This is a common misunderstanding of LLMs. The major, qualitative difference is that LLMs represent their knowledge in a latent space that is composable and can be interpolated. For a significant class of programming problems this is industry changing.

E.g. "solve problem X for which there is copious training data, subject to constraints Y for which there is also copious training data" can actually solve a lot of engineering problems for combinations of X and Y that never previously existed, and instead would take many hours of assembling code from a patchwork of tutorials and StackOverflow posts.

This leaves the unknown issues that require deeper reasoning to established software engineers, but so much of the technology industry is using well known stacks to implement CRUD and moving bytes from A to B for different business needs. This is what LLMs basically turbocharge.

But given a sufficiently hard task for which the data is not in the training set in explicit format, its pretty easy to see how LLMs can't reason.

Now, you still have to be competent enough to formulate the right questions, but the LLMs do all the other stuff for you including copy and paste.

So yes, just a more efficient search engine.

"I'll go down this thread with GPT or Grok and I'll start to get to the edge of what's known in quantum physics and then I'm doing the equivalent of vibe coding, except it's vibe physics"

That interview is practically radioactive levels of cringe for several reasons. This is an excellent takedown of it: https://youtu.be/TMoz3gSXBcY?feature=shared

It feels like this is a lesson we've started to let slip away.