The Tale of the Time Millionaire
There’s a story going around right now that goes something like this: AI makes us so efficient that we’ll soon have more free time than we know what to do with. We’ll work four-hour days, take Fridays off, pick up hobbies, learn instruments. We become time millionaires — rich in hours, finally free from the grind.
It’s a nice story. I don’t think it’s what’s going to happen.
In 1865, the economist William Stanley Jevons noticed something counterintuitive. James Watt’s steam engine had made coal dramatically more efficient. A given task required far less coal than before. You’d expect coal consumption to drop. Instead, it soared. Cheaper energy per unit meant people found entirely new uses for it. Industries that couldn’t justify coal before suddenly could. Factories spread. Railroads expanded. Efficiency didn’t reduce demand — it unlocked it.
This is known as Jevons paradox, and it keeps showing up everywhere.
More fuel-efficient cars didn’t reduce total miles driven. People just drove more — longer commutes became tolerable, road trips got cheaper, households added a second car. The savings per mile got spent on more miles.
Or take laundry. Washing machines became dramatically more efficient over the decades — faster, cheaper to run, easier to use. Did people do the same amount of laundry in less time and enjoy the free hours? No. They raised their standards. Shirts that used to be worn three or four times before washing now go in the hamper after one wear. We wash towels, sheets, and gym clothes at a frequency our grandparents would find absurd. The machines got better, and we just did a lot more laundry.
Every time we make a resource cheaper to use, we use more of it.
I think we’re watching this happen to software right now.
Agentic engineering has made writing code dramatically cheaper. A feature that took a day takes an hour. A prototype that took a week ships in an afternoon. The cost per unit of software has dropped through the floor.
And the response hasn’t been “great, now we need fewer engineers.” The response has been “great, now we can build more things.” Projects that were too small to justify get started. Side features that sat in the backlog for months get built in a day. Internal tools that nobody would have staffed a team for suddenly exist.
I see this in my own work. I don’t write less code than I did a year ago. I write significantly more. The bar for “worth building” has dropped. Ideas that I would have filed away as “nice to have, maybe someday” — I just build them now. The tool I wrote this week to generate banner images for my newsletter? A year ago, that would have stayed on a sticky note. Now it exists, it works, and it took an afternoon.
The same thing is happening at companies. Teams aren’t shrinking. Backlogs aren’t getting shorter. Instead, the definition of what’s worth building is expanding. More experiments, more internal tools, more automation, more custom solutions for problems that were previously solved with spreadsheets and manual processes.
Jevons would have predicted exactly this. When the cost of producing something drops, you don’t produce the same amount more cheaply. You produce more. The efficiency gains get absorbed by increased demand.
This has implications for the time millionaire fantasy. If you’re thinking about AI as a way to do the same work in less time and then go home early, history suggests otherwise. The more likely outcome is the same people doing a lot more work. The bottleneck shifts from execution to deciding what to build — and then to maintaining everything you’ve built.
We’re not going to become time millionaires. We’re going to become people who build, create, and produce at a pace that would have seemed impossible a few years ago. Whether that’s a better outcome depends on how well we manage the abundance — and whether we’re intentional about protecting the time that actually matters.
