For instance, you can put a lot of effort into something, without creating any value for yourself or for others. But it is often true that things of utility need a sufficient amount of compounded effort behind them before they become valuable, otherwise they are common and easy to obtain. Value is necessarily relative.

You could say that people have the responsibility to demonstrate that they put in the effort and created value, but then you get the situation where people naturally optimize perception of effort or value over actual effort or value, because in the end that is what is rewarded. Then you can also say that people also have the responsibility to look a bit closer before estimating real value, but that takes more effort and people naturally strife towards efficiency. I would guess that the problem today is that the balance between these two is off, and we're doing too much of the former and too little of the latter.

Also like some people hinted at this in sibling threads, I think it's different between purely abstract skills, and skills that involve muscle memory. For instance, I could probably stop using my bicycle for a very long time, and still not unlearn how to use it, or learn it again really quickly. Maybe it is because abstract skills are inherently more complex and require more cognitive effort and connections to knowledge overall, and are therefore more fragile.

In addition to your last paragraph: lots of things that we used to do the less efficient way had side-benefits that were not immediately obvious, probably because they compounded over time. Now that we're not doing them anymore, we notice all kinds of widespread societal problems (in particular among young people) that come up that were never there before.

However, when looking at muscle, once you have it you don't need to use it as much in order to maintain it. I wonder if the same is true for skills; in that case, some kind of regiment where you still use the skill you delegate once a week or so could maybe help with avoiding this loss of skill for most part.

The amount of creative expression does not necessarily correlate with impact. Something can be created with nearly zero creative expression, that ends up making a significant impact. In that case you are more of a director than an artist I suppose, in that you direct the high-level process and only make decisions there. You can call it creative thinking in the same way a good businessman makes smart high-level decisions and then delegates what is downstream to others, with decisions being optimized for impact.

I think you can be creative "within a frame" in that sense, e.g. creative in the way you wield an LLM for instance, which is on a different scale compared to being creative on the piano roll with how you organize and brainstorm your melodies. It's just a different skill set at a different granularity altogether. But the one thing that I think holds, is that higher level methods have less creative expression by definition, because you are delegating more decisions to other faculties; you are seeing less of the "creator" in the work.

I guess the less detailed a spec has to be thanks to the tooling, the more likely it is that the LLM will come up with something usable. But it's unclear to me whether that is because of more examples existing due to higher user adoption, or because of fewer decisions/predictions having to be made by the LLM. Maybe it is a bit of both.

It necessarily has to derive it from examples of cameras that fly forward that it knows about, without understanding the exact mathematical underpinnings that allow you to rotate a 3D perspective camera and move along its local coordinate system, let alone knowing how to verify whether its implementation functions as desired, often resulting in dysfunctional garbage. Even with a human in the loop that provides it with feedback and grounds it (I tried), it can't figure this out, and that's just a tiny example.

Math is precise, and an LLM's fuzzy approach is therefore a bad fit for it. It will need an obscene amount of examples to reliably "parrot" mathematical constructs.

Often though, this position is highly subjective and mental in nature. A homeless man could willingly give his food away, and still somehow be fine with that, if he believes that things will be fine regardless somehow (perhaps he has an alternative source of food, or sincerely doesn't think that skipping food once will set him back forever). At the other hand, someone with a difficult and tedious job that pays well may not feel like they have the time or energy to give without necessarily receiving anything in return, even though they may objectively be in a much better overall position for it.

I guess altruism necessarily requires some other essential basic needs to be in abundance first before it can overflow.

What I was getting at is that nothing stops you from asking AI what would be the next best smartwatch app to build, and based on all its aggregated knowledge and other inputs (e.g. search) it has, it can potentially make a better estimation than you or any human of a product that would sell.

Of course whether that is actually true depends on how well its training data is able to model/mimic reality, and how grounded its inputs (e.g. internet) are. You can always help it a bit by steering it into the right direction, providing additional grounding. Was mainly wondering for how long this "additional" guidance would be a necessity, fearing that it won't be for as long as we think.

My honest and rather pessimistic take is that in the long-term any craft that purely lives in the abstract is likely to be doomed.

In that sense, going from pseudocode to a programming language is no different from that, or even translating a piece of code from one programming language to another.

With how rapidly the world has been changing lately, it has become difficult to estimate which of those more specific skills will remain relevant for how long.