Methodology

Our quantitative data infrastructure is the world’s most comprehensive and quality controlled. 

We study more than 5.5 million companies across 350+ startup ecosystems, combine data from the three leading venture funding databases, and then remove duplicates and clean with an AI engine, machine learning techniques, and a manual review. We work with 65+ countries to power and update the data in our reports and policy consultancy work.

Key Definitions

Ecosystem: We define a startup ecosystem as a shared pool of resources, generally located within a 62 mile (100 kilometer) radius around a center point in a given region, with a few exceptions based on local reality. Resources typically include policymakers, accelerators, incubators, coworking spaces, educational institutions, and funding groups.

Exit: An exit, in the context of startups, refers to an event in which the founders, investors, or employees of a startup realize a return on their investment by selling their ownership stake in the company. Exits include IPOs, M&A, buyouts, and reverse mergers. We only include the first exit as relevant.

H1/H2: Fiscal periods of half a year, in which January–June is H1 and July–December is H2. Similarly, Q1, Q2, etc. refers to the four fiscal quarters of a year (January–March, April–June, etc.).

Regions: We define global regions based on UN and World Bank definitions and divide all countries into seven regions: Asia, Europe, Latin America, MENA, North America, Oceania, and sub-Saharan Africa. For a full list of which ecosystems are included in each region, please see here.

Startup: We define a startup as an innovative or technology-driven company that was founded within the last 10 years and that has technology and/or scalability at the core of its business model. In addition to software, this includes startups active in Deep Tech, such as Robotics, Life Sciences, and more.

Unicorn: A startup that meets our definition, has been valued at more than $1 billion, and has not exited.

Sub-Sector Definitions

Sub-sectors are not mutually exclusive nor comprehensive — some startups are in sub-sectors that we do not consider. In addition, we are aware of a clear tech convergence. Technologies such as AI software are increasingly interrelated, and we would expect a similar convergence over time for other startup sub-sectors.

AI-Native: AI-Native companies build LLM models, AI models for domain-specific use cases, and/or AI platforms or tools that tech and traditional businesses can use.

Advertising Tech (Adtech): Captures different types of analytics and digital tools used in the context of advertising and marketing. Extensive and complex systems are used to direct, convey, or monitor advertising to target audiences of any size and scale.

Advanced Manufacturing & Robotics (AMR): The use of smart technology to improve traditional manufacturing of products and/or processes, and the science and technology of robots, their design, manufacture, and application.

Agriculture Tech (Agtech) & New Food: Agtech captures the use of technology in agriculture, horticulture, and aquaculture with the aim of improving yield, efficiency, and profitability through information monitoring and analysis of weather, pests, and soil and air temperature. New Food includes technologies that can be leveraged to create efficiency and sustainability in designing, producing, choosing, delivering, and consuming food.

Artificial Intelligence, Big Data & Analytics (AI & BD): An area of technology devoted to extracting meaning from large sets of raw data, e.g., often including simulations of intelligent behavior in computers.

Blockchain: A decentralized data storage method secured by cryptography. Companies building their product/architecture on top of this decentralized and encrypted technology are defined as Blockchain companies. Cryptocurrencies are one of many innovations utilizing Blockchain.

Cleantech: Sustainable solutions in the fields of energy, water, transportation, agriculture, and manufacturing that include advanced materials, smart grids, water treatment, efficient energy storage, and distributed energy systems.

Cybersecurity: The body of technologies, processes, and practices designed to protect networks, computers, programs, and data from attack, damage, or unauthorized access.

DefenseTech: Technology designed for military, national security, or defense-related use cases. This includes systems and infrastructure such as weapons, defense platforms, intelligence and surveillance (ISR), autonomous and drone-based systems, and secure communications used to support defense operations.

Edtech: Technology devoted to the development and application of tools (including software, hardware, and processes) intended to redesign traditional products and services in education.

Fintech: Technology that aims to improve existing processes, products, and services in the Financial Services industry (including insurance).

Gaming: The development, marketing, and monetization of video games and gambling machines, as well as associated services.

Life Sciences: Life Sciences is concerned with diagnosing, treating, and managing diseases and conditions. This includes startups in Biotech, Pharma, and Medtech (also referred to as medical devices).

Ecosystem Page Metrics

AI-Native Ecosystem Value: A measure of economic impact, calculated as the value of exits and startup valuations from H2 2023–2025 for AI-Native. Ecosystem Value includes all active unicorns.

AI-Native Ecosystem Value Growth: Growth is calculated based on the AI-Native Ecosystem Value from H2 2021–2023 vs. H2 2023–2025.

AI-Native Funding Tier: AI-Native Early-Stage Funding (ESF) Count Growth Tier evaluates the growth in the number of early-stage funding rounds between 2022–23 and 2024–25. Ecosystems are scored on a scale from 1 to 10, where 1 represents the lowest growth and 10 represents the highest.

Ecosystem Value: A measure of economic impact, calculated as the value of exits and startup valuations from H2 2023–2025. Ecosystem Value includes all active unicorns.

Ecosystem Value Growth: Growth is calculated based on the Ecosystem Value from H2 2018–2020 vs. H2 2023–2025.

Median Seed: The median of seed rounds in tech startups in the ecosystem from H2 2023–2025.

Median Series A: The median of Series A rounds in tech startups in the ecosystem from H2 2023–2025.

Software Engineer Salary: Average software engineer salary informed by data from Glassdoor, Salary.com, and PayScale, as well as local sources when available.

Time to Exit: The average age at the time of exit in the ecosystem from 2021–2025.

Top-Funded Startups: Top startups identified based on funding amount from July 2023 to December 2025.

Total Early-Stage Funding: The total seed and Series A funding in tech startups in H2 2023–2025.

Total VC Funding: The total VC funding (seed, Series A, Series B+)  in tech startups from 2021–2025.

Badge Definitions

Highlighting global and regional positioning of Startup Genome member ecosystems across key Success Factors.

Affordable Talent: Measures the ability to hire tech talent at competitive costs.

AI-Native Cluster: A measure of the size and intensity of an ecosystem’s AI startup activity.

Funding Momentum: Measures innovation through early-stage funding and investor activity across the ecosystem (formerly “Funding”).

Funding Runway: Measures the amount of runway tech startups acquire, on average, from a VC round (formerly “Bang for Buck”). 

Performance: Measures the size and performance of an ecosystem based on the accumulated tech startup value created from exits and funding.

R&D Engine: Measures innovation through research and patent activity generated within the ecosystem (formerly “Knowledge”).

Talent Strength: Measures long-term trends over the most significant performance factors and the ability to generate and retain talent in the ecosystem (formerly “Talent”). 

Primary Data Sources

• Startup Genome proprietary data:
  • Interviews of 100+ experts
  • 2017–2024 Startup Ecosystem Survey with more than 10,000 participants per year
  • Startup Genome LLC (2017-2025): startupgenome.com database
• Dealroom: global dataset on funding, exits, and locations of startups and investors including the (2017-2025) dealroom.co database
• Crunchbase: global dataset on funding, exits, and locations of startups and investors including the (2017-2025) crunchbase.com database
• PitchBook (2018-2025), a private capital market data provider
• CB Insights (2019-2025): cbinsights.com database
• Local partners (accelerators, incubators, startup hubs, investors):
  • List of startups
  • List of local exits and funding events

Secondary Data Sources

• Forbes 2000
• GitHub API
• International IP Index
• Meetup.com
• OECD, R&D Spending
• Other sources from Life Sciences Rankings
• Salary data from Glassdoor, Salary.com, and PayScale
• Shanghai Rankings
• Techboard
• Times Higher Education Rankings
• USPTO
• WIPO
• World Bank

Selected Data Timeframes

• Ecosystem Value: Sum of exits and funding rounds in H2 2023–2025.
• Based on long-term research and analysis, we know that it takes around one year for 50% of seed rounds to appear in the major data sources. As such, we use H2 2022 as the most recent period for seed rounds and earlier-stage metrics that are computed to create reliable benchmarks at the ecosystem level.
• For early-stage funding, we take the count of all seed and Series A investments in H2 2022–2024 for seed rounds and H2 2023–2025 for Series A rounds. It takes four to eight weeks for the majority of Series A rounds to appear in our sources.

Ranking Methodology 

Global Startup Ecosystem Ranking 2026
(Top 40)

This ranking identifies the Top 40 ecosystems. These ecosystems are more mature than other ecosystems globally, featuring more exits over $50 million and more funding activities. 

This ranking is a weighted average of the following factor scores:

• Performance: 27.5%
• Funding: 20%
• AI-Native Cluster: 10%
• Market Reach: 20%
• Talent & Experience: 17.5%
• R&D Engine: 5%

We calculate an Ecosystem Index Value for each factor based on the sub-factor and metrics detailed below. The ecosystem scores are multiplied by the above weights to establish the overall rank of each ecosystem. The weights of the factors are determined through correlation analyses and modeling work based on linear regression analyses, using factor indices as independent variables with the performance index as a dependent variable. Finally, adding the actual Performance Index to the ranking formula serves to include the influence of unobserved factors on the performance of an ecosystem.

Ranking Details

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.

• 50% Ecosystem Value
  • Log of sum of all exits and estimated startup valuations from H2 2023–2025 without double counting
• 37.5% Exits
  • 80% volume of exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) from H2 2023–2025
  • 20% exit growth from 2022–2023 vs. 2024–2025
• 12.5% Startup Success
  • 80% growth-stage success (50% ratio of Series C-to-A Startups and 50% from the number of unicorns) from H2 2023–2025
  • 20% early-stage success (ratio of Series B-to-A startups) from H2 2023–2025

Funding

Quantifies funding metrics important to the success of early-stage startups.

• 90% Access
  • 90% early-stage funding volume (80% log of count and 20% log of sum of total early-stage funding deals). The time range for seed rounds is H2 2022–2024 and for Series A rounds is H2 2023–2025.
  • 10% log of early-stage funding growth from seed count from H2 2021–2023 and Series A Count from H2 2022–2024 to seed count from H2 2022–2024 and Series A Count from H2 2023–2025
• 10% Quality and Activity
  • 70% volume of investors (50% log of total number of VCs and CVCs in 2025 and 50% log of total number of investors with $100 million+ assets under management in  2025)
  • 10% experience of investors (50% number of investors with above average exit rates and 50% average years of experience of investors)
  • 20% new investors (50% log of total number of new investors, with less than five years of activity) and 50% ratio of active investors 

AI-Native Cluster

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.

• 50% Transition
  • 80% ratio of AI-Native seed funding to all technology seed funding in H2 2023–2025
  • 20% ratio of AI-Native startups to all technology startups formed in 2021–2025
• 50% Experience
  • 50% Series A Funding ($) in H2 2023 - 2025
  • 50% AI-Native Ecosystem Value
  • Log of sum of all exits and estimated AI-Native startup valuations during H2 2023–2025 without double counting

Market Reach

Measures early-stage startup access to customers, allowing them to scale and potentially “go-global.”

• 75% Local Reach 
  • 60% Scaleup Production
    • 50% ratio of startups with $1 billion+ valuations to GDP from H2 2023–2025
    • 40% ratio of $50 million+ exits to GDP from H2 2023–2025
    • 10% log of ratio of exits over $50 million from H2 2023–2025 to Series A funding from H2 2023–2025
  • 40% Local Market
    • 90% from the log of GDP of the country
    • 10% from tiers of average number of days to commercialization of IP assets
• 25% Global Reach 
  • 60% ratio of tech startups (formed after 2016) with international secondary offices
  • 20% from the log of tech companies with secondary offices in the ecosystem
  • 20% from the log of international investors

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 43% Talent
  • 80% Tech Talent
    • 90% Quality & Access
      • 70% log of count of $50 million+ exits in 2015–2024
      • 10% share of top Github coders to total Github coders (based on the data available in December 2025)
      • 10% log of count of Github coders on github.com with more than 10 followers (based on the data available in December 2025)
      • 10% English Proficiency Score for 2025
    • 10% Cost
      • 50% log of software engineer salary (lower is considered better) from Glassdoor, Salary.com, and PayScale for 2025
      • 50% log of funding runway: ratio of median Series A funding rounds for H2 2023–2025 by software engineer salary
  • 20% Life Sciences
    • 30% STEM students: log of number of STEM students
    • 60% Life Sciences access
      • 70% log of number of Life Sciences disciplines
      • 30% log of number of institutes with Life Sciences-related disciplines
    • 10% Quality
      • 25% average of CNCI score from Shanghai Rankings
      • 25% average of World-Class Faculty score from Shanghai Rankings
      • 25% average IC score from Shanghai Rankings
      • 25% average PUB score from Shanghai Rankings
• 57% Experience
  • 80% startup experience in the ecosystem
    • Log of count of funding of Series A in 2016–2025
  • 20% scaling experience in the ecosystem (the cumulative number of significant exits [over $50 million and $1 billion] over 10 years for startups founded in the ecosystem)
    • 60% log of number of $1 billion+ exits from 2016–2025
    • 40% log of number of $50 million+ exits from 2016–2025

R&D Engine

Measures innovation through research and patent activity.

• 100% patents (the volume, complexity, and potential of all patents created in the ecosystem)
  • 60% log of tier of number of all the patents in the ecosystem in 2015–2024
  • 20% five-year moving average growth of all patents
  • 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces

Emerging Ecosystems Ranking

Emerging ecosystems are startup communities at earlier stages of growth. The methodology for ranking the Top 100 Emerging Ecosystems is designed to reflect this, showcasing the ecosystems displaying high potential to become top global performers in the coming years. The factor weights used to rank these ecosystems differ slightly from those used with the Top 40 ecosystems to reflect their emerging status and emphasize the factors that have more influence in ecosystems that are just beginning to grow. Less weight is given to the number of exits over $50 million and startup activity is more focused on early-stage funding than in the Top 40 ecosystems.

The Emerging Ecosystem Ranking is a weighted average of the following factor scores:

• Performance: 27.5%
• Funding: 30%
• Market Reach: 15%
• AI-Native Cluster: 5%
• Talent & Experience: 17.5%
• R&D Engine: 5%

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.

• 75% Ecosystem Value
  • Log of sum of all exits and estimated startups valuations from H2 2023–2025 without double counting
• 20% Exits
  • 80% volume of exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) from H2 2023–2025
  • 20% Exit Growth Index (scored from 1 to 10) for 2022–2023 vs. 2024–2025
• 5% Startup Success
  • 80% growth-stage success (50% ratio of Series C-to-A startups and 50% log of active unicorns)

Funding

Quantifies funding metrics important to the success of early-stage startups.

• 100% Access
  • 90% early-stage funding volume (80% log of count and 20% log of sum of total early-stage funding deals). The time range for seed rounds is H2 2022–2024 and for Series A rounds is H2 2023–2025  
  • 10% log of early-stage funding growth from seed count from H2 2021–2023 and Series A Count from H2 2022–2024 to seed count from H2 2022–2024 and Series A Count from H2 2023–2025

Market Reach

Measures early-stage startup access to customers allowing them to scale and “go-global.”

• 80% Local Reach
  • 62.5% Scaleup Production
    • 55% ratio of startups with $1 billion+ valuations to GDP from H2 2023–2025
    • 45% ratio of $50 million+ exits to GDP from H2 2023–2025
  • 37.5% Local Market
    • 100% from the log of GDP of the country
• 20% Global Reach
  • 80% ratio of tech startups (formed after 2016) with international secondary offices
  • 20% from the log of tech companies with secondary offices in the ecosystem

AI-Native Cluster

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.

• 50% Transition
  • 20% ratio of AI-Native startups to all technology startups formed from 2021–2025
  • 80% ratio of AI-Native seed funding to all technology seed funding from H2 2023–2025
• 50% Experience
  • 50% Series A Funding ($) from H2 2023–2025
  • 50% AI-Native Ecosystem Value
    • Log of sum of all exits and estimated AI-Native startup valuations from H2 2023–2025 without double counting

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 43% Talent
  • 80% Tech Talent
    • 50% Quality & Access
      • 50% log of count of $50 million+ exits from 2016–2025
      • 30% share of top Github coders to total Github coders
      • 20% log of count of Github coders with more than 10 followers on github.com
    • 50% Cost
      • 50% log of software engineer salary (lower is considered better) from Glassdoor, Salary.com, and PayScale
    • 50% log of funding runway: the ratio of median Series A funding rounds by software engineer salary
  • 20% STEM Students: log of number of STEM students
• 57% Experience
  • 80% Startup Experience in Ecosystem
    • Log of count of Series A funding in 2016–2025
• 20% Scaling Experience in Ecosystem (the cumulative number of significant exits [over $50 million and $1 billion] over 10 years for startups founded in the ecosystem)
  • 60% log of the number of $1 billion+ exits from 2016–2025
  • 40% log of the number of $50 million+ exits from 2016–2025

R&D Engine

Measures innovation through research and patent activity.

• 100% patents (the volume, complexity, and potential of all patents created in the ecosystem)
  • 60% log of tier of number of all the patents in the ecosystem from 2015–2024
  • 20% five-year moving average growth of all patents
  • 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces

Changes from GSER 2025

Startup Genome continuously aims to improve its data and research. This year, the key methodological change is the increased emphasis on the AI-Native Cluster factor, a composite metric measuring how conducive an ecosystem is to AI startup growth. Its weight in the Top 40 ranking has been raised from 5% to 10%, reflecting Startup Genome's view that AI is an increasingly general purpose technology with the potential to fuel expansion across other industries. Additionally, this factor — previously limited to the Top 40 ranking — has now been extended to the Emerging ranking.