Employment Environment

One reason for relatively low unemployment amid mixed economic conditions is the reduced working age population, ages 15 to 64, that has remained below its peak level in 2012 (Figure 2). The impact is more pronounced in developed countries with aging populations. Among the powerhouse tech talent markets, Bengaluru and Toronto had the fastest working-age population growth of 2.4% and 2.2%, respectively, between 2019 and 2024 (Figure 3). Canada’s Waterloo region and Austin were the fastest growing established markets, both higher than all powerhouse markets.

Figure 2: Global Working Age Population Share of Overall Population

Figure 3: Working Age Population by Market

Tech talent is most concentrated in the high-tech services & manufacturing sector, which includes software, cloud services, social media and computer hardware. These sectors benefited from the pandemic-induced increase in online shopping and remote work. But as the pandemic eased by 2022, business conditions changed and many of the major tech companies began reducing their employee headcounts, according to publicly filed annual reports.

Despite this, most of the major tech companies still have higher employee counts than pre-pandemic levels (Figure 4). Additionally, the number of layoffs declined by more than 40% in 2024 vs. 2023 and have stabilized, according to data provider Layoffs.fyi (Figure 5).

Figure 4: Global Big-Tech Employment Growth

Figure 5: Global Tech Layoffs Since COVID Pandemic

Tech industry job growth has continued across most global markets despite a slowdown in 2023. Every region had positive annual average growth between 2018 and 2023. Latin America, Canada and the Asia-Pacific region had the fastest five-year growth, while Europe and the U.S. had the slowest. All but four of the 75 global markets tracked by CBRE (Detroit, Sydney, Boston and Philadelphia) have recorded tech employment growth over the past five years, primarily due to employment losses during the pandemic. Among U.S. and Canada markets, tech job growth ranged from 10.4% in Toronto to -2.2% in Detroit (Figure 6A).

Figure 6A: High-Tech Industry Five-Year Average Annual Employment Growth (2018-2023)

In other global regions, tech job growth ranged from 25.0% in Monterrey, Mexico, 17.5% in Mexico City and 15.5% in Shenzhen to less than 1% in Hong Kong SAR, Melbourne and Sydney (Figure 6B)

Figure 6B: Info & Communications Industry Five-Year Average Annual Employment Growth (2018-2023)

VC funding for up-and-coming companies is a barometer for the tech industry’s health. Tech companies traditionally attract the most VC funding, accounting for more than half the global total since 2012.

Pitchbook data indicates that global venture capital funding increased by 57% year-over-year in 2024 to $369 billion, driven largely by funding to North American and AI-related companies (Figure 7A). VC funding in North America nearly doubled to $218 billion or 59% of the global total. Asia-Pacific and Europe each had about 20% of global VC funding.

Figure 7A: Venture Capital Funding Volume by Global Region

Deal counts decreased for the third consecutive year even though dollar volume increased last year, indicating higher funding rounds to fewer companies. Europe had a much higher share of total VC deal activity at 30% since it has fewer large companies (Figure 7B).

Figure 7B: Venture Capital Deal Count by Global Region

The top global markets for VC funding in 2024 were the San Francisco Bay Area, New York, London, Boston and Beijing, which combined accounted for $145 billion or approximately 39% of the global total (Figure 8).

Figure 8: Top Global Markets for Venture Capital Funding in 2024

AI-related VC investment reached a record-high $129 billion across more than 5,900 deals in 2024, 56% higher than the prior year and 3% more than the previous record in 2021 (Figures 9A and 9B). AI-related companies increased their share of overall VC funding to 35% in 2024 from 21% in 2021. About two-thirds of total AI-related VC funding over the past five years went to North American companies, mostly U.S.

Figure 9A: Artificial Intelligence Venture Capital Funding Volume by Global Region

Figure 9B: Artificial Intelligence Venture Capital Deal Count by Global Region

The top global markets for AI-related VC funding in 2024 were the San Francisco Bay Area, London, New York, Beijing and Paris, which combined accounted for $77 billion or 60% of the global total (Figure 10).

Figure 10: Top Global Markets for Artificial Intelligence Venture Capital Funding (2024)

The long-term growth prospects of the tech industry and tech talent demand remain strong, as global economies digitally transform. Innovations like AI will catalyze the next economic growth cycle, producing significant monetary value, tech talent employment and real estate demand.

By Luis Flores, Senior Analyst, Americas Research

Large language models have exponentially increased in capability over the past few years and can quickly generate cognitive insights. To test this, we posed a detailed prompt to ChatGPT, which generated the following result.

Our Analysis

Large language models can produce high-quality responses, but their full potential remains limited by both the capabilities of the actual AI model and those of the person crafting the prompts.

For example, in my initial attempts to craft the prompt for this AI-generated article, use of the phrase “tech markets” produced results related to technology sectors and industries rather than the cities and geographic areas that I had in mind. This was the most significant disconnect between the intended output and the actual output, but there were others, including no mention of China. Even with the inclusion of “technology companies” in the prompt, ChatGPT did not include the names of any companies until explicitly asked to do so. Conversely, when a set of examples was provided, such as with types of technology industries, the output repeated the words from the examples provided several times. This repetition was less prevalent when the examples were made more open ended by the inclusion of “or other fields.”

Generative AI has shown its prowess in automating simple tasks and quickly providing responses to direct and simple prompts, but its limitations are still clear. Current AI models do not think or understand like humans do, occasionally generate content that contains errors or lacks coherence, can provide significantly different outputs from a single prompt and are limited in what they can produce by the data sources they use. To mitigate these issues, prompts for generative AI models must be explicit and deliberate, while avoiding words or phrases that could produce a biased response.