According to AnthropicAI, new interactive models show how AI may shift tasks, jobs, and GDP by 2030, yet policy gaps on white collar displacement remain.

According to @AnthropicAI, a new model projects AI’s 2030 effects on growth, jobs, and wages, with scenarios benchmarked by 10,000 US respondents.

According to @CNBC, Goldman Sachs finds AI exposed sectors saw slower job openings growth since H2 2022, strongest in Germany, Australia, and the U.S.

According to Ethan Mollick on X (Twitter), the viral 2028 AI crash scenario by Citrini is "hard" science fiction and not a fully plausible path, and he recommends Alex Oleg Imas’s economic analyses of AGI impacts as a better basis for forecasts (source: Ethan Mollick tweet; links to Citrini Research and Alex Imas Substack). According to Citrini Research, the scenario imagines a 38% S&P drawdown, 10.2% unemployment, and credit stress as advanced AI surpasses expectations; however, Mollick frames it as scenario-building rather than prediction (source: Citrini Research post; Ethan Mollick tweet). According to Alex Oleg Imas’s Substack, evaluating AGI economics requires micro-founded mechanisms such as productivity shocks, labor substitution elasticities, and capital deepening paths, which provide more credible planning inputs for businesses than narrative stress tests (source: Alex Imas Substack). For AI leaders, the business takeaway is to model cash-flow sensitivities to AI-driven productivity and labor market shifts under multiple elasticities and adoption curves, instead of anchoring on single dramatic paths (sources: Ethan Mollick tweet; Alex Imas Substack).