Comment on "Artificial Intelligence and Technological Unemployment" by Wang and Wong

This comment, written by J. Carter Braxton (University of Wisconsin), discusses the paper “Artificial Intelligence and Technological Unemployment” by Wang and Wong (2025), which develops and quantifies an equilibrium labor search model to evaluate the employment effects of spreading AI. Wang and Wong’s central finding is that improvements in AI quality will increase productivity by a factor of three while reducing employment by 23%, with approximately half of the employment decline occurring within the next five years. Braxton’s comment serves two purposes: first, to clarify the model’s structural channels through which AI affects employment; and second, to bring empirical evidence from the spread of computers in the 1980s–2000s to bear on the relative magnitude of those channels.

Braxton identifies two competing forces within Wang and Wong’s framework. The job destruction channel arises from endogenous separations: as AI quality improves, firms increasingly replace matched workers with AI, raising outflows from employment. The job creation channel arises from the free-entry condition: rising AI quality increases firm profits on all matches, inducing firms to post more vacancies, which raises workers’ job-finding rates and employment inflows. Whether aggregate employment rises or falls depends on which channel dominates — a quantitative question the authors resolve through calibration, finding the job destruction channel dominant. Braxton notes that three modeling choices (learning-by-using, the requirement that firms must be matched with a worker to adopt AI, and disembodied technological change) each push against the job-destruction result, making the authors’ findings more striking.

Braxton then evaluates the relative strength of these channels using the historical spread of personal computers. Drawing on Bick, Blandin, and Deming (2024), he notes that workplace AI adoption in 2024 follows nearly the same time trend and income-distribution profile as computer adoption in 1984, making computers a plausible historical analog. Using the CPS Computer Supplement (1984–2003), Braxton measures the change in computer usage by occupation and regresses it against the change in employment-to-unemployment (EU) transition rates by occupation. The estimated coefficient is 0.0146 (robust SE 0.0064), indicating that occupations with higher computer adoption rates saw higher flows into unemployment — confirming that a job destruction channel was active during the computer era. However, regressing the change in log occupation-level employment (1980–2000 Census) on the change in computer usage yields a coefficient of 0.7761 (robust SE 0.2658), with a positive slope indicating that occupations more exposed to computers saw higher employment growth. For the computer episode, therefore, the job creation channel dominated the job destruction channel — the opposite of Wang and Wong’s AI projection.

Braxton also cites his own prior work showing that even when job creation and destruction balance in aggregate, workers displaced by technological change face lasting earnings losses and elevated permanent income risk, raising the question of how to optimally insure these workers.

The comment concludes by identifying avenues for future research: introducing occupational heterogeneity (with some occupations more exposed to AI than others) and worker heterogeneity (skills that are complements versus substitutes to AI). The central open question is whether AI is qualitatively different from prior episodes of technological change, and if so, why.

Q1: What are the two central channels through which AI quality affects employment in Wang and Wong’s model, and how do they operate? The job destruction channel operates through endogenous separations: as AI quality (At) improves, firms that are matched with workers are more likely to replace them with AI at rate ρ, adding the term ρµAt Ht It to outflows from employment in the law of motion for employment. The job creation channel operates through the free-entry condition: higher AI quality raises firm profits on all existing matches (because technological change is disembodied, benefiting matches formed today with future AI gains), inducing firms to post more vacancies, which via free entry reduces the firm’s matching probability but raises the worker’s job-finding rate αt and thereby increases employment inflows. The net employment effect depends on which channel quantitatively dominates.

Q2: What is Wang and Wong’s quantitative finding about the aggregate employment and productivity effects of AI? Using a calibrated equilibrium labor search model, Wang and Wong find that the spread of AI will increase productivity by a factor of three while reducing employment by 23%. Approximately half of the employment decline is projected to occur within the next five years. A version of the model holding job-finding rates fixed yields a similar result, indicating that through the lens of their model the job creation channel is quantitatively small and the job destruction channel dominates.

Q3: What three modeling choices push against Wang and Wong’s job-destruction result, and why does Braxton view this as making the finding more striking? First, AI improves through “learning by using” — it learns from all output being produced — which creates an incentive for employment to remain elevated to accelerate AI learning, dampening job destruction. Second, firms can only adopt AI if currently matched with a worker, which creates an incentive for vacancy posting and pushes in favor of job creation. Third, AI improvements are disembodied (raising productivity in all matches, including those formed before the improvement), which increases the value of forming new matches today and strengthens job creation. Because each of these assumptions pushes against the job destruction result, Braxton argues that finding job destruction dominant despite these model features makes the result more striking.

Q4: How does Braxton use the historical spread of computers to assess the job destruction and job creation channels? Braxton measures occupation-level computer adoption as the change in the share of CPS Computer Supplement respondents who reported using a computer at work between 1984 and 2003 (denoted ΔCPUo,84–03), using occupation codes from Autor and Dorn (2013). He then regresses the occupation-level change in EU transition rates (ΔEUo,84–03, from monthly CPS micro data) on ΔCPUo,84–03 to measure the job destruction channel, and separately regresses the change in log occupation-level employment (Δlog Eo,80–00, from the 1980 and 2000 Census IPUMS) on ΔCPUo,84–03 to assess the net employment effect. A positive coefficient on the employment regression indicates job creation dominates; a negative coefficient indicates job destruction dominates.

Q5: What do the regression results show about the job destruction and job creation channels during the computer era? The job destruction regression yields a coefficient of β = 0.0146 (robust SE = 0.0064, R² = 0.0178), indicating that occupations with higher computer adoption rates did see higher employment-to-unemployment transition rates — the job destruction channel was present. However, the employment-level regression yields a coefficient of β = 0.7761 (robust SE = 0.2658, R² = 0.0348), with a positive slope indicating that occupations more exposed to computers experienced higher employment growth between 1980 and 2000. Thus, for the computer episode, the job creation channel dominated the job destruction channel — the opposite of what Wang and Wong project for AI.

Q6: What is the basis for treating the computer episode as a relevant analog to the spread of AI? Braxton cites Bick, Blandin, and Deming (2024), who show that AI adoption in the workplace in 2024 is following nearly the same aggregate time trend as the spread of personal computers in the early 1980s. Moreover, the distribution of AI usage across the income distribution in 2024 is nearly identical to computer usage across the income distribution in 1984: for both technologies, workplace usage peaks between the 80th and 90th percentiles of the income distribution before declining modestly at the top. Bick et al. (2024) also show the similarities hold by education level and age.

Q7: Even if job creation and destruction balance in aggregate, what does prior work suggest about the distributional consequences for workers? Braxton and Taska (2023) show that workers in occupations more exposed to technological change (measured by changes in computer and software task requirements) suffered larger earnings losses following displacement. Braxton, Herkenhoff, Rothbaum, and Schmidt (2024, forthcoming AER) show that workers in occupations more exposed to technological change experienced larger increases in permanent income risk between the 1980s and 2010s. These findings imply that even if AI does not reduce aggregate employment, workers who are displaced will face deteriorating labor market prospects, raising the question of how to optimally provide insurance.

Q8: What policy implication does Braxton draw from the distributional consequences of technological change? Braxton and Taska (2025, forthcoming Review of Economic Dynamics) show that technological change expands the motive for governments to provide retraining subsidies. Braxton argues that if AI represents an acceleration of technological change, even larger retraining subsidies — and potentially other forms of insurance — may be needed for displaced workers.

Q9: What are the main avenues for future research identified in the comment? Braxton identifies two principal directions. First, introducing occupational heterogeneity into the Wang-Wong framework, so that some occupations are more exposed to AI displacement than others, would allow the model to generate richer distributional implications. Second, allowing worker heterogeneity in skills — distinguishing skill dimensions that are complements to AI from those that are substitutes — would permit the model to capture differential effects across the workforce. The overarching research question is whether AI is qualitatively different from prior technological change episodes, and if so, to identify the precise mechanisms that make it different.

Job destruction channel: In the Wang-Wong model, the increase in endogenous separations driven by firms replacing matched workers with AI as AI quality improves. Formally, this is the term ρµAt Ht It in the law of motion for employment, representing separations that occur when a firm adopts AI and the worker and firm cannot renegotiate a mutually acceptable wage.

Job creation channel: The increase in vacancy posting and worker job-finding rates induced by rising AI quality. Because higher AI quality raises firm profits on all matches (via disembodied technological change), the free-entry condition implies firms post more vacancies, lowering the firm’s matching probability but raising the worker’s job-finding rate αt, increasing employment inflows.

Free-entry condition: The equilibrium condition equating the cost of posting a vacancy (κt) to the expected benefit (the probability of matching ft times the firm’s match surplus Πt). This condition pins down the job-finding rate for workers: when firms find it more profitable to post vacancies, αt rises.

Disembodied technological change: The modeling assumption that AI quality improvements raise productivity in all existing matches, not just those formed after the improvement. This means future AI gains benefit matches formed today, increasing the incentive to create new matches and pushing in favor of the job creation channel.

Learning by using: The mechanism in Wang-Wong whereby AI quality (At) improves as a function of current aggregate employment (Ht) and the learning rate µ. Because AI learns from all output being produced, maintaining higher employment accelerates AI improvement, creating a motive that partially offsets the job destruction channel.

Employment-to-unemployment (EU) transition rate: The rate at which employed workers flow into unemployment in a given occupation, used by Braxton as the empirical measure of the job destruction channel during the computer episode. Measured from monthly CPS micro data.

Capitalization effect: The tendency for firms to post more vacancies today in anticipation of future productivity improvements, because the cost of posting is paid upfront while the benefits of a future-better-AI accrue to the match going forward. Referenced by Braxton as relevant to understanding the job creation channel in Wang-Wong’s framework (citing Pissarides (2000), Chapter 3).