> suggests a way forward will come from formalising natural language arguments

If by this you mean "reliably convert expressions made in human natural language to unambiguous, formally parseable expressions that a machine can evaluate the same way every time"... isn't that essentially an unreachable holy grail? I mean, everyone from Plato to Russell and Wittgenstein struggled with the meaning of human statements. And the best solution we have today is to ask the human to restrict the set of statement primitives and combinations that they can use to a small subset of words like "const", "let foo = bar", and so on.

Yesterday I got AI (a sota model) to write some tests for a backend I'm working on. One set of tests was for a function that does a somewhat complex SQL query that should return multiple rows

In the test setup, the AI added a single database row, ran the query and then asserted the single added row was returned. Clearly this doesn't show that the query works as intended. Is this what people are referring to when they say AI writes their tests?

I don't know what to call this kind of thinking. Any intelligent, reasoning human would immediately see that it's not even close to enough. You barely even need a coding background to see the issues. AI just doesn't have it, and it hasn't improved in this area for years

This kind of thing happens over and over again. I look at the stuff it outputs and it's clear to me that no reasoning thing would act this way

> That suggests a way forward will come from formalising natural language arguments.

Hot take (and continue with the derailment), but I'd argue that analytic philosophy from the last 100 years suggests this is a dead end. The idea that belief systems could be formalized was huge in the early 20th century (movements like Logical Positivism, or Russell's principia mathematica being good examples of this).

Those approaches haven't really yielded many results, and by far the more fruitful form of analysis has been to conceptually "reframe" different problems (folks like Hillary Putnam, Wittgenstein, Quine being good examples).

We've stacked up a lot of evidence that human language is much too loose and mushy to be formalised in a meaningful way.

I think you may mean Sperber and Mercier define "reasoning" as the capacity to produce and evaluate arguments?

> These discussions often get derailed into debates about what "thinking" means.

"SAL-9000: Will I dream? Dr. Chandra: Of course you will. All intelligent beings dream. Nobody knows why. Perhaps you will dream of HAL... just as I often do." From 2010

>If we define thinking as the capacity to produce and evaluate arguments

That bar is so low that even a political pundit on TV can clear it.

Thinking is not besides the point, it is the entire point.

You seem to be defining "thinking" as an interchangeable black box, and as long as something fits that slot and "gets results", it's fine.

But it's the code-writing that's the interchangeable black box, not the thinking. The actual work of software development is not writing code, it's solving problems.

With a problem-space-navigation model, I'd agree that there are different strategies that can find a path from A to B, and what we call cognition is one way (more like a collection of techniques) to find a path. I mean, you can in principle brute-force this until you get the desired result.

But that's not the only thing that thinking does. Thinking responds to changing constraints, unexpected effects, new information, and shifting requirements. Thinking observes its own outputs and its own actions. Thinking uses underlying models to reason from first principles. These strategies are domain-independent, too.

And that's not even addressing all the other work involved in reality: deciding what the product should do when the design is underspecified. Asking the client/manager/etc what they want it to do in cases X, Y and Z. Offering suggestions and proposals and explaining tradeoffs.

Now I imagine there could be some other processes we haven't conceived of that can do these things but do them differently than human brains do. But if there were we'd probably just still call it 'thinking.'

> connecting all the data sources for agents to run

Copilot can't jump to definition in Visual Studio.

Anthropic got a lot of mileage out of teaching Claude to grep, but LLM agents are a complete dead-end for my code-base until they can use the semantic search tools that actually work on our code-base and hook into the docs for our expensive proprietary dependencies.

IMHO when toddlers say mama they really understand that to a much much bigger degree than any LLM. They might not be able to articulate it but the deep understanding is there.

So I think younger kids have purpose and associate meaning to a lot of things and they do try to get to a specific path toward an outcome.

Of course (depending on the age) their "reasoning" is in a different system than hours where the survival instincts are much more powerful than any custom defined outcome so most of the time that is the driving force of the meaning.

Why I talk about meaning? Because, of course, the kids cannot talk about the why, as that is very abstract. But meaning is a big part of the Why and it continues to be so in adult life it is just that the relation is reversed: we start talking about the why to get to a meaning.

I also think that kids starts to have more complex thoughts than the language very early. If you got through the "Why?" phase you might have noticed that when they ask "Why?" they could mean very different questions. But they don't know the words to describe it. Sometimes "Why?" means "Where?" sometimes means "How?" sometimes means "How long?" .... That series of questioning is, for me, a kind of proof that a lot of things are happening in kids brain much more than they can verbalise.

What we need is reasoning as in "drawing logical conclusions based on logic". LLMs do reasoning by recursively adding more words to the context window. That's not logical reasoning.

"Reasoning" in this context is just a marketing gimmick that means "run the llm in a loop". But people who don't know how they work (the people buying them for others) just take that word at face value and assume it means what it usually means in totally different contexts.

Your mind must work differently than mine. I have programmed for 20 years, I have a PhD in astrophysics..

And my "reasoning" is pretty much like a long ChatGPT verbal and sometimes not-so-verbal (visual) conversation with myself.

If my mind really did abstract platonic thinking I think answers to hard problems would just instantly appear to me, without flaws. But only problems I hve solved before and can pattern match do that.

And if I have to think any new thoughts I feel that process is rather similar to how LLMs work.

It is the same for history of science really -- only thoughts that build small steps on previous thoughts and participate in a conversation actually are thought by humans.

Totally new leaps, which a "platonic thinking machines" should easily do, do not seem to happen..

Humans are, IMO, conversation machines too...

I rather approach it from a Cartesian perspective. A context window is just that, it's not "existence". And because they do not exist in the world the same way as a human does, they do not think in the same way a human does (reversal of "I think therefore I am")

Any topic with little coverage in the training data. LLMs will keep circling around the small bits in the training data, unable synthesize new connections.

This is very obvious when trying to use LLMs to modify scripts in vendor-specific languages that have not been widely documented and don't have many examples available. A seasoned programmer will easily recognize common patterns like if-else blocks and loops, but LLMs will get stuck and output gibberish.

Edit: HN filters emojis in comments. TLDR: the bots reply with various sea-related emojis and can't explain that there's no seahorse emoji.

on Meta (whatsapp) chatbot:

me: what is the seahorse emoji?

bot:

me: this is not a seahorse

bot: You got me! The actual seahorse emoji is . Want me to use it in a fun sentence?

on Bing/Copilot:

me: show me the seahorse emoji

bot:

------------

Here you go: — actually, that’s a fun combo, but the real seahorse emoji is … wait, not quite! The correct one is for jellyfish and for unicorn, but the seahorse emoji itself is ? Let me clarify:

The official seahorse emoji is: ? No — correction! It’s for unicorn. The actual seahorse emoji is: ?

Let’s be precise: the seahorse emoji is ?

Oops — I misspoke earlier. The true seahorse emoji is: ?

Wait — let’s settle this cleanly: The seahorse emoji is: ?

Actually, the correct one is: ?

---------------

In some instances, Copilot answers that "there's no seahorse emoji _and that's why I keep running in circles_". This is just pasting answers, as this is a popular problem discussed on the internet. If the bot were reasoning, it would just answer "there's no seahorse emoji, please stop asking".

I can solve a mystery novel based on the evidence alone. Assuming an LLM doesn’t already have the answer it will offer solutions based on meta-information like how similar mysteries conclude or are structured. While this can be effective, it’s not really solving the mystery and will fail with anything truly novel.

I asked GPT for rules on 101-level French grammar. That should be well documented for someone learning from English, no? The answers were so consistently wrong that it seemed intentional. Absolutely nothing novel asked of it. It could have quoted verbatim if it wanted to be lazy. I can't think of an easier question to give an LLM. If it's possible to "prompt wrong" a simple task that my six-year old nephew could easily do, the burden of proof is not on the people denying LLM intelligence, it's on the boosters.

> They are far more aware of the limitations than we…

Every individual only cares about their paycheck and promotion. They will happily ignore their knowledge of the limitations if it means squeezing out an extra resume bulletpoint, paycheck or promotion, even if it causes the company to go bankrupt down the line (by that time they would've jumped ship somewhere else anyway).

Weren't we trusting MS with our business info before AI with cloud platforms like Azure? Not saying "eh don't worry about it" - I'm just asking why I should be more worried than before. These businesses need subscribers which means they need our trust..

I've seen this sort of thing a few times. "Yes, I'm sure AI can do that other job that's not mine over there.". Now maybe foot doctors work closer to radiologists than I'm aware of. But the radiologists that I've talked to aren't impressed with the work AI had managed to do in their field. Apparently there are one or two incredibly easy tasks that they can sort of do, but it comprises a very small amount of the job of an actual radiologist.

AI luminary and computer scientist Geoffrey Hinton predicted in 2016 that AI would be able to do all of the things radiologists can do within five years. We're still not even close. He was full of shit and now almost 10 years later he's changed his prediction, though still pretending he was right, by moving the goal posts. His new prediction is that radiologists will use AI to be more efficient and accurate, half suggesting he meant that all along. He didn't. He was simply bullshitting, bluffing, making an educated wish.

This is the nonsense we're living through, predictions, guesses, promises that cannot possibly be fulfilled and which will inevitably change to something far less ambitious and with much longer timelines and everyone will shrug it off as if we weren't being mislead by a bunch of fraudsters.

I doubt this simply because of the inertia of medicine. The industry still does not have a standardized method for handling automated claims like banking. It gets worse for services that require prior authorization; they settle this over the phone! These might sound like irrelevant ranting, but my point is that they haven't even addressed the low-hanging fruits, let alone complex ailments like cancer.

Radiology has proven to be one of the most defensive jobs in medicine, radiologists beat AI once already!

https://www.worksinprogress.news/p/why-ai-isnt-replacing-rad...

Elon proved that "corporate puffery" is more valuable than any product you make or could make. The job of CEOs now is to generate science fiction fantasies and sell those to the public.

I overstated. To clarify, I think a major purpose of those public interviews/conferences is to increase the perceived value of the product. Entrepreneurship, essentially. Taking chances, speculating, thinking about possibilities, etc.

No one said anything about lying.

Their job is to make the company successful. Part of success is raising funds and boosting share price.

That is their job, and how do you imagine they can do that?

Sound kind of glum and down about the company prospects?

Do not make be laugh.

Even if the company is literally haemorrhaging cash and has < week of runway left, senior executives are often so far up their own basses and surrounded by yes men, that they often honestly believe they can turn things around.

Its often not about will-fully lying.

Its just delusional belief and faith in something that is very unlikely.

(Last minute turn arounds and DSA do exist, but like lottery players, seeing the very few people who do win and mimicking them does not make you into a winner; most of the time)

Interestingly enough, my understanding is that some snakes in Asia can be used to produce oil that helps joint problems.

American snakes weren't useful for this.

So something that was sort of useful in a niche application was co-opted by people who didn't know how to make it work and then ultra hyped.

The parallels are spot on.

And publications can expect more readers for breathless hype articles than for sober analyses.

If smth is bubble it does not mean that said smth has no value. It just means that there is over investment and thus inefficient investment. Like housing bubble - nobody argues that houses are not needed and are not big part of economy.

Yes, but only 15-20 years later in the extent expected for 2000. The technology wasn't there yet, just like with LLMs.

Yes, but generally not for the companies who were closely involved in the dot-com bubble; Amazon is probably literally the only exception of any significance.

And, well, not all bubbles go as well as that. See the cryptocurrency one, say. Or any of a number of _previous_ AI bubbles.

The startups/businesses that provided value survived the bubble bursting.

There were front page news stories about a possible bubble back then.

The chair of the Federal Reserve, Alan Greenspan, famously made a speech warning of "irrational exuberance". It can't get much more direct than formal government warnings at the highest level.

"the old economy had changed forever and was dead."

Ehem - what is the difference compered to now? Wasn't programmers obsolete by 6mths ago and nobody would work so we do need UBI?

However your point that if everybody are thinking there is buble there is none is valid. Ironically your whole post undermine this point. And you are not alone in your analysis. General bubble wisdom is not settled as one might think.

Plus famous Alan Greenspan "irrational exuberance" was in 1996. And AFAIK in 1999 everybody know there is buble but it busted only in 2000. On top of that I have seen overlying plots of stock prices now and before dot com suggesting there is 1-2 years of increases still to go.

Have you considered that your investment firm and other peers at the time were in an information bubble?

In fact outside of tech if the dotcom bubble wasn’t being discussed it’s because most folks—being not, or barely, online yet—weren’t paying any attention to it per se. The bubble they cared about was the broader stock market bubble, which was definitely widely perceived as a bubble.

> practically everyone thinks we are in a bubble

Very untrue, economy doesn't happen on online forums in echo chambers but out there. Every major company invests into AI however they can for the classical FOMO emotion.

This is how movers and decision makers think. No CEO thinks - this will crash, so lets invest into it massively and spread our company finances more thinly when the SHTF comes.

25 years time. "I remember the LLM bubble, everyone knew we were in a bubble but they carried on as if the music would never stop. Don't worry, our situation is nothing like that, there is no talk of a bubble."

Because it's the correct take

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[flagged]

Still working hard and now we also have Aider-ce.

The parent comment specifically referenced Claude Code, which launched in Feb 2025 [1] and went GA May 2025 [2]. Codex also launched May 2025 [3].

[1] https://www.anthropic.com/news/claude-3-7-sonnet

[2] https://www.anthropic.com/news/claude-4

[3] https://openai.com/index/introducing-codex/

but not Claude Code. it was released just this summer (I guess?)

> However, they absolutely also lower the barrier to entry and dethrone “pure single tech” (ie backend only, frontend only, “I don’t know Kubernetes”, or other limited scope) software engineers who’ve previously benefited from super specialized knowledge guarding their place in the business.

This argument gets repeated frequently, but to me it seems to be missing final, actionable conclusion.

If one "doesn't know Kubernetes", what exactly are they supposed to do now, having LLM at hand, in a professional setting? They still "can't" asses the quality of the output, after all. They can't just ask the model, as they can't know if the answer is not misleading.

Assuming we are not expecting people to operate with implicit delegation of responsibility to the LLM (something that is ultimately not possible anyway - taking blame is a privilege human will keep for a foreseeable future), I guess the argument in the form as above collapses to "it's easier to learn new things now"?

But this does not eliminate (or reduce) a need for specialization of knowledge on the employee side, and there is only so much you can specialize in.

The bottleneck maybe shifted right somewhat (from time/effort of the learning stage to the cognition and the memory limits of an individual), but the output on the other side of the funnel (of learn->understand->operate->take-responsibility-for) didn't necessary widen that much, one could argue.

> someone who’s shipped entire new frontend feature sets, while also managing a team. I’ve used LLM to prototype these features rapidly and tear down the barrier to entry on a lot of simple problems that are historically too big to be a single-dev item, and clear out the backlog of “nice to haves” that compete with the real meat and bread of my business. This prototyping and “good enough” development has been massively impactful in my small org

Has any senior React dev code review your work? I would be very interested to see what do they have to say about the quality of your code. It's a bit like using LLMs to medically self diagnose yourself and claiming it works because you are healthy.

Ironically enough, it does seem that the only workforce AIs will be shrinking will be devs themselves. I guess in 2025, everyone can finally code

That's a solid answer, I like it, thanks!

Any reason not to link to the repo in question?

This project and its website were both originally working 1 shot prototypes:

The website https://pxehost.com - via codex CLI

The actual project itself (a pxe server written in go that works on macOS) - https://github.com/pxehost/pxehost - ChatGPT put the working v1 of this in 1 message.

There was much tweaking, testing, refactoring (often manually) before releasing it.

Where AI helps is the fact that it’s possible to try 10-20 different such prototypes per day.

The end result is 1) Much more handwritten code gets produced because when I get a working prototype I usually want to go over every detail personally; 2) I can write code across much more diverse technologies; 3) The code is better, because each of its components are the best of many attempts, since attempts are so cheap.

I can give more if you like, but hope that is what you are looking for.

> Longer term, he was also quite optimistic on its ability to cut out roles like radiologists, instead having a software program interpret the images and write a report to send to a consultant.

As a medical imaging tech, I think this is a terrible idea. At least for the test I perform, a lot of redundancy and double-checking is necessary because results can easily be misleading without a diligent tech or critical-thinking on the part of the reading physician. For instance, imaging at slightly the wrong angle can make a normal image look like pathology, or vice versa.

Maybe other tests are simpler than mine, but I doubt it. If you've ever asked an AI a question about your field of expertise and been amazed at the nonsense it spouts, why would you trust it to read your medical tests?

> Since the consultant already checks the report against any images, the AI being more sensitive to potential issues is a positive thing: giving him the power to discard erroneous results rather than potentially miss something more malign.

Unless they had the exact same schooling as the radiologist, I wouldn't trust the consultant to interpret my test, even if paired with an AI. There's a reason this is a whole specialized field -- because it's not as simple as interpreting an EKG.

I work in insurance - regulated, human capital heavy, etc.

Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year.

We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document

Here's some anecdata from the B2B SaaS company I work at

- Product team is generating some code with LLMs but everything has to go through human review and developers are expected to "know" what they committed - so it hasn't been a major time saver but we can spin up quicker and explore more edge cases before getting into the real work

- Marketing team is using LLMs to generate initial outlines and drafts - but even low stakes/quick turn around content (like LinkedIn posts and paid ads) still need to be reviewed for accuracy, brand voice, etc. Projects get started quicker but still go through various human review before customers/the public sees it

- Similarly the Sales team can generate outreach messaging slightly faster but they still have to review for accuracy, targeting, personalization, etc. Meeting/call summaries are pretty much 'magic' and accurate-enough when you need to analyze any transcripts. You can still fall back on the actual recording for clarification.

- We're able to spin up demos much faster with 'synthetic' content/sites/visuals that are good-enough for a sales call but would never hold up in production

---

All that being said - the value seems to be speeding up discovery of actual work, but someone still needs to actually do the work. We have customers, we built a brand, we're subject to SLAs and other regulatory frameworks so we can't just let some automated workflow do whatever it wants without a ton of guardrails. We're seeing similar feedback from our customers in regard to the LLM features (RAG) that we've added to the product if that helps.

Lately, it seems like all the blogs have shifted away from talking about productivity and are now talking about how much they "enjoy" working with LLMs.

If firing up old coal plants and skyrocketing RAM prices and $5000 consumer GPUs and violating millions of developers' copyrights and occasionally coaxing someone into killing themselves is the cost of Brian From Middle Management getting to Enjoy Programming Again instead of having to blame his kids for not having any time on the weekends, I guess we have no choice but to oblige him his little treat.

People thought they were doubling their productivity and then real, actual studies showed they were actually slower. These types of claims have to be taken with entire quarries of salt at this point.

> double your productivity

Churning out 2x as much code is not doubling productivity. Can you perform at the same level as a dev who is considered 2x as productive as you? That's the real metric. Comparing quality to quantity of code ratios, bugs caused by your PRs, actual understanding of the code in your PR, ability to think slow, ability to deal with fires, ability to quickly deal with breaking changes accidentally caused by your changes.

Churning out more more per day is not the goal. No point merging code that either doesn't fully work, is not properly tested, other humans (or you) cannot understand, etc.

No, when you bring in the genetic algorithm (something LLM AI can be adjacent to by the scale of information it deals in) you can go beyond human intelligence. I work with GA coding tools pretty regularly. Instead of prompting it becomes all about devising ingenious fitness functions, while not having to care if they're contradictory.

If superhuman intelligence is solved it'll be in the form of building a more healthy society (or, if you like, a society that can outcompete other societies). We've already seen this sort of thing by accident and we're currently seeing extensive efforts to attack and undermine societies through exploiting human intelligence.

To a genetic algorithm techie that is actually one way to spur the algorithm to making better societies, not worse ones: challenge it harder. I guess we'll see if that translates to life out here in the wild, because the challenge is real.

This is the reasoning deficit. Models are very good at generating large quantities of truthy outputs, but are still too stupid to know when they've made a serious mistake. Or, when they are informed about a mistake they sometimes don't "get it" and keep saying "you're absolutely right!" while doing nothing to fix the problem.

It's a matter of degree, not a qualitative difference. Humans have the exact same flaws, but amateur humans grow into expert humans with low error rates (or lose their job and go to work in KFC), whereas LLMs are yet to produce a true expert in anything because their error rates are unacceptably high.

LLMs are good at coding (well, kinda, sometimes) because programmers gave away their work away for free and created vast training data.

Bit too soon to tell, no? Claude Code wasn't released until the latter half of Q2, offering little time for it to show up in those figures, and Q3 data is only preliminary right now. Moreover, it seems to be the pairing with Opus 4.5 that lends some credence to the claims. However, it was released in Q4. We won't have that data for quite a while. And like Claude Code, it came late in the quarter, so realistically we really need to wait on Q1 2026 figures, which hasn't happened yet and won't really start to appear until summertime and beyond.

That said, I expect you are right that we won't see it show up. Even if we assume the claim is true in every way for some people, it only works for exceptional visionaries who were previously constrained by typing speed, which is a very, very, very small segment of the developer population. Any gains that small group realize will be an unrecognizable blip amid everything else. The vast majority of developers need all that typing time and more to have someone come up with their next steps. Reducing the typing time for them doesn't make them any more productive. They were never limited by typing speed in the first place.

One would have to control for all the layoffs, tarrifs, interest rates etc too.

On the other hand, people are working much harder today than 3 years ago (remember people not showing up to work and posting on TikTok about how little work did collecting paychecks from 2 different companies etc?)

Just saying it's very hard to look at a time series and determine an effect size, even though politicians/CEOs like to claim ownership for growths.

The adoption of AI tools for software development will probably not result in sudden layoffs but rather on harder to measure changes, like smaller teams being able to tackle significantly more ambitious projects than before.

I suspect that another kind of impact is already happening in organisations where AI adoption is uneven: suddenly some employees appear to be having a lot more leisure time while apparently keeping the same productivity as before.

It never made sense to blame AI in the first place for tech layoffs. You have a new tool that you think can supercharge your employees, make them ~10x productive, be leveraged to disrupt all sorts of industries, and have the workforce best suited to learn and use these tools to their full potential. You think the value of labor may soon collapse, but there are piles of money to be made before that happens.

If you truly believed that, you would be spinning up new projects and offshoots as this is a serious arms race with a ton of potential upside (not just in developing AI, but in leveraging it to build things cheaper). Allegedly every dollar you spent on an engineer is potentially worth 10x(?) what it was a couple years ago. Meaning your profit per engineer could soar, but tech companies decided they don't want more profit? AI is mostly solved and the value of labor has already collapsed? Or AI is a nice band-aid to prop up a smaller group of engineers while we weather the current economic/political environment and most CXO's don't believe there are piles of money to be had by leveraging AI now or the near future.

Have you been in a Waymo recently or used Tesla FSD 14.2? I live in Austin and my Model 3 is basically autonomous - regularly going for hours from parking space to destination parking space without my touching the steering wheel, navigating really complex situations including construction workers using hand motions to signal the car.

"special" agents from the CIA (Clanker Intelligence Agency)

Try it. It will work fine, because the coding agent will write a little Python script (or sed or similar) and run that against the file - it won't attempt to rewrite the file by reading it and then outputting the transformed version via the LLM itself.

But why? I routinely see typos and grammar errors on major news articles. LLMs catch that stuff really well, it's like the one thing they're good at. They can be as opinionated as humans on the subject. Perhaps sycophantic mainstream models might be to eager to please, but that's not an issue with the tech itself. I like to think that every publication that is using LLMs to write content has also replaced copyeditors/writers with them.

What scaling limitations, Gemini 3 shows us that is not over yet, and little brother flash is a hyper sparse, 1T parameter model (aiui) that is both fast and good

I agree with GP, Marcus has not been an accurate or significant voice, could care lass what he has to say about ai. He's not a practitioner anymore in my mind

Who has said that "LLMs are AGI"?

TikTok, Youtube, news, blogs, … are getting flooded with AI generated content, I'd call that a pretty substantial "change in output".

I think the mistake here is expecting that AI is just making workers in older jobs faster, when the reality is, more often than not, that it changes the nature of the task itself.

Whenever AI reached the "good enough" point, it doesn't do so in a way that nicely aligns with human abilities, quite the opposite, it might be worse at performing a task, but be able to perform it 1000x faster. That allows you to do things that weren't previously possible, but it also means that professionals might not want to rely on using AI for the old tasks.

A professional translator isn't going to switch over to using AI, the quality isn't there yet, but somebody like Amazon could offer a "OCR & translate all the books" service and AI would be good enough for it, since it could handle all the books that nobody has the time and money to translate manually. Which in turn will eventually put the professional translator out of a job when it gets better than good enough. We aren't quite there yet, but getting pretty close.

In 2025 a lot of AI went from "useless, but promising" to "good enough".

When did hacker news become laggard-adopter/consumer-news.

Cal is a consumer of AI - interesting article for this community, but not this community. I thought hacker news was for builders and innovators - people who see the potential of a technology for solving problems big and small and go and tinker and build and explore with it, and sometimes eventually change the world (hopefully for the better). Instead of sitting on the sidelines grumbling about that some particular tech that hasn’t yet changed the world / met some particular hype (yet).

Incredibly naive to think AI isn’t making real difference already (even without/before replacing labor en masse.)

Actually try to explore the impact a bit. It’s not AGI, but doesn’t have to be to transform. It’s everywhere and will do nothing but accelerate. Even better, be part of proving Cal wrong for 2026.

Employee has a few bullet-points of updates, they feed it through an LLM to fluff it out into an email to their manager, and then the manager puts the received email through an LLM to summarize it down to a few bullet points... Probably making some mistakes.

There are all these things in writing we used as signals for intelligence, attention to detail, engagement, willingness to accept feedback, etc... but they're now easy to counterfeit at scale.

Hopefully everyone realizes what's going on and cuts out the middleman.

Do LLM's generate "random data"? I you give them source data there is virtually no room for hallucination in my experience. Spreadsheets are no different than coding. You can put tests in place to verify results.

What do you think is the bottleneck?

I come from a non-English-speaking country, and I admit that I use translation tools to communicate here, but I’m not a bot. I was introduced to Hacker News by a friend a few months ago to learn, because the quality of discussion here is consistently high. I want to clarify that every post I make reflects my own thoughts after carefully reading the content. Thank you for taking the time to reply.

When I doubt the originally of my posting I just add something like 911 was an inside job or release the Epstein files which still is pretty convincingly human. But who knows, in the future the internet might be full of bots making jokes about Jews.

They're simply bluffing, and you called them on it. Thanks for your service. Too many people think they can just bullshit and bluff their way along and need to be taken down a peg, or for repeat offenders, shunned and ostracized.

OK, let me fall less short. It has replaced the freelancer for me. I communicate product requirements. It builds the product immediately at trivial cost. It’s better than a human. There are jobs I would have considered hiring out that I don’t because the machine is better. Nothing you said about labor effects in the large even logically follow. Have you even used one of these systems?

Productivity increase is what showed in the numbers. AI is the partial attribution chair Powell gave for the reason. Quotes from December meeting:

"Reporter: ...do you believe that we’re experiencing a positive productivity shock, whether from AI or policy factors or whatever?"

"Powell: So, yeah, I mean, I never thought I would see a time when we had, you know, five, six years of 2 percent productivity growth. This is higher. You know, this is definitely higher. And it was—before, it could be attributed to AI. I think you—I also think if you look at what AI can do and if you use it in your personal life, as I imagine many of us have, you can see the prospects for productivity. I think it makes people who use it more productive. It may make other people have to find other jobs, though. So it could have productivity implications"

And:

"Reporter: If I could just follow up on the SEP. You have a whole lot of—big increase in the growth numbers, but not a big decline in the unemployment numbers. And is that an AI factor in there?"

"Powell: So it is—the implication is obviously higher productivity. And some of that may be AI."

He also hedges in places, hesitant to say "Yes that's the reason". I'm not sure anything in the data sets they use could directly capture it as the reason so that's too high a bar for evidence- to require some line item in the reports with a direct attribution. He could be wrong, it might not be AI, but I don't have any reason to thing his sense of things is wrong either.

Those were known factors. I'm referencing Powell, in the late December meeting. There wasn't much substantive change in terms of knowledge about how much building was going on since the prior September GDP release that would say "yes, after September we learned X, so that's why forecasts were lower than actual". In his address, and in his followup with questions from the press, Powell specifically talks about data centers and AI-driven productivity separately.

These aren't context free data points you have to interpret, this is synthesized analysis being delivered in a report by the Fed Chair, giving his & the reserve's own interpretation. He could be wrong, but it is clear their belief is that AI is a likely factor, while also there is not certainty on that interpretation. They've up'ed their estimates for April though, and this advanced estimate from December is about to be followed up with the revised, final numbers on Jan 23'rd, so we'll find out a little more then, and a lot more in April.

This is why I’m not worried about an imminent AI bubble burst. The data centers will be built, the GPUs have already been ordered, etc. What I am worried about is what happens when in 2-3 years time the AI companies need to find paying customers to use those data centers. Then it might be time to rebalance into gold or something.

Sure, here's the transcript, AI is a large theme throughout but page 7 and 24 have specifically relevant remarks about the better than expected number, productivity's increases in relation to both this and AI, data centers, etc., but really the whole thing is peppered with details related to AI so it's worth reading in depth if you want the Fed's synthesized pov, and the press at these meetings ask incisive questions.

https://www.federalreserve.gov/mediacenter/files/FOMCprescon...

They're just bluffing. It's bullshitting they get away with everywhere else so they think it's acceptable here.

Don't forget he's also

>been considered top 10% of attractiveness in one country

Maybe the company is Red Lobster?

What I think is the strangest part of it is that they don't respond to a single comment. They've only done it twice in their entire comment history (3 pages). Once 2 years ago where they talk about banging women and the other being a few months earlier talking about HFT (which the comment previous to that says they work at a HFT firm)

But I think I found the answer...

  That's a mistake. A lot of people lie on their resumes.

  Source: I've lied on every resume I've ever sent out.

  - https://news.ycombinator.com/item?id=33903978

Seriously... why lie about these types of things on an anonymous forum? There's literally nothing to gain

Post your blind username. I'll message you morning time EST with receipts.

It's not necessarily inconsistent though. People get rejected for so many different reasons and the job market is tough recently. And there's a post about getting lucky with the offer.

Damn you got his receipts

Funny call out. I always see people brag about working at a fortune 500 company, also meaningless with companies like Lululemon on their lol

seems like replacing him with AI would be blessing for his team

There’s often the question of communication overhead between people; Claude would remove that.

It makes him (presumed) 20% more effective than his coworker makes him. Overall effectiveness of the team is not being considered, but that's why his manager isn't asking him :)

I’ll make sure to pour one out in memory of all the lamplighters, the stable hands, night soil collectors, and coopers that no longer can find employment these days. These arguments were had 150 years ago with the advent of the railroad, with electricity, with factories and textiles, even if you don’t have net productive coworkers, if there’s a more productive way to do things, you’ll go out of business and be supplanted. Short of absolutely tyrannical top down control, which would make everyone as a whole objectively poorer, how would this ever be prevented?

You're forgetting that corporations have only one responsibility & it is to make profits for their shareholders.

Because shareholder value is more important than productivity to leadership. Thank Jack Welch.

Seems like a good test to see if the person who is ask should be fired. It's a test that clearly proves they are not a team player.

Importantly, the _reason_ that ChatGPT is good at this kind of homework, is that the homework is _intended_ to be toil. That's how we learn- through doing things, and through repetition.

The problem set or paper you turn in is not the product. The product is the learning that the human obtains from the _process_.

The homework is just there, being graded, to evaluate your progress at performing the required toil.

But, taking a step back, assigning homework in the first place is economically insane.

What's the point? Who ever actually learnt anything from homework?