[gwerbin]

I actually agree with you, although I don't agree with the "open your mind or be left behind" sentiment. I left it implicit in my example, but in my actual experience they actually can't put it in production as it is because there are genuine broken things or features they can't add without rewriting big chunks of the system. The worst are the ones that are not obviously broken but are just wrong, like incorrect numbers in a financial report.

But yes, I do agree that some engineers and some engineering teams are slow and cautious where it's not needed, to the point of obstructing rapid prototyping and iteration. And yes I agree that AI will be good to help push them to overcome it.

[8note]

> rewriting big chunks of the system

the costs of doing this is much lower than it has been

testing to make sure thats safe to do maybe hasnt caught up, but its no longer an unreasonable task

[gwerbin]

It's not about turning out the necessary lines of code. It's about doing the critical thinking, validating of assumptions, etc. which was not done the first time around.

Yes, AI can assist with the brute force of broad scale factoring. But without the humans and domain experts involved, you are going to either keep flailing around at the cost of millions of tokens, or something actually really bad in production that you don't even understand and won't work for your business.

The lines of code have never been a bottleneck for the actual engineering team. That's why AI is so good for expediting the prototype cycle and allowing stakeholders to develop their own prototypes, but why you still need someone who actually knows what they're doing to finish the project, whether they are manually typing the lines of code or letting Claude do it.