AI and the Rebound Effect

Apparently AI was supposed to make developers work less. That was the idea, anyway. Write code faster. Automate the boring parts. Finish the same feature in half the time. Close the laptop at 2 p.m. and go become the kind of person who owns hiking shoes for reasons other than taking out the trash. That is not what happened. A lot of developers are working more with AI, not less. Not because AI is useless. The problem is almost the opposite. AI is useful enough that it makes more work feel worth doing. There is a name for this kind of thing: the rebound effect. The Rebound Effect The rebound effect is what happens when something becomes cheaper or easier, and people respond by doing more of it instead of simply enjoying the savings. You make cars more fuel efficient, and people drive more. You make lighting cheaper, and people light more buildings, more streets, and more screens. You make code cheaper to produce, and, well, look around. The washing machine is the easy example. It did make laundry easier. No question. But it did not simply delete laundry from household life. Standards changed. Clothes got washed more often. People owned more clothes. Clean clothes every day became normal instead of fancy. Historian Ruth Schwartz Cowan wrote about this in More Work for Mother. Labor-saving technology often saves labor on the individual task, then quietly raises the standard for how much of that task everyone is expected to do. AI looks a lot like that, except the laundry is software and the hamper is Jira. Why Developers Are Working More The real difference with AI is that it lowers the activation energy of programming. Before AI, a lot of ideas died in the parking lot. You would think, "I could build that," and then immediately remember the setup, the docs, the boilerplate, the weird library behavior, the test data, the CSS problem hiding under the couch, and the two hours you were about to lose to some TypeScript error that looks like it was generated by a cursed obelisk. So you would do the healthy thing and ignore the idea. Now you can ask an AI to scaffold the project, explain the library, write the first pass, generate the test cases, fix the annoying bug, and summarize the docs you did not want to read. The idea that used to feel like a weekend suddenly feels like something you can try before dinner. That sounds great, and it is great. But it changes the math. Instead of eight hours of work becoming four hours of work and four hours of leisure, it becomes eight hours of work with twice as many things attempted. Or ten hours, because now you are on a roll. This is where the rebound effect shows up. AI lowers the cost of doing software work, so more software work clears the bar. The side project becomes reasonable. The refactor becomes reasonable. The docs become reasonable. The "quick" prototype becomes reasonable. The little polish pass becomes reasonable. The feature nobody asked for but you can suddenly imagine shipping by Friday becomes, unfortunately, reasonable. More Things Become Worth Doing This is the key shift: AI does not just make existing tasks faster. It changes which tasks feel worth starting. A task that used to be obviously too much work now sits right on the line. Maybe it is not worth two days. But is it worth forty minutes with an AI assistant? Maybe. And once enough tasks move into that category, your day fills back up. The TODO list does not shrink. It mutates. You close one ticket and notice three adjacent improvements. You fix one bug and ask the AI to search for similar bugs. You write one endpoint and decide the client library should probably be updated too. You generate tests, then notice the tests could be better, then notice the code could be easier to test. This is not laziness. It is not hustle culture either, at least not always. A lot of developers genuinely like building things. AI removes enough friction that the part of your brain that says "eh, tomorrow" gets quieter. Economists have a related extreme called Jevons paradox. Make something more efficient, and total use can go up instead of down. Steam engines got better at using coal, and coal use increased. Programming tools get better at producing software, and we produce more software. Companies Will Not Invent the Four-Day Week for You To be fair, this is not only a personal problem. There is a company version too, and it is not subtle. If one developer with AI can produce what three developers previously produced, companies are unlikely to say, "Great news. Everyone can work Tuesday and Wednesday and spend the rest of the week making soup." They are more likely to say, "Great news. Imagine how much software we can ship now." Then competitors adopt the same tools. Expectations adjust upward. Yesterday's impressive output becomes tomorrow's baseline. The washing machine did not eliminate laundry. It helped make daily clean clothes feel ordinary. AI may do something similar to software. Leisure Is a Choice, Not an Automatic Dividend None of this means AI is bad. I am not interested in pretending that writing boilerplate by hand was some kind of character-building exercise. Getting unstuck faster is good. Exploring more ideas is good. Spending less time fixing infuriating bugs is very good. The issue is what happens after the savings show up. Technology can create surplus time, but surplus time does not automatically turn into leisure. Usually it gets spent. Sometimes by your employer. Sometimes by the market. Sometimes by your own ambition at 11:30 p.m. when you really should know better. So if you feel busier with AI than without it, you are not imagining things. AI made you faster, and then your backlog immediately noticed. The real trick is not learning how to use AI to do more work. Most of us figured that out pretty quickly. The trick is deciding when not to. Top comments (0)