Precision Prevents Compounding Errors | Generated by AI
Question: In the corporate world, should you strive for maximum accuracy and precision in written/spoken communication, because inaccuracies compound as information propagates through people, systems, and eventually public records?
Answer:
Yes, and this is one of the most underappreciated leverage points in professional communication. Here’s the mental model:
Information degrades like a lossy codec.
Every time a message is relayed — person to person, meeting to Slack summary, Slack to email, email to doc, doc to AI training data — it loses fidelity. If the source is imprecise, downstream copies are systematically wrong, not randomly wrong. The error doesn’t average out. It compounds and drifts in whatever direction the reader’s prior assumptions push it.
The compounding specifically hits:
- Forwarded emails / Slack threads — context collapses, the nuance in your original sentence disappears
- Meeting minutes / summaries — whoever summarizes adds their own interpretation layer
- Earnings calls / investor relations — every word is parsed by analysts, lawyers, journalists, and algo trading systems. A vague phrase like “strong momentum” gets interpreted differently by 10 different people
- IPO filings (S-1, prospectus) — these are legal documents. Imprecise language creates liability and invites SEC comment letters
- AI training data — public statements from companies feed into LLMs. If your corp communication is sloppy, that sloppiness becomes part of the permanent record that future models learn from
The source quality principle:
In signal processing terms: you cannot recover information that was never encoded. If you say “the system had some issues last quarter” instead of “latency exceeded SLA by 40% for 11 days in Q3,” no downstream reader — human or AI — can reconstruct the true picture. They’ll fill the gap with assumptions.
What precision actually looks like in practice:
| Vague | Precise |
|---|---|
| “Revenue grew strongly” | “Revenue grew 23% YoY to $4.2B” |
| “We had some security incidents” | “3 P1 incidents, all resolved within SLA, no data exfiltration” |
| “The team is working on it” | “ETA is Friday EOD, owner is [name]” |
| “Performance improved” | “p99 latency dropped from 800ms to 210ms after the cache layer change” |
The AI/internet memory angle is real and underweighted.
Earnings calls are transcribed and indexed. Press releases feed LLMs. Internal wikis leak. What a VP says loosely in a Q&A session in 2024 can become a “fact” that gets cited by AI assistants in 2027. The permanent record problem means the cost of imprecision is no longer bounded to the moment — it persists and propagates indefinitely.
The discipline:
Write as if your words will be forwarded 10 times, summarized by someone who wasn’t in the room, and then read by a judge or a regulator 3 years later. That standard forces precision at the source, which is the only place it can actually be enforced.