Principles Oriented Thinking as a Durable Skill in an AI First World

The skills that survive every industry shakeup aren't the ones you can Google — they're softer, harder to name, and far more durable. In this episode, Jonathan explores principle-oriented thinking: the practice of stripping away the labels we attach to tools, roles, and even ourselves to see what something actually does at its core. It's the difference between handing your coding off to an agent and rethinking your entire workflow around what these new materials are truly capable of. If you've been following along with our recent focus on durable skills, you know we've been hunting for the abilities that translate beyond this month, this year, or whatever AI does to our industry next. Today's skill doesn't have a tidy name you can search for — it's softer than that. Jonathan calls it "principle-oriented thinking": the habit of deconstructing the labels we put on things to understand their core components, properties, and capabilities. It's how NASA engineers turned a sock into a water filter on Apollo 13, and it's how forward-thinking engineers are reframing what AI can actually do rather than jamming it into a predetermined slot. Labels Are Useful Shortcuts — Until They Aren't: Every label, from "software engineer" to "sock," carries baggage, heuristics, and presupposition. That's not a flaw — labels are how we move through the world quickly. But when a label is the only lens you have, it quietly caps how much value you can get out of the thing you're looking at. The Apollo 13 Sock: When the crew needed to fix a life-threatening problem with mismatched parts, the engineers on the ground had to forget what a sock was for and ask what it actually is — a piece of cloth with tensile strength, flexibility, and filtering properties. Strip the assumption that it goes on a foot, and a whole new set of uses opens up. Stop Slotting AI Into Old Roles: The common move is to take one responsibility — coding, debugging, refactoring — hand it to an agent, and keep everything else the same. That works, but it's low-leverage. The more powerful approach starts by asking what the agent is fundamentally capable of, then rebuilding the workflow around those raw materials. See Things as Materials, Not Fixed Functions: When you deconstruct out from under a label, tools and concepts start to look like craftable raw materials. You can then combine them in new, valuable ways they haven't been combined before — alloying old methods with new capabilities to create properties neither had on its own. Reason From Properties, Not Personas: Ask what the actual properties of an LLM are. Non-determinism isn't a bug to apologize for — it's a property you can exploit. The existence of many different models is a property too, which is exactly what makes adversarial review possible. That's principle-oriented thinking applied to agents. Extend the Latticework: Charlie Munger talked about a latticework of mental models that weave together rather than sit in isolation. The durable skill isn't quarantining your concept of "AI" off to the side — it's grafting a new section onto the existing tapestry and letting it reshape everything you already understood. Episode Takeaway: Look at how you spend your time and ask new questions of it. What is the material here? What kind of thinking does the agent actually do? What can a human do that an LLM can't — and the other way around? That's how you avoid believing a sock is only ever good for a foot. 🙏 Today's Episode is Brought To you by: Unblocked Your coding agents have access to your code, your repos, and probably a pile of connected MCPs filling up their context — but access isn't the same as good context, and a bloated context window can actually degrade the very reasoning you're relying on. Agents don't know your architectural decisions, your team's patterns, or why an API was shaped the way it was, so they look in the wrong place and deliver bad outputs you then spend time and tokens correcting. ● Unblocked is the smart context layer your agents are missing. ● Instead of ingesting everything and getting lost, it builds reasoning over shared context. ● It turns code, docs, tickets, and conversations into actionable context — so engineers move faster, agents make better plans and write higher-quality code, and you burn fewer tokens and fewer correction loops. If you're running Claude Code, Cursor, or any agentic workflow, it's worth a look. 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