Linear Digressions

Summer break: back soon by Katie Malone

After a five-year hiatus, the podcast that burned out partly over the tedium of writing episode descriptions is back — and using AI agents to handle exactly that task. The season-11 finale turns the lens on the podcast itself, putting the AI agents built throughout the season to work on real production tasks. It's a fitting, self-referential close to a season spent dissecting how agents actually function — and a honest look at what they can (and can't) take off your plate.

What if building more highways made your commute *slower*? That's the paradox at the heart of AI agent economics: even as per-token inference costs have plummeted dramatically over the past two years, total LLM spending keeps climbing. Drawing on a surprising lesson from Robert Moses's mid-century New York infrastructure projects, this episode unpacks why cheaper compute doesn't necessarily mean cheaper AI — and what's really driving the economics of running agents at scale.

Capabilities get all the attention when it comes to AI agents — but what happens when a highly capable agent makes a bad decision in the real world? Trust, oversight, and control are the unglamorous but critically important flip side of the agentic AI story. This episode digs into the security concerns that emerge when you combine powerful models with real-world tool access, and why judgment (or the lack of it) might matter just as much as raw capability.

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Website: https://lineardigressions.com
Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323
Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT
Substack: https://substack.com/@lineardigressions

Whether you work best solo or thrive in a team, you know collaboration is complicated — and it turns out AI agents face the same tensions. This episode dives into multi-agent systems, exploring how networks of AI agents can overcome the individual limitations of a single model, and what the research says about when collaboration actually helps versus when it just adds noise. Think scaling laws, but for teamwork.

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Website: https://lineardigressions.com
Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323
Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT
Substack: https://substack.com/@lineardigressions