Summary In today's episode, I tackle Cooper's question about handling context drift and hysteresis in long-running persistent agent sessions, explaining why generative AI models cannot remember anything on their own. Here's what this means for you. You gain a clear framework for understanding why memory problems happen in AI agents and how external scaffolding solves…
Summary In today's episode, I explore which entry-level roles face full automation by the end of 2026 and why the answer depends heavily on your company's culture and business model. Here's what this means for you. You gain a clear framework for measuring your own exposure to AI and a practical strategy for staying indispensable…
AI tools are lazy. This should not be a surprise to anyone. After all, they’re trained on our work histories, our discussions, and our examples. They take the same shortcuts we do. They say something is done when it isn’t. They cut corners. Here’s a simple example: test coverage in coding. When you’re using an…
Whoever has the most, best ideas will win. Yesterday I shared a slide talking about how the distance from idea to done is getting shorter and shorter. Projects that took weeks can now take hours. Proofs of concept and MVPs can be generated over a meal. What this means is that if you are creative…
Summary In today's episode, I break down Trust Insights' six-level framework for measuring AI proficiency and explain why most companies are grading their teams against the wrong yardstick. Here's what this means for you. You stop comparing your staff to AI researchers and start mapping proficiency to the actual responsibilities of each role inside your…
I listened to no podcasts today on my drive. You’re probably like, “And?” Normally when I do long distance drives, I catch up on podcasts. Today, I didn’t. Today, I listened to the NotebookLM summaries of papers I needed to catch up on, advanced AI papers from NeurIPS that I didn’t understand well. Over the…
