[avaer]

I don't know, compute is compute. Arguably making complex software with LLMs isn't all that different from training a model to do a thing. You're throwing a lot of compute at the problem and hoping for a stochastic solution. The distinction will become even blurrier with time.

Though I agree it might be informative to split it by industry sector.

[alexjurkiewicz]

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.

[eru]

> 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).

[ThunderSizzle]

Which is probably a backwards anti-pattern companies have built.

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.

[eru]

Eh, computers are so (comparatively) cheap, that you can give all your employees top-of-the-line equipment by that logic.

(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.)

[ThunderSizzle]

Meh - our dev machines are over-provisioned cloud pc's from MS, that run as fast a laptop from 2005. And I'm sure they are paying a premium for the privledge.

My 10 year old desktop sitting next to me is a lot faster, but alas, I can't BYOD.

[fragmede]

> more expensive engineers don't use more expensive computers

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.

[klibertp]

> you're going to get worse output from them

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.

[eru]

I agree, but good computers are so comparatively cheap and engineers so expensive that this logic applies to (nearly?) every one of your engineers, not just the most expensive ones.

[scrollaway]

If you’re going to include AI training in costs, you should include education as part of the costs of an engineer …

[imhoguy]

... and that actually shows - senior engineers have spent actual paid time to train juniors. Plus they used to spent time contributing to open source projects or Stack Overflow, all the stuff which every company benefits from.

[vksv6]

why stop there? Count how long and how much energy it took for evolution to produce that 3 chimp brain that is then educated, and add how long it took culture to produce the knowledge in text books for said education to be possible.

[victorbjorklund]

And why only education? Everything the engineered needed so far should be included. Can’t have a dev that never eaten since they were born.