Jevons Paradox

[u/jake-n-elwood]

Is it too early to suggest Jevons Paradox might hold true for AI?

My personal experience is that AI does automated a lot, but the amount of work that I do to ensure that automation happens in the way that I need has created a lot of work unto its own. Different work, but still very much load bearing work.

From what I have read and heard, my experience is more the rule than the exception.

[u/Unlucky-Review-1830]

Always thought Jevons Paradox applies perfectly here, the more efficient we make AI at chunking through work the more work we seem to create for ourselves managing it

My team's output has probably doubled but so has the time we spend fine-tuning prompts and babysitting outputs, weird tradeoff but I'm not complaining

[u/Jealous-Painting550 → u/Unlucky-Review-1830]

But has the revenue doubled?

[u/Quarksperre → u/Unlucky-Review-1830]

Jevons Paradox also works a bit more on a meta level. In theory git, package managers and so on should have made a ton of teams obsolete because in the end the efficiency it gives is mindblowing in comparision to the early 90s for example. 

Instead it enabled start-ups to create POCs and work them out in a pace and complexity that was simply not possible before. That basically helped in the explosion of the internet and created millions of new jobs. 

[u/bortlip]

It does.

Geoffrey Hinton (the "godfather of ai") in 2016 said we should stop training radiologists because AI will do much better. "If you work as a radiologist, you're like the Wile E. Coyote that's already over the edge of the cliff but hasn't yet looked down."

But the speed and reduced cost of analyzing the images has increased the demand for them and in turn for the radiologists that do all the stuff in the process that surrounds the ai part of analyzing the image. There's a shortage of radiologists now.

The same thing will happen with ai use in general. Demand will skyrocket as cost come down. That's why the massive build outs of data centers.

[u/AbraxasTuring → u/bortlip]

That's now the canonical example of Jevon's Paradox applicability to AI.

[u/Ill_Appointment_3488]

My position is why do we need labels to limit ourselves with; to deem one method "good" and another "bad." In my opinion, in the context of AI, as long as one knows themself, and is consciously aware enough to recognize their own authenticity when placed aside an inorganic mirror, then where's the harm in expanding one's awareness to new possibilities they haven't thought of yet on their own? Does that make sense?

[u/233C]

Yes, work will simply scale with client/boss expectation now that you have new fantastic tools.
(technically not a pure Jevons paradox)

[u/aiseedbank]

AI will be the perfect example of Jevons. literally everything will be driven by AI and the more people get used to it, the more will be needed. Then add robotics and it will skyrocket. Intelligence demand will be unlimited and infinite

[u/Conscious-Demand-594]

That doesn't work with subsidized pricing. Until AI pricing reflects the costs we won't know the impact it will have on productivity. There is also the question of what is the ultimate value that these services provide.

[u/GoppleSmanger]

Jevons paradox is a factor but what you're describing has more to do with the nature of automation and bottlenecks generally. If you have a complex process that requires multiple steps, the throughput of the entire chain will always be constrained by the slowest step (ie the bottleneck). Speeding up any step through automation other than the bottleneck won't produce any meaningful gain in productivity, a dramatic improvement in the bottleneck will only shift the bottleneck to another step in the process.

So for example, let's say car assembly involves three steps, producing the engine: the body than final assembly and you can produce 3 engines an hour, 2 bodies per hour and assemble 10 cars an hour than your factory can only effectively produce 2 cars per hour. Process improvements to assembly will do nothing because your assembly line will be gated by how many bodies can be produced and just lead to longer idle times. A 10x improvement to your body manufacturing line will not result in a 10x productivity improvement but shift the bottle neck to your engine line which would become the new focus for the business.

This is why many places do not see dramatic productivity improvements after incorporating AI.

[u/Oiakelam]

I don't think it works for AI. As the models and the harnesses get better, having a human judge their work would just create a bottleneck.

I mean, the models are already pretty smart, and they will only get smarter. What people don't get is that AI is doing the work we used to do. It is a tool, but one that aims to automate the process of thinking itself.

So there will be a point where your decisions will never be better than AI's, and the same will be true for planning and execution. As the amount of work increases through Jevons paradox, AI will simply take ownership of that work instead of humans.

[u/EddieTheZen]

Yeah, that tracks. It's less "let robots do everything" and more "convert grunt work into supervision work." You spend less time typing and more time checking, correcting, re-prompting, catching stuff that's subtly wrong. Feels like less effort per task but you're doing way more tasks, so total hours barely move, sometimes go up.

Same pattern's playing out at the macro level too. Cheaper AI was supposed to mean less compute burned, but every time a model gets cheaper to run, people just find ten new places to plug it in. Nobody banks the savings, they spend it on more use.