Can India and the EU Catch Up in the Global AI Race?

Many believe the global artificial intelligence (AI) race is already a two-horse sprint between the US and China. But is that the end of the story? While these two giants lead in infrastructure, talent, and funding, countries like India and those in the European Union (EU) still have potential lanes to compete—especially if they play to their strengths.

Technology races are often shaped by first-mover advantage and unexpected breakthroughs. Yet, history shows that competitors can catch up if they survive the early waves. Based on public insights from business and tech leaders, no one is giving up. Here are four key factors shaping the AI competitive landscape:

1. Skilled Workforce Availability

   Building, iterating, and deploying AI models at scale demands a deep bench of engineers, developers, and data scientists. It's estimated that only around 150 core AI engineers in each leading company currently have the expertise in large language models to push forward new versions rapidly.

   Sustaining this growth will require a massive, ongoing influx of skilled talent. India has an edge here, with its vast pool of engineers and dominance in global IT services. The EU, meanwhile, benefits from its academic institutions and immigration-friendly policies that attract global tech talent.

2. Semiconductors and Supply Chain Control

   The world's most advanced AI chips rely heavily on semiconductors produced in Taiwan, and even more critically, on extreme ultraviolet (EUV) lithography equipment from ASML in the Netherlands. This gives the US and EU a distinct advantage in the high-end chip supply chain. With NVIDIA chips delivering up to four times the performance of rivals, it’s a gap that may be hard to bridge without years of R&D investment.

   Still, the EU could build on this edge by scaling its manufacturing base. India, however, remains at a nascent stage in semiconductor capabilities.

3. Energy and Grid Capacity

   AI data centers consume massive amounts of electricity. The US currently has about 1.3 terawatts of generation capacity, with another 0.5 terawatts in development. The EU is similar. India stands at roughly 0.5 terawatts today, with plans to double that. But China leads the pack, with over 3.5 terawatts in operation and more under construction.

   With AI demand growing, energy availability will increasingly dictate scalability. This is a major bottleneck for India and, to some extent, the EU.

4. Capital and Long-Term Funding

   Running large AI programs comes with a steep price tag. Burn rates are often in the billions. Many startups have already shut down after running out of funds. This has left the field dominated by deep-pocketed tech giants in the US and China.

   That said, the EU has the potential to mobilize capital through its corporate sector and financial stability. India still faces a gap in terms of large-scale, long-term funding for AI infrastructure and R&D.

Conclusion

The US leads the AI race, with China close behind but reliant on external supply chains. The EU and India currently lag due to gaps in energy, chips, and coordinated investment. But the race isn’t over.

Smaller, specialized AI models—requiring less compute and infrastructure—could offer a faster path to relevance for India and the EU. With targeted investments in talent development, efficient data centers, and focused innovation, they can still play a meaningful role in the AI future.