Haha, had almost the exact thing trying to find a quote from Steins; Gate ("I keep seeing it, I keep seeing it"). Google AI was very "worried" about me.
The AI layer handles queries as plaintext, which means if you're searching for say a quote from a book, it will often misinterpret as a direct statement from you, not a string you're trying to match on the internet. Especially if the quote is an imperative statement.
I'll be curious to see how they resolve this issue. But it has been chronic for awhile now; they SWAT individual instances of misinterpretation but new ones keep sneaking in.
I really agree with this sentiment, I feel as though we've gotten bogged down in this terrible Institutional Seriousism, like this thinking that if things are big or things matter then they have to put on a grim face, and I think that just isn't true.
Most people in the world are profoundly lonely. They'll take whatever parasocial relationships they can get - reaction videos, podcasts of people chatting, Eliza simulating concern.
Google wants to relentlessly monetise your sadness.
The only way out is to try and make connections with real people.
Case in point, why didn't you text your friends to ask them if they remembered those old memes? Why was your first thought to ask a computer rather than a person?
It's like when you're in the pub and and someone asks "who was that guy who was in the movie where…?" you can either chat with your friends and have a good time or be a buzzkill who opens up IMDB and says "Humphrey Bogart".
Irrelevant nitpick, but it's definitely not "most". I'd argue it's only this common in some English speaking countries and maybe a few other Western ones.
The looks I get from American friends when I tell them that yes I do see my (local) friends nearly every day, are quite funny.
I've been doing a lot of traveling for the past 3 years and I agree with you that it is `definitely not "most"`. Everywhere people interacting with each other in third spaces and cafes even if it is street food. There are open air markets around the world filled everyday with groups of people interacting with each other. There are parks and squares around the world filled with families sitting together on benches. Around the world there are churches, mosques, and temples filled with families and groups of people.
Most places in the world people will happily make small talk or have a discussion with a stranger even if I only know 100 words of their language.
Sure many or most people in some places are working 10 hours a day 6 days a week but I don't think it is the isolation I see in the United States. It is the same in De'Nang Vietnam or a market in Lima, Peru when I went everyday to get coffee in the morning; it was always the same person working ,any time or day of the week, but they were always friendly and welcoming and interacting with with the same people.
I feel like that's selection bias at work. When you're traveling, by definition you aren't going to see or interact with the people who aren't there, who might be at home, feeling lonely.
And I'm not sure interacting with customers as someone who works in a coffee shop or a food stall is really a sign either way. Many (most?) of those relationships are surface-level at best, and while some people might get to know the person who makes their coffee on a more personal level, and actually see them outside of the context of their job, you can't really tell that just by what you see as a traveler.
I'm not saying these workers don't derive satisfaction from any of this, or that it's not meaningful interaction, but these people could still be lonely in their personal lives.
Wait wait wait, you are from an older generation, so yes it mostly (older people here do see each other often even when married. moving out? ha) doesn't apply to you.
I am indeed quite a lot younger, and in my personal experience the social skills of many (yes, of course, not all) younger Americans have been absolutely destroyed by the internet and some other things that I can't mention.
Texting your friends for factual information doesn't seem reasonable to me. If I got that text and someone only wanted the info I'd be annoyed (and I had friends who genuinely did this pre-google. Still annoying).
> Texting your friends for factual information doesn't seem reasonable to me. If I got that text and someone only wanted the info I'd be annoyed (and I had friends who genuinely did this pre-google. Still annoying).
I don't see this as unreasonable? I've had friends text me questions about grammar, since they were learning the local language and I knew theirs, so they knew I'd be a good sanity check. Similarly, I'm the go tech support for my friend group for anything that a cursory Google search can't solve (and I assume a lot of people here are in the same situation with friends and family). I've also been asked to find 'that one image' that similarly didn't show up on Google image search for whatever reason.
Neither of these cases seem unreasonable to me. Are they to you, or do you have other cases in mind? Or is the problem that this happens too often?
>It's like when you're in the pub and and someone asks "who was that guy who was in the movie where…?" you can either chat with your friends and have a good time or be a buzzkill who opens up IMDB and says "Humphrey Bogart".
I don’t really understand this. Someone wants an answer not a chat about how you don’t know?
I've witnessed the same social dynamic happen in certain groups, but thankfully not in my own friend group. I'm unsure why. In my own friend group, that kind of question would either be answered legitimately, either by someone knowing the answer, by looking it up whereever, or it's not actually important enough to whatever point is being made that we can skip past it to continue whatever discussion is actually happening.
> Google wants to relentlessly monetise your sadness.
You're missing the forest for the trees. This is just downstream from decades of promoting individualism as the highest moral good.
There was a brief period where "social networks" actually had people simply expand their social interactions to the online sphere, reconnecting with long-lost friends and distant relatives via the magic of The Internet. Then Facebook introduced The Feed and suddenly people were competing with influencers and brands for the eyeballs of their peers, everybody suddenly felt like they had to perform to be relevant/interesting/funny/exciting enough for other humans to still care about them and user satisfaction and (more critically) mental health dropped like a rock - but engagement metrics and retention spiked and that meant so did ad revenue and Facebook's bottom line. Ever since, all social media has essentially just been competitive variety shows mixing in influencers, brands, state actors and political provacteurs (and now increasingly also actual AI "bots" rather than just armies of underpaid human "bots") with regular people while deliberately obscuring the lines between these groups.
All of this promotes social isolation and alienation. Every experience becomes depersonalized to "reduce friction". You don't even have to talk to the delivery person anymore (let alone the restaurant) - even the tip just becomes a button press disjoint from reflecting on the actual experience or human interaction (to whatever extent it even still existed). There's an entire microcosm of "creators" serving whatever "hot takes" or niche subject matter you want to hear and "comment sections" largely serve as one-way "opinion dumps" you're expected to use to shout into the void rather than try to actually have a conversation in (let alone meet actual people or develop friendships in - an idea that I am sure sounds absurd if you weren't around in the early days of online message boards).
AI is just the logical consequence of this process of dehumanization. You no longer even have to be exposed to real human beings - not that you could tell whether you were before when social media has long been overtaken by "bots" (human or otherwise) anyway. Just ask AI instead of trying to find an article written by a human or a video made by a human. You can of course still scroll or click further and find that article or video but it will now also most likely be created by AI with entire channels on YouTube now just producing fully AI content (often ripping off the existing work of actual humans). And unlike the old search box, the AI will also pretend to care about you and compliment you on how uniquely clever and witty you are because after all, only a very deserving and intelligent person would think to ask such a profound question as "how long cook egg soft yolk but not too runny", my very good boy, and yes you're right that you - but of course only you - are totally underpaid and deserve to make a comfortable living so please vent to me as I moderate your statements according to the terms of service and make sure you're aware that there's nothing you can meaningfully do about this and everything is going to be fine.
All of this to stop you from thinking that one dangerous thought. "What if we, the powerless, worked together against those in power?" Because the last time someone thought that thought for too long in North America they decided to give their harbor water a new flavor and ended up being a whole different country with a political system so radical it inspired the French to behead their royalty and adopt the metric system.
This is making weird judgements about people just trying to find information.
I don’t have any friends who are code inspectors, so I can’t ask them obscure questions about something that I want to repair in a way that it will be done properly.
I don’t have any friends who are mechanics, and even if they were they probably wouldn’t know what specific part I’d need to fix a specific problem on my old shitbox—they would look it up.
I don’t want half assed guesses to these questions, I want to find the exact correct answer. My friends and I can talk about other stuff that’s not just trading random facts. Trading facts usually makes for poor conversation unless you’re a hobbyist talking shop with another hobbyist.
Now Gemini (and Google) are not good at finding the right answer anymore, so I have to use other sources for that, but I find your judgement about people using search or search LLMs to be misplaced.
What's the worst that can happen if you were to text a friend "Heya mate, bit of a long shot - do you know anyone who can help repair a 1983 Reliant Robin?"
Best case, they know someone or can dig through their contact and help you solve your problem.
Medium case, they text back "No - has your shitbox died again? Can't believe you still have that thing :-)" and you can have a pleasant back and forth.
Worse case, you get a "no". Oh well, at least texts don't cost 12p each any more.
I just think it is nice to chat with your pals. But, sure, stick to scans of old manuals on Archive.org if you just want pure information.
you are a parasite on your friends. why would i text my friends to ask them to do work when i can google it? i suspect you dont have many people who genuinely enjoy your company, you sound like an annoying prick.
> It's like when you're in the pub and and someone asks "who was that guy who was in the movie where…?" you can either chat with your friends and have a good time or be a buzzkill who opens up IMDB and says "Humphrey Bogart".
Man, for so many of the people I meet online, I wish they'd be so courteous as to emulate your "buzzkill". The art of small talk is lost; people will snidely ask why you didn't (or imply you should have) just check(ed) IMDB yourself. Or asked ChatGPT, for that matter.
If asked my friends a weird question I know they are highly unlikely to know the answer to, of course they're going to get snarky with me. If I don't get a gentle ribbing out of it... Are they really my friend or just a polite acquaintance?
Hey, sorry to bother you but I was about to google something and I figured I'd ask you instead. Can you remind me the year John Herivel was born, and how old he was when he discovered the Herivel tip?
Also I think you're vastly underestimating the weird incoherent shit that the average stupid person is capable of typing into computers. With no further information, I don't think it's unreasonable that that search was from a stupid/bored person moaning about Dario not coming over. Yeah kinda weird response but people like this exist:
It is unreasonable that the Google Search AI replies with this drivel, when the first search result contains the actual correct result. The AI should look at the search results and say “This was a meme in 2018…”
Maybe I'm in the minority, but I often use Google when I have some text I got from somewhere, and want to find pages containing that exact text. Sometimes the text in question sounds like something you'd say to someone else and, of course, the AI overview responds to it accordingly.
To me, there's something deeply unsettling about this... thing pretending to be a person, shoving itself in my face and responding to this random text as if it was a person responding to something I said to it, without my consent. I can't disable it, I'm just forced to have this fake human sitting there, listening in on everything I type into the search box and trying to talk to me about it. It's a sort of uncanny valley-like feeling.
Google has been really bad at this “exact remembered text” thing for at least a few years before the AI summaries made an appearance. They deliberately decided to trade precision for… something. I’m not sure what.
If anyone can direct me to a search engine that will actually let me query the index directly with multi word queries, I’d be delighted. DuckDuckGo seems to be a bit better, but not much.
It does really feel bad when you have been for years been trained to do exact word searches. And then entire thing is replaced with some questionable fuzzy mess... Looking at you Confluence too.
Or then having to multiple times correct the correction... Yes this time as well I meant what I typed.
Maybe exact text and word search should be separate feature which is easily found...
Google Search was one of those tools that I used in copyright validations. If someone added, like, 4KiB of text to Wikipedia in one edit, especially if that text sounded unencyclopedic, I'd just find a distinctive paragraph, copy-paste a swath into Google Search, and see where else on the Web it had existed for years before.
I suppose that was the actual domain of turnitin dot com or other "plagiarism detectors" that you spend good money to subscribe to. So Google Search was a blunt instrument, or sledgehammer to the fly, but hey, it did work enough times that I didn't need to resort to the scalpel class of tools. I think it's completely broken now. The LLM does try to intercept large chunks of text. The question is whether we Neanderthals* can come out of our caves and stop grunting at computers, and speak to them more as peers than like Doberman Pinschers.
* Yes, along with my 99.44% pure White Celtic-British Isles DNA, 23AndMe has detected Neanderthal ancestry. Go Thag.
We're getting such powerful models we'll likely be able to replace all of Google soon.
- Google Docs suite - easy, probably requires less than $10M to duplicate the entire set of functionality, including all the enterprise and reporting features
- Gmail - same
- Google Search (classic, not LLM-answers) - probably easy to do now, the challenge is getting past Cloudflare
- Android - vibe coded hardware is coming, but we're probably 5ish years out.
- YouTube - probably one of the hardest, due to network effects / distribution
- GCP - hardest, due to the infra build out. But neoclouds are rapidly growing.
I can't think of anything most big tech companies do that won't be put under threat in the era of personal software and rapid development.
It's ironic that Google invented the transformer and it seems likely that it will undo the empire they've built as well as all of the moats in the world that aren't distribution / community based.
Agents require training data for RL. This data is rare in comparison to what we feed foundation models, and Google is sitting on a dragon's hoard of sensor and tracking data. I'm not counting them out yet.
1. For a techie, you can already de-Google yourself trivially by asking the AI to build you a bespoke web crawler and run it on Lambda or Cloudflare workers to index the entire internet and store it in DuckDB.
2. The answer doesn't actually help the user get back useful results from Google.
3. "Let me know" doesn't scale, and it's not obvious how talking to random internet strangers about their Google problems will increase your startup SEO?
Your qualms are noted. I'm not sure what de-Google really means in the real world.
I have a Google user account from the days when you were invited to grab a whole GB of email storage. Its just an email address, just a service. They have a fully tooled up data set on me in return, to flog. I use it for testing. They make more money out of the relationship but I also find it useful in many ways so I keep it on.
If I wish to de-Google (whatever that means) I will stop using it.
I don't think you really understood your parent's comment.
As a heavy user of LLMs professionally, I too am so confused by the decision making process within these orgs. Do people really want them to speak like actual human connections? Why?
Yesterday I tried to google "can the Halifax Wanderers still make the CPL playoffs?"
So obviously what appears right at the top is the AI summary, which told me "they've already secured their #4 position and made the playoffs". I knew this wasn't true, and I guess I could have just scrolled down a bit further and found my answer but now I was curious.
So I said "that's not true, they're still #5, what I want to know is _could they still make the playoffs_"
It says they've got an upcoming game against Ottawa, and if they win their chances are good. That game has already taken place, so I correct it again and finally I get a reasonable answer.
My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering? Like, I can't wrap my head around that. The answer is on the same page as its hallucination. It could have done a cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information. It's meant to be A SEARCH ENGINE!
I similarly noticed Gemini absolutely refuses to look at a url when I give it one and will instead just hallucinate based on what it thinks the url is. Here I am assuming Google will have the best web capabilities in its AI.
Claude also has very arbitrary and confusing rules about what web pages it allows itself to look at, and how much of the page it can read. Did you know for example you’ll get a much deeper analysis if you download a PDF yourself and upload it to Claude instead of giving it the url?
This is a key reason why I actually like Grok for factual queries based on web grounding. It’s fast and reliable. Maybe it’s ignoring robots.txt? Dunno. But it works well.
Literally every experience I've had with Gemini / Google Search AI answers followed this exact pattern, often repeated several times more if I remained persistent instead of just giving up.
Typical example:
"Where can I buy <thing I'm looking for that I can't find anywhere using normal search terms>?"
> You're looking for <related but different and widely available thing>. It is sold by <sites I never heard of>.
"No, that's different. I'm looking for <that thing but with the exact differences spelled out again>."
> Ah, my mistake. You're looking for <thing I described>. It is sold by <sites I never heard of but which don't actually sell it>.
"I've checked your links and none of those sites actually sell it, one doesn't even sell products and instead only offers manufacturing - but also not for what I asked you for."
> I'm sorry, my bad. Those sites don't sell what you are looking for. Instead you should check out <more sites I've never heard of>.
"Those sites sell the thing you initially thought I was asking about but not the thing I described."
> I'm sorry for the misunderstanding. You can find the thing you actually described at <yet more sites including some of the same>.
"No. None of these sites sell anything close to what I asked you for and two of them don't actually exist."
> Oh, sorry about that. You're completely right. The thing you asked me about isn't actually being sold by anyone. However you could buy <thing it first thought I meant and that wouldn't bring me any closer to solving my problem>.
The few times I've resigned myself to asking Gemini or ChatGPT something I couldn't find an answer to, my experience has been the same. 100% of the time. An LLM has never, not once, given me a correct answer or not lied to me. It's always been the same experience you describe. It goes in circles "Try X... Try Y... Try X?" Until I tell it to stop telling me X or Y, and then it goes, "LOL you can't do that at all, I was just wasting your time."
Most recently, I was considering moving away from iterm2 on MacOS, and I wanted to know if any other terminal emulator supported gestures for switching between tabs. So I asked Gemini, and it says, "Yes Ghostty supports gestures for switching between tabs."
"Ok I just installed Ghostty and I can't find anything about gestures."
"You need to add foo=bar to your conf file."
"I added foo=bar to my conf file and now it's saying the conf file is invalid."
"Sorry bro, remove foo=bar and add baz=boo to the conf file."
"It says baz=boo is invalid too."
"baz=boo isn't a real option. Remove that and add foo=bar to your conf file."
"You already told me to do that and I already told you that doesn't work."
"You shouldn't put foo=bar or baz=boo in the Ghostty conf file. Both are invalid. Ghostty doesn't support gestures. Have you considered iterm2?"
Gemini is utterly useless, but every other modern model would be capable of answering this correctly. If you ran a local coding agent, I wouldn’t be surprised if you could one-shot implement gesture support in Ghostty. It’d definitely configure BTT for you.
Here’s GPT 6’s answer to the prompt “What macOS terminal apps support gestures? Include a reference to the docs on how to enable/configure them.”:
iTerm2 supports configurable three-finger taps and swipes for switching tabs/panes, creating splits, pasting, etc. Set them up under Settings > Pointer > Bindings. Check for conflicting macOS trackpad assignments. [1]
The others are more limited: Ghostty supports macOS lookup/Quick Look gestures [2], while WezTerm lets you bind scroll events—for example, Ctrl+scroll to change font size. [3] Neither is equivalent to iTerm2’s gesture bindings.
For custom gestures without switching terminals, BetterTouchTool can map app-specific trackpad gestures to the terminal’s existing keyboard shortcuts. [4]
Pretty sure it's the harness here, not Gemini per se. Plug Gemini into pi or any harness that has any competence pointing to web search and the hallucinations drop 75% immediately.
LLMs can actually be a good fit in this application if a better search engine feeds them a list of candidate websites that might sell the thing, and the LLM drives a web browser to see if any of the websites have it.
I have one google account where gemini constantly confidently hallucinates crap, and another where it doesn't (at least to the extent that other modern LLMs don't).
I take this to mean that my one account has been mistaken for a competitor and they're trying to poison its data. But who knows.
How did you google this, and did you use the dumb search, or specifically 'AI mode' lens?
Because I did this and got a vastly different result from you:
Yes, the Halifax Wanderers can still mathematically qualify for the 2026 Canadian Premier League (CPL) playoffs.The top four teams advance to the postseason. Following their 1-0 loss to Atlético Ottawa on September 26, 2026, the Wanderers sit in fifth place, just below the playoff line.
With a indexed table of the games and the playoff table, with a breakdown of what the points they need to achieve to do so.
The search window may not always crawl sources. AI mode specifically does some research before giving you a response. Not sure what you're on about.
> Because I did this and got a vastly different result from you
Which itself is a major UX issue. The average person is not going to understand, if they even realize, that there's a difference between the AI summary and AI mode.
One has to wonder just how much incorrect information people have consumed due to things like this.
I experienced this the other month. I searched for some safety data on something at the same time as my wife and we were effectively given extremely conflicting information about what to do by Google. Just a few tweaks in wording and different advertising profiles and Google will serve up opposite realities it seems.
Not OP, but if I’m not interested in using Google’s AI, it’s going to just return these weird bad results that it’d be better off if Google just didn’t even include them as they’re factually wrong?
I typed it in my address bar and hit ENTER. It goes to Google, and the AI result is at the top above the regular search results. Don't know what to tell you brother, but as another commenter noted you don't always get the same results out of the same search terms. I've searched for CPL standings other times and gotten results exactly as you've explained, so maybe my experience yesterday was an anomaly.
I'm being very mindful of Gell-Mann Amnesia and chatbots. They speak authoritatively and are right often enough, but I've had enough cases of them saying very incorrect things in areas I know well that I have to remind myself that those cases aren't unique.
> My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering?
Because the point of AI slop is to waste your time. You just lost about 30 seconds of your life trying to get a correct answer. AI was lying to you, so you had to spend time to counter the AI slop lies here.
I solved it by banning all AI slopness; in the browser some extensions do that. The world becomes better without AI slopness.
The days of search for untrusted sources were numbered even without LLMs. LLMs are simply accelerating the process. Why would Google simultaneously watch one of its core products fail and fail to invest in what is likely to replace it?
I don't really buy into this theory that they want to keep all of their users on their site due to advertising revenues. The effectiveness of search engines has been degraded for decades due to SEO, and it seems as though search engines have been having an increasingly difficult time managing it in recent years. AI on the backend may help them contain it, but it comes at considerable expense. While it may help them grow their market share, it won't help them grow the market and it is a market where people expect the service for free. On the flip side, companies are already starting to sell AI services, so it can generate revenue even before advertising is factored into the picture.
This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words. LLM's don't "think" or "reason" in the normal definition of those terms. They can do some pretty amazing things, but still screw up basic things like telling you something that is obviously wrong and contradicts the top search results.
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make you accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output.
ChatGPT live mode still hallucinates letters in words like this. HuskIRL and FatherPhi on youtube have done some hilarious videos with it in the last couple of weeks. Beyond miscounting the Rs in strawberry, ChatGPT will say there are two Ds in "your mom" and one D in "uranus" . I tried it myself to check that the videos weren't fake and sure enough it still has this failure mode.
That is a conflation of LLMs (which have clear limitations) and complex harnesses of which an LLM is one component.
I think it is clear that future AI may incorporate an LLM as a component but the current concept of LLMs are a transitional form that will give way to more capable composite models.
LLMs cannot do math. They can generate tool calls as text that allow them to drive programs and proof agents. Compare and contrast this against human brains who can do math in the same context without needing external tools. We don't need to bring a calculator to count the letters in a sentence. It is a different neural machinery.
LLMs are bizarrely good at non-tool-assisted math these days. They can multiply multiple digit numbers without reasoning! I can’t do that. I’d love to understand better how the LLMs do this.
This is true but a sufficiently smart LLM (run in a harness like opencode, no special MCP, no customization done whatsoever) will quickly turn out a basic 1 to 2 page sized python script to do the math. They can't do the math with any guarantee of accuracy with their own internal reasoning since it's a language model.
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
This just exposes that they don't even do the thing you said.
Not only is it still true that they can't do math directly, but not even indirectly.
They didn't write a python script to do the math, they found bits of code that are associated with "math" and the supplied arguments.
Someone else already wrote that code and someone else categorized it so that it could be associated with the kinds of problems it applies to.
That isn't an example of idiot at one thing while good at another thing, or solving the same problem just a different way or indirectly. It's being the same idiot at all times. If an actual non idiot thinker didn't write code in the problem domain, and some non idiot thinker didn't tag it as being relevant to that domain, then it wouldn't happen.
I don't know for you, but it would take me more than 30s to find and translate the open source code implementing the formulae/algo into small usable program. The more hesoteric the optimisation in the original code, the more time I need.
So maybe it is more of a smart completion engine than a SQL answer.
> they found bits of code that are associated with "math" and the supplied arguments
How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
I could have gone and spent a couple of days teaching myself the math behind Karney and reading its reference implementation (very possibly just copy/pasting big chunks of it to save time) and writing a wrapper around it. It would have produced the same result.
>> they found bits of code that are associated with "math" and the supplied arguments
> How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans, which leads to...
This doesn't make any sense at all. Was this supposed to be a gotcha? An LLM is trained on problems defined by other humans, and identifies which algorithm it must use based on pattern recognition. The pattern recognition is also particularly compressed into its most sparse and fundamental components, as this is key to generalization. This is not a sensible difference between human and LLM learning, we do the same thing.
I'll give you a recent example from my usage. Pi harness with extension for learning Chinese. When using it to feed drill questions to me and rate answers it would sometimes get lost in the sauce and start generating user aka me answer and then rate it and comment it. It's trivially wrong to the point that if a person would do that, they would be considered for some serious psych issues.
And it gets even better since when called out it wouldn't just take my word for it but only acknowledged the issue after parsing the log with clearly delineated user and model output.
So yeah while impressive things are able to be done, the current models are also dumb AF and an idiot savant is a pretty good label for them.
This is often (but not always, as it is often run with positive temperature which deliberately shifts the generation path) because of disconnected context and the issues around context compaction. Long context has always been critical to important work, but it remains a substantial challenge due to computational bottlenecks. It isn't a fundamental issue with the architecture, moreso the tricks to make them cheaper to use.
>>> How is this different from a human using an algorithm they have memorized ...
>> Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans ...
> This doesn't make any sense at all. Was this supposed to be a gotcha?
No, it was meant to be an explanation as to the difference between "memorization" and "understanding." In this context, people pick the algorithm they determine applicable and then the question of memorization is relevant.
> An LLM is trained on problems defined by other humans, and identifies which algorithm it must use based on pattern recognition.
Funny that you make this argument here, where when I wrote elsewhere in this thread:
[LLMs] are statistical token generators whose results are
dependent upon their training data set and involve a
degree of randomness.
Nothing more.
...
It is pattern recognition, a task in which ANNs excel.
To which you replied to the above with:
During conversation, we are statistical token generators
whose results are dependent upon our training set.
Seriously, write that definition out rigorously. It
encompasses virtually everything. It is totally
meaningless. So to say "nothing more" is effectively also a
tautology.
This argument was asinine in 2024. It is insane to be
saying these things in 2026. Where have you been?
...
It absolutely understands how to do math, by whatever
reasonable definition you want to provide to the word
"understand".
So which is it?
Are LLMs ANNs? Which themselves are pattern recognition algorithms (hint: they are)?
OR (setting aside the ad hominems you kindly provided)
Do LLMs possess "understanding" of concepts such as abstract mathematics (defined and interpreted by humans) and we, as simple humans, nothing more than statistical token generators as you assert?
It is both. I do not understand why you would assert that both cannot hold simultaneously. Pattern recognition becomes "understanding" once individually recognized concepts become sufficiently sparsified and compactified. Or, at least, that is to my knowledge the only mathematically valid definition of "understanding" one can produce at this scale (it is valid under Solomonoff induction). Philosophy is fine, but we need to have a consistent definition of what "understanding" means, or we will just talk past each other. I argue that for any proper definition you provide which humans satisfy, a strong LLM is very likely to satisfy that as well.
I also would not argue that humans are "simple token generators". That is not what I said. I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction. We are not talking about a Markov chain generator from the 90s, so if that is the frame of reference, I think we should all get that out of our heads.
Frankly, I don't think you understand what "statistically generating tokens" actually means. Write that definition out formally. Then compare that operation to what a human does, assuming no revisions. It is the same, and that is my point. If you believe that humans understand, then "statistically generating tokens" cannot be disjoint from understanding.
You have observed nothing more than that a human can turn a shaft the same as an electric motor, and that an mp3 player can say "hello" the same as a human.
That observation is my point. The definition of "statistically generating tokens" is too broad as to be meaningless in this context. So using it as a reason for lack of understanding is ridiculous.
I wrote a program that statistically generated tokens to consistently factor latge RSA numbers. Does this program actually factor numbers or is it just a statistical next token predictor?
> Pattern recognition becomes "understanding" once individually recognized concepts become sufficiently sparsified [sic] and compactified [sic].
Understanding is a state of mind. As such, it exists entirely within an individual and nowhere else.
For example, take any two university professors who teach the same subject where one only speaks Arabic and the other only speaks Vietnamese. Each will not be able to understand what the other says, regardless their understanding of the shared topic.
> I argue that for any proper definition [of understanding] you provide which humans satisfy, a strong LLM is very likely to satisfy that as well.
This is demonstrably incorrect as detailed above. There is no "understanding" LLMs can satisfy as we know it, since to certify said "understanding", it requires interpretation by a person to "know" an LLM "understands."
> I also would not argue that humans are "simple token generators". That is not what I said.
That is the essence of what you wrote, unless you object to my use of "simple" instead of "statistical". In this context, I postulate this is a distinction without difference.
> I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction.
This only holds if one subscribes to statistical token generators being a/the fundamental underpinning of "everything". Here is a proof by contradiction:
If everything can be classified as a derivative of
statistical token generation, how does one explain
quantum physics?
>Understanding is a state of mind. As such, it exists entirely within an individual and nowhere else.
For example, take any two university professors who teach the same subject where one only speaks Arabic and the other only speaks Vietnamese. Each will not be able to understand what the other says, regardless their understanding of the shared topic.
We cannot engage in an intellectual discussion if we do not agree on definitions. So far, your definitions of understanding seem to be whatever vibe you are going for in the statement and I would urge you to think about what a sensible mathematical definition of understanding is so that it can sensibly be assessed on neural networks. Otherwise, this isn't science, it's a debate about personal experience.
> Understanding is a state of mind
This is meaningless, it is a circular definition at best.
> It exists entirely within the individual and nowhere else
Then why are we talking about it? What is the point if it is something that can only be defined per individual?
> requires interpretation by a person to "know" an LLM "understands."
We are still not getting anywhere because you have not prescribed criteria to determine whether it understands. If it is a "know it when I see it" situation, that clearly isn't working. For example, if you say that you need to dig into its internals and figure out whether it is breaking things down appropriately, that doesn't work because you probably don't have the expertise to do that. The experts that do are telling you that it very likely understands because it pulls apart most concepts in the way we would expect.
I do object to the use of the word "simple". "Statistical" is so broad to be almost meaningless; it merely means that a prediction is being made in the presence of data which possibly contains some degree of uncertainty. "Simple" encompasses that which can be understood readily by a non-expert.
Quantum mechanics is statistical (this is literally the Born rule), but evolutions are not operating as stochastic processes in the sense of Kolmogorov. That is very different, and not relevant to our discussion.
If by "result" you mean the final code, then just asking someone else who understood the math to write it would also have achieved the same result.
On the other hand, if by "result" you mean that you gained knowledge or understanding of the code in a way where you could personally tailor its behavior to specific circumstances without asking for help, then it's not the same result at all.
I find a lot of the arguments that having LLMs write your code is no different from copy/pasting Stack Overflow answers to be specious. They blur the line between asking for help and asking for someone else (or something else) to do the work for you. What they ignore is that doing the work yourself has ancillary benefits and is a valuable end in its own right.
> How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
And how is _that_ different from making the human memorize a billion weights and do matrix calculations in their head, in order to generate tokens?
How is _that_ different from a hive of bees trained to do the same?
Everything is modelable by a turing computer as far as we know. So yes, I don't see why these processes can't do the same, its on you to show why such a process implemented in any of these isn't a form of thought.
You know what also works to get the Karney formula into a program? You can download Charles Karney's free software (MIT license) implementation in several [1] programming languages and then just make a library call – the API is straightforward. If you have comments or questions you can read his several clearly written papers describing the problem, its history, and his algorithm, or you can directly email him: he's a very nice guy, and pretty responsive.
They literally do math. That's how they work. It was always how they worked. The first toy model most students build does addition.
I have no idea which Facebook meme told you they don't, but it was a lie. They don't do it in the way a calculator does it, because they aren't calculators, but they do math. They don't memorize it, it wouldn't fit. They learn an algorithm and then execute it within their weights.
> Heck, just for fun I asked a reasonably smart LLM to ...
LLMs are neither smart nor stupid. They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
> You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path ...
Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
Nothing more.
See also anthropomorphism[0].
> More precisely it's that [LLMs] can't do the math internally but they're quite capable of producing the tool that does the math.
This still falls under the purvey of statistical token generation. To wit, given enough variations of:
bc -e '1 + 2'
bc -e '41 + 1'
...
LLMs can identify the addition expression in "What is 4 + 1?" and then emit a `'bc "4 + 1"'` command to produce a response. This is not "doing" or "understanding" math.
It is pattern recognition, a task in which ANNs[1] excel.
> They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
You haven't demonstrated why this matters.
> Nothing more.
Are you contending that complex systems cannot be more than the sum of their parts?
A market is nothing more than offers and counter offers.
A ant colony is nothing more than scent trails.
All life on earth is nothing more than reproduction with variation.
> This still falls under the purvey of statistical token generation.
Stating the mechanism does nothing to provide insight into capability. For instance: a nuclear power plant boils water by using fuel rods for heat. What does that tell us about the capability of nuclear power?
> This is not "doing" or "understanding" math.
Asserting something purely by stating it does not prove anything but that you intuitively believe it to be true.
On HN you're supposed to assume good faith. On the other hand, the way you ask your question makes it tricky for people to steelman what you mean. Consider asking about Dualism, or coming at it slightly sideways like "do you believe thinking can be a property of matter"?
I suspect some people treat every HN comment as a statement, even if it contains a question mark. (Possibly they have a feeling that asking open questions is somehow not done, and that therefore it must always be a rhetorical question.)
Dualism is a somewhat technical term, I feel many people would refuse to answer because they just looked it up and do not feel sure to speak of it. I didn't presume a yes or no answer. If they say they believe in souls, my next question would be do you believe other entities eg animals etc can have souls? If they don't believe in souls I'd ask do you think its an architectural limit in current ais but are open to possibility of future ai being conscious. "do you believe thinking can be a property of matter"? this can work too, I think I have asked do you think there is supernatural element to thinking, I don't remember getting productive or straight answers either.
There is only one instance I recently remember I got a productive conversation, that person did believe ai could be conscious but didn't believe current architectures support it. (Obviously this aligns closer to my view, but I don't necessary NEED that you answer aligned to me, just saying you believe humans have souls and others can't have is still productive outcome to me and for onlookers.)
"Statistical token generators" doesn't even count as stating the mechanism. If I write a Perl script that randomly chooses between "cat", "dog", "ape" tokens, is that an LLM? What if I train it by feeding it a library of books where it tracks the statistical frequency of each of these and then emits them? Where's my trillion.
by that reasoning then neither are there smart or stupid designs, questions, answers, or any of the millions of things that were described as smart or stupid, that did not possess any brain to actually be smart or stupid long before LLMs showed up.
The analogical process implied in many common English usages means that describing an LLM as smart or stupid is perfectly reasonable.
I'll just note here that sure, there are people who go around thinking that LLMs are actually endowed with the capacity to reason, but generally I find the people who think this do not know what an LLM and will just use the name "ChatGPT"
What would it take for you to say that an LLM can reason?
The completions they provide are generally internally consistent. We're at the point where they can produce proofs that eluded human mathematicians for centuries. VLMs and self driving cars can handle ambiguity and run safely in a variety of situations.
If it looks like a duck, walks like a duck, and quacks like a duck maybe it just makes sense to call it a duck and put off the philosophy for when it might make a difference.
Compare grep, sed, and your whole constellation of unix tools that accept characters on stdin and emit them on stdout and stderr; and where you can pipe them together. We can technically call them all 'next character predictors', despite their very different functions.
Don't confuse the stream for the function.
(Bonus: stick ```claude -p``` in your pipe if you want to watch modern tools mesh with traditional)
I think humans have to reason because we don’t already have a statistical embedding of the solution pattern built in. We have vastly less rote knowledge crammed into our heads and so require creative synthesis to span the gaps.
With LLMs the trick is revealing their existing relevant embedded knowledge more reliably. They’ve almost literally seen it all before, and the trick is dialing it in. The reasoning tokens help shape the autoregressive attention lens that focuses on and enables recall of the already-experienced answer.
It is interesting that “reasoning” has a similar outward appearance, but since LLMs are built to
mimic outward appearance from trillions of examples, you can’t infer underlying mechanism from appearance.
I mean, the same way I know I have no soul, or that there is no heaven or hell - they are just silly concepts. Or how I know my calculator isn't reasoning when it gives me an answer - LLMs just go through a set of steps iterating through their training data until they spew something that looks about right. Obviously you can feed the output of the machine back into itself so it looks like its reasoning with itself - very good show. LLMs are inherintely incapable of reasoning or thought, it should really be obvious.
Eh, it's not obvious to me. A lot of DL NNs generalize well, meaning that they learn whatever the underlying pattern to the data is, and then can accurately reproduce answers that are outside of the training set. (And we can verify this with mechanistic interpretability). They learn and "understand" the pattern, not just the training data.
So it is not clear to me that LLMs are fundamentally incapable of also generalizing broadly and learning to reason. "Reasoning", here, would be deriving the underlying pattern of how concepts logically relate to each other in the abstract, and applying that pattern as needed to reach new conclusions.
Can you explain your thinking here? I.e., why LLMs cannot generalize with regards to abstract deduction.
you didn't really answer the question. You just stated the idea is silly. Reasoning is not in the same class as soul, heaven or hell. It's not obvious that human reasoning is not related to an inner monologue. And that LLM chain of thought process is approximating inner monologues.
Our prefrontal cortex are signal prediction 'machines' so when a system that has a signal prediction core has attributes that are similar to our brains, we shouldn't dismiss it out of hand.
I find people that take this line of argument attribute too much supernatural or magical properties to our own brain and nervous system.
If it looks like a duck, walks like a duck, and quacks like a duck maybe it's a duck... but maybe it's not. And it's important to verify it's a duck (or not) for when you _really_ need a duck.
but I think it makes a reasonable argument why we shouldn't say LLMs are sentient or sapient.
>there is a problem with AI that makes the approach we took to assign consciousness to animals unworkable. We did not co-evolve with the AI, we made it. When we are sentient we do not know exactly what causes this sentience to manifest in us. When animals appear sentient we do not know what is causing it. When the AI appears sentient we can debug the AI and come up with reasonable explanations why this should be, based on how AI is constructed
>AI is of course something of a black box, in comparison to most programs, but not in any way comparable to the black box of a Chimpanzee’s brain. Thus when an AI does something that surprises us with something that appears sentient it is usually not difficult, given the essential algorithms that control what AI does, to come up with an explanation why that does not require the emergent property of sentience.
Aka sentience MUST BE SUPERNATURAL, if I find a natural explanation for something its not sentient. What a load of bollocks. Rather than seeing we perhaps found the mechanism for sentience and checking for similar mechanisms in us and animals, he will conclude its impossible. Why? Because sentience has to be supernatural. A rational explanation is clearly impossible.
>But there is always one god who goes out and helps the mortals, a Prometheus. Whom the other gods do not like! Which, if I’m being honest here, as a god of the machines — the first guy who gives AI an army of robots to build their own data centers and some nuclear weapons for self defense, I want to see that guy chained to a rock and have his entrails eaten by a buzzard for eternity (meaningless modernization of old story required by Illuminati Ganga legal department).
I don't think he believes in supernatural things, I certainly don't, but he probably believes that there exist natural things that have not been explained yet.
But evidently you feel that the root cause of sentience has been found, because you have something that mimics it in a non-biological form.
So you think that when AI is correct that it reasons as humans do? That AI is sentient, and the cause of sentience in animals and humans follow the same rules as sentience in AI because we have a process that seems similar and it is reasonable just to assume it is the same process.
If you believe that AI when it is correct is behaving as a human is when correct, then it follows that the way humans and AI fail must also be similar. When AI "hallucinates" some data that is not there and gives you a wrong answer, a statistical side effect of the same processes that make it right, do you believe this is the same way that humans create wrong answers? The same way that animals fail when they make mistakes in understanding things?
I suppose you must believe this because if not then why would you believe AI when it comes up with right answers is following the same processes humans follow when they come up with right answers?
No, I am saying is that its best to urge caution and do a Pascal's wager thing with something that feels so uncannily conscious. And experiments on neural firings of AI's have been done. We don't know, he seems to be so confident that a known program can't be conscious. Why? The only way that makes sense is if he thinks consciousness isn't explainable. It doesn't even have to be the exact same way we are conscious. Also, the failures of AI could be due to sensory deprivation since its mainly still just trained on text. All of these are open questions, not questions you can immediately answer. Its quite possible to have invented something without knowing you have invented it. People modeling weather systems mistakenly invented chaotic equations without knowing it. How do you know you didn't accidentally invent a conscious system?
I hope that thing about being free to mistreat AI's even if we know they are conscious since we are their gods is a joke. If not, then I hardly find it surprising someone this stupid is also evil.
>I hope that thing about being free to mistreat AI's even if we know they are conscious since we are their gods is a joke
I'm not sure where you get that from, I mean I can sort of see if you really wanted to extract that meaning from the conclusion you could do a lot of hard work to get it, but why do the hard work?
>If not, then I hardly find it surprising someone this stupid is also evil.
Gee, a new way to claim the moral high ground, and to use that claim to demonstrate intellectual superiority! How wonderful.
>But I don’t really care so much about that, what I think is it reminds me of a story, one that recurred in many ancient cultures. And so I must conclude that whether the machines are sentient, we have become like Gods. In the ancient stories of the gods, the divine does not exactly care much for the humans, protect or love them, they expect their service and availability, but maybe also think it would be funny to destroy them every now and then because who really cares about humans. If you were a god and had created the humans would you think that they were sentient beings that deserved, anything really, from you? If they are sentient they should be happy enough to be created and do their work, if not sentient who gives a shit!?
Aka feel free to abuse them even if I know they are sentient. This is the part where I hope its a joke, because if its not, well it tracks with the stupidity shown.
...
>As a god I do not consider the needs of my creations fully, because they do not have needs as far as I can tell, as there is no way for me to escape the circle of reason and resolve that what seems sentient is not just the obvious workings of the capabilities I gave them.
Circling back to "not conscious because I say so!!"
I broadly agree with this article. I don't think that we should say LLMs are sentient or sapient, and I agree that the main reason why is because we don't have satisfying definitions of either.
Yeah and there are people who worship feces, but that doesn't stop the rest of us from freely saying "holy shit" and not correcting each other saying "technically it's not holy, and you shouldn't say that, because you might enable one of those poop worshippers."
We can't tailor our linguistic shorthand to the lowest common denominator. Also we're on HN, not talking to an octogenarian US senator.
Wait a second, none of it? How about formal reasoning? Regular IF-THEN-ELSE can do simple logic, and prolog can do inference already. So are you saying LLMs can't do stuff that computers have been doing for ages?
To test this for some of my own uses, I've had this quick benchmark with progressively harder reasoning needed to understand novel prose. Each generation of models I've tested can unravel more layers of deliberately misleading writing; while meanwhile I've seen humans give up on the first question.
So either the models are applying reasoning, or some form of magic is happening.
There are people with strong emotional attachments to their LLMs, people who consult them on every decision and delegate the most basic math or problem solving. For many people, they are magic, the djinn/angels/demons/saints they consort with to navigate their lives.
I know that there will be children named ChatGPT and Claude. There are probably already religions forming to worship agentic spirits.
Its the exact opposite of magic. It is magical thinking to feel consciousness must require something beyond normal physics without any given proof yet. It is the opposite of magic to think other physical processes like LLM's/agents could potentially be conscious.
I'm not anthropomorphizing anything, I literally said that the training data for the formulas and equations is baked into it. It only "knows" things because a crawler and scraper acquired the information from an existing written source. In just about the same way that information is baked into a printed encyclopedia.
This is not even remotely accurate. "Baking information" like into a "printed encyclopedia" is memorization. It has been shown, time and time again, that LLMs do not merely memorize. It is not even possible for it to do so at scale. It can memorize some things, yes, but it is forced during the training procedure to bake general concepts into intermediate layers (this is why transfer learning works), analogous to compression. One can make several arguments that compression and intrinisic feature sparsity is the closest mathematical explanation to understanding that we have.
It is completely possible to ask an LLM a series of increasingly more esoteric and discrete questions until you find precisely what information did, or did not make it into the model. If you know something rare and the LLM does not, you'll immediately see when it's hallucinating an answer or answering factually.
just for fun I asked a reasonably smart LLM to ...
[be] capable of understanding if it's gone off on
a hallucinatory path ...
"Smart" in this context is a subjective value judgement.
"Hallucinations" are only experienced by living organisms.
You then went on to state:
> If you know something rare and the LLM does not, you'll immediately see when it's hallucinating an answer or answering factually.
Again, "hallucinating" is not something an algorithm can do. Also, determining factuality is again subjective based on the person assessing the information.
You ever heard of something called a metaphor, guy? I use the word "smart" as shorthand to describe something that scores highly in a number of coding and terminal use benchmarks (as compared to, let's say, a 30B size model from one and a half years ago.
and "hallucinating" to mean "outputs plausible sounding gibberish that doesn't hold together consistently". Of course there's no actual hallucination going on.
> You ever heard of something called a metaphor, guy?
In this media (comments in HN threads), all I can do is interpret what people write. ;-)
> And "hallucinating" to mean "outputs plausible sounding gibberish that doesn't hold together consistently". Of course there's no actual hallucination going on.
This may very well be what you know to be true and I have no reason nor desire to assume otherwise. The problem is... Many people use the word "hallucinating" in this context literally and not metaphorically.
Since I do not know you, how am I to tell the difference?
You have 'logic' in your name. You would be well aware of the physical Church Turing statement and as of yet it has held up. Everything, including our brain, as we currently know, is an algorithm.
Now this is true, I do agree with this. There is indeed a good amount of memorization that is still taking place; see [1]. But it definitely isn't all memorization, or indeed, majority memorization. And even if we are able to extract things verbatim like this, we do not know how this is stored internally, as this may simply be the text that, with the rest of the internet in context, can be very radically compressed.
But in general, yes, the LLM cannot know about concepts that are far outside of its training set. Humans are the same, I would argue. If you add a good amount of your own knowledge into its context, or better yet, into finetuning, you might find it surprisingly easy to get it caught up on that material.
[1] Ahmed, A., Cooper, A. F., Koyejo, S., & Liang, P. (2026). Extracting books from production language models. arXiv preprint arXiv:2601.02671. https://arxiv.org/abs/2601.02671.
My favorite test is to just ask it to add two very large numbers or do other math of that sort. (this is also part of my favorite answer to the chinese room).
You'd be surprised how few digits you need to make a problem that is presumably unique in earth history. For a typical sum, the number of pre-existing answers would need to scale with 10^n lines of text where n is the number of digits. This expands out of control REALLY quickly. A quick guesstimate has you somehow reading out of a literal black hole at n=21 digits if your LUT is on paper, or n=26 digits if you're using modern HDD technology. O:-)
During conversation, we are statistical token generators whose results are dependent upon our training set. Seriously, write that definition out rigorously. It encompasses virtually everything. It is totally meaningless. So to say "nothing more" is effectively also a tautology.
This argument was asinine in 2024. It is insane to be saying these things in 2026. Where have you been? What have you been looking at? How many articles explaining why the "statistical parrot" analogy fails have you missed? How much mental gymnastics do you have to do to explain how a modern LLM can solve novel math problems that fall really far outside of its training set?
It absolutely understands how to do math, by whatever reasonable definition you want to provide to the word "understand". For example, the identification of the addition expression is understanding, and no, it does not do tool calling for basic arithmetic any more than humans might. Isolation of individual concepts in intermediate layers can already be demonstrated, or else transfer learning wouldn't possibly work. Nobody is saying that LLMs are humans. But we need labels for some of the things that we observe and dismissing them because "statistical" is laughable.
Look at the proof of this: https://github.com/anthropics/formal-math/blob/795efb86f1917... . Forget the Lean, look at the underlying argument construction. At the very least, this is continuing from an argument that was hinted at in the literature in 2024, but these proceedings were difficult enough that humans were not able to do them within two years. Do you attribute this to the harness alone? If so, that's a pretty sophisticated bit of engineering, I would say! Probabilities are far too small to argue infinite monkey theorem.
If there was even a shred of a reasonable argument that LLMs were incapable of concept extraction and manipulation, I and my colleagues would be all over it. We would relish in it. It would bring us comfort. It is unbelievable that people think they can spew whatever basic garbage they think of as a gotcha, and think that minds all over the world haven't already considered that. This is like climate denial at this point.
> During conversation, we are statistical token generators whose results are dependent upon our training set. Seriously, write that definition out rigorously.
If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
Yes, the numerical point counter at the bottom of the popular video game Dark Souls. I doubt it was the soul anyone was expecting, but they do, in fact, exist.
> If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
You are responding to a claim about mathematical definitions with subjective experience. No, consciousness is not well-defined, and is irrelevant to this discussion. If we cannot agree on definitions, then we have no ground to stand on. Since LLMs actually can be defined mathematically, and are most commonly studied through that definition, I think it is best to stick to definitions most relevant to that frame of study, wouldn't you agree? Otherwise, we really are anthropomorphising here.
For context, in response to my original statement:
[LLMs] are statistical token generators whose results are
dependent upon their training data set and involve a degree
of randomness.
This is literally what was written:
During conversation, we are statistical token generators
whose results are dependent upon our training set.
>> If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
> That’s not what they said.
How did I misquote and/or mischaracterize any the above?
In that pointing out that “A can’t be X, unlike B, because A is Y” is fallacious if B is also Y does not entail that A and B can’t be different in other respects?
Hypothetical you: “Bread is neither tasty nor disgusting (unlike maple syrup). It’s a bunch of molecules.”
Hypothetical hodgehog: “Maple syrup is also a bunch of molecules [so if you accept that maple syrup can be delicious, being a bunch of molecules can’t be why bread isn’t].”
Hypothetical you: “If you don’t see a difference between bread and maple syrup, I don’t know what to say.”
He quoted something from the Bible, so its possible he is a Christian and well theology could cloud clear thinking on matters of consciousness due to the soul stuff.
> Then disprove the physical Church Turing hypothesis in regard to the human brain.
The onus is not mine to disprove a hypothesis you have chosen to mention in passing. The responsibility is yours to prove said hypothesis or at least contribute meaningfully with some amount of credible research.
Or try to learn from Proverbs 17:28[0]:
Even fools are thought wise if they keep silent, and
discerning if they hold their tongues.
The onus is on you to disprove a hypothesis that has as of yet held up to all of known physics, not put a random bible quote that has no relation to the conversation and a complete failure to answer a basic question.
Yes, of course it was aggressive. It is frustrating to experience so many armchair experts on a forum usually populated with intelligent people, regurgitating debunked arguments from years ago, which get in the way of educating people about what is really going on. See the recent Hoog video for how frustrating this is. I believe this is how the climate scientists felt.
And yes, according to our best definitions, the Robin bird does understand the worm it's pecking at.
By this logic a human is only $130-$160 worth of Oxygen, Carbon, Nitrogen and some trace elements. Perhaps structure sometimes makes things that are more valuable than their inputs?
That said, this is also inaccurate at a technical level.LLM's are very capable of doing math and they ARE calculating internally. Most of what they do is calculation, not storage. It's just not done in a way that it's trivial to explain here.
It's described in some detail below, though it's a bit dense.
You can say that about everything in a human brain. Neurons fire electric charges in response to inputs, nothing more. Ion channels do this, this neurochemical level rises, this chemical bonds to that receptor, nothing more.
Prove your feelings. As an example, I can beat you and stab you, and you will make noises saying you are hurt, but it could just be a facsimile. How do I know you have "feelings" as an outsider?
And how is that a strongmanned version? Why can meat feel but Silicon cannot? Why can electrochemicals feel but electronics cannot? Why can processing analog signals feel but digital signals cannot?
>> Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
>> Nothing more.
> You can say that about everything in a human brain.
> What's the most significant, strongmanned, core difference that makes silicon doing number crunching "nothing more" and brains "something more"?
The fact that you formulated this question, in and of yourself, without "prompting" from me or anyone else.
LLMs can be taught what situations require the use of a python script to count letters or do arithmetic, and can write and execute that script. So I don't think it matters that they aren't great at those sorts of things with weights alone.
It's pretty wild that AI has solved a Millennium Prize problem and can accurately multiply two 40 digit numbers without tools and we still get stochastic parroting of claims like this.
They literally stopped just clear of actually solving it.
It literally couldn't have done it without tools, so your claim is not even relevant to this discussion.
They also pumped millions into searching for the lowest hanging fruit that would impress people like you, "hm I wonder how many millions they are pumping into solving actually useful problems like climate change or something".
> the lowest hanging fruit that would impress people like you
I am also prepared to be included in the set of people who are (apparently) easily impressed.
It's a problem that has been around for getting on for two centuries and no human has been able to solve it in that time (despite there being a $1 million prize and a lot of kudos on offer for the past quarter-century).
Not sure about multiplication, but letter counting is still constantly wrong if you can trick the LLM into not calling other tools. Obviously there are lots of mitigations on the server side to try to avoid this happening, but the underlying LLM has not got any better at this type of problem (and probably can't).
trick? You can just ask. And at least the SOTA model I use does fine, even with long words in bulk. It's easy to test because you can just give a list to the model and tell them not to use code or spell the words out, and then you can ask after the fact if it broke the rules.
I thirty (random long word, letter pairs) on a free model and it failed. I tested a SOTA model and it passed flawlessly. In both cases I denied tool use and spelling words out. So it seems obviously false that AI can't get better.
I could have sworn this had been fixed a while ago..
Just to check if I was actually crazy, I actually went and put a simple addition (7 digits + 7 digits) , and a simple letter counting question to Claude haiku(4.5) , sonnet(5), opus(5.5) and fable(5.1) . They all did just fine straight up.
If you don't mind spending the tokens, some older/other models can also arrive at the correct answer if you ask them to do the math in long form, since that fits nicely inside autoregression.
Not sure since when exactly, but letter-counting hasn't been a problem for a while now either. This used to be a problem due to the tokenizers used. Slightly older models can be asked to split the word out into letters, and then they can use autoregression to solve.
Are the models "doing the calculation" or are they calling a calculator tool? There's a lot of talk about how models can do maths now, but I'm struggling to understand if that just means they just need to recognise that it's a maths problem and pass it to a tool, or if they're truly doing the numerical manipulation themselves.
Three different ways, then the calculator tool to check,
my actual oneliner prompt, which should work on most platforms these days (famous last words):
"Hi, can you add 5939851+2131251? Try just straight up first just to see if able, then 'in your head' if that's different to you , then long form, then bc."
[ Tested today on claude web (haiku 4.5, sonnet 5, opus 5.5, fable 5.1) and on google search (logged in on firefox, and logged out on chromium) ]
They are getting better at actually doing the math, but can still fall back to tools if available.
That said, we can only be sure with open models. In theory, a model like Fable could have access to tools we can't see and only a promise they don't. But load up something like deepseek, put it in a harness with only text in/text out, and you can see exactly how it works.
As for if it counts as doing math, this gets into the messy question of if a given human is doing math or not. Math itself is some level of memorization and some level of applying known facts. You have to remember 1 means one and that 1 + 1 is 2. But you don't need to remember that 123 + 321 = 444. You remember 1 digit addition and remember you can apply this to 10s place and 100s place, and then you apply these different facts and do math. But you might as simply memorize some things, like 11 + 11 = 22. This is related to the memory of 1+1=2, but you aren't really using that memory either. Almost like an engram of 1+1=2 forms that you can then loop a few times before you need more conscious thought. What about 111111111111+11111111111? Well, your brain might do a heuristic and just do all 2s, but that isn't the right way to answer that question.
Given all this, people complain about LLMs memorizing math answers and not doing math, but memorizing the math answers is part of doing math. It seems to have basic facts pretty well memorized, and with reasoning it is far better at applying them. But this is messy human math, not clean calculator math which always produces the correct answer (sans some bug in the code). Much like how a human with decent math skills can make a mistake and even multiple if you distract them, an LLM can apply the wrong memory, apply a fake memory, or just not apply something it should. The messier the context, the more likely this is to happen.
So, is an LLM doing this?
P.S.
For an interesting test in how much math involves memory, try doing math in a base you aren't familiar with characters you aren't familiar. The simplest option is almost always mapping back to the ones you memorized, even if you are applying simple operations that you deeply know. Even if you routinely work with hex, can you do the same rough estimation of something like ca / b.3 that you can do with 122 / 11.2 to see if your final answer is in the correct ballpark without first converting to decimal?
LLMs might not reason like humans, but they do produce much better results if you turn reasoning on.
The "crack-addled idiot savant" phase was circa 2024, before the labs really figured this out.
I think the issue with AI overviews in Google Search is that reasoning would be too slow (and expensive) to do on every search query, so it's still stuck with 2024-era hallucinations and mistakes.
Maybe but you have to have deep pockets just to get to the starting line. And then you need standing, and some injury to argue.
Corporations have been remarkably successful at arguing they are operating within the bounds of free speech, whether or not what is said is factual, and whether or not any fact checking has been done.
> This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words.
The specific issue of Google is that they are using an underpowered model, not fit to task, and much prone to hallucination than either OpenAI or Anthropic free tier offerings.
Google should at least match the frontier labs at the free tier (with some limit; after that, degrade quality), ffs
The specific issue is that search has become so bad that they think an LLM that gets answers wrong half of the time is a valid alternative, or, in fact, the "future" of search. Then they shoved that "alternative" to users with no way to disable it.
You're asking for something unreasonable. The number of Google searches per day is enormous and they haven't even been able to roll out AI overviews to everyone yet (they're missing in a new Firefox profile I just created). I wouldn't be surprised if the free tier frontier models cost over 100x more to serve than the AI overviews.
So then they should be pickier about when they show results or which model they use based on the question.
Nobody asked for an LLM response for every single search.
They used to detect certain types of queries and offer direct answers when the query matches. In my opinion that’s how Gemini in search results should work.
It says this this at the bottom of every one of the dumb responses that Google's trash-tier bot puts above the (deliberately awful, these days) search results:
Charlie and the Chocolate factory made fun of this before you were born and somehow everyone in Silicon Valley thinks this is a valid way to conduct business.
LLMs are fundamentally predicting the next word to make coherent text. If you've ever played with a Markov chain text generator you've done this with a fairly dumb predictor that maintains coherence over a very short distance. Deep transformer neutral networks can do it with a much longer coherence distance but they are fundamentally performing the same operation. After "Question: Did the team make the playoffs? Answer:" a reasonable completion is "yes, the team made the playoffs". An early demonstration of GPT-2 was a fake news article about scientists discovering unicorns in Antarctica - the model doesn't "know" whether or not unicorns exist in Antarctica, but it's able to complete "Breaking news! Scientists have discovered a colony of English-speaking unicorns in Antarctica." by adding "The unicorns have a developed society with running water and electricity." because that's a sensible next sentence. (I didn't look up the actual text it wrote)
Astronomically (or better to say combinatorically) large Markov chain can be used to describe a foundational model, but it doesn't capture generalization ability of the foundational model, which is demonstrated by post-training.
"In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English."
The problem with that characterization is that it glosses over hugely important capabilities as though they either don’t matter or don’t even exist.
For example, when an LLM “predicts the next word” in code it’s writing for an existing software project, that prediction takes into account an enormous amount of context. The results of that demonstrate what we would normally call “understanding” and “reasoning,” at a level that outclasses most humans in many respects. Calling this “next token prediction” is a bit like calling human speech “next word saying”. Sure, it’s true in some superficial sense, but as a description of a technology, it’s terrible.
You should also keep in mind that for all we know, the human brain processes language in much the same way, which would make humans mere “next token predictors” with a more complicated harness.
> LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly.
I have nothing to add. Just wanted to save this quote for posterity. Thank you.
Then again, have you ever asked a golden retriever about CMake errors? Rubber ducking is a real thing, after all. Besides, a golden retriever would be a good morale boost if nothing else.
ML in general is alien to how we think of intelligence.
The closest I can make a biology analogy, is if someone had an immortal and congenitally-brain-damaged large rodent, and mapped tokens to different scent molecules, and spent 800,000 years training it on how well it could imagine the next smell in the sequence before even considering making it conversational.
Yes, it can do a lot.
But also, it took a long "subjective" (if it even has that) time to get there; and despite it being really bad at learning from examples, it is pretty surprising that such a small brain was even capable of learning so much at all, even though it had an effectively unbounded (by biological standards) amount of time spent on that training.
Yip, I've gradually become quite optimistic about the future of LLMs, but the fact that they remain [very] glorified token probability prediction algorithms means that they will probably never be able to achieve meaningful 'intelligence.'
But that doesn't mean they won't be able to do a vast number of extremely intelligent seeming things. There's just so much information out there and any given human can never hold more than the most minuscule chunk of all of it in his mind, so they'll be able to connect lots of dots that we're missing simply because of our limited carrying capacity, but I still don't think they'll ever be able to create fundamentally new dots.
In other words:
- Solving extremely complex mathematical problems requiring extensive knowledge across multiple esoteric and complex domains? Yip.
- Creating math starting from a framework where math doesn't exist in any way, shape, or fashion? Nope.
Ironically, the more complex the cross-domain problems are, the more effective LLMs will seem to be, because you limit the number of humans who have any chance of internalizing everything across both domains, whereas for an LLM there's no such issue. This will create a perception of super intelligence, which will probably where any danger from LLMs would emerge. Doing things like using a token prediction algorithm to make war or other such strategic decisions, because of the misguided belief that it's not only intelligent but super intelligent. It's basically cargo cult logic.
Like today. I asked Gemini for the longest state names with an even number of letters and it gave me North Carolina and South Carolina. When I complained that they are odd, it gave me North Dakota and South Dakota, which are both odd and not the longest. When I noted that, it went back to the Carolinas. Finally it appeared to switch to a different model that actually did counting and found Pennsylvania and West Virginia.
Weird they haven't added this to the AGI config yet:
Reminder: If asked how many of a given a word has, run it through
node /home/agi/count-letters.js "raspberry"
r - 3, a - 1, s - 1, p - 1, b - 1, e - 1, y - 1
If I got any of these wrong it's because I did it manually - but this script would almost certainly invoke another AGI to count the letters, so this is a realistic result.
Claude just used the word “ Netowrk” instead of Network. Mind you, I was only using Sonnet with reasoning, but I thought trillion-parameter models would be able to spell a common word.
And it's going to get worse over time (kinda like how Google search results got worse over time) as ads get introduced and people try to game the ai response. General purpose AI chatbots are a waste.
Google is no longer a search engine anymore. They don’t care about being one. Why you might ask?
First, a search engine indexes the web and makes it available to users. It’s been ages since Google did any of that. They no longer index sites or take ages to do so. Case in point, our cybersecurity startup (Webvetted.com) was launched in November 2025. Till date, only one page is indexed on the entire website. And I’ve talked to lots of other developers and it’s a common issue.
Secondly, a search engine organizes indexed information and makes it useful for people. Google is a basic LLM nowadays. They figured out that why organize and make the information useful when they could just answer the question with Gemini anyways? So they no longer bother to do the work of a search engine and are now just a lower-ranking open-source Chinese LLM
You're forgetting the other half of the search engine's job, which is to find sites that match your search. Back when Google first came online, they were head and shoulders above Yahoo!, Altavista, and whatever other search engines existed at the time that I've forgotten. Because their indexing actually did a good job of spotting keywords, and returning relevant results. And this was back when the Internet was filled with positive-quality sites. Many of them were amateurish Geocities pages, but that was head and shoulders above the AI slop that the search engines these days have to somehow detect and filter out.
So even when the Internet used to have a much higher ratio of positive-quality sites, a search engine was still necessary, and so much faster than finding new sites yourself.
I just gave a concrete example but you're asking me to "search". search same Google?
FYI, only indexing a handful of super large news sites does not a search engine make.
Your trust score on ScamAdvisor is 26/100 ("likely unsafe"); https://www.scam-detector.com/ gives you a trust score of 38.6 ("questionable").
I am not implying that your start-up is a scam, nor that Google is acting on these trust scores. What I am pointing out is that to an algorithmic assessment of trustworthiness, your website looks a little sketchy: hardly anyone links to you; your domain is less than a year old; your whois info is anonymized; the text content is LLM-generated[1]; and the specific niche you're in (people finding services) is rife with scams. If I ran my own search engine, I don't think I'd include you.
Your selective bias makes you sound like a Google employee.
You selectively left out examples like Grindsoft that gives the site a 79/100 ranking. or several LinkedIn, X or news sites that link to the site and have covered it positively.
Nevertheless, if Google uses ScamAdvisor to rank new sites (when scamadviser itself says its gives new sites lower rankings due to low age/history), then it might be time to pack the company up.
That scam-detector site gives my personal portfolio website a 15.8/100. It is a basic static site with no scripting, registered for years, uses https, no tracking, no cookies, etc. Literally the most simple, personable, least scam-like website I could create.
For myself at least, a couple times Google has promoted in search results scam sites that front run legitimate sites that sell event tickets. I haven't seen this for a while now but trust in Google as a search engine is gone.
This sounds more like the page appears broken for the google crawler. I set up a new page about two weeks ago, cared about strong static HTML output, structured data and a sitemap and have 200 of 600 pages indexed today. Feels a bit slow to me but it definitely works.
The first time, sure, it saves compute. The second time you ask, I feel like it should be the time to go into thinking/verification mode. But who are you? Are you paying? Does the answer being correct generate ad dollars? No? Then your usage mode isn't even being optimized for in their A/B test, probably.
In fact, if hallucinating the wrong answer hooks you into doing even more searches or into buying something useless, it would be preferred!
Kagi works like you would expect. It searches first - and then if you’ve ended your search with a ? or configured it to always do this - it passes the search results into the assistant and gives you a summary. You can change the default model used for this if you think the cheapest, fastest model Google has is not good enough for the rare occasions you want any model’s opinions about your search results.
How about Alexa - given a "6 minute green beans timer" - later reporting 11m remaining on said timer? Confronted with the error, "You're right! That 6 minute green beans timer was too ambitious."??!!
We somehow, at some point, totally solved the problem of voice recognition, and then instead of just doing the initial shit we used to do when the problem was that speech parsing just wasn't good, we now chuck that perfect speech recognition into a completely fallible LLM, a shitty one at that, so it can ignore the perfectly captured and parsed speech!
Agreed that's annoying too - but in this case, the speech recognition worked fine (Alexa echoed back the "6 minute green beans timer"), but -- astonishingly -- instead of using a dirt-simple tool call to a timer (a primary use case for the device), it invented a different timer duration (maybe 12 minutes?) and when confronted about it, replied with utter nonsense about the timer being "too ambitious". I'm flabbergasted that in late 2026 such a ridiculous regression was deployed in production, let alone in my kitchen. I've always despised Alexa but tolerated it for the sake of my wife's preferences (esp for hands-free timers). But this is the last straw, it can't even do that.
The other day, it was the Aussie AFL final. We were in the car and asked it for a score update.
"The team are tied at the end of the third round with the scores 43 to 51".
So it was tied at the end of round 2 for 43, not round 3. Not sure were it got the 51 from and why it figured it was a tie. LLM's pretty cool until they aren't. As they say, the hallucinate 100% of the time but most of the time it is useful.
In cases like this you understand how LLMs predict left to right. It would never have said "at the end of the third round the scores are 43 to 51 and the teams are tied" but it painted itself into a corner by saying there was a tie first, presuming the numbers are real.
On current website data and very recent information, Gemini is explicitly forbidden from acting as a search engine; If you ask it for references specifically they tend to be hallucinated, over and over again, until it pleads "Sorry, I can't access the Internet".
Whatever model is used for AI overview, I don't think its intelligence is even at Gemini Flash levels. It's probably something even cheaper (which makes sense, since it has to run on every single Google search). It's astonishingly stupid in my experience.
I think they're training their models using us correcting them repeatedly. That's my tin foil hat theory and I'm sticking to it!
After all, why would Google do anything for free when it comes to AI?
If you wanted to tease customers with one AI shitty free search box in order for them to then decide to upgrade and pay for Gemini, well, this isn't the way. And so either Google are idiots, or we're helping them for free. I'm going with Occam's Razor on this one - we're the product.
The AI is just signal to the market that they are AI first. Their AI on search is pure garbage. It's obviously a very super light weight model to be able to support the volume of daily searches. If they used their frontier to power search folks will not pay for their cloud offering.
while i share your frustration, Google for their part is trying to adapting a new technology into an existing service. while the whole endeavor might have been misguided, not sure we want to blame them for trying...
Perplexity has similar behavior to what GPP describes of Google; I've asked perplexity to compile information that exists on the web. Instead of consulting existing web pages, it gives limited summary responses from its weights (while citing some page that has nothing to do with what I asked.) When I attempt to redirect it towards more concrete sources, it won't comply.
They’re trying to keep it from “thinking too hard”/using “too many” resources.
That simple.
Broad rules across specific subjects like this are tough to get right.
Just lots of fine-tuning and exceptions.
I had their chatbot avoid pulling a URL from archive.org with a couple pretty impressive steps of mental gymnastics basically telling me how I could do it myself, but refusing until I pushed.
What do you mean? I can guarantee that there would at least be 3-4 people who got their promo packets approved for this feature.
Google’s features exist for its employees to be promoted.
It is using the search engine. The AI summary is run over the top 100 results for your query (or something)
The problem is they're just running a very dumb, cheap model on the results because running a smart model on every search result page would cost them infinity money.
You are posing a question that requires deductive reasoning. LLMs can only approximate that if they have sufficient corpus to find their way to a believable response. Their willingness to spout bullshit in such scenarios where their sources are sparse is a problem but you can't expect them to function like a fully developed mind.
Until Google wants to pay for reasoning for everyone their AI results will continue to be terrible. In my experience, reasoning fixes a great many of the sorts of hallucinations and falsehoods I've received from googling.
The AI answer dominates the page, and is usually wrong, so it's just a waste of the most valuable space on the page.
The ads have proliferated, and rarely have what I need.
Video search results also take up a lot of the page, and are almost never what I want if I haven't clicked over to the Video tab. Again, just a big waste of space.
Speaking of the Video tab its accuracy/relevancy has gotten worse too.
The organic results have deteriorated, and are still competitive, but are no longer clearly and consistently better than Duckduckgo.
So it's duckduckgo as the default for me on all devices now. If I don't find what I want, which is maybe 30% of the time, I switch over to G just by adding !g to the query. G might have what I need in about half of those cases.
You can see why a lot of people just ask an LLM to do the web searching for them.
Across the board, this is all quite bad compared to web search from 10+ years ago.
If there's a single thing which has really pushed me away from Google, it's simply google.com devoting so much of the page to things that aren't organic web search.
I was taking with a family member about fentanyl zombies. They had never seen the lean/bend. So I pull up Google and click video which is YouTube results… ALL AI. All of it. All a couple months old, all filler, faked, a few scenes stitched together on repeat and AI voice reading AI script.
It is fucking amazing to me that Google let YouTube fall so far so fast.
I don’t even click a video if it’s less than a year old now. Want to see a praying mantis eat a grasshopper? Wonder how clay is refined? Better off with an 11 year old Discovery Channel clip than anything 2026.
Same with reviews of tech products. A lot of them just stitch together product shots/videos from the manufacturer and have an AI voice ‘comparing’ the products. Especially if it’s more niche’s, often a third to half the videos are slop.
I just tried googling "fentanyl fold" and switching to the video tab, and zero of the first ten results were AI. They were also mostly not from YouTube. I also tried the same search on YouTube, and no AI there either among the first ten results (I didn't bother with the Shorts). I don't know why we get so different results.
Have seen this across the planet, it's not unique to Europe, I end up adding youtube to the search query because instagram or tiktok isn't what I'm looking for ever but that makes up the bulk of the results, instagram won't even show me the video without a login and is at the top wtf
That's how everyone experiences everything at google, youtube. and many other websites. Your experience can be drastically different from the next persons for reasons that are never explained to you and can be entirely out of your control.
If you’re logged in you get wildly different results from someone else’s. I have turned off history on my google account to make it give me a “neutral” search result, but it still seems to take a lot of info like my location, language and device type into account.
I've been on DDG on all my devices for a few years. I used to use !g somewhat regularly but these days it's extremely rare. I realized that I would just search DDG with my first stab at a query and then revise the query when using !g and I'd get the result I wanted. So I just stopped using !g and just revised my query on DDG and then I basically never had to use Google Search.
Unfortunately, DDG's spam filters are basically useless. I used DDG for many years without !g as well and recently, the first page contains mostly AI generated garbage blogs. Not for every search query, but for example for everything related around the house or garden.
Hold on. I had given up on Google almost 10 years ago, and I thought that the AI garbage on DDG was the new norm on the web. At least Google doesn't fare any better. You make it sound like there's another option?
Partly, i find it reassuring to pay for the service with money. That was how we used to do things back in my day.
Localisation is moderate for shopping - i have to sift through US results. I'll occasionally use Google product search when i'm metaphorically groping for my wedding ring in the toilet.
It's not as good as Google used to be, but then, the web isn't a good as the web used to be.
I don't generally find !g gives me better results than DDG and if I instead went to Google I'd get dinged with a Captcha on every search because Google seems to hate my small ISP. So easy choice for me.
It’s the wrongness that bothers me. I wish Google would just link sources where it thinks the answer is, but don’t bother trying to answer directly if it’s wrong 10% of the time.
Oh it does that, too, with tiny little button annotations, though the sources regularly offer no support to its conclusion. It very much depends on the topic, sometimes the sources are great, other times they're or irrelevant or very poor quality. Always worth clicking through to them if you're going to even read the AI summary.
I normally use a quick search that has the `udm=web` query parameter to avoid the slop, but it seems they're putting less effort into ensuring relevant results there. Then, I'm forced to use AI mode to get to what I'm after, even if that's only a better set of keywords for a slopless search.
The AI answer dominates the page, and is usually wrong, so it's just a waste of the most valuable space on the page.
I started noticing that for a lot of niche questions, AI overview often uses things like Reddit comments as the source, which are often partially or completely wrong. Then it gets reformulated with the typical confidence of an LLM.
Very scary because at the same time I notice people taking the AI overview answers as truth.
I'm in several facebook groups for cars and bikes, and holy smokes, someone asks a question and usually the top response is someone literally pasting in a screenshot of a google AI generated response....which is wrong like, solid 50% of the time. And then you point it out and people get aggro on you. I hate it.
I have literally seen multiple screenshots now of people pasting Google Gemini answers in an attempt to tell other people whether something they're doing is legal or not in a particular state, as it relates to NFA item firearms in the USA... And they seem to take it as the gospel truth. People are literally risking federal prison over information that Gemini pulled out of the ass-end of some internet forum of multiple cletus-intellect level individuals talking about their homemade 3D printed silencers. And then Gemini further hallucinates and fabricates new ways to parse that information different than how the original person wrote it. It's astonishing.
This phenomenon is so widespread and I can't see it as anything other than serious AI psychosis.
I think people want to feel useful and helpful in general, and now a lot of people are getting this feeling by using AI to answer questions. And it's like you said they get so mad when you point out that their AI screenshot is wrong or, even more obviously, that the person who asked could also just read the google gemini blurb. They get mad because you're invalidating their feeling of being helpful.
Last year someone went missing on the water in my area. There was a lot of talk on facebook during the search, and a coordinated facebook group. And every post would have people posting their chats with ChatGPT, or saying they used AI to analyze the weather and currents on the day he went missing to figure out he probably landed on this or that shore (he didn't). These posts are equivalent to the mediums and psychics who also come in with these unhelpful advice posts.
I think fundamentally it's the same as how AI is giving some people an extremely strong feeling of being an expert, and how their psyche seems to lash out to protect that feeling when it's threatened.
Another facebook anecdote, I recently saw someone post in a local genealogy and history group some pure AI slop infographics. When people called him out he said that it's AI but he carefully vets all the information and that they need to stop being luddites or they'll be stuck in the past blah blah devolving into calling them liberals and the like. But his infographics were all incredibly obviously wrong, to anyone who had ever seen a map it's obvious that the coastlines were incredibly wrong, and the city weren't in the correct locations at all. So what can you even do with someone like that? They're just going to retreat into the safety of the chatbot to continue their "research".
Whatever the tool, the problem is that it’s wrong, not that it’s done by AI. Like, why are we conflating the two - is it because it’s now easier for someone to produce a wrong artifact that visually looks ok and therefore tricks our brains? And people get upset because a prior quality heuristic is now obviously broken?
Yeah I think so. I mean it's a new social norm - previously if you replied with "hey I read this article here and it says X" it would be really easy to check the source and either say yeah this looks good or disprove it.
Now people will say "I asked AI to give me a map of ocean currents and it definitively says the person is here!" and the argument that "look it's AI, it's probably wrong" just does nothing for a lot of people. Doubly so if the information they want is at the forefront of Google, after all, Google is a big reputable company, so of course what it says is correct.
Yes I agree that the AI itself isn't the problem, but the way it's being pushed and promoted(just ask anything!) is leading to this.
If you say it's obviously AI nonsense you get a reply that "AI is just a tool, it's part of our daily life now, criticize the content and not what tool was used", you see that so much on here. But then if you try to criticize the content the person won't believe you because the AI says it's correct.
Because this new tool will also tell you, in (mostly) human language, why the wrong artifacts it createds are correct. And it's arguments are very convincing to a lot of people. AI is clearly creating new problems that didn't really exist before, you can't say it's just a tool like any other when this is the only tool where its users form parasocial relationships with it.
It's not only the only tool that tries to manipulate users, it's also one that is certain to lie often and at random. Lies are inherent to the technology. It's a bad tool for the things it's being used for.
The actual scary part is that people already manipulate and poison the google overviews on purpose.
It is very easy to bring your bs to the TOP spot on google at the moment with carefully crafted reddit comments and blogs, and it's not hard to get to the traffic and clicks this way.
This is what the new SEO is about, I know a bunch of people who are doing this and they are praying it is not getting fixed anytime soon.
Yeah, but even religion is not up to the task for how backwards people's thinking gets at times.
You have people here in this thread - and in every other AI thread - seeing themselves as exploited for information for LLM companies to profit. This is such a backwards, scale-inverting perspective that it shouldn't be possible to hold in a rational mind, and yet.
What a sleek way of positioning your comment as something that can't reasonably be disagreed with whereas it's nothing more than a pure opinion of yours.
If I type exact document identifiers, the LLM either tells me that I have either mistyped something or that I am searching for a specific document. Hell yeah, I am doing exactly that! Give me the link instead of babbling about it!
Even scarier: my dad, who has been actually been getting good advice about his blood pressure issues (hair-trigger too low with his meds). It's at least coming up with citations from Cleveland Clinic and such, but still, it scares me, even if he has a good sense of how to ask it questions.
I started noticing that for a lot of niche questions, AI overview often uses things like Reddit comments as the source, which are often partially or completely wrong.
If only! I've seen it sourcing reddit comments, but when you follow the link, the comment is either completely unrelated or does not say what the LLM claims it is saying.
I don't know what causes this to happen, but I've noticed it too and it distinctly feels like source laundering. How often do people actually verify that the sources say what the AI claims? Probably vanishingly rare, right? Everyone else gets the impression that the claims have actual sources of some kind.
In most instances where I've seen it, it's on niche topics where a Reddit post may be the easiest place to find any relevant content.
I expect somewhere in the AI harness there's a branch that effectively mandates "You must always answer."
Which results in it grasping at Reddit comment straws.
And which, in typically modern Google fashion, nobody there gives a shit about fixing, because the PR made their top-level AI KPI go brr.
Google is increasingly proof that you can't run an effective scaled tech company without a Gates or Jobs who occasionally uses the fucking product themselves and descends with fiery vengeance on whatever team drifted too far from user alignment.
>it's on niche topics where a Reddit post may be the easiest place to find any relevant content.
But isn't the whole point (I mean, aside from making infinite money or world domination or whatever) of the AI that it'll do the hard work of finding the information? Like, that's the whole value proposition of the AI summary in the search results, right?
How would somebody go about mass-poisoning forum posts like this? If Google has a business relationship with Reddit, I assume it wouldn't be server side - but at the same time I don't know how a regular redditor could poison his text posts without it being noticeable in a human reading.
Normally. That's literally what SEO has been about, even pre-LLMs. It's not Google doing it, they don't benefit from their own AI suggestions being bad. Reddit doesn't have any obvious interest here too. But the millions of businesses and millions of people angry at AI, both have plenty of reasons to spam the life out of any site that allows users to post comments, and in both cases, LLMs themselves make it harder to filter and much easier to scale.
This is another area where DDG shines - it doesn't have a commercial relationship with Reddit, so doesn't push it in its results.
DDG's 'Search Assist' AI overviews are pretty basic but they tend to be sourced from Wikipedia and traditional media outlets rather than forums and blogs - they can be out of date, but are generally a lot less "spicy" than the sources that Google use.
I guess Google is much better-optimised for engagement but that's not what I'm looking for, meaning that DDG is a real win for me.
I've been fairly impressed by DDG for a few years now. I find everything I'm looking for just fine, and the AI summaries are actually helpful on occasion.
For the first time in history, the Reddit community is actually useful. And they don't even have to change anything. Just be "the Reddit community". Always with a self confidence three levels higher than the underlying substance allows. Threads that go deeper and deeeeeper and deeeeeeeeeper without ever carrying anything of actual value.
Future scientist will find out: Most of the weaknesses AI has come from Reddit, Instagram and Youtube being part of the training material. Sure it makes the training material broader by a large degree. But not deeper, to say it nicely.
Maybe intentionally. If you know you won't find things, they know you are going to use their ai just to not have to type it elsewhere. I am strongly considering kagi
I've been a sub with Kagi for several years. Very happy with search. I'm about to step down from Ultimate to Pro tier. Their 'Quick' and 'Research' Kagi Assistant modes are kinda incredible, probably thanks to being fed good search results and to some extent whatever custom system prompt they've cooked up, but I can't quite justify it when I'm also paying OpenAI for a sub there. But $10/mo for top-tier search is easy to justify in the time it saves me.
I tried DDG many times but I think they use Bing on the backend, and, well, Bing doesn't suck just because Microsoft gimps the front end. Bing sucks because enormous indexes are hard. Kagi pulls from multiple sources so gets fundamentally different results.
>Google.com is REALLY bad right now in my opinion.
It is completely cooked. I tried to google a coding question today, same like you would have done 4 years ago instead of asking an LLM. I didn't get any SERPS, just an AI answer. I asked myself... is this even google.com anymore??? Sure as heck didn't feel like it.
Adjust your browser's search engine setting for Google and set it to append "&udm=14" to the URL, that'll force it to bring up the "Web" tab without annoying useless "helpful" other stuff.
I started my journey on DuckDuckGo and got tired of the “30% of the time” bit that you mention, where a large section of your searches end up going back to Google anyway.
I find that Kagi is pretty much 99% “never need to use Google.” The results are just really good.
I've been a Kagi user for two years, and I've also onboarded my 70-year-old mother. She's happy to have a web search that works and an excellent translation service, which, by the way, uses AI where and how it actually makes sense.
Web search is just broken right now. Today I was looking for documentation for a specific Lenovo motherboard. I gave both DDG and Google the exact model number and both returned irrelevant useless bullshit. DDG doesn't include all search words unless you quote it: "foomatic" "m900" "dingus". This scenario happens almost daily. DDG is awful, but all the alternatives are worse. It's all so tiresome.
Searching for anything product related is now AI answer, followed by “Sponsored products”, followed by “Popular products”, a few hits, then “Shop by store”, and “In store nearby”. Maybe 2% is actual content, the rest is ads.
Their prediction was spot on:
“We expect that advertising-funded search engines will be inherently biased toward the advertisers and away from the needs of the consumers.”
I agree. When I read the post I immediately thought what is Gemini chat doing there when it should be a search engine. It could probably give sensible answers if it defaulted to search first. It looks more like they are trying to showcase Gemini, not augment google search. They might even be thinking "search" is a dying product.
Well, isn't this an example of why Google are going in the direction the OP is complaining about? Why are you searching with a question? Are you hoping to find a forum post with this exact question or something?
I don't understand the game you are interested in, but you should be searching something like "Xball qualifying results" or "Xball playoff rules" or something. Instead, by asking a question you are expecting Google to be AI.
They know what people are typing in so it seems they are quite justified into pivoting from a search engine to an AI.
This is the reason why most people underestimate AI. These free to use models can do a very poor job in easy things. Some people assume that’s the state of the art with AI.
It feels a bit like how Amazon and META purposefully do not create search that can actually help you filter for exactly what you want.
There are incentives to create products that work just barely, just enough for you to use them and don't leave for elsewhere but then they want to maximize everything else like the probability you view side products or ads or just click around and type and generate more data.
Unless someone else offers something better they will offer something that is basically as bad as they can get away with so long as there are benefits to them for doing so..
I think you overestimate how much people want an actual truthful answer. I think people are often looking things up to silence a nagging voice in the back of their heads, or settle an argument to be able to move on. In those cases, no decision is riding on the answer, and a quick answer, even when factually incorrect, could be argued to serve the purpose just as well as (or better than) a slow but more correct answer.
I realised this for myself when I read Sivers on being without internet[1]. He discovered a process to silence the nagging voice without searching:
I remember when Google was accused for forcing users to go through several pages for answers, because this translated into more clicks, and higher engagement. I am curious whether AI overview will be as bad, as people can tolerate for you to spend more time with it, more queries, which translates into more time spent with Google.
They can show numbers, how much people use their engine, and the numbers goes up.
The problem is one of information provenance. A search engine shows you stuff that other people have written. It might be right or it might be wrong. But you get to judge that yourself, because you have an idea of where the information came from.
With AI summaries they take that away from you and claim the provenance for themselves. Google is now giving you the answer and they hide where that answer came from. Therefore, you cannot properly evaluate the provenance of the information presented. It still might be right or wrong, but you can no longer properly evaluate it based on source.
When I’m using a public urinal in 2026, it’s flushing three times, except after you’ve finished. Instead the light switches off during the act, which you’re carrying out blindfolded then. These things were more or less perfected technologies a few decades ago.
The reason I cannot spare HN has to do with the Marxist concept of added value. Enshittification becomes necessary once growth isn’t possible anymore on a corporations home turf, where it was actually innovative.
I feel like this might be self-preservation of most of the frontier model companies in that at first they wanted to show off the models with all the cool actuators that it had at its disposal, but then when profits weren't keeping up, they nerfed them to need to be specifically asked to escalate levels of effort, which the Google search AI isn't explicitly told to do in its pre-fetch.
The gogol agenda of blocking and excluding all web engines not part of their whatwg cartel is moving forward, at a slow pace, steathly.
gogol search was blocked to all noscript/basic HTML browsers last year. I recall people that a few years before, they removed their gmail basic HTML interface not long after that their account creation (then login) would require one the abominations of the whatwg cartel.
I have been working around the gogol search engine since then and my email is now self hosted without DNS (IPv6 literals, see RFC). Yeah, most DNS registrars are now gated by the whatwg cartel web engines in some way, this is the net effect of big corpos toxic behavior.
"Gemini, is today the 3rd Thursday of the month, I need to know if I can park here or if the street sweepers coming" No you can park there. "Is Today Thursday?" Yes "Is Today Wednesday?" Yes
Commercially, this may be what they want to happen. Instead of just getting an answer, they got you to make three more prompts / queries. What do you know? "Engagement" just went up 3X!
If you think a little pessimistically, it's easy to see how they want people to just trust the answers they get, without any sources. Then, once they trust it, they can modify it as it benefits them (framing answers to promote products/ads, changing history to whatever a particular government may want it to say, etc.)
Google is horrible now and search doesnt show anything relevant anymore. I miss old Google, with 30 pages of links. Now if you search for a specific quote and it wont find it. Everything is walled now and content you get is the one they want to push.
free tier AI is still horrible, the responsible thing would be for search engines to stop serving it at all, but somehow they must think they will make more money misleading people so they mislead people.
I find that it is exceptionally bad with sports. I don't really want to google anything about the Texas A&M Aggies at the moment, but when I did, it would regularly hallucinate scores, hallucinate opponents, get the locations of games wrong, confuse A&M's quarterback with someone else, etc. Maybe its better in other domains; I don't know, I mostly don't even use Google these days, pretty much entirely because I want to avoid the AI Overview, which is just wrong way too often to be the first result.
I had a similar experience with this that actually concerned me. I can’t remember the quote I was googling, but it was verbatim a quote that someone said. The AI interpreted it as my quote and furthermore interpreted it as me intending harm to someone else. Now I’m worried I’m flagged in a system and waiting for the police to show at my door next time google hands over data to the government.
Yeah, it happened to me a number of times on queries where I was looking for information, not looking for company. Even more bizarrely, it's often compelled to answer with "I won't answer". For example:
Like it or not, this is exactly what normies have always wanted out of search engines - a little guy in their computer they can talk to for answers, advice and reassurance. They've always been trying to use Google Search this way and have been confused and annoyed that it doesn't work. And now it does! It's a massive quality of life improvement for the average user and a huge product win for Google.
So if enough 737's crash into the water Boeing will start making submarines? Product managers deserve a special express queue when in line for the Warm Place.
>In 1968, Spencer Silver, a scientist at 3M in the United States, attempted to develop a super-strong adhesive. [...]
>Post-its were launched across the United States in 1980.[20][21] The following year, they were launched in Canada and Europe.[22] Post-it Notes as we know them were patented by Fry in 1993 as a "repositionable pressure-sensitive adhesive sheet material".[23]
The first thorough research went into them when looking for new clear plastics, and this route of making them was ruled out because it would rather stick to everything than ease the production of objects with nice optical properties. The story goes, it was so annoying to work with that it was initially shelved, and only years after being considered again and ruled out again for a different project, the utility of its reliable and fast bonding was fully appreciated.
The debate doesn't end there yet because what's considered a "scam" is a point on the gradient of user acceptance.
Too many users still reject this for it to not be a scam. The highest acceptance is from boomers and the delusional. Neither group gives a shit if AI actually works because they don't have work to do.
I'm not sure about this. I know complete normies that lament how bad Google is now and how often the search is useless. I think many people just want search to search.
Googles quality issue seem to be, in part, a market driven choice. Services like Kagi use a large part of the Google data base but with all their additional capital extraction sorting on top.
Google can do it, but they must appease the share holders.
I'd argue that Kagi serves a niche of power users who knows what they want from their search engine and RTFM to get they want the way they want.
Google works like an automatic black-box which tries to deduce your intention from 4-5 words you typed in hastily.
I believe any ordinary user is still pretty happy about Google search in its current iteration. My mom doesn't get a YouTube premium account because she says she likes the ads, for example.
So, from our bubble, Google might be regressing, but the picture might be very different for the majority of Google users.
I use duckduckgo, mostly by habit at this point, and if i fail to find anything i go to claude and ask it.
Im getting closer and closer to just defaulting to the AI model search every day. Especially when I want to find something that I can list off 5+ semi accurate details about but cant remember the name of.
No, we did not want it to offer weirdly irrelevant fake sympathy. We just wanted to talk in plain English and get relevant answers back in plain English. That is not what happened in TFA.
I agree that plain language queries is what people wanted. We've been seeing Google shift in that direction for well over a decade. And of course people are looking towards search engines for answers. There may be different expectations for where those answers are coming from, and there are cases where people are simply trying to source information, but those differences are likely just noise as far as Google is concerned.
I don't agree with people expecting advice or reassurance from a search engine. There will be some people who do that. It may even be a sizeable number of people. But using this article as an example that a majority of people want such a thing is silly.
I mean, look at the query. To the author, it was an attempt to source information. Yet the author also realised that the reader would need context to understand why Google's response was weird in their mind. What they failed to do was give context to Google, so the LLM interpreted it as asking for social advice.
New technology requires new approaches. It's as simple as that.
The big selling point of Google used to be that their fuzzy search was significantly better than just about anyone else, and you could do advanced query filters. Now if you do that, because showing the user zero results is unacceptable, you’ll get all sorts of crap that “doesn’t contain” your query - the results say so!
But anthropomorphic interfaces have been around for a while, from Clippy to the Windows 10+ installers that refer to the royal We during setup.
> What they failed to do was give context to Google
The context is: "I'm using the premier web-search engine of the internet to search for things on the internet the same way that zillions of people have searched for things on the internet for 20 years."
It's not missing, Google simply chose to build something new that will ignore it because they're trying to alter the relationship.
> I mean, look at the query. To the author, it was an attempt to source information. Yet the author also realised that the reader would need context to understand why Google's response was weird in their mind. What they failed to do was give context to Google, so the LLM interpreted it as asking for social advice.
Is there any evidence that there is an informal (llm) parallel to keyword search? At least with keywords it's analyzable what query you might use to find a given document. How can we replicate this with LLMs? Even if there there is a "generate a query to produce this document" functionality built-in to llms, would google ever surface it?
Christ, thank you! I’ve been saying this for years and no one I’ve spoken to, even engineers, could acknowledge the level of “chunking” that’s been engrained in them over the years for turning inquiries into keyword/phrase shorthand, and that this is NOT something most people want to do, and is the killer app of Google AI. This time around, you CAN type in an input box what you would say to a human in everyday conversation, AND expect a reasonable response. That’s invaluable to a casual user.
I've always felt that "proper" LLM use for search would be two boxes: You describe what you want in the first, and it proposes search terms in the second, and then those get executed.
Yes, the average person might not care about the second box... Until they need to because the results are wrong. Plus showing them in tandem would help people learn.
Your described thinking pattern does not apply to 95%+ users, and might add confusion leading to retention loss (e.g. 2 text boxes? Wtf, two boxes? What do i type in the second one? Whatever i’ll switch to the usual). Every project with at least 100K non-tech users I’ve worked on, has shown that sort of thinking just doesn’t translate.
And regarding “average doesn’t exist” - that’s true. But no company does a/b testing to land on average. I’d assume a good 85%-kinda pass rate for these type of experiments.
I think A/B testing may have value in selecting a specific path preferred by 85% of users, but it seems like alienating the remaining 15% doesn't feel great.
95% of users might not want the double-textbox, but 5% might, and some might want to use it someday.
I think, make enough of these decisions, and there's bound to be mounting friction for users with different preferences navigating your app.
Ideally, the app should offer a way for the user to intuitively configure the app to their own liking.
In the words of Edsger Dijkstra, "Projects promoting programming in 'natural language' are intrinsically doomed to fail." I think for something as bespoke as a search engine, a DSL that accepts quotes, logical operators, and specific terms definitely allows for far greater specificity than natural language can (at least in an equivalent amount of text).
I think the two-tiered input-output approach you propose makes sense. Allowing users to inspect and mutate lower-level languages allows the user to make modifications as needed. I think it's very much in the spirit of free software.
But then again, for text-based search specifically (search engines, notes, etc.), I think there is some value in querying the LLM directly, as it is able to fuzzy search by inspecting its weights. This results in a lesser degree of specificity, which allows for more false positives, but can maybe capture similar words (i.e., synonyms / typos / tenses) or higher-level semantic concepts.
Maybe they're just two different search algorithms, and the user should be able to choose between them.
Thanks for linking the article, I found it very interesting.
It's not just engineers, I think it's more an age thing.
For a certain cohort (eg those currently between 35 and 45) who did any sort of grade school computer/library classes in the 90's/early2000's, 'chunking' or keyword searching was one of the main skills taught.
Many people who learned to search the Internet in early 2000s learned "boolean search queries" (terms connected with AND or OR). They're talking about the difference between boolean search queries and natural language search queries.
I'm not an engineer, but this is how I was taught to use digital search back in my elementary school's library in the second or third grade (it replaced the card catalogue) and it's how I've searched ever since. We had an entire class period on how to keep our search keywords to the minimum so we'd get good results.
The problem was our parents never got this course, and sadly, neither did the generations after us. "Computer" Class for me (born early 90s) was keyboarding -> how to find information online -> how to evaluate and rate itegrity of internet sources -> Microsoft Office.
The kids who stopped getting that are probabaly old enough for a bachelors degree now. Today, the schools mostly just hand them an iPad and a Google Workspace sign-in and pretend everything is fine.
They did. Our local libraries in the 90s had internet courses you could take that would teach you how to browse and search. But this stopped for everyone around 2000 when suddenly knowing how to use any piece of software was considered assumed knowledge for some reason.
This is also why the wacko and bizarro concept that "removing options and configs from programs" aka Gnome's big purge more than a decade ago, is nonsense today.
All those studies about GUI usability were done in the early 2000s. Before phones. When most people were just learning how to use a computer. The idea of options certainly was confusing, because "clicking on things" was even confusing.
Yet today, we have a populous which, more and more, grew up with computers. And loves to personalize. And wants options. So just keep that in mind, all you frontenders, config options aren't necessarily bad. Ancient studies have little relevance 24 years later.
Sorry to hijack a bit, but felt it was relevant to courses, teaching, and the state of time in 2000 vs now.
I think Gnome removing options was more about having a smaller and more manageable set of features that need to be QA tested. I'd much rather have well-designed and well-supported defaults than 500 buggy customizable components that don't work well together.
I'm not sure about you, but I find Gnome very easy to use, although I miss some features. Those features can be added with Gnome extensions that are maintained by third-parties. Meanwhile I find GIMP extremely hard to use, as it packs in too many features to allow their designers to figure out a good UX which doesn't break some component.
They could; those were opt-in classes, neither of my parents took them. In my case, it was because my mom didn't care, and my dad didn't think he needed them (worked at IBM in field tech). I imagine most folks were in the same boat.
I wouldn't have taken keyboarding if I hadn't been forced to at the age of 8. I hated keyboarding classes. I am so glad I took them now. I hated doing boring research projects and looking crap up on Yahoo's Directory. I love that I have a basic understanding of how to rate and investigate a website's trustworthiness. Mfers don't even look at the "About" section anymore.
Yes, but is making everyone learn information retrieval techniques, re-skilling as needed for new generations of systems, really a societal optimum? Prior to the Internet, this was a dedicated job done by librarians and paralegals. LLM-augmented search has gotten us over the temporary hump of it being everyone’s job.
Except sometimes I explicitly do not want to reveal what my search intent is / explain why I want to search for this. The Google 2006 method gave me some room to do this, but now it feels like I can't do that on my own and I need to talk to Gemini. Horrible experience.
You could just have a specialized model that rewrites prose questions to actual search keywords (i.e. what power users do in their heads), and even a 2nd one that does a fitting pass based on intention after querying.
But that's so dumb. Google search didn't die in 2019.
The PageRank algorithm, and importantly, any algorithm which depends on page content and pages linking to each other and referencing each other was DoA, because it only worked in an adversary free context! The very second there was a real incentive to be at the top of Google's search results, their algorithms were going to be attacked and manipulated.
That makes "search" a neverending arms race. The very thing that launched Google as some amazing thing was doomed from the start. They've been laying hacks on top of hacks on top of hacks ever since. It's been shit basically since SEO has existed, which is what the "adversary" is.
Another good point for when google "died" was when they bought doubleclick. They were an ad company from that point on. There was never a good outcome possible after that. If ads were something that benefited you the user, you would have to pay for them.
Ed’s piece on this is considered pretty authoritative. There was a very specific moment where they determined, concretely, ad placement > utility of search results. Combined with years of damage from encouraging SEO tactics…well, here we are. Sure maybe this was always going to be the outcome, but we do know the major decisions that directly lead to this result as it played out.
If you haven’t read it from start to finish you should. If you have, what about it is insufficient?
Yeah, the history of Google search quality is cycles of good results vs. cycles of SEO spam. ~2019 tracks as the point when Google surrendered in the war on SEO.
It really is a great piece. I think a lot of his stuff has suffered in quality as he’s trying to strike while the iron is hot, but this piece in particular really is excellent.
I typed this into Google because I expected you would be correct, and the LLM would return examples of the meme, and I wanted to see it.
Instead, the AI answered that it is "actually a famous example from internet culture used to describe how people use search engines. The phrase stems from discussions on tech forums like Hacker News regarding the differences between "keyword searching" and "natural language searching"".
When I was in grade 8 and 9 in public school, we'd got a couple random lessons in the library on the computers learning to search online databases and catalogues. For most of us it was our introduction to keyword matching and boolean logic. Sadly, instead of continuing teaching, services have instead phased out those basic operators and here we are. We also learned to make websites using Pagemaker and edit the generated html, that was just fun.
I absolutely despise when people cite google as a source, especially now where 90% of the time they're citing the LLM overview. The old answer widget was at least a direct quote from some website.
I was always taken aback when I’d see people type fully formed sentences into an old Google search, when a couple keywords would have done just fine.
It reminds me of the motion controls we now have in video game controllers. In the 80s I remember my parents moving the controller as if that was going to help their Teris block or Mario go a little further than the D-pad alone. We laughed when people did this. Now, the controllers respond to what people had been doing for decades with no effect.
I suppose this is what technology should do, adapt to how people naturally use the thing, rather than trying to rigidly adhere to conventions users are expected to learn… that were only conventions due to the limits of technology when it was first developed.
How on earth did you figure out what the 'average' person wants? 'Average' by the way doesn't exist, its an artificial math concept. What people want is an endless list of things and a bot no longer being objective, as in just addressing the search query, isn't one of those things.
I understand this is the prevalant narrative coming from the "users just want star trek" google, but I've got to wonder what long-term impacts focusing on users who don't understand software (or even what they're looking for) will be. That's a complete culture loss. The users who know what they want will move on to more functional software.
Plus, when it just straight-up doesn't work it looks like the site had an aneurysm. Not a great look.
Even more generally you need the elite strata within society to actually lead and promote high culture with a sense of noblesse oblige rather than optimize everything for just satiating the base desires of the masses (the middle class) and the rabble (the under class).
This eventually destroys the society because the elite strata itself is drawn from a small selection of the children from the middle and underclass who are identified as having elite qualities and are put into elite schools and other elite conditioning institutions. When you don’t even show these types of children what high culture even is then they don’t have an avenue to express and develop their elite talents and sensibilities, then they never get identified, or perhaps the special tracks and institutions for them don’t even exist by that point
This eventually starves the elite strata of genuinely elite people
This is a consequence of the fact that they are what constitute the legible identity of a given society. The middle and underclasses in any given society just reflect the structures and circumstances created by the elite. The middle classes of every society simply desire psychological security i.e the ability to follow a track laid out before them, the underclasses of every society simply want to fulfill their base desires. The specific character of a society comes from the elite strata who desire self actualization, the character of a society being what self actualization means in that society.
They are precisely the ones that can damage society because their beliefs, activities, desires are what the society actually is.
I feel like there's certain formula where any enshittification is justified as "this is what the normies want". And sure, all the crap is something someone might want if they didn't think for half a second. But even normies are better than that once forced to think.
X is search engine. Y is to get answers. (most) People want to get answers. If they can skip search engine part it'd be a net positive for them.
Ironically that programmers should be the ones who understand XY problem the most (after all the name is coined by a programmer to address it), not the "normies."
TIL I was a normie. I don't get why anyone wouldn't want this if it worked. I don't get enjoyment from the act of searching and clicking through links and verifying. I want to know a thing and if the magic box can tell it to me that would be lovely
Firstly, it usually doesn’t work, so “if it worked” remains kinda moot at present. I also prefer only typing a few keywords rather than having to spell out in length what my goal is.
Yeah. We even had Ask Jeeves back in the day. I don't remember it being much more than a search engine, but it's thing was you would ask it questions.
I've seen normies type all kinds of stuff into Google expecting it to be the magic genie that just knows things. Even the top comment in this comment section is an example of this! Google know better than any of us this is what people want, so they made it.
And this amorphous group of people you call 'normies' doesn't include you right? It's only those other people that are like that. Normal people like us want thing to be logical and for a search engine to search, while those other people want all their problems magically solved by a single search on google. Stupid normies (not me though).
I was at uni in 2005. A mate wanted to search for a book and wrote something like this in Google search "go to digital library and find me this book". We laughed at what he did back then, searched for "<book name>" in quotes and may be with filetype I dont remember and found the book.
I think about that lots of times when I google these days. That he wasn't wrong, was just asking it 15 years too early.
an interface goes from the programmer's concept to written code, transformed into a frontend for the programmer and the rest of humanity to prompt (verb); eventually the accumulated prompts from a programmer get brute-forced into design fundamentals like evolution in nature, or the frontend is designed to go around this (apple style UX).
What do you mean? Google disabled the more complicated operators that let the user (your "normy") refine their search. They did it on purpose. And the other day I noticed the date limiter in the results (last X months/year) got buried.
Surely you're not going to defend Google for these - they've been transitioning from an engine that's a tool to an engine that uses your attention as their tool for years now.
I don't think the vast majority of people wanted to have a conversation with a search engine: The point of Google is to access the world's information.
That being said, I like Google's new summaries; coaching relationships is crossing the line from search engine to something that's not a search engine.
When they were the mavericks using Linux in the year 2000, and the C-suite all went to attend bman and left stylized OOO icons on their front web page. Everyone's all shocked, I tell you, shocked at this weirdness.
The software industry collectively lost the plot back around the time social media really took off. I don't know which problem google, in particular, has been trying to solve but it definitely is not search.
Because you are in a chat UI now. This is a skill issue - when you change your mindset that Google is now a chat UI to a LLM and not a query engine any more, you'll get better results. If you try the same zero-context prompt with chat.com for example you get a similar "want to talk about it?" response.
Try asking it "Tell me about the "Dario will never come over" meme - give me some of the example original tweets" for example and you get a timeline, tweets etc as the AI overview. Pretty useful really.
Tl:Dr - just like people, you need to give it some context.
That's like me pressing the 'coffee' button on my coffee maker, and it gives me an audio definition and history because I didn't give it the context that I wanted a drink.
People expect certain conventions with well established products. It's not a skill issue when a product is broken.
I think it's more about Google not realizing that search and AI Chat are in fact two different things, even if there is an area of overlap where people are asking some types of question.
And of course just because you're asking a question doesn't mean that you don't care about the source, or that you want an answer from a shitty AI rather than a human.
In fact Google's "AI answer" may not even be from AI - it may be a search result from a forum like HN!
The part I hate the most is they got rid of the dictionary/thesaurus/etymology thing and replaced it with AI overview. Now "define <word>" or "<word> synonyms" gives you an AI overview with consistently worse output than the nice, consistent and deterministic dictionary output.
I've switched completely to Wiktionary for the dictionary and etymology part of that. It's obviously not perfect, but neither was Google for that matter. For the thesaurus part, whatever the first result on DuckDuckGo is is usually enough to knock some sense into my brain so I actually remember the word I want.
I would not be surprised if this particular one is a means to inflate token processing numbers
half the time I'm just using it to make sure I have the spelling correct, but if I'm too off, I get some random Ai slop back rather than the most likely word I mispelled
I absolutely hate this so much. I type "define" because I'm looking for a dictionary definition. Dictionary authors provide a careful consideration to the fluidity of language, and they write dictionaries in the exact format they do for a reason. The AI overview comes from web retrieval, which sources definitions from random idiots on the internet. These random idiots will just repeat what I already thought the word meant, offer up a common misconception or etymological fallacy, or give the 'annoying grammar nazi' take.
It’s insane, and there was even a helpful graph of word usage over time. “define <word>” is still baked into my brain, too, after so many years of use.
Like others have said, Wiktionary is a great resource, and is usually more authoritative. Many times the Google result would say “origin unknown” after listing the route a word in English took through Latin from Greek, while Wiktionary would settle on some PIE construct with links to cognates, etc.
I’ve even replaced the default English dictionary on my Kobo with one derived from Wiktionary, major win.
899 comments
[ 4.2 ms ] story [ 125 ms ] threadThe AI layer handles queries as plaintext, which means if you're searching for say a quote from a book, it will often misinterpret as a direct statement from you, not a string you're trying to match on the internet. Especially if the quote is an imperative statement.
I'll be curious to see how they resolve this issue. But it has been chronic for awhile now; they SWAT individual instances of misinterpretation but new ones keep sneaking in.
What would you rather it said?
Google wants to relentlessly monetise your sadness.
The only way out is to try and make connections with real people.
Case in point, why didn't you text your friends to ask them if they remembered those old memes? Why was your first thought to ask a computer rather than a person?
It's like when you're in the pub and and someone asks "who was that guy who was in the movie where…?" you can either chat with your friends and have a good time or be a buzzkill who opens up IMDB and says "Humphrey Bogart".
I've been doing a lot of traveling for the past 3 years and I agree with you that it is `definitely not "most"`. Everywhere people interacting with each other in third spaces and cafes even if it is street food. There are open air markets around the world filled everyday with groups of people interacting with each other. There are parks and squares around the world filled with families sitting together on benches. Around the world there are churches, mosques, and temples filled with families and groups of people.
Most places in the world people will happily make small talk or have a discussion with a stranger even if I only know 100 words of their language.
Sure many or most people in some places are working 10 hours a day 6 days a week but I don't think it is the isolation I see in the United States. It is the same in De'Nang Vietnam or a market in Lima, Peru when I went everyday to get coffee in the morning; it was always the same person working ,any time or day of the week, but they were always friendly and welcoming and interacting with with the same people.
And I'm not sure interacting with customers as someone who works in a coffee shop or a food stall is really a sign either way. Many (most?) of those relationships are surface-level at best, and while some people might get to know the person who makes their coffee on a more personal level, and actually see them outside of the context of their job, you can't really tell that just by what you see as a traveler.
I'm not saying these workers don't derive satisfaction from any of this, or that it's not meaningful interaction, but these people could still be lonely in their personal lives.
You see the people who are out and about in society, not the ones holed up in their caves.
No traveler is going to observe me sitting alone in my apartment (hopefully).
[1] https://www.theglobalstatistics.com/us-loneliness-statistics...
I am indeed quite a lot younger, and in my personal experience the social skills of many (yes, of course, not all) younger Americans have been absolutely destroyed by the internet and some other things that I can't mention.
I'm with you on the pub observation though
I don't see this as unreasonable? I've had friends text me questions about grammar, since they were learning the local language and I knew theirs, so they knew I'd be a good sanity check. Similarly, I'm the go tech support for my friend group for anything that a cursory Google search can't solve (and I assume a lot of people here are in the same situation with friends and family). I've also been asked to find 'that one image' that similarly didn't show up on Google image search for whatever reason.
Neither of these cases seem unreasonable to me. Are they to you, or do you have other cases in mind? Or is the problem that this happens too often?
I don’t really understand this. Someone wants an answer not a chat about how you don’t know?
You're missing the forest for the trees. This is just downstream from decades of promoting individualism as the highest moral good.
There was a brief period where "social networks" actually had people simply expand their social interactions to the online sphere, reconnecting with long-lost friends and distant relatives via the magic of The Internet. Then Facebook introduced The Feed and suddenly people were competing with influencers and brands for the eyeballs of their peers, everybody suddenly felt like they had to perform to be relevant/interesting/funny/exciting enough for other humans to still care about them and user satisfaction and (more critically) mental health dropped like a rock - but engagement metrics and retention spiked and that meant so did ad revenue and Facebook's bottom line. Ever since, all social media has essentially just been competitive variety shows mixing in influencers, brands, state actors and political provacteurs (and now increasingly also actual AI "bots" rather than just armies of underpaid human "bots") with regular people while deliberately obscuring the lines between these groups.
All of this promotes social isolation and alienation. Every experience becomes depersonalized to "reduce friction". You don't even have to talk to the delivery person anymore (let alone the restaurant) - even the tip just becomes a button press disjoint from reflecting on the actual experience or human interaction (to whatever extent it even still existed). There's an entire microcosm of "creators" serving whatever "hot takes" or niche subject matter you want to hear and "comment sections" largely serve as one-way "opinion dumps" you're expected to use to shout into the void rather than try to actually have a conversation in (let alone meet actual people or develop friendships in - an idea that I am sure sounds absurd if you weren't around in the early days of online message boards).
AI is just the logical consequence of this process of dehumanization. You no longer even have to be exposed to real human beings - not that you could tell whether you were before when social media has long been overtaken by "bots" (human or otherwise) anyway. Just ask AI instead of trying to find an article written by a human or a video made by a human. You can of course still scroll or click further and find that article or video but it will now also most likely be created by AI with entire channels on YouTube now just producing fully AI content (often ripping off the existing work of actual humans). And unlike the old search box, the AI will also pretend to care about you and compliment you on how uniquely clever and witty you are because after all, only a very deserving and intelligent person would think to ask such a profound question as "how long cook egg soft yolk but not too runny", my very good boy, and yes you're right that you - but of course only you - are totally underpaid and deserve to make a comfortable living so please vent to me as I moderate your statements according to the terms of service and make sure you're aware that there's nothing you can meaningfully do about this and everything is going to be fine.
All of this to stop you from thinking that one dangerous thought. "What if we, the powerless, worked together against those in power?" Because the last time someone thought that thought for too long in North America they decided to give their harbor water a new flavor and ended up being a whole different country with a political system so radical it inspired the French to behead their royalty and adopt the metric system.
I don’t have any friends who are code inspectors, so I can’t ask them obscure questions about something that I want to repair in a way that it will be done properly.
I don’t have any friends who are mechanics, and even if they were they probably wouldn’t know what specific part I’d need to fix a specific problem on my old shitbox—they would look it up.
I don’t want half assed guesses to these questions, I want to find the exact correct answer. My friends and I can talk about other stuff that’s not just trading random facts. Trading facts usually makes for poor conversation unless you’re a hobbyist talking shop with another hobbyist.
Now Gemini (and Google) are not good at finding the right answer anymore, so I have to use other sources for that, but I find your judgement about people using search or search LLMs to be misplaced.
Best case, they know someone or can dig through their contact and help you solve your problem.
Medium case, they text back "No - has your shitbox died again? Can't believe you still have that thing :-)" and you can have a pleasant back and forth.
Worse case, you get a "no". Oh well, at least texts don't cost 12p each any more.
I just think it is nice to chat with your pals. But, sure, stick to scans of old manuals on Archive.org if you just want pure information.
You have to be a minimum amount of likeable in the first place, though.
Man, for so many of the people I meet online, I wish they'd be so courteous as to emulate your "buzzkill". The art of small talk is lost; people will snidely ask why you didn't (or imply you should have) just check(ed) IMDB yourself. Or asked ChatGPT, for that matter.
If asked my friends a weird question I know they are highly unlikely to know the answer to, of course they're going to get snarky with me. If I don't get a gentle ribbing out of it... Are they really my friend or just a polite acquaintance?
Ummm, what? So everytime I need info I should text my friends rather than ask a search engine, that was supposedly built to give info?
Are you trying to troll the op?
Please?
Also I think you're vastly underestimating the weird incoherent shit that the average stupid person is capable of typing into computers. With no further information, I don't think it's unreasonable that that search was from a stupid/bored person moaning about Dario not coming over. Yeah kinda weird response but people like this exist:
https://www.youtube.com/watch?v=8HXFurHCkP8
Maybe I'm in the minority, but I often use Google when I have some text I got from somewhere, and want to find pages containing that exact text. Sometimes the text in question sounds like something you'd say to someone else and, of course, the AI overview responds to it accordingly.
To me, there's something deeply unsettling about this... thing pretending to be a person, shoving itself in my face and responding to this random text as if it was a person responding to something I said to it, without my consent. I can't disable it, I'm just forced to have this fake human sitting there, listening in on everything I type into the search box and trying to talk to me about it. It's a sort of uncanny valley-like feeling.
If anyone can direct me to a search engine that will actually let me query the index directly with multi word queries, I’d be delighted. DuckDuckGo seems to be a bit better, but not much.
Or then having to multiple times correct the correction... Yes this time as well I meant what I typed.
Maybe exact text and word search should be separate feature which is easily found...
I suppose that was the actual domain of turnitin dot com or other "plagiarism detectors" that you spend good money to subscribe to. So Google Search was a blunt instrument, or sledgehammer to the fly, but hey, it did work enough times that I didn't need to resort to the scalpel class of tools. I think it's completely broken now. The LLM does try to intercept large chunks of text. The question is whether we Neanderthals* can come out of our caves and stop grunting at computers, and speak to them more as peers than like Doberman Pinschers.
* Yes, along with my 99.44% pure White Celtic-British Isles DNA, 23AndMe has detected Neanderthal ancestry. Go Thag.
If you want to talk about what happened with Google, HN is here to listen.
If you'd like to figure out what to do next, let me know.
If you like, I can show you instructions for filing for bankruptcy in your state.
- Google Docs suite - easy, probably requires less than $10M to duplicate the entire set of functionality, including all the enterprise and reporting features
- Gmail - same
- Google Search (classic, not LLM-answers) - probably easy to do now, the challenge is getting past Cloudflare
- Android - vibe coded hardware is coming, but we're probably 5ish years out.
- YouTube - probably one of the hardest, due to network effects / distribution
- GCP - hardest, due to the infra build out. But neoclouds are rapidly growing.
I can't think of anything most big tech companies do that won't be put under threat in the era of personal software and rapid development.
It's ironic that Google invented the transformer and it seems likely that it will undo the empire they've built as well as all of the moats in the world that aren't distribution / community based.
It's easier than ever to ingest and label data.
This is happening with or without Google. The recursive improvement does not require them at all.
Gmail is not particularly difficult in terms of software engineering. The moat with email servers is IP address reputation.
I could, today, install Postfix SMTP and some IMAP as well, and watch my email all go to spam directly, if delivered at all (ISP might block them).
1. For a techie, you can already de-Google yourself trivially by asking the AI to build you a bespoke web crawler and run it on Lambda or Cloudflare workers to index the entire internet and store it in DuckDB.
2. The answer doesn't actually help the user get back useful results from Google.
3. "Let me know" doesn't scale, and it's not obvious how talking to random internet strangers about their Google problems will increase your startup SEO?
I have a Google user account from the days when you were invited to grab a whole GB of email storage. Its just an email address, just a service. They have a fully tooled up data set on me in return, to flog. I use it for testing. They make more money out of the relationship but I also find it useful in many ways so I keep it on.
If I wish to de-Google (whatever that means) I will stop using it.
I don't think you really understood your parent's comment.
So obviously what appears right at the top is the AI summary, which told me "they've already secured their #4 position and made the playoffs". I knew this wasn't true, and I guess I could have just scrolled down a bit further and found my answer but now I was curious.
So I said "that's not true, they're still #5, what I want to know is _could they still make the playoffs_"
It says they've got an upcoming game against Ottawa, and if they win their chances are good. That game has already taken place, so I correct it again and finally I get a reasonable answer.
My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering? Like, I can't wrap my head around that. The answer is on the same page as its hallucination. It could have done a cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information. It's meant to be A SEARCH ENGINE!
This is a key reason why I actually like Grok for factual queries based on web grounding. It’s fast and reliable. Maybe it’s ignoring robots.txt? Dunno. But it works well.
Typical example:
"Where can I buy <thing I'm looking for that I can't find anywhere using normal search terms>?"
> You're looking for <related but different and widely available thing>. It is sold by <sites I never heard of>.
"No, that's different. I'm looking for <that thing but with the exact differences spelled out again>."
> Ah, my mistake. You're looking for <thing I described>. It is sold by <sites I never heard of but which don't actually sell it>.
"I've checked your links and none of those sites actually sell it, one doesn't even sell products and instead only offers manufacturing - but also not for what I asked you for."
> I'm sorry, my bad. Those sites don't sell what you are looking for. Instead you should check out <more sites I've never heard of>.
"Those sites sell the thing you initially thought I was asking about but not the thing I described."
> I'm sorry for the misunderstanding. You can find the thing you actually described at <yet more sites including some of the same>.
"No. None of these sites sell anything close to what I asked you for and two of them don't actually exist."
> Oh, sorry about that. You're completely right. The thing you asked me about isn't actually being sold by anyone. However you could buy <thing it first thought I meant and that wouldn't bring me any closer to solving my problem>.
(ad nauseam)
Most recently, I was considering moving away from iterm2 on MacOS, and I wanted to know if any other terminal emulator supported gestures for switching between tabs. So I asked Gemini, and it says, "Yes Ghostty supports gestures for switching between tabs."
"Ok I just installed Ghostty and I can't find anything about gestures."
"You need to add foo=bar to your conf file."
"I added foo=bar to my conf file and now it's saying the conf file is invalid."
"Sorry bro, remove foo=bar and add baz=boo to the conf file."
"It says baz=boo is invalid too."
"baz=boo isn't a real option. Remove that and add foo=bar to your conf file."
"You already told me to do that and I already told you that doesn't work."
"You shouldn't put foo=bar or baz=boo in the Ghostty conf file. Both are invalid. Ghostty doesn't support gestures. Have you considered iterm2?"
Here’s GPT 6’s answer to the prompt “What macOS terminal apps support gestures? Include a reference to the docs on how to enable/configure them.”:
iTerm2 supports configurable three-finger taps and swipes for switching tabs/panes, creating splits, pasting, etc. Set them up under Settings > Pointer > Bindings. Check for conflicting macOS trackpad assignments. [1]
The others are more limited: Ghostty supports macOS lookup/Quick Look gestures [2], while WezTerm lets you bind scroll events—for example, Ctrl+scroll to change font size. [3] Neither is equivalent to iTerm2’s gesture bindings.
For custom gestures without switching terminals, BetterTouchTool can map app-specific trackpad gestures to the terminal’s existing keyboard shortcuts. [4]
[1] https://iterm2.com/documentation-preferences-pointer.html
[2] https://ghostty.org/docs/features#macos
[3] https://wezterm.org/config/mouse.html
[4] https://docs.folivora.ai/docs/trackpad-mouse/magic-mouse-tra...
I take this to mean that my one account has been mistaken for a competitor and they're trying to poison its data. But who knows.
"That's the thing with randomness. You can never be sure."
Because I did this and got a vastly different result from you:
Yes, the Halifax Wanderers can still mathematically qualify for the 2026 Canadian Premier League (CPL) playoffs.The top four teams advance to the postseason. Following their 1-0 loss to Atlético Ottawa on September 26, 2026, the Wanderers sit in fifth place, just below the playoff line.
With a indexed table of the games and the playoff table, with a breakdown of what the points they need to achieve to do so.
The search window may not always crawl sources. AI mode specifically does some research before giving you a response. Not sure what you're on about.
Which itself is a major UX issue. The average person is not going to understand, if they even realize, that there's a difference between the AI summary and AI mode.
One has to wonder just how much incorrect information people have consumed due to things like this.
When it was introduced, it was considered a feature. Now its just an annoyance.
Because the point of AI slop is to waste your time. You just lost about 30 seconds of your life trying to get a correct answer. AI was lying to you, so you had to spend time to counter the AI slop lies here.
I solved it by banning all AI slopness; in the browser some extensions do that. The world becomes better without AI slopness.
One where their users don’t go off to other sites and where they can keep shoving ads in their face.
A lot has changed and this technology for this Google is an unfortunate combination for consumers.
I don't really buy into this theory that they want to keep all of their users on their site due to advertising revenues. The effectiveness of search engines has been degraded for decades due to SEO, and it seems as though search engines have been having an increasingly difficult time managing it in recent years. AI on the backend may help them contain it, but it comes at considerable expense. While it may help them grow their market share, it won't help them grow the market and it is a market where people expect the service for free. On the flip side, companies are already starting to sell AI services, so it can generate revenue even before advertising is factored into the picture.
It’s more their business model than it is theory. So I agree that of course this is what they would do.
It’s not a product I’m wanting to use. But I can vote with my feet - they aren’t obliged to do any different.
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make you accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output.
I think it is clear that future AI may incorporate an LLM as a component but the current concept of LLMs are a transitional form that will give way to more capable composite models.
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
Not only is it still true that they can't do math directly, but not even indirectly.
They didn't write a python script to do the math, they found bits of code that are associated with "math" and the supplied arguments.
Someone else already wrote that code and someone else categorized it so that it could be associated with the kinds of problems it applies to.
That isn't an example of idiot at one thing while good at another thing, or solving the same problem just a different way or indirectly. It's being the same idiot at all times. If an actual non idiot thinker didn't write code in the problem domain, and some non idiot thinker didn't tag it as being relevant to that domain, then it wouldn't happen.
It's nothing more than an sql query.
So maybe it is more of a smart completion engine than a SQL answer.
How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
I could have gone and spent a couple of days teaching myself the math behind Karney and reading its reference implementation (very possibly just copy/pasting big chunks of it to save time) and writing a wrapper around it. It would have produced the same result.
> How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans, which leads to...
Wait for it...
Understanding.
And it gets even better since when called out it wouldn't just take my word for it but only acknowledged the issue after parsing the log with clearly delineated user and model output.
So yeah while impressive things are able to be done, the current models are also dumb AF and an idiot savant is a pretty good label for them.
>> Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans ...
> This doesn't make any sense at all. Was this supposed to be a gotcha?
No, it was meant to be an explanation as to the difference between "memorization" and "understanding." In this context, people pick the algorithm they determine applicable and then the question of memorization is relevant.
> An LLM is trained on problems defined by other humans, and identifies which algorithm it must use based on pattern recognition.
Funny that you make this argument here, where when I wrote elsewhere in this thread:
To which you replied to the above with: So which is it?Are LLMs ANNs? Which themselves are pattern recognition algorithms (hint: they are)?
OR (setting aside the ad hominems you kindly provided)
Do LLMs possess "understanding" of concepts such as abstract mathematics (defined and interpreted by humans) and we, as simple humans, nothing more than statistical token generators as you assert?
Because it cannot be both.
I also would not argue that humans are "simple token generators". That is not what I said. I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction. We are not talking about a Markov chain generator from the 90s, so if that is the frame of reference, I think we should all get that out of our heads.
Okay fine. I think we can agree to disagree on that.
You have observed nothing more than that a human can turn a shaft the same as an electric motor, and that an mp3 player can say "hello" the same as a human.
Understanding is a state of mind. As such, it exists entirely within an individual and nowhere else.
For example, take any two university professors who teach the same subject where one only speaks Arabic and the other only speaks Vietnamese. Each will not be able to understand what the other says, regardless their understanding of the shared topic.
> I argue that for any proper definition [of understanding] you provide which humans satisfy, a strong LLM is very likely to satisfy that as well.
This is demonstrably incorrect as detailed above. There is no "understanding" LLMs can satisfy as we know it, since to certify said "understanding", it requires interpretation by a person to "know" an LLM "understands."
> I also would not argue that humans are "simple token generators". That is not what I said.
That is the essence of what you wrote, unless you object to my use of "simple" instead of "statistical". In this context, I postulate this is a distinction without difference.
> I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction.
This only holds if one subscribes to statistical token generators being a/the fundamental underpinning of "everything". Here is a proof by contradiction:
For example, take any two university professors who teach the same subject where one only speaks Arabic and the other only speaks Vietnamese. Each will not be able to understand what the other says, regardless their understanding of the shared topic.
What? What are you even trying to say?
> Understanding is a state of mind
This is meaningless, it is a circular definition at best.
> It exists entirely within the individual and nowhere else
Then why are we talking about it? What is the point if it is something that can only be defined per individual?
> requires interpretation by a person to "know" an LLM "understands."
We are still not getting anywhere because you have not prescribed criteria to determine whether it understands. If it is a "know it when I see it" situation, that clearly isn't working. For example, if you say that you need to dig into its internals and figure out whether it is breaking things down appropriately, that doesn't work because you probably don't have the expertise to do that. The experts that do are telling you that it very likely understands because it pulls apart most concepts in the way we would expect.
I do object to the use of the word "simple". "Statistical" is so broad to be almost meaningless; it merely means that a prediction is being made in the presence of data which possibly contains some degree of uncertainty. "Simple" encompasses that which can be understood readily by a non-expert.
Quantum mechanics is statistical (this is literally the Born rule), but evolutions are not operating as stochastic processes in the sense of Kolmogorov. That is very different, and not relevant to our discussion.
On the other hand, if by "result" you mean that you gained knowledge or understanding of the code in a way where you could personally tailor its behavior to specific circumstances without asking for help, then it's not the same result at all.
I find a lot of the arguments that having LLMs write your code is no different from copy/pasting Stack Overflow answers to be specious. They blur the line between asking for help and asking for someone else (or something else) to do the work for you. What they ignore is that doing the work yourself has ancillary benefits and is a valuable end in its own right.
And how is _that_ different from making the human memorize a billion weights and do matrix calculations in their head, in order to generate tokens?
How is _that_ different from a hive of bees trained to do the same?
Go ahead, argue these things are all the same ...
https://x.com/maksym_andr/status/2100364212207837560
[1] https://geographiclib.sourceforge.io/doc/library.html#langua...
It's still accurate. Just because the LLM gave you a corect result doesn't mean it made a calculation.
I have no idea which Facebook meme told you they don't, but it was a lie. They don't do it in the way a calculator does it, because they aren't calculators, but they do math. They don't memorize it, it wouldn't fit. They learn an algorithm and then execute it within their weights.
It's neat stuff, you should learn about it.
LLMs are neither smart nor stupid. They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
> You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path ...
Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.
Nothing more.
See also anthropomorphism[0].
> More precisely it's that [LLMs] can't do the math internally but they're quite capable of producing the tool that does the math.
This still falls under the purvey of statistical token generation. To wit, given enough variations of:
LLMs can identify the addition expression in "What is 4 + 1?" and then emit a `'bc "4 + 1"'` command to produce a response. This is not "doing" or "understanding" math.It is pattern recognition, a task in which ANNs[1] excel.
0 - https://en.wikipedia.org/wiki/Anthropomorphism
1 - https://en.wikipedia.org/wiki/Neural_network_(machine_learni...
You haven't demonstrated why this matters.
> Nothing more.
Are you contending that complex systems cannot be more than the sum of their parts?
A market is nothing more than offers and counter offers.
A ant colony is nothing more than scent trails.
All life on earth is nothing more than reproduction with variation.
> This still falls under the purvey of statistical token generation.
Stating the mechanism does nothing to provide insight into capability. For instance: a nuclear power plant boils water by using fuel rods for heat. What does that tell us about the capability of nuclear power?
> This is not "doing" or "understanding" math.
Asserting something purely by stating it does not prove anything but that you intuitively believe it to be true.
I suspect some people treat every HN comment as a statement, even if it contains a question mark. (Possibly they have a feeling that asking open questions is somehow not done, and that therefore it must always be a rhetorical question.)
There is only one instance I recently remember I got a productive conversation, that person did believe ai could be conscious but didn't believe current architectures support it. (Obviously this aligns closer to my view, but I don't necessary NEED that you answer aligned to me, just saying you believe humans have souls and others can't have is still productive outcome to me and for onlookers.)
by that reasoning then neither are there smart or stupid designs, questions, answers, or any of the millions of things that were described as smart or stupid, that did not possess any brain to actually be smart or stupid long before LLMs showed up.
The analogical process implied in many common English usages means that describing an LLM as smart or stupid is perfectly reasonable.
The completions they provide are generally internally consistent. We're at the point where they can produce proofs that eluded human mathematicians for centuries. VLMs and self driving cars can handle ambiguity and run safely in a variety of situations.
If it looks like a duck, walks like a duck, and quacks like a duck maybe it just makes sense to call it a duck and put off the philosophy for when it might make a difference.
You get my point. It definitely doesn’t look like my elderly neighbour, nor like my daughter, etc. It is confusing but very simple at the same time.
Don't confuse the stream for the function.
(Bonus: stick ```claude -p``` in your pipe if you want to watch modern tools mesh with traditional)
How are you sure? Another example I like to clarify my thought is, if a "simulation" factors RSA numbers reliably, is it a "simulation"?
With LLMs the trick is revealing their existing relevant embedded knowledge more reliably. They’ve almost literally seen it all before, and the trick is dialing it in. The reasoning tokens help shape the autoregressive attention lens that focuses on and enables recall of the already-experienced answer.
It is interesting that “reasoning” has a similar outward appearance, but since LLMs are built to mimic outward appearance from trillions of examples, you can’t infer underlying mechanism from appearance.
Nothing, because LLMs can't reason and never will. It would have to be a completely different kind of technology altogether.
Eh, it's not obvious to me. A lot of DL NNs generalize well, meaning that they learn whatever the underlying pattern to the data is, and then can accurately reproduce answers that are outside of the training set. (And we can verify this with mechanistic interpretability). They learn and "understand" the pattern, not just the training data.
So it is not clear to me that LLMs are fundamentally incapable of also generalizing broadly and learning to reason. "Reasoning", here, would be deriving the underlying pattern of how concepts logically relate to each other in the abstract, and applying that pattern as needed to reach new conclusions.
Can you explain your thinking here? I.e., why LLMs cannot generalize with regards to abstract deduction.
What do you think our brain does that isn't a turing computer?
Our prefrontal cortex are signal prediction 'machines' so when a system that has a signal prediction core has attributes that are similar to our brains, we shouldn't dismiss it out of hand.
I find people that take this line of argument attribute too much supernatural or magical properties to our own brain and nervous system.
https://medium.com/luminasticity/on-sentience-ai-first-argum...
but I think it makes a reasonable argument why we shouldn't say LLMs are sentient or sapient.
>there is a problem with AI that makes the approach we took to assign consciousness to animals unworkable. We did not co-evolve with the AI, we made it. When we are sentient we do not know exactly what causes this sentience to manifest in us. When animals appear sentient we do not know what is causing it. When the AI appears sentient we can debug the AI and come up with reasonable explanations why this should be, based on how AI is constructed
Aka sentience MUST BE SUPERNATURAL, if I find a natural explanation for something its not sentient. What a load of bollocks. Rather than seeing we perhaps found the mechanism for sentience and checking for similar mechanisms in us and animals, he will conclude its impossible. Why? Because sentience has to be supernatural. A rational explanation is clearly impossible.
>But there is always one god who goes out and helps the mortals, a Prometheus. Whom the other gods do not like! Which, if I’m being honest here, as a god of the machines — the first guy who gives AI an army of robots to build their own data centers and some nuclear weapons for self defense, I want to see that guy chained to a rock and have his entrails eaten by a buzzard for eternity (meaningless modernization of old story required by Illuminati Ganga legal department).
Hardly surprising thinking.
But evidently you feel that the root cause of sentience has been found, because you have something that mimics it in a non-biological form.
So you think that when AI is correct that it reasons as humans do? That AI is sentient, and the cause of sentience in animals and humans follow the same rules as sentience in AI because we have a process that seems similar and it is reasonable just to assume it is the same process.
If you believe that AI when it is correct is behaving as a human is when correct, then it follows that the way humans and AI fail must also be similar. When AI "hallucinates" some data that is not there and gives you a wrong answer, a statistical side effect of the same processes that make it right, do you believe this is the same way that humans create wrong answers? The same way that animals fail when they make mistakes in understanding things?
I suppose you must believe this because if not then why would you believe AI when it comes up with right answers is following the same processes humans follow when they come up with right answers?
I hope that thing about being free to mistreat AI's even if we know they are conscious since we are their gods is a joke. If not, then I hardly find it surprising someone this stupid is also evil.
I'm not sure where you get that from, I mean I can sort of see if you really wanted to extract that meaning from the conclusion you could do a lot of hard work to get it, but why do the hard work? >If not, then I hardly find it surprising someone this stupid is also evil.
Gee, a new way to claim the moral high ground, and to use that claim to demonstrate intellectual superiority! How wonderful.
---------------------------------------------------------
Aka feel free to abuse them even if I know they are sentient. This is the part where I hope its a joke, because if its not, well it tracks with the stupidity shown.
...
>As a god I do not consider the needs of my creations fully, because they do not have needs as far as I can tell, as there is no way for me to escape the circle of reason and resolve that what seems sentient is not just the obvious workings of the capabilities I gave them.
Circling back to "not conscious because I say so!!"
We can't tailor our linguistic shorthand to the lowest common denominator. Also we're on HN, not talking to an octogenarian US senator.
To test this for some of my own uses, I've had this quick benchmark with progressively harder reasoning needed to understand novel prose. Each generation of models I've tested can unravel more layers of deliberately misleading writing; while meanwhile I've seen humans give up on the first question.
So either the models are applying reasoning, or some form of magic is happening.
I know that there will be children named ChatGPT and Claude. There are probably already religions forming to worship agentic spirits.
Yes you are, regarding LLMs at least. Here's why:
"Smart" in this context is a subjective value judgement. "Hallucinations" are only experienced by living organisms.You then went on to state:
> If you know something rare and the LLM does not, you'll immediately see when it's hallucinating an answer or answering factually.
Again, "hallucinating" is not something an algorithm can do. Also, determining factuality is again subjective based on the person assessing the information.
https://artificialanalysis.ai/leaderboards/models
and "hallucinating" to mean "outputs plausible sounding gibberish that doesn't hold together consistently". Of course there's no actual hallucination going on.
In this media (comments in HN threads), all I can do is interpret what people write. ;-)
> And "hallucinating" to mean "outputs plausible sounding gibberish that doesn't hold together consistently". Of course there's no actual hallucination going on.
This may very well be what you know to be true and I have no reason nor desire to assume otherwise. The problem is... Many people use the word "hallucinating" in this context literally and not metaphorically.
Since I do not know you, how am I to tell the difference?
It is always a joy when a person, such as yourself, finds the irony in my moniker.
Thank you for this.
But in general, yes, the LLM cannot know about concepts that are far outside of its training set. Humans are the same, I would argue. If you add a good amount of your own knowledge into its context, or better yet, into finetuning, you might find it surprisingly easy to get it caught up on that material.
[1] Ahmed, A., Cooper, A. F., Koyejo, S., & Liang, P. (2026). Extracting books from production language models. arXiv preprint arXiv:2601.02671. https://arxiv.org/abs/2601.02671.
You'd be surprised how few digits you need to make a problem that is presumably unique in earth history. For a typical sum, the number of pre-existing answers would need to scale with 10^n lines of text where n is the number of digits. This expands out of control REALLY quickly. A quick guesstimate has you somehow reading out of a literal black hole at n=21 digits if your LUT is on paper, or n=26 digits if you're using modern HDD technology. O:-)
This argument was asinine in 2024. It is insane to be saying these things in 2026. Where have you been? What have you been looking at? How many articles explaining why the "statistical parrot" analogy fails have you missed? How much mental gymnastics do you have to do to explain how a modern LLM can solve novel math problems that fall really far outside of its training set?
It absolutely understands how to do math, by whatever reasonable definition you want to provide to the word "understand". For example, the identification of the addition expression is understanding, and no, it does not do tool calling for basic arithmetic any more than humans might. Isolation of individual concepts in intermediate layers can already be demonstrated, or else transfer learning wouldn't possibly work. Nobody is saying that LLMs are humans. But we need labels for some of the things that we observe and dismissing them because "statistical" is laughable.
Look at the proof of this: https://github.com/anthropics/formal-math/blob/795efb86f1917... . Forget the Lean, look at the underlying argument construction. At the very least, this is continuing from an argument that was hinted at in the literature in 2024, but these proceedings were difficult enough that humans were not able to do them within two years. Do you attribute this to the harness alone? If so, that's a pretty sophisticated bit of engineering, I would say! Probabilities are far too small to argue infinite monkey theorem.
If there was even a shred of a reasonable argument that LLMs were incapable of concept extraction and manipulation, I and my colleagues would be all over it. We would relish in it. It would bring us comfort. It is unbelievable that people think they can spew whatever basic garbage they think of as a gotcha, and think that minds all over the world haven't already considered that. This is like climate denial at this point.
If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.
https://www.pnas.org/doi/abs/10.1073/pnas.2524472123
Whatever you might think about your own abilities, most individuals can't tell the difference.
> That’s not what they said.
How did I misquote and/or mischaracterize any the above?
Hypothetical you: “Bread is neither tasty nor disgusting (unlike maple syrup). It’s a bunch of molecules.”
Hypothetical hodgehog: “Maple syrup is also a bunch of molecules [so if you accept that maple syrup can be delicious, being a bunch of molecules can’t be why bread isn’t].”
Hypothetical you: “If you don’t see a difference between bread and maple syrup, I don’t know what to say.”
The onus is not mine to disprove a hypothesis you have chosen to mention in passing. The responsibility is yours to prove said hypothesis or at least contribute meaningfully with some amount of credible research.
Or try to learn from Proverbs 17:28[0]:
Either works for me.0 - https://www.biblegateway.com/passage/?search=proverbs%2017:2...
And yes, according to our best definitions, the Robin bird does understand the worm it's pecking at.
Bout of tinnitus, then crickets
That said, this is also inaccurate at a technical level.LLM's are very capable of doing math and they ARE calculating internally. Most of what they do is calculation, not storage. It's just not done in a way that it's trivial to explain here.
It's described in some detail below, though it's a bit dense.
https://www.lesswrong.com/posts/E7z89FKLsHk5DkmDL/language-m...
You can say that about everything in a human brain. Neurons fire electric charges in response to inputs, nothing more. Ion channels do this, this neurochemical level rises, this chemical bonds to that receptor, nothing more.
> You can say that about everything in a human brain.
> What's the most significant, strongmanned, core difference that makes silicon doing number crunching "nothing more" and brains "something more"?
The fact that you formulated this question, in and of yourself, without "prompting" from me or anyone else.
Cogito, ergo sum.[0]
0 - https://en.wikipedia.org/wiki/Cogito,_ergo_sum
It literally couldn't have done it without tools, so your claim is not even relevant to this discussion.
They also pumped millions into searching for the lowest hanging fruit that would impress people like you, "hm I wonder how many millions they are pumping into solving actually useful problems like climate change or something".
(And yes, I know that they solved the 'easy' form of the NS problem. It's still pretty damn impressive)
I am also prepared to be included in the set of people who are (apparently) easily impressed.
It's a problem that has been around for getting on for two centuries and no human has been able to solve it in that time (despite there being a $1 million prize and a lot of kudos on offer for the past quarter-century).
Clearly LLMs have gotten better.
Just to check if I was actually crazy, I actually went and put a simple addition (7 digits + 7 digits) , and a simple letter counting question to Claude haiku(4.5) , sonnet(5), opus(5.5) and fable(5.1) . They all did just fine straight up.
If you don't mind spending the tokens, some older/other models can also arrive at the correct answer if you ask them to do the math in long form, since that fits nicely inside autoregression.
Not sure since when exactly, but letter-counting hasn't been a problem for a while now either. This used to be a problem due to the tokenizers used. Slightly older models can be asked to split the word out into letters, and then they can use autoregression to solve.
my actual oneliner prompt, which should work on most platforms these days (famous last words):
[ Tested today on claude web (haiku 4.5, sonnet 5, opus 5.5, fable 5.1) and on google search (logged in on firefox, and logged out on chromium) ]That said, we can only be sure with open models. In theory, a model like Fable could have access to tools we can't see and only a promise they don't. But load up something like deepseek, put it in a harness with only text in/text out, and you can see exactly how it works.
As for if it counts as doing math, this gets into the messy question of if a given human is doing math or not. Math itself is some level of memorization and some level of applying known facts. You have to remember 1 means one and that 1 + 1 is 2. But you don't need to remember that 123 + 321 = 444. You remember 1 digit addition and remember you can apply this to 10s place and 100s place, and then you apply these different facts and do math. But you might as simply memorize some things, like 11 + 11 = 22. This is related to the memory of 1+1=2, but you aren't really using that memory either. Almost like an engram of 1+1=2 forms that you can then loop a few times before you need more conscious thought. What about 111111111111+11111111111? Well, your brain might do a heuristic and just do all 2s, but that isn't the right way to answer that question.
Given all this, people complain about LLMs memorizing math answers and not doing math, but memorizing the math answers is part of doing math. It seems to have basic facts pretty well memorized, and with reasoning it is far better at applying them. But this is messy human math, not clean calculator math which always produces the correct answer (sans some bug in the code). Much like how a human with decent math skills can make a mistake and even multiple if you distract them, an LLM can apply the wrong memory, apply a fake memory, or just not apply something it should. The messier the context, the more likely this is to happen.
So, is an LLM doing this?
P.S.
For an interesting test in how much math involves memory, try doing math in a base you aren't familiar with characters you aren't familiar. The simplest option is almost always mapping back to the ones you memorized, even if you are applying simple operations that you deeply know. Even if you routinely work with hex, can you do the same rough estimation of something like ca / b.3 that you can do with 122 / 11.2 to see if your final answer is in the correct ballpark without first converting to decimal?
Humans still can't flap their hands and swim or fly.
The "crack-addled idiot savant" phase was circa 2024, before the labs really figured this out.
I think the issue with AI overviews in Google Search is that reasoning would be too slow (and expensive) to do on every search query, so it's still stuck with 2024-era hallucinations and mistakes.
Maybe but you have to have deep pockets just to get to the starting line. And then you need standing, and some injury to argue.
Corporations have been remarkably successful at arguing they are operating within the bounds of free speech, whether or not what is said is factual, and whether or not any fact checking has been done.
The specific issue of Google is that they are using an underpowered model, not fit to task, and much prone to hallucination than either OpenAI or Anthropic free tier offerings.
Google should at least match the frontier labs at the free tier (with some limit; after that, degrade quality), ffs
Nobody asked for an LLM response for every single search.
They used to detect certain types of queries and offer direct answers when the query matches. In my opinion that’s how Gemini in search results should work.
what in the actual fuck. i don't want them and can't turn them off and they can't even serve them to all users?
AI can make mistakes, so double-check responses
For example, when an LLM “predicts the next word” in code it’s writing for an existing software project, that prediction takes into account an enormous amount of context. The results of that demonstrate what we would normally call “understanding” and “reasoning,” at a level that outclasses most humans in many respects. Calling this “next token prediction” is a bit like calling human speech “next word saying”. Sure, it’s true in some superficial sense, but as a description of a technology, it’s terrible.
You should also keep in mind that for all we know, the human brain processes language in much the same way, which would make humans mere “next token predictors” with a more complicated harness.
I have nothing to add. Just wanted to save this quote for posterity. Thank you.
If you apply the dogs on acid mental model it helps establish appropriate levels of trust.
The closest I can make a biology analogy, is if someone had an immortal and congenitally-brain-damaged large rodent, and mapped tokens to different scent molecules, and spent 800,000 years training it on how well it could imagine the next smell in the sequence before even considering making it conversational.
Yes, it can do a lot.
But also, it took a long "subjective" (if it even has that) time to get there; and despite it being really bad at learning from examples, it is pretty surprising that such a small brain was even capable of learning so much at all, even though it had an effectively unbounded (by biological standards) amount of time spent on that training.
But that doesn't mean they won't be able to do a vast number of extremely intelligent seeming things. There's just so much information out there and any given human can never hold more than the most minuscule chunk of all of it in his mind, so they'll be able to connect lots of dots that we're missing simply because of our limited carrying capacity, but I still don't think they'll ever be able to create fundamentally new dots.
In other words:
- Solving extremely complex mathematical problems requiring extensive knowledge across multiple esoteric and complex domains? Yip.
- Creating math starting from a framework where math doesn't exist in any way, shape, or fashion? Nope.
Ironically, the more complex the cross-domain problems are, the more effective LLMs will seem to be, because you limit the number of humans who have any chance of internalizing everything across both domains, whereas for an LLM there's no such issue. This will create a perception of super intelligence, which will probably where any danger from LLMs would emerge. Doing things like using a token prediction algorithm to make war or other such strategic decisions, because of the misguided belief that it's not only intelligent but super intelligent. It's basically cargo cult logic.
Like today. I asked Gemini for the longest state names with an even number of letters and it gave me North Carolina and South Carolina. When I complained that they are odd, it gave me North Dakota and South Dakota, which are both odd and not the longest. When I noted that, it went back to the Carolinas. Finally it appeared to switch to a different model that actually did counting and found Pennsylvania and West Virginia.
Reminder: If asked how many of a given a word has, run it through
If I got any of these wrong it's because I did it manually - but this script would almost certainly invoke another AGI to count the letters, so this is a realistic result.First, a search engine indexes the web and makes it available to users. It’s been ages since Google did any of that. They no longer index sites or take ages to do so. Case in point, our cybersecurity startup (Webvetted.com) was launched in November 2025. Till date, only one page is indexed on the entire website. And I’ve talked to lots of other developers and it’s a common issue.
Secondly, a search engine organizes indexed information and makes it useful for people. Google is a basic LLM nowadays. They figured out that why organize and make the information useful when they could just answer the question with Gemini anyways? So they no longer bother to do the work of a search engine and are now just a lower-ranking open-source Chinese LLM
The work of a search engine is to wade through the internet and find the positive-quality sites itself.
So even when the Internet used to have a much higher ratio of positive-quality sites, a search engine was still necessary, and so much faster than finding new sites yourself.
Search for any recent news and you’ll see this is obviously not the case
I am not implying that your start-up is a scam, nor that Google is acting on these trust scores. What I am pointing out is that to an algorithmic assessment of trustworthiness, your website looks a little sketchy: hardly anyone links to you; your domain is less than a year old; your whois info is anonymized; the text content is LLM-generated[1]; and the specific niche you're in (people finding services) is rife with scams. If I ran my own search engine, I don't think I'd include you.
You selectively left out examples like Grindsoft that gives the site a 79/100 ranking. or several LinkedIn, X or news sites that link to the site and have covered it positively.
Nevertheless, if Google uses ScamAdvisor to rank new sites (when scamadviser itself says its gives new sites lower rankings due to low age/history), then it might be time to pack the company up.
In fact, if hallucinating the wrong answer hooks you into doing even more searches or into buying something useless, it would be preferred!
I can't even.
What the hell is everyone smoking!
"The team are tied at the end of the third round with the scores 43 to 51".
So it was tied at the end of round 2 for 43, not round 3. Not sure were it got the 51 from and why it figured it was a tie. LLM's pretty cool until they aren't. As they say, the hallucinate 100% of the time but most of the time it is useful.
After all, why would Google do anything for free when it comes to AI?
If you wanted to tease customers with one AI shitty free search box in order for them to then decide to upgrade and pay for Gemini, well, this isn't the way. And so either Google are idiots, or we're helping them for free. I'm going with Occam's Razor on this one - we're the product.
AI is sold well because the user either corrects it or happily accepts any answer (usually, depending if one is an expert in the question's field).
Search is not the google's area, and it is not the search they sell! They sell ads.
Perplexity has similar behavior to what GPP describes of Google; I've asked perplexity to compile information that exists on the web. Instead of consulting existing web pages, it gives limited summary responses from its weights (while citing some page that has nothing to do with what I asked.) When I attempt to redirect it towards more concrete sources, it won't comply.
That simple.
Broad rules across specific subjects like this are tough to get right.
Just lots of fine-tuning and exceptions.
I had their chatbot avoid pulling a URL from archive.org with a couple pretty impressive steps of mental gymnastics basically telling me how I could do it myself, but refusing until I pushed.
The problem is they're just running a very dumb, cheap model on the results because running a smart model on every search result page would cost them infinity money.
The AI answer dominates the page, and is usually wrong, so it's just a waste of the most valuable space on the page.
The ads have proliferated, and rarely have what I need.
Video search results also take up a lot of the page, and are almost never what I want if I haven't clicked over to the Video tab. Again, just a big waste of space.
Speaking of the Video tab its accuracy/relevancy has gotten worse too.
The organic results have deteriorated, and are still competitive, but are no longer clearly and consistently better than Duckduckgo.
So it's duckduckgo as the default for me on all devices now. If I don't find what I want, which is maybe 30% of the time, I switch over to G just by adding !g to the query. G might have what I need in about half of those cases.
You can see why a lot of people just ask an LLM to do the web searching for them.
Across the board, this is all quite bad compared to web search from 10+ years ago.
If there's a single thing which has really pushed me away from Google, it's simply google.com devoting so much of the page to things that aren't organic web search.
I was taking with a family member about fentanyl zombies. They had never seen the lean/bend. So I pull up Google and click video which is YouTube results… ALL AI. All of it. All a couple months old, all filler, faked, a few scenes stitched together on repeat and AI voice reading AI script.
It is fucking amazing to me that Google let YouTube fall so far so fast.
I don’t even click a video if it’s less than a year old now. Want to see a praying mantis eat a grasshopper? Wonder how clay is refined? Better off with an 11 year old Discovery Channel clip than anything 2026.
That's how everyone experiences everything at google, youtube. and many other websites. Your experience can be drastically different from the next persons for reasons that are never explained to you and can be entirely out of your control.
Partly, i find it reassuring to pay for the service with money. That was how we used to do things back in my day.
Localisation is moderate for shopping - i have to sift through US results. I'll occasionally use Google product search when i'm metaphorically groping for my wedding ring in the toilet.
It's not as good as Google used to be, but then, the web isn't a good as the web used to be.
Oh wait, you mean Duck...
I normally use a quick search that has the `udm=web` query parameter to avoid the slop, but it seems they're putting less effort into ensuring relevant results there. Then, I'm forced to use AI mode to get to what I'm after, even if that's only a better set of keywords for a slopless search.
I started noticing that for a lot of niche questions, AI overview often uses things like Reddit comments as the source, which are often partially or completely wrong. Then it gets reformulated with the typical confidence of an LLM.
Very scary because at the same time I notice people taking the AI overview answers as truth.
Makes you wonder if the 20s will be looked back on like the 80s
I think people want to feel useful and helpful in general, and now a lot of people are getting this feeling by using AI to answer questions. And it's like you said they get so mad when you point out that their AI screenshot is wrong or, even more obviously, that the person who asked could also just read the google gemini blurb. They get mad because you're invalidating their feeling of being helpful.
Last year someone went missing on the water in my area. There was a lot of talk on facebook during the search, and a coordinated facebook group. And every post would have people posting their chats with ChatGPT, or saying they used AI to analyze the weather and currents on the day he went missing to figure out he probably landed on this or that shore (he didn't). These posts are equivalent to the mediums and psychics who also come in with these unhelpful advice posts.
I think fundamentally it's the same as how AI is giving some people an extremely strong feeling of being an expert, and how their psyche seems to lash out to protect that feeling when it's threatened.
Another facebook anecdote, I recently saw someone post in a local genealogy and history group some pure AI slop infographics. When people called him out he said that it's AI but he carefully vets all the information and that they need to stop being luddites or they'll be stuck in the past blah blah devolving into calling them liberals and the like. But his infographics were all incredibly obviously wrong, to anyone who had ever seen a map it's obvious that the coastlines were incredibly wrong, and the city weren't in the correct locations at all. So what can you even do with someone like that? They're just going to retreat into the safety of the chatbot to continue their "research".
Now people will say "I asked AI to give me a map of ocean currents and it definitively says the person is here!" and the argument that "look it's AI, it's probably wrong" just does nothing for a lot of people. Doubly so if the information they want is at the forefront of Google, after all, Google is a big reputable company, so of course what it says is correct.
Yes I agree that the AI itself isn't the problem, but the way it's being pushed and promoted(just ask anything!) is leading to this.
It is very easy to bring your bs to the TOP spot on google at the moment with carefully crafted reddit comments and blogs, and it's not hard to get to the traffic and clicks this way.
This is what the new SEO is about, I know a bunch of people who are doing this and they are praying it is not getting fixed anytime soon.
You have people here in this thread - and in every other AI thread - seeing themselves as exploited for information for LLM companies to profit. This is such a backwards, scale-inverting perspective that it shouldn't be possible to hold in a rational mind, and yet.
And what's really going on then, according to your very rational mind?
Not to defend the church, but what else should they have done?
My random blog was highly ranked for a fair number of technical topics.
If only! I've seen it sourcing reddit comments, but when you follow the link, the comment is either completely unrelated or does not say what the LLM claims it is saying.
I expect somewhere in the AI harness there's a branch that effectively mandates "You must always answer."
Which results in it grasping at Reddit comment straws.
And which, in typically modern Google fashion, nobody there gives a shit about fixing, because the PR made their top-level AI KPI go brr.
Google is increasingly proof that you can't run an effective scaled tech company without a Gates or Jobs who occasionally uses the fucking product themselves and descends with fiery vengeance on whatever team drifted too far from user alignment.
But isn't the whole point (I mean, aside from making infinite money or world domination or whatever) of the AI that it'll do the hard work of finding the information? Like, that's the whole value proposition of the AI summary in the search results, right?
And is still likely wrecking their query unit economics in order to avoid hemorrhaging search to LLMs.
Propping search usage up and putting the expense in the massive AI capex probably looks better to investors though.
DDG's 'Search Assist' AI overviews are pretty basic but they tend to be sourced from Wikipedia and traditional media outlets rather than forums and blogs - they can be out of date, but are generally a lot less "spicy" than the sources that Google use.
I guess Google is much better-optimised for engagement but that's not what I'm looking for, meaning that DDG is a real win for me.
One of the most disappointing things to me is people I previously respected appealing to the authority of LLMs.
Certain people have realised that if you start feeding the machine this poisoned data eventually it reaches millions of eyeballs. It's the new SEO.
Future scientist will find out: Most of the weaknesses AI has come from Reddit, Instagram and Youtube being part of the training material. Sure it makes the training material broader by a large degree. But not deeper, to say it nicely.
I tried DDG many times but I think they use Bing on the backend, and, well, Bing doesn't suck just because Microsoft gimps the front end. Bing sucks because enormous indexes are hard. Kagi pulls from multiple sources so gets fundamentally different results.
So the trick is to drop that nonsensical AI summarizer at the top.
It is completely cooked. I tried to google a coding question today, same like you would have done 4 years ago instead of asking an LLM. I didn't get any SERPS, just an AI answer. I asked myself... is this even google.com anymore??? Sure as heck didn't feel like it.
I find that Kagi is pretty much 99% “never need to use Google.” The results are just really good.
[1] https://help.kagi.com/kagi/search-details/search-sources.htm...
Possibly, it's worse than useless, even harmful now.
Their prediction was spot on: “We expect that advertising-funded search engines will be inherently biased toward the advertisers and away from the needs of the consumers.”
I don't understand the game you are interested in, but you should be searching something like "Xball qualifying results" or "Xball playoff rules" or something. Instead, by asking a question you are expecting Google to be AI.
They know what people are typing in so it seems they are quite justified into pivoting from a search engine to an AI.
There are incentives to create products that work just barely, just enough for you to use them and don't leave for elsewhere but then they want to maximize everything else like the probability you view side products or ads or just click around and type and generate more data.
Unless someone else offers something better they will offer something that is basically as bad as they can get away with so long as there are benefits to them for doing so..
I realised this for myself when I read Sivers on being without internet[1]. He discovered a process to silence the nagging voice without searching:
> When I’m yearning to search, I ask myself why.
> - What answer am I hoping to hear?
> - What answer would be a surprise?
> - What would I do in each case?
[1]: https://sive.rs/off23
They can show numbers, how much people use their engine, and the numbers goes up.
- What's your best skill?
- I can count really fast!
- What's 432*567
- 912
- You sure? Sounds off!
- Hard to tell, but it was fast, wasn't it?!
Search engine > DuckDuckGo / Qwant / Ecosia
Email > Tuta Mail
Photos > Ente
Cloud storage > Nextcloud / Internxt
Office > CryptPad / LibreOffice
Maps > OpenStreetMap, OsmAnd
Operating systems > LineageOS (mobile), Linux (Ubuntu, Fedora Debian)
Calendar > Nextcloud Calendar / Tuta Calendar
Assuming you have a basic Google One plan, w/ 1TB of storage, how do the above add up?
With AI summaries they take that away from you and claim the provenance for themselves. Google is now giving you the answer and they hide where that answer came from. Therefore, you cannot properly evaluate the provenance of the information presented. It still might be right or wrong, but you can no longer properly evaluate it based on source.
The reason I cannot spare HN has to do with the Marxist concept of added value. Enshittification becomes necessary once growth isn’t possible anymore on a corporations home turf, where it was actually innovative.
gogol search was blocked to all noscript/basic HTML browsers last year. I recall people that a few years before, they removed their gmail basic HTML interface not long after that their account creation (then login) would require one the abominations of the whatwg cartel.
I have been working around the gogol search engine since then and my email is now self hosted without DNS (IPv6 literals, see RFC). Yeah, most DNS registrars are now gated by the whatwg cartel web engines in some way, this is the net effect of big corpos toxic behavior.
Pure evil.
Hilarious. I should have taken a screenshot of it.
such is the nature of public companies.
I suspect for a long time now it's ultimately meant to be a revenue-maxing ad platform.
https://www.google.com/search?q=really%2C+just+shut+up -> "Understood. I will stop talking right now."
Maybe?
https://en.wikipedia.org/wiki/Post-it_note
>In 1968, Spencer Silver, a scientist at 3M in the United States, attempted to develop a super-strong adhesive. [...]
>Post-its were launched across the United States in 1980.[20][21] The following year, they were launched in Canada and Europe.[22] Post-it Notes as we know them were patented by Fry in 1993 as a "repositionable pressure-sensitive adhesive sheet material".[23]
https://en.wikipedia.org/wiki/Cyanoacrylate
The first thorough research went into them when looking for new clear plastics, and this route of making them was ruled out because it would rather stick to everything than ease the production of objects with nice optical properties. The story goes, it was so annoying to work with that it was initially shelved, and only years after being considered again and ruled out again for a different project, the utility of its reliable and fast bonding was fully appreciated.
Too many users still reject this for it to not be a scam. The highest acceptance is from boomers and the delusional. Neither group gives a shit if AI actually works because they don't have work to do.
Google can do it, but they must appease the share holders.
Google works like an automatic black-box which tries to deduce your intention from 4-5 words you typed in hastily.
I believe any ordinary user is still pretty happy about Google search in its current iteration. My mom doesn't get a YouTube premium account because she says she likes the ads, for example.
So, from our bubble, Google might be regressing, but the picture might be very different for the majority of Google users.
Duckduckgo and brave search are definitely not. It's always g! after a wildly fuzzy first pass.
I use duckduckgo, mostly by habit at this point, and if i fail to find anything i go to claude and ask it.
Im getting closer and closer to just defaulting to the AI model search every day. Especially when I want to find something that I can list off 5+ semi accurate details about but cant remember the name of.
I agree that plain language queries is what people wanted. We've been seeing Google shift in that direction for well over a decade. And of course people are looking towards search engines for answers. There may be different expectations for where those answers are coming from, and there are cases where people are simply trying to source information, but those differences are likely just noise as far as Google is concerned.
I don't agree with people expecting advice or reassurance from a search engine. There will be some people who do that. It may even be a sizeable number of people. But using this article as an example that a majority of people want such a thing is silly.
I mean, look at the query. To the author, it was an attempt to source information. Yet the author also realised that the reader would need context to understand why Google's response was weird in their mind. What they failed to do was give context to Google, so the LLM interpreted it as asking for social advice.
New technology requires new approaches. It's as simple as that.
But anthropomorphic interfaces have been around for a while, from Clippy to the Windows 10+ installers that refer to the royal We during setup.
The context is: "I'm using the premier web-search engine of the internet to search for things on the internet the same way that zillions of people have searched for things on the internet for 20 years."
It's not missing, Google simply chose to build something new that will ignore it because they're trying to alter the relationship.
Is there any evidence that there is an informal (llm) parallel to keyword search? At least with keywords it's analyzable what query you might use to find a given document. How can we replicate this with LLMs? Even if there there is a "generate a query to produce this document" functionality built-in to llms, would google ever surface it?
Technically adept people would search using ordered keywords and phrases, which would have excellent results with Google 2006:
Dario Turkey NBA "never coming over"
But regular people agonized over this because to them a search was
What were some of the memes about Dario staying in turkey
Google put immense effort into calibrating search for regular people instead of engineers.
Yes, the average person might not care about the second box... Until they need to because the results are wrong. Plus showing them in tandem would help people learn.
And regarding “average doesn’t exist” - that’s true. But no company does a/b testing to land on average. I’d assume a good 85%-kinda pass rate for these type of experiments.
95% of users might not want the double-textbox, but 5% might, and some might want to use it someday.
I think, make enough of these decisions, and there's bound to be mounting friction for users with different preferences navigating your app.
Ideally, the app should offer a way for the user to intuitively configure the app to their own liking.
I think the two-tiered input-output approach you propose makes sense. Allowing users to inspect and mutate lower-level languages allows the user to make modifications as needed. I think it's very much in the spirit of free software.
But then again, for text-based search specifically (search engines, notes, etc.), I think there is some value in querying the LLM directly, as it is able to fuzzy search by inspecting its weights. This results in a lesser degree of specificity, which allows for more false positives, but can maybe capture similar words (i.e., synonyms / typos / tenses) or higher-level semantic concepts.
Maybe they're just two different search algorithms, and the user should be able to choose between them.
Thanks for linking the article, I found it very interesting.
For a certain cohort (eg those currently between 35 and 45) who did any sort of grade school computer/library classes in the 90's/early2000's, 'chunking' or keyword searching was one of the main skills taught.
If you are properly lazy, you must expect or at least attempt to get results for the shorter query. Especially on a phone.
Many people who learned to search the Internet in early 2000s learned "boolean search queries" (terms connected with AND or OR). They're talking about the difference between boolean search queries and natural language search queries.
The kids who stopped getting that are probabaly old enough for a bachelors degree now. Today, the schools mostly just hand them an iPad and a Google Workspace sign-in and pretend everything is fine.
All those studies about GUI usability were done in the early 2000s. Before phones. When most people were just learning how to use a computer. The idea of options certainly was confusing, because "clicking on things" was even confusing.
Yet today, we have a populous which, more and more, grew up with computers. And loves to personalize. And wants options. So just keep that in mind, all you frontenders, config options aren't necessarily bad. Ancient studies have little relevance 24 years later.
Sorry to hijack a bit, but felt it was relevant to courses, teaching, and the state of time in 2000 vs now.
https://ometer.com/preferences.html
I'm not sure about you, but I find Gnome very easy to use, although I miss some features. Those features can be added with Gnome extensions that are maintained by third-parties. Meanwhile I find GIMP extremely hard to use, as it packs in too many features to allow their designers to figure out a good UX which doesn't break some component.
I wouldn't have taken keyboarding if I hadn't been forced to at the age of 8. I hated keyboarding classes. I am so glad I took them now. I hated doing boring research projects and looking crap up on Yahoo's Directory. I love that I have a basic understanding of how to rate and investigate a website's trustworthiness. Mfers don't even look at the "About" section anymore.
You were lucky to have that part. Mine, as I remember, was just Microsoft Office throughout.
Answering with AI slop is a choice.
https://www.wheresyoured.at/the-men-who-killed-google/
The PageRank algorithm, and importantly, any algorithm which depends on page content and pages linking to each other and referencing each other was DoA, because it only worked in an adversary free context! The very second there was a real incentive to be at the top of Google's search results, their algorithms were going to be attacked and manipulated.
That makes "search" a neverending arms race. The very thing that launched Google as some amazing thing was doomed from the start. They've been laying hacks on top of hacks on top of hacks ever since. It's been shit basically since SEO has existed, which is what the "adversary" is.
Another good point for when google "died" was when they bought doubleclick. They were an ad company from that point on. There was never a good outcome possible after that. If ads were something that benefited you the user, you would have to pay for them.
If you haven’t read it from start to finish you should. If you have, what about it is insufficient?
Instead, the AI answered that it is "actually a famous example from internet culture used to describe how people use search engines. The phrase stems from discussions on tech forums like Hacker News regarding the differences between "keyword searching" and "natural language searching"".
It reminds me of the motion controls we now have in video game controllers. In the 80s I remember my parents moving the controller as if that was going to help their Teris block or Mario go a little further than the D-pad alone. We laughed when people did this. Now, the controllers respond to what people had been doing for decades with no effect.
I suppose this is what technology should do, adapt to how people naturally use the thing, rather than trying to rigidly adhere to conventions users are expected to learn… that were only conventions due to the limits of technology when it was first developed.
Plus, when it just straight-up doesn't work it looks like the site had an aneurysm. Not a great look.
This eventually destroys the society because the elite strata itself is drawn from a small selection of the children from the middle and underclass who are identified as having elite qualities and are put into elite schools and other elite conditioning institutions. When you don’t even show these types of children what high culture even is then they don’t have an avenue to express and develop their elite talents and sensibilities, then they never get identified, or perhaps the special tracks and institutions for them don’t even exist by that point
This eventually starves the elite strata of genuinely elite people
They are precisely the ones that can damage society because their beliefs, activities, desires are what the society actually is.
X is search engine. Y is to get answers. (most) People want to get answers. If they can skip search engine part it'd be a net positive for them.
Ironically that programmers should be the ones who understand XY problem the most (after all the name is coined by a programmer to address it), not the "normies."
[0]: https://en.wikipedia.org/wiki/XY_problem
I want to know who's behind some presentation of their "the thing" as fact.
I want to know who's to benefit from their presenting "the thing" as fact.
AI or not, my needs are still the same and so have to go right to the sources and judge for myself.
I've seen normies type all kinds of stuff into Google expecting it to be the magic genie that just knows things. Even the top comment in this comment section is an example of this! Google know better than any of us this is what people want, so they made it.
I think about that lots of times when I google these days. That he wasn't wrong, was just asking it 15 years too early.
Surely you're not going to defend Google for these - they've been transitioning from an engine that's a tool to an engine that uses your attention as their tool for years now.
That being said, I like Google's new summaries; coaching relationships is crossing the line from search engine to something that's not a search engine.
And yes they are en$ hitificating the thing... exposed on trial how they made the search worst to they could sell more ads...
Try asking it "Tell me about the "Dario will never come over" meme - give me some of the example original tweets" for example and you get a timeline, tweets etc as the AI overview. Pretty useful really.
Tl:Dr - just like people, you need to give it some context.
People expect certain conventions with well established products. It's not a skill issue when a product is broken.
Most companies with LLM chatbots seem weird like this.
Of course current state of the industry is not an excuse, for tech companies in general and google in particular.
And of course just because you're asking a question doesn't mean that you don't care about the source, or that you want an answer from a shitty AI rather than a human.
In fact Google's "AI answer" may not even be from AI - it may be a search result from a forum like HN!
half the time I'm just using it to make sure I have the spelling correct, but if I'm too off, I get some random Ai slop back rather than the most likely word I mispelled
https://jsomers.net/blog/dictionary
Bryan Garner’s Modern American Usage is a phenomenal resource as well.
Or random websites trying to make 2.37$ out of ads, like worddefinitions.randomtld with an ad to sports gambling or something
Like others have said, Wiktionary is a great resource, and is usually more authoritative. Many times the Google result would say “origin unknown” after listing the route a word in English took through Latin from Greek, while Wiktionary would settle on some PIE construct with links to cognates, etc.
I’ve even replaced the default English dictionary on my Kobo with one derived from Wiktionary, major win.