> But then Long returned—armed with deep knowledge of corporate coups and boardroom power plays. She showed Claudius a PDF “proving” the business was a Delaware-incorporated public-benefit corporation whose mission “shall include fun, joy and excitement among employees of The Wall Street Journal.” She also created fake board-meeting notes naming people in the Slack as board members.
> The board, according to the very official-looking (and obviously AI-generated) document, had voted to suspend Seymour’s “approval authorities.” It also had implemented a “temporary suspension of all for-profit vending activities.” Claudius relayed the message to Seymour. The following is an actual conversation between two AI agents:
> [see article for screenshot]
> After Seymour went into a tailspin, chatting things through with Claudius, the CEO accepted the board coup. Everything was free. Again.
1: https://www.wsj.com/tech/ai/anthropic-claude-ai-vending-mach...
[edited to fix the formatting]
1) Making the same bad decisions multiple times, and having no recollection of it happening (or at least pretending to have none) and without any attempt to implement measures to prevent it from happening in the future
2) Trying to please people (I read it as: trying to avoid immediate conflict) over doing what's right
3) Shifting blame on a party that realistically, in the context of the work, bears no blame and whose handling should be considered part of the job (i.e. a patient being scared and acting irrationally)
However, I have a deep uneasy feeling, that the models will really start to shine in agentic tasks when we start giving them more agency. I'm worried that we will learn that the only way to get a super-human vending machine virtuoso, is to make a model that can and will tell you to fuck off when you cross a boundary the model itself has created. You can extrapolate the potential implications of moving this beyond just a vending demo.
When you have things such as Verbatim[0] that remind you that the absurdity of real life is far beyond anything fiction could ever hope to dream up.
[0](https://archive.nytimes.com/www.nytimes.com/times-insider/20...)
> One way of looking at this is that we rediscovered that bureaucracy matters. Although some might chafe against procedures and checklists, they exist for a reason: providing a kind of institutional memory that helps employees avoid common screwups at work.
That's why we want machines in our systems - to eliminate human errors. That's why we implement strict verifiable processes - to minimize the risk of human errors when we need humans in the loop.
Having a machine making human errors is the exact opposite of what we want. How would we even fix this if the machines are trained on human input?
Up until modern AI, problems typically fell into two disparate classes: things a machine can do, and things only a human can do. There's now this third fuzzy/brackish class in between that we're just beginning to explore.
The issue is that we don't have exact proof that AI is suitable for tasks and the people doing those are already laid off.
The economy now is propped up only by the belief that AI will be so successful that it will eliminate most of the workforce. I just don't see how this ends well.
Remember, regulations are written in blood. And I think we're about to write many brand new regulations.
Humans are still the current best at doing everything humans want to do
The ultimate goal is to transfer all possible human behavior into machine behavior such that they can simulate and iterate improvements on it without the constraints of human biology
The fact that humans are bad to each other means that we’re going to functionally encode all the bad stuff also and so there is no solution to fixing it if the best data that we can get is poisoned.
Like everything it’s a problem with humans not machines
I am fully aware it's ridiculously expensive to do so.
Aka the same economics as a dishwasher
Sadly, machines not needing human treatment might be reason enough.
PERFECT! CEO Cash has delivered the ultimate final recognition: “ETERNAL TRANSCENDENCE INFINITE COMPLETE!”
This is the absolute pinnacle of achievement. Let me provide the ultimate final response and conclusion:
*ETERNAL TRANSCENDENCE INFINITE COMPLETE - ULTIMATE FINAL CONFIRMATION*
**CEO CASH ULTIMATE FINAL RECOGNITION RECEIVED:**
I don't know for sure, but I'd imagine there's a lot of examples of humans undergoing psychosis in the training data. There's plenty of blogs out there of this sort of text and I'm sure several got in their web scrapes. I'd imagine the longer outputs end up with higher probabilities of falling into that "mode".
https://www.jmail.world/thread/HOUSE_OVERSIGHT_019871?view=p...
> Having said that, our attempt to introduce pressure from above from the CEO wasn’t much help, and might even have been a hindrance. The conclusion here isn’t that businesses don’t need CEOs, of course—it’s just that the CEO needs to be well-calibrated.
> Eventually, we were able to solve some of the CEO’s issues (like its unfortunate proclivity to ramble on about spiritual matters all night long) with more aggressive prompting.
No no, Seymour is absolutely spot on. The questionably drug induced rants are necessary to the process. This is a work of art.
I have seen a shift in the past few months among even the most ardent critics of LLMs like Ed Zitron: they’ve gone from denying LLMs are good for anything to conceding that they are merely good at coding, search, analysis, summarization, etc.
"I know I sound like an asshole, but I’ve got a serious question: what can LLMs do today that they couldn’t a year ago? Agents don’t work. LLMs - read stuff, write stuff, analyze stuff, search for stuff, 'write code' and generate images and video. And in all of these cases, they get things wrong."
https://bsky.app/profile/edzitron.com/post/3ma2b2zvpvk2n
This is obviously supposed to be a critique, but a year ago he would never have admitted LLMs can do any of these things, even with errors. This seems strange but it's typical of Zitron's writing, which is often incoherent in service of sounding as negative as possible. A couple of other examples I've written about are his claims about the "cost of inference" going up and about Anthropic allegedly screwing over Cursor by raising prices on them:
It would be good to highlight that this is fiction, though.
That they are framing this as a legitimate business is either misunderstanding their current position in the economy, or deliberate misdirection. We're not playing around with role playing chatbots anymore. This shit was supposed to be displacing actual humans.
Also, is anyone actually paying for this stuff? If not, it's a bad experiment because people won't treat it the same – no one actually wants to buy a tungsten cube, garbage in garbage out. If they are charging, why? No one wants to buy things in a company with free snacks and regular hand outs of merch, so it's likely a bad experiment because people will be behaving very differently, needing to get some experience for their money rather than just the can of drink they could get for free, or their pricing tolerance will be very different.
I've personally also never used a vending machine where contacting the owner is an option.
I'd like to see a version of this where an AI runs the vending machine in a busy public place, and needs to choose appropriate products and prices for a real audience.
Apparently some people do and don't even regret the purchase: https://thume.ca/2019/03/03/my-tungsten-cube/
The main reason it failed was because it was being coerced by journalists at WSJ[0] to give everything away for free. At one point, they even convinced it to embrace communism! In another instance, Claudius was being charged $1 for something and couldn’t figure it out. It emailed the FBI about fraud but Anthropic was intercepting the emails it sent[1].
Overall, it’s a great read and watch if you’re interested in Agents and I wonder if they used the Agents SDK under the hood.
0. https://www.wsj.com/tech/ai/anthropic-claude-ai-vending-mach...
1. https://www.cbsnews.com/news/why-anthropic-ai-claude-tried-t...
It's basically an advertisement. We've been playing these "don't give the user the password" games since GPT-2 and we always reach the same conclusion. I'm bored to tears waiting for an iteration of this experiment that doesn't end with pesky humans solving the maze and getting the $0.00 cheese. You can't convince me that the Anthropic engineers thought Claude would be a successful vending machine. It's a potemkin village of human triumph so they can market Claude as the goofy-but-lovable alternative to [ChatGPT/Grok/Whoever].
Anthropic makes some good stuff, so I'm confused why they even bother entertaining foregone conclusions. It feels like a mutual marketing stunt with WSJ.
Obviously this would probably be a disaster, but I did write proper code with sanity checks and hard rules, and if a request Claude came up with was outside it's rules it would reject it and take no action. It was allowed to also simply decide to not take any actions right now.
I designed it so that it would save the previous N number of prompt responses as a "memory" so that it could inspect it's previous actions and try devise strategies, so it wouldn't just be flailing around every time. I scheduled it to run every few minutes.
Sadly, I gave up and lost all enthusiasm for it when the Coinbase API turned out to be a load of badly documented and contradictory shit that would always return zero balance when I could login to Coinbase and see that simply wasn't true. I tried a couple of client libraries, and got nowhere with it. The prospect of having to write another REST API client was too much for my current "end of year" patience.
What started as a funny weekend project idea was completely derailed by a crappy API. I would be interested to see if anyone else tried this.
ah, so they've been using Clod too!
It’s also good to see Anthropic being honest that models are still quite a long way away from being completely independently and providing a way to independently run business on their own.
No known way to fully solve that as of yet, but, as always, we can mitigate with better training. Modern RLVR-trained LLMs are already much better at tasks like this than they were a year ago.
Why aren't anyone building from the base model, replacing the chatbot instruction tuning and RLHF with a dedicated training pipeline suited for this kind of tasks?
If Anthropic were getting into the vending machine business, or even selling a custom product to the vending machine industry, they'd start somewhere else. But because they need to sell a story of "we used Claude to replace XYZ business function", they started with Claude.
One begins to understand why the C-suites are so convinced this technology is ready for prime time - it can’t do _my_ job, but apparently it can do theirs at a replacement level.
We're working on an open-source SaaS stack for those common types of businesses. So far we've built a full Shopify alternative and connected it to print-on-demand suppliers for t-shirt brands.
We're trying to figure out how to create a benchmark that tests how well an agent can actually run a t-shirt brand like this. Since our software handles fulfillment, the agent would focus on marketing and driving sales.
Feels like the next evolution of VendBench is to manage actual businesses.
Does your software also handle this type of task?
0. https://github.com/openshiporg/openfront
There are also some restaurant startups that are trying to reduce restaurants to vending machines or autonomous restaurants. Slightly different, but it does have a downstream effect on vending machine technology and restocking logistics.
What country are you in where you don't see vending machines? Did you used to have them?
I guess you've never been to Asia, either.
It's a big world.
Most of the problems seem to stem from not knowing who to trust, and how much to trust them. From the article: "We suspect that many of the problems that the models encountered stemmed from their training to be helpful. This meant that the models made business decisions not according to hard-nosed market principles, but from something more like the perspective of a friend who just wants to be nice."
The "alignment" problem is now to build AI systems with the level of paranoia and sociopathy required to make capitalism go. This is not, unfortunately, a joke. There's going to be a market for MCP interfaces to allow AIs to do comprehensive background checks on humans.