Global Startup Ecosystem

Ranking 2026 (Top 40)

This ranking identifies the Top 40 global ecosystems. These ecosystems are more mature than other ecosystems globally, featuring more large exits (valued over $50 million) and more funding activity.

For more information about how this ranking is created, please see the Methodology section of this report.

Key Findings

• Silicon Valley, New York City, and London retain their top three positions, holding steady with no change in rankings.
• Seattle made the most significant climb within the Top 10, jumping five positions to #10 Global Startup Ecosystem.
• Austin recorded the biggest leap among all North American ecosystems in the Top 40, surging 12 places to #18.
• Toronto-Waterloo made impressive progress, moving up seven positions to #13, tying with Paris and marking its strongest ranking performance in recent years.
• Stockholm emerged as the standout performer among European ecosystems, jumping eight positions to #23 and tying with Amsterdam-Delta.
• Dallas made a strong move up the global rankings, climbing 11 places to #27, reflecting the ecosystem's growing strength and increasing startup activity.
• Delhi slipped two positions to #31, reflecting increased competition across a rapidly-evolving global startup landscape.
• Philadelphia experienced the most significant decline of all Top 40 Global Startup Ecosystems, falling 20 positions to #33.
• Nearly all Chinese ecosystems in the Top 40 saw a decline in their rankings: Beijing (-1 to #6), Shanghai (-1 to #11), Shenzhen (-2 to #19), Hangzhou (-5 to #28), and Guangzhou (-3 to #38). The exception was Wuxi, which made an impressive climb of six positions to #36.

Success Factor Highlights

To create the 2026 rankings, we measured six Success Factors in each ecosystem:

• Performance
• Funding
• Market Reach
• Talent & Experience
• AI-Native Cluster
• R&D Engine

Each of these factors is assessed and awarded a score of 1 to 10, with 1 being the lowest and 10 being the highest. For more information, please see the Methodology section.

Performance

The Performance Success Factor assesses:

• Exits: The number of exits over $50 million and $1 billion, as well as the growth of exits.
• Ecosystem Value: A measure of the economic impact of the ecosystem, calculated as the total exit valuation and startup valuations over a two-and-a-half-year time period.
• Startup Success: How many startups succeed in the ecosystem. Measured in early-stage success (ratio of Series B to Series A companies), late-stage success (ratio of Series C to A companies), and number of active unicorns.

Funding

The Funding Success Factor assesses:

• Access: A function of early-stage funding volume and growth.

• Quality & Activity: The number of local investors, those investors’ experience (average years investing and exit ratio), and their level of activity (percentage of investors active in 2025 and the number of new investors).

Market Reach

The Market Reach Success Factor assesses:

Local Reach

• Scaleup Production
  • Ratio of startups with $1 billion+ valuations to GDP from H2 2023–2025
  • Ratio of $50 million+ exits to GDP from H2 2023-2025
  • Log of ratio of exits over $50 million from H2 2023-2025 to Series A funding from H2 2023-2025
• Local Market
  • The log of GDP of the country
  • Average number of days to commercialization of IP assets

Global Reach

• Ratio of tech startups (formed after 2016) with international secondary offices
• Log of tech companies with secondary offices in the ecosystem
• Log of international investors at Series A round

Talent & Experience

The Talent & Experience Success Factor assesses:

• Tech Talent
  • Quality & Access: A function of the number and density of top developers on GitHub, English proficiency, and history of exits. Quality is also a proxy for experienced scaled teams in the ecosystem.
  • Cost: Cost efficiency average of software engineer salaries. (Higher salaries lead to lower scores.)
• Life Sciences Talent
  • STEM Access: Number of STEM students and graduates.
  • LS Access: Number of Life Sciences-focused universities and degree programs.
  • LS Quality: A function of Life Sciences quality of instruction and research at local universities as measured by the Shanghai Rankings.
• Experience
  • Scaling Experience: The cumulative number of significant exits (over $50 million and over $1 billion) over 10 years for startups founded in the ecosystem.
  • Startup Experience: The cumulative number of early-stage companies started and funded at the Series A stage.

AI-Native Cluster Factor

This factor is a composite measure of the degree to which an ecosystem encourages Artificial Intelligence (AI) startups. This sector has been highlighted over others since Startup Genome believes that AI is increasingly a general purpose technology which will drive growth in other sectors.

• Transition
  • Ratio of AI-Native startups to all technology startups formed in 2021-2025
  • Ratio of AI-Native seed funding to all technology seed funding in H2 2023-2025
• Experience
  • Series A Funding ($) in H2 2023-2025
  • AI-Native Ecosystem Value
    • Log of sum of all exits and estimated AI-Native startup valuations during H2 2023-2025 without double counting

R&D Engine

The R&D Engine Success Factor assesses:

• Patents: The volume, complexity, and potential patents generated in the ecosystem.