Reading Path 06 · Language as Infrastructure
Language is often treated as a layer placed on top of a system: naming, documentation, copy, terminology or prompts.
The writing develops a different view.
Names determine what teams can see and discuss. Product language shapes user expectations and trust. Metric definitions determine what can be compared. Instructions shape machine behavior. Capability contracts define what an AI system is permitted to do. Even text files can become executable operating artifacts.
This path follows that progression from language as representation to language as infrastructure.
The central question is not whether wording matters. It is what happens when words, definitions and instructions become part of the mechanism through which people and machines coordinate, interpret evidence and act.
Naming a dysfunction doesn’t solve it, but it makes it visible. Without shared language, teams treat symptoms, not systems: feature soup, strategy drift, prioritization theater. Name it to align, track patterns, and move from vague complaints to structural diagnosis and action.
Ghosts in the System: Why Naming Dysfunction Isn’t Fluff. It’s Infrastructure →That skateboard-to-car graphic isn't an MVP. It's a delivery roadmap. MVPs are for learning: the smallest test to validate riskiest assumptions. In complex orgs, call things correctly: MVP for learning, POC for feasibility, V1 for shipping. Clarity prevents waste and misaligned bets. Validate first.
It’s a Great Metaphor, But It’s Still Not an MVP →Words shape trust. Product language isn’t neutral: it trains mental models. “Friend” now means barely acquainted; “I’m thinking...” makes users over-trust AI. Audit copy, cut unearned metaphors, and test for over-trust. Tech runs on tokens; products run on words. Clarity is a trust contract.
When Words Lie: The Invisible Risk in Your Product →Instruction design turns a chaotic GPT into a reliable tool. Replace a 'don't do' list with a clear operating model: retrieve verbatim from the knowledge base, format consistently, handle exceptions, and test like software. Positive, explicit rules cut variance and improve UX.
From “Don’t Do” to “Do Well”: Designing Instructions That Make AI Useful →Teams don’t argue about numbers; they argue about definitions. Inconsistent metrics like MAU erode trust, stall decisions, and mislead AI at scale. Fix it upstream: build a Metric Dictionary with clear names, sources, formulas, and owners. One name, one definition.
Beyond the Dashboard | Principle 9: Reconcile Metric Definitions Before Analysis →Enterprise AI needs governance for prompts and instructions, not only models. A framework for ownership, evaluation and behavioral consistency.
Instruction Governance: The Missing Layer of Enterprise AI (Part 1 of 4) →Before deploying enterprise AI, define capabilities, boundaries, inputs and expectations. A practical capability-contract approach.
The Semantic Supply Chain: Capability Contract & Inputs →Further reading