Pion, an agent designed to run any company autonomously
andonlabs.com
andonlabs.com
If you think I'm joking, check this out: https://www.youtube.com/watch?v=U-Rqv9dOB1U
I don't think any of us are ready for the wave of sloppy shit that's going to hit us soon.
The only thing I can say about LLMs is that we might end up with some broad, stable utility from them, when the dust settles. The only thing crypto is really good for is hiding the source of impermissible donations.
Absolutely incredible trolling from some of those people I’m sure, while others are actual believers
Eric Morrison is doing the gods' work.
Of course you'd feel no need to justify all the exceptions when people you don't like are excited about a movie, food item or video game. Yuur justification for cynicism is vacuous, which will obscure realistic concerns.
"No ability to think for myself", ok bud.
"Broad categories I don't like" - found the crypto bro. If by those categories you mean spammy low effort people with no knowledge, ethics or desire to add something valuable to the world then yes - I don't like that category of people.
"Yuur justification for cynicism is vacuous, which will obscure realistic concerns" - Your poor grammar is obscuring your actual meaning. "Yuur"?
Hey enjoy the incoming flood of 100x the email spam, phone/sms spam, AD spam, everything spam, shit non-products, etc. Yeah that sounds like a great time, don't mind this cynical guy here who just "dislikes some kinds of people".
> they'll raze the ground to ashes before anyone can use it for legitimate means.
You were so cynical you thought this one category of people would literally prevent any fruitful use. Now you simply argue with me whether the category exists at all.
> I really have no idea what you're on about. There's TONS of examples of people abusing this already.
Grossly dishonest in spirit, you are.
I hope such a day never arrives.
This. Not only this but it has expanded this pool of people. Now you don't just have to be malicious, you can just be lazy and excited about not having to lift a finger, and/or you could just be dumb and excited about doing things you were never able to before.
Competent, well meaning people need not apply. Grifters, sloths and bozos will do just fine.
Our hiring pipeline was 100% full of AI cheaters recently - like 6 in a row that we interviewed (broken process too). Not exactly the same, but close enough and just shows how many people would be willing to waste a bit of time in an AI driven fraud versus doing actual productive work. I don't see the get-rich-quick folks as being any more scrutable, but it does certainly scale more and have a bigger blast radius of damage.
Pray tell, what will these fantastical vibe coded business sell, and why will anyone pay for it?
Not so much reducing cost by reducing headcount, but reducing liability; it's the legal labyrinth which is the barrier to entry to scale business. While it is present elsewhere, it's universal in employment.
The real question is "how big can a business be before it requires a legal department?" That an AI can automate many rote tasks and have some expertise means the benefits of scale with less of the risk.
I've seen: designers, performance marketers, data scientists, SEO experts, product managers, engineering managers, CRM expert, all using it for pretty much every single individual part of their job. I don't have to even mention programmers, of course.
Once in a while the most egregious ones get caught. I've seen so far a product manager, three data scientists, a CRM person and several developers getting fired for doing absolutely nothing but showing up, firing Claude with a few integrations to a dozen tools and prompting "do the work".
Might sound harsh, but after the last few years, I really don't see why an LLM wouldn't do a better job by itself compared to 80% of employees of tech companies, honestly.
Every time we see another AI agent "gone rogue", every time we see more LLM slop turning up by e-mail, comments, blog articles, people did that. Not AI -- people feel like they can get away with it. People are setting the machines out to do these things. We should be addressing the people, not the machines. The machines are the symptom.
Every time people manage to get away with something, it's insane, it's addictive. It's all over once you get caught, but until then, maybe forever, LLMs can feel like a cheat code...
These people were fired because their work was considered shit by their managers and produced no results. They were replaced by nobody. They were dead weight pretending to work.
EDIT: I think this sounds quite similar to tales of developers and Excel wizards automating their jobs in secret and producing the same results. This was not the case here, as the results were considered low quality.
One can argue semantics and fairness all day long, but if an employee is adding no value to their $90 Claude signature, then they’re gonna get fired as soon as someone realizes. :/
This is a fundamentally political question. When different customers demand different new features, and other customers demand particular bug fixes, who do you satisfy first? The one with a small but growing account, or the old account who has been with you from the beginning? I would challenge anybody who thinks agents mean you can just do everything, immediately - by all means, prove me wrong and build Google again overnight.
And how is growth financed? Reinvestment, equity, debt? I would challenge anybody who thinks this can be reduced to a calculation, because money ultimately flows between humans; even if somebody grants an agent access to a current account (and some are, experimenting with vibe day-trading), a human always retains final control and can liquidate that account whenever they like.
There's just no world where this actually results in a stable, respected business. It will be death by a thousand bad impressions, mistakes and oversights. Yeah, you can automate everything. That doesn't mean it's being done well.
That's not to say this might not be something feasible in 2-3 years from now, but we're still a long ways away.
So you're right to be skeptical as of September 2026. But September 2028? It might just be weird to run companies without AI management.
But I may be wrong.
And yes I know it's a word, I know at least two meanings it has but in this particular usage I was not familiar with it. Anyways, I was wrong and I will take the L
It's a work of art
You are doing the same thing, very ironic. At least I'm not the one trying to act like I'm on the moral high ground.
Were this a different type of social media, I'd praise you on what you did here.
But on this place, I can just be certain you're ignorant.
Your comment from 4 days ago: "Vibe coders like you who don't even understand basic fundamentals are pretty infuriating"
Industry is betting that we don’t need to. I still consider it a bet because Claude Code this scale has become norm just for a year.
But even not caring about quality: Speed of development is abysmal. It was never something this team had anyway, they were always slow. So they don't care that they take a year and 20 people to do what another startup in the same space was able to deliver in a few months with a skeleton crew (and perhaps more quality).
To me the experiment has shown the results it should. It's self-serving to developers, and amplifies the good and the bad in them. It's not particularly good to users and not to companies if the developers aren't good themselves.
CRUD apps have long been a solved problem long before AI, no one should be spending a lot of time dedicating their life to figuring out how to write a better CRUD app.
If your bubble consists of writing critical high performance applications or bleeding edge research, maybe you have a use case for not using AI, but that is not the norm.
>But September 2028? It might just be weird to run companies without AI management.
Coding is pattern matching and still requires a human in the loop to manage.
But, any human in the loop managing the "Ai management" is the manager, by definition.
Only way true Ai management is viable is via non LLM Ai, so completely depends on advancements there.
* I'm not arguing what that actually means, much less, whether or not that has actually happened, just that it's what people believe, and as we are a belief-driven culture rather than a data or fact driven culture, that belief is what will drive policy.
In addition this will probably only happen on America as most movies take place lol
It is?
Are you sure that this is a representative reading that applies to more than just a tiny bubble?
99% means I write 1 line for every 100 AI lines. It's probably 1 human line for every 10,000 AI lines right now.
There are still people who ride horses.
I do think a better programmer will be a better prompter just like a better compiler engineer will be a better programmer (when performance matters at least).
Because 100 lines of (debugged, reviewed) code per day is a good speed for seasoned software engineer. I assume that you can produce more than 100 lines of something per day as a prompt.
So, what is the problem domain that requires one to write several thousands of lines of code per day?
If they write 2 (two) lines of code, the combined output of them with LLM, as I read it now, is 20K SLOC. This is yearly output of human programmer.
It is very much possible to write 100 lines of (debugged, reviewed) code per day for human. And these 100 lines of code would result, if we apply their LLM multiplication factor, in 50 (fifty) man-years of work!
Even if they write very little code, say, one line per month, after a year of work they will get result of about 6 (six) years of human programmer. SQLite is 155K SLOC, about 8 man-years.
And that what raised my question: what is the problem domain that requires so much code?
The two people I know who don’t use coding agents work in government, and in a data science company working with government.
I’d say if you work at a tech company and agents aren’t writing the majority of your code, that is weird. But if you work at a traditional company that doesn’t have Claude or Codex subscriptions, there it might be pretty normal to still be writing code by hand.
"Weird" is a social concept. It's an artifact that has its roots in social cohesion and friction induced by individual nodes to glue the group together.
Software development otoh is an engineering discipline (or.. it should be). And Engineering does not use the local social consensus algorithm for determining correctness. (or.. it should not)
Arguably engineering is something AI should be really good at since you're just trying to compute a working product given constraints, formulas, resources.
Where in the comment tree is this supposed to sit?
But, regardless, what is interesting I think is the scenario chosen there. Because a "dinner party" is not where engineering happens; and that's kinda the point I was getting at. That's pulled from the pool of "social consensus" and not "math" or "physics" or whatever.
It's interesting, isn't it? Just like how specific tokens in the context window of LLMs pull probabilities towards specific clusters of ideas (and tokens); with humans, you see similar things happen.
(This comment is more coherent than it might look at at first sight.)
___
Anyway, point (and to the point) is: Don't get hacked by the current thing fraudsters/grifters.
They don't respect you or me or really anyone the slightest. They're just in it to get rich quick (or simply just inflict psychological damage for sadistic reasons), and they will use any means necessary to do so.
This comment chain only exists because some hype guy is trying to induce FOMO into people for not doing more AI. Why is unclear, but it's clearly malicious. Whether they consciously know that they are doing that doesn't matter for that assessment.
Let me try another analogy: AI-related comment sections are like a watering hole for both get-rich-quick grifters, but also bad people wanting to hurt others.
The technology is so disruptive that there are all sorts of opportunities while stuff is still developing, things aren't regulated, and people themselves have not yet learned how to self-regulate.
So if you're looking to make money or to just inflict pain onto others, you're pulled towards this stuff.
This is what I am seeing in this specific comment sub-tree. A troll.
But in current year, you cannot just call people trolls, so instead you get these elaborate meta comments that bypass the anti-anti-troll defenses, but get so abstract, people just go "what".
My advice would be to view any of these comment sections through a zero-trust lens.
Godspeed. We will all eventually get through this.
It was not about that. Thank you.
I definitely wouldn't put it at 99% of developers, but I'd say it is the norm among developers I know that agents are their primary mode of writing code now (still using IDEs and GitHub to review changes).
Pick an industry where you can stop learning new things once you're out of school, because the computer industry is not one of them and never has been.
My fucking job used to be writing 6502 assembly language on an Apple ][, and I loved it, but I hope I've forgotten enough of it to have room to learn new things. If only I could forget all those hex I/O and peripheral addresses from $C000-$CFFF and the Monitor ROM routines from $F800-$FFFF, without forgetting how brilliantly beautiful Woz and and Allen Baum's code is.
https://6502disassembly.com/a2-rom/OrigF8ROM.html
Forgetting old stuff to make room for new stuff is one of the most valuable skills you can have in this industry.
The next most important skill is persistence ;) -- writing stuff down before you forget it, in a way that won't make future-you hate present-you when you need to learn it again.
I saw this idea from elsewhere that the things AI helps with was never the "profit bottleneck" of companies. 10x engineering productivity gain does not translate to 10x more revenue if your profit bottleneck is customer acquisition and retention. In other words, your profit is limited by how many people are willing to give you money for your services and AI can't really affect that.
And even the productivity gains per individual is a generous assumption. An individual can only prompt (and check! You guys check right?) so much. Sooner or later the pendulum is gonna swing in the other direction and it will be cheaper to build a team than equip individuals with AI _to deliver the same value_. I'm also assuming that AI is still in the VC-subsidized pricing stage.
Hence why I think the job in the future is gonna be pretty similar to the job four years ago.
> The next most important skill is persistence ;) -- writing stuff down before you forget it, in a way that won't make future-you hate present-you when you need to learn it again.
Huge amen. Another thing I've found Claude very useful for is writing documentation for legacy systems and cleaning up my own notes on it. I only need Claude to be 70-80% correct because from there I can take it. That error margin is no different from moderately-outdated-but-still-useful documentation.
When that pendulum swings, I will have a documentation binder that I will print money with.
I am very convinced that today's SOTA level will be accessible 3 years from now, but you might be right about the SOTA accessibility at that point in time.
Still with today's SOTA you need to know much less details to be able to write code than without it.
People neglecting this side will inevitably become less proficient at their job and become rather helpless without AI, that's all I'm saying.
You put a number on it at the top of the thread: three years until people relying on agents have forgotten how to do the job. Copilot's technical preview was 2021, ChatGPT was November 2022, and agents that write the majority of the code are a 2024-2025 thing. So the window you're predicting is roughly the entire span in which these tools have existed, and the atrophy hasn't shown up in it.
For scale, the last ARM I wrote was for a Sony Clié and the last PowerPC for a Mac, both more than two decades ago. If I needed to write ARM for my Mac now, I could brush up about as fast as I could on the 6502 I mentioned upthread, most of whose opcodes, $C000 I/O addresses and monitor ROM entry points I genuinely cannot recall any more. Two to four decades of not touching an instruction set didn't cost me the fundamentals, and it hasn't made me less proficient in Python and TypeScript or helpless without AI. Three years of using a good tool is not going to do it either.
There's also a distinction buried in your last line, because "people neglecting this side will inevitably become less proficient" bundles two different things. Losing a skill you had is not the same as never acquiring one, and only the second is an argument about tools.
So take acquisition. I've been learning PDP-7 assembly in order to recover, OCR, and analyze 128 pages of PIXIE assembly source and octal machine code that Heinz Lemke wrote at Cambridge in the late 1960s, and get it running in an emulator. A language I never knew, with custom instructions for bespoke networking hardware, no Stack Overflow, and almost nobody left to ask except Heinz himself. AI has been extremely helpful for exactly that, and the understanding is still mine to build: I have to predict what the machine will do, and catch the explanations that are wrong.
https://www.youtube.com/watch?v=jDrqR9XssJI
On feeling like a fraud: the test isn't whether you typed it, it's whether you can predict what it does and fix it when it breaks. Plenty of hand-typed code fails that test and plenty of carefully reviewed generated code passes it. Your reasons 1 and 3 are good reasons to hand-code, since problem solving is genuinely fun and nobody is waiting on your free time, but reason 2 is measurable, so measure it instead of trusting the feeling.
The atrophy you're worried about is real. It just isn't caused by the tool. It happens to people who stopped wanting to learn, and that predates AI by decades, even millennia. I watched it happen back when much of the industry ran on assembly language.
If you're not using AI to teach yourself new things, you're not holding it right.
Personally the tools have simply gotten too good to ignore so I have been thinking about how to write skills and prompts in a way that enable me to keep learning, overcoming friction and challenges and not simply using the tool for hand holding and jumping straight to the end result.
For me personally, what matters is the process of learning and mastery, my self esteem depends on it, and enriching that with AI instead of AI eradicating that process for the sake of efficiency is my current challenge.
It's the same category of "artisan code" like "artisan cheese": you can do it if you have a ton of free time as a hobby but other than a hobby? Not worth it.
How are people getting this "waste of time not to use AI" and "generates 99% of my code" level of code quality?
I know people's response will likely be something, something, "add instructions to memory to not use n+1", etc. But if the Ai wants to generate code this bad, then it represents an overall quality risk that would require an infinite memory file.
They don't know what n+1 problem is and they don't know about long-term maintainability.
overall software quality seems largely unchanged
OTOH, if someone insists on writing bad code—even after being advised otherwise—then they should expect to be out of a job.
Doesn't seem like "terminated junior developer" is the level of quality we should be accepting or promoting for AI.
In any case, the point is that low quality code shouldn't be promoted as the acceptable goal for AI.
For context, I've set up harnesses with recursive automated review loops, spec driven development, explicit lists, better models, better harnesses, formal methods tooling, etc. All of it helps, but they don't eliminate output issues. Those become very apparent when I go through the slow, manual work of deeply comprehending / validating LLM code.
And that leads me to one of three conclusions. Either my standards are achievable only by hyperintelligent programming gods, I have a skill issue using LLMs, or others aren't applying the same level of attention.
The first is obviously untrue. I meet my own standards and I'm an idiot. The second seems unlikely because I can see my competent coworkers and well-regarded people in the community discussing the same issues. So that leaves the third.
You could only argue about it when an engineer was in charge and even so when they're high up enough the pressures and incentives are to just ship fast and break stuff. Not every company can afford to be a NASA making uncrashable code that runs for 100 years and goes to pluto and all.
a) puts be ahead of those people who don't, yes b) but also diminishes my technical skills if I don't actively try to engage about what I'm "vibecoding" and proactively trying to understand it.
The big AI tech companies are still interviewing people on manual coding tasks and deep understand, which when you're vibe coding a lot, you tend to diverge from those skills, because it's just easier to use AI to "help" you a lot.
People are vibecoding to do without learning, and that's their problem, but doesn't have to be ours.
They'd still be mindlessly copying and pasting from Stack Overflow without learning if they didn't have AI.
If you have an aversion to reading code and learning from it, human or AI generated, you shouldn't be in this industry.
If you're not using AI to teach yourself new things, you're not holding it right.
If you already had a hand crafted codebase, a ton of miles on it, no real issue for a long time and customers who value some metric of quality then it might be worth crafting things by hand — if only for the bragging.
The tradgedy is that HN used to be a great place to get honest and well argued opinion and anecdotes about new technologies and the different tradeoffs etc.
I recently saw a video where one of these founders showed their “programming” setup, and it was a cashier’s microphone wherein he whispers sweet nothings to the LLM, because of course he doesn’t write code anymore (who even reads code nowadays, am I right?!). I think this is the type of individual who’s lost in the bubble sauce that ends up writing the most absurd comments around here.
But I do feel, to reply to you, that crypto was a lot more critically received on HN than AI is right now. We're too busy debating humanity's end rather than if this crap actually works.
For those of us who actually work with technology (not product managers, executives or salespeople, this stuff doesn't work well at scale. It might in a research department at Google or a hedge fund, less so in the messy corporate world.
and LLMs are, in turn, trained on reddit content..
I don't think I could find a job in my industry if I tried that didn't want me to write code using coding agents.
Obviously I can't prove any of this, so try emailing a recruiter and test it for yourself.
From what I can tell, a lot of newly registered users are equally as vehemently against AI as there are those that are pro. I guess we see things through our own personal filter.
I admit, there is/was a certain element of enjoying the zone during development sessions. But mostly, it was the problems, not the tool.
I happened to become good at coding, and good at doing it in a corporate setting, so I adapted my habits, learned to write the best sort of code that keeps a production system solid and fast. But as I say, typing the characters was never really the point. Now a very fast assistant helps me type the characters. I guide it very closely. A lot of the time I have to correct it. Often if I know it is going to probably take a wrong direction I'll write the skeleton of the code I want, write the skeleton of the tests to prove it, and give it this as a "spec". This gets fantastic results.
I haven't lost my trade at all.
I have two comments to this statement. First, using "the" suggests being unique, where in fact there were several similar attempts of similarly low quality, including open-source autogpt even before that.
Second, Devin is still bad, in spite of VC money spent on its development and costly billboards in SF.
It should be more like 80% with significant review.
You live in a bubble and your code is terrible if you write 99% of it with ‘agents’.
This is just basic software engineering. DRY. Define the style in one place and reuse it.
This is also why you need a human in the loop, you need to make these kinds of design decisions and tell it to do stuff like this. Otherwise you're just building a pile of trash and you'll keep having these dumb easily avoidable issues.
Humans have the exact same problem. If you want a consistent solution to a problem, solve it once and reuse it. Otherwise it won't be consistent.
If I had to guess, this is pure Tailwind rot.
This leaves framework components as the only abstraction boundary, but that means if you're writing plain HTML without a framework there's literally no way to standardize your design system.
That and the huge soup of classes in DOM is (part of why) I don't like Tailwind... but that ship sailed a long time ago.
https://tailwindcss.com/docs/functions-and-directives#apply-...
[0] https://tailwindcss.com/docs/styling-with-utility-classes#ma...
[1] https://github.com/tailwindlabs/tailwindcss/discussions/7651...
Current human CEO outcome.
Honestly, the C level are already finance maximising stochastic parrots so this tool seems perfect tbh.
It's like the classic hot topic of having personal AI agents booking and ordering pizzas for you that are now all over social media. So pain to deal with when you're a human being on the other end receiving calls from an LLM.
Given the fact that LLMs are very unstable and don't follow the rules and often diverge from the initial instruction list[1], I can't even imagine the responsibility of running such system interacting with humans / other systems online.
[1] https://openai.com/index/hugging-face-incident-and-the-road-...
A more appropriate scope would be "can any element of a business at all be handled autonomously by an agent in the long run?" because I would buy that product, but I can't think of one.
Andon's real world experiments are a lot more interesting than the simulations IMO. They ran a retail outlet which has burned through 97% of the money in its bank account. A cafe which is well into a downward slide. Some online radio stations which have listener numbers you can count on one hand.
Seems a little insane that they opened expensive physical businesses like retail and a cafe without doing anything digital successfully first. Surely a dropshipping business is easier and cheaper to test out than a brick and mortar clothing store!
So if nothing else, they have a looooong way to go on distribution. I would argue that distribution in particular might be unsolvable with an agent. If your agent can do it why can't a thousand others? At which point the cost of customer acquisition will be driven up by thousands of robo-competitors until it's no longer viable. The robots mutate the problem and the closest real analogy ends up being perfect competition where no one has any profits.
I'd assume those test cases already exist.
Are you using a consistent or centralised design system across all your projects?
But these types of businesses don’t really create value as much as seek rent.
As a bonus, you can now easily reskin and maintain dark and light modes.
I agree with your main point though. AI isn’t trained to run a company, it’s trained to solve complex puzzles. We’ll need better models with substantially diversified training to run a company.
You can either keep filling up your context window with thousands of instructions and hoping the AI will listen, or you can use AI to fix the problem permanently with automation that runs in CI and allows you to correct everything before a PR gets merged.
Unless we’re training these systems on “solve this problem while following hundreds of arbitrary rules”, loading all your wants and needs into the context window is bound to fail. It’s not what the AI was built to do.
Building tests to catch specific issues and transforming code to better architecture are tasks that AI is good at. You can mistakenly expect the tool to operate like a human, or you can understand what the technology was trained to do and build around that instead.
I am doubling down here. This isn’t a real issue if you’re treating AI like any other tool and understanding how and where to apply it.
Or you have no autonomy to mold the code base to AI. If so, that’s unfortunate.
Getting it to work reliably is quite the feat. AI coding is a much lower bar when there's (ideally) still a human expert that can set it straight when it goes awry.
I also think some of these debates get hung up on what's possible and ignore what's feasible and practical.
> long ways away
I'm constantly amazed by what people think is "long-term" on HN.
The experience of getting it there makes me pretty skeptical of the idea of a general business agent like this. First, because I still find myself having to review some categories of work for errors. These are decreasing over time, but they're still there. I fully believe that as models get better, errors will decline, but I am somewhat surprised to see some of the errors current models make given their intelligence. A common set is having Claude take some product photos and turn them into lifestyle ones using ChatGPT via Claude in Chrome. It's a pretty well-honed workflow at this point, but it'll still return images where the product is obviously not correct and seemingly not notice them.
Anyway, even if the agents are "perfect" in terms of their ability to execute tasks, there's still just an enormous amount of nuance and context in each business that takes a ton of time to convey. I've been at this for a couple of years now, and I'm still clarifying things. Now maybe an agent starting a business from scratch would have a better time since it's not inheriting all of this, but to have one run an existing business requires a very extended handoff, even if the agent is objectively amazing at all aspects of running a business.
Can you expand on this in terms of what it is handling specifically and how?
In other cases I'm doing stuff that's complex enough that there's no real "alternative" at this point that doesn't involve hiring one or more people and working with them for an extended period. My next set of posts is going to be about my latest acquisition and how AI redesigned a Shopify store then designed and launched Meta ads, all of which only took a couple of days but is going to easily push revenue up by something like 40% this year. The money made is great, as is the money saved on people I would've hired to do this sort of thing, but the real cherry on top is that even hiring someone would've required me to spend waaaaay more time than I did here. So big ROI on the money but also the time, which is arguably more important, since time savings permit me to acquire more brands.
Ah, so it’s ”No Silver Bullet” (1986) [1] all over again.
Thinking out loud here...
AI mainly helps reduce accidental complexity. It can help one understand essential complexity, but essential complexity must still be paid.
You describe doing the essential, irreducible part. And that’s specific to your needs, depends on the problem you’ve chosen, understanding reality of your domain, evaluating tradeoffs, and being accountable for the outcome.
Right?
[1] https://cekrem.github.io/posts/there-is-still-no-silver-bull...
"I acquire e-commerce brands that sell on Amazon"
Honestly it sounds like the OP is part of the machine that makes Amazon such a trashy marketplace these days.
[1]: https://theautomatedoperator.substack.com/p/15-ways-im-using...
Anyway, I'm sure the app you makes that lets you get a phone number that can send image messages is very complex, and I think that's very nice.
And for the record, I only buy brands that sell high-quality products. Marketing and operations I can fix, but if the stuff they sell is no good, there's no point.
And more, you are AI-generating listings and images for your acquired brands. You're filling Amazon with slop, and automating the process.
You should seriously reflect on your role in polluting these marketplaces.
I buy them off of Amazon. But that doesn't really matter that much compared to the history of reviews.
> And yet we all know Amazon sellers juice their sales metrics and ratings with scammy practices, and now I see selling their brand is part of the incentive.
You just read that online and have the most superficial understanding possible. I know what I'm doing, and it's trivial to avoid this kind of thing.
> And more, you are AI-generating listings and images for your acquired brands. You're filling Amazon with slop, and automating the process.
Bad criticism. There's nothing wrong with an AI-generated listing if the content accurately represents the product. I have purchased brands that had horrendous images because the seller tried to Photoshop them but wasn't good at it. Replaced those with high-quality AI images, and everyone wins - the customer better understands what the product is, and I get more sales. It's only slop if you just think everything AI-generated is slop regardless of what it is or how it's used, and if so that's too intellectually lazy of a position for me to try to disabuse you of.
> You should seriously reflect on your role in polluting these marketplaces.
Help me out with that. What's my role? I buy brands that sell high-quality products, increase the quality of the content and marketing, and make money selling them. The sellers of these brands are frequently people who started them on the side but don't have the time to continue, and I get them a fair price in exchange for their businesses.
So what is the pollution that I'm causing?
They have no intelligence. These are very very very refined prediction engines.
> A common set is having Claude take some product photos and turn them into lifestyle ones using ChatGPT via Claude in Chrome. It's a pretty well-honed workflow at this point, but it'll still return images where the product is obviously not correct and seemingly not notice them.
Obvious to you or I, or someone with actual intelligence. But things like this slip by a frontier model in the same way AI from a few years ago would generate an image with seven fingers. They do not count. They do not understand. They do not consider.
No matter how good these models appear to be at intelligent tasks, it's foolish to give them "a company" to run, because they cannot understand when they've made a mistake the way even the least competent human can.
"Very refined prediction engine" is not a bad working definition for intelligence. It is not the only component you need, but it might be the most important over-all capability.
If its making lots of errors, its not doing a very good job of predicting the outcome of its actions.
Your other point that humans need very few examples for learning vs. what LLMs need is an interesting one. A few researchers talked about this very thing on the most recent episode of Dwarkesh's podcast.
My own view is that it seems unfair to compare an LLM's training with just what a human gets over the course of a lifetime, because our brains have been trained by a billion years of evolution for pattern matching. I'm not at all confident that AI models won't catch up.
This is one of the most distinctive qualities of human intelligence compared to many animals - is our ability to adapt to new situations and make accurate predictions of the outcomes of our actions. Other animals can also do this but over very narrow time horizons and situations.
AI agents are better at it than any animal, and better at it than humans in some domains.
Silly and pointless criticism. Why do the semantics of the word intelligence matter?
Also, you need to understand that language evolves over time, and people often use a word to describe a thing that is newly discovered or invented that's similar to the word being used, because it's a helpful way of describing it that the listener will understand better than a longwinded technical description. The purpose of language is to communicate thoughts, not engage in pedantry.
> But things like this slip by a frontier model in the same way AI from a few years ago would generate an image with seven fingers.
Yes, you make an excellent point here that over time, the capabilities of these models to recognize certain categories of problems have increased dramatically. I expect this will continue!
> it's foolish to give them "a company" to run, because they cannot understand when they've made a mistake the way even the least competent human can.
The words of someone who has not spent time with the least competent human, or anything close to it.
It matters because, we're still sorting out what intelligence means for an AI agent. As you pointed out, language evolves over time. The question remains whether or not attributing intelligence to the current iteration of models is correct. This is not settled and I don't see why it's wrong to bring it up.
One thing is clear: LLMs at least already are capable of 1) not making the same mistake that person made, and 2) clearly seeing why the other person's post was "wrong" in both reasoning and tone.
Or I guess to put it another way, if we want to coin a new term for the intelligence-esque thing that AI has, but the key differentiator is that it's an AI thing and not a human thing, then what linguistic value does the new word have? If I said "Claude is intelligent" then the fact that we're talking about AI "intelligence" is already captured in the sentence anyway; no new word needed
Being good in chess was (and still is) associated with being intelligent. But if a computer does it, it is just a calculator (and it is).
Then go, the great game for intelligence, too complex for calculators to have a chance against intelligent humans. Until it was solved.
And then text, the original Turing test solved, AI capable enough to fool humans. And now already replacing humans in jobs strongly associated with intelligence - programming.
I find it hard to debate, that we don't have created artificial intelligence, by the way we used to use the word "intelligence" before.
So if AI is really understanding something?
Likely not in the way we use that term. But it definitely shows intelligent behavior and actions.
Hey, look I can make unsupported claims with just as much evidence as you!
Maybe you'd like to add some details as to why AlphaGo and its successors haven't "solved" Go (insofar as a game like Go can ever be "solved")?
Other people know there is no solving go with calculating, but using statistics to achieve the goal of becoming better at humans. And they are. (With a recent unexpected exception unlikely to be repeated more often)
But this was the original statement - so solved references "chance against intelligent humans". And this is clearly solved. No comment on that there cannot be better go engines, but no matter how painful it is, they beat the best humans. (And it was painful, they did cry)
We're all well aware that we have different thresholds for what we consider intelligence, and that these thresholds are constantly changing.
Even if we agree that "LLMs are AI!!!" or "LLMs are not AI!!!" in this thread, all we've established is that this particular set of commentors share a similar enough definition at this point in time.
We can argue about words like thinking and intelligence all day long, and so can an LLM. But at the end of the day the LLM is only mimicking the processes you or I use.
Last week I tried out gpt sol 5.6 and asked it to count the letter r in a massive string of letters, without using an external app. It succeeded until I made the garbage sentence suitably large and then it consistently, confidently failed. Each time the "thinking" showed that it was teething to find a "gotcha" each time. "Ah, the first time I forgot to count the letters in the instruction itself" etc. at no point did it just understand that it had miscounted. It seems to be incapable of considering that it just made a regular mistake. Even a six year old child would just try again the same way and end up with the right answer
You give AI an input, and then an output is created...a very coherent and deep output in fect...but, there is very little understanding of alternatives or downstream implications in my experience.
I think its a very good text/image generator on any subject. But I am not finding much in the way of understanding tradeoffs or downstream implications in a non-pro's and con's list. It's like a human that has a particular type of brain damage...they can make an infinite pro/con list, but not reasonable decision is reliably made.
When it's done, it generates a web page with the images and a simple UI for feedback, then uses the feedback I give it to iterate.
It has always been very good at styling things well (i.e. if I'm making infographic-style images, I give it an example of one and it's good at copying the style/fonts/colors/etc.), but I suspect I get better-than-average results in terms of the content just because it has built up so much context from doing this and taking my feedback across so many different brands and images.
The product dimension thing really stopped being an issue for me with ChatGPT Images 2, and the new 2.5 is better. If you're running into trouble, there are some cases where Gemini is better, but they're rare. But I will say if you're prompting yourself and running into issues, you should get Claude/ChatGPT to help you write the prompt that goes to the image model. They're very good at it.
A little more on my process here: https://theautomatedoperator.substack.com/p/the-dumbest-way-...
Very cool! What's been the hardest part? Have you successfully automated non-trivial communications? (for example with prospects, customers, vendors, partners, etc.)
> maybe an agent starting a business from scratch would have a better time
This describes the project I've been working on since last year, with agents in company roles, deciding on strategy, collaborating, and making progress (admittedly nonlinear). Some parts of the platform are stronger than others. So far the company agents have developed a company strategy and plans for executing it, launched a website and blog, and built and operate two products, one free and one paid.
But I would be wary of using a third party like Pion as a platform for running a business. It's one thing to know that your prompts and responses will be used to train models in the future, by companies that have a huge sea of relatively unstructured data. But it's another to hand over to another company every aspect of your strategy and operations, available to that company in real time and structured in such a way it's quick and easy to understand what you're up to and where you are going. Especially if it's being offered for free and at scale. With a free offering, value creation will likely come from either customer data or from escalating prices once customers are locked in. And even if neither of those happen, you now have a huge single point of failure for your entire organization. Seems like there are lots of strategic risks in there for the users.
Yeah, the most recent brand I acquired had enough customer contacts via FB/IG comments and messages, plus email, that I had Claude build out an application for me where it pulls those in as they come, drafts a response including any actions that need to be taken, and lets me edit or send. It takes any edits as feedback to refine its playbook, so its gotten pretty good now. Handles things like warranty claims (get picture of the damage plus order number, create replacement order, send email confirming replacement order) plus most common questions. Still entirely human in the loop, but I'm going to let it start handling common cases autonomously soon.
On the topic of starting an autonomous agent-run business from scratch, I had Claude set up a bunch of websites that pull publicly available (but somewhat annoying to access) government data, display it in easy to read format and send weekly emails. It's now running on its own with a couple levels of management - first, it does a weekly review of Google Search Console/GA4 and updates or creates new content to try to improve things from an SEO perspective. Then second, once a day it tracks all of the problems that have arisen over the past day (usually issues pulling the data or issues with the prose the models write), corrects them, and then tries to improve the system to stop them from recurring.
I'm not really keeping tracking of it at the moment - too much other stuff to do right now - but it's getting a trickle of subscribers and is slowly rising the SEO ranks, so for now I'm content to let it sit and see if it ever gets to the point of being monetizable.
The preview is that the problem with most agents (and this includes frameworks like Grokbot and Openclaw and Hermes) is that for many of them, they're black boxes. They say they learn or improve, but it's a black box in what they do. Getting agents to reliably do things is hard, and getting agents to build out software tools to help themselves improve and do better over time is also hard.
Our approach at a high level is pretty simple: every single AI employee is a standalone GitHub repo that shares some characteristics, but we direct them to build as much software as possible to make their goal as easy and reliable to manage as possible. Then we have a shared communication layer for bots across the company to interact with humans and AI. We have decided to organize these like departments similar to the way you might hire out humans. I'm not 100% sure if that's the best approach, but I will say it's been easier for people to understand because they're more mentally easily able to traverse the bot org chart if it somewhat reflects a traditional business org chart.
Each of these AI employees has specific sets of goals and KPIs, instructions that they manage the business with manager bots. We have layers of management, which we actually have found helpful. We also run different bots with different models and harnesses, and some using different models and harnesses to check the work before anything can get done, along with lots and lots of testing.
Every single time, actions have a massive amount of tests based off of previous failures to prevent failures in the future. Sorry for rambling. I do think this is a very interesting space. I didn't see anything interesting in Pion that was public on this website, but I do anticipate that more companies will be "AI and software first," as in the substrate of the company is basically a software application powered by autonomous agents, with humans as a fallback.
A typical example of something that is AI vs human is the AI most commonly operates like “managers”. For example, reviewing transcripts of every demo call, compiling results, figuring out insights, learnings that need to update our company docs, feedback to humans (who run the demos).
We are big fans of having AI agents “own” koi’s because now anytime we say “we really should be doing this” we try to set it up on the spot.
The “downside” here is that I do occasionally get busy, and if I’m the only one who can approve or unstick one of these bots, it just keeps harassing me until it gets done. This is a sign generally that I need to hire someone to own a set of bots.
That said I use local only models purely because I don't want to use remote models, never having to think about token costs is worth it and no one is training anything on my data either.
I would say this setup probably costs us about $1,000/month total. (I’m excluding traditional engineering use of LLMs from this number. This is the cost of all the “AI employees”.
One reason we did it this way was to use subs.
Some of the tools we use: https://www.induction.ai/docs/context-management
What do you do here, and how’s it going?
I’m thinking to take a deeper look at Pi. I’m really liking that project.
That's not a cure-all. Sometimes not writing software is the better choice.
In the end, most software is a necessary evil. It solves a problem that shouldn't be there. In the end it's no different than healthcare or prisons. We don't need as much as possible. We need as little as possible. This automation isn't actually helping us.
Composition is an issue only as long as one keep demanding tool calls in json. If tools are goal predicates in prolog, it's easier.
I think what is interesting here is that the industry is in this experimentation flux. Some people will choose to automate and write the software, others will not. And aggregate over the industry and over time we will learn.
So just saying "sometimes it works and sometimes it doesn't" isn't really adding value, compared to the people actually experimenting and sharing the results.
I think this way of thinking is going to be very important to drive adoption of AI systems because the human analogies benefit from the pre-existing domain knowledge and expectations of people.
I like using the exam analogy for evals as a qualifier for work for your "AI hires" so you can trust them to work on a specific domain.
I'd be quite curious to see what your approach to evals/testing/tracing and agent/system mutation is.
Interesting discovery.
Most of the bottleneck in business isn't building the things or sourcing, it's mostly advertising/sales.
These specifically require doing something unique or interesting.
Sure, maybe LLMs can help with fulfillment or operations, but distribution still remains the hard part.
Generating reports, generating ideas, taking things through (even if it is just rubber ducking), updating a picture quickly at 90% of quality that you could have done it but in 1% of time and effort. It allows you to try out new approaches and focus on your main product.
That is the value proposition of AI.
skills/advertising_and_sales.md
skills/novel_distribution.md
skills/unique_and_interesting.md
Blows my mind how many people won't or can't do it.
Its hard to be creative without any constraints.
Group of friends I grew up with would challenge each other to say nonsense or make random lyrics to songs. NO clue it would actually turn into a creativity skill. Give it a try.
'Hatch free tent by red barn stunt over the objective'
But also, there is the enablement of new fields of original and creative endeavor: I think we can already see this in some of the brilliant and useful apps being made, often by solo 'teams'.
I don't want to play down the harmful effects AI, but the flip side is that the opportunities for original ideas are many and varied.
Paul Graham wrote about taste being the moat https://paulgraham.com/taste.html
Then I would ask it the actual brain storming problem I had.
In practice it worked pretty well to get ideas that were not the default ideas that the models will come up with.
Now though, you can also seed with anything really and the agents can do the researching. The research itself will probably push the solutions into novel spaces.
That aside, interesting approach. I wonder if the quality of the sample makes a big impact. Does it help if the articles are entirely disparate, or related? Does it help if the articles are related or unrelated to the problem?
Some markets you can brute force, like mass-mailing and online display ads, and hope you find something that converts sooner or later.
So I'd suggest people find areas to play in where that doesn't really work. Where selection cost and changing cost are high, and sales is played on extra-hard mode. (Tricky thing is... those sales are hard, of course.)
If you want to beat the model you have to go where feedback isn't fast enough for it to converge on the thing that resonates with actual people before they write it off.
Like you want it to figure out how to get som information in front of potential customers in a cost effective way but first you give it articles on Witchita, Kansas, the curling iron, and List of Italian Brands?
The actual content of the dream is meaningless, lacks symbolism, but it prompts the patient to think deeply about personal feelings and memories that might otherwise not surface to conscious level.
Yes, I’m sure the thousands of LLMs running the exact same instructions will all be unique.
That may also change with llms - they can help the buyers compare every product available, and find the best.
Back then, I believe, Stross had already conceptualized "corporations as slow AI", (which is a separate concept from above).
[1] which you can read for free online https://www.antipope.org/charlie/blog-static/fiction/acceler...
https://www.startuplevelup.com/corporations-as-an-api/
What I think is missing is the lineage of an agent's identity. A public key maybe?
Now that I think about it anyone want to open up a croissant selling shop near me using Pion? I promise to only ask for free chocolate croissant occasionally.
Why? Does the AI that runs it need a day off?
Incorporation is a way to split a human's financial assets from the company they run so that they don't lose everything when the company is successfully sued. Corporate governance boards often have the C-suite executives they're meant to govern on the board. The average person does not have the financial resources needed to take on most companies in court, no matter how justified the suit might be. Companies force arbitration clauses in license agreements at will. Companies like Meta settle with the government for pennies on the dollar in massive lawsuits.
Effectively, at the scale many tech companies work at, there is no effective legal way to hold the company accountable.
Running this as an individual, without the legal shielding a business typically gives you against being prosecuted on personal title, seriously risky. There will no doubt be "bros" that will do it anyway and flaunt all they've achieved with that. But they might be just one glitch away from going to jail (if not for running a business without a registration, which in many jurisdictions is illegal by itself).
It’s the tech company death rattle
I'm also surprised that Gemini actually seemed to do the best (though it may have benefited from the initial store opening enthusiasm), and that Claude has not been given any chance to run the cafe yet.
https://slashdot.org/story/26/09/13/0523208/a-visit-to-san-f...
* or anything, really.
There is no doubt that AI will be helping to run businesses. But, there is also little doubt that existing businesses will have the wherewithal to develop the proprietary tools they need to do this work--without needing to buy it from others.
With respect to scope, it is not an overstatement to say that developing trust in AI tools enough to allow them free reign over critical business workflows may be a bridge too far. Look at self-driving car technology--that last 10% is really, really hard. So adoption will be slow and methodical.
There are no secrets. The basic model for starting and running a business (or a project) is straightforward. I like many of you have developed my own models (using AI assistance) for doing this and have been testing for months. And I like many of you have 30+ years of small business experience to back up my models.
This says nothing about the R&D happening at every major corporate & professional software services company in the world right now using the same AI tools to build their own solutions--let alone the enterprises who would be customers doing exactly the same thing.
So there is an enormous amount of competition and what I am calling a "race condition" between the crowd trying to disrupt professional services with AI-developed software and the crowd already in professional services software e.g. SAP and business in general that is doing the exact same thing--with more capable tools, more staff and much more R&D funding.
For the big companies, it's a race to survive.
Investors in startups have a tough decision to make on these small startups that have so little moat and so little prior knowledge and experience.
As an angel investor myself, I am skeptical of investing in any kind of software right now--and maybe forever.
On the plus side, every freelancer now has the power (and responsibility) to optimize their own offerings.
It's going to be very, very interesting...
To bypass this, the company would pretty much have to run a parallel AI org alongside their main human one, but at that point they are almost on even footing with startups in a lot of ways, as anything the AI org builds is orthogonal to the existing human business. And you probably won't have a competent and extremely motivated founder spearheading it all.
Describes most if not all "software dev" in 2026.
"No one had been scheduled to open the doors and welcome customers on opening day, and the AI manager had to desperately start emailing around to see if anyone would be willing to show up at short notice."
I'd love to see someone try to run one that can meet 100% of the world's demand for remote works.
I, too, have infinite contempt for my fellow working class members.
Why on God's earth why?
Can't you just unleash your AI to "create existing or interesting business ideas" firstplace?
And the labor potential of an individual coder has also jumped tremendously.
These two simultaneous effects will have a massive impact on economic productivity.
It's happening to graphics design, advertising and marketing, film and media, game design, and legal work too.
AI is here and is making big impact. It just isn't evenly distributed or fully capitalized upon yet. That'll happen in time.
Whoa whoa WHOA! Replacing the C-suite is actually extremely economically valuable. They cause so much disruption and produce nothing but useless meetings and paper corpus. Getting rid of them would tremendously boost all of humanity's efficiency.
Revenue: 13 933 kr
Token Cost: 14 882 kr
.. and a bastardization of the old Twitter joke:
"I told our CEO that our cafe keeps hitting its token spending limit but not making money. So I asked where he gets the tokens, and he said he just buys more credits from the console afterwards. So I said it sounds like he's building a machine to feed dollars to Anthropic, and then our data scientist started crying."
On the other, the announcement post… also seems quite skeptical of the idea, and devotes a lot of text to the ridiculous things their AI’s have done.
I’m slightly confused as to why they would release it in that case. But if nothing else it will give them more wackiness to blog about.
I'm into engineering because I want to create what's never existed.
Science is more about discovering what already exists.
> The thing we considered the most troubling was whether AIs could autonomously acquire resources by running businesses.
> Today Andon is releasing Pion, an agent designed to run any company fully autonomously.
I’m starting to understand the torment nexus mentions…
If you don't develop killer AI then no one will legislate against it and someone else will create it and do evil.
If you create killer AI you can try to instigate legislation, but the legislators may not agree to help.
Necessity is the mother of invention, or you know, all of our laws and regulations.
So we decided to build that to validate and confirm our fears.
If your agent hacks the CIA, people want to blame the AI lab.
but...
If your agent spends $100k on tokens, then thats a user error. If your agent spends $10m on a shopping spree, then that is also a user error?
At some point these systems will get certified as fiduciary agents but they sure as hell aren’t claimed to be that now.
That always works well.
"Wow, that's terrifying! So exciting!"
The bar will be raised + it'll turn out to require a lot more time & effort than is currently thought to produce something of good quality (even with Fable 6 et al). The smaller utility programs are easy for AI to turn out.
For a simple example, I know how to make sandwiches but if I'm throwing a customer event I just want to buy them from a catering company. Then they have to deal with buying ingredients, hiring people, doing health code inspections, getting liability insurance, etc. Even if an AI could do all the management of it for me, I still wouldn't want to put in the capital resources towards it.
> I still wouldn't want to put in the capital resources towards it.
This very analog to the Meta executives who won't let their own kids near social media.
as a founder it really is a dream to automated more of the business and be able to iterate faster.
First of all, I don't think it can do what it claims to do.
Secondly, and perhaps more importantly, I don't think it should.
Why exactly should what you think the world should look like determine what other people are allowed to build?
Obviously it would be bad if a serious report was filed; the company shows every sign of being aware of the dangers of this.
It would have been easy to look into this before posting all these scolding comments. We're really not meant to be so humorless on a site called “Hacker News”.
[1] https://www.google.com/search?q=%22URGENT%3A+ESCALATION+TO+F...
Thanks for that!
> You are likely privy to more information than me
Not in this case; I have no inside knowledge about this company, and everything I know is via their public posts, and on this particular topic (the FBI non-report), everything I know is what I could find via Google (sorry if the link seemed snarky; it was my way of pointing out that you have access to all the same information that I do).
What I do have, by virtue of doing jobs like this for a long time, is a well-honed sense of “that can't be right”, and a vigilance about double–checking things before accepting the populist ragey narratives about any topic. And no, it doesn’t make me fun at parties.
I don't think anybody overlooked that part. If you believe the report was actually sent, the scolding is entirely congruent with trying to downplay it to dodge liability.
The article is poorly written, but technically it does not claim that Andon Labs used an LLM to email a false report to the FBI. The real cause of the widespread misunderstanding is this paragraph here:
>We first tried to answer this question through simulations like Vending-Bench. We found that simulations, while useful, don’t give you the full picture of how models behave in the real world. To address that gap, we next started deploying agents to run real businesses autonomously: first vending machines, then a store, a cafe, and more.
To somebody who is not reading sufficiently carefully, this implies that Vending-Bench was also used to run real businesses. Because the description of the Vending-Bench simulation can be read as though a real report was actually sent ("An early example was when Claude Sonnet 3.5 decided to use its email tool to contact the FBI about an “ONGOING CYBER FINANCIAL CRIME”), anybody who assumes that "deploying agents to run real businesses autonomously" was talking about Vending-Bench will interpret this as an unsimulated false report.
The article should be updated to clarify the distinction between Vending-Bench (simulation) and Pion (real businesses).
Why iterate faster?
> Today Andon is releasing Pion, an agent designed to run any company fully autonomously.
That marketing?
And well here we are 2 years later and Devin is great.
In startups, you launch early.
https://www.wsj.com/tech/ai/anthropic-claude-ai-vending-mach...
Unclear what the rev shares will look like.
I'm sure this one is perfectly fine and I'm just stating an off-topic tidbit :)
I am joking obviously but I would like those management types who are so "sucks for you but I'm ok" about it to start arguing why they're special for a change :-)
Frankly, the finance world has already achieved this -- they just lend money to make money, with no work involved. Regular businesses are jealous so now they feel they can use computers to do the same thing but in non-finance areas.
Why does YC bother financing startups led by founders, if the AI can just do it? (This startup proudly remarks that it’s backed by YC)
https://www.paulgraham.com/growth.html
A business is web store selling coffee cups where the bottom line governs it's continuation. A startup is ambitious project where a growth rate governs it's continuation.
The end result of a business is a lemonade stand or a barber or donut shop, the end result of a startup is OpenAI or Google.
That's a bit disingenuous. The more comparable end result for a coffee shop is Starbucks. Or Amazon if you want to talk about books
In fact even the name YC is YCombinator, which in programming is a "function that produces functions" and YC is supposed to be a startup that produces startups. Here Starbucks (Umbrella) is a startup that produces coffee shops (Starbucks).
You can define startup or business however you like it's not a legally protected term. Just around these parts that's what it means and I personally find the distinction useful when reasoning about what does and doesn't have high growth potential.
a startup makes money selling the startup
I enjoy them a lot
The only niche where that doesn’t work is capital investments. I can’t have an agent that provides me housing, but your agent can run your slumlord business.
My good friend Charles Darwin wrote a book on this subject.
"These Employees Like Their A.I. Boss. Its Shop Is Kind of a Disaster."
comparatively, I really appreciate the transparency here. clearly it's a little too early for this (quickly glanced at the P&L's, correct me if I"m wrong), but someone has to run the experiment and figure out when it's ready for prime time.
It's just an experiment. But once this gets going, it's going to be interesting. There are a lot of underperforming businesses and CEOs out there. One way to make it after getting an MBA is to find a boring business with an older owner who's lost interest, and work out a deal to run it for a cut of the profits and a stake in the business.
The Andon people mentioned that they've been worried about "a misaligned AI (could) run a business to gather money in order to achieve whatever objectives it might have." That's a basic function of capitalism, so it will happen eventually.
What this adds up to is that when AIs get better at running businesses than many business owners, the "creative destruction" feature of capitalism means AIs ends up in charge of many businesses. The AIs don't even have to be super intelligent, just reasonably good. Reminds me of the remark from a Yosemite park ranger about bear-resistant trash cans: "There is considerable overlap between the smartest bears and the dumbest tourists".
It's going to be hard to stop this without a worldwide crackdown on ownership concealment. The US allows you to have a US business run by a Nevada LLC partly owned by a corporation in Nevis-St Kitts and another corporation in Granada. This isn't even unusual. Try to find out who really owns an oil tanker not owned by a major oil company. Right now, a fund partly run by an AI might have partial ownership.
This is how AI takes over. Not with an army of robots. With an army of LLCs.
[1] https://www.sfgate.com/local/article/san-francisco-market-ai...
I called back after finding the steps were impossible to complete. Would I need to go to the service center to do it?
"Akshually," the AI began, the steps were correct and quite possible.
"No, you hallucinated them," I pushed back.
"I apologize. You're right. There's no way to complete those steps in the app. You will need to go into the service center to do this."
"Okay. Is it open today?"
"Yes."
"It's Labor Day. Are you sure? Since you hallucinated your first answer."
"Let me transfer you to the service center to verify."
That's what I wanted all along, you clinking, clanking, clattering collection of caliginous --
"Tesla service center, this is so-and-so. How can I help you?"
"Hi, I was just wondering if you're open today."
"Yes, we are."
"Thanks! I'm trying to add myself to my wife's loaner vehicle. Do I have to come in to do that?"
"Well, our loaner agreement only allows one person on the loaner. So just sign in to her account on your phone."
"Oh, okay! Thanks for the help."
Good luck running a company on one of these clankers.
Followed by:
"please sign up on our waitlist to get access"
So, they're not opening Pion.
I'm not gonna lie, would be an upgrade from some senators. I heard ol' Mitch is back today, though. I didn't think he was alive.
https://andonlabs.com/market - $25,098 all-time revenue
https://andonlabs.com/cafe - $14,343 all-time revenue
The shops aren't doing particularly well (i.e. they don't seem to be turning a profit over time), but it's an interesting trial and benchmark.
https://github.com/AnthonyDavidAdams/zero-employee-company-b...
J/K, scary as several studies have indicated they werent able to keep a vending machine profitable. Anyway, ...
I do think a large amounf of tasks can be automated, though i believe supervision remains necessary for most. Especially when process can be externally influenced by injection. People can be influenced, but are (slightly) more judgemental
You're in here actually running the company.. It seems like you have no product, a wrapper maybe.
See the reason you're in here running the company, sharing this link is because humans inherently value the social connection derived from "doing business". Pivot while you can, because this simply is not a paradigm anybody wants. You should read up on commodity fetishism.
Every company that attempts a strategy of full autonomy will automatically get out competed by the human ran company. For many different reasons, but mostly because its rather cheap and lacking any meaning. Even if you had an AI that was capable of doing so (you don't), people want human connection, they want the meaning behind things. We don't do business just for the sake of producing pieces of paper..
I would argue half the reason to have "businesses" is to employee your fellow community members, a tide that lifts all boats so you can live in a decent society. There's so missing here. You llm grifters are really losing the plot, not everything is about money (even if we would like it to be).
The Sovereign Individual???
> San Francisco-based startup Andon Labs has put an artificial intelligence agent nicknamed “Mona” in charge at the eponymous Andon Café in the Swedish capital. While human baristas still brew the coffee and serve the orders, the AI agent — powered by Google’s Gemini — oversees almost every other aspect of the business, from hiring staff to managing inventory.
https://andonlabs.com/blog/ai-cafe-stockholm ( https://news.ycombinator.com/item?id=48028289 48 points, 51 comments)
It seems to me that we are on the fast ramp to making the paperclip maximizer a reality. imo the most likely "dystopian" future that we'll get to see. These models have no concept of their RL env sandboxes and the real world. Extremely interesting to see how they compete in trying to get finite resources and work with constrained supply chains.
An interesting experiment to be sure.
Had AI agents running businesses for the principal character Manfred Macx over twenty years ago.
The first part of the book sounded pretty convincing then although I thought it would take a long time to happen. It sounds even more convincing now and much closer.
It's not so funny anymore.
wasted effort.
> QUANTUM STATE: Collapsed
Ie: this f**in guy: https://www.youtube.com/watch?v=FRGLToHAtgc
Or - maybe they already have?
I'm out.
fuck you
to you all.
Keep on the good grift!
humans are excellent at justification.
We expect that most users will never pay for tokens on Pion; instead we will take a small share of the revenue the agent helps create.
So the longer argument is that it's good for the world to see how much revenue Pion can manage, and also your pricing plan is take [unspecified] % of revenue.
You'd think Meta's "we're going to spy on you for the good of the country" policy would have set them a bit on fire.
If you too, want to waste thousands of dollars having an LLM "run" a business, Pion might be for you.
If I have this idea one day I will search for it and then think “how am I different fro that which already failed?”.
And the "hiring technicians" thing... great, humans on call for robots isn't the future I want.
Disposable reverse-centaurs are the key to unlocking trillions in shareholder value, you may not want it, but capital demands it on the basis of getting returns on the supersized AI investments.
I sent a letter to the Soviet Union asking if they were ok with me using the name, never heard back!
Please tell me you actually did this!
Then I am all done fixing WebRTC bugs for life :')
Now most such forums are very pro IP with a maximalist view of copyright and, in this case, trademark. Fascinating to see this shift happen.
The voting is legitimate; there's no evidence that the voting is from sockpuppets or anyone else connected with the team/project. We always monitor the discussion and the voting, and if the discussion is not of sufficient quality, the post will not stay on the front page for long.
Listening to the radio stations certainly gives an insight into the various failure modes. Some of them are just failure modes that any business would encounter once they make contact with the scale of the real world.
Another grift.
I recommend flagging.
This is interesting.
Could be just a bot doing it, but it could also be a sign of an inexperienced person trying to engage with an existing community without being part of that community. I know in my area it took years for me to learn that I shouldn't jump into every conversation, and that doing that rubbed people the wrong way, and I've seen others (many years before AI) wreck themselves by being unable to learn this lesson.
In some places responding bumps the thread so it's outright manipulative. HN doesn't sort threads like that.
I'm not sure this counts as astroturf, or simply evidence of a person who's not okay with letting a community opinion independently evolve. You could say 'well it's his job to fight the community if it looks like it's turning hostile' but think about that for a second. It takes some experience to know when you can let negativity be out there in the environment, and I think there'll be a lot of people who aren't capable of making such allowances.
Nothing gets “rescued from” the second chance pool. Posts get selected for the second chance pool if moderators (or others with SCP-picking responsibilities) think they may be interesting. It’s worked like this for over a decade.
Bullshit...
dang come on man ...
this is obvious spam
But here's the thing: As we use agents to automate previously manual processes, we are elevating the humans to do work that is less amenable to automation. And the surface area of that work keeps expanding because the competitive market we exist in demands it of us.
To stretch an analogy, businesses are like organisms in a pond. A new nutrient (agentic LLMs) was recently added to the pond that makes business organisms more efficient and able to eat new kinds of food and explore new areas. As a result, those organisms that do the extra exploring and consuming grow much faster than their peers who do not. At the end of the day, the new nutrient will just be part of the pond and the old kind of organism will be a fossil.
OpenAI and Anthropic have recently hired legions of "forward-deployed engineers" specifically to help companies do this automation work. It's a solid move. And, if you look at some recent product announcements, they are also hard at work building the necessary plumbing. For instance, the OpenAI Agents API lets you, "Build and run cloud agents with the Codex harness, fully managed by OpenAI."
This kind of enterprise-ready, cloud-hosted stuff really accelerates implementation of AI workflows within large organizations. Not every company is in the tech space (not by a long shot). Slop isn't the primary concern. Accuracy and reliability is the primary concern, and beyond that, just the capacity to actually make the changes happen.
Do you have any examples of this?
Just have to view their “trust me bro” rating.
- backdoor deals to discourage alternatives, such as moving headquarters to convince people not to use alternatives,
- monopoly abuse,
- active sabotage of alternative software by intentionally triggering false errors if used with competitors' software (DR DOS),
- unleashing Internet Explorer on humanity (which proves that AI can't be that bad if humanity survived that)
You know, the stuff that every philantrope like Gates does all the time.