All-You-Can-Eat: Can You Balance AI’s Appetite for Data & Privacy?

The data privacy diet is a fluctuating one, that varies across time, regions, and adapts to (tech) disruptions. Data has proven to be one of our most valuable assets today – one whose protection varies from a legislation to another. But as regulations grow stricter, a potential data wall might emerge, threatening smaller startups' competitiveness and raising questions about shifting focus to application layers rather than LLMs. From the user perspective, building ethical AI is crucial, as current models may pose significant dangers. Understanding the origins of training data is equally important—where this data comes from directly impacts individual privacy rights. Parallels with the social media industry and its challenges around privacy offer interesting lessons. How can we reconcile increasingly strict privacy regulations with data-hungry business models while avoiding past pitfalls?