Labor Supply

The U.S. and China are leaders in university quality by country, with 186 and 132 universities ranked among the top 1,000 globally, respectively, according to U.S. News & World Report (Figure 12). The U.K., Germany and Italy had the next most.

Figure 12: Top 1,000 Universities by Country

The size of tech talent markets is a key consideration for employers formulating workforce location and growth strategies (Figure 13). Eight markets had more than 500,000 tech talent workers in 2023, all of which were in the Asia-Pacific region. Three markets had more than 1 million tech talent workers: Beijing, Bengaluru and Shanghai.

The largest markets outside of India and China were Tokyo with 500,000+ tech talent workers and London, New York, Paris, the San Francisco Bay Area and Toronto with between 300,000 and 500,000. Mexico City and Sao Paulo were the only Latin American market with more than 200,000 tech talent workers.

Figure 13: Tech Talent Workforce by Market (2023)

AI development, one of today’s most in-demand skills, was also quantified for the 15 largest workforces by country (Figure 14). The U.S. and India have the largest AI-development workforces at approximately 406,000 and 375,000, respectively, according to Linkedin Talent Insights. The U.K., Germany, Canada, Brazil and France each exceeded 50,000. There is continued high demand for AI-development talent, especially in Poland, the U.S. and Germany where current jobs postings are about 8% of existing AI-related professionals.

Bengaluru, San Francisco Bay Area, New York, Delhi and Hyderabad are the largest AI-development talent markets. The San Francisco Bay Area currently has the most AI-development job postings, followed by Bengaluru, Washington, D.C., New York and Seattle. Chinese markets were excluded because LinkedIn data was unavailable (see China note below).

Figure 14: Artificial Intelligence Tech Talent by Country & Metro Market* (Top 15)

By Udit Sabharwal, Associate Director, Asia-Pacific Consulting

Tech talent strategies have undergone significant transformation over the past two decades, driven by shifting corporate demands and macroeconomic factors. Initially, companies relied heavily on the concept of collective wisdom to guide their location and talent acquisition strategies, significantly influenced by where the largest tech companies established offices. This focus resulted in a dense concentration of tech talent in prominent hubs such as San Francisco, London, Bengaluru and Mexico City.

As this talent pool became increasingly clustered in well-established hubs, companies faced higher employee turnover and wages. They began to strategically position back-office and mid-office operations in low- and middle-income countries that offered a compelling mix of scale and cost efficiency.

However, the COVID pandemic has reshaped these dynamics with the rise of remote work leading to increased talent mobility. The movement of talent across international borders has become an increasingly pivotal issue for corporate occupiers, particularly as hybrid work schedules have made office workers more mobile than ever. Global talent migration is on the rise and shows no signs of slowing. Countries such as Germany, the U.K., Canada and the U.S. are primary recipients of this international migration, with younger workers and those from less-developed countries driving this trend as they seek better career prospects and improved lifestyles.

This transformed the feasibility of a distributed workforce and allowed companies to address geographical concentration risks, which has become a core concern for C-suites around the world. Talent diversification is now emerging on two fronts: regionally across Asia-Pacific, EMEA, Latin America and North America, and nationally by leveraging a broader array of cities and maintaining robust business continuity plans. Companies that have been successful in navigating concentration risks have used a three-pronged strategy:

1. Comprehensive Risk Basket: Developing a holistic risk matrix that encompasses all the risks that may impact a business in any location is critical. These include political, economic, geographic, supply-chain, financing, trade and social risks.
2. Peer Benchmarks: Comparing the location and talent footprint with that of peers offers insights on relative workforce and location concentration risks, potentially revealing necessary adjustments.
3. Futureproofing: Analyzing the overall workforce by size, location and function to build in risk thresholds. This creates alignment between the business units within a company, as well as its overall strategic direction.

By proactively recognizing and mitigating these risks within talent strategies, companies can strengthen resilience against market fluctuations and empower innovation through a more diverse workforce, setting the stage for sustainable growth in today’s dynamic environment.

By Stephen Fleetwood, Head of Europe Location and Labor Consulting

AI will force companies to rethink the determinants of location strategy and site selection. The prevailing trend of nearshoring or offshoring IT functions to low-cost locations will be challenged.

AI will automate tasks and ultimately jobs, but it will also augment existing roles and create entirely new ones. Companies will need to consider how AI can reduce costs and increase revenue by enabling advanced analytics and new thinking that will challenge entrenched business models.

The demand for top AI-development talent has extended beyond the major clusters in the San Francisco Bay Area, Seattle, New York, London and Paris. India remains the most scalable, low-cost destination for tech talent. The scale of the talent pools in Bengaluru or Hyderabad rivals the largest clusters in the U.S. However, the salary differential between high and low-cost countries is narrowing even as other viable low-cost countries are starting to appear on corporate radars. So where will companies choose to locate?

Automation of tasks and jobs began long ago, but the emergence of newer AI technologies will accelerate the process. The jobs most likely to be automated are those routine, repetitive or basic analytical tasks that can be scripted through AI. Many of these jobs have already been offshored to low-cost countries.

AI is already augmenting existing jobs related to data analytics and decision-making, although there is far greater potential. These jobs are a mix of revenue-generating and operations functions.

New roles have been created with high demand for skills such as deep learning, machine learning, natural language processing and algorithms. These roles are more focused on revenue generation than cost reduction and likely will be in higher-cost headquarters or regional office locations.

Many companies are now searching for tech talent with relevant sector experience, which is more likely found in established rather than emerging locations.

The launch of Open AI’s first model capable of advanced reasoning before generating responses is already having a big impact on the productivity of software developers. This means an experienced developer in a high-cost location could do the same amount of work as a team of developers in a low-cost location. While non-revenue-generating processes likely will remain in low-cost countries, the higher value functions could be reshored to higher-cost locations with no increase in labor costs and in some cases a reduction. As a result, offshoring will not be an automatic IT location decision.