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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

#271

Post by jauntywundrkind »

dymk wrote: post #220
kurige wrote: post #204

Earlier quoted context omitted.

> 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.

Its crazy that people think caching is such a silver bullet, despite the cost of long context windows still being ridiculously high even with caching. https://blog.exe.dev/expensively-quadratic https://news.ycombinator.com/item?id=47000034

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

#272

Post by canes123456 »

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 use it as an ide. I am a security engineer but there a bunch of predictable things I need to write code for. Onboarding logs, writing detection rules, SOAR type stuff. It makes a diff and locally tests all the permutations I describe than I review the code.

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

#273

Post by raducu »

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…

> notice more and more are consumed $1k in tokens a month

I've said it before: if you allow people to see how much others spent, they will try to climb up the "leaderboard".

It takes just ONE little praise for using tokens or one perk gained, and the GAME IS ON among the developers!

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

#274

Post by at-fates-hands »

52-6F-62 wrote: post #230
bs7280 wrote: post #175

Earlier quoted context omitted.

I have ancedotal examples of claude code choosing a solution to a problem that is ridiculously token inefficient. One example - was giving several agents different sub problems to solve in a complex ML / forecasting problem. Each agent would write + run + read a jupyter notebook. This worked ok, the notebooks would be verbose but it was fine... until one of them wrote out hundreds of thousands of rows to a cell outpu…

> have claude read all your emails every day But that is exactly what it is sold to people to do as a panacea: consume all the data, produce insights. Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.

>> Nobody is being instructed to be judicious. Everyone is being instructed to use it as much as possible for all problem areas.

Do you think this is because the LLM owners have such a massive ROI they're trying to cover so they're actively encouraging teams not to be judicious so then you get into this vicious cycle where both the LLMs and companies are both burning through cast like crazy?

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

#275

Post by linkregister »

I wonder how much of this AI budget was spent on their LLM-heavy CI/CD pipeline: https://www.uber.com/us/en/blog/ureview/

I'm considering rolling out something similar but am not sure if it would exceed the expenses of Claude Code Review at an estimated $20 per PR.

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

#277

Post by DarenWatson »

There is a major disconnect in that people think token usage is exclusively tied to human typing rates...it isn't true. When software developers evolve to using self-managing CLI tools (like Claude Code - the source article mentions this), they are not merely chatting; they are unleashing loops of agency.

When you enter one single inquiry of "find and fix the memory leak in the billing service" you are not submitting just one single inquiry. The tool is searching through an entire code repository for relevant code, pulling 15 related files into context (easily 200k+ tokens) proposing a fix, running the test suite and failing, taking an entire stack trace of errors into context and looping to keep iterating towards the solution.. In that process you can loop multiple times (10+) in a very short period of times (within 5 minutes). While you grab a cup of coffee you will have consumed $20 in token usage. At the enterprise level (like with Uber) when you multiply that out by thousands of software developers using it as a personal shell tool your budget disappears very very quickly.

And on your point about the junior developer: Comparing $100,000/year in tokens to hiring a junior developer is such a ridiculous false equivalency that even makes you question whether they even understand how to make such a comparison.

The cost to a business of one junior engineer with a $100,000 salary is not just the $100,000 in salary but also an additional $40,000+ in benefits and taxes, as well as in hardware.

Also, you are disregarding another cost of hiring junior engineers that is their mentorship cost. Each week, your senior and staff engineers spend hours mentoring junior engineers by reviewing their code, pairing with them, and unblocking their progress. Mentoring requires a substantial amount of time and will be expensive to your business.

The return on investment (ROI) for the $10,000 monthly expenditure on tokens is not so much about replacing the junior engineer with the AI. Instead, the ROI is that your senior engineers can use the huge amount of compute power to create boilerplate and tests, and refactor their code 3x quicker than if they had to mentor junior engineers. In addition, LLMs do not sleep, require one-on-ones, or leave for another company for 20% more pay in 18 months, when the value to the code base made them an asset to your business.

Lastly, the main reason that Uber has problems with their AI business is that due to the UX of these agentic tools, developers think of the API calls made to the AI as free and as a result, treat them like a basic grep command.

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

#278

Post by Galanwe »

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.

From my experience, this happens essentially by three means:

- Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_, which did not spend time curating their _base_ agent knowledge. They end up with a single meta conversation with huge context where they feel the agent is "educated", and feel like any new conversation with the agent is a loss of time because they have to re-educate it.

- Level 1 (intermediate users) heavy explicit use of subagents: Once you discover the prompt pattern of "spawn 5 subagents to analyze your solution, each analyzing a different angle, summarize their findings", it can become addictive. It's not a bad habit per se, but if you're not careful it can drastically overspend your credits.

Level 3 (expert users) extreme multitasking. Just genuinely having 10 worktrees perpetually in parallel and cycling between them in between agent responses. Again, not necessarily bad in itself, but can exponentially conse credits.

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

#279

Post by starkparker »

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…

At least your workplace doesn't frame raw usage as a leaderboard, with awards given out for topping it

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

#280

Post by th0raway »

cyanydeez wrote: post #55
jp57 wrote: post #38

Earlier quoted context omitted.

Claude is a mediocre programmer that can do great things with great supervision, but it can't make mediocre human programmers into good ones, because they can't provide great supervision. It will try and try and try, though.

id bet its the LLM doom loop: vaguely ask it to do something, tab to news.ycombinator.com for 30 minutes, tab back, noticed it misunderstood the prompt. Restart with new improved prompt, tab back to HN. So yeah, probably the same thing people do anyway, just not compile time its now generating time.

We opened the Cloud Code floodgates all at once in my org. After a few months we looked at stats, and asked managers for impressions on performance changes. The API cost per engineer doesn't correlate with the apparent increases in performance, but it sure seems that the vast majority of people that used to have good reviews got a lot better, while the bottom third just didn't, even though they use the LLMs about as much. It makes the performance differences in teams look like an abyss. Someone appears stuck in a task, and we see what they've been prompting, and then one of the best seniors comes in, actually asks the questions well, and the LLM does all the debugging and all the fixing in 20 minutes.

It's not that the best performers are magical prompt engineers providing detailed instructions: They ask better questions that the LLM knows how to try to answer, and provide the specific information that the LLM would take a while finding. It's as if some people just had no "theory of mind" of the LLM, and what it can know, and others just do. It's not a living thing or anything like that, but it's still so useful to predict it, put yourself in it's shoes, so to speak. Just like you'd do with a new hire, or a random junior.

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