Hacker News
The article hypothesizes that the introduction of AI coding assistants, rather than directly improving development speed, exposes and exacerbates existing bottlenecks in the development process, forcing teams to adapt and tighten their feedback loops for genuine improvement.
AI 生成摘要
文章推測，AI程式碼助手並非直接提升開發速度，而是暴露並加劇了開發流程中現有的瓶頸，迫使團隊適應並縮緊回饋循環以實現真正的改進。
Codemanship's Blog
Code Craft Codified
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:
Lead times shrink
Reliability improves
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”.
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.
Founder of Codemanship Ltd and code craft coach and trainer
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