If your global AI talent strategy assumes the mobility-based model of the 2010-2020 decade, where top AI researchers and engineers moved internationally with relative ease and concentrated in a handful of global hubs, that model is now under strain. Mobility is slowing across regions. Both inflows and outflows of top AI authors and inventors are declining. The US remains the primary global attractor of top AI talent, but its lead is rapidly narrowing. India is transitioning from net exporter to net absorber of AI talent. The Middle East and North Africa are making incremental gains. The pattern is that talent is increasingly staying within national or regional systems rather than circulating globally.

The structural observation is consistent across regions: net flows may remain stable in some cases, but both inflows and outflows are declining. This is the mobility-slowdown signal: the rate at which talent moves between countries is falling regardless of whether net direction stays the same. The implication is that the global AI talent market is becoming less liquid.

The US position is the central data point. The US has been the dominant global attractor of top AI talent since the data series begins. Through 2018-2022, the US net inflow was substantial. By 2023-2025, the net inflow has narrowed materially. The US lead is rapidly narrowing: the rate at which the US attracts top global AI talent net of departures has slowed.

India is the inverse case. Historically, India has been a major net exporter of AI talent, primarily to the US. By 2023-2025, India is transitioning from net exporter to net absorber. Indian AI authors and inventors are increasingly remaining in India, or returning to India after international training. The well-documented pattern of India-US talent flow that defined the prior decade is reversing.

The Middle East and North Africa region shows incremental gains. The region's AI talent base is small in absolute terms but the directional signal, net inflow rather than net outflow, represents new positioning. The UAE's deliberate talent attraction programmes, Saudi Arabia's investment in AI infrastructure and research, and Israel's continued role as a cybersecurity-AI hub all contribute.

The contested question is what is driving the slowdown. Three interpretations are defensible from the same data, and the strategic implication differs across them.

Interpretation 1: visa and immigration policy is the primary driver. The argument: the US has materially restricted skilled worker visa programmes since 2022, including H-1B, OPT extensions, and research-track visas. The restrictions reduce inflow into the US. India's growth in domestic AI infrastructure and the reduction in US visa availability together produce the India inflow-outflow shift. The Middle East gains reflect deliberate policy programmes (UAE Golden Visa, Saudi research initiatives) that compete on visa availability. Under this interpretation, the talent mobility slowdown is a function of immigration policy; if policy reverses, mobility resumes. The slowdown is therefore reversible on a policy timeline.

Interpretation 2: regional AI infrastructure is the primary driver. The argument: as AI infrastructure (compute, research institutions, industry partnerships, funding) has built in non-US regions, the relative attractiveness of working in those regions has risen. Indian AI researchers who would have moved to the US in 2018 can now do comparable work at Indian institutions and Indian-funded companies. UAE researchers can access state-backed compute and funding without leaving. Under this interpretation, the talent mobility slowdown reflects the success of regional infrastructure-building. The slowdown is structural and is likely to persist or deepen as infrastructure continues to expand.

Interpretation 3: geopolitical risk and cultural preference are the drivers. The argument: international researchers face increased political risk in cross-border placements (visa unpredictability, return restrictions, sanctions exposure). Researchers may also have cultural and family preferences for staying closer to home. As both political risk and remote-work feasibility have increased, the cost-benefit of international placement has shifted. Under this interpretation, the slowdown reflects researcher preferences operating against an environment with rising costs of mobility. The slowdown is durable absent major shifts in the global political environment.

The data does not adjudicate between these interpretations. The likely answer is that all three contribute. The strategic implications differ across interpretations:

If Interpretation 1 is dominant: organisations should plan for policy-cycle variability in talent supply. Visa changes can shift the trajectory quickly. Hiring strategies should build in flexibility.

If Interpretation 2 is dominant: organisations should plan for sustained structural change. Regional AI talent supply will continue to grow, and the relative competitive landscape for talent will continue to shift.

If Interpretation 3 is dominant: organisations should plan for hybrid and remote talent engagement. Researchers who would have relocated may instead engage through remote partnerships, sabbaticals, or short-term placements that allow them to remain primarily home-based.

The recommended planning posture: build for all three interpretations. The talent strategy that absorbs the mobility slowdown without committing to one interpretation will be most resilient.

Three structural implications follow for organisations setting global AI talent strategy.

The first implication: the mobility-based talent acquisition model is operating against a less liquid market. The 2018 strategy of "identify top global talent, attract them to US/UK/Singapore/etc., concentrate them in our headquarters" is producing fewer placements per attempt than it did. The framework needs adjustment toward more distributed talent engagement: placing organisational presence near talent rather than expecting talent to relocate to organisational presence.

The second implication: regional talent hubs are becoming more important. India's transition to net absorber means Indian AI talent is increasingly available to organisations operating in or near India. UAE's gains mean Middle East operations have access to a growing regional talent base. China's relatively closed talent system limits direct access but produces a substantial domestic AI workforce. Organisations operating in these regions have access to talent that is harder to access through international mobility-based hiring.

The third implication: remote and hybrid AI work models become structurally more important. The mobility-based default of "AI researcher relocates to AI hub" is producing fewer placements. The remote-or-hybrid alternative, "AI researcher remains in regional hub, collaborates remotely with global team," has been more viable since 2020 and is increasingly the operating reality. Organisations that have built infrastructure for distributed AI teamwork are better positioned for the slowdown scenario.

The contested question for strategy planning: how durable is the slowdown? The recommendation: assume durable for planning purposes but monitor the visa-policy signal as the leading indicator for any reversal. Plans that build for the slowdown will be aligned with the current trajectory. Plans that wait for the mobility-based model to return may wait through the planning horizon.

Sources

  • Primary: Stanford AI Index 2026, Chapter 8 (Policy and Governance) 8.3 — hai.stanford.edu/ai-index/2026
  • Cross-border talent flow tracking: Zeki Data, 2025 — inflow and outflow of top AI authors and inventors by region, 2016–2025
  • Cross-reference: Stanford AI Index 2026 Chapter 1, 1.8 and Chapter 4, 4.4 — AI talent concentration and labour market
  • Policy mechanisms referenced: US H-1B and OPT extension policy; UAE Golden Visa programme; Saudi Arabia AI research initiatives; Israel cybersecurity-AI hub