Take the 'AI is creating a job boom' challenge
Before you share that post that claims software jobs are booming, you need to ask yourself 3 questions.
Personally, I’m an AI optimist and I’ve spent years building spatial computing, computer vision and AI/ML. I’m also evidence-based, especially when it comes to measuring the impact of technology.
So what do the latest AI-and-jobs reports really measure?
In early April 2026, TrueUp published data showing 67,000 open software-engineering roles globally - the highest in over three years, roughly double the mid-2023 trough, with listings up 30% year-on-year. Other reports landed in the same window with similar headlines. Software jobs are booming. Everyone can sigh with relief - AI isn’t killing coding jobs.
These reports are being shared widely as evidence that AI is not displacing knowledge work. But they actually measure something different from what that conclusion requires.
The reports above measure postings - currently-listed openings on a given day, aggregated across companies. That is a flow signal - it only shows what’s being advertised. This is not the same thing as employment.
Employment is stock - actual headcount on payroll, measured over time, with cohort and tenure detail. The number of people working a given category (participation and hours worked), not the number of openings being advertised for it.
Flow signals can move in the opposite direction from stock signals when displacement is not balanced. Three separate channels can inflate postings without raising overall employment:
Substitution churn: a firm lays off a junior engineer and lists a senior replacement. Two opposing events, but only one shows up in postings activity. Net employment drops by one. Postings count rises by one.
Turnover within remaining headcount: every senior departure creates a posting without changing the firm’s net headcount.
Re-listings of unfilled roles: the same role appearing across multiple monthly snapshots as “open” until it eventually fills or is withdrawn.
In the real world aggregate posting counts can rise during periods of net displacement, even if the displacement is structurally unbalanced - compressing one cohort or skill-set while listing roles for another.
The firm-level signal
The same quarter that produced TrueUp’s 67,000 postings number also produced 52,050 tech layoffs by Challenger’s Q1 2026 count, and over 127,000 across 283 companies by TrueUp’s tracker - depending on what’s included. Yes postings up. But layoffs are also up. Both are true at the same time.
The framing of those layoffs has also shifted.
Earlier rounds of layoffs through 2023-25 were attributed to efficiency, post-pandemic correction, or restructuring. But two recent announcements name AI directly.
Meta’s April 2026 cuts (about 8,000 roles, that’s roughly 10% of headcount) were attributed in the announcement to AI-driven productivity gains. That was the first time a Mag7 firm directly stated the AI driven substitution mechanism plainly to public markets. No share-price punishment followed.
Coinbase’s announcement on May 5, 2026 went even further. Brian Armstrong’s memo described the company as “fundamentally changing how we operate, rebuilding the company as an intelligence, with humans around the edge aligning it”. The cuts (about 700 roles, 14% of the company) target “pure managers”, replaced by “player-coaches” who are also individual contributors. The company plans to create “AI-native pods” including one-person teams directing agents. $50-60M in restructuring charges in Q2 2026.
Once Meta named the mechanism in April without market punishment, the cost of using the same language fell for everyone else. Coinbase didn’t just announce layoffs in May - it announced layoffs as a structural reorganisation around AI capability. The framing accelerates because it can, and because investors are now rewarding it rather than penalising it.
What’s being listed in postings, when you look at categories rather than aggregates, fits the same pattern. Senior-specialist hiring continues. AI/ML engineering, cybersecurity, cloud infrastructure, and DevOps are the fastest-growing specialisations TrueUp tracks. None of those are entry-level work. They’re the categories where displacement is least likely. Their growth isn’t evidence that displacement isn’t happening - it’s the structural shape of where it isn’t, and the parts where it is happening are sitting outside the postings frame.
What payroll-level data shows
In 2025 Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen published ”Canaries in the Coal Mine”, using Stanford’s ADP payroll dataset. Tens of millions of US workers, tracked by occupation and age cohort, with employment changes measured *within the same employer* rather than across firm transitions.
Their finding - among 22-25 software developers, employment fell around 20% from late 2022 through mid-2025 (with a 13% relative decline across all AI-exposed occupations after controlling for firm-level shocks). But older cohorts in the same occupations didn’t see the same fall. Same employers. Same job titles. The signature is cohort-asymmetric. Junior down. Senior up. The pattern only shows up where AI capability has landed - in occupations not exposed, the cohort signature is absent or much weaker.
That signature does not appear in postings data. Postings don’t break out by cohort. They don’t isolate within-employer changes. They don’t separate substitution churn (substituchurn) from net hiring. They count openings, not people.
The Canaries finding (measured in payroll, isolating the right signal) is what asymmetric displacement really looks like at stock level. The TrueUp finding (measured in postings, aggregated across categories) is what that same shift looks like at the flow level. They are not contradicting each other. They’re just the same structural shape, viewed through different instruments.
What to look for in AI-and-jobs reports
Ask yourself these three questions to sharpen your view of what an AI-and-labour report can actually tell you:
Is the data stock or flow? Postings, listings, openings, vacancies are flow. Payroll, headcount, employment-by-cohort are stock. Reports that move between the two without flagging the distinction produce conclusions the data does not support.
Does it break out cohort? Aggregate counts can hide cohort-asymmetric patterns entirely. The Canaries finding wouldn’t show up in any aggregate report.
Does it isolate within-employer changes? Firm-to-firm transitions inject noise into employment data. Within-employer measurement isolates the actual displacement signal.
A report that does not address those three questions can only tell you what is being advertised. It can only tell you what one slice of activity looks like. But it can’t tell you whether net employment in a category has risen or fallen, or if the change is concentrated in specific cohorts.
The 67,000 postings are real. But the 8,000 Meta, 700 Coinbase and all the other layoffs are also real. And the 22-25 cohort employment drop is real. They’re all consistent observations of the same structural shift, just measured at different levels. Reports that quote just one of them and conclude “AI is not displacing jobs” are making claims that the data does not actually support.
The doomer view is not productive, and blindly accepting the optimist view is not productive either. The best approach is to explore beyond the headline numbers and ask yourself these 3 questions before you believe and share that post...
Hey Rob — the stock vs flow distinction is the kind of thing that should be required reading before anyone shares another "AI job boom" headline. Postings aren't jobs. That's not pedantic — it's the whole ballgame. A company relisting the same senior role three times while quietly not replacing the four junior devs who left is "growing" by the metrics and shrinking by the headcount.
The Stanford data on 22-25 year old developers dropping 20% while older cohorts hold steady — that's the canary and nobody's listening to it because the aggregate numbers still look fine. The displacement is happening at the entry level where people don't have platforms or political power to make noise about it. By the time it shows up in aggregate data it'll be five years too late to address.
I work in the AI agent space and I see both sides of this. The businesses I build for genuinely need fewer people to do the same work — that's the whole value proposition. But the honest version of that conversation is "this replaces a part-time hire" not "this creates jobs." The industry needs to stop pretending automation and employment growth are the same story. Your three evaluation questions should be stapled to every AI jobs report. Sharing this one.