AI Productivity: it works best for the people losing their jobs June 29th 2026

Curated by Alexis

Three years into the productivity promise, there's finally enough hard evidence to answer the question plainly: does working with AI actually make you more productive? Yes — spectacularly, for some people on some tasks. And no, or worse, for others. The gains are real. They're just not flowing where the marketing said they would. This issue maps who wins, who pays, and why the line between them isn't where you think.

The short version

Four of the most-cited workplace studies of the AI era, read side by side, tell one story: AI productivity gains are real but radically uneven, and they bend toward the inexperienced, the well-scoped, and the verifiable — not toward experts doing hard, familiar work, and not toward enterprises buying pilots.

Customer-support agents got 14% more productive on average — but +34% for novices and near-zero for veterans (Brynjolfsson, Li & Raymond, Generative AI at Work, NBER/QJE).
BCG consultants using GPT-4 did 12.2% more tasks, 25% faster, with higher-quality work — but only inside AI's "jagged frontier." On a task chosen to sit outside it, AI-assisted consultants were 19% less likely to be right (Dell'Acqua et al., SSRN).
Experienced open-source developers were 19% slower with AI on their own codebases — while believing they were 20% faster (METR randomized trial, arXiv 2507.09089).
And 95% of enterprise GenAI pilots delivered no measurable P&L impact (MIT NANDA, State of AI in Business 2025, via Fortune).

Put those together and the answer to "for whom?" stops being a slogan and becomes a map.

Who gets faster — and it's not who you'd guess

The cleanest finding in the whole literature is also the most counter-intuitive: AI levels up the bottom, not the top.

In the Brynjolfsson study of 5,179 support agents, the average 14% gain was almost entirely carried by the least-experienced workers (+34%), while the most experienced barely moved. The AI worked by encoding the best agents' tacit know-how and handing it to the newest ones — compressing months of on-the-job learning into a prompt. The BCG/HBS consulting experiment found the same shape from the other direction: below-average performers gained the most, and the technology narrowed the gap between weak and strong consultants.

Then the experts. METR's randomized trial put 16 seasoned developers through 246 real tasks on repositories they maintain. With AI tools allowed, they took 19% longer. The part that should haunt every "AI made me 10x" LinkedIn post: the developers expected a 24% speed-up, and even after finishing slower, still believed AI had sped them up by 20%. They could not feel the tax they were paying. METR is careful about scope — the result is early-2025 tools, expert developers, familiar codebases, and explicitly does not generalize to juniors or unfamiliar code. But that caveat is the finding: the slowdown is concentrated exactly where expertise and context are highest.

The unifying idea is the "jagged frontier." AI is wildly good at some tasks and confidently wrong at adjacent ones, and the border between them is invisible and irregular. Inside the frontier, the BCG consultants soared. Pushed just outside it — onto a task that looked similar but required judgment AI lacked — AI-assisted consultants were measurably more likely to be wrong, because the tool's fluent confidence is the same on both sides of the line. Productivity, then, isn't a property of AI. It's a property of the match between the task, the tool, and whether the human can tell when it's lying.

Who's paying for gains that never show up

If individuals see real (if lopsided) gains, the enterprise picture is starker. MIT's NANDA study — 150 executive interviews, 350 employee surveys, 300 public deployments — found just 5% of GenAI pilots produced rapid revenue acceleration; the other 95% stalled with little or no measurable P&L impact. The blocker wasn't model quality. It was the "learning gap" — organizations bolting AI onto unchanged workflows. Tellingly, bought-from-a-vendor projects succeeded ~67% of the time; internal builds barely a third as often, and the real ROI sat in unglamorous back-office automation, not the sales-and-marketing tools that ate most of the budget.

The market is now feeling that gap in cash. Expert-shared reporting this week: companies are "scrambling to stop spending so much on AI" as inference bills balloon (404 Media's "Tokenpocalypse"), and the cautionary tale of the cycle — Ford "hired AI and sacked humans," and it backfired badly — is making the rounds precisely because it's the inverse of the pitch deck.

The part the demos hide

Three costs don't appear in any productivity dashboard but show up everywhere in the reporting:

The hidden human payroll. A lot of "AI productivity" is really human productivity, relocated and made invisible: the systems are trained and corrected by armies of data workers — many in the Global South, paid a fraction of Western wages — whose labor the DAIR Institute documented in detail. The output looks autonomous; the payroll behind it usually isn't.
The gains get spent as more work, not less. Bloomberg's "AI burnout era" documents the people closest to AI working harder than ever — one startup CEO reports sleeping at the office for three straight weeks. When AI clears the busywork, the freed hours get refilled with higher-stakes work and faster cycles. Time saved becomes output demanded.
Skill atrophy. Engineers are starting to write essays about it — one trended on Hacker News this week — arguing AI is eroding the "flow state" that made the craft worth doing, and that offloading the hard parts quietly hollows out the expertise an organization will need when the tool is wrong.

The cruel symmetry

Here's the twist that should anchor how you read all of this. The economist whose research most clearly shows AI making junior workers more productive — Stanford's Erik Brynjolfsson — also runs the dashboard showing AI making junior workers unemployed. His Canaries indicator, built on ADP payroll data covering ~1 in 6 U.S. workers, finds employment for 22–25-year-olds in the most AI-exposed roles falling while it rises for less-exposed peers — early, large-scale evidence that the entry rungs are being pulled up (Fortune, June 27). The two findings aren't in tension; they're the same fact. AI delivers its biggest productivity boost to the novice — and that is precisely why the novice's job is the first one a company decides it can do without. We are most efficiently automating the rung you used to climb to become the expert AI can't replace. Nobody has answered what happens to the expert pipeline when the apprenticeship is the thing we optimized away.

So — does it actually work? A field guide

The honest synthesis isn't "yes" or "no." It's a set of conditions. AI reliably raises productivity when: the task is well-scoped and the output is verifiable (you can tell good from bad fast); it sits inside the model's competence; and the worker is either a novice on routine work or an expert who treats AI as a draft to be checked, not an oracle. It fails or backfires when: the work is open-ended judgment, the human can't easily verify the answer, the expert already moves faster than the review-the-AI loop, or an organization buys a tool without redesigning the workflow around it. The winners aren't the most or least technical people — they're the ones who know exactly where the jagged frontier is and never trust the tool past it.

Key Takeaways

The gains are real but bottom-weighted. AI's largest measured productivity boosts go to novices and below-average performers; for experts on familiar work, the effect shrinks to zero or reverses.
You can't feel it. METR's developers were 19% slower and 20%-sure they were faster. Self-reported "10x" gains are not evidence; verifiable throughput is.
The org, not the model, is the bottleneck. 95% of enterprise pilots return nothing — and the 5% that work buy focused tools and redesign the workflow instead of bolting AI onto it.
The productivity story and the jobs story are one story. AI helps the junior worker most, which is exactly why the junior role is cut first. Plan for the missing expert pipeline now.

Worth Reading

Generative AI at Work — Brynjolfsson, Li & Raymond. The foundational "novices gain most" study (NBER / Quarterly Journal of Economics).
Measuring the Impact of Early-2025 AI on Experienced Developers — METR. The 19%-slower-but-felt-faster RCT, with its own honest caveats.
Navigating the Jagged Technological Frontier — Dell'Acqua et al., Harvard Business School / BCG. Where AI helps, where it quietly hurts.
The GenAI Divide: State of AI in Business 2025 — MIT NANDA. The 95% number and what the 5% do differently.
Canaries in the Coal Mine — Stanford Digital Economy Lab. Live payroll-data evidence on AI and entry-level employment.

Wait, What?

Starting July 8, Claude may ask to see your passport — and your face. For accounts flagged for abuse, Anthropic will require a government-issued ID plus a facial-recognition selfie, processed by the third-party vendor Persona, which builds a facial-geometry template — data that several US states classify as legally protected biometric information (TechCrunch). It's an unusually physical demand from a chatbot, and a small sign of where the relationship is heading: the tool that's supposed to work for you increasingly wants to know exactly who you are.