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Comment by throwitaway222 | Hacker News Reader

> monthly AI spending limits have reportedly been slashed from $100,000 per employee...

what. I can see a team of 20 costing 100k per month (but rare), but per person?

Considering that’s a healthy portion of a salary for an additional employee per person, the fact they’re slashing spending sure makes it look like AI wasn’t even a 2x multiplier at minimum.

You could only conclude that if they would have dropped LLMs completely. It just means they see the benefit/cost optimum at a lower point than 100k/programmer.

I also doubt a longterm 2x multiplier for most developers.

I don't think you need the LLMs to be completely dropped to indicate this. Even entry-level faang programmer salaries are in the 300k range, so the fact that LLMs aren't worth 100k/programmer does tell us something about the marginal usefulness. If the drop was motivated purely by price-per-output, it would indicate the LLMs are less useful than adding another 1/3 engineer. However, as discussed in other comments, there are other motivations as well, notably dogfooding homegrown tooling.

Let’s say you could get 10x speed up with 10k and 11x speed up with 100k. The 100k option still wouldn’t make sense.

It’s a contrived example, but to me it’s not improbable that a lot of the spend comes from tokens that are used very inefficiently. After all developers were pushed to try using LLMs without looking at costs.

I’m not saying the others are wrong, just that you can only tell something about marginal cost effectiveness, not total cost effectiveness of LLMs by measures to reduce AI spending

The marginal utility over $10k/mo/employee isn't super high now, but maybe there is some future where it is. shrug

Not even close to 300k

Sorry you’re right, I said entry level but in my mind I was thinking after one promotion, not sure how I messed that up. L5 software dev total comp was about 300k when I left Amazon two years ago. Levels.fyi reports it’s lower now [0], but I am suspicious of that. Maybe it’s brought down by non-HCOL locations.

[0] https://www.levels.fyi/

Even if it was a net negative, you still might want to titrate the exit a bit just to prevent the total chaos of broken workflows. Can't really draw the conclusions you want to draw.

We're sitting on a year of more or less capable coding models and I have not exactly seen a revolution in new software being released. AI is likely a force multiplier for specific subsets of individuals and workflows. Coding is not a bottleneck for a lot of systems.

I suspect that SaaS is silently being eaten alive as an industry.

You often pay for regulatory compliance and liability with SaaS. Would you risk running your own vibe-coded payroll or accounting system for your company?

Yep. The SaaS-is-doomed crowd are missing this part. It's the Red Hat model (debatable how well they do it, but they exist, so..)

I think SaaS will be fine. How the field works might change, but in the end marginal cost of software is still zero and when you've got a quality, well-tested and well-designed product it's going to be obviously better than things vibed together.

Talk to the emulator community. Huge leaps being made there. Or binary decompiling topics. Huge leaps there. There is more than web applications

Can you link to some of the huge leaps in emulators and binary decompilation that have caught your attention? (I hope that doesn't sound like a challenge. I'm just interested.)

https://www.youtube.com/ModernVintageGamer has some great videos about emulation and he's been talking about AI recompilations quite a bit lately.

PS5 is a good one: https://github.com/KytyPS5/KytyPS5

That's the thing though. My hobby projects have been doing better than ever. My job is more or less the same it's always been, just with more unit tests (that cover...who knows) and slightly better tooling/CI because it's now less tedious to fix that shit when it gets mangled.

AI is very good at creating small codebases. It still sucks shit at maintaining huge ones where someone needs to understand how the codebase works.

I've seen what happens when companies just vibe code an entire mega codebase and it's really bad. AI is fucking terrible at grand architecture decisions.

That's monthly, not yearly.

Oops, yikes.

Very likely a negative multiplier.

It’s mostly from people using their personal accounts to run LLM services that serve a larger team or organization. At least at Microsoft, it’s still impressively hard to get access to an LLM for service usage with high enough rate limits to be useful, making running services on dev boxes much more appealing (despite the countless drawbacks that few people seem to care about around security, compliance, reliability, etc).

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