You say "amazing that it took months to figure this out" as if the answer to the question is obvious.

But it's not. Some FAANGs are doing amazing things with unlimited tokens. Other companies have no clue what to do with tokens, they've just told their engineers to max them.

It really depends on how you're using the tokens. If you're just using them for Codex and Claude Code - yeah, tokenmaxxing is incredibly dumb.

In other words, people who are productive get more done when you scale up what they're already doing, and people who aren't productive will not magically become productive when you scale up what they're already doing. That's incredibly obvious, because we've seen how this plays out repeatedly in so many different ways (lines of code, commits, tickets closed, etc.), and it has nothing to do with tokens or even programming, but just how trying to manage people works.

> Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens.

Unlimited tokens is different from “use AI a lot or we will fire you, and we are counting token consumption as usage”. Obviously the latter is stupid and yet it was done in many places.

> But it's not. Some FAANGs are doing amazing things with unlimited tokens

Giving someone unlimited access to a resources is not the same as directing or incentivizing them to use it for the sake of using it which is what the parent comment criticized.

As for the other FAANGs, Meta and Google have (not good but still) frontier models of their own, so they are very different from a company paying API costs per token.

Where can I see those amazing things done by FAANGs?

> Some FAANGs are doing amazing things with unlimited tokens.

Would love to know what things!

Show me some fang that have made nice outwards facing products through a fully embraced AI workflow?

AI is an accelerator that engineers should know and have access to, but it's not something that should have mandated usage and quotas around. It's also absolutely dangerous for young engineers and the like - it fundamentally denies you of the "learning" aspect. I'm now seeing in interviews young graduates being given AI tasks to complete and they come back with a correct solution and no concept of how it is working.

You learn and reinforce learning by DOING and reading in depth. High level summaries don't teach anything and are the kinds of things only VPs care about. So, unless the intention in the future is for everyone to be a VP using AI to do the work, we need some middle ground here and some real thought around implementation of these tools or there's going to be a generational canyon gap of knowledge between being able to "say" and being able to "do".