Oct 11, 2026

Why every company now needs to think and operate like a lab team | Josh Woodward (VP Google Labs)

Josh Woodward, head of Google Labs, the Gemini app, and AI Studio, explains why every company needs to operate like a labs team in the AI era. He covers where great ideas really come from, the almost-possible framework, how to recognize product-market fit by watching people's eyes, when to kill an idea, the skills rising in value such as unlearning rate and explosive endurance, why roles are not collapsing into a single builder, and how to structure an internal lab that graduates ideas without being crowded out.

Oct 11, 2026

Building AI Agents with Real-Time Web Data — Yohan Raju, Bright Data

Yohan Raju of Bright Data shows how an AI agent can combine a live Amazon listing with Reddit sentiment to produce a purchase recommendation, arguing that agents need fresh, structured web data rather than relying only on pre-trained knowledge or pre-indexed retrieval.

Aug 30, 2026

SOTA Generative Media Panel — Dumitru Erhan, Shane Gu & Nicole Brichtova, Google DeepMind

This detailed discussion with Google AI researchers highlights the latest generative video models (NanoBanana 2 Lite, Gemini Omni Flash APIs) and their applications, including video editing, education, and localization. The conversation critically examines whether language is an adequate intermediate representation for AI, especially for sensory experiences like audio and taste, where human vocabulary is limited. It exposes the unreliability of human preferences in evaluating AI-generated content, citing the "Instagram filter effect" and "wedding ring" reward hacking as examples. The researchers emphasize the importance of high-quality, real-world data and the critical role of forward-deployed engineers in bridging the gap between cutting-edge research and practical applications.

Aug 17, 2026

Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI

Experiments with an AI tutor reveal that not compacting context often outperforms summarization, leading to better recall, lower cost, and faster responses, primarily due to LLM API prompt caching. Compaction invalidates the cache, making it unexpectedly expensive. While cloud environments benefit from full history, local hardware limits necessitate compaction, highlighting the need to prioritize actual constraints over default compaction strategies.

Aug 17, 2026

Reading Group July 2026 - Loop Engineering

This session provides an in-depth exploration of Loop Engineering, a paradigm shift from manual AI prompting to designing autonomous systems that orchestrate AI agents. Speakers share practical experiences, from building production-grade platforms with automated code generation and adversarial AI reviews to experimental loop structures and foundational infrastructure layers. Key discussions address challenges like managing token costs, preventing agent chaos, and implementing robust verification mechanisms for industrializing software development.

Jul 17, 2026

Research to Reality with Google DeepMind — Benoit Schillings, Google DeepMind, VP of Technology

Benoit Schillings, VP of Technology at Google DeepMind, explores the evolution of AI's role in software development, highlighting the transition from human-limited coding to an AI frontier where syntax generation is solved. He delves into the power of self-play for model training, the shifting economics of software engineering, and the imperative for active guardrails. Schillings also discusses the need for inductive architecture, advanced model planning, multimodal reasoning (as seen in Gemini), and the potential for AI to drive scientific breakthroughs in fields like chemistry and biology by uncovering patterns imperceptible to human bias.