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When AI Costs More Than the Engineer

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Re: When AI Costs More Than the Engineer

#101

Post by acka »

spiderfarmer wrote: post #4

I'm not a VC guru but in my opinion you can't include the time and money it takes to grow a tree and mine the iron to compare the time it takes to hammer in a nail with a hammer versus using your fist.

Your framing assumes that because the hammer is there, its manufacturing cost vanishes. In reality, someone had to pay that cost, and a holistic view has to decide whether building and maintaining that entire tool chain is actually cheaper than just paying humans to do the job. Often, we still choose the tool even when it raises true total cost per unit, because we value convenience or speed. That’s exactly what’s happening with many AI deployments.

Re: When AI Costs More Than the Engineer

#102

Post by klibertp »

fragmede wrote: post #93
eru wrote: post #38

Earlier quoted context omitted.

> 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).

> 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.

> 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.

Re: When AI Costs More Than the Engineer

#103

Post by zild3d »

gnfargbl wrote: post #57
NitpickLawyer wrote: post #30

Earlier quoted context omitted.

> but you can achieve the same at 1/10th of the cost. For some tasks, sure. But not for all tasks. And for some tasks, cost per token is irrelevant if it provides real benefits that are oom compared to what you had. Local models are indeed becoming "good enough" for some tasks, but there are still tasks that they can't touch. There's a recent benchmark for kernel writing. Fable wrote a kernel that provides ~30% more…

You're looking at the status quo and ignoring the trajectory. The best current open models are about as good as closed models from ~1.5 generations ago. The rate of improvement of all models is converging to zero. It follows that in a few generations, open models inferencing will be about as good as closed model inferencing. The problem is going to become that there's no incentive for anyone to run the stupidly-expen…

>The rate of improvement of all models is converging to zero.

Curious where you draw this conclusion from? Most benchmarks still show continual steady progress https://metr.org/time-horizons/

Re: When AI Costs More Than the Engineer

#105

Post by imtringued »

sevenzero wrote: post #46
schnitzelstoat wrote: post #36

Earlier quoted context omitted.

My company has a Claude Code and Codex one and I use Claude Code because I am more familiar with it. That said, I just use Opus for planning and Sonnet for implementation and it's pretty cheap. Codex seems decent too so I should try it out some more. But you can get an awful lot done even with just like $200 a month at API pricing if you are careful not to waste a powerful model on an easy task, or carry around a blo…

With cheaper models I really meant cheaper subscriptions but used the wrong vocabulary. We still use Claude Opus (if thats what 4.6 is?). We just have the 20 bucks subscription and I barely use up my token limits in my day to day work. I often wonder what kinda features other devs implement compared to me, if they need that many tokens? It kind of feels impractical to bloat up an app with features one barely understa…

Yeah, I noticed this as well. The most common trap I've seen though is when people suddenly get the idea "code is cheap now" and then start working on low value projects but then it turns out that it wasn't as cheap as they thought.

Re: When AI Costs More Than the Engineer

#107

Post by emil-lp »

A_Duck wrote: post #13

Analogous 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?

Why do authors spend so much time coming up with a book when they can literally pick one up for free at the library?

Re: When AI Costs More Than the Engineer

#108

Post by gnfargbl »

Tade0 wrote: post #81
gnfargbl wrote: post #29

Working regularly with AI is like managing a small team of unbelievably knowledgeable, very smart, and occasionally crashingly naïve junior developers. Because they're so knowledgeable and smart, they can get a lot done very quickly. Because they make a proportion of howling errors, you have to keep a close eye on them -- or carefully train another agent to do it for you, in which case you now have to keep a close ey…

I get the feeling that either I'm using LLMs wrong, or everyone else is. Outside of enthusiastic use of Tab and some one-off scripts, I don't really tell it to write code. Instead I ask vague questions about the codebase and its inner workings. Reading other people's code has always been my Achilles' heel - particularly if it's a huge project and has a lot of undocumented conventions. LLMs are brilliant at explaining…

You're not using them wrong at all. Part of the reason LLMs are good at writing code is that they're good at understanding code. You're just not using the full menu of capabilities -- which is totally fine.

Re: When AI Costs More Than the Engineer

#109

Post by gnfargbl »

zild3d wrote: post #103
gnfargbl wrote: post #57

Earlier quoted context omitted.

You're looking at the status quo and ignoring the trajectory. The best current open models are about as good as closed models from ~1.5 generations ago. The rate of improvement of all models is converging to zero. It follows that in a few generations, open models inferencing will be about as good as closed model inferencing. The problem is going to become that there's no incentive for anyone to run the stupidly-expen…

>The rate of improvement of all models is converging to zero. Curious where you draw this conclusion from? Most benchmarks still show continual steady progress https://metr.org/time-horizons/

To me it seems like a first-year physics scaling laws problem. To get linear improvements in capability, you appear to need need exponential (or at least superlinear) increases in model size. We have no technical nor business solution for that kind of scaling, so the long-term outcome is obvious.

Re: When AI Costs More Than the Engineer

#110

Post by rh94 »

geon wrote: post #2

Garbage. 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.

Excatly, further more it really depends on who is using it and how value is compared not $. Senior engineers growing without ai will probably deliver 10x in value then ppl who just found out about ai yesterday. Doesn’t matter how much you spent dolar wise
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