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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

#241

Post by Eldt »

adi_kurian wrote: post #93
embedding-shape wrote: post #74

Earlier quoted context omitted.

> I just can't figure how _how_ to burn that much money a month responsibly. Same but in regards to quotas. I'm on the 200 EUR ChatGPT plan, so presumable have the highest quota, using the "most expensive" models, on highest reasoning, in fast-mode (1.5x quota usage) and after a full day of almost exclusively doing programming with agents, I still get nowhere close to hitting my quota. In fact, since I started using…

Codex quota is suspiciously high right now. Either way, the subscription plans are not sustainable, and perhaps less relevant to any discussion about corporate API use. The prosumer developer plans are an insane deal. It is a golden age right now and it will end. If you tried to use the APIs to achieve the same thing, you would be spending thousands upon thousands of dollars a month. My completely unfounded conjectur…

Codex quota is/was 2x its normal amount for some promotion or something. I thought it ran out today but can't check right now

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

#242

Post by ok123456 »

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.

Will this result in people moving away from large monorepos to per-unit, quasi-micro repositories to save in token use?

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

#243

Post by steveBK123 »

bonesss wrote: post #181
steveBK123 wrote: post #116

Earlier quoted context omitted.

My friend is a CTO at a non-tech company and he's now dealing with code from non-SWEs trying to self serve with LLMs. But it's like a kid running a lemonade stand. Total DIY weekend project quality stuff that they are demanding go live. Hardcoded credentials, no concept of dev/qa/prod environments, no logging, no tests, no source control. I'm not really sure teaching basic SWE practices / SDLC / system design to peop…

It’s the same dilemma as old: it’s easier to teach a doctor UML than a coder Doctoring. But, critically, that’s about making doctor-facing IT systems not performing their skilled jobs. Bringing code does not help, but a validated user story with flow diagrams, a UI suggestion, and a valid ticket could. That’s the bridge to gap. Were I that CTO I’d explain that code carries liability, SWEs can end up in jail for malfe…

The ROI on teaching UML to a doctor is pretty low though right?

Non-technical people are not writing tickets, they are just slinging slop.

Another anecdote of things I've seen - a non technical person setting up some web scraping monstrosity with 200k lines of code. They beat their chest about how they didn't need the IT org. 1 month goes by and of course it breaks as soon as anything on the website changes and now they have a gun to ITs head to "fix it" and take it over.

This outcome for a DIY brittle web scraper is obvious to anyone that's ever written code, but shocking to someone who thinks LLMs are magic.

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

#244

Post by dunham »

ivirshup wrote: post #180
AlotOfReading wrote: post #114

Earlier quoted context omitted.

As always, improving accessibility for humans makes automation more effective. If the humans need to remember a PhD's worth of source code/documentation to contribute effectively, your codebase stinks.

People at my company have started writing docs specifically for claude. They're quite useful for me too, but kinda disappointing they never wrote these docs for their colleagues.

I recently saw this with the logseq api - the published api was an auto-generated stub. So I tried to grep the source code for the function and found detailed documentation written for claude. So I guess one benefit of all of this is that it's making people actually document things and maybe plan a little bit before implementing.

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

#245

Post by steveBK123 »

solenoid0937 wrote: post #167
steveBK123 wrote: post #103

Earlier quoted context omitted.

Your reply answers how you are able to spend money, not if it is returning sufficient dollar value per spend.. > I genuinely challenge someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value.

The problem with HN is that everyone here thinks like an engineer, not like a business owner. $10k a month on tokens is just not that much when you're already making $2M per engineer. If their productivity has increased even 10% then the spend was well worth it. Case in point, Meta made 33% more revenue this earnings report. Now you can nitpick and ask for attribution down to the dollar, but macro trends speak for th…

Go look up a multi-year chart of their revenue and find the inflection point where the AI made it go up faster (there isn't). In fact revenue growth used to be higher pre-2023.

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

#246

Post by stavros »

codebolt wrote: post #211
kansface wrote: post #189

Earlier quoted context omitted.

> 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?

I don't know about the GP, but my workflow is similar to theirs, but I aim to ship low thousands of lines per week. The fewer the better. I even tell the agent to only write high SNR tests, otherwise it just adds useless "make sure this function returns this thing we hardcoded".

I usually succeed, BTW. I spend a lot of time planning, but usually each PR is a few hundred lines, and fairly easily reviewable.

I mostly work with Python backends, though these days it might be any language (Ruby, Go, TS).

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

#247

Post by esseph »

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.

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

Depends on your subscription type. Some are just a flat monthly fee.

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

#249

Post by o10449366 »

Animats wrote: post #171

What is Uber developing? They're an app and a car allocator back end. Both work OK. Why are they spending so much? They gave up on self-driving, so that's not it.

this is the most tired hn comment ever

"X is just Y - why is it so complicated?"

its lazy and boring to read these on every thread about a disliked big company

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

#250

Post by dwedge »

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.

Ironically this is accidentally begging the question - that breaking them up into LLM context windows would be good because it would be to fit them in LLM context windows.

Maybe you're right but I'm aghast at how much of engineering over the last 15 years has been breaking up working monoliths to fit better within the budget of an external provider (first it was AWS). Those prices can change.

There are good reasons to use microservices but so often they're used for the wrong reasons.

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