Oct 25, 2026

Why Frontier Models Fail as Buy-Side Analysts - Janie Gu, Google DeepMind & Suyeol Yun, LinqAlpha

Janie Gu of Google DeepMind and Kaggle explains why most AI benchmarks are unreliable: they don't reflect real work, they are costly to keep current, and their scores are hard to verify. Drawing on a reproduction of 65 industry benchmarks, she details the failure modes and how Kaggle is building auditable, containerized evaluation infrastructure. Suyeol Yun of LinqAlpha then presents MandateBench, which tests whether models can carry out buy-side equity analyst work from a fund mandate to a full investment memo.

Oct 24, 2026

Proving Alpha: How a Hedge Fund Measures AI's ROI - Hari Kumar, Contour Asset Management

How Contour Asset Management, a $6B fundamental long/short technology, media and telecom fund, measures the return on AI spend through live A/B tests of analyst research, a cost per useful insight metric, timing agents that resurface old ideas, and AI workflows across trading, operations and compliance.

Oct 23, 2026

Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca

Arca, an AI-native financial services firm, runs its client business through long-horizon agents that own goals end to end across days of stop-start work. Aman Singh, who leads AI there, explains the signal system, operational context, harness, persistent memory, and shared spaces that let agents pull in people at the right moment, and why steering agents becomes the way to steer the business.

Oct 20, 2026

The World Wild Web & Bots With Buying Power

This episode asks whether a model described as dangerously capable is a real security problem or a marketing narrative. It uses the Astra sandbox incident as a starting point, then covers how agents are moving from chat into the browser to book, file, draw, and shop, and what that shift means for payments, advertising, and merchant data.

Oct 19, 2026

What Is Loop Engineering? Building Better AI Agents

Martin Keen explains loop engineering as the next step beyond prompt engineering: designing systems where AI agents act, observe, adjust, and repeat toward verifiable goals. The discussion covers goal design, triggers, context and persistent state, tools via MCP and APIs, verifier subagents, and why humans must remain in the loop to manage comprehension debt, intent debt, and cognitive surrender.

Oct 18, 2026

Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS

Nick Nisi and Zack Proser from WorkOS explore how to move "Beyond Done" with AI agents, ensuring their work is truly complete and reliable. They demonstrate building robust verification loops using custom CLIs, browser tools, and LLM judges, allowing agents to self-check and self-correct, preventing costly mistakes and accelerating development velocity for a technical audience.