Employment Trends and Artificial Intelligence in the Israeli Labor Market
Generative Artificial Intelligence (AI) is emerging as one of the most significant technological revolutions of our time, with growing influence across many domains — including the economy and the labor market. The rapid pace of technological development and its swift adoption for work-related purposes underscore both the necessity — and the challenge — of conducting empirical research on its impact on human employment, as well as the need to formulate effective public policy to address the risks and opportunities this technology presents for Israeli workers. The speed at which generative AI is advancing gives rise to a classic “moving target” problem: it is difficult to study a technology that is evolving faster than conventional tools of measurement and analysis can keep up with. In this paper, we respond to that challenge by cross-referencing the capabilities of generative AI models from two successive generations — before and after the release of GPT-41 — with Israeli employment data from 2023–2024, alongside an analysis of trends in both technology and employment. Our findings indicate that for the average Israeli worker in 2024, a large language model such as Claude, Gemini, or ChatGPT can perform at least 17% of typical work tasks without any auxiliary tools. When additional tools are available — such as image or voice generation, internet access, or specialized datasets — this figure rises to 51%. In our estimation, these rates were at least 5.3% lower in 2023. We also find that the capabilities of generative AI help explain, to some extent, the occupational profile of the unemployed and those who have stopped looking for work. Individuals with skills that AI is able to substitute for are more likely to be out of work than those with skills that AI complements — suggesting a differential impact on employment. We begin by reviewing key research findings from Israel and abroad over the past two years. We then explain the methodology we use and present the “big picture” of the Israeli labor market. Next, we offer a current snapshot broken down by industry, education level, residential district, sector, and gender, and examine the varying trends in exposure to generative AI. Finally, we explore whether AI exposure helps explain non-employment in Israel and conclude by summarizing our findings.