Yves here. Please welcome Goran Lazarevski, who sent us an article of his on LinkedIn which he then revamped and expanded for publication here. Lazarevski, an AI industry participant, takes issue with the cheery views of economists Lukas Althoff and Hugo Reichardt on the labor market effects of AI. As Lazarevski summarizes and you can read in full in Task-Specific Technical Change and Comparative Advantage. The authors come to the astonishing conclusion that AI will both substantially increase wages and reduce inequality. Lazarevski unpacks the flaws in their model and presents an alternative view, of likely labor market effects using a more perspective of how they operate.
Note that both the Althoff/Reichardt article and Lazarevski’s forecast assume that AI will be widely employed by businesses and will take over many tasks performed now. Some business commentators suggest that many of the announced corporate headcount cuts that cite AI adoption as a major driver are Wall-Street-pleasing exaggerations, that most of these reductions are to roll back Covid-era overhiring or otherwise rationalize their workforces. More and more reports of AI not resulting in cost reductions and unduly high and perhaps rising levels of AI errors may also dent the current inevitable seeming trajectory to pervasive use. Nevertheless, Lazarevski’s forecast is consistent with what our technology kingpins are trying to achieve.
By Goran Lazarevski, an economist currently working in Paris as AI Solution Design Lead at Pfizer
Over the past two centuries, technological revolutions have continuously reshaped the labor market, redefining the division of labor between humans and machines. Each wave of innovation, from industrial mechanization to digital automation, has altered not just productivity but the social fabric of work itself, creating new winners and losers and shifting the balance between capital and labor. Today, generative AI represents a new inflection point in this long evolution: it touches the cognitive and creative domains once thought uniquely human, threatening to fracture the labor market into sharply diverging strata.
A new economic paper by Althoff and Reichardt has been making waves in the press this week. It estimates that GenAI implementation in the economy will result in average wage increases of 21% and will substantially narrow wage inequality—the opposite of what most people fear.
So should we believe economists, given their long history of confidently modeling golden futures that reality politely ignores? As Joan Robinson said, the purpose of studying economists is to not be fooled be economists. In what follows, I explain how the authors’ conclusions rest on fragile assumptions within the neoclassical framework and offer an alternative more realistic way of thinking about AI’s effect on the labor market.
The model elegantly formalizes the dynamics from a microeconomic perspective. It defines occupations as bundles of tasks requiring different skills to be completed. Workers are modeled as forward‑looking, optimizing agents endowed with innate skills who choose occupations and can accumulate further skills while on the job. AI can affect production in three ways:
- Automation: AI replaces human labor entirely because it’s cheaper.
- Augmentation: AI boosts worker productivity.
- Simplification: AI lowers the skill requirement for a task.
The simplification channel is the authors’ novel contribution to the literature, and it fully drives the anticipated reduction in wage inequality as it empowers lower-skilled workers to compete for more jobs.
The authors then calibrate the model using historical data in order to estimate the effect of a hypothetical full implementation of 2024 frontier LLM capabilities in the US economy. They get remarkably optimistic conclusions, implying that workers can now look forward to their future under the AI overlords.
But hidden beneath the model’s impressive technical architecture lie standard neoclassical assumptions, which if relaxed could completely reverse the model’s results. Labor markets are treated as perfectly competitive, so wages for each occupation and skill are set to that worker’s marginal product contribution. As a consequence, an AI-induced productivity boost automatically raises higher wages, even in those occupations where full AI automation of routine tasks displaces large number of workers who are then seamlessly reassigned to other tasks where they’d be more productive thanks to AI augmentation and task simplification. Remember, in the neoclassical utopia that mainstream economists inhabit nobody gets fired because perfectly competitive markets assume away unemployment – contrary to recent evidence from AI-exposed occupations experiencing declines in employment. In this neoclassical world, supply creates its own demand (Say’s law), as profits automatically turn into investment and output expands to the exact level needed to accommodate these displaced workers.
In reality, wages are determined by bargaining, not perfect market clearing. In those negotiations between employer and employee, it is most often the employer that has the upper hand due to his monopsony position and/or informational leverage. Employers can even leverage AI to obtain such leverage, also known as AI monopsony. Drawing from the work of Kalecki, the productivity gains from AI-augmented labor will be partially captured by the employer, at a rate that is greater for low-skill occupations than high-skill ones (due to skill scarcity translating into bargaining leverage). This channel can totally flip the predicted effect on wage inequality – turning wage convergence into widening inequality.
Moreover, when a task is fully automated or even simplified to become accessible to less skilled workers, there would now be a larger pool of less specialized workers with a more limited skill set, intensifying competition for jobs and further eroding their bargaining power. This happens because larger labor pools worsen outside options, reduce union threat points and heighten intra-worker competition, shrinking workers’ surplus share. Wages can stagnate or fall despite productivity rising. The resulting pressure on wages and layoffs lowers aggregate consumption, potentially triggering a recession even as profits skyrocket and investment remains strong. The most recent example was the 2001 dot-com recession with similar technological driving factors as today. Studies by Autor and Acemoglu show job polarization trends predating GenAI, but are likely to intensify under its diffusion.
In the post-Keynesian framework described above, multiple forces interact in opposing directions, leaving no straightforward conclusion about their overall effect on wages. Nonetheless, the system’s core dynamics remain driven by skill scarcity. AI primarily automates mid-level cognitive tasks, sharply reducing the demand for those underlying cognitive abilities once central to white-collar employment. What remains are roles demanding advanced interpersonal capacities, high-order reasoning, and practical or technical skills anchored in the physical world (at least until the robots come for those too).
This creates a structural challenge. Our education system is still designed to produce exactly the mid-level cognitive competencies that tools like ChatGPT now provide instantly and at almost no cost. While the school-to-college pipeline aspires to develop higher-order cognitive and creative skills, the question is how many graduates can truly reach that level, especially when the bar rapidly rises due to model advancement. If this trajectory holds, the labor market will fragment into four broad strata:
- Cognitive professionals capable of building and critically evaluating AI automation systems and their outputs;
- Skilled trade and technical workers, performing embodied, locally bound tasks;
- Creative workers talented enough to market their human authenticity;
- Everyone else, including a wide swath of displaced cognitive workers (with a potential differentiation for strong interpersonal communicators in sales, caregiving, social work etc.).
All of a sudden, former analysts, marketers, and technical writers will all be competing for the same type of menial jobs requiring only baseline cognitive skills. (This doesn’t mean these jobs will cease to exist, but there will be a lot fewer of them and they will be technically undemanding.) In this scenario, the bargaining power and wages of the workers saddled in this group will collapse, while top-tier cognitive professionals will see their compensation surge. Today’s weak unions, gig contracts, and corporate monopsony power tilt the scales even further towards inequality, letting firms pocket most AI gains while sidelined workers get scraps. This completely inverts the paper’s conclusion that AI will reduce wage inequality, and this, unfortunately, seems like the more plausible future. The question is not whether AI can make the economy bigger, but who will have the leverage to claim the new wealth it creates.
