Professor Anil Gupta of the University of Maryland’s Robert H. Smith School of Business counters Bill Gates’ concerns about AI-induced job losses, citing current labor data and advocating for a shorter workweek as a potential solution.
COLLEGE PARK, MD — In a recent discussion spurred by Bill Gates’ extensive commentary on the implications of artificial intelligence (AI) for the labor market, Anil K. Gupta, a professor at the University of Maryland’s Robert H. Smith School of Business, has presented a counterargument. Gates, in a detailed 6,000-word essay and subsequent remarks to The New York Times, expressed alarm over the potential for AI to displace millions of jobs and emphasized the need for new policy interventions, including a controversial “token tax” on AI usage to mitigate these risks and fund retraining programs. Gupta, however, maintains that current evidence does not support Gates’ more pessimistic outlook on immediate job displacement.
Current Labor Market Indicators
Gupta emphasizes that macroeconomic data refute Gates’ dire predictions regarding job losses. He references the AI Maps dataset, which reveals that U.S. employers posted 4.2 million new jobs in the fourth quarter of 2025, an increase from 4.0 million in the same period in 2019 and a notable rise from 3.7 million in the first quarter of 2018. “Job creation has not collapsed. If anything, it has grown,” Gupta stated, highlighting the downward trend in the unemployment rate, which was recorded at 4.1% in July 2026, a decrease from 4.4% in March of the same year. These figures suggest a labor market that is not only resilient but also evolving in response to technological advancements.
Gupta expresses skepticism regarding the notion that young workers will suffer the most due to AI developments. He argues that the transition to an AI-centric economy necessitates a workforce proficient in AI technologies. “Younger workers—who are exposed to AI tools in college and are more open to new technologies—are more AI-native than mid-career professionals,” he explained. Furthermore, he notes that younger employees tend to be less expensive to hire, making them appealing candidates for roles where AI tools augment human labor rather than replace it.
Job Postings for Recent Graduates
Further data supports Gupta’s assertions regarding entry-level job opportunities. The UMD-LinkUp AI Maps analysis indicates that job postings explicitly targeting recent graduates, defined as candidates with up to one year of experience, constituted 12.6% of all U.S. job postings in the fourth quarter of 2025. This marks an increase from 11.7% in the same quarter the previous year and significantly exceeds the 8.7% recorded in the first quarter of 2018. “If AI were eliminating entry-level opportunities, we would see the opposite trend,” Gupta remarked, reinforcing his position that the current labor market remains robust for young professionals.
Moreover, Gupta notes a shift in how employers are utilizing young talent, as major financial institutions have begun assigning interns to work on developing AI applications, contrasting with previous years when interns focused on more traditional tasks. This trend reflects an adaptation within industries that recognize the growing importance of AI fluency among younger employees.
Critique of the Proposed Token Tax
In addition to his analysis of workforce implications, Gupta critiques Gates’ proposed “token tax” on AI compute. He argues that as AI technology evolves, token consumption is surging while the cost per token is decreasing significantly. For instance, OpenAI has reported a reduction in API prices for certain models by over 95% in recent years. “Given these trends, a fixed per-token tax would become an increasingly larger share of the cost of inference as token prices fall, while aggregate tax liabilities would rise rapidly as token use expands,” Gupta explained. He posits that such a tax structure is impractical, as it would require governments to frequently adjust tax rates to align with fluctuating costs.
Gupta also raises concerns that implementing a token tax through commercial API providers could unfairly burden smaller and mid-sized organizations compared to larger companies that can afford to run their own AI models. This disparity in capacity could stifle innovation and limit the competitive landscape in the AI sector.
Proposed Solutions to Labor Displacement
Looking ahead, Gupta proposes a potential strategy to address any future labor displacement that may arise from AI advancements: a reduction in the standard workweek. He references historical precedents, noting that prior to World War II, the typical workweek was six days, which transitioned to five days. “We can transition from five to four,” he suggested, advocating for a societal shift that could help accommodate the changes brought about by AI technology.
Gupta points to early experiments with four-day workweeks, which have shown promising results in terms of productivity and employee satisfaction. He estimates that adopting a four-day workweek could effectively absorb up to a 20% substitution of human labor by AI without leading to significant job losses. “AI will reshape work. But the data do not support a narrative of imminent, widespread job loss—and certainly not one that falls hardest on young workers,” he concluded, emphasizing the need for a balanced perspective on the evolving labor landscape as AI continues to advance.
In summary, while Bill Gates raises valid concerns regarding the future of work in the age of AI, Anil Gupta’s analysis provides a counterpoint grounded in current labor data and trends. The ongoing dialogue about the implications of AI for employment underscores the importance of addressing both potential risks and opportunities as society navigates this transformative technological landscape.
