Leadership in technology and business relies heavily on one factor: access to the world’s top talent.

That’s why countries and companies increasingly compete for the world’s best and brightest. But real-time numbers capturing the pulse of this competition are hard to come by—much of the movement of this highly mobile group remains hidden from official statistics. BCG’s Top Talent Tracker fills this gap by tracking the movement of close to 200 million highly skilled workers in real time, giving actionable feedback to policymakers and senior executives vying for such talent or eager to locate talent hot spots.

BCG’s Top Talent Tracker focuses on two subsets of highly skilled talent: workers with expertise in STEM (science, technology, engineering, and mathematics) and AI (artificial intelligence). These two groups are in high demand and are highly mobile. (See “About Our Research.”)

Consider this: Of the close to 200 million highly skilled people who we are able to track, 1.2% move annually for work. In contrast, 1.9% of the 3 million STEM experts and 2.2% of the 1.4 million AI experts move each year. Put another way, for every 100 AI experts, approximately 11 move internationally every five years, and their mobility is about 80% higher than that of other highly skilled workers.

Where they go—which company, which city, which country—matters profoundly and has the potential to shape geopolitical, technological, and economic advantage. Evidence shows that complex innovation and technology challenges are best solved by a blend of cross-functional, cross-national teams of top talent. That’s why countries that are more open to the influx of global talent are also the ones that tend to invent more (as measured by patents) and to grow faster. As countries jockey for leadership in emerging technologies like battery technologies, generative AI, and semiconductors (to name just a few), their ability to attract the best and brightest has a clear impact on their competitiveness. Case in point: A nation that leads in the attraction of talent in a given technology is 17 times more likely to also be the leader in that technology. And, as is well known, highly skilled first- or second-generation immigrants often go on to found or co-lead notable companies, such as BioNTech, OpenAI, Nvidia, or Snowflake.

BCG’s Top Talent Tracker allows us to explore the dynamic nature of this competition for STEM and AI talent. For example, as of Q4 2024, the US remains the undisputed hot spot for top talent worldwide—but countries in the Middle East are catching up fast.

Winning the competition for the world’s best and brightest is a team sport best played by public and private players together. Leaders across all sectors can start by answering a series of questions.

Private-sector leaders should ask:

• Are talent and talent development truly top priorities for us?
• Do we lack the right talent (for example, in digital or GenAI)? What’s the financial impact?
• What is our employee value proposition—what promise can we make to highly skilled workers?

Public-sector leaders of top talent destination countries and cities should ask:

• Do we have a fit-for-purpose immigration system for highly skilled workers?
• How can we build state- and city-level innovation hubs to attract talent?
• How could we use talent funds to incentivize companies to attract and retain talent ?

Public-sector leaders of top talent origin countries should ask:

• How can we upskill our workforces to find meaningful work at home and abroad?
• Do we have a clear talent brand to attract and reattract talent?
• How can we enhance companies’ capacity to retain our best talent?

The global competition for top talent is already in full swing, but the outcome is far from decided. The true winners will be those that set clear objectives and lead the way with innovative approaches, such as talent funds, before others catch on. BCG’s Top Talent Tracker is designed to guide those who are ready to embrace this new era of competition for technological, economic, and geostrategic advantage.

The authors express their gratitude to Christophe Haesler for data analysis.