The United States and the European Union face the 2026-2027 sprint with two different recipes for artificial intelligence:
- USA. US: increase salaries/bonuses for AI profiles over the next two years, with incentives for engineers, data scientists and product teams, seeking to retain talent and accelerate the production of advanced models.
- EU: fund of 1 billion euros (€1B) to promote its own capabilities: computing, data centers, supercomputing, startups and business adoption, with a more balanced focus on infrastructure and industrial fabric.
1) What each model finances
- USA. US (salaries/bonuses): variable spending, fast and directly aimed at talent. It favors immediate hiring and retention, but does not guarantee investment in shared infrastructure.
- EU (€1B fund): capital spending on hardware, sovereign clouds, supercomputing centers, calls for SMEs and deep tech. Slower to execute, but leaves lasting assets.
2) Impact speed
- USA. US: almost instantaneous impact on talent traction and deployment capacity; risk of wage inflation and gap between large and small companies.
- EU: gradual impact (calls, tenders), but it can expand the industrial base and reduce dependence on external suppliers.
3) Talent and wage gap
- USA. USA: reinforces its flexible labor market advantage. You can absorb foreign specialists with fast visas and aggressive packages.
- EU: commitment to retaining talent with projects and financing, not just salary. Challenge: bureaucracy and speed in calls; opportunity: public careers in centers of excellence.
4) Infrastructure and technological sovereignty
- USA. US: The emphasis is on applying AI on infrastructure already dominated by local hyperscalers. Risk: lock-in in suppliers and dependence on chips/energy.
- EU: €1B fund prioritizes infra-sovereign, open clouds and supercomputing. Objective: reduce dependence on chips, clouds and non-European foundries.
5) Concentration risk
- USA. US: When wages rise, the likely winners are Big Tech and unicorns; SMEs and the public sector may be left behind.
- EU: if the calls are well designed, the fund can be distributed in public-private consortia and SMEs; if designed poorly, it can also concentrate on few integrators.
6) Results horizon
- USA. US: value in 6-18 months: more releases, more products with generative AI; risk of fatigue if there are no clear returns or if regulation arrives late.
- EU: value in 18-36 months: more data centers, industry adoption programs, deep tech startups; risk of slowness and underutilization if demand does not keep up.
7) Governance and ethics
- USA. US: prioritizes execution; Ethics depends on internal company policies and sectoral guides. Advantage: speed. Risk: externalities (bias, privacy, security).
- EU: the fund was created accompanied by the regulatory framework (AI Act) and risk assessment requirements. Advantage: common standards. Risk: regulatory overload for SMEs.
##8) Suggested Success Metrics
- USA. US: time to contract, 12-24 month retention, speed of deployment to production, % of models audited.
- EU: MW of available computing, % use of sovereign infrastructure, number of beneficiary SMEs, TRL of deep tech projects, interoperability with open standards.
Convergence possible
Both strategies can be complementary: competitive salaries to attract talent and an infrastructure fund that reduces dependencies. The challenge for the EU will be to accelerate implementation and avoid concentration; for the US, mitigate gaps and ethical risks derived from speed.