In this episode of the Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Alex Dimakis, a renowned AI researcher and professor at UC Berkeley. With a background in deep learning, graphical models, and foundational AI frameworks, Alex provides unparalleled insights into the evolving landscape of AI. The discussion delves into the detailing of foundation models, modular AI architectures, fine-tuning, and the role of synthetic data in post-training. They also explore practical applications, challenges in creating reasoning frameworks, and the future of AI specialization and generalization. As Alex puts it, "To deep seek or not, that’s the $1 trillion question." Tune in to hear his take on how companies can bridge the gap between large generalist models and smaller specialized agents to achieve meaningful AI outcomes.