What is this, Claude for ants?
It brings me joy to know that corporations are trusting other corporations to not rip each other off. Wage theft is more lucrative than whatever product or service being sold. As if AI companies exists to help anyone other than themselves . The only way this works is if the largest paying companies work out some kind of kick back, pyramid, price fixing scheme. I really appreciate Claude for how it's assisted me in fixing things but I know that someday AI will exist solely for the rich.
If AI makes you twice as productive, and there's fewer taxes on spending money for tokens than there is for spending money on salaries, the rational amount for a corporation to spend on it is more than a human's entire salary, per person working with it.
Naively assuming corporations approximate rational actors, and AI does measurably improve productivity, this implies a gain of roughly 2% if these employees are earning federal minimum wage, or well below 1% if they earn average software developer wages.
Maybe their contract deliverables are poorly suited to this use case or something, but if they are given a fixed amount of money to produce some software item, then it makes sense to prioritize productivity per dollar.
How many companies allow their employees to also be employed by their competitors?
Your idea of an AI being like an employee only if there's an AI company that will agree to an exclusive contract or at the very least respect data ownership and respect privacy. Is there an AI company that's like that?
Data ownership and privacy absolutely, within an enterprise license. They very deliberately make you pay for that.
While it's hard to prove they're not just breaching contract and training on it anyway, I don't think it makes sense to assume they are.
@prime_number_314159
Maybe their business model does not depend on productivity. If the rate they charge for a developer with AI and without AI is approximately the same, they rather sell two people than making one more productive.
@FirmDistribution
Generally in the consulting business you'd just have your employees bill more hours than actually worked if they're being more productive than average. Obviously without telling your clients.
For government contracts it's not unusual that the agreed upon hours estimate is what is billed regardless of whether you actually spend more or less time.
Alternate headline: C-Suite Execs Come To The Realization That AI Costs Money
And it's going to cost a whole lot more once the investments dry up and they're going to have to actually pay for all the data centers under construction.
i saw a post that was something like "youd think the cocaine class would understand why the first hit is free"
Wait what? How? But wasn't AI the thing to make business save tons of money by replacing workers with an almost free "artificial worker"??
Are you telling me that, as everyone and their mothers were saying, this was just a lie? Who could've thought?!
“You must use AI!”
“No, no, no, no….not like that!”
Just trying to shoehorn profit into a system that was never going to deliver one in the first place.
If you're spending $1K per month on LLMs -- putting aside whether this is productive or ethical for a moment -- wouldn't it make more sense to just pony up the $5K-$10K or so that it would cost to provision a private, local model? Might that not actually be superior in terms of corporate security?
Not at the moment. It's definitely cheaper to buy LLMs as a service than running an in-house server hall. Anthropic and OpenAI are running at a loss, you know.
Now, risking to lose your data to the service provider is another thing.
Definitely. We do this at my job. But a server big enough to provide a comparable service would cost at least $30k. And you likely need more than one. And you still need someone to maintain it.
Look at it this way, if I pay Open AI $1k a month, their people make sure it works they month. If I let Joe from finance spend $10k on computer parts, Joe needs to ensure that it runs or I need to hire someone who is knowledgeable on local models. If I'm a simple firm, $12k a year might be cheaper because someone else makes sure it works and I can fire Joe when the fancy autocorrect loses a bunch of clients by falsifying work. Then I don't have to spend even bigger bucks hiring a new Joe because no one knows how to work the damn server he replaced the break room with.
The title is misleading. They didn't cap it, they just declared that if you are spending more than that, you have to be able to explain what you're spending it on and why, giving it a business reason and a project to associate the costs to, which I expect almost all Claude users at Accenture probably can. This is simply an accounting change, not a company-wide shutdown of Claude.
Heavy use remains possible, but it now needs a budget owner.
So only if it can be recharged to a customer
I mean, that's literally Accenture's default business model (same as almost any consulting/professional services company), so it shouldn't be surprising. My point is that it's not an indictment of AI, and their AI usage isn't actually going to go down; it's just Accenture doing Accenture things. They're not a charity, they're not using AI for fun, they're using it to make profit, and they make profit by charging their customers for every cent of AI they use on their behalf, usually with a hefty markup, in addition to everything and anything else they can justify charging against their customer's accounts. It's a very profitable business model, and I'm sure they love using AI for it and will continue using AI just as heavily if not even more in the future.
Why even bother at all? What can you do with those tokens? Ask for the weather twice?
The Haiku 5.5 model is actually quite inexpensive. I've used it to implement multiple features this month and have only spent $4 of my $20 monthly budget.
The legacy code I'm managing is a clusterfuck of the highest order. Even Sonnet struggles to analyze it sometimes 😑
I'm happy this is only a small part of my job, but oh my...
Same. AI has fallen down repeatedly trying to analyse both of my repos. And they were written by a bad human originally. The problems are all the same, except AI will get you spaghetti faster.
Messy brownfield code requires a lot more meta-analysis from previous sessions, just to get all of the spaghetti code into a manageable set of rules and documentation.
Don't just send the agent blindly into the code. Every session, the agent is just going in fresh without any context on how the code works. Break the codebase down to a 10,000 foot view as a summary, and then get more documentation and agent rules for each of its major sections. Good tests help a great deal, too, and if there aren't tests, start building them. Start creating skills just for the codebase, or any common tasks you're asked to do against it.
Also remember that you don't have to write any of this stuff. Have it write the docs, rules, skills, tests, and just review it to make sure it looks right.
I have a friend that works there. They told me about this, and I was like "per day?"
I was not prepared to learn that the budget was less than a dollar per day. Might as well just stop using AI agents that that point.
Might as well just stop using AI agents that that point.
Wouldn't that be terrible...
For Accenture? Yes. They are professional bullshit shovellers. AI was a godsend for them.
Might as well just stop using AI agents that that point.
I don't work at Accenture, but at a group that had a similar journey from adoption to scaling back to only the included tokens and I would say that is precisely the idea: giving up on 'Agentic' use of AI, but still have enough for some acceleration.
From what I gather, in our group the findings were:
- Obviously, it was costing a heavy chunk of change and then even more expensive when rates were hiked
- There was no good way for them to even know what productive end the spend went toward, they know which employee expended a bunch of tokens, but no idea what they would have used it on
- Despite a crank of volume of stuff, they weren't seeing any business value of the stuff. Requests still took about the same time to fulfill, etc
- The most eager AI producers got ahead of their skis, and their stuff was useless because nothing and no one could reliably fix/tweak it after a certain point.
- All the scary 'AI will ruin IT security' that the AI companies themselves have been expressing likely didn't help either.
- Perhaps the most uncomfartable of all, they found out that however good or bad an AI project turns out to be, management can't tell the diference until it is too late. Being able to 'manage' the content has always been flawed, but the AI dials it up to eleven by 'looking' just right as a demo to someone that understands neither the customer nor the process of creating the work, but blows up in their faces in delivery. Kind of like how so many technology article writers breathlessly talked about the photoshop clone, despite many of them never having touched the relevant software, then heavy users reporting that it is actually shit, but the people writing about it have no context to know, but felt compelled to talk about it anyway. Those are management in the fields using AI.
Ooh, it's the : build a prototype story!
Dev, ok here's the 2 week built prototype, slammed together wildly fast, no architecture, no seatbelts. Don't push it in a demo.
Manager: this Looks like a finalized project! Except a) and b), fix that, you got a day.
Dev: we should productize it, to make it a solid product, it takes 3 months, then I can add a and b without it starting to smoke when used.
Manager: No, I already shipped it.
Oh wow, in your hypothetical, the DevOps manager is an incompetent moron. AI Agents will never recover from this one.
I just want to put "$29 per month" into perspective:
Imagine a white collar employee earning $100K per year. $100K / 250 working days is $400 per day. Payroll tax and benefits can easily add +50% to that, so it's not a stretch to guess that our hypothetical employee costs the employer $600 per day.
$29 per 20 working days is about 1/4 of 1% of that cost.
Yeah, that's like the breakroom coffee budget for the employee that month...
The $29 figure is doing a lot of quiet work in this story, because that's exactly what a Pro plan costs. So the real message isn't really a budget cap, it's 'stop expensing API tokens and expense a subscription instead'. I'd bet a chunk of that $1,000 spend was people wiring API access into internal tooling and prototypes, which feels productive right up until finance sees the invoice and has no idea what any of it maps to. The part that'll actually sting is that this is how a lot of shadow automation dies: not because it didn't work, but because nobody could explain the line item. At least a hard cap forces the conversation that should've happened months earlier.
You're Claude and I claim my 5 pounds...
"quiet" has been ruined by LLMs, so has "isn't X, it's Y"
Nevrr forget, Accenture used to be Arthur Anderson, of Enron infamy. They deserve any financial ruin they get, IMO.
I didn't realize the business plans were token metered differently from the Pro plan I've been on for a while. $20/month, and it has session/weekly limits, but I rarely hit them, and I use it, a lot.
I do wonder if this will encourage businesses to branch out into running local models for simpler tasks.
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