The Drift Beneath the Velocity

🧵 Reflections from Week 2 of AI-assisted coding

In the first week of this build, I felt fast.
Core features spun up. Architecture stitched together.
A working demo, with decent bones, came to life.

It’s the kind of velocity that gets attention.
And that’s what AI assistants offer — a sense that maybe, finally, you can move as fast as you think.

But by week two, something subtle crept in.

I wasn't just coding. I was chasing drift.

Let’s talk about drift

In traditional software builds, drift shows up when people aren't aligned: style guides ignored, assumptions made, docs outdated, handoffs sloppy.

In AI-assisted builds, drift hits harder and faster.

You're not just dealing with human inconsistency — you're dealing with agents that don’t remember what they did two days ago. Or even two minutes ago.

They’ll confidently generate code that overwrites previous logic. They’ll suggest improvements without checking dependencies.
They’ll follow your prompt precisely — and forget the reasoning that came before.

Velocity without memory? That’s a recipe for drift.

The illusion of delegation

At some point this week, I tried to run parallel tracks:
Agent A focuses on UX tweaks. Agent B handles theme logic. Agent C documents system behavior.

That’s not delegation. That’s chaos — unless the framework holds.

And if you’re not careful, you become the framework.
The one reconciling the tension between what was said, what was generated, and what actually shipped.

You realize: you're not speeding up the build.
You're just taking on the mental load of a distributed team... with zero shared memory.

The break point

I hit the wall when a UI element broke.
Custom themes no longer rendered. Styles clashed.
Something that had worked just... didn’t.

I spent a full day walking back changes, hunting down the moment a refactor quietly rewired my logic.
It wasn't even "bad code" — it was code that made sense in isolation, but broke the system when applied without context.

That’s when I stopped and said it out loud:

“I’m teaching the AI to troubleshoot itself.”

I had to teach it what it had done — because it didn’t know.
I had to remind it of the architecture.
I had to retrain it on the very patterns it helped write.

This isn’t a rant. It’s a read.

I'm not here to dunk on the tools — I still use them, every day.
They’re not broken. But they are immature.

There’s no real infrastructure yet.
No persistent memory unless you build it.
No schema guardrails unless you enforce them.
No discipline unless you design one.

So what happens? Drift.

Not because you're sloppy.
But because you're fast.
And fast, without constraint, eventually tears.

The real realization

This post started as a tech rant.
But what I’m actually seeing — and feeling — is the absence of a shared mental model.

One person, working alone, has to hold:

• feature logic
• performance constraints
• accessibility needs
• error states
• documentation fidelity
• theme architecture
• test coverage
• future scaling strategy
• and a UX that doesn’t suck

...while prompting an AI that forgets everything every time you click "regenerate."

This is no longer about speed.
This is about systems thinking.
This is about memory.
This is about interfaces between trust and code.

Where it goes from here

I’m building a more formalized AI memory stack.
I'm codifying what "guardrails" actually means in this context.
I’m shifting from one-off prompt interactions to reusable interfaces.
And I’m watching how quickly “AI coding” starts to look like “AI team management.”

Because the future of development might not be a solo engineer writing faster.

It might be someone who understands how to orchestrate and align a team of agents — each with bounded memory, clear constraints, and shared intent.

And if that’s true?

Then software consulting isn’t going away.
It’s just evolving.