Startups that advertise AI or sustainability find it harder to raise money in Europe, and firms that emphasize both suffer the biggest funding shortfall; investors appear to penalize these signals rather than reward them.

Main Finding

Startups in Europe that signal the use of AI systems receive smaller funding amounts on average; startups signaling a sustainability focus also face fundraising penalties; and startups that signal both AI and sustainability encounter the largest negative association with funding. Results are robust to multiple specifications.

Key Points

• Signaling perspective: Investors interpret AI and sustainability signals when evaluating startups; these signals appear to increase perceived uncertainty or risk.
• AI-only signaling is associated with lower funding sizes.
• Sustainability-only signaling shows a similar negative relationship with funding.
• AI + sustainability combined leads to an even stronger negative association with funding size than either signal alone.
• Robustness checks and alternative model specifications were performed and support the main conclusions.

Data & Methods

• Empirical approach: The study uses a signaling theory lens and econometric models to estimate the relationship between firm signals (AI, sustainability, both) and funding size.
• Outcome variable: startup funding size (amount raised).
• Key independent variables: observable signals of AI integration and sustainability focus (operationalized from firm descriptions, product/industry tags, or other firm-level indicators).
• Controls: standard observable covariates (e.g., firm age, sector, team characteristics, country/region) to isolate the association between signals and funding.
• Estimation strategy: regression-based analysis with multiple specifications; robustness checks and alternative model formulations are used to test sensitivity of results.
• Limitations noted by the authors (implicit from methods): potential measurement error in signal proxies, omitted variable bias or endogeneity (investor preferences and unobserved quality), and generalizability constrained to the European startup ecosystem.

Implications for AI Economics

• Investor behavior: Investors may penalize perceived technical or strategic complexity (AI, sustainability) in early-stage funding decisions, suggesting risk/ambiguity discounting for these signals.
• Strategic signaling for entrepreneurs: Startups should be mindful of how they present AI and sustainability—investors may require clearer, lower-uncertainty evidence of market traction, business model viability, or technical maturity to justify larger investments.
• Market formation and specialism: The findings point to potential demand for specialized investors and intermediaries (e.g., VCs with AI or green expertise) who can better assess and value these combined propositions.
• Policy implications: If AI + sustainability startups are systematically underfunded, public policy (grants, blended finance, targeted incubators) could correct market frictions and support socially valuable but perceived-risky ventures.
• Research directions: Need for causal identification of investor penalties (e.g., instrumental variables, natural experiments), heterogeneity analysis by sector and investor type, and exploration of mechanisms (perceived risk, greenwashing concerns, longer time-to-profit).