Aug 13, 2026

Why Old GPUs Keep Gaining Value

Steve Hou, Head of Research at Silicon Data, unpacks the data behind the AI compute market, revealing persistent tightness in GPU rentals and rising residual values, despite talk of oversupply. He discusses the methodologies behind their GPU price indices and forward curves, the evolving hardware landscape beyond Nvidia (including AMD, Cerebras, and TPUs), and trends in LLM token economics. The conversation also delves into the complex financial aspects of AI data center buildouts, the rise of specialized models, China's emerging AI hardware ecosystem, and the growing importance of power constraints and distributed AI infrastructure.

Aug 11, 2026

The RAG Mistake Almost Every Team Is Making (with Pete Johnson)

Pete Johnson, Field CTO of AI at MongoDB, discusses effective AI strategies, why most organizations struggle with AI ROI, and how to build reliable AI systems. He covers the importance of choosing the right embedding models for RAG pipelines, introduces Matryoshka embeddings, and explains the evolution of agentic memory to combat token maxing and ensure consistency in production AI.

Aug 08, 2026

CAN CHINA BEAT WAYMO?

This episode discusses three critical topics in AI: the true nature of recent AI agent "breakouts," arguing they highlight governance and security flaws rather than model danger; the role of AGI narratives in fueling the current AI investment bubble and questioning its sustainability; and China's aggressive strategy in the global robotaxi market, potentially outpacing Western counterparts like Waymo.

Aug 04, 2026

What Are Large Database Models? AI for SQL Data

Martin Keen introduces Large Database Models (LDMs), a new AI paradigm that brings advanced analytical capabilities directly into SQL and relational databases. This allows for semantic queries on the 99% of enterprise data traditionally locked away, enabling faster, more secure insights without costly data movement.

Jul 31, 2026

Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

Mahesh Sathiamoorthy of Bespoke Labs argues that high-quality data and curated RL environments are the true bottlenecks for post-training LLMs, especially for building reliable, autonomous agents. He grounds this in experiences with OpenThoughts, a reasoning dataset, highlighting counterintuitive lessons like the importance of diverse reasoning traces and the fact that stronger teachers aren't always best. A key takeaway, reinforced by their Curator tooling, is that a disciplined curation stack is essential for transforming base models into capable, post-trained agents for real-world applications like credit card compliance.

Jul 31, 2026

Decagon’s Playbook for Building Enterprise AI Applications

Jesse Zhang and Ashwin Sreenivas, co-founders of Decagon, discuss their company's transition to open-source models for enterprise AI, emphasizing how fine-tuned small models outperform frontier models on specific tasks. They delve into the role of application-layer companies in an AI-first world, their product-driven 'glass box' approach for enterprises, and the transformative power of their 'Duet Autopilot' agent, which builds other AI agents. The conversation also covers AI's impact on jobs, highlighting the Jevons Paradox in customer support.