Linear Digressions

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

Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly doing the wrong thing, or looping endlessly without ever crashing in an obvious way. This episode tackles one of the thorniest problems in the agentic world: evaluation. If failure is hard to see, how do you measure it systematically? And how do you know when your agent is actually working?

Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and sometimes surprising ways AI agents can fail, from subtle reasoning errors to cascading task breakdowns. It's episode six in the show's ongoing season arc on AI agents, and failure modes turn out to be a surprisingly rich topic worth unpacking in detail.

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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