Investors in India are increasing due-diligence standards for artificial intelligence startups as the country's AI boom accelerates [1, 2].
This shift indicates a transition from speculative investing to a risk-mitigation phase. By prioritizing technical infrastructure over marketing narratives, investors aim to ensure long-term viability in a volatile tech landscape.
Funding decisions are no longer based solely on a compelling AI story. Instead, venture capitalists and investors are evaluating startups based on specific technical and legal criteria, including AI governance and data ownership [1, 2]. Model performance and cybersecurity have also become primary benchmarks for securing capital [1, 2].
The trend is emerging as the rapid growth of the sector prompts a need for more rigorous oversight. Investors are focusing on how companies manage their data and the security protocols they implement to protect intellectual property, a move designed to prevent systemic failures as these companies scale.
Founders, such as Karan Bhatty of Millow, are navigating this new environment where technical scrutiny is the baseline for investment [1, 2]. The focus on governance suggests that the initial wave of enthusiasm for generative AI is being replaced by a demand for sustainable and secure business models.
This rigorous approach to funding reflects a broader global trend toward stability in the AI sector. By tightening the requirements for entry, investors are forcing startups to address critical vulnerabilities in their architecture before they reach the mass market [1, 2].
“Investors are tightening due-diligence standards and funding AI startups based on factors such as AI governance.”
The shift toward stricter due diligence in India suggests the AI market is maturing. Moving away from 'hype-based' funding toward metrics like data ownership and cybersecurity indicates that investors now view AI not just as a growth opportunity, but as a sector with significant operational and legal risks that must be managed to avoid catastrophic failure.
