This certainly does. If we think from this angle, it really begs the question of what language/tech stack to use if a company wants to start a new project. On one hand, if company uses a very well tech stack, development and rewrites will be faster due to AI having way more examples to draw from. In certain cases, AI will handle some edge cases which are difficult to come by/replicate under strictest test procedures. Overall, that results in faster workflow. On the other hand, if this company choose a newer stack which may be better better than older popular frameworks, development time will increase (along with rewrite time)but the product might be better. we have to see how companies handle this in the future, given this is also affected by how cheap/expensive token consumption becomes. Using something pretrained vs training and then using an AI has cost implications when done in a large scale. It will be interesting to see what directions companies go to, faster workflows and delivery using AI or potentially a better product using more manually written proprietary code with lesser AI involvement.

I don't think that holds. Internal docs for bespoke frameworks, with examples, are effective at steering AI. The main thing is that both the API and the docs are well written. Easier said than done, but you can ask AI how to write effective documentation for AI.

>if company uses a very well tech stack, development and rewrites will be faster due to AI having way more examples to draw from.

Eh maybe not.

Stuff that has a lot of deprecated features is honestly burdensome on AI. It keeps rediscovering the deprecated features as the understanding that they are deprecated fall outside of the context window.

What you need is something that either never deprecates syntax, or is <10 years old with minimal changes over that time.