AI training uses wildly more compute than most companies, who are generally building domain specific CRUD apps.
Compare AI costs per-engineer-salary-dollar, because more expensive engineers probably need more expensive AI.
Compare AI costs per-engineer-salary-dollar, because more expensive engineers probably need more expensive AI.
Let's see how this works out in the long run. For a historical analog, more expensive engineers don't use more expensive computers (by and large).
Your most expensive engineer's time is most valuable, so if you give them standard issue which is half the speed, you are throttling the value you can get from your engineer. Not to mention the mental drain of your cursor barely being able to move due to all the bloated virtual networking systemization.
It would seem to make sense to give more valuable employees faster equipment, so that their time isn't spent toiling with the slow machine, but rather actually producing value.
(Well, for this to make sense, you need good computers to be cheap, but the returns to diminish quickly after that. Which used to be the case until fairly recently: single core performance was pretty much capped, and after your whole compile is parallelised adding more cores to a machine didn't really help your best engineer either. Similarly for RAM.)
My 10 year old desktop sitting next to me is a lot faster, but alas, I can't BYOD.
They don't? If you give your best engineers substandard hardware to work on, you're going to get worse output from them compared to if you give them more expensive computers to work with.
Not completely true. Giving developers hardware that is too beefy is the main reason why so much software breaks down when run on users' machines, which are generally old, on spotty Internet connections, and RAM-starved. Devs just don't need to think about performance unless it's really asymptotically bad, while the users bear the full brunt of inefficiencies.