Foundation models in pathology are failing not due to size or training duration but because they are built on flawed assumptions about data scalability and generalization. Clinical performance has plateaued, as models struggle with variability across institutions and real-world applications, highlighting a need for task-specific approaches instead of generalized solutions. Alternative methods, like weakly supervised learning, have shown promise in achieving high accuracy without the limitations of foundation models.

Apple has introduced the Foundation Models Framework at WWDC, enabling developers to integrate powerful on-device AI capabilities into their apps. This framework emphasizes privacy and offline functionality, allowing applications like education and outdoor apps to utilize Apple's AI without incurring inference costs. Developers can start testing the framework today through the Apple Developer Program, with a public beta expected next month.

Mark Zuckerberg announced the establishment of Meta Superintelligence Labs (MSL), which will focus on advancing AI superintelligence and be led by key hires including Alexandr Wang and Nat Friedman. The initiative comes as part of a significant investment into AI talent, aiming to enhance Meta's capabilities in developing advanced foundation models and products.

Apple's Foundation Models framework, introduced with iOS 26, empowers developers to create innovative, privacy-focused AI features for apps, enabling offline functionality and cost-free AI inference. Apps across health, education, and productivity are harnessing this technology to enhance user experiences, personalize interactions, and improve data management, all while ensuring user privacy.