Comment by alpha21io
2 hours ago
Is there evidence that overall software developer positions are declining relative to other jobs? It wouldn't surprise me if Jevons paradox applied here.
2 hours ago
Is there evidence that overall software developer positions are declining relative to other jobs? It wouldn't surprise me if Jevons paradox applied here.
There is mixed evidence on the impact of AI on developer jobs, but on the balance it seems that to the extent AI is impacting dev jobs, it is mostly the junior roles that are being affected.
There is even some evidence that suggests senior roles are more in demand (and getting correspondingly higher pay too) which gels with the common observation that senior workers are more effective at managing the AI which is doing the work of (and hence displacing) junior workers.
Asking a chatbot or Google AI overviews should surface relevant studies.
I have been feeling like “mostly junior roles being affected” has been a false indicator or false narrative.
Junior roles are always affected when industries have high unemployment rates.
I don’t think AI has eliminated the need for junior engineers, nor will it, it’s just that companies have their pick of available experienced hires.
I would think that junior engineers would make more sense in an era where AI can fix and catch rookie mistakes.
Exactly. At least in my part of the world (hardware) we are hiring more software folks these days and fewer pure hardware
> Jevons paradox
Thanks! I've heard numerous examples of this. E.g. paint rollers, and later paint sprayers, made painting so much more efficient and cheaper that people wanted their houses and apartments painted more often, ultimately increasing house painter employment. But I didn't realize the phenomenon had a name.
The broader topic of marginal utility is worth looking into if this is interesting to you.
The reason why AI induces more demand is more interesting than simply observing a correlation. At the root of the paradox is the dissatisfaction with AI as it raises the bar for human work. In that sense, AI functions like training wheels more than actually doing anything useful on its own. More people can ride the bike well as a result.
This is the "law of diminishing marginal utility". In other words, LLMs are an NLP-powered google search 2.0. That sums up pretty well why AI hit a plateau a couple of years ago. There's only so much training data and humans become more efficient and creative once exposed to the exact information they were always looking for, but could never find in the past.
https://en.wikipedia.org/wiki/Marginal_utility