William Stanley Jevons (via University of Manchester Libraries), CC BY-SA 4.0, via Wikimedia Commons.
Here’s something that shouldn’t be true but is: when things get cheaper, we often spend more on them.
Think about lemons. 🍋
In the 1800s, a lemon was an exotic luxury, a tropical fruit shipped from far away at great expense. Only the wealthy could afford them regularly. As shipping and agriculture improved, the price per lemon plummeted.
And did we buy fewer lemons? Did we spend less on them?
Of course not. We put them in everything. Lemonade, lemon meringue pie, lemon zest on fish, lemon in our water, lemon-scented cleaning products. The cheaper lemons got, the more uses we found for them, and the more we spent on them in aggregate.
This is Jevons Paradox, and it’s one of those ideas in economics that sounds wrong until you see it everywhere. Most people who invoke it do so in passing, a quick reference to sound clever in a meeting. But the man behind it and his original argument deserve a deeper look, because the pattern he identified in 1865 is playing out right now with AI and software engineering.
The Man Behind the Paradox
William Stanley Jevons was born in Liverpool in 1835, the ninth of eleven children in a family of iron merchants and hardware manufacturers. He was, by any measure, a polymath.
Jevons studied chemistry and mathematics at University College London, then spent five years in Sydney, Australia as an assayer at the Royal Mint, testing the purity of gold during the Australian gold rush. He returned to England and became one of the founders of the marginalist revolution in economics, fundamentally changing how economists think about value. He also built a mechanical reasoning machine called the “logic piano”, essentially an early mechanical computer, decades before anything resembling modern computing.
Jevons died in 1882 at the age of 46, drowning while swimming near Hastings. He left behind a body of work that economists and logicians still reference today.
But the thing most people know him for, if they know him at all, is a book he published in 1865 at the age of 29.
The Coal Question
In 1865, Jevons published The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal Mines.
The prevailing wisdom at the time was reassuring: James Watt’s far more efficient steam engine (yes, the watt is named after him) would reduce Britain’s coal consumption. After all, if each engine uses less coal per unit of work, the nation should need less coal overall. Simple math, right?
Jevons saw it differently.
He argued that Watt’s improvements made coal-powered industry so much more economical that it opened up entirely new applications. Before the efficient steam engine, coal powered a narrow set of uses, mostly pumping water out of mines. After Watt, coal powered factories, locomotives, steamships, and heating. The efficiency gains didn’t reduce demand, they exploded it.
As Jevons wrote in his book:
“It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth.”
This is the paradox: technological improvements that increase the efficiency of resource use tend to increase the total consumption of that resource, not decrease it. The more efficiently you can use something, the more uses you find for it, and the more of it you consume.
Watt’s steam engine wasn’t a conservation device. It was a device that unlocked more value.
The Cloud Proved Him Right
I’ve watched this paradox play out firsthand over a decade of working with cloud computing.
Earlier in my career, I had a manager who would side-eye me about the cost of a single thickly provisioned API gateway. It was a meaningful line item, the kind of thing that showed up in budget reviews and required justification.
A few years later, that same offering became serverless: consumption-based, pay-per-call, scaling to actual demand. The cost per unit fell so far that the side-eye went away.
And what could I do with that newfound freedom?
I wouldn’t run one API gateway more cheaply. I would run a hundred of them for different use cases, produce far more value, spend roughly the same amount of money, and nobody would bat an eye.
This is Jevons Paradox in the real world.
The broader cloud story follows the same pattern. Year after year, the cost per compute unit has dropped. Serverless architectures and managed services emerged that scale to demand (you only pay for what you use). Spot instances, reserved capacity, autoscaling. All of these drove the unit economics down.
And yet, cloud bills across the industry went up, not down.
Not because companies were being wasteful (although some were), but because cheaper compute unlocked use cases that weren’t viable before. Workloads that couldn’t justify the infrastructure cost at $X per hour suddenly made perfect sense at $X/10 per hour. So companies ran more workloads. And more. And more.
The cloud got cheaper. We used more of it. Jevons was right.
AI Is the Next Steam Engine
I see the same pattern emerging with AI and software engineering, and I want to take a clear stance here:
AI will increase the demand for software engineers, not decrease it.
I know that’s not the dominant narrative right now. The headlines are full of layoffs and hiring slowdowns, with companies citing AI-driven efficiency as the reason. But look closer at these stories and the picture gets murkier. It’s hard to separate “AI made us more efficient” from “we needed to cut costs anyway and AI is a convenient narrative.”
Correlation is not causation, and a convenient story is not evidence.
There will be an initial period (we may be in it now) where some companies look at AI-assisted engineering and see an opportunity to do the same work with fewer people. And some of those companies will be right, for a time.
But businesses are always insatiable. They always have a backlog that stretches to the horizon, features they can’t build, markets they can’t enter, technical debt they can’t address, experiments they can’t run. The bottleneck has always been the cost and availability of engineering talent.
When AI makes each engineer significantly more productive, when it turns a task that took a week into one that takes a day, businesses won’t say “great, we can cut 80% of our engineers.” They’ll say “great, now we can finally build that thing we’ve been putting off for three years.”
Just like Watt’s steam engine didn’t make Britain say “wonderful, we need less coal.” It made Britain say “what else can we power with this?”
(I’ve also seen AI compared to electricity and the printing press. Pick your poison / analogy.)
I’ve Seen This in My Own Work
I wrote recently about acceleration and compound engineering, how a dev machine setup practice I’ve carried across jobs for years took me months to modernize with GitHub Copilot last summer, but this year, Claude Code wrapped it in CI in ninety minutes and added a new Linux distro in under an hour.
Here’s the thing: I didn’t take those efficiency gains and go sit on a beach (although that sounds really nice). I immediately turned around and did more. More distros. More CI. More polish. Things that weren’t worth the effort before became trivially achievable, so I did them.
My backlog didn’t get smaller. It got different. Projects that I’d mentally filed under “someday when I have a free month” became “I can knock that out this afternoon.” And when those were done, I found new things to build that I hadn’t even considered before, because the cost of attempting them had dropped below the threshold where they were worth thinking about.
This is exactly what Jevons described. The efficiency of the steam engine didn’t reduce coal consumption. It unlocked new applications that nobody had previously considered viable.
The Counterarguments
It’s worth noting that Jevons Paradox doesn’t always hold. Economists have identified cases where efficiency improvements really do reduce total consumption.
The key variable is elasticity of demand. If demand for a resource is relatively inelastic, if people don’t actually want much more of it even when it’s cheap, then efficiency gains can reduce total consumption. LED lightbulbs appear to have actually reduced total electricity used for lighting, because there’s only so much light people want in their homes.
But zoom out from lighting and look at electricity as a whole. Demand for power is surging, driven largely by data centers that didn’t exist at this scale a decade ago. LEDs saved watts in the living room; AI is consuming megawatts in the server farm. Same resource, opposite outcomes, depending on where the demand is elastic.
And software? Software demand is about as elastic as it gets. Every business wants more of it. Every industry is being reshaped by it. The backlog of software that the world wants built is effectively infinite.
When the constraint is demand, efficiency gains can reduce consumption. When the constraint is supply, efficiency gains blow the doors off. Software engineering is firmly in the latter camp.
Token Budgets Are the New Cloud Bills
Here’s my prediction for how this plays out:
The engineers who learn to wield AI effectively, who build the compound practices (testing, CI, structured prompting, agentic workflows) and discover entirely new ways of delivering software, will be the ones everyone wants to hire. There will be more to build than ever before.
And businesses won’t spend less on software engineering. They’ll spend differently. Where today the cost is primarily headcount, a growing share is already token budgets, the cost of the AI compute that amplifies each engineer’s output. We saw the same shift in cloud computing: the engineers who learned to automate infrastructure were the ones everyone wanted.
Just like cloud bills replaced data center CapEx and then grew beyond what most people expected, token budgets will become a major line item that grows year over year. As the cost per token comes down, our consumption will go up.
William Stanley Jevons figured this out about coal over 160 years ago. The cloud proved him right. AI will prove him right again.
The paradox endures: when something gets cheaper, we don’t use less. We use more.
This is not a threat to software engineers, but there’s an uncomfortable flip side. If the cost per token keeps dropping and engineers around you are multiplying their output with these tools, then choosing not to use them makes you the expensive resource per unit of output. You don’t want to be the one still rationing coal while everyone else is building modern steam engines. We have the greatest of power tools now, and it’s up to us to use them.