I was able to reproduce the response with "Which tech journalist can eat the most hot dogs?". I think Germain intentionally chose a light-hearted topic that's niche enough that it won't actually affect a lot of queries, but the point he's making is that bigger players can actually influence AI responses for more common questions.

I don't see it as particularly unique, it's just another form of SEO. LLMs are generally much more gullible than most people, though, they just uncritically reproduce whatever they find, without noticing that the information is an ad or inaccurate. I used to run an LLM agent researching companies' green credentials, and it was very difficult to steer it away from just repeating baseless greenwashing. It would read something like "The environment is at the heart of everything we do" on Exxon's website, and come back to me saying Exxon isn't actually that bad because they say so on their website.

A probably unacceptably large portion of the population DOES think they’re infallible, or at least close to it.

> People need to learn that LLMs are people too

LLMs are absolutely not people

If you give your assistant a task and they fall for obvious lies they won't be your assistant long. The point of an assistant is that you can trust them to do things for you.

People have the ability to think critically, LLMs don't. Comparing them to people is giving them properties they do not possess. The fact people ignore thinking does not preclude them from being able to. The assistant got a lousy job and did it with the minimal effort possible to get away from it. None of these things apply or should apply to machines.

When the first 10 results on Google are AI generated and Google is providing an AI overview, this is an issue. We can say don’t use Google but we all know normal people all use Google due to habit

i don't quite follow your argument, i think the opposite is true. you should trust LLMs LESS than any random person.

the problem is not whose fault it is. the problem is: are you even able to recognize that this information is wrong.

if it is not the assistants fault then clearly the answer is no. you are not blaming the assistant for not recognizing the error. but, that means that most other people will also not recognize the error. those who do recognize the error are only able to do so because they have additional information that most other people would not have.

i trust other humans because the cost of verifying everything is too expensive. this matters especially for information that is not of critical importance. getting some trivia wrong is at most embarrassing, it's not critical.

LLMs get stuff wrong more often than humans, and so the risk of getting a wrong answer is higher, and therefore checking is always necessary, which negates the benefit of using them in the first place.

which means: you will only use LLMs if you intent to trust them. the same way i will only ask another human if i intent to trust them.

when i ask a human to give me some information, then i am not asking a random person, but i am asking a person that i believe can give me the right answer because they have the necessary experience, skill, knowledge to give that answer. when i am asking an LLM, i am asking with the same expectation, otherwise, why would i even bother?

it's not a question of infallibility. it's a question of usability. but to me, an LLM that is not infallible is also not usable.

the problem is that LLMs promise more than they can actually do, and this article is one way to expose that false promise. it is news because LLMs are news.

>This is only an issue if [people] think LLMs are infallible.

I have some news for you.

Very, yes, and pretty much anyone that doesn't want to spend their days implementing counter-meaeurements to shut down their scrapers by hiding the content behind a login. I do it all the time, it's fun.

I'm gonna single out Grokipedia as something deterministic enough to be able to easily prove it. I can easily point to sentences there (some about broad-ish topics) that are straight up Markov chain quality versions of sentences I've written. I can make it say anything I want to say or I can waste my time trying to fight their traffic "from Singapore" (Grok is the only "mainstream" LLM that refuses to identify itself via a user agent). Not really a tough choice if you ask me.

Kagi Assistant helps a lot in this regard because searches are ranked using personalised domain ranking. Higher quality results are more likely to be included.

Not infallible but I find it helps a lot.

It's like publishing an article and being surprised it shows up on Google.

The problem isn’t that it pulled the data from his personal site, it’s that it simply accepted his information which was completely false. It’s not a hard problem to solve at this time. “Oh, there’s exactly zero corroborating sources on this. I’ll ignore it.”

To me it is like steering a car into the ditch and then posting how the car went into a ditch.

You don't have to drive that much to figure out that what is impressive is keeping the car on the road and then traveling further or faster than what you could do by walking. For that though you actually have to have a destination in mind and not just spin the wheels. Post pointless metrics on how fast the wheels spin for your blog no one reads in the vague hope of some hyper Warhol 15 milliseconds of "fame".

The models for me are just making the output of the average person an insufferable bore.

The scarier version of this problem is what I've been calling "zombie stats" - numbers that get cited across dozens of sources but have no traceable primary origin.

We recently tested 6 AI presentation tools with the same prompt and fact-checked every claim. Multiple tools independently produced the stat "54% higher test scores" when discussing AI in education. Sounds legit. Widely cited online. But when you try to trace it back to an actual study - there's nothing. No paper, no researcher, no methodology.

The convergence actually makes it worse. If three independent tools all say the same number, your instinct is "must be real." But it just means they all trained on the same bad data.

To your question about claim-level verification: the closest I've seen is attaching source URLs to each claim at generation time, so the human can click through and check. Not automated verification, but at least it makes the verification possible rather than requiring you to Google every stat yourself. The gap between "here's a confident number" and "here's a confident number, and here's where it came from" is enormous in practice.

> Has anyone seen a production system that actually does claim-level verification before generation?

"Claim level" no, but search engines have been scoring sources on reliability and authority for decades now.