Jun 18, 2026

The AI Frontier: from FLOPs to Megawatts — Anjney Midha, AMP

Anjney Midha unpacks the critical bottlenecks in AI scaling beyond just GPU acquisition, advocating for responsible infrastructure, community-aligned data centers, and an independent system operator model for compute. He discusses the perils of research hoarding, the rise of researcher CEOs, and how Anthropic's culture of "preparedness" and "output maxing" led to its success, while also highlighting his personal mission to use AI for precise end-of-life prediction.

May 22, 2026

Reflections of AI: A Trilogy in 4 Parts • Rasmus Lystrøm • GOTO 2025

In a talk styled as "A Trilogy in Four Parts", Rasmus Lystrøm critically examines the real-world impact of Generative AI, debunking productivity myths and highlighting hidden costs like degraded code quality and environmental strain, while advocating for a return to solving real user problems with valuable, often simpler, technology.

May 21, 2026

"Garbage In, Garbage Out" is a LIE

Terrence Lee-St. John, author of "From Garbage to Gold," challenges the "garbage in, garbage out" mantra. He presents a data-architectural theory explaining why models trained on noisy, high-dimensional tabular data can achieve robust predictive performance by focusing on recovering latent signals rather than exhaustive data cleaning.

May 20, 2026

Inference, not prediction — Prof. Michael I. Jordan on what modern AI is still missing

Michael I. Jordan, a leading figure in machine learning and statistics, argues for reframing AI from a race for disembodied superintelligence to the design of collective economic systems. He critiques the AGI hype, advocates for integrating economic principles and robust uncertainty quantification into ML, and proposes a new intellectual framework for building technology that augments, rather than replaces, human systems.

May 11, 2026

Predictive vs Generative AI: How They Work and When to Use Each

Predictive AI forecasts what will happen next based on historical data, while Generative AI creates new content by asking what something could look like. This summary explores their fundamental differences in outputs, data types, underlying models like transformers and diffusion systems, and how they can be used together in enterprise applications.

Apr 30, 2026

Robots Don't Need More Compute. They Need This.

Encord co-founders Eric and Ulrich discuss their $60M Series C, the company's origins before the AI hype, and their focus on building the essential data infrastructure for physical AI and robotics—the next frontier after LLMs.