Ignoring 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.

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sevenzero 4 hours ago | parent [-]

>it still makes sense to pay for the LLM Enterprise subscriptions.

Does it though? I do not see any advantages in my day to day job over using the cheaper models.

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schnitzelstoat 4 hours ago | parent [-]

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 bloated context window etc.

I think a lot of the 'tokenmaxxing' people spending thousands every month are simply using the tools ineffectively (like having loads of Opus agents doing tasks that Sonnet or even Haiku could do). I suspect this will only get worse now with the release of Fable, but Anthropic must love it.

When you say the cheaper models do you mean like Deepseek or GLM? I haven't tried those but they look interesting. It'd be nice to shift to open weights and not be tied to one company.

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sevenzero 4 hours ago | parent [-]

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 understands? I've just been reading about these devs using x-amount of tokens, having that y-amount of steps perfected AI workflow, but none of them ever talk about what they actually implement all day...

	▲	imtringued an hour ago \| parent [-]
		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.