Sales teams often use terms like "champion" inconsistently. Companies can combat this and prevent AI hallucinations by using dedicated AI agents to analyze internal language. These agents build a company-specific dictionary, or "semantic model," to ensure consistent definitions for both humans and AI.

History shows that major breakthroughs are often preceded by someone who meticulously defines a problem, attracting solvers to it. However, society celebrates the solver, not the definer. Spending more time on precise problem definition is a powerful, yet under-appreciated, path to innovation.

Effective AI prompting is a high-level form of programming that requires a rich, specific vocabulary. Experts in fields like art history or software engineering can generate superior results because they can provide more precise instructions (e.g., specific styles, frameworks), making deep domain knowledge more valuable than ever.

AI curiosity involves individuals testing tools in isolation. AI fluency is a collective capability where teams share a common language, integrated workflows, and a foundational understanding of how AI drives strategy. This fluency is built through consistent, shared learning and processes.

Frameworks are not an innate way of thinking but a tool developed out of necessity. They arise when you must reteach or reuse a complex thought process so often that you create mental shorthand to avoid re-deriving the decision set every time. It's about crystallizing a process for scalability.

Linguist Anne Kerzan reframes jargon not as a flaw, but as a specialized lexicon for a profession. It provides useful shortcuts and creates a sense of shared identity for insiders. The negative perception arises from being an outsider or when jargon is used to obscure meaning, such as with corporate euphemisms.

As AI capabilities become commoditized, the key to superior output is the user's domain expertise. An expert with precise vocabulary can guide an AI to produce better results in one attempt than a novice can in many, because they can articulate the desired outcome more effectively.

Years of focusing on MVPs has weakened the ability of product teams to imagine magical, delightful features. AI prototyping tools make ambitious ideas easier to build, helping teams reignite their creative muscles and aim for awesome products, not just viable ones.

Rive deliberately uses correct engineering terminology like "view models" instead of simplified alternatives like "variables." This opinionated choice forces a common language, leveling up designers' technical understanding and improving collaboration by ensuring everyone works with the same concepts and constraints.