The Velocity Paradox
When Thinking Becomes Faster Than Building
The Bottleneck Has Moved
For the first time in my career, I’m generating bugfixes faster than I can test them.
Not “thinking of” fixes. Not “designing” solutions. Actually generating concrete, implementable code changes. The AI coding assistants do it in minutes. But then I wait 10 minutes for Docker to rebuild. And another 10 for the next fix. And another.
This is not a Docker problem. This is a phase transition.
The Traditional Development Rhythm
Software development has always had a natural rhythm:
Think (minutes to hours): What needs to change?
Code (hours to days): Implement the change
Verify (minutes): Does it work?
The bottleneck was coding. We optimized everything around making implementation faster: better IDEs, code completion, frameworks, libraries, Stack Overflow.
The build/test cycle? That was just overhead. Ten minutes to rebuild a container? Annoying but negligible compared to the hours spent coding.
The AI-Assisted Reality
With AI coding assistants:
Think (minutes): What needs to change?
Code (minutes): AI implements it
Verify (minutes): Does it work?
Suddenly those 10-minute builds aren’t overhead anymore. They’re 30-50% of my development cycle.
I am coming up with bugfixes faster than I can build and test them.
Why This Matters
This isn’t just about Docker being slow. It’s about what happens when we remove the traditional “breathing room” from development.
Waiting for compilation wasn’t just waste. Those pauses were when you’d:
Notice you’d made a logical error before committing it
Let the design marinate and realize a better approach
Catch systemic issues before they propagated
Context-switch and come back with fresh eyes
AI removes those forced pause points. You can generate solutions faster than you can evaluate them.
The New Constraint
The bottleneck is now human verification.
AI can generate code. AI can even write tests. But integration - confirming that all the pieces work together in the actual system - still requires building, deploying, and observing real behavior.
I’m a single integration point. The AI can produce in parallel (conceptually), but I must serialize verification. That’s the constraint.
What This Means
This is new territory. We’ve optimized software development for decades around the assumption that implementation is the expensive part.
What happens when implementation becomes nearly free, but verification stays expensive?
Some possibilities:
Better testing infrastructure becomes critical - Can AI help verify fixes without full rebuilds?
Parallel verification environments - Test multiple changes simultaneously
Higher tolerance for technical debt - Document designed-but-unverified fixes, batch test later
Different development patterns - Maybe we need more upfront design because implementation is so cheap
AI-assisted integration testing - Can AI help validate system-level behavior?
The Deeper Question
When thinking becomes faster than execution, what’s the human’s role?
Not “writing code” anymore - AI does that faster. Not “knowing syntax” - AI handles that. Not even “debugging” in the traditional sense - AI can spot logic errors.
Integration. Judgment. System-level reasoning.
The human becomes the orchestrator, the validator, the one who can hold the entire system in their head and say “yes, this change makes sense in context” or “no, this will break something three layers away.”
This is what 10-100x productivity looks like in practice. Not just faster coding. A fundamental restructuring of where humans and AI focus their effort.
This is part of an ongoing series exploring what actually changes when AI can write code at human level. Not hype, not doom - just observations from the trenches.
I’d push back a bit on the idea that the human who “holds the whole system in their head” should be the final integration point. In distributed systems / CI-CD, that’s a single point of failure.
That role is powerful for direction and systems thinking — but velocity at scale comes from an iron wall of automated tests. Once critical flows are covered, “tests pass” becomes a much better proxy for “safe to ship” than any one person’s judgment.
As a PM, BDD was the unlock for me. I could define behavior without over-prescribing implementation, and just write Gherkin for both developers and testers in one shot.
Feels like there’s a BDD++ opportunity here: a syntax that feeds both a coding agent and a test agent — handling deterministic cases directly, and expanding into compiled evals for fuzzier behavior.