I think the concern isn't so much about the current state of AI replacing software engineers, but more "what if it keeps getting better at this same rate?"

I don't really agree with the reasoning [1], and I don't think we can expect this same rate of progress indefinitely, but I do understand the concern.

[1] https://en.wikipedia.org/wiki/Jevons_paradox

Jevons paradox doesn't always apply (it depends on the shape of supply-demand curves) and it is entirely possible for technology to eliminate careers. For example, a professional translator can work far faster now than twenty years ago, but the result is that positions for professional translators are rapidly disappearing rather than growing. There's a finite demand for paid translation work and it's fairly saturated. There are also far fewer personal secretaries now than there were in the '70s. That used to be a very common and reasonably well-paying career. It may happen that increasing the efficiency of software development results in even more and even-better-paid software developers, but this isn't a guaranteed outcome.

| "what if it keeps getting better at this same rate?"

All relevant and recent evidence points to logarithmic improvement, not the exponential we were told (promised) in the beginning.

We're likely waiting at this point for another breakthrough on the level of the attention paper. That could be next year, it could be 5-10 years from now, it could be 50 years from now. There's no point in prediction.

If software developers wind up replaced by AI, I think it's safe to say every industry's labor will be replaced. Trade jobs won't be far behind, because robotics will be nipping at their heels.

If software falls, everything falls.

But as we've seen, these models can't do the job themselves. They're best thought of as an exoskeleton that requires a pilot. They make mistakes, and those mistakes multiply into a mess if a human isn't around. They don't get the big picture, and it's not clear they ever will with the current models and techniques.

The only field that has truly been disrupted is graphics design and art. The image and video models are sublime and truly deliver 10,000x speed, cost, and talent reductions.

This is probably for three reasons:

1. There's so much straightforward training data

2. The laws of optics and structure seem correspondingly easier than the rules governing intelligence. Simple animals evolved vision hundreds of millions of years ago, and we have all the math and algorithmic implementations already. Not so, for intelligence.

3. Mistakes don't multiply. You can brush up the canvas easily and deliver the job as a smaller work than, say, a 100k LOC program with failure modes.

You imply the models have been improving in some capacity.