Google got one thing wrong and nearly destroyed the internet - people need to have an incentive to contribute content online, and that incentive should not be to game the system for advertising.
This in particular dawned on me when asking Claude for instructions in taking apart my dryer. There was literally only one webpage on the internet left with instructions for my particular dryer - the page was more or less unusable with rotten links and riddled with adware. Claude did it's best but filled in the missing diagrams with hallucinations.
I was imaging if LLMs could finally solve the micropayments solution people have always proposed for the internet. Part of my monthly payment gets split between all of the sites that the LLM scraped knowledge. Paid out like Spotify pays out artists.
It might not be a lot of money, but it would certainly be more than the pitiful ad revenue you get from posting content online right now. And if I want to upload corrected instructions for repairing this dryer I would have reason to.
So, mostly to fraudulent AI spam?
AI makes this problem worse in both directions. It makes it fantastically easy to produce ""content"". So if you're scraping content, or browsing content, you're going to run in to increasing amounts of AI. Micropayments makes this worse, because it's then a means of getting paid to produce spam. The problem comes when you want the ""content"" to be connected to real questions like "how does my dryer work" or "what is going to happen to oil availability six months from now".
AI trainers didn't pay book authors until forced to. $3,000 ended up being a pretty high value! But it was also a one-off. Everyone writing books from now on is going to have to deal with being free grist to the machine.
That's probably not the best comparison. Spotify only benefits the big players resp. those with the most bots. If you actually want to support specific artists, you'd have to use Bandcamp or similar sites.
Would love to know exactly what the latest process is to keep slop out of training data.
There's way more value, if seeking out answers, in following the links to external sources, scraping books, and other sources that aren't "unwashed masses saying whatever they want".
:)
It has already done so, and we can be confident in saying that.
Verified content will always be relatively expensive when compared to AI content.
Visits to wikipedia and most sites have dropped. Rtings has gone full paywall. Ad revenue for producing Verified content will be too meager to allow for public consumption.
Theres jokes about GenAI being the great filter; while I doubt this, I do hope this is the final push that makes us think of how we want our information commons to be nurtured.
As a software user I wish I could do the same for all the software I use.
This system is usually called taxes.
Which then pay for the universal healthcare, free education, affordable housing, libraries, parks,.. and so on.
LLM doesn't need to invent it, we should stop allowing them (people and companies behind LLM) to avoid it.
> Reporters may use AI tools vetted and approved for our workflow to assist with research, including navigating large volumes of material, summarizing background documents, and searching datasets.
If this is their official policy, Ars Technica bears as much responsibility as the author they fired for the fabricated reporting. LLMs are terrible at accurately summarizing anything. They very randomly latch on to certain keywords and construct a narrative from them, with the result being something that is plausibly correct but in which the details are incorrect, usually subtly so, or important information is omitted because it wasn't part of the random selection of attention.
You cannot permit your employees to use LLMs in this manner and then tell them it's entirely their fault when it makes mistakes, because you gave them permission to use something that will make mistakes 100% without fail. My takeaway from this is to never trust anything that Ars reports because their policy is to rely on plausible generated fictional research and their solution to getting caught is to fire employees rather than taking accountability for doing actual research.
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Edit: Two replies have found complaint with the fact that I didn't quote the following sentence, so here you go:
> Even then, AI output is never treated as an authoritative source. Everything must be verified.
If I wasn't clear, I consider this to be part of what makes the policy self-contradictory. In my eyes, this is equivalent to providing all of your employees with a flamethrower, and then saying they bear all responsibility for the fires they start. "Hey, don't blame us for giving them flamethrowers, it's company policy not to burn everything to the ground!". Rather than firing the flamethrower-wielding employees when the inevitable burning happens, maybe don't give them flamethrowers.
“Even then, AI output is never treated as an authoritative source. Everything must be verified.”
I believe this policy can never result in a positive outcome. The policy implicitly suggests that verification means taking shortcuts and letting fabrications slip through in the name of "efficiency", with the follow-up sentence existing solely so that Ars won't take accountability for enabling such a policy but instead place the blame entirely on the reporters it told to take shortcuts.
You still need to verify it, but "find the right things to read in the first place" is often a time intensive process in itself.
(You might, at that point, argue that "what if LLM fails to find a key article/paper/whatever", which I think is both a reasonable worry, and an unreasonable standard to apply. "What if your google search doesn't return it" is an obvious counterpoint, and I don't think you can make a reasonable argument that you journalists should be forced to cross-compare SERPs from Google/Bing/DuckDuckGo/AltaVista or whatever.)
With that said, a good RAG solution would come with metadata to point to where it was sourced from.
Sometimes you have a weak hunch that may take hours to validate. Putting an LLM to doing the preliminary investigation on that can be fruitful. Particularly if, as if often the case, you don't have a weak hunch, but a small basket of them.
You still need to check the junk you dig up using the metal detector.
I get where you're coming from (I'm learning more and more over time that every sentence or line of code I "trust" an AI with, will eventually come back to bite me), but this is too absolutist. Really, no positive result, ever, in any context? We need more nuanced understanding of this technology than "always good" or "always bad."
Yes you can. The same way Wikipedia (or, way back when, a paper encyclopedia) can be used for research but you have to verify everything with other sources because it is known there are errors and deficiencies in such sources. Or using outsourced dev resource (meat-based outsourced devs can be as faulty as an LLM, some would argue sometimes more so) without reviewing their code before implemeting it in production.
Should they also ban them from talking to people as sources of information, because people can be misinformed or actively lie, rather than instead insisting that information found from such sources be sense-checked before use in an article?
Personally I barely touch LLMs at all (at some point this is going to wind up DayJob where they think the tech will make me more efficient…) but if someone is properly using them as a different form of search engine, or to pick out related keywords/phrases that are associated with what they are looking for but they might not have thought of themselves, that would be valid IMO. Using them in these ways is very different from doing a direct copy+paste of the LLM output and calling it a day. There is a difference between using a tool to help with your task and using a tool to be lazy.
> it's company policy not to burn everything to the ground!
The flamethrower example is silly hyperbole IMO, and a bad example anyway because everywhere where potentially dangerous equipment is actually made available for someone's job you will find policies exactly like this. Military use: “we gave them flamethrowers for X and specifically trained them not to deploy them near civilians, the relevant people have been court-martialled and duly punished for the burnign down of that school”. Civilian use: “the use of flamethrowers to initiate controlled land-clearance burns must be properly signed-off before work commences, and the work should only be signed of to be performed by those who have been through the full operation and safety training programs or without an environmental risk assessment”.
Didn't one of the magazine's editors share the byline?
Everything occurred exactly as predicted.
I don't know what you've been doing, but the summaries I get from my LLMs have been rather accurate.
And in any event, summaries are just that - summaries.
They don't need to be 100% accurate. Demanding that is unreasonable.
If an intern was routinely making up stuff in the summaries they provided to their bosses, they'd be let go.
I think you are perhaps stuck in 2023?
What failed was extracting verbatim quotes, not summarizing.
If you want an LLM to do verbatim anything, it has to be a tool call. So I’m not surprised.
AI policy without AI usage is easy to read and write.
> We don't use them. That's it.
This sounds overfit to their earlier incident.
Besides, I expect they already have a policy on accurate quotes.
> Anyone who uses AI tools in our editorial workflow is responsible for the accuracy and integrity of the resulting work.
AFAICT that's the actual simplified policy. Reasonable!
When you write things you just need to think about what you want to say and write it. It's not hard.
If you make an LLM generate the text, and you still bear responsibility for whether it's correct or not, you haven't actually reduced the amount of work you have to do at all because now instead of "thinking -> writing" you need "reading -> thinking -> rewriting."
"Reading is easier than writing" sounds like something that someone who never writes anything would think. In both code and natural languages writing is easier than reading. Text is after all a representation of thought, so to write you just need to represent your thoughts in a way others can understand, but in order to read you must decipher what the author was trying to convey from their written word. If the author is an LLM that is going to be a lot more difficult and annoying.
I've had similar feelings about LLM's "completing" code. It "feels" like I'm more productive, but at the same time the "completions" feel more like constant interjections that won't let me get into a flow of writing, and often when things don't work it's because the AI quickly generated code that looked correct at first glance, so I left it that way, but when I tried to run the program it turns out it was wrong, and when it happens I can't help but think: of course it's wrong, I haven't written the code yet. There is code, the LLM wrote it. But I haven't written it. I haven't done the work. So it's like having a "placeholder" that you still HAVE to check but there is no metadata that says this is a WIP, no # TODO: check the LLM output. It's a placeholder camouflaged as finished work.
\2 Why Crikey retracted an article that we found out was written with AI help (2026) - https://www.crikey.com.au/2026/03/19/crikey-responds-to-ai-c...
Yesterday, we published an article by a contributor who later confirmed they used AI in some aspects of its production.
This goes against our editorial policies. As a result, we’ve taken down the story and the preceding three stories in the series.
The Australian Media Watch team looked at this tension closely and felt the rejection was unfair, pointing out that while slop is bad, assistance (subject to terms and conditions) can enhance.
- Media Watch, likely geolocked to AU, might need a proxy - https://www.abc.net.au/mediawatch/episodes/ep-08/106487250
Ars Technica fires reporter after AI controversy involving fabricated quotes (606 points, 394 comments)
https://news.ycombinator.com/item?id=47226608
Editor's Note: Retraction of article containing fabricated quotations (308 points, 211 comments)
How is this different from anyone else publishing AI slop images on their blog? Those people also direct the AI through prompting and evaluate the results.
I mean, use AI images, so long as they are not crap, but why keep up this charade of "we're authoring the slop".
As opposed to what? This is a little facetious, but what could it possibly mean to have creative direction and editorial judgement without human involvement?
Presumably we're talking about image generated by a diffusion model or something, but further, an image which is generated without being edited by any human. The prompt used to generate the image isn't written by a human, and it can't really be based on the contents of the (human authored and edited) article either. No human may select the service or model used, and once generated the image is published sight unseen without being reviewed by any human.
If some kind of agentic AI does any of these things it is one which appears ex nihilo, spontaneously appearing without being created or directed by any human.