A frontend dev doing tailwind integration for his day job is gonna see very different speedups than someone working in a niche scientific codebase. Taking the average makes about as much sense as taking the average of the speedup from calculators for a mathematician, a farmer, and an elementary school student.

A junior employee doesn't have that knowledge, so they are expected the follow rules to ensure that they don't inadvertently break something.

What exactly are these dozens of breakthroughs? Most frontier models architectures today still look very much like GPT2 at their core. There were various of improvements like instructgpt, finetuning techniques, efficiency improvements with kv caches, faster attention, lora, better tokenizers, etc. Most of these are for making things run faster. The biggest differentiator has probably been data curation and post-training data and the ability to fit more into the model. But I think we had few breakthroughs that would fall into the category of different technologies.

Now I would love to tell you about all the amazing magical healthy benefits that have come with that, but unfortunately there are none. I feel no difference at all.

There is indeed something useful about trying to write elegant code. Not because others read it. But because that's how you learn about the engineering tradeoffs and abstraction that exist everywhere.

I'm curious what their long term vision they pitched investors is.

I was prototyping something with pi under the hood for a personal project, going to switch off it now.