Worst thing is, some of these bullshit answers will be medical, some of them financial, it seems pretty certain people are being harmed.
To stick with your post, consider people asking medical or financial questions. For a wide variety of reasons, many of such questions don't have an answer. In such cases, AI is still going to take a crack at it. AI shouldn't be blamed for "bullshit answers" to such questions.
Before using AI, I think people should stop and ask themselves, "Is there really a single answer to this question? Is AI the right choice?"
Earlier today, I searched "pixel 10 wifi 7" because I was confused that GSMArena showed my Pixel 8 supports Wifi 7, but the Pixel 10 only Wifi 6. Gemini confidently claimed that the Pixel 10 does support Wifi 7 -- but that's not true at all. Only the Pixel 10 _Pro_ supports it, as I discovered when actually reading the non-AI search results.
And this is a question about a Google product!
It's really amazing we can make machines do that, and it's really depressing that we think a stochastic bullshit machine is going to give us something we can rely on.
Ask a human a question like this, and they also have a chance of getting it wrong, even when confident.
Why would a human know specs for a random phone off the top of their head? The human response is either "I don't know" or "let me look that up", not a hallucination.
Claude is OK at saying when it can’t find good information, but it’s still 50/50 on citing a source that has nothing to do with its claim.
Also, I asked a thinking model with browsing enabled and got this:
> The Google Pixel 10 is expected to support Wi-Fi 7 (802.11be), based on the Qualcomm Snapdragon 8 Gen 4 / Tensor G5 chipset it will likely use, which includes an integrated Wi-Fi 7 modem. Specific finalized specs aren't confirmed until Google's official announcement.
(Model GLM-5-Turbo - two months old - using Kilo Code in the "Ask" profile; in its thinking token churn it reasoned that it should keep the response brief and direct. Perhaps not the best suite of model+harness for this task, but it's what I had to hand that's not quantized to shit, is a thinking model, and has a web search tool available to it.)
We google something specifically because the humans within reach don't know. The goal of searching is, well, to search pages - we're trying to find a site when we use google search.
The goal when using an LLM is generally different; we want an answer, not a site.
AI agents can verify and summarize URLs, but a plain LLM can not.
They can still be useful, e.g. they're significantly better at finding "I want a thing that does x but not y and it must be blue, or maybe two things that can be glued together to do that" than classic search. But they'll routinely miss extremely obvious answers because the related search it ran didn't find it, or completely screw up what something can actually do. Checking more pages of results by hand or asking humans who know even a little about those fields is still wildly more useful... but they're absolutely slaughtering the sites where people do that, by stealing all the real traffic and sending DDoS-level automated requests.
I built a retro game clone once and I used that project as a way to try out AI. While it wasn't perfect, it definitely wasn't wrong about everything. I'd go so far as to say it was probably correct (or damn close) 75% of the time.
I see people on HN all the time saying AI is terrible, but that just isn't the experience I'm having. I'm willing to admit it may have something to do with me not being able to recognize I'm being fed bullshit. Or, I may be asking really simple questions. Who knows? But AI seems like a pretty useful tool for average people.
I know bits from non-IT fields like RC planes and quads, electric motors, aerodynamics, mountain biking, cars. I often use Claude (Opus 4.7 on Max sub atm) to brainstorm new ideas or refine my understanding of some phenomena, and almost without fail, I can get it to claim something ridiculously stupid or contradictory 5-10 messages in. I can usually catch it, because I don't venture far from what I'm already familiar with, and I also need explanations to be thorough and things to make total sense to me before I accept them, but not everyone is that pedantic.
Though the inconsistency of results between users is definitely another frustrating thing.
It's really depressing how bad things are getting...
An interesting aspect of this is the decrease in quality feedback on th organic links. If most people never get down to the actual links there is very little to tell which ones were good or if they had any relevance.
There is also that less incentive to properly maintain the search algorithms to fight SEO and spam.
For all intents and purpose, organic search results have been given a death sentence and are just waiting for the last moment.
What a wildly irresponsible company
It's a bold position to say that it's the users fault for being lied to by Google. There isn't a "single answer" to most questions. It's still Google's job to provide answers that are accurate and reflect the best information available on complicated topics. That's what they're trying to sell us anyway. When google's AI can't live up to the hype "You shouldn't be asking AI such difficult questions" is not a great response, especially when people are just trying to get web search results and AI is suddenly interrupting with an opinion nobody asked for.
With the power of LLMs you can Google a standard library function and get an inaccurate summarisation of a Reddit discussion where neither side knows what they're talking about
There's even the meme where people ask if the code was the result of a stack overflow question, or answer
I have no idea why this is, but it is impossible that these links are primary sources of the data, if such things even exists at all. In which case, why list them?
It is certainly seems possible that the actual sources of the data is the output of some other LLM.
My guess is you can see this happening with the bots on Reddit where they are refining the answers to one certain thing, often getting two or three the same responses in a row from different users because they have been enforcing themselves by digesting the output of other bots. Waiting to see when they cut down the sentences and start talking garbage.
Whatever it says is a waste of time 99% of the time. Although people believe it, or consider it worthwhile majority of the time because its so simple to use. It's always there, always instant and appears at the very top.
I would much rather people shove a question into a locally running Qwen model and tell me what it said rather than use the nonsense search model. I hate it.
/rant over.
Highly doubtful.
e.g. search for "how do you make money with options"
Google's AI says
"When you buy a Call, you are betting the stock price will go up. When you buy a Put, you are betting it will go down."
Wrong right off the bat, because it ingested a whole bunch of get-rich-quick bull on the internet. The correct version is that if you buy a call you are betting the stock price will go up more than the market expects it to.
I hope it at least has real citations to actual websites like, I dunno, fidelity or some other reasonably competent authority that can explain all the details?
It's an answer that's too short for an expert to find useful, and useless to a layperson unless all they want to do is reply to a post on twitter.
I've never searched for a financial question where I did not want to know all the weird details because why would I search for it unless I was considering doing it? Seems like someone who doesn't care about the answer is going to be more an edge case than I am.
People shoot themselves in the foot because they think NVDA is going to go up after earnings, buy call options, and then even though the stock goes up they lose money because they did not understand IV crush.
People looking for one-sentence explanations should really not be playing with options. In finance you should understand what you're buying thoroughly. If you just want to bet that "NVDA goes up", you should just buy NVDA stock; that is the trade that accurately captures that bet.
While I agree that AI gets things wrong a lot, and someone should read significantly more before getting into actually trading options, this does give a decent overview to give a layperson an idea of what they are, and some key terms on what to look for if they want to dive deeper. That said, with this info alone, there are some sharp edges that would leave the person open to unnecessary risk if they went on this information alone.
> Call Options: You buy these when you believe a stock's price will go up. If the stock rises past your strike price, the option's value increases, allowing you to sell it for a profit or exercise it to buy the stock at a discount.
> Put Options: You buy these when you believe a stock's price will go down. If the stock falls below your strike price, you profit.
Which leaves me wondering if changing the search textually busts some cache that they update using a slower/smarter model.
Newtonian physics is actually wrong, the founding of any country will be wrong, biology is wrong, nutrition is wrong… what can we even teach? what should we teach in this lens? serious question.
If you want to learn about finance, you can learn about it from people who actually know what they're talking about. You can choose to listen to Jim Simons or Warren Buffet or whoever actually knows a thing or two instead of the rando dude you met at the bar. The AI summaries, on the other hand, ingested a lot of internet garbage.
I picked finance as an example because anecdotally, most of the information on the internet by pure token volume is wrong. The Youtubers drawing lines on charts want your attention because they make money from page views; the financial advisors want your annual fees; the brokerages want you to gamble and get your commisions or PFOF (in the case of zero-commision brokers); the market makers and HFTs want your spreads; Reddit users want to show off their lucky, statistically insignificant profit charts for karma points. None of the above have an intention to give you good information.
You can easily make money buying a call without the stock price moving a single cent (IV increases). Funny enough the stock can even go down and with a large enough IV increase you still make money.
Funny enough, the LLM's prove to be more trustworthy than the the posters here on HN like you who are full of shit.
> What scares me is the rampant inaccuracy
With AI ads you get all the power from big data aggregation, the trust/framing of an authoritative voice, and cheap personalization that specifically optimizes for what convinces you. It's too powerful. Even if it only works a small percentage of the time we're interacting with these things so frequently that a small percent is a large number. They're already feeding user profiles into these machines and there's explicit talk about having the LLMs optimize ad campaigns. It's already dystopian if it's ads to get you to spend your money, but people seem to dismiss that. Do we not care that this is also being used in the same way to convince you to believe certain things? To join certain political organizations?
Yeah, these things help me write more lines of code faster (if we include all the lines from our design docs) but I don't like the idea of pointing a supercomputer at my brain and someone else using it to try to manipulate me. That's not a game I'll win. It's not a game you'll win either.
https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2Fn...
What scares me is the rampant inaccuracy. In my experience, the AI responses are wrong about 65% of the time. I just did a search today about an error talking about a disconnected link between apps, and Google AI result summary told me that the error was related to my pulling a USB drive too quickly in windows. The ONLY word similar to my query and that AI response was the word "disconnect". Everything else was clearly about the SaaS apps.
I have people coming to me, asking me questions, then telling my Google told them something else, so now I have to waste time convincing them that it's wrong. Over the past 2 years AI has done nothing for me but complicate my work life.
And of course, this could be because the model is crap, but it could be because they want me to keep refining my query over and over for more ad views. Either way, it's a terrible experience.