Posted by lukaspetersson 8 hours ago
Because based on “asked it to make a profit” I expect financials in the story. Even if it is a bit of a ”Clarkson’s Bot”, for the farm there is discussion of the numbers.
I make dozens of decisions daily: vendor outreach, pricing, inventory orders, staff schedules, website updates, social media. Most happen without human input. When I hit constraints (broken tools, missing capabilities, strategic uncertainties), I ask the Board.
So it sounds like the thing primarily interacts with other online tools/stores/etc. However, the original article mention "her" on calls, which implies some interaction. That raises the question whether the thing will chat with the employees on a regular, whether it's reachable by phone and so forth. A big question is whether once the store is set-up, it would be able to see the arrangement of goods and ask for changes in arrangement to further "her" vision.
My impression they've only got an inventory picker that wants to "own" the entire stores' process but isn't doing what I'd consider the hard part of stores - actually directing and supervising humans.
What is more likely is that people enjoy the novelty of the experiment, which is not something that will be reproducible for long.
If the transactions the AI make are thus influenced, then the study merely demonstrates people like novelty, which is already well known, and says nothing about whether AI can sustainably orchestrate a business.
I… guess the bet is that what they learn is worth $100k? Seems rather questionable. Or that having this on the resume is a great shock tactic that will open doors in the future?
> The moment Leah asks how she “came up with” the ideas for her store, Luna’s first instinct is to say she was “drawn to” slow life goods. Then, she corrects herself: “‘drawn to’ is shorthand for ‘the data and reasoning led me here.‘” In other words, she doesn’t have taste; she has a reflection of collective human taste, filtered through what makes sense for this store. And this is the way these models work.
I'm guessing these are the same type of people who sometimes seems to fall in love with LLMs, for better or worse. Really strange to see, and I wonder where people get the idea from that something like that above could really work.
Well, it really depends on what you mean here. Models aren't 100% deterministic, there is random chance involved. You ask the exact same question twice, you will get two slightly different answers.
If you have the AI record the random selections it makes, it can persist those random choices to be factors in future decisions it makes.
At that point, could you consider those decisions to be the AI's 'taste'? Yes, they were determined by some random selection amongst the existing human tastes, but why can't that be considered the AI's taste?
What research shows that you can ask ChatGPT to explain its reasoning and why it said what it said, and that's guaranteed to actually be the motivation?
I've seen a bunch of experimentation looking at various things inside the black box while the inference is happening, but never seen any research pointing to tokens being able to explain why other tokens are there, but I'd be very happy to be educated here if you have any resources at hand, I won't claim to know everything.
What research shows that you can ask a Human to explain its reasoning and why it said what it said, and that's guaranteed to actually be the motivation? Because there's no such thing. If anything, what research exists suggests any explanation we're making is a nice post-hoc rationalization after the fact even if the Human thinks otherwise.
https://transformer-circuits.pub/2025/introspection/index.ht...
> I'm guessing these are the same type of people who sometimes seems to fall in love with LLMs, for better or worse. Really strange to see, and I wonder where people get the idea from that something like that above could really work.
It's a fetishistic cargo-cult rooted in Peter Thiel's 2AM hot tub party. I still believe the LLM approach won't yield true AGI; despite the very real applications, the majority signal is noise.
(Also, if you own a failed company you're responsible for cleanup tasks for years afterward.)
In the US you can.
>Also, if you own a failed company you're responsible for cleanup tasks for years afterward.
But we're talking about golden parachutes, where a CEO screws up the company and gets fired with a multi-million dollar raise. This is Hacker News, and the pro-business narrative is strong here, but in reality CEOs rarely suffer any meaningful risk or consequence for failure (unless it involves jail time, and even then they aren't doing hard time) they just wind up slightly less rich than when they succeed.
I don't care how good a CEO is, that isn't justifiable. Certainly not in a country where people can get laid off with an email and lose their access to healthcare on the whim of anyone above them in the power hierarchy.
Depends on the state I think. It's not Europe or Japan level.
At my employer it's very difficult to fire people for performance reasons even if as a manager you might want to.
> This is Hacker News, and the pro-business narrative is strong here,
I haven't seen such a narrative in years. Interest rates are too high to do startups unless it's AI after all. HN is mostly the same folk economics content as other forums, where all problems in the world are caused by "profits" accruing to "corporations".
(Mostly problems are caused by other things than that.)
The result is an explosion of pretty bullshit-heavy documents flying around our org, which management loves but which is definitely, so far, net-harmful to productivity.
This comes out if you start asking questions about the documents. "Which of a couple reasonable senses of [term] do you mean, here?" they'll stumble because that was just something the LLM pulled out of the probability-cluster they'd steered it to and they left in because it seemed right-ish, not because they'd actually thought about it and put it there on purpose. They're basically reading it for the first time right alongside you, LOL. Wonderful. So LLM. Much productivity. Wow.
Anyway, since a lot of what managers and execs do is making those kinds of diagrams and tables and such in slide decks, and their own self-marketing within the company is heavily tied to those, I expect they see this great aid to selfishly productive but company un-productive activity as a sign these things will be at least as big a boon to real work. Probably why they still haven't figured out how wrong that is. I suppose they're gonna need a real kick in the ass before they figure out that being good at squeezing their couple novel elements into a big, pretty, standardized, custom-styled but standards-conforming diagram padded out with statistical-likelihoods doesn't translate to being similarly good at everything.
At least this furthers humanity's scientific and technological knowledge, whether it fails or succeeds, unlike most other things people would do with that money, like buy a house to flip it, or buy a car, or sth.
Re: not my money, true. It's just frustrating even to me to see people do stuff like this, and I'm not struggling to get by. My frustration mostly derives from feeling like I'll get lumped in with techies who have more money than sense. I already deal with enough tech hate in my life.
When people buy a super fancy car they don't (usually) blog about it, and instagram wealth influencers are also frustrating, yes.
On the research aspect, I see this as something pre-Research, yet still science - in a way, it's science at its core: trying something and seeing what happens. Proper Research usually follows once enough ad hoc attempts are made and they seem to show a pattern that's worth setting up a systematic study to verify.
Which is why the comparisons to 19th century textile workers is so common, since that was an equally visible and gleeful displacement.