Forrester's J.P. Gownder questions the belief that AI will drive significant productivity gains. He points to historical data from the US Bureau of Labor Statistics, which shows that productivity growth was 2.7% annually from 1947 to 1973 but dropped to 2.1% between 1990 and 2001, and fell further to 1.5% from 2007 to 2019. Despite advances in technology, including the PC revolution, these figures illustrate a disconnect between tech adoption and productivity outcomes. Gownder references the Solow Paradox, which suggests that technological advancements often don’t translate into productivity increases. Gownder's research indicates that AI could eliminate about 6% of jobs, roughly 10.4 million, by 2030. He argues that these job losses are likely permanent, as they are structurally replaced rather than temporarily affected by economic cycles. To assess which jobs are most at risk, Forrester analyzed around 800 job types and 34 skills, employing a methodology similar to a 2013 study by Oxford scholars on job susceptibility to automation. The effectiveness of AI in business remains questionable. Gownder cites an MIT study revealing that 95% of generative AI projects fail to deliver tangible financial benefits. He emphasizes that many recent job cuts stem from budget constraints rather than a direct shift to AI, suggesting companies are using AI as a potential solution rather than an immediate replacement. The current job market reflects hesitancy, with businesses freezing hiring while they explore AI capabilities. Gownder draws parallels to past job losses in manufacturing due to globalization, arguing that today's labor shifts may also stem more from cost-cutting measures than from AI itself.