Uber torches 2026 AI budget on Claude Code in four months • Hacker News Board

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

#121

Post by ambicapter » Fri, May 01, 2026, 5:14 PM UTC

rogerrogerr wrote: post #40

ageitgey wrote: post #13

Earlier quoted context omitted.

Anthropic has a very "interesting" business model where you get subscription pricing as long as you are under 150 employees. When you hit 151, you have to start paying API prices overnight for everyone, and your total bill instantly multiplies. They are getting you hooked on cheaper tokens, then raking you in when you get scale. I'm sure Uber gets a break on list price, but I doubt they are anywhere near <150 employe…

Strange pricing model for a company selling the idea of having fewer employees.

Don't the incentives align? If you have fewer employees, then you pay less...

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

#122

Post by Aurornis » Fri, May 01, 2026, 5:14 PM UTC

brokencode wrote: post #30

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…

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.

The codebase and the topic you're working on are huge variables.

I don't use LLMs to write code (other than simple refactors and throwaway stuff) but I do use them heavily to crawl through big codebases and identify which files and functions I need to understand.

Some of the codebases I explore will burn through tokens at a rapid rate because there is so much complex code to get through. If I use the $20 Claude plan and Opus I can go through my entire 5-hour allocation in a single prompt exploring the codebase some times, and it's justified.

Other times I'm working on simple topics, even in a large codebase, and it will sip tokens because it only needs to walk a couple files to get to what it needs to answer my questions.

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

#123

Post by Retr0id » Fri, May 01, 2026, 5:14 PM UTC

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.

The whole service might fit in a context window but the details of the system around it will still be relevant.

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

#124

Post by Salgat » Fri, May 01, 2026, 5:15 PM UTC

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…

Do lots of deep research and code reviews on large legacy codebases. I've created lots of documentation to reduce token consumption but it's still a lot of token consumption.

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

#125

Post by trjordan » Fri, May 01, 2026, 5:15 PM UTC

> figuring out if the company can afford this level of productivity at scale

This is the thing that boggles my mind. They spent their budget. They have 4 months of data. What do they have to show for it?

I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it.

But are you saying that Uber made this tool available, urged everybody to use it, and is confused about what happens when it worked? It's one thing if they decide AI isn't productive enough to be worth the cost.

Are they out of ideas on what to build next, or something?

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

#126

Post by sorry_outta_gas » Fri, May 01, 2026, 5:15 PM UTC

swiftcoder wrote: post #29

entropicdrifter wrote: post #24

Earlier quoted context omitted.

I'm on the same page. Do people not analyze the problems themselves? Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? I don't get it.

> Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works? That is exactly what they are doing, yes

[dead]

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

#127

Post by Aurornis » Fri, May 01, 2026, 5:15 PM UTC

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.

In my experience, the result is just more crawling across the separate microservices and additional reasoning to confirm how it all fits together.

The monolithic codebases are easier to crawl for any problem that can't be conveniently isolated to a single microservice.

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

#128

Post by conartist6 » Fri, May 01, 2026, 5:17 PM UTC

lukan wrote: post #90

conartist6 wrote: post #77

Earlier quoted context omitted.

So if the AI could do the same work on huge codebases with far fewer tokens, would it be good or bad for the AI companies do you think?

It would be good for the first AI company offering this.

Or an anti-ai company of course too; one whose goal was to level the playing field between humans and AIs again

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

#129

Post by readitalready » Fri, May 01, 2026, 5:17 PM UTC

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 think companies are charged API prices vs individual prices. That alone is 10x for Anthropic. Not sure though.

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

#130

Post by jcgrillo » Fri, May 01, 2026, 5:17 PM UTC

Sherveen wrote: post #18

MichaelNolan wrote: post #3

> 95% of Uber engineers now use AI tools monthly with 70% of committed code originating from AI. Well, that’s to be expected when using AI tools becomes relevant in your performance evaluation.

I don't understand this critique. (1) Did you previously think you weren't getting paid for doing what a company wants you to do, aka what THEY thought was productive? (2) Do you think all this AI generated code is useless? Edit: y'all are some whiney folk, ain't ya?

> (1) ...getting paid for doing what a company wants you to do...?

At my previous company, when the thing they thought they wanted me to do (which was not the thing they actually wanted... but whatever) diverged from my values I quit. You can just do things.

> (2) Do you think all this AI generated code is useless?

Almost universally, yes. Especially in organizations that historically haven't been particularly careful about hiring and have a huge number of young, inexperienced people. There are exceptions but they're rare enough that throwing that particular baby out with the bathwater isn't a big loss.