In 1865, the English economist William Stanley Jevons published The Coal Question. His observation was counterintuitive and has stayed that way for 160 years: James Watt’s steam engine was dramatically more efficient than its predecessor, and British coal consumption went up, not down. The efficiency made coal-powered machinery cheaper to operate, which expanded demand for it faster than the savings could accumulate. More efficient use of a resource leads to more total consumption of it, not less.
The same rebound effect has appeared in other technologies, though the mechanism and scale vary. Digital offices were supposed to eliminate paper; paper consumption rose for decades. Wider highways were supposed to reduce congestion; they filled with traffic. More efficient engines were supposed to reduce fuel demand; people drove more.
Now apply the same logic to engineering headcount and AI.
The assumption behind the layoffs
The prevailing corporate narrative through early 2026 ran like this: AI makes each engineer more productive, therefore you need fewer engineers, therefore cutting headcount captures that efficiency as savings. Gartner found that eighty percent of large enterprises had cut staff while piloting AI, and that the cuts showed no correlation with improved ROI. I covered that finding in an earlier piece on LinkedIn on why AI layoffs aren’t paying off, and it told you what was happening.
It didn’t tell you why, but the Jevons paradox does.
Where the efficiency went
In June 2026, Ramp Economics Lab and Revelio Labs published a study joining firm-level AI spend with workforce data across more than 21,000 US companies. Not surveys, not self-reported estimates, but transactional spend records matched to employment data.
Their finding contradicted the layoff thesis directly. Companies that adopted AI with high intensity, defined as the top third of per-employee AI spending at roughly thirty dollars per employee per month in the first three months, grew headcount by 10.2% over the two years following adoption. Entry-level headcount in that cohort grew 12%, and the share of entry-level workers in the total workforce increased by over a percentage point relative to the control group.
The lead economist on the study, Ara Kharazian, phrased the mechanism without naming it: these companies “can go do more things now.”
That is the Jevons paradox, stated in plain language by an economist sitting on the data. AI made each unit of engineering output cheaper to produce, and the companies that adopted it intensely didn’t bank the savings as headcount reduction. The data shows headcount growth rather than headcount savings. The Jevons interpretation is that firms absorbed the cheaper production by taking on more work, though the study measures workforce outcomes, not the intermediate flow of engineering capacity into specific features or products.
Low-intensity adopters, the companies that dabbled rather than committed, saw no statistically significant headcount change in either direction. The reabsorption mechanism requires actually using the technology. Dabbling produces neither savings nor growth.
A second study, a different lens, the same direction
A separate Revelio Labs analysis, their monthly AI Labor Market Tracker published in July 2026, approached the question from a different angle entirely. Where the Ramp study identified adoption from spend data, the tracker identified AI-adopting firms from job postings for AI-integrator roles. Different populations, different measurement methodology.
The directional finding converged as firms that had adopted AI grew headcount 27% more than non-adopters since October 2022. The growth was concentrated in senior roles, up 31%, compared with just 6% for junior positions.
The adoption signal changed, but neither study found the headcount contraction that the layoff narrative predicts. The specific composition differs, the Ramp study found entry-level hiring growing fastest while the Revelio tracker found senior roles dominating, and neither source reconciles the discrepancy. Whether AI-driven growth favors junior hires brought in for AI-native skills or senior hires needed to direct AI output is a genuinely open question.
Both datasets challenge the assumption that intensive AI adoption automatically translates into fewer workers.
The confound worth naming
There is a legitimate objection to this evidence, AI-adopting firms were already growing faster before they adopted AI. The adoption isn’t random. Larger, more engineering-intensive, venture-backed, faster-growing companies are more likely to adopt AI intensely, and they were already on an upward trajectory.
This means the 10.2% headcount growth figure cannot be read as “AI caused this hiring.” Some portion of it is the growth these companies would have experienced regardless. The Jevons-paradox argument doesn’t require AI to be the sole cause of growth. It requires only that adopting AI intensely doesn’t shrink headcount the way the layoff narrative assumes, and the data supports that narrower claim cleanly. Companies that committed to AI grew their teams. Companies that blamed layoffs on AI, according to a separate Revelio analysis cited in Forbes, actually lagged their industry peers in overall AI adoption. The layoffs weren’t a consequence of efficiency. The evidence suggests some were ordinary restructuring with an AI efficiency story attached.
What Jevons couldn’t tell you
The paradox offers a plausible explanation for the headcount result: the efficiency may have been absorbed by expanded output rather than banked as savings. It doesn’t explain what happened inside the teams that stayed.
The team-level version of this mechanism is one I’ve covered before in the context of AI comprehension debt: saved writing time became more review work. When AI compresses the cost of producing code, the immediate result is more code, not fewer coders. Pull request volume rises, review queues back up, engineers who used to spend their time writing now spend it verifying what the machine wrote. Harness surveyed 700 engineering practitioners across five countries and found that 81% spend more time in code review since their teams adopted AI tools. Twenty-eight percent reported the increase at 30% or more. Faros AI tracked 10,000 developers across over a thousand teams: tasks completed rose 21%, pull requests merged rose 98%, and review time climbed 91%.
For the engineers who remained, saved writing time became more review work and tighter delivery expectations.
One practitioner account on Reddit’s r/developersIndia, in a thread titled “Anyone else feel we’re having to work more due to AI?”, described tighter deadlines, heavier review loads, and no pay increase despite the new tooling. The efficiency gain was real and entirely consumed by expanded expectations.
Lakshmanan, in Visualizing Generative AI (2025), stated: “As coding becomes more efficient because of GenAI coding assistants, the drop in price of a unit of code can lead to more demand for product features, and therefore an increase in demand for programmers.” Alongside the production rebound, Lakshmanan named a macro-level dampener: the Baumol effect, where cost savings from more productive sectors are absorbed by less productive ones, slowing overall economic growth. Efficiency in software development may not translate into broader productivity gains even when it translates into more software.
The measurement trap
The companies doing layoffs measured the input side, fewer engineers producing the same output, and called it efficiency. What they failed to measure was the output side: what happens to the organization’s capacity to take on new work, maintain existing systems, respond to incidents, and retain the institutional knowledge that lets any of those things function?
I wrote about this dynamic in an earlier post on the measurement problem. When your metrics reward the visible, they punish the invisible. Headcount reduction is visible on a quarterly earnings call, the institutional knowledge that walked out the door, the context that took years to accumulate, the capacity to maintain systems that the remaining team didn’t build, those costs arrive later and show up in a different budget line.
Gartner predicts that 50% of companies attributing headcount cuts to AI will rehire for similar functions by 2027. Forrester found that over half of companies that cut staff for AI already regret it. Careerminds found that 68.3% of companies that cut staff for AI had already rehired for similar functions. The boomerang is already happening, and it is expensive: you pay severance on the way out, a hiring premium on the way back in, and the knowledge gap in between never fully closes.
What we don’t know yet
The Ramp/Revelio data covers 24 months post-adoption. The Jevons paradox in Britain’s coal economy played out over decades. Twenty-four months is enough to challenge the assumption that AI efficiency automatically becomes headcount savings, but it is not long enough to establish how the pattern evolves.
The evidence leaves three questions unresolved.
First, the junior-versus-senior hiring tension. The two best available datasets point in opposite directions on who gets hired: the Ramp study says entry-level workers (AI-native skills), the Revelio tracker says senior roles (judgment to direct AI output). Both are plausible, neither is conclusive, and the answer matters enormously for how you staff.
Second, whether the reabsorption is durable. Companies “doing more things” with the same or more people only works as long as the market rewards doing more things. A contraction that punishes excess scope would test whether the Jevons mechanism survives downward pressure.
Third, whether this is genuinely about AI or about the kind of company that adopts AI: fast-growing companies were already hiring before they adopted AI, and separating the technology’s effect from the trajectory those companies were already on will take longer datasets and better controls than anyone currently has.
The practical takeaway
The data does not support using projected AI efficiency as a standalone justification for cutting engineering headcount.
If your organization adopted AI and your teams are producing more, the Jevons-compatible response is to invest that increased capacity into work that was previously too expensive to attempt. But choose the work deliberately: pick initiatives with a named business owner, measure time from intent to validated outcome rather than just output volume, track review and rework load alongside throughput, and preserve enough capacity for maintenance and incident response before committing to additional scope. The companies in the Ramp study’s top cohort didn’t plan for Jevons. They just had enough sense to use the efficiency for growth instead of banking it as savings that never materialized.
And if someone at the next board meeting proposes headcount reduction as the AI ROI capture mechanism, you now have a 160-year-old economic principle and firm-level data across 21,000 companies to explain why it won’t work the way they think.
We don’t know yet whether the pattern holds long-term. We know enough to stop repeating the mistake short-term.
Ricardo
