Using coding agents gave me a new appreciation for the Jevons paradox, a concept that received a lot of attention earlier this year when DeepSeek R1′s release in January coincided with a sudden drop in Nvidia’s stock price, possibly as the supposed efficiency gains of the model made many traders assume this would lead to a decrease in hardware demand. The stock eventually bounced back though, with Jevons paradox being cited as one of the reasons, as it predicted that efficiency gains would lead to an increase in hardware demand rather than a decrease.
I recently realized that Github Copilot’s agent mode with GPT5 is way more capable than I would have imagined, and I started using it a lot, starting a bunch of small to medium-sized projects. I’d just start with an empty directory, write a projectOutline.md file to describe what I ultimately want to achieve, and let the agent take it from there (occasionally making some suggestions for refactorings and writing more unit + end2end tests, to keep things stable and scalable). This way it would just take me something like 5-50 prompts and a few hours of work to reach an MVP or prototype state in these projects that otherwise would have taken weeks.
The naive reaction to this would be to assume I would be much faster with my coding projects and hence would have to spend less time on coding. But, as Jevons paradox would predict, the opposite was the case—it just caused me to work on way more projects, many that I otherwise would never have started, and I spent much more time on this than I would have otherwise (over a given time frame). So even though coding became much faster (I may be wrong, but I’m pretty confident this is true in net dev time despite some contrary evidence, and I’m extremely certain it’s true in calendar time, as my output increased ~30x basically overnight—not because my coding speed was that slow beforehand, but because I never prioritized it as it wasn’t worth doing over other activities), the total time I spent programming increased a lot.
This will probably get old quickly (with the current frontier models), as with most projects I might hit a “wall” where the agents don’t do a great job of further iterative improvements, I suppose. But either way, it was interesting to experience this first-hand, how “getting faster at something” caused me to spend much more, rather than less, time on it, as obvious as this effect may be in hindsight.
Using coding agents gave me a new appreciation for the Jevons paradox, a concept that received a lot of attention earlier this year when DeepSeek R1′s release in January coincided with a sudden drop in Nvidia’s stock price, possibly as the supposed efficiency gains of the model made many traders assume this would lead to a decrease in hardware demand. The stock eventually bounced back though, with Jevons paradox being cited as one of the reasons, as it predicted that efficiency gains would lead to an increase in hardware demand rather than a decrease.
I recently realized that Github Copilot’s agent mode with GPT5 is way more capable than I would have imagined, and I started using it a lot, starting a bunch of small to medium-sized projects. I’d just start with an empty directory, write a projectOutline.md file to describe what I ultimately want to achieve, and let the agent take it from there (occasionally making some suggestions for refactorings and writing more unit + end2end tests, to keep things stable and scalable). This way it would just take me something like 5-50 prompts and a few hours of work to reach an MVP or prototype state in these projects that otherwise would have taken weeks.
The naive reaction to this would be to assume I would be much faster with my coding projects and hence would have to spend less time on coding. But, as Jevons paradox would predict, the opposite was the case—it just caused me to work on way more projects, many that I otherwise would never have started, and I spent much more time on this than I would have otherwise (over a given time frame). So even though coding became much faster (I may be wrong, but I’m pretty confident this is true in net dev time despite some contrary evidence, and I’m extremely certain it’s true in calendar time, as my output increased ~30x basically overnight—not because my coding speed was that slow beforehand, but because I never prioritized it as it wasn’t worth doing over other activities), the total time I spent programming increased a lot.
This will probably get old quickly (with the current frontier models), as with most projects I might hit a “wall” where the agents don’t do a great job of further iterative improvements, I suppose. But either way, it was interesting to experience this first-hand, how “getting faster at something” caused me to spend much more, rather than less, time on it, as obvious as this effect may be in hindsight.