Access to High-Value Data Sets and Adopting Proprietary or Open-Source Models

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Access to high-value private data sets is crucial for training generative AI models, but organizations with limited capabilities in building software often possess the most desirable data. This leads to a debate on whether proprietary or open-source models will prevail in the future. The dilemma arises as organizations with the best data may opt for proprietary solutions rather than developing open-source models. Determining the adoption route in this context remains challenging, as organizations aim to leverage their rich data effectively.

George Mathew is a Managing Director at Insight Partners where he invested in Weights & Biases, Jasper, and others.

He has over 20 years of experience developing high-growth technology startups including most recently being CEO of Kespry.

George joins the podcast to talk about his path to becoming an investor, his data-first thesis about investment, the AI business landscape, his book recommendations, and more.

Starting her career as a software developer, Jocelyn Houle is now a Senior Director of Product Management at Securiti.ai, a unified data protection and governance platform. Before that, she was an Operating Partner at Capital One Ventures investing in data and AI startups. Jocelyn has been a founder of two startups and a full life cycle, technical product manager at large companies like Fannie Mae, Microsoft and Capital One. Follow Jocelyn on LinkedIn or Twitter @jocelynbyrne.