The bear case being set at 40% of employee costs is still quite wild.
When AI Costs More Than the Engineer
11–20 of 128 posts
Re: When AI Costs More Than the Engineer
#12Ignoring the bizarre inclusion of training compute for the AI company estimates, the other comparisons are still valid.
> The rest of the software market trails. The top 1% of companies spend $89k per engineer per year on AI, 40% of a fully-loaded $224k senior engineer salary. The median spends $137. That is the gap : ... 0.4x at the top of the market, near zero at the median.
So it's not more expensive than an engineer it's 40% as expensive, and for many companies use-cases the cost is virtually negligible.
Even here in Europe where developers are much cheaper than in the US, it still makes sense to pay for the LLM Enterprise subscriptions.
Re: When AI Costs More Than the Engineer
#13Analogous statement:
Evian use 1.25 million litres of water per employee per year. When can we expect other non-bottled-water corporations to rise to this level of water usage?
Re: When AI Costs More Than the Engineer
#14[deleted]
Re: When AI Costs More Than the Engineer
#15Mr. Mark Zuckerberg is particularly not happy about these stats. He was promised something else and he has already fired like half of the company.
It is really crazy people didn't think this through.
Re: When AI Costs More Than the Engineer
#16Garbage. You can't include training by the companies that develop an llm in the comparison against companies that merely use the same llm. Apples and potatoes.
Exactly, it's like saying Shell is spending a fortune on fuel compared to what they spend on employees, if you count oil extraction costs as 'fuel'.
So where are these training costs getting paid from?
Re: When AI Costs More Than the Engineer
#17Excellent, with stunning insight like this, you can see why this VC is earning the big bucks.
This is almost economics level of line projection.
It would be good to understand _why_ anthropics "AI" bill is so high. First, They are going to be renting a lot of inference compute just to service customers (Meta's Capex bill is about 2x its wage bill) It then also needs a huge amount of infra to both run training and experimentation. THats probably a third of the cost. (storage and physical infra to get the most out of storage and compute is hard. Then getting it reliable, so that shit state doesn't propogate across the shared memory plane is very hard.
The other thing to note is that claude usage inside anthropic is tiny compared to the customer's usage. even with uber agents at "mythos++" its going to be at best a few thousand servers. not like the massive fleet needed to serve the paying customer.
So using anthropic as some sort of rational target to base any kind of prediction is madness. Its like looking at lyons tea rooms and going yeah, every company is going to spin up an R&D arm to make a company specific computer: https://www.sciencemuseum.org.uk/objects-and-stories/meet-le...
ALSO this assumes that the current way of running LLMs is the way forward. Custom software is expensive (in both time and tokens) to look after, its much easier and cheaper to buy it in from SaaS companies and let them figure that shit out. (yes I know SaaS apocalypse, but you are paying for real world experience, and a packaged way of doing things, rather than experimenting your self, where in a lot of cases the company doing the experimentation doesn't know what its doing)
Re: When AI Costs More Than the Engineer
#18[flagged]
Re: When AI Costs More Than the Engineer
#19Earlier quoted context omitted.
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.
If you’re going to include AI training in costs, you should include education as part of the costs of an engineer …
... 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.
Re: When AI Costs More Than the Engineer
#20Earlier quoted context omitted.
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.
If you’re going to include AI training in costs, you should include education as part of the costs of an engineer …
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.