# Chief Economists Warn Global Growth Faces Geopolitical and AI Forces > Source: > Published: 2026-05-28 17:40:37.086762+00:00 # Chief Economists Warn Global Growth Faces Geopolitical and AI Forces According to a World Economic Forum survey published May 28, 2026 and reported by Hospitality Trends, nearly **nine in ten** chief economists expect global growth to weaken over the next 12 months. The survey finds **94%** expect global inflation to rise, citing the closure of the Strait of Hormuz as a driver of higher energy and food costs and supply-chain disruption. The survey also reports **92%** of respondents expect greater AI adoption over the coming year, though respondents showed cooled optimism about the speed of productivity gains. Saadia Zahidi, Managing Director at the World Economic Forum, is quoted: "The longer the disruption lasts, the heavier the long-term cost for those who can least afford it." Editorial analysis: Industry observers should interpret the survey as signaling elevated macro risk alongside continued expectations for AI diffusion, not a deterministic forecast of near-term productivity gains. ### What happened According to the World Economic Forum survey published May 28, 2026 and reported by Hospitality Trends, nearly **nine in ten** chief economists expect global growth to weaken over the next 12 months. The survey reports **94%** of respondents expect global inflation to rise and identifies the closure of the **Strait of Hormuz** as a key short-term shock to energy, food costs, and supply chains. The survey also finds **92%** of chief economists expect greater **AI** adoption over the coming year, but it notes reduced optimism about how quickly AI will translate into cross-industry productivity gains. Saadia Zahidi, Managing Director at the World Economic Forum, is quoted: "The longer the disruption lasts, the heavier the long-term cost for those who can least afford it." ### Editorial analysis - technical context Industry-pattern observations: macro shocks that constrain energy flows and logistics typically raise stochastic volatility for demand and input costs, creating headwinds for corporate forecasting and ML-driven production optimization. Separately, broad survey-level expectations of AI adoption do not by themselves imply immediate productivity lift; historical diffusion of general-purpose technologies often shows a lag between adoption and measurable productivity gains. For practitioners building ML systems, that lag commonly manifests as integration, retraining, and operationalization burdens that extend beyond initial PoC success. ### Context and significance Editorial analysis: The World Economic Forum results place two competing forces in the same frame: elevated geopolitical risk that raises inflation and supply-chain fragility, and continued expectations for AI-driven transformation. For data teams and ML engineers, that combination means model inputs (cost forecasts, demand signals, supply-chain data) may become more volatile while organizational appetite for AI projects remains high. Survey sentiment alone does not quantify investment levels, timelines, or sectoral distribution of AI benefits. ### What to watch - •Duration of Strait of Hormuz disruptions and measured impacts on energy and commodity price indices, which would validate the survey's inflation expectations. - •Sectoral indicators of AI deployment: capital expenditure in cloud GPUs, enterprise AI procurement announcements, and published benchmarks for productionized models. - •Labor-market signals in analytics and ML roles, and corporate disclosures on productivity metrics tied to AI projects, which will show whether adoption expectations convert into measurable gains. For practitioners: monitor input-data stability and cost-forecast variance, and prioritize instrumentation that separates short-term shock response from longer-term ML-driven efficiency gains. ## Scoring Rationale The story signals meaningful macro headwinds and continued expectations for AI adoption, which matter to AI teams for forecasting, data stability, and prioritization. It is notable but not technical or immediate enough to be frontier-changing for ML research. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets [Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget) [High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page) [Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model) 250 free problems · No credit card [See all Ad Tech problems](/problems/datasets/adtech)