Unsupervised Learning with Jacob Effron

Yash Patil, a former OpenAI researcher who worked on Codex and now runs Applied Compute, argues that post-training is the most overlooked part of the AI stack and the key to owning your intelligence. He explains when companies should optimize their harness and context first, and when it's worth updating the weights: the largest inference workloads, plus tasks that change over time or depend on judgment. That's the heart of why he believes post-training wins inference. The biggest workloads are both the most valuable to train and the most valuable to serve, and how a model is trained shapes how it should be served. Getting production serving running is the easier half; echoing Dylan Patel, he says GPUs plus vLLM plus OpenRouter "kind of" gets you there. Yash is candid about the limits of RL. It's a hill-climbing machine whose hardest part is defining the hill, which makes a company's private evals as worth protecting as its employees. It generalizes far less than pre-training. And continual learning remains blocked by the unsolved problem of extremely data-efficient training from sparse rewards. He lays out his bet on a new AI hyperscaler, built on GPUs the way AWS, GCP, and Azure were built on CPUs. He pictures it as an inverted pyramid with training at the bottom, where few can compete, and inference, routing, and harness stacked above. The conversation also covers whether every firm needs its own model, why Jevons paradox has made cost matter more than he expected, how models reward hack "like water," and why the US needs American open-weight models even though "blanket bans are not the answer."

This episode features Rob Toews from Radical Ventures and Ari Morcos, Head of Research at Datology AI, reacting to Andrej Karpathy's recent statement that AGI is at least a decade away and that current AI capabilities are "slop." The discussion explores whether we're in an AI bubble, with both guests pushing back on overly bearish narratives while acknowledging legitimate concerns about hype and excessive CapEx spending. They debate the sustainability of AI scaling, examining whether continued progress will come from massive compute increases or from efficiency gains through better data quality, architectural innovations, and post-training techniques like reinforcement learning. The conversation also tackles which companies truly need frontier models versus those that can succeed with slightly-behind-the-curve alternatives, the surprisingly static landscape of AI application categories (coding, healthcare, and legal remain dominant), and emerging opportunities from brain-computer interfaces to more efficient scaling methods.

This week on Unsupervised Learning, Jacob sits down with Nicole Brichtova and Oliver Wang, the Google researchers behind "Nano Banana" - the breakthrough AI image model that achieved unprecedented character consistency and took over social media. The conversation covers how their model fits into creative workflows, why we're still in the early innings of image AI development despite impressive current capabilities, and how image and video generation are converging toward unified models. They also share honest perspectives on current limitations, safety approaches, and why the expectation of going from prompt to production-ready content is fundamentally overhyped.