Iterate Through Failure
Everyone knows SpaceX fails fast. Blow up rockets. Learn. Iterate.
What most don't know: How close it came to not working.
2006. Falcon 1 exploded 25 seconds after launch. Fuel leak.
2007. Second launch. Rocket tumbled out of control. Stage separation failure.
2008. Third launch. Collision during stage separation. Another explosion.
Elon Musk warned the team: If the fourth launch fails, SpaceX faces bankruptcy.
We say avoid failure. Actually, iterate through failure.
September 2008. Fourth launch succeeded. First privately funded rocket to reach orbit.
Each explosion wasn't just a loss. It was data.
Engineers analyzed thrust ratios. Modified fuel mixtures. Recalculated every variable.
Each failure built better models.
Better models enabled faster fixes.
Faster fixes meant smarter subsequent attempts.
The cycles accelerated.
Fast-forward to today: Falcon 9 has a 99% success rate. Two failures in 232 launches.
The mechanism: Cycle velocity compounds. Each iteration doesn't just teach you one thing. It builds the mental models that make the next iteration faster and smarter.
Three failures in two years taught SpaceX more than a decade of careful planning could have.
The learning didn't add up. It multiplied.
Startup ships daily, enterprise ships quarterly? The startup isn't just moving faster. They're learning 12x faster, which builds models that make them learn even faster the next month.
Writer publishes 200 okay posts vs perfecting 5 great ones? The volume isn't the point. The 200 cycles of feedback create pattern recognition the 5-post writer never develops.
Your competitor plans carefully to avoid mistakes.
You iterate rapidly through them.
They're optimizing for first-time correctness. You're optimizing for learning velocity.
Five years later, they're still planning. You've run 500 cycles and your models are so refined you look like a genius.
You're not smarter.
You're faster.
What would change if you optimized for iteration speed instead of avoiding failure?