The Calm Before the Storm: AI's Impact on the Labor Market

The Measurement of the Status Quo – Foundation and Method of the Study

The recent study by Gimbel et al. (2025), "Evaluating the Impact of AI on the Labor Market," makes a significant contribution to objectifying a highly emotional debate. The central research question is as clear as it is relevant: What measurable impact has AI had on the structure of the U.S. labor market since the introduction of ChatGPT, and does this differ from previous technological upheavals? The authors counter the public's fear of massive job losses with a sober, data-driven analysis, thereby creating an essential foundation for further discussion.

The methodological heart of the study is a "dissimilarity index," which measures the change in the composition of occupations over time. This approach allows for a quantitative comparison of the current transformational dynamics with historical disruptions, such as the introduction of PCs and the internet. To isolate the specific impact of AI, the authors rely on two central conceptual pillars.

First, the investigation is focused on a fixed time frame of 33 months following the release of ChatGPT to enable a direct before-and-after comparison.

Second, theoretical "exposure" scores from OpenAI and real "usage" data from Anthropic are used to identify occupations that are at potential risk from AI. The implicit assumption is that a profound change would have to manifest itself in these aggregated data and within this time frame.

The Central Results – A Picture of Stability

From this methodological setup, the authors derive a clear and, at first glance, reassuring conclusion: a broad, economy-wide disruption of the labor market by AI is not yet discernible.

Their key findings can be summarized in three points.

First, the analysis of macro-data shows that while the occupational structure is changing slightly faster than in the control period, this trend had already begun before the widespread availability of generative AI. A direct causality cannot, therefore, be proven. Second, and this is the core of the argument, there is no significant correlation between the theoretical "exposure level" of an occupation to AI and actual changes in employment or unemployment figures. Occupations with high theoretical exposure do not show a systematically worse development than others. Third, even among vulnerable groups such as young university graduates, a dramatic structural break attributable to AI is absent.

Logically derived from their assumptions, the authors conclude that the empirical evidence does not support fears of an imminent wave of automation and job displacements. History teaches, they conclude, that profound technological changes unfold over decades, not months.

Why the Stability is Deceptive – A Nanoeconomic Re-evaluation

The observations presented are valuable and empirically sound. However, a re-evaluation from a systems-theoretical and nanoeconomic perspective reveals that the underlying interpretative framework steers the conclusions in a potentially misleading direction. The problem is not the data, but the assumptions through which we view it.

The first assumption, that change must be visible in aggregated data, overlooks the fundamental nature of complex systems. Real transformations do not begin at the macro-level but at the nano-level: with the single, improved decision of an individual actor. Here, AI potentially provides a marginal improvement in information (∆Info) that optimizes a chain of actions. This "AI-Butterfly-Effect"—countless, local efficiency gains—is invisible in the fog of averages. Looking at macro-data is like measuring the average temperature of a country to predict the weather in a single village.

The second assumption, the linearity of time, is equally deceptive in dynamic systems. A system can absorb tensions and appear stable for long periods before it abruptly reaches a tipping point and transitions into a new state. Using a 33-month period as a benchmark is like observing a slowly heating pot of water for the first 33 minutes and concluding that water never boils. The current stability could be the latency phase before a nonlinear structural break—the all-too-familiar calm before the storm.

The third assumption, that "exposure" is a valid proxy for causality, misses the core of AI's value proposition.

The economic leverage of AI usage does not come from the number of tasks that can be automated in theory. Rather, it comes from improving the informational basis at a critical information point (CRIPO), which governs a strategically important decision within a workflow. This improvement propagates to the critical action point (CRAP) and results in a better outcome. Thus, optimizing a single business decision with more precise, AI-enabled forecasts can be more impactful than automating a hundred internal emails (See my article "The Simple Nanoeconomics of AI" for the complete nanoeconomic framework).

Conclusion

If one examines the Yale Budget Labs study data under these more realistic assumptions, a different picture emerges.

The absence of a macro signal does not prove the absence of an effect on the labor market. Rather, it is precisely what systems theory and the nanoeconomic framework would predict for the current phase of local, silent adaptation that has not yet manifested in macro-data. These marginal local changes are the literal butterfly flaps that could lead to massive changes and transformations at tipping points. On a large scale, this AI-Butterfly Effect can completely change the picture.

From this it follows, first, that we must look for different signals: not unemployment figures, but early indicators of productivity gains at the process level. Second, it means that political and corporate strategies must not react to visible disruptions, but must proactively strengthen the system's ability to adapt and learn in order to be prepared for the inevitable, though unpredictable, change. The current calm is not an all-clear signal. Rather, it is a call to action for researchers and practitioners alike.

Sources:

Gerlach, S. (2025). The Simple Nanoeconomics of AI: An Economic World Model for Exploring AI Impacts. Available at SSRN 5394649. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5394649

Gimbel, M., Kinder, M., Kendall, J., & Lee, M. (2025, October 1). Evaluating the impact of AI on the labor market: Current state of affairs. The Budget Lab at Yale. https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs