For a long time, we treated documentation as a hygiene issue.
Something you should do. Something that becomes important later. Something that doesn't really affect today's productivity.
AI made it obvious how wrong that assumption was.
When AI made us faster — and somehow slower
Six months ago, we introduced AI into our development workflow.
Everything sped up immediately:
- Code drafts appeared in seconds
- API specifications wrote themselves
- Refactoring suggestions were always available
- Every question got a confident answer
On paper, productivity skyrocketed.
But in practice, something strange happened.
We spent more time re-reading documents than writing them.
Decisions that felt settled kept resurfacing in later conversations.
Team members hesitated: "Wait, which version is correct?"
We weren't slower at producing output.
We were slower at deciding.
That's when it became clear:
Documentation wasn't a side task. It was the bottleneck all along.
AI didn't create the problem. It exposed it.
The incident that changed everything
Three months in, we hit context size overflow. When AI tried to refactor a core module, it couldn't hold everything in memory.
So we started deleting old docs.
Then we realized: we couldn't tell which ones were safe to delete.
Some had context that explained why we chose approach X over Y. Some were half-finished, but nobody remembered if that was intentional.
The AI had been rewriting documentation to "keep it clean." We'd been accepting those changes because they sounded better.
But each rewrite erased context. Each simplification removed constraints we'd forgotten about.
We had been moving fast, but we'd lost the ability to look back.
The real cost of bad documentation
Bad documentation doesn't just mean outdated text.
It means:
- No clear source of truth — multiple docs claim authority
- Silent overwrites — past decisions vanish without trace
- Context stripped from conclusions — "We decided X" without "because Y was impossible"
- Responsibility blurred over time — who decided this? who can change it?
Before AI, this damage accumulated slowly. Humans compensated with memory, hallway conversations, and institutional knowledge.
AI removed that buffer. It treats all documents as equally authoritative. It can't guess which decision was provisional, or which constraint no longer applies.
So the hidden cost surfaced immediately:
Decision friction is the most expensive form of inefficiency.
Speed is not productivity
Most productivity tools optimize for output speed.
AI is extremely good at that.
But speed without boundaries creates a new kind of waste:
- Confident answers that ignore critical context
- Solutions that quietly expand scope beyond original intent
- Decisions that appear final but were never validated
- Documentation that looks complete but isn't usable three months later
Real productivity isn't about generating more artifacts.
It's about:
- Reducing rework — not doing the same decision twice
- Making decisions safer — knowing what you can change
- Lowering cognitive load — trusting your docs
- Preserving intent over time — understanding why, not just what
And documentation sits at the center of all of this.
What we changed: documentation as a system, not text
We stopped treating documentation as "content" and started treating it as infrastructure.
Infrastructure that:
- Preserves context, not just conclusions
- Tracks responsibility, not just results
- Makes uncertainty explicit instead of hiding it
- Refuses silent changes — every edit is visible
- Stops when judgment is required
Some concrete shifts we made:
Every document now has a lifecycle
Active / Deprecated / Archived — never deleted, only transitioned
Past decisions are never overwritten
When a decision changes, we mark the old one deprecated and write a new one. The history stays visible.
Assumptions are labeled as assumptions
We tag claims as [Explicit], [Inferred], or [Assumed].
AI manages structure and history
It can reorganize, summarize, format—but it cannot decide.
This wasn't about controlling AI. It was about rebuilding trust.
The most productive thing our AI does
Sometimes, our AI does nothing.
It stops and says:
- "This change would affect decisions outside current scope. Human review required."
- "This document conflicts with an earlier constraint. Which takes priority?"
At first, this felt like friction. We wanted AI to solve problems, not create more questions.
But over time, the effect was undeniable:
- Fewer emergency reversals of "completed" work
- Cleaner decision boundaries
- Faster progress overall
The paradox became clear: Stopping early is faster than fixing later.
Why this isn't for everyone
If you want AI to:
- Decide for you
- Smooth over uncertainty
- Always move forward, no matter what
This approach will feel uncomfortable.
It assumes:
- Humans remain responsible for judgment
- Boundaries matter more than speed
- Productivity is cumulative, not immediate
It requires accepting that sometimes the fastest path is to slow down and clarify.
But if you've ever felt that AI made your system faster and more fragile at the same time—
If you've ever had to reverse a week of work because nobody could remember why a decision was made—
If you've ever asked "which version is correct?" and gotten three different answers—
Then you already know:
Documentation isn't a chore.
It's your productivity engine.
The shift
AI didn't break documentation. It finally made its importance impossible to ignore.
For years, we could get away with messy docs because humans filled the gaps.
AI exposed every gap. Every ambiguity. Every silent assumption.
It forced us to ask: What does it actually mean for documentation to work?
The answer wasn't about better writing. It was about better infrastructure.
Documentation isn't about describing what you did.
It's about preserving your ability to decide what to do next.
That's the problem AI made visible.
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