llm/e6f7e516-f0a0-4424-8f8f-157aae85c74e/batch-4-effe7205-e135-40c7-8d18-7df14345de3c-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": "46509013",
"text": "> 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?\n\nExactly, so many of these claims are complete nonsense. I'm supposed to believe that boards/investors would be fine with companies doing massive layoffs to maintain flat/minuscule growth, when they could keep or expand their current staffing and massively expand their market share and profits with all this increased productivity?\n\nIt's ridiculous. If this stuff had truly increased productivity at the levels claimed we would see firms pouring money into technical staff to capitalize on this newfound leverage."
}
,
{
"id": "46506844",
"text": "Agents as staff replacements that can tackle tasks you would normally assign to a human employee didn't happen in 2025.\n\nAgents as LLMs calling tools in a loop to perform tasks that can be handled by typing commands into a computer absolutely did.\n\nClaude Code turns out to be misnamed: it's useful for way more than just writing code, once you figure out how to give it access to tools for other purposes.\n\nI think the browser agents (like the horribly named \"ChatGPT Agent\" - way to burn a key namespace on a tech demo!) have acted as a distraction from this. Clicking links is still pretty hard. Running Bash commands on the other hand is practically a solved problem."
}
,
{
"id": "46506882",
"text": "We still sandbox, quarantine and restrict them though, because they can't really behave as agents, but they're effective in limited contexts. Like the way waymo cars kind of drive on a track I guess? Still very useful, but not the agents that were being sold, really.\n\nEdit: should we call them \"special agents\"? ;-)"
}
,
{
"id": "46513444",
"text": "\"special\" agents from the CIA (Clanker Intelligence Agency)"
}
,
{
"id": "46508848",
"text": "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."
}
,
{
"id": "46513557",
"text": "Thanks for collecting training data for the rest of us, 'coz I don't trust Musk with my life."
}
,
{
"id": "46509751",
"text": "> Agents as LLMs calling tools in a loop to perform tasks that can be handled by typing commands into a computer absolutely did.\n\nI think that this still isn't true for even very mundane tasks like \"read CSV file and translate column B in column C\" for files with more than ~200 lines. The LLM will simply refuse to do the work and you'll have to stitch the badly formatted answer excerpts together yourself."
}
,
{
"id": "46514107",
"text": "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."
}
,
{
"id": "46513890",
"text": "I really don’t agree with the author here. Perplexity has, for me, largely replaced Cal Newport’s job (read other journalists work and synthesize celebrity and pundit takes on topic X). I think the take that Claude isn’t literally a human so agents failed is silly and a sign of motivated reasoning. Business processes are going to lag the cutting edge by years in any conditions and by generations if there is no market pressure. But Codex isn’t capable of doing a substantial portion of what I would have had to pay a freelancer/consultant to do? Any LLM can’t replace a writer for a content mill? Nonsense. Newport needs to open his eyes and think harder about how a journalist can deliver value in the emerging market."
}
,
{
"id": "46514262",
"text": "But it isn’t joining the workforce. Your perspective is that it could, but the point that it hasn’t is the one that’s salient. Codex might be able to do a substantial portion of what a freelancer can do, but even you fell short of saying it can replace the freelancer. As long as every ai agent needs its hand held the effect on the labor force is an increase in costs and an increase in outputs where quality doesn’t matter. It’s not a reduction of labor forces"
}
,
{
"id": "46509963",
"text": "Have y’all tried Claude code using opus 4.5 - I believe it has fully joined the workforce, had my grandma build and deploy her own blog with a built in cms and an admin portal, post editor, integrate uploads with GitHub, add ci/cd and took about 2 hours mostly because she types slow"
}
,
{
"id": "46512860",
"text": "Terrible productivity loss vs. signing up for a hosted Wordpress site."
}
,
{
"id": "46510280",
"text": "I think the issue is that everybody assumes the economy operates under some kind of \"free market\" conditions: limited by available labor but with unlimited potential demand. In that situation, AI could indeed cause massive unemployment.\n\nBut this is perhaps not the case. By pesimistic estimates half of the people work in bs jobs that have no real value to society, and every capitalist is focused on rent extraction now. If the economy can operate under such conditions, it doesn't really need more productivity growth, it is already demand-limited."
}
,
{
"id": "46512061",
"text": "Link to the NewYorker piece: https://archive.ph/VQ1fT"
}
,
{
"id": "46509986",
"text": "I use an agent in all my day to day coding now. It's a lot small tasks to speed me up, but it's definitely in use."
}
,
{
"id": "46512806",
"text": "Claude Code became a critical part of my workflow in March 2025. It is now the primary tool."
}
,
{
"id": "46505990",
"text": "> In one example I cite in my article, ChatGPT Agent spends fourteen minutes futilely trying to select a value from a drop-down menu on a real estate website\n\nMan dude, don't automate toil add an API to the website.It's supposed to have one!"
}
,
{
"id": "46506137",
"text": "It probably has one that the web form is already using, but if agentic AI requires specialized APIs, it's going to be a while before reality meets the hype."
}
,
{
"id": "46507167",
"text": "This article seems based in a poorly defined statement. What does \"joining the workforce\" actually mean?\n\nThere are plenty of jobs that have already been pretty much replaced by AI: certain forms of journalism, low-end photoshop work, logo generation, copywriting. What does the OP need to see in order to believe that AI has \"joined the workforce\"?"
}
,
{
"id": "46507396",
"text": "It was from Altman's blog:\n\n> 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\n\"materially change the output of companies\" seems fairly defined and didn't happen in most cases. I guess some kicked out more slop but I don't think that's what he meant."
}
,
{
"id": "46510175",
"text": "TikTok, Youtube, news, blogs, … are getting flooded with AI generated content, I'd call that a pretty substantial \"change in output\".\n\nI 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.\n\nWhenever 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.\n\nA 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.\n\nIn 2025 a lot of AI went from \"useless, but promising\" to \"good enough\"."
}
,
{
"id": "46509380",
"text": "Headline only response: Same reason my toddler children didn't."
}
,
{
"id": "46508543",
"text": "Agentic AI companies are doing millions in revenue. Just because agents haven’t spread to the entire economy yet doesn’t mean they are not useful for relatively complex tasks."
}
,
{
"id": "46509691",
"text": "I'm curious about how they count revenue..."
}
,
{
"id": "46508561",
"text": "Okay but so is like, the 3 store chain which does my cars tires.\n\nMillions in revenue ain't hard to hit with extremely modest business."
}
,
{
"id": "46511243",
"text": "One million companies with a dollar in revenue?"
}
,
{
"id": "46508575",
"text": "And just because people are thowing money at an AI company doesnt mean they have or will ever have a marketable product.\n\nThe #1 product of nearly every AI company is hope, hope that one day they will replace the need to pay real employees. Hope like that allows a company to cut costs and fund dividends ... in the short term. The long term is some other person's problem. (Ill change my mind the day Bill Gates trusts MS copilot with his personal banking details.)"
}
,
{
"id": "46508739",
"text": "When did hacker news become laggard-adopter/consumer-news.\n\nCal 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).\n\nIncredibly naive to think AI isn’t making real difference already (even without/before replacing labor en masse.)\n\nActually 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."
}
,
{
"id": "46509459",
"text": "not much in income tho"
}
,
{
"id": "46509341",
"text": "And we enter the Trough of Discontent..."
}
,
{
"id": "46506149",
"text": "I've seen organizations where 300 of 500 people could effectively be replaced by AI, just by having some of the the remaining 200 orchestrate and manage automation workflows that are trivially within the capabilities of current frontier models.\n\nThere's a whole lot of bullshit jobs and work that will get increasingly and opaquely automated by AI. You won't see jobs go away unless or until organizations deliberately set out to reduce staff. People will use AI throughout the course of their days to get a couple of \"hours\" of tasks done in a few minutes, here and there, throughout the week. I've already seen reports and projects and writing that clearly comes from AI in my own workplace. Right now, very few people know how to recognize and assess the difference between human and AI output, and even fewer how to calibrate work assignments.\n\nSpreadsheet AIs are fantastic, reports and charting have just hit their stride, and a whole lot of people are going to appear to be very productive without putting a whole lot of effort into it. And then one day, when sufficiently knowledgable and aware people make it into management, all sorts of jobs are going to go quietly away, until everything is automated, because it doesn't make sense to pay a human 6 figures what an AI can do for 3 figures in a year.\n\nI'd love to see every manager in the world start charting the Pareto curves for their workplaces, in alongside actual hours worked per employee - work output is going to be very wonky, and the lazy, clever, and ambitious people are all going to be using AI very heavily.\n\nSimilar to this guy: https://news.ycombinator.com/item?id=11850241\n\nhttps://www.reddit.com/r/BestofRedditorUpdates/comments/tm8m...\n\nPart of the problem is that people don't know how to measure work effectively to begin with, let alone in the context of AI chatbots that can effectively do better work than anyone a significant portion of the adult population of the planet.\n\nThe teams that fully embrace it, use the tools openly and transparently, and are able to effectively contrast good and poor use of the tools, will take off."
}
,
{
"id": "46506548",
"text": "> Spreadsheet AI\n\nIf you don't mind, could you please write a few examples of what LLMs do in Spreadsheets? Because that's probably the last place where I would allow LLMs, since they tend to generate random data and spreadsheets being notoriously hard to debug due all the hidden formulas and complex dependencies.\n\nSay you have an accounting workbook with 50 or so sheets with tables depending on each other and they contain very important info like inventory and finances. Just a typical small to medium business setup (big corporations also do it). Now what? Do you allow LLMs to edit files like that directly? Do you verify changes afterwards and how?"
}
,
{
"id": "46507080",
"text": "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."
}
,
{
"id": "46506672",
"text": "It seems like we are using AI to automate the unimportant parts of jobs that we shouldn’t have been doing anyway. Things like endless status reports or emails.\n\nBut from what I’ve seen it just makes that work output even less meaningful—who wants to read AI generated 10 pages that could have been two bullet points?\n\nAnd it doesn’t actually improve productivity because that was never the bottleneck of those jobs anyway. If anything, having some easy rote work is a nice way to break up the pace."
}
,
{
"id": "46508853",
"text": "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.\n\nThere 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.\n\nHopefully everyone realizes what's going on and cuts out the middleman."
}
,
{
"id": "46506334",
"text": "> because it doesn't make sense to pay a human 6 figures what an AI can do for 3 figures in a year.\n\nHumans have one bit over \"AI\": You can't blame and fire \"AI\" when it inevitably goes wrong."
}
,
{
"id": "46506348",
"text": "> I've seen organizations where 300 of 500 people could effectively be replaced by AI, just by having some of the the remaining 200 orchestrate and manage automation workflows that are trivially within the capabilities of current frontier models\n\nCurious, what industries? And what capabilities do LLMs present to automate these positions that previous technologies do not?\n\n'Bullshit jobs' and the potential to automate them are very real, but I think many of them could have been automated long before LLMs, and I don't think the introduction of LLMs is going to solve the bottleneck that prevents jobs like these from being automated."
}
,
{
"id": "46506457",
"text": "What do you think is the bottleneck?"
}
,
{
"id": "46506523",
"text": "The percentage of jobs that are actually bullshit as opposed to the percentage of jobs the person making the claim thinks are bullshit merely because they are not that person's own job.\n\nWhich is, of course, conveniently never a bullshit job but a Very Important One."
}
,
{
"id": "46510012",
"text": "AI doing a bullshit job isn't a productivity increase though; it's at best a cost cut. It would be an even bigger cost cut to remove the bullshit job"
}
,
{
"id": "46506551",
"text": "Which are the best spreadsheet AIs?"
}
,
{
"id": "46506331",
"text": "everyone excited about AI agents doesn’t have to evaluate the actual output they do\n\nVery few people do\n\nso neither Altman, the many CEOs industry wide, Engineering Managers, Software Engineers, “Forward Deployed Engineers” have to actually inspect\n\ntheir demos show good looking output\n\nits just the people in support roles that have to be like “wait a minute, this is very inconsistent”\n\nall while everyone is doing their best not to get replaced\n\nits clanker discrimination and mixed with clanker incompetence"
}
,
{
"id": "46509283",
"text": "\"good looking output\" is exactly the problem. They're all good at good looking output which survives a first glance."
}
,
{
"id": "46510331",
"text": "I predict all house cats will be replaced by robots by 2027. People just do not realise how great of an effect the AI and robotics will have on home pet ownership. However as a CEO of a publicly listed company ”Robot-cats-that-are-totally-like-awesome-and-are-gonna-like-totally-be-as-lovable-as-real-ones Inc.” I am seeing this change from first row seat. We plan to be on the helm of the new pet-robot future, which not furry and cute, but cold and boring.\n\nSources? What, but, you are not a journalist, you are not suppose to challenge what I say, I’m a CEO! No I’m not just using media to create artificial hype to pull investors and make money on bullshit that is never gonna work! How can you say that! It’s a real thing, trust me bro!"
}
,
{
"id": "46506258",
"text": "> But for now, I want to emphasize a broader point: I’m hoping 2026 will be the year we stop caring about what people believe AI might do, and instead start reacting to its real, present capabilities.\n\nyes, 100%\n\nI think that way too often, discussions of the current state of tech get derailed by talking about predictions of future improvements.\n\nhypothetical thought experiment:\n\nI set a New Year's resolution for myself of drinking less alcohol.\n\non New Year's Eve, I get pulled over for driving drunk.\n\nthe officer wants to give me a sobriety test. I respond that I have projected my alcohol consumption will have decreased 80% YoY by Q2 2026.\n\nthe officer is going to smile and nod...and then insist on giving me the sobriety test.\n\ncompare this with a non-hypothetical anecdote:\n\nI was talking with a friend about the environmental impacts of AI, and mentioned the methane turbines in Memphis [0] that are being used to power Elon Musk's MechaHitler slash CSAM generator.\n\nthe friend says \"oh, but they're working on building nuclear power plants for AI datacenters\".\n\nand that's technically true...but it misses the broader point.\n\nif someone lives downwind of that data center, and they have a kid who develops asthma, you can try to tell them \"oh in 5 years it'll be nuclear powered\". and your prediction might be correct...but their kid still has asthma.\n\n0: https://time.com/7308925/elon-musk-memphis-ai-data-center/"
}
,
{
"id": "46507051",
"text": "In December of 2025, I took five tickets I was assigned in Jira and threw them at codex, which just did them, and with the help of MCPs, codex was able to read the ticket, generate some code, test the code, update gitlab, create a merge request on Gitlab, and update the Jira with the MR. CodeRabbit then reviewed the MR before a human had to look at it. It didn't happen in 2025, but I see it happening for 2026."
}
,
{
"id": "46509348",
"text": "the title is bit misleading, and all the article sez is to\n\n```\n\nTo find out more about why 2025 failed to become the Year of the AI Agent, I recommend reading my full New Yorker piece .\n\n```\n\nso essentially, just go and read the new-yorker piece here: https://archive.ph/VQ1fT"
}
,
{
"id": "46506652",
"text": "Once again, more evidence mounts that AI is massively overhyped and limited in usefulness, and once again we will see people making grandiose claims (without evidence of course) and predictions that will inevitably fall flat in the future. We are, of course, perpetually just 3-6 months away from when everything changes.\n\nI think Carmack is right, LLM's are not the route to AGI."
}
,
{
"id": "46505977",
"text": "Pretty ironic that he complains about Kahn citing someone who told him AI agents are capable of replacing 80% of call center employees, right after quoting Gary Marcus of all people, claiming LLMs will never live up to the hype.\n\nIf you want to focus on what AI agents are actually capable of today, the last person I'd pay any attention to is Marcus, who has been wrong about nearly everything related to AI for years, and does nothing but double down."
}
,
{
"id": "46506196",
"text": "What has he been wrong about? He was way ahead of predicting the scaling limitations, llm not making it to agi."
}
]
</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