don't want to give it "dangerous" access to my entire mac).

I don't understand what people are doing with their side projects that is leading them to churn through tokens so quickly, to the point of requiring two $200/month subscriptions and a bunch of token charges besides.