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Uber torches 2026 AI budget on Claude Code in four months

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Re: Uber torches 2026 AI budget on Claude Code in four months

#211

Post by codebolt »

kansface wrote: post #189
abuani wrote: post #17

I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someon…

> I just can't figure how _how_ to burn that much money a month responsibly. I always have a few agents (2-5) doing research and working on plans in parallel. A plan is a thorough and unambiguous document describing the process to implement some feature. It contains goals, non-goals, data models, access patterns, explicit semantics, migrations, phasing, requirements, acceptance criteria, phased and final. Plans often…

Just out of curiosity, what type of systems are you working on? What type of features did you implement on your 100k LOC week?

Re: Uber torches 2026 AI budget on Claude Code in four months

#214

Post by pier25 »

hyperpape wrote: post #59

I love how these articles drop, and all of a sudden HN is filled with people who think engineering productivity is simple to measure. Yes, productivity implies revenue (or cost reduction), and revenue is measurable. However: 1. You spend money today to build features that drive revenue in the future, so when expenses go up rapidly today, you don’t yet have the revenue to measure. 2. It’s inherently a counterfactual c…

> You spend money today to build features that drive revenue in the future

Totally but new features in their app or better software are not going to increase Uber's revenue/profit significantly.

Re: Uber torches 2026 AI budget on Claude Code in four months

#216

Post by phillipcarter »

AntiUSAbah wrote: post #172
phillipcarter wrote: post #157

> Monthly API costs per engineer ranged from $500 to $2,000 as adoption skyrocketed across the company. That's...not exactly a lot per engineer. It sounds like they just didn't budget correctly. Especially if the net of that work is more features that would have otherwise required hiring more engineers, which would cost a lot more than $500 to $2000 a month.

Its a lot. Its a lot for being able to generate that many tokens. And i'm not talking about some genies 10x developer who is working with multiply git worktrees on x tasks in parallel in high quality

No, it's really not a lot at all, especially if you've got a mandate to maximize your AI usage, which many engineering orgs have right now. I burned $216 USD using Claude Code in March just doing some casual development on the side and certainly not as a part of any professional workplace mandate.

Re: Uber torches 2026 AI budget on Claude Code in four months

#217

Post by swader999 »

quaintdev wrote: post #105
brokencode wrote: post #30

Earlier quoted context omitted.

Really depends on the repo you’re working in. If it’s very large, especially if the tool needs to refer to documentation for a lot of custom frameworks and APIs, you often end up needing very large context windows that burn through tokens faster. If it’s smaller or sticks with common frameworks that the model was trained on, it’s able to do a lot more with smaller context windows and token usage is way lower.

Begs the question if we should move on to minimal microservices so that whole project lives in context of llm. I hardly have to do anything when I'm working with small project with llm.

Generally speaking no. Treat your IP (the code that runs your business, makes your business competitive or special) as precious and don't make it subservient to infra. It should be in the format (code, architecture, structure) that best serves it.

Re: Uber torches 2026 AI budget on Claude Code in four months

#218

Post by fhn »

abuani wrote: post #17

I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someon…

a good way to prevent companies from adopting AI (and keeping your job) is to waste tokens making AI cost prohibitive

Re: Uber torches 2026 AI budget on Claude Code in four months

#219

Post by Ekaros »

robotresearcher wrote: post #112
sikozu wrote: post #65

Earlier quoted context omitted.

So he's being paid and is sitting there letting an AI tool do his work for him? Insanity.

We didn’t mind when typesetting was automated. Or when compilers were invented. Why is this different?

So I take we can soon replace coders entirely. Just fire all of them. And let some intern under VP prompt the whole thing?

Re: Uber torches 2026 AI budget on Claude Code in four months

#220

Post by dymk »

kurige wrote: post #204
BeetleB wrote: post #170

Earlier quoted context omitted.

First: There's the obvious "If the company is letting me do it, I'll be wasteful." This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. People have already mentioned the size/complexity of the codebase. I'm new to my team and the codebase isn't huge, but it's large enough that…

> This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. I see this repeated by others, including coworkers. It completely ignores caching. Caching itself is complicated, but the "longer context window = more expensive" is not 100% true and you are hampering yourself if you're no…

It’s crazy that people don’t understand cached tokens despite them being priced separately on the cost pages of every single provider.
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