Very Sparkly (Evolving Thinking on GenAI)

Tired of my GenX references yet? No? Good, here is another one.

Let me explain. As you may notice from my more recent blog entries, "AI" is something I have been frequently thinking about. Especially trying to work through the hype, where GenAI is very sparkly and attention grabbing, to a place where I see how AI makes things better. Further, what is the intersection of my years of engineering and building AI solutions? Is there one? Take for instance this entry from a year ago where this journey started to gain traction for me. Or me thinking about Vibe coding back in July.

I’ve been thinking about what “engineering” even means now that anyone can spin up an LLM and build something that looks like it works. It’s too easy to confuse “the demo runs” with “the system works.” So here’s where I’ve landed - four realities that to consider. These are not my original work (thanks Nate), but landed in my conciseness at the exact right time.

1. If you can’t write the invariant, you haven’t engineered it.
This is the difference between vibe coding and engineering. “Make it work” is not the same as “define what working means.” AI gives you plausibility, not correctness. The invariant is what keeps you from gambling with probabilities and calling it architecture.

2. If you can’t measure it in production, you didn’t build it.
Anyone can make a demo now. Real engineering starts when actual users show up and start breaking things in creative ways. Observability isn’t a bonus -it’s the only way to know if the magic still works after it leaves the notebook.

3. If you can’t explain why it failed to a regulator, you haven’t owned it.
“The AI did it” won’t fly with the FDA, SEC, or anyone with a pulse. If you can’t walk a smart non-engineer through what went wrong, you don’t understand your own system. Ownership means accountability, not just implementation.

4. Good system design still matters.
Data is still the asset. Models are temporary. The LLM you love today will be obsolete in six months. Build so you can swap the engine without rebuilding the car. The fundamentals haven’t changed - only the excuses have. The data represents your value / asset, the LLM is a processor to help you realize the value of your data.

Bottom line: these form a lifecycle - specify, verify, and own. The tools changed. The responsibility didn’t.