llm/e6f7e516-f0a0-4424-8f8f-157aae85c74e/batch-5-da147681-84b2-4d95-9aff-a08052a8103c-input.json
The following is content for you to classify. Do not respond to the comments—classify them.
<topics>
1. Reasoning vs. Pattern Matching
Related: Debates on whether LLMs truly think or merely predict tokens based on training data. Includes comparisons to human cognition, the definition of "reasoning" as argument production versus evaluation, and the argument that LLMs are "lobotomized" without external loops or formalization.
2. AI-Assisted Coding Reality
Related: Divergent experiences with tools like Claude Code and Codex. While some report massive productivity boosts and shipping entire features solo, others describe "lazy" AI, subtle logic bugs in generated tests (e.g., SQL query validation), and the danger of unverified code bloat.
3. The AI Economic Bubble
Related: Comparisons to the dot-com crash, with arguments that current valuation relies on "science fiction fantasies" and hype rather than revenue. Counter-arguments suggest the infrastructure (datacenters, GPUs) provides real value similar to the fiber build-out, even if a market correction is imminent.
4. Workforce Displacement and Automation
Related: Fears and anecdotes regarding job security, including a "Staff SWE" preferring AI to coworkers and contractors losing bids to smaller, AI-equipped teams. Discussions cover the automation of "bullshit jobs," the potential for a "winner take all" economy, and management incentives to cut labor costs.
5. Definition of Agentic Success
Related: Disagreement over whether AI "joined the workforce." Some argue failing to replace humans entirely (the "secretary" model) is a failure of 2025 predictions, while others claim deep integration as a tool (automating loops, drafting emails) constitutes a successful, albeit different, type of joining.
6. Verification and Hallucination Risks
Related: The critical need for external validation mechanisms. Commenters note that coding agents succeed because compilers/linters act as truth-checkers, whereas open-ended tasks (spreadsheets, emails) lack rigorous feedback loops, making hallucinations and "truthy" errors dangerous and hard to detect.
7. Impact on Skill and Learning
Related: Concerns about the long-term effects on human expertise. Topics include "skill atrophy" where juniors bypass learning fundamentals, the educational crisis evidenced by Chegg's collapse, and the difficulty of debugging AI code without deep institutional knowledge or "muscle memory" of the system.
8. Corporate Hype vs. Utility
Related: Cynicism toward executive predictions (Altman, Hinton) viewed as efforts to pump stock prices or attract investment. Users contrast "corporate puffery" and "vaporware" with the practical, often mundane utility of AI in specific B2B workflows like insurance claim processing or data extraction.
9. Integration into Legacy Systems
Related: The challenge of applying AI to real-world, messy environments versus greenfield demos. Discussion includes the difficulty of getting agents to work with proprietary codebases, expensive dependencies, lack of documentation for obscure vendor tools, and the failure of browser agents on standard web forms.
10. Formalization of Natural Language
Related: Theoretical discussions on overcoming LLM limitations by mapping natural language to formal logic or proof systems (like Lean). Skeptics argue human language is too "mushy" or context-dependent for this to be a silver bullet for AGI or perfect reasoning.
11. Medical and Specialized Fields
Related: Debates on AI in radiology and medicine. While some see potential in automated reporting and "second opinions" to catch errors, professionals argue that current models struggle with complex cases, over-report issues, and lack the nuance required for high-stakes diagnostics.
12. The Secretary vs. Replacement Model
Related: The shift in expectations from AI as an autonomous employee to AI as a productivity-enhancing assistant. Users describe workflows where humans act as orchestrators or managers of AI output rather than performing the rote work, effectively reviving the role of the personal secretary.
13. Software Engineering Evolution
Related: Predictions that the discipline is shifting from "writing code" to "managing entropy" and system design. Some view this as empowering "cowboy devs" to move fast, while others fear a future of unmaintainable "vibe coded" software that no human fully understands.
14. Productivity Metrics and Paradoxes
Related: Skepticism regarding "2x productivity" claims. Commenters argue that generating more code doesn't equal value, noting that debugging, communicating, and context-gathering are the real bottlenecks, and that AI might simply be increasing the volume of low-quality output or "slop."
0. Does not fit well in any category
</topics>
<comments_to_classify>
[
{
"id": "46506293",
"text": "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\n\nI 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"
}
,
{
"id": "46506570",
"text": "Gary Marcus is just a free Outrage As A Service. People politically align with him and then feel the need to go along with the rest of the charade."
}
,
{
"id": "46507122",
"text": "Well clearly LLMs are not AGI, and all such calls of them being 'AGI' have been a pump and dump scam. So he got that dead right for years."
}
,
{
"id": "46508134",
"text": "Who has said that \"LLMs are AGI\"?"
}
,
{
"id": "46508889",
"text": "Probably any sales and marketing departments of companies with an \"AI\" product (based on an LLM) which is presented as having AGI-like capabilities.\n\nI'm doubt parent poster was referring to anyone phrasing it in those literal terms. Kind of like how \"some people claim flavored water can cure cancer\" doesn't mean that's the literal pitch being given for the snake-oil."
}
,
{
"id": "46511497",
"text": "That's complete bullshit, and you know it. The big labs are saying that AGI will be here soon, not that it's here now. Please prove me wrong."
}
,
{
"id": "46512312",
"text": "\"We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents 'join the workforce' and materially change the output of companies.\"\n\nWe know how to build it and it will be entering the workforce in 2025. Well, we're in 2026 now and we don't have it in the workforce or anywhere else because they haven't built it because they don't really know how to build it because they're hucksters selling vaporware built on dead end technologies they cannot admit to."
}
,
{
"id": "46510349",
"text": "https://news.ycombinator.com/item?id=46494830 This was on HN yesterday."
}
,
{
"id": "46511489",
"text": "lol, yes, Gary Marcus is worth quoting because some random blogger said AGI is here."
}
,
{
"id": "46506430",
"text": "There needs to be a companion to Betteridge’s law that addresses AI-related headlines with “because since the beginning of time the field of artificial intelligence over-promises and under-delivers.”"
}
,
{
"id": "46506056",
"text": "Cal Newport looked in the wrong places. He has no visibility into the usage of ChatGPT to do homework. The collapse of Chegg should tell you, with no other public information, that if 30% of students were already cheating somehow, somewhat weakly, they are now doing super-powerful cheating, and surely more than 30% of students at this stage.\n\nIt’s also kind of stupid to hand wave away, programming. Programmers are where all the early adopters of software are. He’s merely conflating an adoption curve with capabilities. Programmers, I’m sure, were also the first to use Google and smartphones. “It doesn’t work for me” is missing the critical word “yet” at the end, and really, is it saying much that forecasts about adoption in the metric, “years until when Cal Newport’s arbitrary criteria of what agent and adoption means meets some threshold only inside Cal Newport’s head” is hard to do?\n\nThere are 700m active weeklies for ChatGPT. It has joined the workforce! It just isn’t being paid the salaries."
}
,
{
"id": "46506172",
"text": "Wow, homework is an insane example of a \"workforce.\"\n\nHomework is in some ways the opposite of actual economic labor. Students pay to attend school, and homework is (theoretically) part of that education; something designed to help students learn more effectively. They are most certainly not paid for it.\n\nHaving a LLM do that \"work\" is economically insane. The desired learning does not happen, and the labor of grading and giving feedback is entirely wasted.\n\nStudents use ChatGPT for it because of perverse incentives of the educational system. It has no bearing on economic production of value."
}
,
{
"id": "46506727",
"text": "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.\n\nThe problem set or paper you turn in is not the product. The product is the learning that the human obtains from the _process_.\n\nThe homework is just there, being graded, to evaluate your progress at performing the required toil."
}
,
{
"id": "46509456",
"text": "> The homework is just there, being graded, to evaluate your progress at performing the required toil.\n\nThere’s the problem, some students don’t want an education, they just want a qualification, even if it means cheating on the evaluation."
}
,
{
"id": "46516395",
"text": "But, taking a step back, assigning homework in the first place is economically insane.\n\nWhat's the point? Who ever actually learnt anything from homework?"
}
,
{
"id": "46506550",
"text": "> He’s merely conflating an adoption curve with capabilities.\n\nSure, programmers would still adopt LLMs faster than the rest of the work-force whether or not the LLMs were good at writing code. But you have to at credit at least some of that adoption rate to the fact that LLMs are significantly better at text (e.g. code) generation than they are at most other white-collar tasks (e.g. using a web browser)"
}
,
{
"id": "46506155",
"text": "read it again. he criticizes the hype built around 2025 as the Year X for agents. many were thinking that \"we'll carry PCs in our pockets\" when Windows Mobile-powered devices came out. many predicted 2003 as the Year X for what we now call smartphones.\n\nno, it was 2008, with the iPhone launch."
}
,
{
"id": "46509298",
"text": "A brief history of programming:\n\n1. Punch cards -> Assembly languages\n\n2. Assembly languages -> Compiled languages\n\n3. Compiled languages -> Interpreted languages\n\n4. Interpreted languages -> Agentic LLM prompting\n\nI've tried the latest and greatest agentic CLI and toolings with the public SOTA models.\n\nI think this is a productivity jump equivalent to maybe punch cards -> compiled languages, and that's it. Something like a 40% increase, but nowhere close to exponential."
}
,
{
"id": "46509314",
"text": "1. Punch cards -> Assembly languages\n\nErr, in my direct experience it was Punch Cards -> FORTAN\n\nHere, for example, is the Punch Card for a single FORTRAN statement: https://en.wikipedia.org/wiki/File:FortranCardPROJ039.agr.jp...\n\nPunchCards were an input technology, they were in no way limited to either assembley languages or to FORTRAN.\n\nYou might be thinking of programming in assembly via switch flipping or plug jacking."
}
,
{
"id": "46512368",
"text": "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."
}
,
{
"id": "46509448",
"text": "That's jump if you are a junior. It falls down hard for the seniors doing more complex stuff.\n\nI'm also reminding that we tried whole \"make it look like human language\" with COBOL and it turned out that language wasn't a bottleneck, the ability of people to specify exactly what they want was the bottleneck. Once you have exact spec, even writing code on your own isn't all that hard but extracting that from stakeolders have always been the harder part of the programming."
}
,
{
"id": "46509373",
"text": "Except punch cards are a data storage format, not a language. Some of the the earliest computers were programmed by plugboard ( https://en.wikipedia.org/wiki/Plugboard#Early_computers ) so that might be thought of as a precursor to machine language / assembly language.\n\nAnd compiled and interpreted languages evolved alongside each other in the 1950s-1970s."
}
,
{
"id": "46508817",
"text": "It pretty much did join the work force. Listen to the fed chair, listen to related analysis, the unexpected overperformance of GDP isn’t directly attributed AI but it is very much in the “how did that happen?” conversation. And there’s plenty of softer, more anecdotal evidence in addition to that to respond to the headline with “It did.” The fact that it has been gradual and subtle as the very first agent tools reach production readiness, gain awareness in the public, start being used…? That really doesn’t seem at all unexpected as the path than “joining” would follow."
}
,
{
"id": "46508940",
"text": "> the unexpected overperformance of GDP isn’t directly attributed AI but it is very much in the “how did that happen?” conversation.\n\nWe spent an amount of money on data centers that was so large that it managed to overcome a self-imposed kick in the nuts from tariffs and then some. The amount of money involved rivals the creation of the railroad system in the United States. Of course GDP overperformed in that scenario.\n\nWhere did AI tool use show up in the productivity numbers?"
}
,
{
"id": "46509192",
"text": "My understanding was that the growth came mainly from things like building data centers and buying chips. Boring old fashioned stuff."
}
,
{
"id": "46509346",
"text": "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."
}
,
{
"id": "46509765",
"text": "The energy isn't available and that is going to take much longer to build."
}
,
{
"id": "46509046",
"text": ">Listen to the fed chair, listen to related analysis, the unexpected overperformance of GDP isn’t directly attributed AI but it is very much in the “how did that happen?” conversation\n\nI would very much like to read this if you have a link"
}
,
{
"id": "46512378",
"text": "They're just bluffing. It's bullshitting they get away with everywhere else so they think it's acceptable here."
}
,
{
"id": "46509457",
"text": "> the unexpected overperformance of GDP isn’t directly attributed AI but it is very much in the “how did that happen?” conversation.\n\nit's builing datacenters and buying servers and GPUs. It isn't directly attributed to AI because it isn't caused by use of AI, but blowing the AI bubble"
}
,
{
"id": "46508742",
"text": "I'm a staff level SWE at a company that you've all heard of (not a flex, just providing context).\n\nIf my manager said to me tomorrow: \"I have to either get rid of one of your coworkers or your use of AI tools, which is it?\"\n\nI would, without any hesitation, ask that he fire one of my coworkers. Gemini / Claude is way more useful to me than any particular coworker.\n\nAnd now I'm preparing for my post-software career because that coworker is going to be me in a few years.\n\nObviously I hope that I'm wrong, but I don't think I am."
}
,
{
"id": "46509053",
"text": "Interesting that this guy claims to be a \"staff level SWE at a major company\", yet one year ago he was on HN posting about how horrible of a time he was having getting a SWE job, how he's failed multiple interviews, including at FAANGs, was being rejected for even no-name small startups, had failed multiple interviews because of inability on algorithm questions ... and yet within the last year he was supposedly successfully hired on at a \"major company\" for a staff-level senior coding position."
}
,
{
"id": "46509101",
"text": "Don't forget he's also\n\n>been considered top 10% of attractiveness in one country"
}
,
{
"id": "46509092",
"text": "Maybe the company is Red Lobster?"
}
,
{
"id": "46509117",
"text": "That would be a flex."
}
,
{
"id": "46509126",
"text": "Pay me in biscuits"
}
,
{
"id": "46509171",
"text": "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)\n\nBut I think I found the answer...\n\nThat's a mistake. A lot of people lie on their resumes.\n\nSource: I've lied on every resume I've ever sent out.\n\n- https://news.ycombinator.com/item?id=33903978\n\nSomething tells me they aren't the most honest person. That something is thw09j9m...\n\nSeriously... why lie about these types of things on an anonymous forum? There's literally nothing to gain"
}
,
{
"id": "46509707",
"text": "Back in my days this was called \"trolling\"."
}
,
{
"id": "46510595",
"text": "Trolling is more than lying. It requires having bait. And sometimes a boat."
}
,
{
"id": "46509245",
"text": "Post your blind username. I'll message you morning time EST with receipts."
}
,
{
"id": "46509251",
"text": "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."
}
,
{
"id": "46509135",
"text": "Damn you got his receipts"
}
,
{
"id": "46509465",
"text": "seems like replacing him with AI would be blessing for his team"
}
,
{
"id": "46509118",
"text": "Funny call out. I always see people brag about working at a fortune 500 company, also meaningless with companies like Lululemon on their lol"
}
,
{
"id": "46508813",
"text": "Is that a useful thought experiment? Claude benefits you as an individual more than a coworker, but I find I hard to believe your use of Claude is more of a value add to the business than an additional coworker. Especially since that coworker will also have access to Claude.\n\nIn the past we also just raised the floor on productivity, do you think this will be different?"
}
,
{
"id": "46509003",
"text": "There’s often the question of communication overhead between people; Claude would remove that."
}
,
{
"id": "46509045",
"text": "No that’s not true at all. Humans can deal with ambiguity and operate independently. Claude can’t do that. You’re trading one “problem” for an entirely different one in this hypothetical."
}
,
{
"id": "46509059",
"text": "Isn't that what polishing 'the prompt' does? Refine the communication like an editor does for a publication? Only in this case it's instructions for how to get a transformer to mine an existing set of code to produce some sort of vaguely useful output.\n\nThe human factor adds knowledge of the why that refines the results. Not just any algorithm or a standard pattern that fits, but the correct solution for the correct question."
}
,
{
"id": "46509091",
"text": "people talking as if communication overhead is bad. That overhead makes someone else able to substitute for you (or other one) when needs happen, and sometimes can discover concerns earlier."
}
,
{
"id": "46509047",
"text": "> There’s often the question of communication overhead between people; Claude would remove that.\n\n... and replace that with communication overhead with claude ?"
}
]
</comments_to_classify>
Based on the comments above, assign each to up to 3 relevant topics.
Return ONLY a JSON array with this exact structure (no other text):
[
{
"id": "comment_id_1",
"topics": [
1,
3,
5
]
}
,
{
"id": "comment_id_2",
"topics": [
2
]
}
,
{
"id": "comment_id_3",
"topics": [
0
]
}
,
...
]
Rules:
- Each comment can have 0 to 3 topics
- Use 1-based topic indices for matches
- Use index 0 if the comment does not fit well in any category
- Only assign topics that are genuinely relevant to the comment
Remember: Output ONLY the JSON array, no other text.
50