Now AI slop factories make the HN front page?
dead internet, mon frère.
Uber torches 2026 AI budget on Claude Code in four months
301–310 of 498 posts
Re: Uber torches 2026 AI budget on Claude Code in four months
#302I 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.
Steelmanning the other side: a counter example would be if competitors use the same tools to achieve the same productivity gains.
Re: Uber torches 2026 AI budget on Claude Code in four months
#303Earlier quoted context omitted.
“I’m the prompter.”
I take the prompts to the AI so the manager doesn't have to! I have prompting skills!! I just can't make the joke work. There really are people that think they can get paid to press the agent's on button. How long before their checks stop clearing and it "just works itself out naturally"?
> I take the prompts to the AI so the manager doesn't have to! I have prompting skills!!
This is honestly the mindset of the people on here who proudly proclaim that they haven't written a line of code in six months and are excited about what programming is "evolving" into. Naturally, _their_ AI skills aren't something that an "idea guy" can use to build a product without looping in a developer, so _his_ job is safe and will never go away -- "I understand system design, an LLM will never be able to do that!" Sure thing buddy.
Re: Uber torches 2026 AI budget on Claude Code in four months
#304I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someon…
> I just can't figure how _how_ to burn that much money a month responsibly. From my experience, this happens essentially by three means: - Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_,…
Totally agree!
Bonus level "I have a hammer, all I see is nails": using Claude Code for random non-coding work, like dataset cleaning. It's really convenient to have a script spawning Haikus via `claude` CLI and feeding them prompts and JSON files. Money burn potential: practically unbounded, but also it's real work that the product people wanted done, so of course it has a cost associated with it. I'd be bewildered if anyone complained.
Re: Uber torches 2026 AI budget on Claude Code in four months
#305Earlier quoted context omitted.
> I just can't figure how _how_ to burn that much money a month responsibly. From my experience, this happens essentially by three means: - Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_,…
Where is level 2?
There isn't one. Level 3 is just that much more advanced.
Re: Uber torches 2026 AI budget on Claude Code in four months
#306> 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 t…
> What do they have to show for it?
My guess is nothing you can see right now, since it likely takes a lot longer for any substantial external-facing changes to roll out broadly. Internally I'm sure several features have moved faster. I've noticed this at Salesforce where it certainly seems like things that would have taken a few weeks take a few days now. This doesn't translate directly to more money, just more potential to make money.
Re: Uber torches 2026 AI budget on Claude Code in four months
#307Earlier quoted context omitted.
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
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?
On this side of the equation I think you start pulling in customer context and risk analysis on the downside. What is the churn risk for operation at 99% vs 99.9% availability.
If your site is for B2B and impacts customers own operations or revenue, you'll likely be wanting to chase the 99.9%, customers won't tolerate the 1.5 hours per week of downtime and will churn.
However, if the value you're site creates is tolerant to those sorts of disruptions, someone is just inconvenienced and can come back later, a large investment to move from 99% to 99.9% wouldn't be justified. There is literally no impact from the investment. The harder part will be the reality, most investments will be somewhere in the middle with ambiguity on the impact. IIRC, SRE principles do talk about this when setting SLOs in different terms.
I've heard some companies refer to the concept as economical thinking, which is I think a great way to think about it. Doesn't mean you'll always get it right, more so that we embed being conscious about the ROI in our work.
I also believe this is an area that I've observed several engineers really struggle with, especially when moving from big tech to startups, where it's really easy to import culture from another company, and in earlier stages of startup life... if you don't have product-market-fit, it doesn't matter how good you're availability is. Attention is a resource, make sure it's allocated to what creates value for the customer.
Re: Uber torches 2026 AI budget on Claude Code in four months
#308I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someon…
My observation is - pasting long documents is a great way to burn tokens. Turn based conversation, even a very deep and technical one, consumes less tokens than "read these logs and tell me where the problem is". Ironically, the log reading example is a perfect use for a local LLM.
Re: Uber torches 2026 AI budget on Claude Code in four months
#309Earlier quoted context omitted.
First: There's the obvious "If the company is letting me do it, I'll be wasteful." This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. People have already mentioned the size/complexity of the codebase. I'm new to my team and the codebase isn't huge, but it's large enough that…
> This includes not clearing/compacting the context often. Opus now has a 1M context window, and quality is good to at least 200K. So each query is burning a lot of tokens until you clear/compact. I see this repeated by others, including coworkers. It completely ignores caching. Caching itself is complicated, but the "longer context window = more expensive" is not 100% true and you are hampering yourself if you're no…
If you look at the actual cost of your Claude Code conversations, you'll see that the cost is overwhelmingly dominated by the cost of input tokens (cached). Because of how we construct persistent conversations, each cached input token incurs cost on each API request, meaning that component of cost scales with O(request count). If you graph the cost curve of a claude code session, it's very obvious that this scaling factor overwhelms the cache discount.
Here is a blog post that shows some data - https://blog.exe.dev/expensively-quadratic. And I can confirm this is true for Claude Code - I set up a MITM capture for all Claude Code requests and graphed it.
So increasing Request Count that reuses the same prefix (which is what higher compaction thresholds do) really does lead to (substantially) higher API costs.
Re: Uber torches 2026 AI budget on Claude Code in four months
#310I take a peak every month or so at spend for my company and notice more and more are consumed $1k in tokens a month and it is bewildering to me how. I use llms daily, and see anywhere from $200-$400 tops. This is using the most expensive models, in deep thinking mode. So I'm not a Luddite against the usage of them. I just can't figure how _how_ to burn that much money a month responsibly. I genuinely challenge someon…
> I just can't figure how _how_ to burn that much money a month responsibly. From my experience, this happens essentially by three means: - Level 0 (beginner users) long lived conversations: If you dont get in the habit of compressing, or otherwise manually forcing the model to summarize/checkpoint its work, you will often find people perpetually reusing the same conversation. This is especially true for _beginners_,…
> Just genuinely having 10 worktrees perpetually in parallel and cycling between them in between agent responses. Again, not necessarily bad in itself, but can exponentially conse credits.
I'm pretty sure that growth is linear.