CDL Research
Creative Destruction Lab (CDL) is a nonprofit organization that delivers an objectives-based program for massively scalable, seed-stage, science- and technology-based companies.
Research and data drawn from Creative Destruction Lab’s program captures one of the least understood phases of firm growth: early startup formation. It includes multi-year data on roughly 15,000 applicants, 9,000 founders, and 2,000 mentors across 27 advanced technology domains, from therapeutics to quantum computing. Purpose-built for research, the data link venture characteristics, longitudinal firm outcomes, and detailed mentor–founder interactions, enabling rigorous study of entrepreneurship, finance, advice, and technology transfer.
If you are interested in learning more about the CDL dataset, including data structure, research applications, and access to de-identified CDL data for academic use, it is necessary to submit an application. The application involves four main steps: (1) a research proposal submission, (2) ethics approval from the home institution of the Principal Investigator, (3) a Data Transfer Agreement (between the Principal Investigator and CDL), and (4) Non-Disclosure Agreements for all members of the research team accessing the data. The research proposal submission is accessible via this link. Additional information about the process can be obtained by clicking this link.
- “Information frictions and employee sorting between startups” (Bryan, Hoffman, & Sariri, 2026)
Would workers apply to better firms if they were more informed about firm quality? Collaborating with 26 science-based startups, we create a custom job board and invite business school alumni to apply. The job board randomizes across applicants to show coarse expert ratings of all startups’ science and/or business model quality. - “Database, Methodological Tools, and Research Opportunities: Creative Destruction Lab and Early-Stage Technology Ventures” (Sariri et al., 2024) (working paper)
We introduce a new dataset built from a global non-profit startup program for early-stage high-technology startups called Creative Destruction Lab (CDL). The early stages of startup formation remain one of the least understood aspects of firm growth. The nature of this program and the data collected from its operations are well suited to investigate open questions in entrepreneurial strategy, entrepreneurial finance, advice, and technology transfer. - “Business-to-Investor Marketing: The Interplay of Costly and Costless Signals” (Nyilasy et al., 2024)
Marketing to investors—especially when seeking funding for startups—is unique, with investors facing extreme uncertainty. This study uses foundational work in marketing, economics, management, finance, and psychology, as well as theories-in-use development with angel and venture capital investors, to build a business-to-investor marketing theory. The theory proposes that investors rely on marketing signals from startups, whether they are costly (financial, social, human, and intellectual resource endowments) or costless (verbal passion and concreteness). - “Enhancing Deep-Tech Innovation: An Equilibrium Analysis of Joint Ventures” (de Véricourt & Gurkan, 2024)
We investigate the innovation process within a joint venture that demands progress in both scientific and business domains. We analyze this setup in a game that captures the search decisions of two agents, one with expertise in science and the other in business, based on their initial ideas’ maturity, search costs, and the uncertainty associated with the innovation process. In contrast to existing literature, our equilibrium characterization reveals that the agents’ decisions to search are non-monotone in the maturity levels of the initial ideas. - “The Economics of Advice: Evidence from Startup Mentoring” (Sariri, 2024)
We investigate the innovation process within a joint venture that demands progress in both scientific and business domains. We analyze this setup in a game that captures the search decisions of two agents, one with expertise in science and the other in business, based on their initial ideas’ maturity, search costs, and the uncertainty associated with the innovation process. In contrast to existing literature, our equilibrium characterization reveals that the agents’ decisions to search are non-monotone in the maturity levels of the initial ideas. - “A structural model of mentorship in startup accelerators: Matching, learning, and value creation” (Nejad, 2024) (working paper)
Entrepreneurial success depends on reducing uncertainty about the quality of ideas and selecting effective strategies to bring the idea to market. Mentorship plays a critical role in this process. In this paper, I examine how mentorship improves entrepreneurial outcomes within the Creative Destruction Lab (CDL), a global mentorship-driven startup accelerator, through two channels: the direct effect of improving startup quality and the screening effect of identifying high-quality startups. Using mentorship interaction data from CDL, I apply machine learning algorithms to generate quantifiable measures of mentors’ advice. - “Early-stage venture financing” (Parra & Winter, 2022)
This paper develops a theory of venture financing at the earliest stages. Ventures choose between issuing equity or a “SAFE,” which gives investors the right to a number of shares to be determined by a future equity price. Our key assumption is that between two rounds of financing the market learns information that is initially private to the entrepreneur. - “A Taxonomy for Technology Venture Ecosystems” (Sako & Qian, 2021)
We develop a taxonomy – Oxford Venture Ecosystem Taxonomy (OVET) — to classify technology startup ventures along nine dimensions: (1) the area of work, (2) purpose of technology use, (3) technology stack, (4) platform business model, (5) type of clients, (6) value capture strategy, (7) founder and funder characteristics, (8) geographical footprint, and (9) funding cycle. This paper provides a theory and method for developing taxonomies, emphasizing the importance of clarifying the purpose for which a taxonomy is used and the determination of the appropriate level of abstraction. - “Venture Idea Assessment (VIA): Development of a needed concept, measure, and research agenda” (Davidsson et al., 2021)
To address challenges constraining prior research on evaluation of entrepreneurial projects, we develop the concept of Venture Idea Assessment (VIA) and validate an instrument to capture it. VIA concerns the assessment of Venture Ideas (VI) unbundled from assessment of any agents with whom they may be associated. The assessment can be performed by anybody at any stage of the venture development process, not just by potential founders at its outset. - “Market for Judgement: The Creative Destruction Lab” (Lakhani et al., 2019, Harvard Business School Case)
This case study examines Creative Destruction Lab (CDL), a university-based seed-stage accelerator founded by Professor Ajay Agrawal at the University of Toronto’s Rotman School of Management in 2012, which pioneered a unique model where experienced entrepreneurs collaboratively mentor science-based startups in an MBA-style setting. The case captures a pivotal moment at CDL’s first Super Session as Agrawal contemplates the program’s future expansion into new streams including space, health, cities, and blockchain-AI, raising questions about the scalability and sustainability of this unconventional approach to startup acceleration.