The End of Code as We Know It

Why reviewing AI-written software will feel more like turning a key than reading a diff


When the robot ships the feature

For most of computing history, code existed so humans could tell silicon what to do. We argued over tabs vs. spaces, filed nit-picks on variable names, and convinced ourselves that a pristine diff meant a healthy system.

Large-language models do not share our love of diffs. They drink documentation, emit functions, and happily rearrange architectures we spent quarters perfecting. Today the typical engineer reviews AI-generated patches against a familiar backdrop: an existing codebase. We still know where the dragons are buried, so we flag the dragons. Life is good—​for now.

Tomorrow’s blank repo

Now imagine the green-field product that ships with zero hand-written code. The AI scaffolds the repo, invents micro-services, writes Terraform, and wires up the observability that will tell us if anything went sideways. There is no “old code” to anchor a human review.

At that point the code becomes an abstraction of an abstraction—​a byte stream produced by another program. Peering into it feels about as useful as poking around the compiled binaries hiding under /usr/bin. We could read them, sure, but why?

Shifting the review surface

The shift looks a lot like getting into a car with push-button start:

Traditional reviewAI-era review
Read the diffPress Start
Verify each lineVerify the behavior
Ask “Why this loop?”Ask “Did the engine turn over?”

Our future checklists will care less about for-loops and more about inputs, outputs, and failure envelopes:

  • Is latency still under 200 ms at P99?
  • Does the billing API refuse negative invoices?
  • Can I roll back to yesterday’s prompt if today’s build catches fire?

”AI slop” is a tooling problem, not a human one

It is tempting to argue that humans must tighten the bolts on every model-written line or drown in sloppy code. I disagree. The fix is better model alignment, not endless human janitorial work.¹

If the generator can learn your performance budgets, threat model, and compliance rules, it can self-enforce those constraints. Review then shifts up yet another level: we audit the policies that guide generation, not the output itself.

Towards just-in-time software

Picture software that crystallizes only when a user shows up, tuned to their request, then evaporates when no one needs it. No pre-compiled artifacts, no fixed language runtime, just live thoughts turning into running systems. In that world the concept of “code” dissolves into something closer to conversation history.

At that point, the most valuable programmers will be the ones who articulate problems clearly enough that the machine can solve them safely. Syntax will matter about as much as spark-plug gap settings matter to the driver who only presses “Start.”

Where does that leave us?

If the how of software fades into the background, our leverage shifts to defining the why. Product-thinking, domain expertise, and rigorous safety constraints become the new “clean code.” The engineers who thrive will be equal parts systems thinker, prompt whisperer, and quality advocate—​guardians of outcomes rather than caretakers of source files.

So keep your lint rules if they spark joy, but start practicing a different skill: explaining your intent so clearly that two billion transistors and a trillion-parameter model can’t possibly misunderstand you. The next diff you review may be measured in behaviors shipped, not lines changed. And that’s a future worth pressing “Start” for.


¹ Think garbage collection versus free(): once the platform got smarter, we happily stopped counting pointers.