Knowledge as Infrastructure

The simplest form of force multiplication is sharing what you know. This has always been true in good organizations. But when what you know is how to work effectively with AI, the multiplier effect becomes concrete and measurable. When you figure out how to get a large language model to review code in a particular way, that's useful to you once. When you write it down as a prompt template and make it available to your team, it's useful to everyone. When you document not just the prompt but the reasoning behind it — what kinds of code it catches, what it misses, when to use it versus human review — you've built a piece of infrastructure.

This is not about being generous. It's about understanding that your time is limited and your team's time is limited, but a piece of written knowledge or a well-constructed template or a decision framework can be used by many people simultaneously. The return on investing your effort in creating that infrastructure is much higher than the return on using the same capability just for yourself.

What does this look like in practice? You develop a system for writing direction briefs that consistently produce better outputs from AI tools. You document the system and share it with colleagues. You refine it based on their feedback. Eventually, a document that took you a week to develop the first time becomes something the team uses in an hour, across dozens of projects. Your initial investment in thinking clearly compounds across the organization.

Or you figure out how to structure data in a way that makes it easier to verify AI output for factual accuracy in your domain. You share the checklist. People adopt it. The quality of what moves forward improves. And the thing that took you weeks to develop becomes a standard everyone uses.