Comment by PaulRobinson

I agree with you on all of this, and found myself wondering if the author had actually studied the Industrial Revolution at all.

The Industrial Revolution created a flywheel: you built machines that could build lots of things better and for less cost than before, including the parts to make better machines that could build things even better and for less cost than before, including the parts to make better machines... and on and on.

The key part to industrialisation in the 19th-century framing, is that you have in-built iterative improvement: by driving down cost, you increase demand (the author covers this), which increases investment in driving down costs, which increases demand, and so on.

Critically, this flywheel has exponential outputs, not linear. The author shows Jevons paradox, and the curve is right there - note the lack of straight line.

I'm not sure we're seeing this in AI software generation yet.

Costs are shifting in people's minds, from developer salaries to spending on tokens, so there's a feeling of cost reduction, but that's because a great deal of that seems to be heavily subsidised today.

It's also not clear that these AI tools are being used to produce exponentially better AI tools - despite the jump we saw ~GPT-3.5, quantitive improvement in output seems to remain linear as a function of cost, not exponential. Yet investment input seems to be exponential (this makes it feel more like a bubble).

I'm not saying that industrialisation of the type the author refers to isn't possible (and I'd even say most industrialisation of software happened back in the 1960s/70s), or that the flywheel can't pick up with AI, just that we're not quite where they think it is.

I'd also argue it's not a given that we're going to see the output of "industrialisation" drive us towards "junk" as a natural order of things - if anything we'll know it's not a junk bubble when we do in fact see the opposite, which is what optimists are betting on being just around the corner.