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TRUVACE RECORD VERSION
record: TRV-2026-2481
version: 2
kind: revised
reason: Refiled from Gain to Trace after a re-read of the full paper: its headline result comes with a stated cost, and the record now carries both.
timestamp: 2026-11-12T08:33:53.076074Z
status: published
lens: trace
sector: labor
headline: Task-Specific Technical Change and Comparative Advantage
dek: Artificial intelligence (AI) reshapes workers’ comparative advantage by altering the tasks they perform and the skills those tasks require. We develop a dynamic task-based model to quantify the general-equilibrium effects of task-specific technical change. Workers have multidimensional skills, choose occupations, and accumulate skills on the job; occupations combine tasks, and productivity depends on how workers’ skills match task requirements. We develop a computationally efficient procedure to estimate the mod…
gain_title: In a model-based projection for the moderate AI scenario, the 90-10 wage ratio falls 24 percent and average wages rise 28 percent, with the largest welfare gains going to the lowest-skill workers.
problem_title: In the same model, skill-dependent adoption costs cut the projected average wage gain from 28 percent to 0.3 percent, and AI lowers the returns to skills, with verbal skills losing the most value.
trace_subject: Projected wage and skill effects of AI under a task-based labor market model
gain_reading: In a model-based projection for the moderate AI scenario, the 90-10 wage ratio falls 24 percent and average wages rise 28 percent, with the largest welfare gains going to the lowest-skill workers.
gain_evidence: In the moderate AI scenario, the 90-10 wage ratio falls by 24 percent, and average wages rise by 28 percent. | The gains are largest for the lowest-skill workers who benefit disproportionately from simplification.
problem_reading: In the same model, skill-dependent adoption costs cut the projected average wage gain from 28 percent to 0.3 percent, and AI lowers the returns to skills, with verbal skills losing the most value.
problem_evidence: Average wage gains, however, shrink from 28 to 0.3 percent. | Verbal skills depreciate most, while manual and math skills retain much of their value.
quick_read: Two economists, Lukas Althoff of Stanford and Hugo Reichardt of CREI, built a dynamic model of the US labor market in which AI can augment, automate or simplify tasks. In their moderate AI scenario, the ratio of 90th- to 10th-percentile wages falls by 24 percent and average wages rise by 28 percent.

The equalizing force is simplification: when AI lowers the skills a task requires, lower-skill workers can compete for jobs that were previously out of reach. Automation and augmentation have small distributional effects but drive most of the average wage increase.

The results depend on adoption. When the model includes adoption costs, which are highest for lower-skill workers, the 90-10 ratio still falls by 15 percent but average wage gains shrink from 28 to 0.3 percent. These are model projections across slow, moderate and rapid scenarios, not measured outcomes.
limitation: The results come from a structural model run under slow, moderate and rapid scenarios for AI capabilities, which are described as uncertain and evolving; they are projections rather than observed wage changes, and the paper is a working paper not yet peer-reviewed.
tag: Dual reading
key_points: The model was estimated on NLSY79 panel data on wages, occupations and skills, combined with O*NET data covering 19,530 tasks linked to 974 occupations. | AI's task-level capabilities were estimated by prompting several AI models (Qwen 2.5, gpt-oss, GPT-4o); in the moderate scenario, generative AI saves 25.2 percent of worker time and 22.4 percent of tasks are automatable. | Welfare gains for current workers in the moderate scenario equal a rise in lifetime wages of 15 to 45 percent, largest for the lowest-skill workers and most variable among older workers. | Administrative occupations such as financial clerks see a large decline while construction and manufacturing occupations become more important; occupations with the biggest employment gains often see the largest relative wage falls. | Mapped to college majors, engineering, mechanics and architecture gain value while law, political science and philosophy lose value. | Because simplification reduces the incentive to build skills, average skill levels fall, which dampens AI's positive growth effects. | In US job postings, occupations predicted to be more simplified by AI show a decline in required expertise of 0.4 standard deviations by early 2026 after the ChatGPT release.
rundown: The authors add simplification to the standard automation and augmentation channels of task-based models. Workers have five-dimensional skills (manual, math, social, technical, verbal), choose occupations each period and learn on the job by performing tasks that exceed their current skills. They estimate the model by maximum likelihood on NLSY79 data following workers from age 25 to 2022.

To quantify AI, they used AI models to rate task-level skill requirements across 35 skills, with 683,550 queries per model. These ratings correlate 0.79 to 0.93 with O*NET occupation-level measures and 0.70 with the average of two human experts on 200 skill-task combinations for economists. Slow, moderate and rapid scenarios follow earlier forecasting work.

Beyond wages, AI changes what skills are worth. Because workers build skills by doing demanding tasks, lowering requirements slows skill accumulation. Occupations with the largest employment gains tend to be those whose relative wages fall most, since simplification enlarges the pool of qualified workers and increases competition.

Adoption has been slower among lower-skill workers, so the authors added skill-dependent adoption costs estimated from current adoption data. These costs dampen but do not eliminate the decline in inequality. The authors conclude that lowering adoption costs is a key policy lever for raising wages, especially among lower-skill workers.
sources:
- peer_reviewed | NBER Working Paper | https://doi.org/10.3386/w35353 | 2026-06-01
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