If you're paying an engineer $X and they're getting 3x the amount of work done you should be happy paying up to $2X in AI tool usage.
In reality many companies start complaining at their employees when they hit $0.1*X or less.
I think the problem is more for long lived projects and developers as regular employees. As you keep working on the product, you should be developing your own mental model of everything: business, tech stack, how the company approaches things. And that's where LLM value drops down a lot, or it should, otherwise there is a bigger problem with the developer. A huge LLM cost for a consultant is in my opinion a lot more justified than a huge recurring cost for someone who's been working on the same thing for at least 9+ months.
After arriving at a seemingly workable design it helps spot oversights I've made either directly or by providing a stand in for a person to walk though the logic with (unlike a real person it doesn't become annoyed or impatient). Then it magically materializes any and all boilerplate along with 90% of the solution near instantaneously.
Yeah it confidently makes some truly bizarre errors that no half competent person ever would. Failing to go over the output with a fine toothed comb is inevitably leaving landmines in place. But I agree with you that your own mental model is required - if you have that then you will be able to spot the pitfalls assuming you actually put in the effort to read the output and really think about it instead of skimming. (As an example an LLM recently materialized an nftables filter chain for me that would have taken me quit a while to figure out on my own but for no apparent reason omitted a single key detail that would have completely broken all networking if it had been used blindly.)
But did you do it well? It’s not a snack, but a question based on general observation that every is praising the process, but no comment on the outcome.
The future scapegoat, aka maintainer.
So scaling horizontally in different markets seems like an advantage if a product is already mature. Which is exactly what Anthropic and Open AI are doing because they want to put their tentacles in everything.
Exactly this. Unless the new features directly drive new users or new revenue from existing users, for many products iterating 2x faster does not mean 2x ROI.
Additionally, from anecdotes here, at work, and in my network.. a lot of the unlocked developer velocity is going to fun/frivolous/extra things. I think part of it is developers have their own features they want for themselves that are the easiest and most direct thing to deploy LLMs against, in the absence of good direction.
Yes it's cool you finally achieved 100% test coverage, or you wrote a new utility that makes your job easier, or cleared the 2 year old ticket that was 100 deep in your backlog, etc. But there were ROI reasons these things were not done previously.
That's already putting aside the fact that development going 3x faster doesn't increase end to end output by 3x because <100% of a SWEs job is development.
https://en.wiktionary.org/wiki/yak_shaving
And I'll have you know I'm a Grand Master Yak Shaver.
(I think they are being irrational, and that the mental model they have of AI costs -- "how much are we spending on tooling for this developer?" -- is going to shift over time to something more sensible, but those kinds of short-sighted companies are the ones that are having cost panics.)
What company pays developers $40000/month and are they hiring?
Based on such a basic mistake, I wouldn't trust the rest of your comment. Everything you listed feels like made up arbitrary excuses. Most of the overhead per employee is fixed, that doubling rule of thumb is assuming an employee with a salary closer to the median. At the salary you're claiming, the overhead shrinks to something like 30% on top of the base salary.
If anyone took your numbers seriously they would run into the weird conclusion that an employee paid $50k actually costs a company $300k. That's the type of math error you're doing here.
The mistake of 'h' instead of 'y' typo?
Overhead is not fixed per employee - things like taxes and benefits scale with salary, not to mention the cost of tooling (hardware, software, AI token budget, etc) tend to be higher for higher-compensated employees.
You definitely shouldn't plan your business based on my comment, but you'd be foolish to ignore it.
Meta, Google, Amazon, Apple, OpenAI, Anthropic, Netflix, Nvidia, to name a few
we need to start using that median instead of using salaries from the Magnificent 7.
... and that also improves the company's financials.
Otherwise it's all fluff.