I think this gets to a fundamental problem with the way the AI labs have been selling and hyping AI. People keep on saying that the AI is actually thinking and it's not just pattern matching. Well, as someone that uses AI tools and develops AI tools, my tools are much more useful when I treat the AI as a pattern matching next-token predictor than an actual intelligence. If I accidentally slip too many details into the context, all of a sudden the AI fails to generalize. That sounds like pattern matching and next token prediction to me.

> This isn’t to say “AI” technology won’t lead to some extremely good tools. But I argue this comes from increased amounts of search and optimization and patterns to crib from, not from any magic “the AI is doing the coding”

* I can tell claude code to crank out some basic crud api and it will crank it out in a minute saving me an hour or so.

* I need an implementation of an algorithm that has been coded a million times on github, I ask the AI to do it and it cranks out a correct working implementation.

If I only use the AI in its wheelhouse it works very well, otherwise it sucks.

I think this comes down to levels of intelligence. Not knowledge, I mean intelligence. We often underestimate the amount of thinking/reasoning that goes into a certain task. Sometimes the AI can surprise you and do something very thoughtful, this often feels like magic.

Both CRUD and boilerplate are arguably a tooling issue. But there are also a bunch of things only AI will let you do.

My tests with full trace level logging enabled can get very verbose. It takes serious time for a human to parse where in the 100 lines of text the relevant part is.

Just telling an AI: "Run the tests and identify the root cause" works well enough, that nowadays it is always my first step.