Accuracy vs Simplicity

Subject matter experts often need to explain something they know deeply to someone who has to make a decision.

The details matter. The nuance matters. And accuracy often feels like the safest choice—especially when you care about being taken seriously. But accuracy and simplicity are often in tension.

As you add precision, caveats, and edge cases, the explanation becomes harder to follow. As you simplify, you start to worry about what you’re leaving out—and whether that omission will come back to bite you.

The mistake is treating this as a communication problem. The real challenge isn’t how to explain this accurately. It’s how accurate it needs to be for the purpose it’s serving. That depends on what you’re trying to enable:

• understanding vs completeness
• direction vs documentation
• decision vs defensibility

Those aren’t technical choices. They’re judgment calls.

It helps to be explicit about what you’re optimizing for before you start explaining. Naming what must be preserved, and what can be safely simplified, often makes the tradeoff clearer.

AI can help with that kind of thinking—not just by rewriting your explanation, but by helping you test it. You can ask whether something is clearer without a detail, what might be misleading if it’s removed, or how someone unfamiliar would interpret what you’ve written.

That doesn’t remove the tradeoff. It just makes it visible. Accuracy still matters. So do clarity and simplicity. The choice is deciding which one deserves priority this time and that is rarely obvious.