Artificial Intelligence and the Global Startup Ecosystem

Artificial intelligence (AI) is reshaping the global startup landscape in profound and far-reaching ways—from how founders build companies, to how investors allocate capital, to how innovation emerges and spreads across industries and regions. This project aims to conduct a systematic, data-driven study of the evolving AI-driven entrepreneurial ecosystem worldwide, examining both the transformative potential of AI and the structural frictions that shape startup formation, scaling, and financing.

The research will assemble and analyze large-scale international datasets that combine startup characteristics (e.g., PitchBook/Crunchbase), employment and skill trajectories (e.g., LinkedIn/Revelio), patenting and scientific output, and venture financing patterns across countries. Using modern empirical tools—including machine learning–based text analysis, innovation network mapping, and causal inference methods—the project seeks to answer several core questions:

How is AI changing the rate and direction of startup formation across different industries and regions globally?
In what ways do AI capabilities affect founders’ skill composition, team structure, and early strategic choices?
How does AI adoption influence venture capital investment behavior, fund performance, and screening efficiency across markets?
What new geographic or sectoral clusters of AI-native startups are emerging worldwide, and what do these patterns imply for innovation and economic development?

The broader goal is to provide an evidence-based understanding of how AI is reorganizing entrepreneurial activity at a global scale—lowering (or reshaping) barriers to entry, transforming comparative advantage, and altering the competitive dynamics of innovation.

This role offers a unique opportunity to participate in a frontier research agenda at the intersection of AI, innovation, and entrepreneurial finance, and to build practical research skills that are increasingly essential in modern empirical work, especially for students considering PhD studies or careers in data-driven research.

Requisite Skills and Qualifications:

The research assistant will work on tasks including data collection and cleaning, constructing novel measures of AI-related innovation, conducting empirical analysis in Stata/Python, reviewing relevant academic literature, and assisting in preparing research presentations and manuscripts.

Beyond strong quantitative and organizational skills, the project will particularly benefit from candidates with experience—or a strong interest—in AI-related tools and methods, such as large-scale text analysis, embedding models, LLM-based classification, NLP pipelines, and basic machine learning techniques for prediction and clustering.