DePIN network developer Verida has successfully raised $5 million in seed funding to tackle the growing concerns over user data privacy in the era of Big Tech and generative AI. The funding round saw participation from notable investors including O-DE Capital Partners, ChaiTech Ventures, Simurg Labs, Gate Labs, HASH CIB, Bison Capital, Amesten Capital, and Evan Cheng from Mysten Labs. Additionally, part of the capital was secured through community pre-sales of the Verida Storage Credit token.

The infusion of capital will be utilized to develop alternative data storage solutions aimed at internet users who wish to protect their data from being exploited to train AI models. "Verida is poised to disrupt the AI landscape by putting users back in control of their data, unlocking more secure, privacy-preserving personalized AI for everyone," a Verida representative stated.

With this latest funding round, Verida's valuation has climbed to $50 million. The company plans to use the fresh capital to enhance its infrastructure for personal data ownership. Verida’s network will offer decentralized storage and encryption services for users' personal information, ensuring data is distributed across numerous nodes managed by different network participants. This approach aims to enhance security and privacy, giving users greater control over their data.

The rise of the AI market has heightened concerns regarding the use of personal data to train language-learning models. Recently, social media giant Meta announced its intention to leverage data from its European users to improve its AI models. Similarly, X (formerly known as Twitter) updated its privacy policy last year to indicate that it would use users' biometric data for AI training purposes.

Verida’s efforts to address these concerns by developing a robust personal data ownership infrastructure represent a significant step towards protecting user privacy in an increasingly AI-driven world. By offering decentralized storage and encryption, Verida aims to provide a secure alternative for users who are wary of their data being utilized without consent in the training of AI models.