Here’s my hypothesis (and I’ve seen real-world examples with client teams that make me ask this question):
Dev teams bring “AI” coding assistants into their daily workflows. Very quickly, much more code starts hitting the feedback loops in their process: testing, code review, integration, user feedback.
This starts to overwhelm those loops. Delays get longer, more Bad Stuff leaks through into production, systems get less stable and teams end up spending more and more time playing whack-a-mole with issues.
Far from making these teams faster overall, the traffic jams get worse and their journeys take even longer.
So some teams adapt*. They reduce batch sizes going into the feedback loops: fewer changes being tested, reviewed and integrated at a time, in tighter feedback loops.
We know that if you tighten these feedback loops, three things tend to happen:
1. Lead times shrink
2. Reliability improves
3. Cost of change goes down
My hypothesis is that when we see positive systemic impact from “AI” code generation, it’s actually more attributable to the team adapting to it, and not directly to the “AI” itself.
“AI” code generation load-tests your dev process and forces you to address the worst bottlenecks.
Basically, “Your Waterfall is showing”.
* And, of course, some teams run in the exact opposite direction, getting even more Waterfall. Silly Billies!
It just so happens that I specialise in helping development teams build the technical skills needed to shrink lead times, improve reliability and lower cost of change – with and without AI.
I know, right! What a happy coincidence!
Visit my website to find out more.
