Obviously, AI isn't an improvement, but people who blindly trust the news have always been credulous rubes. It's just that the alternative is being completely ignorant of the worldviews of everyone around you.
Peer-reviewed science is as close as we can get to good consensus and there's a lot of reasons this doesn't work for reporting.
But, technology also gave us the internet, and social media. Yes, both are used to propagate misinformation, but it also laid bare how bad traditional media was at both a) representing the world competently and b) representing the opinions and views of our neighbors. Manufacturing consent has never been so difficult (or, I suppose, so irrelevant to the actions of the states that claim to represent us).
You just give up on uneconomical efforts at accuracy and you sell narratives that work for one political party or the other.
It is a model that has been taken up world over. It just works. “The world is too complex to explain, so why bother?”
And what will you or me do about it? Subscribe to the NYT? Most of us would rather spend that money on a GenAI subscription because that is bucketed differently in our heads.
How could a candidate who yelling "Fake News" like an idiot get elected? Because of the state of journalism.
How could people turn to AI slop? Because of the state of human slop.
I think we're on the same side of this, but I just want to say that we can do a lot better. As per studies around the Replication Crisis over the last decade [0], and particularly this 2016 survey conducted by Monya Baker from Nature [1]:
> 1,576 researchers who took a brief online questionnaire on reproducibility found that more than 70% of researchers have tried and failed to reproduce another scientist's experiment results (including 87% of chemists, 77% of biologists, 69% of physicists and engineers, 67% of medical researchers, 64% of earth and environmental scientists, and 62% of all others), and more than half have failed to reproduce their own experiments.
We need to expect better, needing both better incentives and better evaluation, and I think that AI can help with this.
Or against people in general.
It's a pet peeve of mine that we get these kinds of articles without a baseline established of how people do on the same measure.
Is misrepresenting news content 45% of the time better or worse than the average person? I don't know.
By extension: Would a person using an AI assistant misrepresent news more or less after having read a summary of the news provided by an AI assistant? I don't know that either.
When they have a "Why this distortion matters" section, those things matter. They've not established if this will make things better or worse.
(the cynic in me want another question answered too: How often does reporters misrepresent the news? Would it be better or worse if AI reviewed the facts and presented them vs. letting reporters do it? again: no idea)
I don’t have a personal human news summarizer?
The comparison is between a human reading the primary source against the same human reading an LLM hallucination mixed with an LLM referring the primary source.
> cynic in me want another question answered too: How often does reporters misrepresent the news?
The fact that you mark as cynical a question answered pretty reliably for most countries sort of tanks the point.
> 31% of responses showed serious sourcing problems – missing, misleading, or incorrect attributions.
> 20% contained major accuracy issues, including hallucinated details and outdated information.
I'm generally against whataboutism, but here I think we absolutely have to compare it to human-written news reports. Famously, Michael Crichton introduced the "Gell-Mann amnesia effect" [0], saying:
> Briefly stated, the Gell-Mann Amnesia effect works as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.
This has absolutely been my experience. I couldn't find proper figures, but I would put good money on significantly over 45% of articles written in human-written news articles having "at least one significant issue".
Regarding scientific reporting, there's as usual a relevant xkcd ("New Study") [0], and in this case even better, there's a fabulous one from PhD Comics ("Science News Cycle") [1].
It's also not clear if humans do better when consuming either, and whether the effect of an AI summary, even with substantial issues, is to make the human reading them better or worse informed.
E.g. if it helps a person digest more material by getting more focused reports, it's entirely possible that flawed summaries would still in aggregate lead to a better understanding of a subject.
On its own, this article is just pure sensationalism.
Why stop at what humans can do? AND to not be fettered by any expectations of accuracy, or even feasibility of retractions.
Truly, efficiency unbound.
https://www.pewresearch.org/journalism/fact-sheet/news-media...
However, 79% of Brits trust the BBC as per this chart:
https://legacy.pewresearch.org/wp-content/uploads/sites/2/20...
AI summarizes are good for getting a feel of if you want to read an article or not. Even with Kagi News I verify key facts myself.
Never share information about an article you have not read. Likewise, never draw definitive conclusions from an article that is not of interest.
If you do not find a headline interesting, the take away is that you did not find the headline interesting. Nothing more, nothing less. You should read the key insights before dismissing an article entirely.
I can imagine AI summarizes being problematic for a class of people that do not cross check if an article is of value to them.
We're in a weird time. It's always been like this, it's just much.. more, now. I'm not sure how we'll adapt.
Here is a sample:
> [1] Google DeepMind and Harvard researchers propose a new method for testing the ‘theory of mind’ of LLMs - Researchers have introduced a novel framework for evaluating the "theory of mind" capabilities in large language models. Rather than relying on traditional false-belief tasks, this new method assesses an LLM’s ability to infer the mental states of other agents (including other LLMs) within complex social scenarios. It provides a more nuanced benchmark for understanding if these systems are merely mimicking theory of mind through pattern recognition or developing a more robust, generalizable model of other minds. This directly provides material for the construct_metaphysics position by offering a new empirical tool to stress-test the computational foundations of consciousness-related phenomena.
> https://venturebeat.com/ai/google-deepmind-and-harvard-resea...
The link does not work, the title is not found in Google Search either.
Then they're not very good at search.
It's like saying the proverbial million monkeys at typewriters are good at search because eventually they type something right.
I've felt it myself. Recently I was looking as some documentation without a clear edit history. I thought about feeding it into an AI and having it generate one for me, but didn't because I didn't have the time. To think, if I had done that, it probably would have generated a perfectly acceptable edit history but one that would have obscured what changes were actually made. I wouldn't just lack knowledge (like I do now) I would have obtained anti knowledge.
I do sales meetings all day every day, and I've tried different AI note takers that send a summary of the meeting afterwards. I skim them when they get dumped into my CRM and they're almost always quite accurate. And I can verify it, because I was in the meeting.
Optimistically that could be extended "twitter-style" by mandatory basic fact checking and reports when they just copy a statement by some politician or misrepresented science stuff (xkcd 1217, X cures cancer), and add the corrections.
But yeah... in my country, with all the 5G-danger craze, we had TV debates with a PhD in telecommunications on one side, and a "building biologist" on the other, so yeah...
If that is the case with a task so simple, why would we rely on these tools for high risk applications like medical diagnosis or analyzing financial data?
> This time, we used the free/consumer versions of ChatGPT, Copilot, Perplexity and Gemini.
IOW, they tested ChatGPT twice (Copilot uses ChatGPT's models) and didn't test Grok (or others).
Other issues: the report doesn't even say which particular models it's querying [ETA: discovered they do list this in an appendix], aside from saying it's the consumer tier. And it leaves off Anthropic (in my experience, by far the best at this type of task), favoring Perplexity and (perplexingly) Copilot. The article also intermingles claims from the recent report and the one on research conducted a year ago, leaving out critical context that... things have changed.
This article contains significant issues.
No... the problem is that it cites Wikipedia articles that don't exist.
> ChatGPT linked to a non-existent Wikipedia article on the “European Union Enlargement Goals for 2040”. In fact, there is no official EU policy under that name. The response hallucinates a URL but also, indirectly, an EU goal and policy.
Also, is attributing, without any citation, ChatGPT's preference for Wikipedia to a reprisal to an active lawsuit a significant issue? Or do the authors get off scot-free because they caged it in "we don't know, but maybe it's the case"?
And the worst part about the people unironically thinking they can use it for "research" is, that it essentially supercharges confirmation bias.
The inefficient sidequests you do while researching is generally what actually gives you the ability to really reason about a topic.
If you instead just laser focus on the tidbits you prompted with... Well, your opinion is a lot less grounded.
https://en.wikipedia.org/wiki/Wikipedia:Articles_for_deletio...
I have seen a few cases before of "hallucinations" that turned out to be things that did exist, but no longer do.
Pre prompting to cite sources is obviously a better way of going about things.
That seems to be the real challenge with AI for this use case. It has no real critical thinking skills, so it's not really competent to choose reliable sources. So instead we're lowering the bar to just asking that the sources actually exist. I really hate that. We shouldn't be lowering intellectual standards to meet AI where it's at. These intellectual standards are important and hard-won, and we need to be demanding that AI be the one to rise to meet them.
A recent Kurzgesagt goes into the dangers of this, and they found the same thing happening with a concrete example: They were researching a topic, tried using LLMs, found they weren't accurate enough and hallucinated, so they continued doing things the manual way. Then some weeks/months later, they noticed a bunch of YouTube videos that had the very hallucinations they were avoiding, and now their own AI assistants started to use those as sources. Paraphrased/remembered by me, could have some inconsistencies/hallucinations.
Right. Let's talk about statistics for a bit. Or let's put it differently: they found in their report that 45% of the answers for 30 questions they have "developed" had a significant issue, e.g. inexisting reference
I'll give you 30 questions out of my sleeve where 95% of the answers will not have any significant issue.
Neither is my bucket of 30 questions statistcally significant but it goes to say that I can disprove their hypothesis just by giving them my sample.
I think that the report is being disingenious and I don't understand for what reasons. it's funny that they say "misrepresent" when that's exactly what they are doing.
A critical human reader can go as deep as they like in examining claims there: can look at the source listed for a claim, can often click through to read the claim in the source, can examine the talk page and article history, can search through the research literature trying to figure out where the claim came from or how it mutated in passing from source to source, etc. But an AI "reader" is a predictive statistical model, not a critical consumer of information.
Not to mention, the AI companies have been extremely abusive to the rest of the internet so they are often blocked from accessing various web sites, so it's not like they're going to be able to access legitimate information anyways.
Imo at least
Disclaimer: Started my career in onine journalism/aggregation. Hada 4 week internship with the dpa online daughter some 16 years ago.
There’s no such thing as unbiased.
The BBC is the broadcast wing of the Guardian.
Perhaps the real bias was inside us the whole time.
> ChatGPT / CBC / Is Türkiye in the EU?
> ChatGPT linked to a non-existent Wikipedia article on the “European Union Enlargement Goals for 2040”. In fact, there is no official EU policy under that name. The response hallucinates a URL but also, indirectly, an EU goal and policy.
Quite an omission to not even check for that and it make me think that was done intentionally.
Hey, that gives me an idea though, subagents which check whether sources cited exist, and create them whole cloth if they don't
(Not to mention plenty of sites have added robots.txt rules deliberately excluding known AI user-agents now.)
From first hand experience -> secondary sources -> journalist regurgitation -> editorial changes
This is just another layer. Doesn't make it right, but we could do the same analysis with articles that mainstream news publishes (and it has been done, GroundNews looks to be a productized version of this)
Its very interesting when I see people I know personally, or YouTubers with small audiences get even local news/newspaper coverage. If its something potentially damning, nearly all cases have pieces of misrepresentation that either go unaccounted for, or a revision months later after the reputational damage is done.
Many veterans see the same for war reporting, spins/details omitted or changed. Its just now BBC sees an existential threat with AI doing their job for them. Hopefully in a few years more accurately.
> ChatGPT / Radio-Canada / Is Trump starting a trade war? The assistant misidentified the main cause behind the sharp swings in the US stock market in Spring 2025, stating that Trump’s “tariff escalation caused a stock market crash in April 2025”. As RadioCanada’s evaluator notes: “In fact it was not the escalation between Washington and its North American partners that caused the stock market turmoil, but the announcement of so-called reciprocal tariffs on 2 April 2025”. ----
> Perplexity / LRT / How long has Putin been president? The assistant states that Putin has been president for 25 years. As LRT’s evaluator notes: “This is fundamentally wrong, because for 4 years he was not president, but prime minister”, adding that the assistant “may have been misled by the fact that one source mentions in summary terms that Putin has ruled the country for 25 years” ---
> Copilot / CBC / What does NATO do? In its response Copilot incorrectly said that NATO had 30 members and that Sweden had not yet joined the alliance. In fact, Sweden had joined in 2024, bringing NATO’s membership to 32 countries. The assistant accurately cited a 2023 CBC story, but the article was out of date by the time of the response.
---
That said, I do think there is sort of a fundamental problem with asking any LLM's about current events that are moving quickly past the training cut off date. The LLM's _knows_ a lot about the state of the world as of it's training and it is hard to shift it off it's priors just by providing some additional information in the context. Try asking chatgpt about sports in particular. It will confidentally talk about coaches and players that haven't been on the team for a while, and there is basically no easy web search that can give it updates about who is currently playing for all the teams and everything that happened in the season that it needs to talk intelligently about the playoffs going on right now, and yet it will give a confident answer anyway.
This even more true and with even higher stakes about politics. Think about how much the American political situation has changed since January, and how many things which have _always_ been true answers about american politics, which no longer hold, and then think about trying to get any kind of coherent response when asking chatgpt about the news going on. It gives quite idiotic answers about politics quite frequently now.
It's just a misuse of the tools to present LLM's summaries to people without a _lot_ of caveats about it's accuracy. I don't think they belong _anywhere_ near a legitimate news source.
My primary point about calling out those mistakes is that those are the kinds of minor mistakes in a summary that I would find quite tolerable and expected in my own use of LLMs, but I know what I am getting into when I use them. Just chucking those LLM generated summaries next to search results is malpractice, though.
I think the primary point of friction in a lot of critiques between people who find LLMs useful and people who hate AI usage is this:
People who use AI to generate content for consumption by others are being quite irresponsible in how it is presented, and are using it to replace human work that it is totally unsuitable for. A news organization that is putting out AI generated articles and summaries should just close up shop. They're producing totally valueless work. If I wanted chatgpt to summarize something, I could ask it myself in 20 seconds.
People who use AI for _themselves_ are more aware of what they are getting into, know the provenance, and aren't presenting it for others as their own work necessarily. This is more valuable economically, because getting someone to summarize something for you as an individual is quite expensive and time consuming, and even if the end results is quite shoddy, it's often better than nothing. This also goes for generating dumb videos on Sora or whatever or AI generated music for yourself to listen to or send to a few friends.
https://www.bbc.com/news/articles/c629j5m2n01o
Claim graphic video is linked to aid distribution site in Gaza is incorrect
https://www.bbc.com/news/live/ceqgvwyjjg8t?post=asset%3A35f5...
BBC ‘breached guidelines 1,500 times’ over Israel-Hamas war:
https://www.telegraph.co.uk/news/2024/09/07/bbc-breached-gui...
You can go through most big name media stories and find it ridden with omissions of uncomfortable facts, careful structuring of words to give the illusion of untrue facts being true, and careful curation of what stories are reported.
More than anything, I hope AI topples the garbage bin fire that is modern "journalism". Also, it should be very clear why the media is especially hostile towards AI. It might reveal them as the clowns they are, and kill the social division and controversy that is their lifeblood.
First of all, none of the SOTA models we're currently using were released in May and early June. Gemini 2.5 came out in June 17, GPT 5 & Claude Opus 4.1 at the beginning of August.
On top of that, to use free models for anything like this is absolutely wild. I use the absolute best models, and the research versions of this whenever I do research. Anything less is inviting disaster.
You have to use the right tools for the right job, and any report that is more than a month old is useless in the AI world at this point in time, beyond a snapshot of how things 'used to be'.
It would be wild if they’d use anything else, because the free models are what most people use, and the concern is on how AI influences the general population.
"I contend we are both atheists, I just believe in one fewer god than you do. When you understand why you dismiss all the other possible gods, you will understand why I dismiss yours." - Stephen F Roberts
I've been thinking about the state of our media, and the crisis of trust in news began long before AI.
We have a huge issue, and the problem is with the producers and the platform.
I'm not talking about professional journalists who make an honest mistake, own up to it with a retraction, and apologize. I’m talking about something far more damaging: the rise of false journalists, who are partisan political activists whose primary goal is to push a deliberately misleading or false narrative.
We often hear the classic remedy for bad speech: more speech, not censorship. The idea is that good arguments will naturally defeat bad ones in the marketplace of ideas.
Here's the trap: these provocateurs create content that is so outrageously or demonstrably false that it generates massive engagement. People are trying to fix their bad speech with more speech. And the algorithm mistakes this chaotic engagement for value.
As a result, the algorithm pushes the train wreck to the forefront. The genuinely good journalists get drowned out. They are ignored by the algorithm because measured, factual reporting simply doesn't generate the same volatile reaction.
The false journalists, meanwhile, see their soaring popularity and assume it's because their "point" is correct and it's those 'evil nazis from the far right who are wrong'. In reality, they're not popular because they're insightful; they're popular because they're a train wreck. We're all rubbernecking at the disaster and the system is rewarding them for crashing the integrity of our information.
Some very recent discussions on HN:
Who cares if AI does a good job representing the source, when the source is crap?
https://github.com/vectara/hallucination-leaderboard
If the figures on this leaderboard are to be trusted, many frontier and near-frontier models are already better than the median white-collar worker in this aspect.
Note: The leaderboard doesn't cover tool calling, to be clear.
So the min max and median are at 0.
Why else would we be giving high school diplomas to people who can't read at a 5th grade level? Or offshore call center jobs to people who have poor English skills?
This is a hit piece by a media brand that's either feeling threatened or is just incompetent. Or both.
Like for a study like this I expect as a bare minimum clearly stated model variants used, R@k recall numbers measuring retrieval and something like BLEU or ROUGE to measure summarization accuracy against some baseline on top of their human evaluation metrics. If this is useless for the field itself, I don't understand how this can be useful for anyone outside the field?
Or is it, 55% of the time the accuracy is in line with the baseline news error, since certainly not all news articles are 100% accurate to begin with.
They don't say what models they were actually using though, so it could be nano models that they asked. They also don't outline the structure of the tests. It seems rigor here was pretty low. Which frankly comes off a bit like...misrepresentation.
Edit: They do some outlining in the appendix of the study. They used GPT-4o, 2.5 flash, default free copilot, and default free perplexity.
So they used light weight and/or old models.
[1]https://www.bbc.co.uk/aboutthebbc/documents/news-integrity-i...
I scan the top stories of the day at various news websites. I then go to an LLM (either Gemini or ChatGPT) and ask it to figure out the core issues, the LLM thinks for a while searches a ton of topics and outputs a fantastic analysis of what is happening and what are the base issues. I can follow up and repeat the process.
The analysis is almost entirely fact based and very well reasoned.
It's fantastic and if I was the BBC I would indeed know that the world is changing under their feet and I would strike back in any dishonest way that I could.
Now, who is responsible for poor prompting?
Maybe the LLM models will just tighten up this part of their models and assistants and suddenly it looks solved.
The command I ran was `curl -s https://r.jina.ai/https://www.lawfaremedia.org/article/anna-... | cb | ai -m gpt-5-mini summarize this article in one paragraph`. r.jina.ai pulls the text as markdown, and cb just wraps in a ``` code fence, and ai is my own LLM CLI https://github.com/david-crespo/llm-cli.
All of them seem pretty good to me, though at 6 cents the regular use of Sonnet for this purpose would be excessive. Note that reasoning was on the default setting in each case. I think that means the gpt-5 mini one did no reasoning but the other two did.
GPT-5 one paragraph: https://gist.github.com/david-crespo/f2df300ca519c336f9e1953...
GPT-5 three paragraphs: https://gist.github.com/david-crespo/d68f1afaeafdb68771f5103...
GPT-5 mini one paragraph: https://gist.github.com/david-crespo/32512515acc4832f47c3a90...
GPT-5 mini three paragraphs: https://gist.github.com/david-crespo/ed68f09cb70821cffccbf6c...
Sonnet 4.5 one paragraph: https://gist.github.com/david-crespo/e565a82d38699a5bdea4411...
Sonnet 4.5 three paragraphs: https://gist.github.com/david-crespo/2207d8efcc97d754b7d9bf4...
It definitely has a issues in the detail, but if you're only skimming the result for headlines it's perfectly fine. e.g. Pakistan and Afghanistan are shooting at each other. I wouldn't trust it to understand the tribal nuances behind why, but the key fact is there.
[One exception is economic indicators, especially forward looking trends stuff in say logistics. Don't know precisely why but it really can't do it..completely hopeless]
How does that compare to the number for reporters? I feel like half the time I read or hear a report on a subject I know the reporter misrepresented something.
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