At some point, speed became the goal.
I streamlined prompts. I cut review time. I trusted first outputs. The faster AI moved, the more efficient I felt. On paper, everything improved.
In practice, accuracy quietly deteriorated.
Speed changed what I paid attention to
When AI got faster, I read less carefully.
Outputs arrived instantly, and I treated that immediacy as reliability. I skimmed instead of analyzed. I checked tone instead of logic. Errors didn’t disappear — they blended in.
Speed shifted my attention away from substance.
Faster workflows amplified small mistakes
AI doesn’t fail loudly.
Small inaccuracies, weak assumptions, or missing context often sound reasonable. When workflows are optimized for speed, those issues slip through unchecked.
What started as minor inaccuracies accumulated into flawed conclusions. By the time problems surfaced, tracing them back was difficult.
Optimization reduced verification
To go faster, something had to give.
That something was verification.
I skipped fact-checks. I stopped stress-testing reasoning. I trusted structure over substance. The system worked — until it didn’t.
Speed made accuracy optional. And optional accuracy doesn’t last.
The illusion of efficiency was convincing
Everything looked productive:
- More output
- Shorter turnaround times
- Fewer perceived blockers
But much of that efficiency was cosmetic. It optimized delivery, not correctness. When scrutiny increased, confidence collapsed.
Accuracy required deliberate friction
The fix wasn’t slowing everything down forever.
It was reintroducing intentional friction:
- Reading outputs fully
- Questioning assumptions
- Comparing results against reality
- Making edits instead of regenerating
Those pauses restored accuracy without destroying efficiency.
Why speed must follow accuracy, not replace it
AI speed is powerful — but only when accuracy leads.
When speed becomes the priority, judgment erodes. When accuracy sets the standard, speed becomes a multiplier instead of a liability.
This is why learning approaches like those emphasized by Coursiv focus on control before acceleration.
Because in real work, fast wrong answers are worse than slow correct ones.
And accuracy, once lost, is much harder to rebuild than speed.
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