The first AI labor signal may be hiring, not layoffs

Peter McCrory's argument is narrower than the headline suggests. The head of economics at Anthropic says AI has not produced a material rise in US unemployment so far, including among workers in jobs with high exposure to current AI automation. He also says this is his personal view, not an official Anthropic position. The interesting question is therefore not whether AI has already replaced the labor market. It is why a technology with visible adoption and rapid capability gains still looks, in the aggregate, more like an amplifier of workers than a substitute for them. 1

McCrory begins with a basic fact: the US labor market remains relatively stable. He cites a June unemployment rate of 4.2%, a job-openings-to-unemployed ratio just above one in April, a prime-age employment-to-population ratio near multi-decade highs, and consistently low weekly initial unemployment claims. Those measures do not look like an economy in which AI has suddenly removed millions of jobs. 1

He argues that AI is already large enough to leave a macroeconomic trace. In the figures he discusses, 20% of firms use AI in at least one business function, adoption reaches 40% in the information sector, and quality-adjusted AI output grew by more than 2,000% in both 2024 and 2025. He also points to labor-productivity growth of about 2% per year from 2022 to 2026, compared with 1.6% in the four years before the pandemic. These are the episode's presentation of McCrory's evidence, not a claim that the podcast independently audited each estimate. 1

The more revealing evidence sits below the unemployment headline. Anthropic's March labor-market report found no systematic increase in unemployment for workers in highly exposed occupations, but it also found tentative evidence that hiring into the most exposed jobs had slowed for workers aged 22 to 25. The estimated job-finding rate in those occupations fell by about 14% relative to 2022, a result the authors describe as barely statistically significant and open to other explanations. 2

That distinction matters. A worker who is never hired does not necessarily appear in the unemployment statistics. They may stay in an existing job, take a different job, return to school, or leave the labor force. McCrory emphasizes that the period since 2022 also included pandemic aftershocks, monetary tightening, commodity volatility, trade uncertainty, and what he describes as a low-hire, low-fire labor market. The young-worker signal is plausible, but it is not clean proof of AI displacement. 1

McCrory's explanation is that AI is skill-biased and labor-augmenting. Models can automate pieces of work, but people still choose what to ask for, judge whether the output is useful, and repair failures at the edge of the model's capabilities. In the episode's formulation, the remaining tasks often involve interpersonal coordination, physical-world interaction, or context that is difficult to specify in a prompt. 1

This is also why an occupation-level exposure score cannot be read as a layoff forecast. Anthropic's March report says that no occupation in its O*NET-based analysis had all of its associated tasks systematically handled by Claude. Technical writing, data entry, customer support, and programming may contain many tasks that AI can perform reliably, but the jobs remain bundles of tasks. Automating one bundle can change the job, reduce demand for some versions of it, or make room for new work without immediately eliminating the occupation. 2

The June Economic Index adds a second piece of evidence. In a linked sample of about 9,700 Claude users, people who used Claude in more automated ways were more optimistic about future pay, job security, and their ability to find work. A majority reported productivity gains in speed, scope, or quality, and 57% said AI had made their skills more valuable. The survey is not representative of the general population, and these are self-reports, so the findings show how active users experience the technology rather than what the whole labor market is doing. 3

The episode's most useful claim is not that expertise has won permanently. It is that expertise still determines how much value a worker can extract from an agent.

McCrory says Anthropic's seven-month analysis of Claude Code usage found that people with more domain expertise completed tasks more often and recovered more reliably when Claude made a mistake. Users made the planning decisions and delegated implementation. The episode summarizes the result as a decline in the value of straightforward coding implementation alongside a rise in the value of complementary skills such as delegation, evaluation, and domain judgment. 1

Anthropic's companion study, based on roughly 400,000 Claude Code sessions from October 2025 through April 2026, gives that idea a measurable shape. In a typical session, users made about 70% of planning decisions while Claude made about 80% of execution decisions. Expert-rated sessions triggered longer action chains and more output per prompt than novice-rated sessions. Verified success occurred in 15% of novice sessions versus 28% to 33% of intermediate-or-higher sessions, although the researchers caution that success is inferred from transcripts and telemetry rather than observed economic outcomes. 4

That pattern points to a different near-term labor-market shift. The scarce skill may move upward from writing every line of code to defining the problem, recognizing a bad answer, and directing a system through ambiguity. It does not make entry-level work irrelevant. It makes the path by which a junior worker acquires those skills more important, and potentially more fragile if routine tasks disappear before they provide enough practice.

The episode's counterpoint comes from Trace Cohen, who argues that AI's first visible effect may be fewer junior roles, smaller teams, slower backfilling, and higher expectations for each employee. One AI-using worker may handle work that previously required several people, even while total unemployment remains low. The point is presented as a hypothesis, not as a settled finding, but it identifies the variables a headline unemployment rate misses. 1

McCrory leaves the door open to a different future. If agents become reliable at complex, long-horizon tasks, the current complementarity between expertise and AI could weaken. His essay also treats automated innovation as a special case: systems that improve the systems themselves could change the pace of substitution faster than ordinary labor statistics can capture. For now, he expects faster productivity growth but not a noticeable AI-driven increase in unemployment over the next year. 1

The practical conclusion is modest. Current evidence does not support a story of broad AI-caused unemployment, but it also does not justify assuming that nothing is happening. The better watch list is hiring by age and occupation, average team size, the tasks bundled into junior roles, and whether domain expertise continues to predict successful agent use. Those measures can change before the layoffs arrive, if they arrive at all.

Read the full AI Daily Brief episode on Spotify.