On hacker news, a very tech literate place, I see people thinking modern AI models can’t generate working code.

The other day in real life I was talking to a friend of mine about ChatGPT. They didn’t know you needed to turn on “thinking” to get higher quality results. This is a technical person who has worked at Amazon.

You can’t expect revolutionary impact while people are still learning how to even use the thing. We’re so early.

But from my social feed the impression was that it us taking over the world:)

I asked it because I am building something similar since some tome and I thought its over they were faster than me but as it appears there’s no real adoption yet. Maybe there will be some once they release it as part of ChatGPT but even then it looks like too early as actually few people are using the more advanced tools.

It’s definitely in very early stage.

If you have been working on a usecase similar to OpenClaw for sometime now I'd actually say you are in a great position to start raising now.

Being first to market is not a significant moat in most cases. Few people want to invest in the first company in a category - it's too risky. If there are a couple of other early players then the risk profile has been reduced.

That said, you NEED to concentrate on GTM - technology is commodified, distribution is not.

> It appears that so far the mainstream success in AI is limited to slop generation and even that is actually small number of people generating huge amounts of slop

The growth of AI slop has been exponential, but the application of agents for domain specific usecases has been decently successful.

The biggest reason you don't hear about it on HN is because domain-specific applications are not well known on HN, and most enterprises are not publicizing the fact that they are using these tools internally.

Furthermore, almost anyone who is shipping something with actual enterprise usage is under fairly onerous NDAs right now and every company has someone monitoring HN like a hawk.

On my app the tech is based on running agent generated code on JavaScriptCore to do things like OpenClaw, I’m wrapping the JS engine with the missing functionality like networking, file access and database access so I believe I will not have a problem with releasing it on Apple AppStore as I use their native stack. Then since this stack is also OS, I’m making a version that will run on Linux, the idea being users develops their solution on their device(iOS&Mac currently) see it working and and then deploys on a server with a tap of a button, so it keeps running.

You need to answer these questions in order to decide whether a Show HN makes sense versus a much more targeted launch.

If you do not know how to answer these questions you need to find a cofounder asap. Technology is commodified. GTM, sales, and packaging is what turns technology into products. Building and selling and fundraising as 1 person is a one-way ticket to burnout, which only makes you and your product less attractive.

I also highly recommend chatting with your network to understand common types of problems. Once you've identified a couple classes of problems and personas for whom your story resonates, then you can decide what approach to take.

Best of luck!

Thanks for the advice! I’m at a stage where I want to have such tool and see who else wants it. Not sure yet about it’s viability as a business and what is the exact market. Maybe I will find out by putting it into the wild and that’s why I consider to release it as a mobile app first.

But even with that persona, it should already answer your question whether posting on HN and producthunt should be a core part of your strategy. Not a lot of social media managers or compliance people around here. And even for crypto traders there are better places to pitch products to them

You need to narrow it down to a single and specific persona and business domain.

This is because it takes years to fully flesh out and productionize a workflow from scratch, so concentrating on a business domain you know intimately well helps you build that muscle, which you can then repeat if you are able to hit revenue metrics for a Series A/B.

Monitoring specific user accounts or keywords? Is this typically done by a social media reputation management service?

Shocking results, I say!

Most people are just "not that deep in there" as most people on HN.

A guy attached Claude to his socials, groundbreaking tech.

While they were deep in software development in general, no body of them read any of the essential/required daily industrial news (also not that one related to doing software development in sector ABC)

:-)

So no, even people somehow attached to a topic are not necessarily somehow deeper involved.

If one lets AI FOMO since the release of chatgpt drive them they'd be glued to their screen 24/7.

OAI wants to keep the hype train going. That is all. OpenClaw is just a project that attracted the interests of people messing about with LLMs. Which as a proportion of economically active people is.... tiny.

They brought him (Pete) over as he seems to have some way of thinking about LLMs in the form of a product. Will he have repeatable success on a large scale? Who knows. I doubt it personally.

1. People who only think of using AI in very specific scenarios. They don’t know when you use it outside of the obvious “to write code” situations and they don’t really use AI effectively and get deflated when AI outputs the occasional garbage. They think “isn’t AI supposed to be good at writing code?”

2. People who let AI do all the thinking. Sometimes they’ll use AI to do everything and you have to tell them to throw it all away because it makes no sense. These people also tend to dump analyses straight from AI into Slack because they lack the tools to verify if a given analysis is correct.

To be honest, I help them by teaching them fairly rigid workflows like “you can use AI if you are in this specific situation.” I think most people will only pick up tools effectively if there is a clear template. It’s basically on-the-job training.

As for the last example, for all the money being spent on this area, if someone is expected to perform a workflow based on the kind of question they're supposed to ask, that's a failure in the packaging and discoverability aspect of the product, the leaky abstraction only helps some of us who know why it's there.

I think this is the prior you should investigate. That may be what HN used to be. But it's been a long time since it has been an active reality. You can still see actual expert opinions on HN, but they are the minority more and more.

From personal experience, I've also noticed that some of the most toxic discourse and responses I've received on this platform are overwhelmingly from post-2022 users.

Really? Can you show any examples of someone claiming AI models cannot generate working code? I haven't seen anyone make that claim in years, even from the most skeptical critics.

I started working today on a project I hadn’t touched in a while but I now needed to as it was involved in an incident where I needed to address some shortcomings. I knew the fix I needed to do but I went about my usual AI assisted workflow because of course I’m lazy the last thing I want to do is interrupt my normal work to fix this stupid problem.

The AI doesn’t know anything about the full scope of all the things in my head about my company’s environment and the information I need to convey to it. I can give it a lot of instructions but it’s impossible to write out everything in my head across multiple systems.

The AI did write working code, but despite writing the code way faster than me, it made small but critical mistakes that I wouldn’t have made on my first draft.

For example, it just added in a command flag that I knew that it didn’t need, and it actually probably should have known it, too. Basically it changed a line of code that it didn’t need to touch.

It also didn’t realize that the curled URL was going to redirect so we needed an -L flag. Maybe it should have but my brain knew it already.

It also misinterpreted some changes in direction that a human never would have. It confused my local repository for the remote one because I originally thought I was going to set a mirror, but I changed plans and used a manual package upload to curl from. So it out the remote URL in some places where the local one should have been.

Finally, it seems to have just created some strange text gore while editing the readme where it deleted existing content for seemingly no reason other than some kind of readline snafu.

So yes it produced very fast great code that would have taken me way longer to do, but I had to go back and consume a very similar amount of time to fix so many things that I might as well have just done it manually.

But hey I’m glad my company is paying $XX/month for my lazy workday machine.

This is your problem: How should it know if you do not provide it?

Use Claude - in the pro version you can submit files for each project which are setting the context: This can be files, source code, SQL scripts, screenshots whatever - then the output will be based on your context given by providing these files.

If I was truly going to automate this one-time task I would have to give the AI access to my browser or an API token for the repository provider, so I’m either giving it dangerous modification capability via browser automation or I’m spending even more time setting up API access and trusting that it actually knows how to interact with the service via API calls.

My company doesn’t provide Claude, they give me GitHub Copilot Pro or whatever it’s called, and when I provided the website it needed to get the RPM files I was working with it didn’t actually do anything with it. It just wrote a readme file that told me what to do. Like I mention it also just eventually mistook the remote repository as my local internal repository.

And one of the specific commands it screwed up was in my existing script and was already correct, it just decided to change it for no discernible reason. I didn’t ask it to do anything related to that particular line.

With such a high error rate, I would be hesitant to actually integrate AI to other systems to try to achieve a more fully automated workflow.

They can do a tolerable job with super popular /simple things like web dev and Python. It really depends on what you're doing.

> Github Copilot has been great in getting that code coverage up marginally but ass otherwise.

When someone claims that AI can't generate working code I assume that it means consistently generating working code. We're talking about a tool. It has to work more often than not and on codebases that we tend to work with, i.e legacy code.

Personally I don't claim that because I'm using everyday to generate working code.

It takes time to get an intuition for the kinds of problems they've seen in pre-training, what environments it faced in RL, and what kind of bizarre biases and blindspots it has. Learning to google was hard, learning to use other peoples libraries was hard, and its on par with those skills at least.

If there is a well known design pattern you know, thats a great thing to shout out. Knowing what to add to the context takes time and taste. If you are asking for pieces so large that you can't trust them, ask for smaller pieces and their composition. Its a force multiplier, and your taste for abstractions as a programmer is one of the factors.

In early usenet/forum days, the XY problem described users asking for implementation details of their X solution to Y problem, rather than asking how to solve Y. In llm prompting, people fall into the opposite. They have an X implementation they want to see, and rather than ask for it, they describe the Y problem and expect the LLM to arrive at the same X solution. Just ask for the implementation you want.

Asking bots to ask bots seems to be another skill as well.

If not, you don't know how to use it efficiently.

A large part of using AI efficiently is to significantly lower that review burden by having it do far more of the verification and cleanup itself before you even look at it.

- PRD and spec fulfillment review

- code review + correction loops

- security review + corrections

- addl. test coverage and tidying

- addl. type checks and tidying

- addl. lint checks and tidying

- maybe more I haven't listed

And these are run after each commit, so you can only imagine the costs per engineer doing this 10, 20, 50+ times per day depending on how much work they're knocking out.

The question is what your time is worth for the company, and which tasks costs less to have an agent automate than having you do.

The other constraint is, for those who are being laid off (maybe because of cost reduction to support an AI budget for a smaller team to use), engineers wanting to expand their skill set and practice these levels of usage + efficiency are effectively unable to with their own funding, making it more difficult to find employment as expectations heighten.

Prior to AI entering the fray, software development was largely free for everyone, allowing anyone with enough time and motivation to build the skills towards gainful employment. As AI becomes more prevalent and expectations around how it's used become higher, fewer and fewer applicants will be able to claim they have the experience necessary because it was out of reach due to costs.

Right now you need to be Uncle Moneybags to do this in your personal life.

If you're lucky, your employer is footing the bill but otherwise... Ugh. It's like converting your app running perfectly fine on a cheap VPS to AWS Lambda. In theory, it's fine but in reality the next bill you get could make you faint.

I'm unconvinced AI reviewing AI is the answer here, because all LLMs have the same flaws. To me, the harness/guard rails for AI should be different technologies that work differently and in a more formal sense. IE, static code analysis, linters, tests, etc.

(Linting has actually been, by far, the BEST code quality enforcers for the agents I've run so far, and it's a lot cheaper and more configurable than running more agents.)

My current project that I started this weekend is a rust client server game with the client compiled into web assembly.

I do these projects without reading the code at all as a way to gauge what I can possibly do with AI without reading code, purely operating as a PM with technical intuition and architectural opinions.

So far Opus 4.6 has been capable of building it all out. I have to catch issues and I have asked it for refactoring analysis to see if it could optimize the file structure/components, but I haven't read the code at all.

At work I certainly read all the code. But would recommend people try to build something non trivial without looking at the code. It does take skill though, so maybe start small and build up the intuition on how they have issues, etc. I think you'll be surprised how much your technical intuition can scale even when you are not looking at the code.

I have friends in security on major platforms who are impressed by the security review of the SOT models. Certainly better than the average bootstrapped founder.

I'm building to play with my friends online.

They vibe-coded a complete rewrite of their products in a few months without any human review. Hundreds of thousands LOC. I feel sorry for the remaining engineers having to learn everything they just generated, and are now having customers use.

I agree. I'm constantly correcting the code it generates. But then, I do the same for humans when I review their PRs, and the LLM generated the code in a 100th of the time (or whatever figure you prefer).

Last time he said: "yes yes I know about ChatGPT, but I do not use it at work or home."

Therefore, most people wont even know about Gemini, Grok or even Claude.

I am completely flooded with comments and stories about how great LLMs are at coding. I am curious to see how you get a different picture than this? Can you point me to a thread or a story that supports your view? At the moment, individuals thinking AI cannot generate working code seem almost inexistent to me.

Folks like this have never used AI inside of an IDE or one of the CLI AI tools. Without that perspective, AI seems mostly like a gimmick.

What makes you think people this will ever change? Have you seen how well people know their already existing tools?

A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer.

Larger implementations like automating customer service might be more costly, but I think there are enough short term supposed benefits that something should be showing there.

Assistant is dispatching a courier to get medical records. AI auto completes to include the address. Normally they wouldn't put the address, the courier knows who we work with, but AI added it so why not. Except it's the wrong address because it's for a different doctor with the same name. At least they knew to verify it, but still mistakes like this happening at scale is making the other time savings pretty close to a wash.

See also: AI-Generated “Workslop” Is Destroying Productivity [1]

[1] https://hbr.org/2025/09/ai-generated-workslop-is-destroying-...

What AI has done is accelerate and magnify both the positives and the negatives.

There are a lot of white-collar tasks that have far lower quality and correctness bars. "Researching" by plugging things into google. Writing reports summarizing how a trend that an exec saw a report on can be applied to the company. Generating new values to share at a company all-hands.

Tons of these that never touch the "real world." Your assistant story is like a coding task - maybe someone ran some tests, maybe they didn't, but it was verifiable. No shortage of "the tests passed, but they weren't the right test, this broke some customers and had to be fixed by hand" coding stories out there like it. There are pages and pages of unverifiable bullshit that people are sleepwalking through, too, though.

Nobody already knows if those things helped or hurt, so nobody will ever even notice a hallucination.

But everyone in all those fields is going to be trying really really hard to enumerate all the reasons it's special and AI won't work well for them. The "management says do more, workers figure out ways to be lazier" see-saw is ancient, but this could skew far towards "management demands more from fewer people" spectrum for a while.

In all areas where there's less easy ways to judge output there is going to be correspondingly more value to getting "good" people. Some AI that can produce readable reports isn't "good" - what matters is the quality of the work and the insight put into it which can only be ensured by looking at the workers reputation and past history.

That's not obvious at all if the AI writing the tests is different than the AI writing the code being tested. Put into an adversarial and critical mode, the same model outputs very different results.

Someone needs to build an agentic tool that does strict, enforced TDD.

Obviously this is only partially true but it's true enough.

It takes humans quite a long time to learn the external context that lets them write good tests IMO. We have trouble feeding enough context into AIs to give them equal ability. One is often talking about companies where nobody bothers to write down more than 1/20th of what is needed to be an effective developer. So you go to some place and 5 years later you might be lucky to know 80% of the context in your limited area after 100s of meetings and talking to people and handling customer complaints etc.

I have been doing this with coding agents across LLM providers for a while now, with very successful results. Grok seems particularly happy to tell Anthropic where it’s cutting corners, but I get great insights from O3 and Gemini too.

Except the test suite isnt just something that appears and the bugs dont necessarily get covered by the test suite.

The bugginess of a lot of the software i use has spiked in a very noticeable way, probably due to this.

>But everyone in all those fields is going to be trying really really hard to enumerate all the reasons it's special and AI won't work well for them.

No, not everyone. Half of them are trying to lean in to the changing social reality.

The gaslighting from the executive side, on the other hand, is nearly constant.

Consider, for example, the following python code:

    x = (5)

    x = (5,)

Now imagine trying to spot that one missing comma among the 20kloc of code one so proudly claims AI helped them "write", especially if it's in a cold path. You won't see it.

Disagree.

Even though performing checks on dynamic PLs is much harder than on static ones, PLs are designed to be non-ambiguous. There should be exactly 1 interpretation for any syntactically valid expression. Your example will unambiguously resolve to an error in a standard-conforming Python interpreter.

On the other hand, natural languages are not restricted by ambiguity. That's why something like Poe's law exists. There's simply no way to resolve the ambiguity by just staring at the words themselves, you need additional information to know the author's intent.

In other words, an "English interpreter" cannot exist. Remove the ambiguities, you get "interpreter" and you'll end up with non-ambiguous, Python-COBOL-like languages.

With that said, I agree with your point that blindly accepting 20kloc is certainly not a good idea.

Those are both syntactically valid lines of code. (it's actually one of python's many warts). They are not ambiguous in any way. one is a number, the other is a tuple. They return something of a completely different type.

My example will unambiguously NOT give an error because they are standard conforming. Which you would have noticed had you actually took 5 seconds to try typing them in the repl.

  > Those are both syntactically valid lines of code. (it's actually one of python's many warts). They are not ambiguous in any way. one is a number, the other is a tuple. They return something of a completely different type.

  > "Now imagine trying to spot that one missing comma among the 20kloc of code"

My reply, "Your example will unambiguously resolve to an error in a standard-conforming Python interpreter." tried to respond to the possibility of such an issue. Even though it's probably not the program behavior you want, Python, being a programming language, will be 100% guaranteed to interpret it unambiguously.

I admit, I should have phrased it a bit more unambiguously than leaving it like that.

Even if it's hard, you can try running a type checker to statically catch such problems. Even if it's not possible in cases of heavy usage of Python's dynamic typing feature, you can just run it and check the behavior at runtime. It might be hard to check, but not impossible.

On the other hand, it's impossible to perform a perfectly consistent "check" on this reply or an email written in a natural language, the person reading it might interpret the message in a completely different way.

The few times I've tried giving LLMs a shot I've had them warning me of not putting some validations in, when that exact validation was exactly 1 line below where they stopped looking.

And even if it did pass an AI code review, that's meaningless anyway. It still needs to be reviewed by an actual human before putting it into production. And that person would still get scrolling blindness whether or not the ai "reviewer" actually detected the error or not.

I didn't say they were guaranteed to find it: I said they were really good at finding these sorts of errors. Not perfect: just really good. I also didn't make any assumption: I said in my experience, by which I mean the code you shared is similar to a portion of the errors that I've seen LLMs find.

Which LLMs have you used for code generation?

I mostly use claude-opus-4-6 at the moment for development, and have had mostly good experiences. This is not to say it never gets anything wrong, but I'm definitely more productive with it than without it. On GitHub I've been using Copilot for more limited tasks as an agent: I find it's decent at code review, but more variable at fixing problems it finds, and so I quite often opt for manual fixes.

And then the other question is, how do you use them? I tend to keep them on quite a short leash, so I don't give them huge tasks, and on those occasions where I am doing something larger or more complex, I tend to write out quite a detailed and prescriptive prompt (which might take 15 minutes to do, but then it'll go and spend 10 minutes to generate code that might have taken me several hours to write "the old way").

That book was very different than what I expected from all of the internet comment takes about it. The premise was really thin and did't actually support the idea that the jobs don't generate value. It was comparing to a hypothetical world where everything is perfectly organized, everyone is perfectly behaved, everything is perfectly ordered, and therefore we don't have to have certain jobs that only exist to counter other imperfect things in society.

He couldn't even keep that straight, though. There's a part where he argues that open source work is valuable but corporate programmers are doing bullshit work that isn't socially productive because they're connecting disparate things together with glue code? It didn't make sense and you could see that he didn't really understand software, other than how he imagined it fitting into his idealized world where everything anarchist and open source is good and everything corporate and capitalist is bad. Once you see how little he understands about a topic you're familiar with, it's hard to unsee it in his discussions of everything else.

That said, he still wasn't arguing that the work didn't generate economic value. Jobs that don't provide value for a company are cut, eventually. They exist because the company gets more benefit out of the job existing than it costs to employ those people. The "bullshit jobs" idea was more about feelings and notions of societal impact than economic value.

I tried to respond to the specific conversation about Bullshit Jobs above. In my experience, the way this book is brought up so frequently in online conversations is used as a prop for whatever the commenter wants it to mean, not what the book actually says.

I think Graeber did a fantastic job of picking "bullshit jobs" as a topic because it sounds like something that everyone implicitly understands, but how it's used in conversation and how Graeber actually wrote about the topic are basically two different things

> Jobs that don't provide value for a company are cut, eventually.

Uhm, seems like Greaber is not the only one drawing conclusions from a hypothetical perfect world

I thought he made a case for both societal and economic impact.

Not necessarily, I’ve seen a lot of jobs that were just flying under the radar. Sort of like a cockroach that skitters when light is on but roams freely in the dark.

I don't know if maybe he wasn't explaining it well enough, but that kind of reasoning makes some sense.

A lot of code is written because you want the output from Foo to be the input to Bar and then you need some glue to put them together. This is pretty common when Foo and Bar are made by different people. With open source, someone writes the glue code, publishes it, and then nobody else has to write it because they just use what's published.

In corporate bureaucracies, Company A writes the glue code but then doesn't publish it, so Company B which has the same problem has to write it again, but they don't publish it either. A hundred companies are then doing the work that only really needed to be done once, which makes for 100 times as much work, a 1% efficiency rate and 99 bullshit jobs.

Sure, but there's no such thing as "the company." That's shorthand - a convenient metaphor for a particular bunch of people doing some things. So those jobs can exist if some people - even one person - gets more benefit out of the job existing than it costs that person to employ them. For example, a senior manager padding his department with non-jobs to increase headcount, because it gives him increased prestige and power, and the cost to him of employing that person is zero. Will those jobs get cut "eventually"? Maybe, but I've seen them go on for decades.

But he states that expressis verbis, so your discovery is not that spectacular.

Although he gives examples of jobs, or some aspects of jobs, that don't help to deliver what specific institutions aim to deliver. Example would be bureaucratization of academia.

This is not true at all. You can find plenty of examples going either way but it’s far from truth from being a universal reality

Maybe you're lucky enough to be doing cutting edge research or do something that really seriously impacts human beings, but I've done plenty of "mission critical right fucking now" work that a week from now (or even hours from now, when I worked for a content marketing business) is beyond irrelevant. It's an amazing thing watching marketing types set money on fire burning super expensive developer time (but salaried, so they discount the cost to zero) just to make their campaigns like 2-3% more efficient.

I've intentionally sat on plenty of projects that somebody was pushing really hard for because they thought it was the absolute right necessary thing at the time and the stakeholder realized was pointless/worthless after a good long shit and shower. This one move has saved literally man years of work to be done and IMO is the #1 most important skill people need to learn ("when to just do nothing").

Companies are obviously not hesitant to lay off anyone, especially for cost saving. It is interesting how you think that people are laid off because they’re unproductive.

Not recognizing the essential role of sales seemed to be a common mistake.

It identified advertising as part of the category that it classed as heavily-bullshit-jobs for reason of being zero-sum—your competitor spends more, so you spent more to avoid falling behind, standard red queen’s race. (Another in this category was the military, which is kinda the classic case of this—see also, the Missile Gap, the dreadnought arms race, et c.) But not sales, IIRC.

Like when a competing country builds their tenth battleship, so you commission another one to match them. The world would have been better if neither had been build. Money changed hands (one supposes) but the aim of the whole exercise had no effect. It was similar to paying people to dig holes a fill them back in again, to the tune of serious money. This was so utterly stupid and wasteful that there was a whole treaty about it, to try to prevent so many bullshit jobs from being created again.

Or when Pepsi increases their ad spending in Brazil, so Coca Cola counters, and much of the money ends up accomplishing little except keeping things just how they were. That component or quality of the ad industry, the book claims, is bullshit, on account of not doing any good.

The book treats of several ways in which a job might be bullshit, and just kinda mentions this one as an aside: the zero-sum activity. It mostly covers other sorts, but this is the closest I can recall it coming to declaring sales “bullshit” (the book rarely, bordering on never, paints even most of an entire industry or field as bullshit, and advertising isn’t sales, but it’s as close as it got, as I recall)

Is it a waste of effort when two companies try to make the best electric car?

If it is wasteful what does aw orld look like where nobody ever spends resources on a goal which overlaps with someone else?

- Which products get included in the candidate list? Every product in existence which claims use? - how many results can it return? And in what order? - which attributes or description of the product is provided to the llm? Who provides it? - how are the claims in those descriptions verified? - what if my business believes the claims or description of our product is false? - how will the llm change its relative valuations based on demand?

> The only way advertising won't exist or won't be needed is when humanity becomes a hive mind and removes all competition.

I don't need advertisement to pick the best product for myself. I have a list of requirements that I need fulfilled – why do I need advertisement for it?

No. They can ban particular modes. They can’t stop people from using power and money to spread ideas.

In the US hedge funds are banned from advertising and all they did is change their forms of presentation to things like presenting at conferences or on podcasts.

If there was a socialist fantasy of a government review board for which all products were submitted before being listed in a government catalog. Then advertising would be lobbying and jockeying that review board to view your product in a particular way. Or merely to go through the process and ensure correct information was kept.

It says stuff like why can’t a customer just order from an online form? The employee who helps them doesn’t do anything except make them feel better. Must be a bullshit job. It talks specifically about my employees filling internal roles like this.

> advertising

I understand the arms race argument, but it’s really hard to see what an alternative looks like. People can spend money to make you more aware of something. You can limit some modes, but that kind of just exists.

I don’t see how they aren’t performing an important function.

There's nothing inherent to socialism that would preclude advertising. It's an economic system where the means of production (capital) is owned by the workers or the state. In market socialism you still have worker cooperatives competing on the market.

If you've read much else you should be able to engage with text properly, and construct charitable interpretations of author's claims or arguments.

Did I miss something?

The example used here was advertising. And then when we push on the example the fallback is to the subjective - feeling unfilled, definition.

So I am still look for concrete examples of bullshit jobs to justify the original comment that AI will find efficiencies by letting us throw these away.

Got any? You are an expert on the text so I’m hoping you can identify one.

See what I mean? We push on where these fake jobs are and you fallback to a subjective internal definition we can’t inspect.

And now let me remind you of the context. If the real definition of bullshit isn’t economic slack, but internal dissatisfaction then this comment would be false:

> What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.

Ever actually lived in anything approaching one? Yeah, if the stores are empty, it does not make sense to produce ads for stuff that isn't there ...

... but we still had ads on TV, surprisingly, even for stuff that was in shortage (= almost everything). Why? Because the Plan said so, and disrespecting the Plan too openly would stray dangerously close to the crime of sabotage.

You have no idea.

And people won't give up their shops and fields and other means of production to the government voluntarily, at least not en masse. Thus they have to be forced at a gunpoint, and they always were.

All the subsequent horror is downstream from that. This is what is inherent to building a socialist economy: mass expropriation of the former "exploitative class". The bad management of the stolen assets is just a consequence, because ideologically brainwashed partisans are usually bad at managing anything including themselves.

Yugoslavia was extremely successful, with economic growth that matched or exceeded most capitalist European economies post-WW2. In some ways it wasn't as free as western societies are today but it definitely wasn't totalitarian, and in many ways it was more free - there's a philosophical question in there about what freedom really is. For example Yugoslavia made abortion a constitutionally protected right in the 70s.

I don't want to debate the nuances of what's better now and what was better then as that's beside the point, which is that the idiosyncrasies of the terrible Soviet economy are not inherent to "socialism", just like the idiosyncrasies of the US economy aren't inherent to capitalism.

It is the model, introduced basically everywhere where socialism was taken seriously. It is like saying that cars with four wheels are just one terrible model, because there were a few cars with three wheels.

Yugoslavia was a mixed economy with a lot of economic power remaining in private hands. You cannot point at it and say "hey, successful socialism". Tito was a mortal enemy of Stalin, stroke a balanced neither-East-nor-West, but fairly friendly to the West policy already in 1950, and his collectivization efforts were a fraction of what Marxist-Leninist doctrine demands.

You also shouldn't discount the effect of sending young Yugoslavs to work in West Germany on the total balance sheet. A massive influx of remittances in Deutsche Mark was an important factor in Yugoslavia getting richer, and there was nothing socialist about it, it was an overflow of quick economic growth in a capitalist country.

> You cannot point at it and say "hey, successful socialism"

Yes I can because ideological purity doesn't exist in the real world. All of our countries are a mix of capitalist and socialist ideas yet we call them "capitalist" because that's the current predominant organization.

> Tito was a mortal enemy of Stalin, stroke a balanced neither-East-nor-West, but fairly friendly to the West policy already in 1950, and his collectivization efforts were a fraction of what Marxist-Leninist doctrine demands.

You're making my point for me, Yugoslavia was completely different from USSR yet still socialist. Socialism is not synonymous with Marxist-Leninist doctrine. It's a fairly simple core idea that has an infinite number of possible implementations, one of them being market socialism with worker cooperatives.

Aside from that short period post-WW2, no socialist or communist nation has been allowed to exist without interference from the US through oppressive economic sanctions that would cripple and destroy any economy regardless of its economic system, but people love nothing more than to draw conclusions from these obviously-invalid "experiments".

"You" (and I mean the collective you) are essentially hijacking the word "socialism" to simply mean "everything that was bad about the USSR". The system has been teaching and conditioning people to do that for decades, but we should really be more conscious and stop doing that.

That is what COMECON was supposed to solve, but if you aggregate a heap of losers, you won't create a winning team.

"Socialism is not synonymous with Marxist-Leninist doctrine. It's a fairly simple core idea that has an infinite number of possible implementations, one of them being market socialism with worker cooperatives."

Of that infinite number, the violent Soviet-like version became the most widespread because it was the only one that was somewhat stable when implemented on a countrywide scale. That stability was bought by blood, of course.

No one is sabotaging worker cooperatives in Europe and lefty parties used to given them extra support, but they just don't seem to be able to grow well. The largest one is located in Basque Country and it is debatable if its size is partly caused by Basque nationalism, which is not a very socialist idea. Aside from that one, worker cooperatives of more than 1000 people are rare birds.

"The system has been teaching and conditioning people to do that for decades, but we should really be more conscious and stop doing that."

No one in the former socialist bloc will experiment with that quagmire again. For some reason, socialism is a catnip of intellectuals who continue to defend it, but real-world workers dislike it and defect from various attempts to build it at every opportunity.

We should stop trying to ride dead horses. Collective ownership of means of production on a macro scale is every bit as dead as divine right of kings to rule. There are still Curtis Yarvin types of intellectual who subscribe to the latter idea, but it is pining for the fjords. So is socialism.

What kind of disingenuous argument is that? Existence of COMECON doesn't neutralize the enormous disadvantage and economic pressure of having sanctioned imposed on you.

> Of that infinite number

I'm glad we agree that Soviet communism is not synonymous with "socialism".

> Aside from that one, worker cooperatives of more than 1000 people are rare birds.

You're applying pointless capitalist metrics to non-capitalist organizations and moralizing about how they don't live up to them.

> No one in the former socialist bloc will experiment with that quagmire again.

You're experimenting with socialist policies and values right now, you just don't want to call it by that name because of your weird fixation. Do public healthcare, transport, education, social security benefits ring any bells?

If you talked to people from ex-Yugoslavia, you'd know that many would be happy to return to that time.

> We should stop trying to ride dead horses.

We should stop declaring horses extinct when it's just your own horse that has died.

The Soviets and their satellites (like the DDR), had another problem related to arbitrage, and that is that their professionals (such as doctors and engineers and scientists, all of whom received high quality, free, state-subsidized education), were being poached by the Western Bloc countries (a Soviet or East German engineer would work for half the local salary in France or West Germany, _and_ they would be a second class citizen, easy to frighten with deportation -- the half-salary was _much_ greater than what they could earn in the Eastern Bloc). The iron curtain was erected to prevent this kind of arbitrage (why should the Soviets and satellites subsidize Western medicine and engineering? Shouldn't a capitalist market system be able to sustain itself? Well no, market systems are inefficient by design, and so they only work as _open_ systems and not _closed_ systems -- they need to _externalize_ the costs and _internalize_ the gains, which is why colonialism was a thing to begin with, and why the "third world" is _still_ a thing).

Note that after the Berlin Wall fell, the first thing to happen was mass migrations of all kinds of professionals (such as architects and doctors) and semi-professionals (such as welders and metal-workers), creating an economic decline in the East, and an economic and demographic boom in the West (the reunification of Germany was basically a _demographic_ subsidy -- in spite of the smaller size, East Germany had much higher birth rates for _decades_; and after the East German labor pool was integrated, Western economies sought to integrate the remaining Eastern labor pools (more former Yugoslavs live abroad in Germany than in any other non-Yugo part of the world [the USA numbers are iffy, but if true Croatians are the only exception, with ~2M residents in USA, which seems unlikely]).

The problem, in the end, is that all of these countries are bound by economic considerations (this is thesis of Marx, by the way), and they cannot escape the vicious arbitrage cycle (I mean, here in the USA, we have aggressively been brain-draining _ourselves_ since at least 1980, which is why we have the extreme polarization, stagnation, and instability _today_ -- it is reminiscent of the Soviet situation in the mid 1980s to late 1990s). Not without something like a world government (if there is only one account to manage, there is no possibility of deficit or surplus, unless measured inter-temporally), or an alternative flavor of globalization.

Internationalism is a wonderful ideology, and one that I support. You can make the case that Yugoslavia, the USSR, etc, were an early experiment in Internationalism, that each succumbed to corruption and unclear thinking (a citizenry that is _inclusive_ by nature and can _think_ clearly is a hard requirement for any successful polity). Globalization, on the other hand, has a bit of an Achilles Heel: when countries asked why they should open their borders and economies to outsider/foreigners, they were told, "so that we can all get rich!". The problem is that once the economic gains get squeezed out of globalization, countries will start looking for new ways to rich, even if it means reversing decades of integration. Appealing to people's greed only works to the extent that you can placate their appetites. We should have justified Internationalism using _intrinsic_ arguments: "we should integrate because learning how others see and experience the world is intrinsically beautiful, and worth struggling for".

Note that most of these economic pathologies disappear, when the reserve currency (dollar) is replaced with a self-balancing currency (like Keynes' Bancor: https://en.wikipedia.org/wiki/Bancor). We have the tools, but everyone wants to feel like the only/greatest winner. These are the first people that have to be exiled.

Most "Bullshit Jobs" can already be automated, but can isnt always should or will. Graeber is a capex thinker in an opex world.

> There’s not much of value to obtain from the book.

Anthropological insight has much more value than anything economists may produce on economy.

Modern economics is literally a bullshit job generating process or complex system.

Using AI to output noise and learn nothing at breakneck speeds is worse than simply looking out the window, because you now have a false sense of security about your understanding of the material.

Relatedly, I think people get the sense that 'getting better at prompting' is purely a one-way issue of training the robot to give better outputs. But you are also training yourself to only ask the sorts of questions that it can answer well. Those questions that it will no longer occur to you to ask (not just of the robot, but of yourself) might be the most pertinent ones!

I could go either way on the future of this, but if you take the argument that we're still early days, this may not hold. They're notoriously bad at this so far.

We could still be in the PC DOS 3.X era in this timeline. Wait until we hit the Windows 3.1, or 95 equivalent. Personally, I have seen shocking improvements in the past 3 months with the latest models.

Either way, being able to generate or compress loads of text very quickly with no understanding of the contents simply is not the bottleneck of information transfer between human beings.

But for coding, the latest models are able to read my codebase for context, understand my question, and implement a solution with nuance, using existing structures and paradigms. It hasn't missed since January.

One of them even said: "As an embedded engineer, you will appreciate that ...". I had never told it that was my title, it is nowhere in my soul.md or codebase. It just inferred that I, the user, was one. Based on the arm toolchain and code.

It was a bit creepy, tbh. They can definitely infer context to some degree.

While we're speculating, here's mine: we're in the Windows 7 phase of AI.

IOW, everything from this point on might be better tech, but is going to be worse in practice.

Might not make a difference. I believe we are already at the point of negative returns - doubling context from 800k tokens to 1600k tokens loses a larger percentage of context than halving it from 800k tokens to 400k tokens.

I predict that in the next two to five years we're going to see a breakthrough in AI that doesn't involve LLMs but makes them 10x more effective at reasoning and completely eliminates the hallucination problem.

We currently have "high thinking" models that double and triple-check their own output and we call that "reasoning" but that's not really what it's doing. It's just passing its own output through itself a few times and hoping that it catches mistakes. It kind of works, but it's very slow and takes a lot more resources.

What we need instead is a reasoning model that can be called upon to perform logic-based tests on LLM output or even better, before the output is generated (if that's even possible—not sure if it is).

My guess is that it'll end up something like a "logic-trained" model instead of a "shitloads of raw data trained" model. Imagine a couple terabytes of truth statements like, "rabbits are mammals" and "mammals have mammary glands." Then, whenever the LLM wants to generate output suggesting someone put rocks on pizza, it fails the internal truth check, "rocks are not edible by humans" or even better, "rocks are not suitable as a pizza topping" which it had placed into the training data set as a result of regression testing.

Over time, such a "logic model" would grow and grow—just like a human mind—until it did a pretty good job at reasoning.

I would argue that the research needed to enable such "vector operation" is nowhere near the stage to come to fruition in the next decade. So, my prediction is, maybe, 20-50 years for this to happen, if not more.

Given time I could see this happening again.

That is true, but then again also with google. You could see why some people want to go back to the "read the book" era where you didn't have google to query anything and had to make the real questions.

i may put this into my email signature with your permission, this is a whip-smart sentence.

and it is true. i used AI to "curate information" for me when i was heads-down deep in learning mode, about sound and music.

there was enough all-important info being omitted, i soon realized i was developing a textbook case of superficial, incomplete knowledge.

i stopped using AI and did it all over again through books and learning by doing. in retrospect, i'm glad to have had that experience because it taught me something about knowledge and learning.

mostly that something boils down to RTFM. a good manual or technical book written by an expert doesn't have a lot of fluff. what exactly are you expecting the AI to do? zip the rar file? it will do something, it might look great, lossless compression it will be not.

P.S. not a prompt skill issue. i was up to date on cutting edge prompting techniques and using multiple frontier models. i was developing an app using local models and audio analysis AI-powered libraries. in other words i was up to my neck immersed in AI.

after i grokked as much as i could, given my limited math knowledge, of the underlying tech from reading the theory, i realized the skill issue invectives don't hold water. if things break exactly in the way they're expected to break as per their design, it's a little too much on the nose. even appealing to your impostor syndrome won't work.

P.P.S. it's interesting how a lot of the slogans of the AI party are weaponizing trauma triggers or appealing to character weaknesses.

"hop on the train, commit fully, or you'll be left behind" > fear of abandonment trigger

"pah, skill issue. my prompts on the other hand...i'm afraid i can't share them as this IP is making me millions of passive income as we speak (i know you won't probe further cause asking a person about their finances is impolite)" > imposter syndrome inducer par excellence, also FOMO -- thinking to yourself "how long can the gold rush last? this person is raking it in!! what am i doing? the miserable sod i am"

1. outlandish claims (Claude writes ALL the code) noone can seem to reproduce, and indeed everyone non-affiliated is having a very different experience

2. some of the darkest patterns you've seen in marketing are the key tenets of the gospel

3. it's probably a duck.

i've been 100% clear on the grift since October '25. Steve Eisman of the "Big Short" was just hopping onto the hype train back then. i thought...oh. how much analysis does this guru of analysts really make? now Steve sings of AI panic and blood in the streets.

these things really make you think, about what an economy even is. it sure doesn't seem to have a lot to do with supply and demand, products and services, and all those archaisms.

I keep seeing this statement in threads about AI, and maybe it’s just from you, but high SNR is a good thing.

See https://en.wikipedia.org/wiki/Signal-to-noise_ratio

I think the rest of your post is very valid. It’s the mental equivalent of this article https://news.ycombinator.com/item?id=47049088

Two inverses do make a right, it seems.

I read that article recently, so the similarities might not be entirely coincidental. 'JPEG of thought' is gonna stay in my vocabulary for a while.

"Skill floor" is another one. People generally interpret that one as "must be at least this tall to ride", but it actually means "amount of effort that translates to result". Something that has a high skill floor (if you write "high floor of skill" it makes more sense) means that with very little input you can gain a lot of result. Whereas a low skill floor means something behaves more linearly, where very little input only gains very little result.

Even though its just the antonym, "skill ceiling" is much more intuitive in that regard.

https://imgur.com/tOHltkx

The confusion comes from people using "skill floor" for "learning curve" instead of "effectiveness".

But this is a thing where definitions have shifted over time. Like jealousy. People use "jealousy" when they really mean "envy", but correcting someone on it will usually just get you scorn and ridicule, because like I mentioned, language is fluid.

To put it simply, think assault rifle vs sniper rifle. Anyone can use the AR and spray and pray and do pretty okay. You can't do that with the sniper rifle. So the AR has a high skill floor (minimum effectiveness) whereas the sniper rifle has a low skill floor (low minimum effectiveness). But the AR has a low skill ceiling too a point where you can put in endless amounts of skill and see no improvement in effectiveness. The sniper being an infinite range OHKO can scale to the end given aim skill and map knowledge.

Another example would be Reinhardt in Overwatch. You can tell a noob to "look in that direction and deploy shield" and they will contribute to the team. You can't put a noob on Widowmaker and have them contribute (as) significantly.

When AI is doing that, it loses all value as a proof of work (just as it does for a school report).

My AI writes for your AI to read is low value. But there is probably still some value in "My AI takes these notes and makes them into a concise readable doc".

https://en.wikipedia.org/wiki/Telephone_game

OTOH, what happens continuously is that technology is used to automate bureaucracy and even allows it to grow some complexity.

Ofcourse they don't read the reports, who has time to read it? But don't even think about not sending the report, they like to have the option of reading it if they choose to do so.

A closed door removes agency from a cat, an absent report removes agency from a manager.

Indeed, productivity has decreased, because now there’s more output that is waste and you are paying to generate that excess waste.

That’s the solution if you’re the business owner.

That’s definitely not the solution if you’re a manager in charge of this useless activity, in fact, you should increase the amount of reports being written as much as humanly possible. The more underlings under you= more power and prestige.

This is the principal-agent problem writ large. As the comment mentioned above, also see Graeber’s Bullshit Jobs essay and book.

https://imgur.com/T4DAGG8

I recommend using https://catbox.moe/ which can even use remote-links so pasting the imgur link in it can also work.

https://files.catbox.moe/4dhvok.jpeg

It's the other way round, Imgur banned UK access so that they wouldn't have to worry about the UK's stupid, authoritarian Online "Safety" Act.

In the end when jobs are done right they seem to disappear. We notice crappy software or a poorly done HVAC system not clean carpets.

Interesting on how the very example you give for "oh this job isn't really bullshit" ultimately ends up being useless for the business itself, and exists only as a result of regulation.

No, health insurance being provided by employers, or tax withholding aren't useful things for anyone, except for the state who now offloads its costs onto private businesses.

These should be handled by the government, equally for all.

This is certainly possible, but it's called communism.

Also, part of that etc is doing payroll so there’s some reason for you to show up at work every day.

That's not why companies have HR; sure, it's a nice side-effect, but it's not the reason for HR.

HR exists primarily to protect the company from the employees.

HR wouldn't tell me what would be the best and most correct course of action, the only thing that they said was that it was my responsibility as an employee to find out. Well, what did they think I was doing.

Companies don’t survive if nobody is paid to show up every day or if they keep paying every single ex employee that ever worked for the company. It’s harder to attract new employees if you don’t offer competitive salaries or benefits. HR is a tiny part of most companies, but without that work being done the company would absolutely fail.

Similarly a specific ratio of flight attendants to passengers are required by the FAA in case of an emergency. Airlines use them for other stuff but they wouldn’t have nearly as many if the job was just passing out food.

Cattle? You actually think that about other people?

We dont matter to them, one was or the other. They dont see us as a threat, just as bugs.

Nobody really understands a job they haven't done themselves, and "arguing" that 90% of them are "bullshit" has no other possible explanation than a combination of ignorance (you don't understand the jobs well enough to judge whether they are useful) and arrogance (you think you can make that judgement better than the 90% of people doing those jobs).

That's fine if you disagree, I'm not aiming to be the authority on bullshit jobs.

This doesn't change the fact that you and I are cattle for corpo/neo-feudals.

No. If that's the case, your statement was unclear: since you didn't specify who else thinks those people were cattle, the implication is that you think it. Especially since you prefaced your statement with "I’d argue."

And the interpretation...

> It seems more like they're implying it's those at the top think that about other people.

...beggars belief. What indication has "the top" given to show they have that kind of foresight and control? The closest is the AI-bros advocacy of UBI, which (for the record) has gone nowhere.

I was half a mind to point that out in my original comment, but didn't get around to it.

I never said it was clear? Two commenters got it right, two wrong, so it wasn’t THAT unobvious.

> What indication has "the top" given to show they have that kind of foresight and control? The closest is the AI-bros advocacy of UBI, which (for the record) has gone nowhere.

Tech bros selling “no more software engineers” to cost optimizers, dictatorships in US, Russia, China pressing with their heels on our freedoms, Europe cracking down on encryption, Dutch trying to tax unrealized (!) gains, do I really need to continue?

> Tech bros selling “no more software engineers” to cost optimizers, dictatorships in US, Russia, China pressing with their heels on our freedoms, Europe cracking down on encryption, Dutch trying to tax unrealized (!) gains, do I really need to continue?

All those things are non sequiturs, though, some directly contradicting the statement I was responding to, as you claim it should be interpreted. If "90% of modern jobs are bullshit to keep cattle occupied" that implies "the top" deliberately engineered (or at least maintains) an economy where 90% jobs are bullshit (unnecessary). But that's obviously not the case, as the priority of "the top" is to gather more money to themselves in the short to medium term, and they very frequently cut jobs to accomplish that. "Tech bros selling “no more software engineers” to cost optimizers," is a new iteration of that. If "the top" was really trying "to keep cattle occupied" they wouldn't be cutting jobs left and right.

We don't live in a command economy, there's no group of people with an incentive to create "bullshit" jobs "to keep cattle occupied."

Then everyone saves time, which they can spend producing more things which other people will not read and/or not reading the things that other people produce (using llms)?

Productivity through the roof.

Eat the Rich

Sadly AI is only capable of doing work that has already been done, thousands of times.

Looking at job numbers that seems to be happening. A lot less employment needed, freeing up people to do other things.

    Looking at job numbers

Wild take to cite a recession when last quarter growth was 4.4%.

Secondly, GDP is the best measure of output / value-add we have, and it's significantly up, despite jobs being down.

Output going up with fewer people means more productivity. That's the point that was being made.

Recessions are measured in economics by tracking GDP, which the person I replied to said we're in. We're not.

Whatever concept of "the economy" you had in mind to bring more nuance and refinement to a discussion, which is possible and welcome, and which you haven't bothered to add, doesn't refute the basics above.

My current understanding of the general consensus is that many companies have been eating the tariffs with the hope SCOTUS will strike them. If they are upheld, prices will likely rise significantly

In the private market are there really so many companies delivering reports no one reads ? Why would management keep at it then ? The goal is to maximize profits. Now sure there are pockets of inefficiency even in the private sector but surely not that much - whatever the companies are doing - someone is buying it from them, otherwise they fail. That's capitalism. Yes there is perhaps 20% of employees who don't pull their weight but its not the majority.

An interesting take.

I am not sure that is true, though. Assume for a moment that Google would waste 50% of their profits. Truly, a huge inefficiency. However, would that make it likely some other corp could take their search/ad market share from them? I doubt it, given the abyss of a moat.

One could say: True, therefore search is not a reasonably functioning capitalist market.

Yeah, I know, this can turn into "no true capitalist market". Still, it seems reasonable to say that many markets work in a certain kind of way (with lots of competition), and search is not one of those markets.

Regardless of their effectivity, it means LLMs are not useful for them.

Just this month the hospital in my municipality submitted an application to put in a new concrete pad for a new generator beside the old one that they, per the application, intend to retire/remove and replace with a storage shed on it's pad once the new one is operational.

Full page intro about how the hospital is saving the world, such a great thing for the community and all manner of vapid buzzword bullshit. dozens of pages of re-hashing bullshit about the environmental conditions, etc, etc, (i.e. basically reiterating stuff from when they built the facility), etc, etc.

God knows how many people and hours it took to compile it (we'll ignore the labor wasted in the public sector circulating and reading it).

All for a project that 50yr ago wouldn't have required 1/100th of the labor expenditure just to be kicked off. All that labor, squandered on nothing that makes anyone any richer. No goods made. No services rendered.

You're conflating private ownership with the organizations nominal financial structure. It has nothing to do with the structure model of the organization and everything to do with resources wasted on TPS reports. This waste has to come from somewhere. Something is necessarily being forgone whether that's profit, reinvestment in the organization or competitive edge that benefits the customer (e.g. lower cost or higher quality for same cost). The same is true for a for profit company, or any other organization.

FWIW the hospital is technically nonprofit as is typical for hospitals. And I assure you, they still have all the wrong incentives despite this.

I'm not smart enough to understand the macro-economics or incentive structures that lead to this happening, but I've seen many 100+ man teams that output whose output is something you could reasonably expect from a 5 man team.

In finance, you have to produce truly astounding amounts of regulatory reports that won't be read... until there is a crash, or a lawsuit, or an investigation etc. And then they better have been right!

"What would you say you do here?" --Office Space

Was a LLM used during that optimization? Yes.

Who will correlate the sudden productivity improvement with our optimization of the data flow with the availability of a LLM to do such optimizations fast enough that no project+consultants+management is needed ?

No one.

Just like no one is evaluating the value of a hammer or a ladder when you build a house.

This is where the whole "show me what you built with AI" meme comes from, and currently there's no substitute for SWEs. Maybe next year or next next year, but mostly the usage is generating boring stuff like internal tool frontends, tests, etc. That's not nothing, but because actually writing the code was at best 20% of the time cost anyway, the gains aren't huge, and won't be until AI gets into the other parts of the SDLC (or the SDLC changes).

It’s easy to convince yourself that it is, and anyone can massage some internal metric enough to prove their desired outcome.

I think broadly that's a paradoxical statement; improving office productivity should translate to higher gdp; whatever it is you're doing in some office - even if you're selling paper or making bombs, if you're more productive it means you're selling more (or using less resources to sell the same amount); that should translate to higher gdp (at least higher gdp per worker, there's the issue of what happens to gdp when many workers get fired).

Society as a whole is no better off since no value or wealth was generated, but the number did go up.

A whole bunch of our economy is broken windows like this to varying degrees.

Edit: If anyone haven't watched Yes Minster, you should go and watch it, it is a documentary on UK Government that is still true today as it was 40-50 years ago.

i.e. it's not the LLM, it's that they're not being used properly.

I get accused of the no true Scotsman argument because I think agile can be done right, for example. Is work bullshit because an LLM doesn't help it?

i'm going to need you to go ahead and come in on sunday

And it is of this lowly commenter's opinion that proofreading for accuracy and clarity is harder than writing it yourself and defending it later.

Claude Code has rate limits for a reason: I expect they are carefully designed to ensure that the average user doesn't end up losing Anthropic money, and that even extreme heavy users don't cause big enough losses for it to be a problem.

Everything I've heard makes me believe the margins on inference are quite high. The AI labs lose money because of the R&D and training costs, not because they're giving electricity and server operational costs away for free.

I'll be convinced they're actually making money when they stop asking for $30 billion funding rounds. None of that money is free! Whoever is giving them that money wants a return on their investment, somehow.

Once that happens, whomever is left standing can dial back the training investment to whatever their share of inference can bear.

Or, if there's two people left standing, they may compete with each other on price rather than performance and each end up with cloud compute's margins.

Training costs are fixed at whatever billions of dollars per year.

If inference is profitable they might conceivably make a profit if they can build a model that's good enough to sign up vast numbers of paying customers.

If they lose even more money on each new customer they don't have any path to profitability at all.

In theory they can increase prices once the customers will be hocked up. That's how many startups works.

I mean we just have to look at old discussions about Uber for the exact same arguments. Uber, after all these years, still is at a negative 10 % lifetime ROI , and that company doesn't even have to meaningfully invest in hardware.

IMO this will probably develop like the railroad boom in the first half of the 19th century: All the AI-only first movers like OpenAI and Anthropic will go bust, just like most railroad companies who laid the tracks, because they can't escape the training treadmill. But the tech itself will stay, and even become a meaningful productivity booster over the next decades.

He often gathers good information but his analysis of that information appears to be heavily influenced by the conclusions he's already trying to reach.

I do pay attention to him but I'd like to see similar conclusions from other analysts against the same data before I treat them as robust.

I don't personally have the knowledge or experience of company finance to be able to confidently evaluate his findings myself!

Which means that training needs to be ongoing. So the revenue covers the inference? So what? All that means is that it doesn't cover your costs and you're operating at a loss. Because it doesn't cover the training that you can't stop doing either.

>Training costs are fixed at whatever billions of dollars per year.

Which I think is the part people disagree with.

Capex is probably the biggest hurdle, but I can see how electricity cost might become a factor under heavy use.

I was downvoted big time. Ah, I love it when people provide an example so it can finally be exposed without me having to say anything.

Unfortunately this is a huge problem on here - many people step outside of their domains, even if on the surface it seems simple, but post gibberish and completely mangled stuff. How does this benefit people who get exposed to crap?

People form very strong opinions on topic they barely understand. I'd say since they know little the opinions come mostly from emotions, which is hardly a good path for objective and deeper knowledge.

Seems like a pretty dumb take. It’s like saying it only takes $X in electricity and raw materials to produce a widget that I sell for $Y. Since $Y is bigger than $X, I’m making money! Just ignore that I have to pay people to work the lines. Ignore that I had to pay huge amounts to build the factory. Ignore every other cost.

They can’t just fire everyone and stop training new models.

Gross profit = revenues - cost of goods sold

Operating profit = Gross profit - operating expenses including depreciation & amortisation

Net profit = Operating profit - net interest expense - taxes

If I am on a roll, I will flip on Extra Usage. I prototyped a fully functional and useful niche app in ~6 total hours and $20 of extra usage, and it's solid enough and proved enough value to continue investing in and eventually ship to the App store.

Without Claude I likely wouldn't have gotten to the finished prototype version to use in the real world.

For Indy dev, I think LLMs are a new source of solutions. This app is too niche to justify building and marketing without LLM assistance. It likely won't earn more than $25k/year but good enough!

For people doing work with LLMs as an assistant for codebase searching, reviews, double checks, and things like that the $20/month plan is more than fine. The closer you get to vibecoding and trying to get the LLM to do all the work, the more you need the $100 and $200 plans.

On the ChatGPT side, the $20/month subscription plan for GPT Codex feels extremely generous right now. I tried getting to the end of my window usage limit one day and could not.

> so the "just another $20 SaaS" argument doesn't sound too good

Having seen several company's SaaS bills, even $100/month or $200/month for developers would barely change anything.

but at that point you could go for a bugger one and split amongst headcount

We do have a way to see the financial impact - just add up Anthropic and oAI's reported revenues -> something like $30b in annual run rate. Given growth rates, (stratospheric), it seems reasonable to conclude informed buyers see economic and/or strategic benefit in excess of their spend. I certainly do!

That puts the benefits to the economy at just around where Mastercard's benefits are, on a dollar basis. But with a lot more growth. Add something in there for MS and GOOG, and we're probably at least another $5b up. There are only like 30 US companies with > $100bn in revenues; at current growth rates, we'll see combined revenues in this range in a year.

All this is sort of peanuts though against 29 trillion GDP, 0.3%. Well not peanuts, it's boosting the US GDP by 10% of its historical growth rate, but the bull case from singularity folks is like 10%+ GDP growth; if we start seeing that, we'll know it.

All that said, there is real value being added to the economy today by these companies. And no doubt a lot of time and effort spent figuring out what the hell to do with it as well.

Does profitable always equal useful? Might other cultures justifiably think differently, like the Amish?

I also didn’t talk profitable. Upshot, though, I don’t think it’s just a US thing to say that when money exchanges hands, generally both parties feel they are better off, and therefore there is value implied in a transaction.

As to what it will be used for: yes.

As measured by whom? The same managers who demanded we all return to the office 5 days a week because the only way they can measure productivity is butts in seats?

And that's the point here: value is handicapped by the web interface, and we are stuck there for the foreseeable future until the tech teams get their priorities straight and build decent data integration layers, and workflow management platforms.

Real life example: A client came to me asking how to compare orders against order confirmation from the vendor. They come as PDF files. Which made me wonder: Wait, you don't have any kind of API or at least structured data that the vendor gives you?

Nope.

And here you are. I am not talking about a niche business. I assume that's a broader problem. Tech can probably automate everything and this since 30 years. Still business lack of "proper" IT processes, because at the end every company is unique and requires particular measures to be "fully" onboarded to IT based improvements like that.

I've seen this play out with time sheets. Every day factory floor workers write up every job they do on time sheets, paper and pencil, but management wants excel spreadsheets with pretty plots. Solution? One old typist who can type up all the time sheets every day. No OCR trouble, no expensive developer time, if she encounters illegible numbers she just cross references against the job fliers to figure it out instead of throwing errors the first time a 0 looks like a 6.

- protect yourself from data loss / secret leaks - what it can and can't do - trust issues & hallucinations - Can't just enable Claude for Excel and expect people to become Excel wizards.

I think it could definitely already create economic benefit, after someone instructed clearly how to use it and how to integrate it in your work. Most people are really not good at figuring that out on their own, in a busy workday, when left to their own devices and companies are just finding out where the ball is moving and what to organize around too.

So I can totally see a lot of failed experiments and people slowly figuring stuff out, and all of that not translating to measurable surpluses in a corp, in a setup similar to what OP laid out.

This shit will take like 10 years to adopt properly, at least in most boomer companies.

I use these all the time nowadays and they are great tools when utilized properly but I have hard time seeing it replace functions completely due to humans having limited cognitive capacity to multitask and still needing to review stuff and build infra to actually utilize all this..

Talking about macro economics, I don’t think that number is correct.

Getting people to figure out how to enter questions is easy. Getting people to a point where they don't burn up all the savings by getting into unproductive conversations with the agent when it gets something wrong, is not so easy.

Only until the loans come due. We're still in the "uber undercutting medalian cabs" part of the game.

ChatGPT just lets you generate slop, that may be helpful. For the vast majority of industries it doesn’t actually offer much. Your meme departments like HR might be able to push out their slop quicker, but that doesn’t improve profitability.

InfoSec and Legal would like a word with you...

I read an article yesterday about people working insane hours at companies that have bet heavily on AI. My interpretation is that a worker runs out of juice after a few hours, but the AI has no limit and can work its human tender to death.

I mean, sure. If you want to use it for 20 minutes and wait for two hours at a time.

My company is spending 20-50x that much per head easily from the firmwide cost numbers being reported.

They had to set circuit breakers because some users hit $200/day.

[0]: https://www.almendron.com/tribuna/wp-content/uploads/2018/03...

https://www.nber.org/system/files/working_papers/w25148/w251...

I don't agree that real GDP measures what he thinks it measures, but he opines

>Data released this week offers a striking corrective to the narrative that AI has yet to have an impact on the US economy as a whole. While initial reports suggested a year of steady labour expansion in the US, the new figures reveal that total payroll growth was revised downward by approximately 403,000 jobs. Crucially, this downward revision occurred while real GDP remained robust, including a 3.7 per cent growth rate in the fourth quarter. This decoupling — maintaining high output with significantly lower labour input — is the hallmark of productivity growth.

https://www.ft.com/content/4b51d0b4-bbfe-4f05-b50a-1d485d419...

[0] on the basis that IT and AI are not general technologies in the mold of the dynamo, keyword "intangibles", see section 4 p21, A method to measure intangibles

Is consumer spending measured in total dollars spent? If so, isn't that curious wrinkle in an economy of rising prices, and decreasing purchasing power?

If true, I believe less quantity could be purchased at a higher cost per person, making it appear that consumer spending is up.

Presumably these numbers are benchmarked/peg to some sort of constant and/or standardization

https://en.wikipedia.org/wiki/Personal_consumption_expenditu...

The 1990s boom was in large part due to connectivity -- millions[1] of computers joined the Internet.

[1] _ In the 1990s. Today, there are billions of devices connected, most of them Android devices.

(Think about this old black/white or green mainframe screens - horrible looking but it gets their job done)

https://fortune.com/2026/02/15/ai-productivity-liftoff-doubl...

Source of the Stanford-approved opinion: https://www.ft.com/content/4b51d0b4-bbfe-4f05-b50a-1d485d419...

https://www.apolloacademy.com/waiting-for-the-ai-j-curve/

The thing to note is, verifying if something got done is harder and takes time in the same ballpark as doing the work.

If people are serious about AI productivity, lets start by addressing how we can verify program correctness quickly. Everything else is just a Ferrari between two traffic red lights.

Is that somewhat substantiated assumption? I recall learning on University in 2001 the history of AI and that initial frameworks were written in 70's and that prediction was we will reach human-like intelligence by 2000. Just because Sama came up with this somewhat breakthrough of an AI, it doesn't mean that equal improvement leaps will be done on a monthly/annual basis going forward. We may as well not make another huge leaps or reach what some say human intelligence level in 10 years or so.

Is it fair to say that wall street is betting America's collective pensions on AI...

I think it's more likely that people "feel" more productive, and/or we're measuring bad things (lines of code is an awful way to measure productivity -- especially considering that these agents duplicate code all the time so bloat is a given unless you actively work to recombine things and create new abstractions)

So we are in the process of "adapting a technology". Welcome, keep calm, observe, don't be ashamed to feel emotions like fear, excitement, anger and all else.

While adapting, we learn how to use it better and better. At first, we try "do all the work for me", then "ok, that was bad, plan what you would do, good, adjust, ok do it like this" etc etc.

A couple of years into the future this knowledge is just "passed on". If productivity grew and we "figured out how to get more out of the universe", then no jobs had to be lost, just readapted. And "investors" get happy not by "replacing workers", but by "reaping win-win rewards" from the universe at large.

There are dangers of course, like "maybe this is truly a huge win-win, but some loses can be hidden, like ecology", but "I hope there are people really addressing these problems and this win-win will help them be more productive as well".

Wide spread internet access turned expensive toys (PCs) into useful assets.

Maybe! Or it might never pan out, or it may pan out way better. Complicated things like this rarely turn out the way people expect, no matter how smart.

But I can say that, judging by historical artifacts, a lot of it was along the same broad lines as AI. And we maybe don’t realize how serious people were about it back then. The technology that actually changed the world was so comparatively boring and pragmatic that the stuff that was being hyped back then seems comically overwrought. It’s easy to assume it must have been a joke all along.

The combination of attention-draining social media walled gardens, and the high performance pocket-computers (which are really designed for consumption instead of productivity), created a positive feedback loop that helped destroy the productivity that we won by defeating the paradox in the 1990s. And we have been struggling against this new paradox for twenty years, since. AI seems like it should defeat the paradox because it is a kind of hands-free system, perfect for mobile phones -- but this is really just a very expensive solution to a problem that we have created and allowed to fester. We could just shun the walled gardens, and demand to be paid for our attention and data.

The new productivity paradox (which I do not think AI in its current form can fix[1][2]), is the price that we pay for a prosperous and valuable advertising industry. And as long as the web is seen as an ad-channel, and as long as the web is always vibrating in your pocket, we will keep paying this price. We will eventually end up (metaphorically) lobotomizing our children, and families, and communities, so that the grand-children of ad-executives and tech-bros and frat-bros can grow up healthy, psychologically stable, educated, and comfortably wealthy. (Brain drain: now available literally everywhere).

[1]: It is telling that most LLMs are centralized, and are most useful as search-engines/information-retrieval-systems. The centralization makes them _spyware_, and their ability to directly answer any question, encourages users to actually ask direct questions, instead of stringing search-terms together. This makes the prompts high-signal advertising data (i.e. instead inferring what you are looking for from the search-string, these companies can see _exactly_ what you are looking for and why -- and with LLMs, they can probably turn these promps into joint-probability-tables or whatever other kind of serialization they need to figure out which products to sell you (either on the web or directly in the response to your prompt)).

[2]: As far as copyright infringement goes, LLM outputs may require mass clean-room rewrites (so your productivity, as pathetic as it already is, now gets _halved_ long term) of text, prose, code, and anything else that is produced with them, because of how copyright law works. In legal arts this is called _the fruit of the poison tree_, and any short-term productivity gains, may become long term liabilities that need to be replaced due to _legal mandate_ -- so even if LLMs can eventually produce perfect and faultless outputs, the copyright laws _in all 200+ countries_ would have to be torn down and rebuilt (and this will certainly come at great expense).