Contrary to the narrative that AI is eliminating office jobs, the US has added 3 million white-collar positions since late 2022. Tech-adjacent roles like software developers and paralegals have also seen significant growth in numbers and real wages, indicating augmentation over replacement.

High-profile predictions of AI-driven mass unemployment often don't stand up to basic data analysis. For example, a claim that 90% of the Philippines' economy relies on customer service was found to be only 6-7%. Similarly, even dire forecasts for "entry-level white-collar" job loss translate to manageable overall unemployment increases, not Great Depression-level crises.

Contrary to fears of mass unemployment, AI will create new industries and roles. While transitional unemployment will occur, the demand for more energy, AI-related regulation (e.g., government lawyers), and new leisure sectors will generate significant job growth, offsetting the displacement from automation.

While proclaiming AI will create jobs, tech giants like Google and Meta have seen profits soar while their employee counts have fallen from 2022 peaks. This data from AI's biggest adopters provides concrete evidence that fuels public skepticism and fears of widespread, technology-driven job losses.

A viral chart linking ChatGPT's launch to falling job openings is misleading. Job openings began declining months earlier, largely due to Fed interest rate hikes. This highlights how complex macroeconomic trends are often oversimplified in popular narratives that rush to assign blame to new technology.

Contrary to the media narrative, LinkedIn's data reveals that AI is currently a net job creator. The recent wave of layoffs and hiring freezes is primarily driven by macroeconomic pressures like interest rates, not automation.

The panic-inducing Citrini paper, which caused a market sell-off, assumes a static economy where AI only destroys jobs. It completely ignores historical precedents where new efficiencies unlock unforeseen demand and create entirely new industries, a concept similar to the Jevons paradox.

Even if AI triples productivity growth, the resulting job churn would only equal that of 1870-1930. That period is historically remembered as one of vast opportunity and creation of new industries, suggesting fears of a jobless future are misplaced.

Skeptics argue the AI-driven productivity boom theory is based on thin evidence. The downward job revisions fueling the theory were concentrated in government, mining, and manufacturing—not the white-collar sectors supposedly most impacted by AI, suggesting other economic factors are at play.