Eka Sudarmaji

Type of the article: Research Article

Abstract
This study examines the funding gap between AI and non-AI startups using a cross-sectional dataset of 2,850 global startups drawn from Kaggle Public Domain. The sample was processed in four stages: text normalization, median imputation, outlier screening, and keyword-based classification. This produced 1,156 AI startups (40.6%) and 1,694 non-AI startups (59.4%), identified through keywords such as "Artificial Intelligence," "Machine Learning," "Deep Learning," "Natural Language Processing," "Computer Vision," and "Generative AI." The dependent variable was each company's last disclosed funding amount in millions of US dollars. Independent variables included AI classification (binary), founding year, employee count, market size in billions of USD, and industry dummy variables. The analysis used multivariable OLS regression with HC3 robust standard errors and Welch's t-tests across 15 industries. The results showed remarkably similar funding levels: $115.18 million for AI startups versus $117.98 million for non-AI startups. Regression analysis found no statistically significant relationship between AI classification and funding (β = 0.89, p = .869), with the model explaining 38.7% of funding variance. Employee count was the strongest predictor (β = 0.13, p < .001), while founding year and market size had no significant effects. These findings challenge the widely held belief that AI startups attract premium investment. As AI matures from a novel technology into standard infrastructure, its signalling power in venture capital markets appears to be fading. What matters most to investors is not a technology label, but how well a business is built and run.