10 ms·

Uber torches 2026 AI budget on Claude Code in four months

402 points by lwhsiao 5mo ago

bhagyeshsp 5mo ago
Wonderful, so when will I see novel features in my Uber app?
- danaw 5mo ago
  if you mean novel bugs than probably at the next app update
  - bhagyeshsp 5mo ago
    Hahaha.. good one :D
- lattalayta 5mo ago
  You can now reportedly book a hotel from the Uber app...which is totally a useful feature that I'm sure everyone will start to use /s https://investor.uber.com/news-events/news/press-release-details/2026/Uber-Expands-into-Travel-with-Hotel-Bookings-and-New-In-App-Features/default.aspx https://investor.uber.com/news-events/news/press-release-det...
  - bhagyeshsp 5mo ago
    I didn't know this. There's a term for this--which everyone of us now know--enshitification.
    - urbandw311er 5mo ago
      I think it’s more feature bloat to be honest. And trying to deliver on massively over-juiced profit projections.
      - 4ffg 5mo ago
        this is what happens when you have a spreadsheet fiddler as a CEO and no vision. By extension this use of tokens + token rationing constraint is another example of that - productivity by plastering shit all over the wall as opposed to taking it slower. Eventually something will happen and people will realise what a mistake they made.
PessimalDecimal 5mo ago
Is this a submarine? https://paulgraham.com/submarine.html https://paulgraham.com/submarine.html
- guywithahat 5mo ago
  It's funny how Paul is recommending people use PR firms, while in more recent videos michael seibel and others have strongly recommended against using them. It's interesting how things shift in ~20 years
MichaelNolan 5mo ago
> 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.
- miltonlost 5mo ago
  When managers and VPs all say, you must use AI or else you will not work here, then yes, people will use it.
- Sherveen 5mo ago
  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?
  - danaw 5mo ago
    you're missing their point; LLM use is often a part of your evaluation at some of these larger companies and they expect you to use them heavily or you will get a lashing
  - RHSeeger 5mo ago
    I think the point was that, when you make a metric goal of "you must use AI this much", then people will use AI even in ways that isn't adding to productivity.
  - bobsomers 5mo ago
    Not OP, but: 1. At my level, the company is not just paying me to do a task the way they want it done, they are paying for my experience to orchestrate the best way to do it. They want an outcome, and I'm responsible for figuring out how to get to that outcome with the right balance of cost, correctness, etc. But yes, the most dystopian reality is what you said. 2. It's not useless, but the AI generated code is absolutely lower quality than what I would have written myself, but there is no desire to clean it up. Companies have always had a disastrously bad understanding of technical debt and they finally have tool they can shove down developers throats that trades even more velocity for even less quality. They're going to take that trade every single time.
NicuCalcea 5mo ago
Can these AI-generated articles not be prompted to at least cite the primary sources? How do I know any of this is true? Here's a much better article: https://aimagazine.com/news/why-uber-has-already-burned-through-its-ai-budget https://aimagazine.com/news/why-uber-has-already-burned-thro...
- fcarraldo 5mo ago
  The OP isn't a good article, but this one is about an entirely different subject?
  - NicuCalcea 5mo ago
    Ah sorry, it's one of those annoying websites that automatically load another article when you scroll down too far. Updated the link.
woah 5mo ago
It's very easy to blow through hundreds of dollars a session using API tokens especially with the 1m context if you aren't careful about clearing old context. At the same time the subscription will allow the same usage for hundreds of dollars a month. Either Anthropic is absolutely hosing API users, massively subsidizing subscriptions, or a little bit of both.
- internetter 5mo ago
  https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes-to-war-for-ai-coding-dominance/ https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes... "Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute"
  - jackp96 5mo ago
    Really curious how many people actually get close to that level of usage? Their general business plan only offers the $100 version, with pay-as-you-go above that. If 95% of people are using $100 of value a month, the whales may not be hurting them that badly.
    - bakies 5mo ago
      I wrote my own "harness" and it exposes the api dollar cost since those come back in the responses even while using my sub. The conversations are typically $40-$60 and the longs ones with multiple compactions get to $100+ I say "Harness" because it's just a web interface that uses `cluade -p` so I can run it in containers and remotely access it.
  - beering 5mo ago
    That’s based on Anthropic’s retail price right? Not a fair comparison, like saying that Netflix must be losing money because every movie rental is $4 and a Netflix subscriber can watch 20 movies in a month.
internetter 5mo ago
I know I'm responding to AI right now, but > which means figuring out if the company can afford this level of productivity at scale. If it was actually productive, then the revenue would increase and affordability wouldn't be a question.
- sonofhans 5mo ago
  Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?
- orf 5mo ago
  Not every change a developer makes increases revenue, and the changes that do often have a lag time.
  - guywithahat 5mo ago
    This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).
  - fg137 5mo ago
    I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain
    - fragmede 5mo ago
      What if you're the SRE and the code fixes mean the site goes from 99% uptime to 99.9% up? How do you measure the revenue from that?
mkozlows 5mo ago
This terrible unsourced article seems to be citing this information piece: https://www.theinformation.com/newsletters/applied-ai/uber-cto-shows-claude-code-can-blow-ai-budgets https://www.theinformation.com/newsletters/applied-ai/uber-c... ... but the key fact about "$500-$2000" per engineer does not appear there, and seems to be fabricated.
- dcre 5mo ago
  Thank you for the link.
AstroBen 5mo ago
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KolmogorovComp 5mo ago
Honest question, does Uber need that much R&D? And do they expect the ROI to be positive?
- deleted 5mo ago
  [deleted]
- danaw 5mo ago
  i assume this also includes their self driving vehicle research and trucking, not just their consumer mobile app dev
  - Sohcahtoa82 5mo ago
    Uber cancelled their self-driving research years ago.
    - danaw 5mo ago
      a quick search shows they're not developing their own AVs but they're heavily investing in them via partnerships and other experimental projects
- freakynit 5mo ago
  Imagine making your product compliant across 100+ countries while regulatiions, labor-laws, tax rules, insurance requirements, and data privacy laws keep changing. Imagine itegrating dozens of payment methods - many of them highly localized - across emerging and developed markets, while dealing with fraud, chargebacks, KYC, AML, and settlement complexities. Imagine processing trillions of data points every day - rides, location updates, pricing signals, ETAs, traffic conditions, demand forecasts, payments, support events.... storing it efficiently, querying it in near real time, generating reports, and keeping the whole pipeline reliable. I have woorked in data engineering, and can tell you confidently that this alone requires an enormous R&d budget. Then there are the apps - not just customer-facing, but driver-facing, courier-facing, merchant-facing, fleet-management, onboarding, support, operations, compliance, finance, and hundreds of internal tools and dashboards. Then come the integrations. Companies running at Uber's scale genemrally have hundreds of tjese - mapping providers, payment processors, banks, identity verification, tax systems, telecoms, customer support platforms, fraud detection, analytics, ERP, CRM, and more. ... And then there are even more... Real-time routing and dispatch optimization Dynamic pricing and marketplace balancing Fraud detection and account security Driver/rider safety systems ML models for ETA, demand forecasting, incentives, and churn prevention Experimentation infrastructure for thousands of A/B tests Reliability engineering across globally distributed systems Data centers / cloud optimization at massive scale Localization across languages, currencies, addresses, and cultural norms Customer support automation at global scale Autonomous vehicle research, mapping, and computer vision ... to be fair, this is all what I could thing of based on my own work experience in related fields... there is definitely as many more systems in reality as mentioned abpve.
jeffbee 5mo ago
It's obvious that the word productivity has been used in this discussion to mean something other than the plain meaning of the word. If AI was productive, there would be no question about whether it could be afforded. If you're asking whether you can afford it then it isn't productive by definition. They are using it to mean a mechanism that produces prodigious amounts of toxic waste. That does not conform to the historical understanding of the word.
abuani 5mo ago
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 someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value. At a corporate level, I'd much rather hire a junior engineer who spends $100-$200/month and becomes productive then try and rationalize $100k/year in token spend.
- wolttam 5mo ago
  It turns out writing good prompts helps to keep token usage down as the model wastes fewer tokens discovering context it needs that wasn't hinted at in the prompt. Whereas a good prompt will give solid leads to all the specifics needed to complete the task.
- CyberDildonics 5mo ago
  They keep forgetting to put "make no mistakes", "think deeply" and "get it right the first time" in their prompts. When people have no ability to understand what they are doing, they will just rerun it endlessly hoping they get something passable. When that doesn't happen they burn money.
  - dpark 5mo ago
    I doubt most of this is from rerunning the same prompts over and over. This token burn is more likely from people using swarms of agents and orchestrators for “efficiency”. “I’ve got 2 dozen agents churning through the backlog to build this feature that would take one agent an hour to implement.”
    - cyanydeez 5mo ago
      managers call meetings and agents call swarms.
- 5mo ago
davidcann 5mo ago
> 70% of committed code originating from AI. How are they calculating that? They could be using my tool, Buildermark, but I do t think they are: https://buildermark.dev https://buildermark.dev
ninjagoo 5mo ago
According to [1], there are about 5500 people in Engineering at Uber. Using $1250 as the mid-point of the $ spend range, that comes to about $6.8 Million in engineering AI spend, ballpark, with the range being $2.75 Million - $12 Million. The article lists $3.4 Billion as the R&D spend. The AI spend does not appear to be a significant chunk of R&D spending (0.3% in 4 months or 1% annualized). If they didn't plan for it, sure, it's not peanuts in the budget, but in context not that much. The real question is, what did they get for that amount? The article claims that 70% of the code commit is now AI-generated, so presumably the code passed review and tests. Did it accelerate the feature count? did it reduce quality problems? Did it lead to other benefits? Sadly the article is silent on the outcomes, besides the higher spend. Maybe 4 months is too soon to assess the benefits. On the other hand, in an agile world ... [1] https://www.unifygtm.com/insights-headcount/uber https://www.unifygtm.com/insights-headcount/uber
- mkozlows 5mo ago
  Everything in this article is purely fake. The numbers don't add up, don't match any reported info, and are just fiction.
- yorwba 5mo ago
  The actual source https://www.theinformation.com/newsletters/applied-ai/uber-cto-shows-claude-code-can-blow-ai-budgets https://www.theinformation.com/newsletters/applied-ai/uber-c... says "about 11% of real, live updates to the code in its backend systems are being written by AI agents built primarily with Claude Code, up from just a fraction of a percent three months ago" and "He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools."
- pier25 5mo ago
  > that comes to about $6.8 Million in engineering AI spend That would be per month. Per year it would be $81.6M. A small fraction compared to the R&D budget but still a huge amount of cash to spend on something with (apparently) very little impact on the whole business.
AndrewKemendo 5mo ago
This continues to boggle my mind so hopefully somebody can explain how this is happening. I’ve been using all these tools since they started popping out around 2021 personally and professionally. I probably built four or five products at this point with assistance, not to mention the thousands and thousands of back-and-forth conversations for research or search or rubber ducking or whatever. I have never spent more than whatever the professional max plan is that is consistently $20 a month. I asked a friend of mine who spent a couple hundred dollars in like an few hours how they did it. The answer was they basically getting these agent groups of agents stuck in a loop and they’re constantly just generating verbose bullshit that is not even interrogated and doesn’t come out with any artifact that is inspectable no matter how expert you are. The couple of stories I have heard of these massive crazy spends are people literally just assuming these things can complete an entire human task in one shot, so they continue to hit the “spin the wheel” button until they get something closer to what they want But I’ve yet to see that actually work and it actually flies in the face of every instruction guide or documentation or prompt engineering process that has been described over the last almost 5 years
taf2 5mo ago
i bet someone mentioned openclaw one too many times
dataranger 5mo ago
we run an agentic pipeline in a different domain (data sourcing) and the only way the math works is to be ruthless about which stages actually need which model. As a founder, the question I always have is "what is the marginal value per token relative to engineer-hours saved." More of a gut feel at the moment, but would be great to calculate.
hyperpape 5mo ago
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 consideration: you have these features completed today, using AI. You’re profitable/unprofitable. So AI is productive/unproductive, right? No. You have to estimate what you would’ve gotten done without AI, and how much revenue you would’ve had then. 3. Business is often a Red Queen’s race. If you don’t make improvements, it’s often the case that you’ll lose revenue, as competitors take advantage. 4. Most likely, AI use is a mixture of working on things that matter and people throwing shit against the wall “because it’s easy now.” Actually measuring the potential productivity improvements means figuring out how to keep the first category and avoid the second. This isn’t me arguing for or against AI. It’s just me telling you not to be lazy and say “if it were productive you’d be able to measure it.”
- jcgrillo 5mo ago
  If it were 10x productive you'd be able to measure it indirectly, you'd be unable to avoid measuring it. So the initial claims were clearly lies. The research question is: Is it >1.0x productive? I agree that's very hard to measure. But given what this shit costs, it had better be answerable, and the multiple had better justify the cost.
- dijit 5mo ago
  > HN is filled with people who think engineering productivity is simple to measure. I think the prevailing (correct) consensus is that developer productivity is actually very hard to measure, and every time it is attempted the measure is immediately made a target making the whole thing pointless even if it had been a solid measurement- which it wasn't. IDK where you're getting the idea here that measuring productivity of anyone who isn't a factory worker is easy.
  - hyperpape 5mo ago
    I do not think it is easy, like I said. I am saying other people are acting like it’s easy. See the second comment on this article. https://news.ycombinator.com/item?id=47976781 https://news.ycombinator.com/item?id=47976781 See @emp17344 responding to me.
jcgrillo 5mo ago
AI token austerity when
tunesmith 5mo ago
I think as it becomes more common for executives to think we can replace software engineering with agents, I wonder if they might be basing their decisions off of unrealistic perceptions of the average software engineer. I guess I'm mulling two somewhat contradictory senses: 1. You get out of it what you put into it. A savvy CTO might be incredibly excited by everything they can do with agents, and improperly think that all the software engineers can do the same thing, when in reality your org's average software engineers might not have the creativity to even think of many cases where it could save them work. So by mandating agent usage, you might find that productivity hasn't improved while AI costs have increased. 2. When using AI, there are two gaps that become more obvious. First is the gap of: who tells the agent what to do? In many orgs, product isn't technically savvy enough to come up with a detailed spec/plan that LLM can use. And many cog-in-machine developers aren't positioned to come up with the spec, they just want to implement it. By expecting work to be implemented by agent-using developers, you might instead find a lot of idle workers waiting for work to show up. Second is the qa/review cycle. You've introduced a big change to the org but are you really saving cost or shifting it? I'm all for introducing LLM as optional to help existing developers increase velocity and quality, but I think the "let's restructure the org" movement is really dicey, especially for mid-size or smaller employers.
- tills13 5mo ago
  > You get out of it what you put into it. Beyond that, it's a force multiplier and it doesn't care if the force is positive or negative. Someone with poor software engineering principals can use AI to make an absolute mess quickly.
- joshuastuden 5mo ago
  You're basically arguing for massive headcount reductions.
  - tunesmith 5mo ago
    How so?
    - 5mo ago
saos 5mo ago
Interesting. Some companies have rolled it out to every department with a small budget. I wonder how this will end as AI becomes more expensive to use. If you can't quantify ROI then I guess you're cooked.
uncircle 5mo ago
Now AI slop factories make the HN front page?
- zombittack 5mo ago
  dead internet, mon frère.
tribune 5mo ago
Might as well get while the getting is good and Anthropic is subsidizing the cost of compute
dwa3592 5mo ago
I am confused - what did they ship based on this spending? - it is totally alright to spend that money if it made significant progress in some area. or did the engineers just chill and let claude take over daily duties? (this is also a benefit for employees in my opinion)
cassianoleal 5mo ago
> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity That's a bit of a logical leap with no demonstrable increase in productivity. All this shows is that they're spending a lot more on AI than they budgeted for. Nothing else.
- rconti 5mo ago
  Could be negative! All it shows is that Uber is probably incentivizing token usage just like so many other companies are. You get what you measure.
- Cyphus 5mo ago
  I think the tech industry in general is taking advantage of the fact that software productivity is hard to quantify to say whatever they want about their AI productivity gains. Apparently we are past the point of having to justify anything and can just equivocate increased AI spend with success.
geetee 5mo ago
No mention of if it actually improved outcomes.
- 2ndorderthought 5mo ago
  That's not the point silly goose.
Cyphus 5mo ago
> what started as an experiment in productivity became a runaway success Successfully burning through cash and tokens, alright, but what have they gotten out of it?
trjordan 5mo ago
> 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?
- bakugo 5mo ago
  > I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it. I'm glad to see we've reached the point of AI discourse at which anything that might be construed as criticism must be prefixed by "I'm also part of the cult, I'm not a non-believer, but" to avoid being dismissed as a heretic.
  - woah 5mo ago
    Since AI has become a partisan political football it makes sense
- zeafoamrun 5mo ago
  The personal max and teams plan actually are an amazing bargain compared to the API PAYG cost you get with Enterprise. I guess they really need their Enterprise features though, otherwise they could just tell users to expense a $200 max sub. Enterprises gonna Enterprise.
  - Raed667 5mo ago
    Entreprise gets you the written agreement that the data you send to Claude will never be used for model training
    - zeafoamrun 5mo ago
      It also gives the plan admins the ability to surveil in automated fashion what the company employees are prompting.
Animats 5mo ago
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.
- jitler 5mo ago
  > Both work OK If only. The optimizations they do on their matching algorithm has made the UX so terrible, I regularly use Lyft instead now.
- o10449366 5mo ago
  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
- urbandw311er 5mo ago
  This is a really underrated comment. It’s a great question and speaks volumes as to what the hell so many modern tech cos are actually doing with all their resource. Didn’t Elon strip most of the team at Twitter away, after some awful false starts, it pretty much ran fine on about 80% less human resource?
  - apf6 5mo ago
    There’s a difference between: 1) the minimum number of employees it takes to maintain the core product Vs 2) All the employees that it makes sense to hire for revenue and market expansion. Internet comments usually assume that (1) is the goal. But think of say the sales department. If every salesperson you hire brings in new company revenue that’s greater than their salary + overhead, then why not hire 1000 of them?
mattas 5mo ago
Wonder how many tokens would be saved if everyone just put “be brief” in their prompts. Also wonder if there is some perverse incentive for models to be verbose to juice tokens.
Painsawman123 5mo ago
If they burned through their ML budget in four months while using heavily subsidized models, we're going to see companies burn through their ML budgets in less than a week once those subsidies are no longer in place and they have to pay per tokens used.....
retired 5mo ago
Have we reached a point yet where companies are spending millions a year on software licenses, cloud and AI to the point where the return isn't worth it? Years ago I did work for a company that was spending over a million on Oracle product licenses and I was part of the consultant team they hired to rip it all out and just go for simple maintainable code based on open source products. Not only did it transform into a codebase that the average newly hired developer could maintain, you also had the savings of not paying Oracle a significant portion of your revenue. I feel like that will repeat itself in a few years time with the current cloud and AI train everyone is on. I haven't been in a professional setting for a while, I just code for fun nowadays so perhaps I'm somewhat out of the loop.
phillipcarter 5mo ago
> 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.
- AntiUSAbah 5mo ago
  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
  - phillipcarter 5mo ago
    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.
    - AntiUSAbah 5mo ago
      Yeah guess what I use at work. Guess fuerther what the ask is? Exactly claude code, maximize AI usage.
ookblah 5mo ago
this is pointless without knowing what they are measuring. you could genuinely moving faster or you could be optimizing for engineers in a rat race to push more code because all their peers are now doing it because those are the metrics you are measuring for "ai productivity".
pier25 5mo ago
> the AI coding tools represent a meaningful chunk that nobody expected would require this much capital so quickly Surprised Pikachu moment. And it's going to become even more expensive when AI companies start charging to actually make a profit.
monooso 5mo ago
> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity. This infers value from spend, which makes no sense. Burning the budget tells us engineers like the tool, not that it's producing value. Show me how to make two dollars whilst spending one, and budget isn't a problem.
tzury 5mo ago
What are the sources for the “facts” presented in this post?
deferredgrant 5mo ago
[flagged]
bahmboo 5mo ago
There are no sources or references.
robmay 5mo ago
Most people don't have the team and time to do heavy token efficiency engineering. But that's all we do. marketplace.neurometric.ai has a bunch of task specific small models, and we charge flat monthly fees. We bear the token risk.
alansaber 5mo ago
There's a line where the unfettered spending is just wasteful, we are well past the line
theusus 5mo ago
It's GPT 5.5 and it still can't do exactly the same thing I want. So, I think companies should call AI a lost cause.
S0y 5mo ago
But did it make them more productive?
- freakynit 5mo ago
  Oh it does... but what happens after 6 months is an entirely different story. A codebase that has exploded in size 2-3 times in just a few months,... internal architecture that is not layers of simple parts anymore, but, layers of complex architectures corresponding to individual agentic runs,... a codebase that now has 10 times more if-else and individual codepaths because you were not clear enough in your requirements, and used the phrase "handle all cases",... a codebase that neither you, nor anyone else now understands properly, thus, can't comment on what's possible anymore, and and at what costs when your manager or PM asks,...and finally, due to combined effect of these, a need for an ever increasing token budget, and constantly increasing fragilty of new AI-generated code due to repeated context compactions. And we haven't even touched on the security and performance elements yet. The right way to use these tools is to use them as, what I like to call, "code-monkeys". You tell them exactly what you want, where you want, how to do it, and how to architecture it, and more.. and then make them code.
dcre 5mo ago
While this is a fundamentally stupid story to begin with, it was at least reported somewhat better in other venues. The original report came from The Information, and at least this Yahoo Finance[0] writeup mentioned that. This article has very little content and no sourcing. [0]: https://finance.yahoo.com/sectors/technology/articles/ubers-anthropic-ai-push-hits-223109852.html https://finance.yahoo.com/sectors/technology/articles/ubers-...
redsocksfan45 5mo ago
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hybrid_study 5mo ago
The more I use Claude Code the harder it is for me to believe this behavior is a byproduct of the model. Behavior = ridiculously token inefficiencies
jimnotgym 5mo ago
I didn't see a bit where they said how this transformed into more productivity and more profit? What is the point in using AI to make developers more productive if you don't either have more features coded making more money, or fewer developers saving cost?
ilia-a 5mo ago
Not surprising, hit my 5h limit on Claude Code Max Plan, had some credits so switched to extended (api). 40 minutes later $30 credits gone... so yeah, I can see how this can happen.
linkregister 5mo ago
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/ 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.
zombiwoof 5mo ago
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DarenWatson 5mo ago
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.
ssfrr 5mo ago
It's wild that the article frames this as > what started as an experiment in productivity became a runaway success and > figuring out if the company can afford this level of productivity at scale It seems like they're equating "developers are spending a ton of money on this" with "this is creating a ton of value". I'm not saying that AI tools aren't valuable, but the article doesn't question this equivalence at all.
- giraffe_lady 5mo ago
  Bizarrely I feel like that reflects how a lot of tech leadership are viewing it? I can't explain this behavior but this is the first time I've seen this inversion: leaders believing money spent on something is itself value. I have dev friends who are legitimately under an edict to burn more tokens! It's freakish.
the_arun 5mo ago
I didn't see the article mentioning the outcomes achieved because of using AI compared to not using AI. I might be missing it. Mainly, Uber is a business. So profit & loss - both need to be measured to understand the equation.
jjcm 5mo ago
Speaking as someone who's bootstrapping here, I'm often envious of engineers at these larger companies, but I also worry that the incentives are screwed up. If I were an engineer at Uber, why wouldn't I select gpt 5.5 pro @ very high thinking + fast mode for a prompt? There's no incentive not to use the most powerful (and thus most expensive) model for even the smallest of changes. I tried one of these prompts for some tests I'm doing for image->html conversion, and a single prompt cost me $40. For someone that's paying that themselves, I'd pretty much never use this configuration. For someone at a large company where someone else is footing the bill, I'd spin these up regularly (the output was significantly better, fwiw). For engineers they're being rated on what they deliver, not the expenditure to get there. There are ways to do this cheaply, but there are no incentives for engineers to do so.
- threatofrain 5mo ago
  Companies may first want to see how fast you can scale work and then trim it back down for efficiency.
  - SkyEyedGreyWyrm 5mo ago
    How could they implement it? Try testing a bunch of models (closed and open sourced) and then seeing which one gives the best returns for it's cost? And then how do they check if it's being properly used, I have read of people just throwing their token budgets to the fire so that they show high usage for KPIs, while the most obvious cases of "X do this very wasteful thing" will be culled quickly (hopefully), I don't see how non-technical management can see through the thinnest layer of malicious compliance
- beering 5mo ago
  image->html is a pretty involved task though. That’s basically a frontend dev’s job. $40 wouldn’t cover an hour of their time.
- everforward 5mo ago
  SWE's are expensive; median salary is $133k (not counting health insurance, payroll taxes, etc). If you can shave off an hour of dev time with $40 in LLM credits, that's $26.50 cheaper than having them do it without. I'm not entirely convinced it works out that way so far, but that's the theory. Trying to bring down LLM costs is sort of a double-edged sword, because the dev needs to be cutting LLM costs by more than what you're paying them. If it takes them a day to bring costs down by $1 an invocation, then it takes almost 2 years to recoup the salary costs. It's worse because LLMs currently change so much I wouldn't be confident that their solution won't be broken before the 2 year period. Will we still be tool calling in 2 years, or will that be something new? Will thinking still be a thing, or will it be superceded by something else? I don't think anyone knows, even the frontier providers.
bilekas 5mo ago
I don't know, maybe this will make companies see the actual value in their engineering team. In my company they are starting to see the rotten fruits of the AI push, but it's come at the cost of many jobs, little planning and big ideas. Exactly how Anthropic, OpenAI and co are selling it.
pstuart 5mo ago
My company has an all you can eat policy, but I think we'd be well served by being thoughtful in optimizing usage so that we still have the overall capabilities but don't burn extra tokens by sloppy use.
somewhereoutth 5mo ago
> When developer productivity tools become so valuable that engineers blow the entire budget in four months, the issue isn't the tool but that the budget was invented too early to forecast this adoption curve. Where oh where can I find clients like these??
dyauspitr 5mo ago
I don’t understand. On the ChatGPT pro plan for $200/month, I am essentially running it 24/7 including nights and I can barely get it under the 40% usage mark. Why are companies not using this?
- claudiug 5mo ago
  when performance means using AI. is easy to make it happen
J_Shelby_J 5mo ago
I use a cli tool to build a document of all relevant code and then use ChatGPT 5.5 pro to plan a feature and generate an implementation plan, and then review and edit and paste it into codex on high to implement. And it works because it won’t stop until the rust compiles. But the code is garbage and makes bad decisions that no junior would. Unmaintainable junk and sometimes I spend more time refactoring than if I would of just built it myself. People here talking about generating 100ks LoC a month and I’m wondering if it’s a skill issue with me, or Codex or if I should pull all my investments out of companies heavily invested in AI like uber.
segmondy 5mo ago
They could have bought all their engines their own massive GPU. They could have built out their own DC. Nuts...
paulbjensen 5mo ago
This Claudemaxxing phenomenon is amusing as hell. I've been able to get by with the $20pm Pro subscription and reap great value out of Claude Code. I feel like it really is about: - Don't feed it the works of Shakespeare into the context window if all it's working on is a few files. I actually don't have a Claude.md file in my projects. - I write the prompt as if I was giving instructions to another developer or to myself on how I want to approach a specific coding, with a numbered step plan. I've actually been able to take the details written into a Jira ticket on a work project, feed it into Clade Code, and get really good results from it. - If you are responsible for the output, then you need to review the output - that does put a natural constraint on the tool's usage, but ultimately it is you who uses the tool, not the other way around. I feel like that's the thing - you have to find the right cadence, just like with running or driving a car - you need to find the level at which you control the car, at which you maintain a consistent pace, and at which you get code that does what you need it to do and meets the quality threshold you want.
wald3n 5mo ago
This doesn’t work at all
AtNightWeCode 5mo ago
Uber must be the biggest tech company that got lucky with timing. They are so incredible stupid and incompetent. How on earth do you end up with that cost for AI per user.
keeda 5mo ago
Relevant Pragmatic Engineer newsletter with many more cases along these lines, along with how some people are handling them: https://newsletter.pragmaticengineer.com/p/the-pulse-token-spend-breaks-budgets https://newsletter.pragmaticengineer.com/p/the-pulse-token-s... Tokenmaxxing seems more and more like a way to encourage experimentation and learning, and incidents like this are a part of learning. Like, today devs simply use the most expensive model by default, even to do extremely simple things. This is obviously wasteful and costly, and budgets will soon be imposed, but this is how they're figuring out the economics. For instance, like we estimate story points, we may estimate token budgets. At that point, why waste time and money invoking a model for a simple refactor when you could do it with a few keystrokes in an IDE? And why use a frontier model when an open-source local model could spit out that throwaway script? Local models can be tokenmaxxed, but frontier models will still be needed and will be used judiciously. Those are essentially trade-offs, and will eventually be empirically driven, which is what engineering is largely about. So economics will soon push engineers back to do what they're paid to do: engineering. Just that it will look very different compared to what we're used to.
- great_psy 5mo ago
  This is the first time I heard about estimating tokens for a task. I feel like you’re on to something. Management will pick this up, and make it part of the sprint planning. Engineers will pull out their hair wondering how you can do that. That’s like estimating how many CPU cycles a task will take. How many instructions will your laptop use while you work on something.
  - keeda 5mo ago
    Yeah, and I expect estimating token budgets is going to go the same trajectory (along with the same accompanying annoyances) as estimating and tracking story points! But done with the right mindset and proper awareness of the inherent uncertainty, you can sometimes achieve some reasonable estimates over time by starting with some T-shirt size estimates and then adapting based on actual numbers. Soon enough the team gets a sense of the nuances of the projects and its dependencies, and estimates get more accurate. As such, the example of estimating CPU cycles for tasks is actually relevant. For instance it is a common practice in real-time embedded systems running on tiny micro-controllers. But it is also possible to get good estimates for more complex applications / OS's / architectures simply by benchmarking them over time. The most common problem with planning and task estimation is that the corporate dynamics around it are not healthy: leadership often uses those as an SLA instead of the SWAG that they are. I worked on a team where our estimates never matched the actual time taken, partially due to rather unpredictable dependencies and high-priority tasks frequently interrupting us. But because we were clearly very high-functioning, management never held that against us. Those were some healthy corporate dynamics; not all places have that.
maplethorpe 5mo ago
In the Uber Eats app I can't even request a refund for an incorrect order anymore, because the UI doesn't allow me to scroll down to the "submit" button. It's been like this for months. I finally got my explanation.
glimshe 5mo ago
I spend $20/month on Gemini Pro and it greatly increased my productivity. I'm still in charge and only use AI for the more tedious or toughest problems. I can't see how these people could be spending this much productively.
mancerayder 5mo ago
And here comes the reining in of spending. If companies are anywhere like I'm seeing: 1 - Company mandate, start using AI 2 - You're afraid? Here's a mandate! 3 - (Devs and others discover Claude Code features where the coolest burn mad tokens) 4 - Um, yeah we're going to have to take a look at the spend here 5 _ What's 5? We know steps 3 and 4 will cycle a bit more, and we know it's going to cost more - these were startup teaser costs.
mohamedabdallah 5mo ago
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tokyoproductj 5mo ago
AI might not make engineering cheaper — just more elastic. Instead of paying for engineers, you’re effectively paying per unit of thinking. At scale, that could get very expensive very quickly.
pwr1 5mo ago
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ttariq 5mo ago
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pradeep1177 5mo ago
For people using Claude Code heavily, do you know which turns are burning the most tokens, or do you only see the final bill/limit?
macAndPeach 5mo ago
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rp1-run 5mo ago
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tellmefrankie 5mo ago
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StaxReport 5mo ago
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402 points by lwhsiao 5mo ago

bhagyeshsp 5mo ago

Wonderful, so when will I see novel features in my Uber app?

danaw 5mo ago
if you mean novel bugs than probably at the next app update
- bhagyeshsp 5mo ago
  Hahaha.. good one :D
lattalayta 5mo ago
You can now reportedly book a hotel from the Uber app...which is totally a useful feature that I'm sure everyone will start to use /s https://investor.uber.com/news-events/news/press-release-details/2026/Uber-Expands-into-Travel-with-Hotel-Bookings-and-New-In-App-Features/default.aspx https://investor.uber.com/news-events/news/press-release-det...
- bhagyeshsp 5mo ago
  I didn't know this. There's a term for this--which everyone of us now know--enshitification.
  - urbandw311er 5mo ago
    I think it’s more feature bloat to be honest. And trying to deliver on massively over-juiced profit projections.
    - 4ffg 5mo ago
      this is what happens when you have a spreadsheet fiddler as a CEO and no vision. By extension this use of tokens + token rationing constraint is another example of that - productivity by plastering shit all over the wall as opposed to taking it slower. Eventually something will happen and people will realise what a mistake they made.

PessimalDecimal 5mo ago

Is this a submarine? https://paulgraham.com/submarine.html https://paulgraham.com/submarine.html

guywithahat 5mo ago
It's funny how Paul is recommending people use PR firms, while in more recent videos michael seibel and others have strongly recommended against using them. It's interesting how things shift in ~20 years

MichaelNolan 5mo ago

> 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.

miltonlost 5mo ago
When managers and VPs all say, you must use AI or else you will not work here, then yes, people will use it.
Sherveen 5mo ago
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?
- danaw 5mo ago
  you're missing their point; LLM use is often a part of your evaluation at some of these larger companies and they expect you to use them heavily or you will get a lashing
- RHSeeger 5mo ago
  I think the point was that, when you make a metric goal of "you must use AI this much", then people will use AI even in ways that isn't adding to productivity.
- bobsomers 5mo ago
  Not OP, but: 1. At my level, the company is not just paying me to do a task the way they want it done, they are paying for my experience to orchestrate the best way to do it. They want an outcome, and I'm responsible for figuring out how to get to that outcome with the right balance of cost, correctness, etc. But yes, the most dystopian reality is what you said. 2. It's not useless, but the AI generated code is absolutely lower quality than what I would have written myself, but there is no desire to clean it up. Companies have always had a disastrously bad understanding of technical debt and they finally have tool they can shove down developers throats that trades even more velocity for even less quality. They're going to take that trade every single time.

NicuCalcea 5mo ago

Can these AI-generated articles not be prompted to at least cite the primary sources? How do I know any of this is true? Here's a much better article: https://aimagazine.com/news/why-uber-has-already-burned-through-its-ai-budget https://aimagazine.com/news/why-uber-has-already-burned-thro...

fcarraldo 5mo ago
The OP isn't a good article, but this one is about an entirely different subject?
- NicuCalcea 5mo ago
  Ah sorry, it's one of those annoying websites that automatically load another article when you scroll down too far. Updated the link.

woah 5mo ago

It's very easy to blow through hundreds of dollars a session using API tokens especially with the 1m context if you aren't careful about clearing old context. At the same time the subscription will allow the same usage for hundreds of dollars a month. Either Anthropic is absolutely hosing API users, massively subsidizing subscriptions, or a little bit of both.

internetter 5mo ago
https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes-to-war-for-ai-coding-dominance/ https://www.forbes.com/sites/annatong/2026/03/05/cursor-goes... "Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute"
- jackp96 5mo ago
  Really curious how many people actually get close to that level of usage? Their general business plan only offers the $100 version, with pay-as-you-go above that. If 95% of people are using $100 of value a month, the whales may not be hurting them that badly.
  - bakies 5mo ago
    I wrote my own "harness" and it exposes the api dollar cost since those come back in the responses even while using my sub. The conversations are typically $40-$60 and the longs ones with multiple compactions get to $100+ I say "Harness" because it's just a web interface that uses `cluade -p` so I can run it in containers and remotely access it.
- beering 5mo ago
  That’s based on Anthropic’s retail price right? Not a fair comparison, like saying that Netflix must be losing money because every movie rental is $4 and a Netflix subscriber can watch 20 movies in a month.

internetter 5mo ago

I know I'm responding to AI right now, but > which means figuring out if the company can afford this level of productivity at scale. If it was actually productive, then the revenue would increase and affordability wouldn't be a question.

sonofhans 5mo ago
Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?
orf 5mo ago
Not every change a developer makes increases revenue, and the changes that do often have a lag time.
- guywithahat 5mo ago
  This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).
- fg137 5mo ago
  I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain
  - fragmede 5mo ago
    What if you're the SRE and the code fixes mean the site goes from 99% uptime to 99.9% up? How do you measure the revenue from that?

mkozlows 5mo ago

This terrible unsourced article seems to be citing this information piece: https://www.theinformation.com/newsletters/applied-ai/uber-cto-shows-claude-code-can-blow-ai-budgets https://www.theinformation.com/newsletters/applied-ai/uber-c... ... but the key fact about "$500-$2000" per engineer does not appear there, and seems to be fabricated.

dcre 5mo ago
Thank you for the link.

AstroBen 5mo ago

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KolmogorovComp 5mo ago

Honest question, does Uber need that much R&D? And do they expect the ROI to be positive?

deleted 5mo ago
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danaw 5mo ago
i assume this also includes their self driving vehicle research and trucking, not just their consumer mobile app dev
- Sohcahtoa82 5mo ago
  Uber cancelled their self-driving research years ago.
  - danaw 5mo ago
    a quick search shows they're not developing their own AVs but they're heavily investing in them via partnerships and other experimental projects
freakynit 5mo ago
Imagine making your product compliant across 100+ countries while regulatiions, labor-laws, tax rules, insurance requirements, and data privacy laws keep changing. Imagine itegrating dozens of payment methods - many of them highly localized - across emerging and developed markets, while dealing with fraud, chargebacks, KYC, AML, and settlement complexities. Imagine processing trillions of data points every day - rides, location updates, pricing signals, ETAs, traffic conditions, demand forecasts, payments, support events.... storing it efficiently, querying it in near real time, generating reports, and keeping the whole pipeline reliable. I have woorked in data engineering, and can tell you confidently that this alone requires an enormous R&d budget. Then there are the apps - not just customer-facing, but driver-facing, courier-facing, merchant-facing, fleet-management, onboarding, support, operations, compliance, finance, and hundreds of internal tools and dashboards. Then come the integrations. Companies running at Uber's scale genemrally have hundreds of tjese - mapping providers, payment processors, banks, identity verification, tax systems, telecoms, customer support platforms, fraud detection, analytics, ERP, CRM, and more. ... And then there are even more... Real-time routing and dispatch optimization Dynamic pricing and marketplace balancing Fraud detection and account security Driver/rider safety systems ML models for ETA, demand forecasting, incentives, and churn prevention Experimentation infrastructure for thousands of A/B tests Reliability engineering across globally distributed systems Data centers / cloud optimization at massive scale Localization across languages, currencies, addresses, and cultural norms Customer support automation at global scale Autonomous vehicle research, mapping, and computer vision ... to be fair, this is all what I could thing of based on my own work experience in related fields... there is definitely as many more systems in reality as mentioned abpve.

jeffbee 5mo ago

It's obvious that the word productivity has been used in this discussion to mean something other than the plain meaning of the word. If AI was productive, there would be no question about whether it could be afforded. If you're asking whether you can afford it then it isn't productive by definition. They are using it to mean a mechanism that produces prodigious amounts of toxic waste. That does not conform to the historical understanding of the word.

abuani 5mo ago

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 someone spending $5-$10k a month to demonstrate how that turns into $50-$100k in value. At a corporate level, I'd much rather hire a junior engineer who spends $100-$200/month and becomes productive then try and rationalize $100k/year in token spend.

wolttam 5mo ago
It turns out writing good prompts helps to keep token usage down as the model wastes fewer tokens discovering context it needs that wasn't hinted at in the prompt. Whereas a good prompt will give solid leads to all the specifics needed to complete the task.
CyberDildonics 5mo ago
They keep forgetting to put "make no mistakes", "think deeply" and "get it right the first time" in their prompts. When people have no ability to understand what they are doing, they will just rerun it endlessly hoping they get something passable. When that doesn't happen they burn money.
- dpark 5mo ago
  I doubt most of this is from rerunning the same prompts over and over. This token burn is more likely from people using swarms of agents and orchestrators for “efficiency”. “I’ve got 2 dozen agents churning through the backlog to build this feature that would take one agent an hour to implement.”
  - cyanydeez 5mo ago
    managers call meetings and agents call swarms.
5mo ago

davidcann 5mo ago

> 70% of committed code originating from AI. How are they calculating that? They could be using my tool, Buildermark, but I do t think they are: https://buildermark.dev https://buildermark.dev

ninjagoo 5mo ago

According to [1], there are about 5500 people in Engineering at Uber. Using $1250 as the mid-point of the $ spend range, that comes to about $6.8 Million in engineering AI spend, ballpark, with the range being $2.75 Million - $12 Million. The article lists $3.4 Billion as the R&D spend. The AI spend does not appear to be a significant chunk of R&D spending (0.3% in 4 months or 1% annualized). If they didn't plan for it, sure, it's not peanuts in the budget, but in context not that much. The real question is, what did they get for that amount? The article claims that 70% of the code commit is now AI-generated, so presumably the code passed review and tests. Did it accelerate the feature count? did it reduce quality problems? Did it lead to other benefits? Sadly the article is silent on the outcomes, besides the higher spend. Maybe 4 months is too soon to assess the benefits. On the other hand, in an agile world ... [1] https://www.unifygtm.com/insights-headcount/uber https://www.unifygtm.com/insights-headcount/uber

mkozlows 5mo ago
Everything in this article is purely fake. The numbers don't add up, don't match any reported info, and are just fiction.
yorwba 5mo ago
The actual source https://www.theinformation.com/newsletters/applied-ai/uber-cto-shows-claude-code-can-blow-ai-budgets https://www.theinformation.com/newsletters/applied-ai/uber-c... says "about 11% of real, live updates to the code in its backend systems are being written by AI agents built primarily with Claude Code, up from just a fraction of a percent three months ago" and "He wouldn’t disclose exact figures of the company’s software budget or what it spends on AI coding tools."
pier25 5mo ago
> that comes to about $6.8 Million in engineering AI spend That would be per month. Per year it would be $81.6M. A small fraction compared to the R&D budget but still a huge amount of cash to spend on something with (apparently) very little impact on the whole business.

AndrewKemendo 5mo ago

This continues to boggle my mind so hopefully somebody can explain how this is happening. I’ve been using all these tools since they started popping out around 2021 personally and professionally. I probably built four or five products at this point with assistance, not to mention the thousands and thousands of back-and-forth conversations for research or search or rubber ducking or whatever. I have never spent more than whatever the professional max plan is that is consistently $20 a month. I asked a friend of mine who spent a couple hundred dollars in like an few hours how they did it. The answer was they basically getting these agent groups of agents stuck in a loop and they’re constantly just generating verbose bullshit that is not even interrogated and doesn’t come out with any artifact that is inspectable no matter how expert you are. The couple of stories I have heard of these massive crazy spends are people literally just assuming these things can complete an entire human task in one shot, so they continue to hit the “spin the wheel” button until they get something closer to what they want But I’ve yet to see that actually work and it actually flies in the face of every instruction guide or documentation or prompt engineering process that has been described over the last almost 5 years

taf2 5mo ago

i bet someone mentioned openclaw one too many times

dataranger 5mo ago

we run an agentic pipeline in a different domain (data sourcing) and the only way the math works is to be ruthless about which stages actually need which model. As a founder, the question I always have is "what is the marginal value per token relative to engineer-hours saved." More of a gut feel at the moment, but would be great to calculate.

hyperpape 5mo ago

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 consideration: you have these features completed today, using AI. You’re profitable/unprofitable. So AI is productive/unproductive, right? No. You have to estimate what you would’ve gotten done without AI, and how much revenue you would’ve had then. 3. Business is often a Red Queen’s race. If you don’t make improvements, it’s often the case that you’ll lose revenue, as competitors take advantage. 4. Most likely, AI use is a mixture of working on things that matter and people throwing shit against the wall “because it’s easy now.” Actually measuring the potential productivity improvements means figuring out how to keep the first category and avoid the second. This isn’t me arguing for or against AI. It’s just me telling you not to be lazy and say “if it were productive you’d be able to measure it.”

jcgrillo 5mo ago
If it were 10x productive you'd be able to measure it indirectly, you'd be unable to avoid measuring it. So the initial claims were clearly lies. The research question is: Is it >1.0x productive? I agree that's very hard to measure. But given what this shit costs, it had better be answerable, and the multiple had better justify the cost.
dijit 5mo ago
> HN is filled with people who think engineering productivity is simple to measure. I think the prevailing (correct) consensus is that developer productivity is actually very hard to measure, and every time it is attempted the measure is immediately made a target making the whole thing pointless even if it had been a solid measurement- which it wasn't. IDK where you're getting the idea here that measuring productivity of anyone who isn't a factory worker is easy.
- hyperpape 5mo ago
  I do not think it is easy, like I said. I am saying other people are acting like it’s easy. See the second comment on this article. https://news.ycombinator.com/item?id=47976781 https://news.ycombinator.com/item?id=47976781 See @emp17344 responding to me.

jcgrillo 5mo ago

AI token austerity when

tunesmith 5mo ago

I think as it becomes more common for executives to think we can replace software engineering with agents, I wonder if they might be basing their decisions off of unrealistic perceptions of the average software engineer. I guess I'm mulling two somewhat contradictory senses: 1. You get out of it what you put into it. A savvy CTO might be incredibly excited by everything they can do with agents, and improperly think that all the software engineers can do the same thing, when in reality your org's average software engineers might not have the creativity to even think of many cases where it could save them work. So by mandating agent usage, you might find that productivity hasn't improved while AI costs have increased. 2. When using AI, there are two gaps that become more obvious. First is the gap of: who tells the agent what to do? In many orgs, product isn't technically savvy enough to come up with a detailed spec/plan that LLM can use. And many cog-in-machine developers aren't positioned to come up with the spec, they just want to implement it. By expecting work to be implemented by agent-using developers, you might instead find a lot of idle workers waiting for work to show up. Second is the qa/review cycle. You've introduced a big change to the org but are you really saving cost or shifting it? I'm all for introducing LLM as optional to help existing developers increase velocity and quality, but I think the "let's restructure the org" movement is really dicey, especially for mid-size or smaller employers.

tills13 5mo ago
> You get out of it what you put into it. Beyond that, it's a force multiplier and it doesn't care if the force is positive or negative. Someone with poor software engineering principals can use AI to make an absolute mess quickly.
joshuastuden 5mo ago
You're basically arguing for massive headcount reductions.
- tunesmith 5mo ago
  How so?
  - 5mo ago

saos 5mo ago

Interesting. Some companies have rolled it out to every department with a small budget. I wonder how this will end as AI becomes more expensive to use. If you can't quantify ROI then I guess you're cooked.

uncircle 5mo ago

Now AI slop factories make the HN front page?

zombittack 5mo ago
dead internet, mon frère.

tribune 5mo ago

Might as well get while the getting is good and Anthropic is subsidizing the cost of compute

dwa3592 5mo ago

I am confused - what did they ship based on this spending? - it is totally alright to spend that money if it made significant progress in some area. or did the engineers just chill and let claude take over daily duties? (this is also a benefit for employees in my opinion)

cassianoleal 5mo ago

> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity That's a bit of a logical leap with no demonstrable increase in productivity. All this shows is that they're spending a lot more on AI than they budgeted for. Nothing else.

rconti 5mo ago
Could be negative! All it shows is that Uber is probably incentivizing token usage just like so many other companies are. You get what you measure.
Cyphus 5mo ago
I think the tech industry in general is taking advantage of the fact that software productivity is hard to quantify to say whatever they want about their AI productivity gains. Apparently we are past the point of having to justify anything and can just equivocate increased AI spend with success.

geetee 5mo ago

No mention of if it actually improved outcomes.

2ndorderthought 5mo ago
That's not the point silly goose.

Cyphus 5mo ago

> what started as an experiment in productivity became a runaway success Successfully burning through cash and tokens, alright, but what have they gotten out of it?

trjordan 5mo ago

> 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?

bakugo 5mo ago
> I'm not a hater; I'm not a luddite. I have a $200 Max plan and I use it. I'm glad to see we've reached the point of AI discourse at which anything that might be construed as criticism must be prefixed by "I'm also part of the cult, I'm not a non-believer, but" to avoid being dismissed as a heretic.
- woah 5mo ago
  Since AI has become a partisan political football it makes sense
zeafoamrun 5mo ago
The personal max and teams plan actually are an amazing bargain compared to the API PAYG cost you get with Enterprise. I guess they really need their Enterprise features though, otherwise they could just tell users to expense a $200 max sub. Enterprises gonna Enterprise.
- Raed667 5mo ago
  Entreprise gets you the written agreement that the data you send to Claude will never be used for model training
  - zeafoamrun 5mo ago
    It also gives the plan admins the ability to surveil in automated fashion what the company employees are prompting.

Animats 5mo ago

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.

jitler 5mo ago
> Both work OK If only. The optimizations they do on their matching algorithm has made the UX so terrible, I regularly use Lyft instead now.
o10449366 5mo ago
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
urbandw311er 5mo ago
This is a really underrated comment. It’s a great question and speaks volumes as to what the hell so many modern tech cos are actually doing with all their resource. Didn’t Elon strip most of the team at Twitter away, after some awful false starts, it pretty much ran fine on about 80% less human resource?
- apf6 5mo ago
  There’s a difference between: 1) the minimum number of employees it takes to maintain the core product Vs 2) All the employees that it makes sense to hire for revenue and market expansion. Internet comments usually assume that (1) is the goal. But think of say the sales department. If every salesperson you hire brings in new company revenue that’s greater than their salary + overhead, then why not hire 1000 of them?

mattas 5mo ago

Wonder how many tokens would be saved if everyone just put “be brief” in their prompts. Also wonder if there is some perverse incentive for models to be verbose to juice tokens.

Painsawman123 5mo ago

If they burned through their ML budget in four months while using heavily subsidized models, we're going to see companies burn through their ML budgets in less than a week once those subsidies are no longer in place and they have to pay per tokens used.....

retired 5mo ago

Have we reached a point yet where companies are spending millions a year on software licenses, cloud and AI to the point where the return isn't worth it? Years ago I did work for a company that was spending over a million on Oracle product licenses and I was part of the consultant team they hired to rip it all out and just go for simple maintainable code based on open source products. Not only did it transform into a codebase that the average newly hired developer could maintain, you also had the savings of not paying Oracle a significant portion of your revenue. I feel like that will repeat itself in a few years time with the current cloud and AI train everyone is on. I haven't been in a professional setting for a while, I just code for fun nowadays so perhaps I'm somewhat out of the loop.

phillipcarter 5mo ago

> 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.

AntiUSAbah 5mo ago
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
- phillipcarter 5mo ago
  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.
  - AntiUSAbah 5mo ago
    Yeah guess what I use at work. Guess fuerther what the ask is? Exactly claude code, maximize AI usage.

ookblah 5mo ago

this is pointless without knowing what they are measuring. you could genuinely moving faster or you could be optimizing for engineers in a rat race to push more code because all their peers are now doing it because those are the metrics you are measuring for "ai productivity".

pier25 5mo ago

> the AI coding tools represent a meaningful chunk that nobody expected would require this much capital so quickly Surprised Pikachu moment. And it's going to become even more expensive when AI companies start charging to actually make a profit.

monooso 5mo ago

> Uber's unexpected budget burn matters because it signals how valuable AI tools have become to engineering productivity. This infers value from spend, which makes no sense. Burning the budget tells us engineers like the tool, not that it's producing value. Show me how to make two dollars whilst spending one, and budget isn't a problem.

tzury 5mo ago

What are the sources for the “facts” presented in this post?

deferredgrant 5mo ago

[flagged]

bahmboo 5mo ago

There are no sources or references.

robmay 5mo ago

Most people don't have the team and time to do heavy token efficiency engineering. But that's all we do. marketplace.neurometric.ai has a bunch of task specific small models, and we charge flat monthly fees. We bear the token risk.

alansaber 5mo ago

There's a line where the unfettered spending is just wasteful, we are well past the line

theusus 5mo ago

It's GPT 5.5 and it still can't do exactly the same thing I want. So, I think companies should call AI a lost cause.

S0y 5mo ago

But did it make them more productive?

freakynit 5mo ago
Oh it does... but what happens after 6 months is an entirely different story. A codebase that has exploded in size 2-3 times in just a few months,... internal architecture that is not layers of simple parts anymore, but, layers of complex architectures corresponding to individual agentic runs,... a codebase that now has 10 times more if-else and individual codepaths because you were not clear enough in your requirements, and used the phrase "handle all cases",... a codebase that neither you, nor anyone else now understands properly, thus, can't comment on what's possible anymore, and and at what costs when your manager or PM asks,...and finally, due to combined effect of these, a need for an ever increasing token budget, and constantly increasing fragilty of new AI-generated code due to repeated context compactions. And we haven't even touched on the security and performance elements yet. The right way to use these tools is to use them as, what I like to call, "code-monkeys". You tell them exactly what you want, where you want, how to do it, and how to architecture it, and more.. and then make them code.

dcre 5mo ago

While this is a fundamentally stupid story to begin with, it was at least reported somewhat better in other venues. The original report came from The Information, and at least this Yahoo Finance[0] writeup mentioned that. This article has very little content and no sourcing. [0]: https://finance.yahoo.com/sectors/technology/articles/ubers-anthropic-ai-push-hits-223109852.html https://finance.yahoo.com/sectors/technology/articles/ubers-...

redsocksfan45 5mo ago

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hybrid_study 5mo ago

The more I use Claude Code the harder it is for me to believe this behavior is a byproduct of the model. Behavior = ridiculously token inefficiencies

jimnotgym 5mo ago

I didn't see a bit where they said how this transformed into more productivity and more profit? What is the point in using AI to make developers more productive if you don't either have more features coded making more money, or fewer developers saving cost?

ilia-a 5mo ago

Not surprising, hit my 5h limit on Claude Code Max Plan, had some credits so switched to extended (api). 40 minutes later $30 credits gone... so yeah, I can see how this can happen.

linkregister 5mo ago

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/ 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.

zombiwoof 5mo ago

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DarenWatson 5mo ago

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.

ssfrr 5mo ago

It's wild that the article frames this as > what started as an experiment in productivity became a runaway success and > figuring out if the company can afford this level of productivity at scale It seems like they're equating "developers are spending a ton of money on this" with "this is creating a ton of value". I'm not saying that AI tools aren't valuable, but the article doesn't question this equivalence at all.

giraffe_lady 5mo ago
Bizarrely I feel like that reflects how a lot of tech leadership are viewing it? I can't explain this behavior but this is the first time I've seen this inversion: leaders believing money spent on something is itself value. I have dev friends who are legitimately under an edict to burn more tokens! It's freakish.

the_arun 5mo ago

I didn't see the article mentioning the outcomes achieved because of using AI compared to not using AI. I might be missing it. Mainly, Uber is a business. So profit & loss - both need to be measured to understand the equation.

jjcm 5mo ago

Speaking as someone who's bootstrapping here, I'm often envious of engineers at these larger companies, but I also worry that the incentives are screwed up. If I were an engineer at Uber, why wouldn't I select gpt 5.5 pro @ very high thinking + fast mode for a prompt? There's no incentive not to use the most powerful (and thus most expensive) model for even the smallest of changes. I tried one of these prompts for some tests I'm doing for image->html conversion, and a single prompt cost me $40. For someone that's paying that themselves, I'd pretty much never use this configuration. For someone at a large company where someone else is footing the bill, I'd spin these up regularly (the output was significantly better, fwiw). For engineers they're being rated on what they deliver, not the expenditure to get there. There are ways to do this cheaply, but there are no incentives for engineers to do so.

threatofrain 5mo ago
Companies may first want to see how fast you can scale work and then trim it back down for efficiency.
- SkyEyedGreyWyrm 5mo ago
  How could they implement it? Try testing a bunch of models (closed and open sourced) and then seeing which one gives the best returns for it's cost? And then how do they check if it's being properly used, I have read of people just throwing their token budgets to the fire so that they show high usage for KPIs, while the most obvious cases of "X do this very wasteful thing" will be culled quickly (hopefully), I don't see how non-technical management can see through the thinnest layer of malicious compliance
beering 5mo ago
image->html is a pretty involved task though. That’s basically a frontend dev’s job. $40 wouldn’t cover an hour of their time.
everforward 5mo ago
SWE's are expensive; median salary is $133k (not counting health insurance, payroll taxes, etc). If you can shave off an hour of dev time with $40 in LLM credits, that's $26.50 cheaper than having them do it without. I'm not entirely convinced it works out that way so far, but that's the theory. Trying to bring down LLM costs is sort of a double-edged sword, because the dev needs to be cutting LLM costs by more than what you're paying them. If it takes them a day to bring costs down by $1 an invocation, then it takes almost 2 years to recoup the salary costs. It's worse because LLMs currently change so much I wouldn't be confident that their solution won't be broken before the 2 year period. Will we still be tool calling in 2 years, or will that be something new? Will thinking still be a thing, or will it be superceded by something else? I don't think anyone knows, even the frontier providers.

bilekas 5mo ago

I don't know, maybe this will make companies see the actual value in their engineering team. In my company they are starting to see the rotten fruits of the AI push, but it's come at the cost of many jobs, little planning and big ideas. Exactly how Anthropic, OpenAI and co are selling it.

pstuart 5mo ago

My company has an all you can eat policy, but I think we'd be well served by being thoughtful in optimizing usage so that we still have the overall capabilities but don't burn extra tokens by sloppy use.

somewhereoutth 5mo ago

> When developer productivity tools become so valuable that engineers blow the entire budget in four months, the issue isn't the tool but that the budget was invented too early to forecast this adoption curve. Where oh where can I find clients like these??

dyauspitr 5mo ago

I don’t understand. On the ChatGPT pro plan for $200/month, I am essentially running it 24/7 including nights and I can barely get it under the 40% usage mark. Why are companies not using this?

claudiug 5mo ago
when performance means using AI. is easy to make it happen

J_Shelby_J 5mo ago

I use a cli tool to build a document of all relevant code and then use ChatGPT 5.5 pro to plan a feature and generate an implementation plan, and then review and edit and paste it into codex on high to implement. And it works because it won’t stop until the rust compiles. But the code is garbage and makes bad decisions that no junior would. Unmaintainable junk and sometimes I spend more time refactoring than if I would of just built it myself. People here talking about generating 100ks LoC a month and I’m wondering if it’s a skill issue with me, or Codex or if I should pull all my investments out of companies heavily invested in AI like uber.

segmondy 5mo ago

They could have bought all their engines their own massive GPU. They could have built out their own DC. Nuts...

paulbjensen 5mo ago

This Claudemaxxing phenomenon is amusing as hell. I've been able to get by with the $20pm Pro subscription and reap great value out of Claude Code. I feel like it really is about: - Don't feed it the works of Shakespeare into the context window if all it's working on is a few files. I actually don't have a Claude.md file in my projects. - I write the prompt as if I was giving instructions to another developer or to myself on how I want to approach a specific coding, with a numbered step plan. I've actually been able to take the details written into a Jira ticket on a work project, feed it into Clade Code, and get really good results from it. - If you are responsible for the output, then you need to review the output - that does put a natural constraint on the tool's usage, but ultimately it is you who uses the tool, not the other way around. I feel like that's the thing - you have to find the right cadence, just like with running or driving a car - you need to find the level at which you control the car, at which you maintain a consistent pace, and at which you get code that does what you need it to do and meets the quality threshold you want.

wald3n 5mo ago

This doesn’t work at all

AtNightWeCode 5mo ago

Uber must be the biggest tech company that got lucky with timing. They are so incredible stupid and incompetent. How on earth do you end up with that cost for AI per user.

keeda 5mo ago

Relevant Pragmatic Engineer newsletter with many more cases along these lines, along with how some people are handling them: https://newsletter.pragmaticengineer.com/p/the-pulse-token-spend-breaks-budgets https://newsletter.pragmaticengineer.com/p/the-pulse-token-s... Tokenmaxxing seems more and more like a way to encourage experimentation and learning, and incidents like this are a part of learning. Like, today devs simply use the most expensive model by default, even to do extremely simple things. This is obviously wasteful and costly, and budgets will soon be imposed, but this is how they're figuring out the economics. For instance, like we estimate story points, we may estimate token budgets. At that point, why waste time and money invoking a model for a simple refactor when you could do it with a few keystrokes in an IDE? And why use a frontier model when an open-source local model could spit out that throwaway script? Local models can be tokenmaxxed, but frontier models will still be needed and will be used judiciously. Those are essentially trade-offs, and will eventually be empirically driven, which is what engineering is largely about. So economics will soon push engineers back to do what they're paid to do: engineering. Just that it will look very different compared to what we're used to.

great_psy 5mo ago
This is the first time I heard about estimating tokens for a task. I feel like you’re on to something. Management will pick this up, and make it part of the sprint planning. Engineers will pull out their hair wondering how you can do that. That’s like estimating how many CPU cycles a task will take. How many instructions will your laptop use while you work on something.
- keeda 5mo ago
  Yeah, and I expect estimating token budgets is going to go the same trajectory (along with the same accompanying annoyances) as estimating and tracking story points! But done with the right mindset and proper awareness of the inherent uncertainty, you can sometimes achieve some reasonable estimates over time by starting with some T-shirt size estimates and then adapting based on actual numbers. Soon enough the team gets a sense of the nuances of the projects and its dependencies, and estimates get more accurate. As such, the example of estimating CPU cycles for tasks is actually relevant. For instance it is a common practice in real-time embedded systems running on tiny micro-controllers. But it is also possible to get good estimates for more complex applications / OS's / architectures simply by benchmarking them over time. The most common problem with planning and task estimation is that the corporate dynamics around it are not healthy: leadership often uses those as an SLA instead of the SWAG that they are. I worked on a team where our estimates never matched the actual time taken, partially due to rather unpredictable dependencies and high-priority tasks frequently interrupting us. But because we were clearly very high-functioning, management never held that against us. Those were some healthy corporate dynamics; not all places have that.

maplethorpe 5mo ago

In the Uber Eats app I can't even request a refund for an incorrect order anymore, because the UI doesn't allow me to scroll down to the "submit" button. It's been like this for months. I finally got my explanation.

glimshe 5mo ago

I spend $20/month on Gemini Pro and it greatly increased my productivity. I'm still in charge and only use AI for the more tedious or toughest problems. I can't see how these people could be spending this much productively.

mancerayder 5mo ago

And here comes the reining in of spending. If companies are anywhere like I'm seeing: 1 - Company mandate, start using AI 2 - You're afraid? Here's a mandate! 3 - (Devs and others discover Claude Code features where the coolest burn mad tokens) 4 - Um, yeah we're going to have to take a look at the spend here 5 _ What's 5? We know steps 3 and 4 will cycle a bit more, and we know it's going to cost more - these were startup teaser costs.

mohamedabdallah 5mo ago

[flagged]

tokyoproductj 5mo ago

AI might not make engineering cheaper — just more elastic. Instead of paying for engineers, you’re effectively paying per unit of thinking. At scale, that could get very expensive very quickly.

pwr1 5mo ago

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ttariq 5mo ago

[flagged]

pradeep1177 5mo ago

For people using Claude Code heavily, do you know which turns are burning the most tokens, or do you only see the final bill/limit?

macAndPeach 5mo ago

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rp1-run 5mo ago

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tellmefrankie 5mo ago

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StaxReport 5mo ago

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