Code Was Never the Unit of Value
AI Isn’t Changing Software Engineering — It’s Revealing What It Always Was
Anthony Mendonca recently wrote a thoughtful article titled “The Code is Dead, Long Live the Craft.”
His article captures something important about the moment we’re in.
But I think the shift runs even deeper.
The real change isn’t that code is disappearing.
And it’s not that code is becoming less important.
The truth is simpler.
Code was never the unit of value.
It only appeared that way because software was historically opaque, specialized, and difficult to commoditize.
For decades, writing software required rare expertise. Systems were complex. Tooling was immature. Feedback loops were slow.
That created a kind of professional insulation.
If you couldn’t understand the code, you had to trust the people who wrote it.
And because of that, organizations often treated lines of code, complexity, and engineering effort as proxies for value.
But those were always proxies.
Customers never bought code.
They bought:
Code was simply the artifact required to produce those outcomes.
AI isn’t changing where value lives.
It’s simply making that reality impossible to ignore.
My First Lesson in Software Engineering
I’ve been programming for more than 45 years since I was 5 years old.
My father believed computers were tools for productive work, not entertainment. If I wanted to use our Apple II for games, I had to write them myself.
So I did.
At five years old I started learning BASIC and assembly to build simple games.
I didn’t realize it at the time, but that experience taught me something fundamental:
The code was never the point.
The goal was the game.
The code was simply the artifact necessary to produce the outcome.
That lesson has never stopped being true.
Learning Lean From the Source
Later in my career I had the unusual privilege of working closely with Shingijutsu Consulting, widely regarded as the world’s foremost experts in the Toyota Production System (TPS).
While working at Boeing—first as an aerospace engineer and later as a lead software engineer—I spent about a year working full-time as a simultaneous interpreter for Shingijutsu’s Japanese consultants.
Being fluent in Japanese allowed me to translate directly between Toyota veterans and Boeing engineering teams.
It was an extraordinary education.
For a time I also served as an AIW (Accelerated Improvement Workshop) facilitator, helping bring Lean thinking into engineering organizations.
Watching Toyota-trained practitioners analyze complex systems was eye-opening.
Their perspective was relentless and simple:
Where is the value?
And equally important:
Where is the waste?
That lens has shaped much of how I think about engineering systems and leadership. I’ve written about related themes around intentional system design and leadership here:
Lean’s Brutally Simple Definition of Value
Lean thinking defines Value-Added (VA) work very precisely:
Anything that changes the fit, form, or function of a product in a way that a customer would willingly pay at least one penny for.
Everything else is Non-Value-Added (NVA).
That definition is brutally clarifying.
Every meeting. Every email. Every status report.
All NVA.
If you can’t go back to your customer and say:
"We had a fantastic internal meeting today."
…and they respond:
"Great — let me pay you for that."
Then by definition, the meeting is not value-added.
That doesn’t mean it’s useless.
Some NVA work is necessary.
Planning. Coordination. Architecture alignment. Progress checks.
Lean calls this Necessary NVA.
But a large portion of NVA is something else entirely.
Waste.
Toyota calls that muda.
The Math Is Brutal
In most traditional organizations the distribution looks roughly like this:
A familiar pattern.
It’s essentially a power-law distribution.
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Inside that 80% NVA bucket:
Which leads to a startling implication.
If you simply eliminated the waste—nothing else—the math looks like this:
Monday and Tuesday: Necessary NVA work. Wednesday: Value-Added work. Thursday and Friday: nothing at all.
You would deliver exactly the same customer value.
At 40% lower cost.
That’s the power of eliminating waste.
AI Is Compressing Non-Value-Added Work
For decades software engineering required enormous amounts of NVA work:
Important work.
But rarely where customer value actually lives.
AI is rapidly compressing these categories.
Large language models can now generate:
in seconds.
Which forces a new question.
If AI eliminates large portions of NVA work…
Where do engineers create value now?
Engineers Become Systems Designers Again
The best engineers were never just coders.
They were systems thinkers.
AI simply makes that reality visible again.
The role increasingly becomes:
In other words: SYSTEMS ENGINEERING.
Because once you understand value creation, engineering stops being about writing code.
Truthfully, it never really was—any more than building a house is about hammering nails.
It becomes about designing systems that reliably produce outcomes customers are willing to pay for.
Discipline Matters More, Not Less
Ironically, the rise of AI makes disciplined engineering practices more important, not less.
Practices like:
These practices aren’t academic ideals.
They are how you build environments where AI agents can operate safely and productively.
Without structure, AI simply produces chaos faster.
Working With AI Agents Feels Familiar
If you’ve ever led an engineering team, working with AI agents will feel remarkably familiar.
The pattern is almost identical.
You:
The difference is speed.
Instead of sprint cycles measured in weeks, you may run dozens of iterations in a single day.
Toyota Already Solved This Problem
The deeper lesson from Toyota was never about manufacturing.
It was about systems thinking.
Design systems where:
AI doesn’t invalidate those principles.
It amplifies them.
Which means the engineers who will thrive in this new era will not be the ones who write the most code.
They will be the ones who best understand:
The Future Engineer
The future engineer looks less like a programmer and more like a systems architect directing teams of human and machine collaborators.
You describe outcomes.
You build environments.
You assemble tools.
You design feedback loops.
You guide systems toward measurable results.
Code still exists.
But it is no longer the center of gravity.
It never really was.
Shout out to Anthony Mendonca for his recent post at https://medium.com/@anthonyjmendonca/the-code-is-dead-long-live-the-craft-6af1190710c6 that inspired my own article.