AI has dramatically lowered the cost of building software.
With agentic development, copilots, and increasingly capable models, it’s never been easier to go from idea to working implementation. You can explore solution space faster, test assumptions sooner, and ship proofs of concept in days rather than weeks.
That’s a genuine shift — and a powerful one.
But there’s a pattern I’ve seen repeatedly in startups (and honestly, in any business trying to solve something genuinely hard), and AI is accelerating it:
We mistake shipping for being ready to scale.
Complexity is often necessary — at first
If the problem you’re trying to solve is interesting, the path to a solution is almost always complex.
You explore dead ends.
You overbuild.
You introduce abstractions that feel clever at the time.
You make trade-offs you don’t fully understand yet.
You overbuild.
You introduce abstractions that feel clever at the time.
You make trade-offs you don’t fully understand yet.
That complexity is often required to get to a proof of concept. It’s how you learn where the real value actually lives.
The mistake comes after the solution works.
Once something is validated, teams often keep building on top of the proof, rather than through it.
And that’s where things quietly start to degrade.
The cost of skipping simplification
When you build on top of an unsimplified solution, you’re not just taking on technical debt in the narrow sense.
You accumulate:
- conceptual debt — “why does this exist again?”
- operational debt — “only two people know how this works”
- cognitive debt — “every change feels risky”
Each new feature has to negotiate with history instead of extending a clean core.
Velocity slows.
Onboarding gets harder.
Confidence drops.
Onboarding gets harder.
Confidence drops.
Eventually, even small changes feel expensive.
The missing phase: deliberate simplification
What’s often missing is a very intentional step after validation:
Strip the solution back to its simplest possible form while preserving the value it proved.
Amazon has talked for years about the idea of “invent and simplify” — not innovation that piles on complexity, but innovation that ends in something clearer, more durable, and easier to operate.
https://www.aboutamazon.com/about-us/leadership-principles
https://www.aboutamazon.com/about-us/leadership-principles
37signals describe a metaphor I’ve always liked: the wind tunnel test.
You take your V1 and put it under pressure.
Anything that flies off the edges probably wasn’t essential.
What remains is the epicentre — the thing that actually matters.
Anything that flies off the edges probably wasn’t essential.
What remains is the epicentre — the thing that actually matters.
This phase can feel uncomfortable. You delete code. You remove features you just finished. You challenge decisions that recently felt “right”.
But this is the moment where a system becomes scalable instead of fragile.
Why this matters more in the age of AI
This simplification step has always mattered. But AI makes it more important than ever.
I recently wrote about agentic development — using AI agents to plan, reason about, and implement software end-to-end:
https://world.hey.com/johnnybutler/agentic-development-the-shift-is-already-here-3fe1a512
https://world.hey.com/johnnybutler/agentic-development-the-shift-is-already-here-3fe1a512
Agentic tools are incredibly powerful. They let teams explore solution space faster than ever before and reach working implementations with much less friction
But there’s a trade-off.
Agentic development can introduce:
- known unnecessary complexity (extra layers, defensive abstractions, “just in case” logic)
- unknown unnecessary complexity (things that work, but no one fully reasoned about)
AI is excellent at helping us build.
Humans still have to decide what’s worth keeping.
Humans still have to decide what’s worth keeping.
Used well, agentic development actually reinforces the right pattern:
- explore boldly
- validate quickly
- then simplify ruthlessly
Used poorly, it accelerates the exact behaviours that slow teams down later.
A pattern I’ve learned to trust
The pattern that’s held up best for me looks like this:
- Allow complexity while discovering the truth
- Prove the value decisively
- Pause
- Simplify aggressively back to the core
- Only then scale, evolve, and extend
Skipping step 4 almost always shows up later — in delivery speed, system reliability, team morale, or all three.
Complexity isn’t the enemy.
Unexamined complexity is.
Unexamined complexity is.
"To simplify before you understand the details is ignorance, To simplify after you understand the details is genius."
AI helps you ship. Simplification is what helps you scale.