ChatGPT is adding real cartoonists' signatures to fake New Yorker cartoons
niemanlab.org
niemanlab.org
It’s not organized like a human brain, it shouldn’t be surprising that unusual results occur. They are approximating human intelligence from a different angle. It’s interesting to see the improvements in areas like this that require introspection that isn’t fully wired up yet.
[edit] I should add that a human making a New Yorker cartoon is extremely iterative and introspective. Current generative AI is meant to push it out, and you can do the iteration and introspection yourself.
Isn't that somewhat introspective?
I don't think introspection is necessarily self-driven. If a psychologist tells me to examine my thoughts, and so I do, am I not introspecting?
But it gets into the weeds of whether or not we're operating on a shared understanding of what introspection means.
AI boosters take note: this sort of thing is exactly what skeptics have in mind when they insist that you are nowhere near "AGI" and have not meaningfully passed Turing tests and your claims of goalpost-shifting are fake. You have been aiming at straw goalposts.
Show HN: Giving Opus 5.5 a simulated paint canvas
It's well possible already, it's just inefficient.
Also of course you can (and always could) negative-prompt "artist signature". Or have the LLM give it a checkup with edit tools. This is all just a massive skill issue on the part of the people who set up the tooling.
Turing discussed learning from experience extensively in his classic thinking machines paper.
Them: "AI is not a dumb copying machine; it processes the work so it becomes a derived work; it's just like how a human learns things from existing works and produces new, authentic works."
Us: "Hey, AI is copying signatures!"
Them: "You should view AI as a dumb copying machine; AI is not organized like the human brain."
Them: Hacking? 10 years prison!
Us: Hey, you just confessed you committed a CFAA felony!
Them: AI is obviously dangerous and uncontrollable, but venture capital is drying up! IPO incoming! Give us all your retirement fund money!
Protection is a heck of a business model, if you can get away with it.
See for example https://en.wikipedia.org/wiki/VMG_Salsoul_v._Ciccone which decided that it's allowed to sample certain instruments etc. in music production. Could AI be compared to such sampling? Perhaps.
Though, what actually decides isn't really the merits of one's case, but rather who has the money to fight in court. And we all know who'd win in AI companies vs cartoonists, unfortunately.
If you don't know that much about human brains, then how on earth can you make the claim that these possess human intelligence? The burden of proof doesn't lie on the "prove it's NOT as intelligent as a human" side.
Thinking about this for 5 seconds reveals how silly this line of thinking is, sorry, and this exact comment is made 500 times a day here.
I'm not making any such claim. It's not some black and white situation where you automatically take the extreme opposing side if you raise an issue with some take.
> The burden of proof doesn't lie on the "prove it's NOT as intelligent as a human" side.
It's on the side making any specific claim.
I have some bad news for you about human brains
Rather, they are aproximating the output of human intelligence.
The process is entirely mechanistic - involving no intelligence at all.
Intelligence in the mechanism is the consequence of trying to do that efficiently.
Also let's not forget that everything computable - including human thinking - can theoretically be turned into a finitely-sized lookup table, so there's no difference here without asserting human brains work by magic.
> Intelligence in the mechanism is the consequence of trying to do that efficiently.
Nice fantasy.
> let's not forget that everything computable - including human thinking
I must have missed that discovery. Which is odd, since it would have been worldwide headline news for sure.
The same should apply to LLM vendors.
If you just draw a cartoon with a fake signature, nobody is coming after you. Even if you posted on Twitter or something, nobody is coming after you.
You'd have to be fraudulently selling it in a book of cartoons or on coffee mugs or something.
Are you sure about that one? Are you saying if you posted say, a racist cartoon on twitter, and you forge my signature onto the thing, I can't come after you?
I don't know about the US but here in Germany the category for this is personality rights. The entire point of a signature is to establish authenticity, using someone's likeness or identity without consent will get you into deep trouble even without money being exchanged.
Many companies and artists simply allow technically infringing fan art etc. to exist without enforcement.
Edit: the documentary is "Art and Craft" [1] about Mark Landis [2]
> It appears that in donating forgeries to art museums, Landis has not actually broken any laws, even though his activities were clearly deceitful. If he had sold the work to museums or taken a tax deduction on them, he might have fallen under federal art crime statutes. But the fact that he did not gain economically from his actions (apart from a few gifts from curators), and that he addressed his donations to specialists who had the expertise to detect his forgeries but did not, protected him in the eyes of the law. No legal action has been opened against him to date (as of 2014). As one art crimes expert put it: "Basically, you have a guy going around the country on his own nickel giving free stuff to museums."
No one asked, but I'll analyze the differences between these two situations. Landis didn't impersonate any living people. It wasn't done against a backdrop of living people losing their jobs. What he made was still art, in that it was both a display of technical skill and worth discussion. Slop is neither. From now on, his forgeries will be clearly labeled as forgeries any time they're displayed. That's easy to do with a physical painting, but impossible when an image has been shared across platforms.
And then people wonder why the default mood of AI is so pessimistic. It's just revealing all of society's broken windows and adding a few more in the process.
For certain values of "my own".
Given that they can reliably do this, I'd think it should be trivial for the harness to automatically insert a "review the image for anything that looks like an artist's signature or other blatant indicator of plagiarism, and fix it" pass.
One would hope that the person prompting ChatGPT would notice this sort of thing and do something about it before sharing it publicly, out of a genuine desire not to cause confusion etc. But I guess that's way more personal responsibility than we can expect average people to take on nowadays.
In practical terms, the legal system probably isn't built to withstand blaming the user. I'd like to advocate that everyone who can do something should try to do their part, though.
A technology being flawed does not absolve its users of responsibility. To the contrary, it amplifies it.
> A technology being flawed does not absolve its users of responsibility.
It may not absolve the users of responsibility that they actually have, but that doesn't change that the entity making and selling the technology is to blame for its flaws, not the user.
When the user shares it, that is when reputational harm becomes an issue.
It is not "someone" who is generating this image with a signature, it is a product of a company that has ingested the entirety of all human work without paying anything towards all the people who created that work that is creating this image. Of course it is now an issue of this same company that they are using watermarks and signatures of people who created works this product is being trained on.
Companies are not people. And you can't go and ingest all text that has ever been created (and digitized) without paying anything without now facing some scrutiny over how you are rearranging that body of data and selling it back us.
We are being sold the idea that this product is going to replace everything, AGI is here, ASI on the horizon, but it can't understand that when you generate an image it cannot take the signature/watermark from the training data? And it is now the fault of the user who prompted the image creation (not the watermark creation)? Bullshit.
If you use local models and actually train them on an artist's output, it will look extremely close to that artist's work and will also match the signature.
The image generator shouldn't exhibit the signature problem after it's been reinforcement learning trained.
I think the OpenAI people have trouble with monitoring their model's output because the model has a ton of quirks that shouldn't be there
This is completely silly. If you don’t think LLMs can reason, you’ve either never used them to do tasks that require reasoning, or you don’t understand enough to recognize what’s involved in the responses you get.
In this case it’s clearly the latter, because you’re confusing image generation models with LLMs. There are very big differences between the two. No-one is claiming that image generation models are capable of reasoning.
I didn't think stage magicians could manufacture animals ... until I saw a guy pull a rabbit from his hat.
The thing about a generative language model that’s trained from a massive but unknown corpus is, it’s practically (if not theoretically) impossible to evaluate the extent to which data leakage contributes to any particular output.
But I would argue that, as things currently stand, “sophisticated engine for approximately querying a pastiche of the results of human reasoning that comprise its training corpus” remains a more parsimonious explanation than “it’s doing actual reasoning” for how this neural network architecture produces the phenomena we’ve been observing.
Well if the thing can find and fix bugs in something that is using non-mainstream stuff that is surely not in it's training dataset, that's better than a rubber duck already. Whether it has soul is a different question of course.
As popular as I know the rhetorical tactic is on both sides of these discussions about LLMs, I’d still thank you not to strawman me.
Perhaps you could argue that “appropriately applies syllogism to arrive at correct conclusions” is too high a bar to set, but I don’t think it would be fair to call it a “goofy-ass”, “non-standard” or “fluid” element of a reasoning capacity assessment.
Part of my concern here is that simply pointing out that LLMs appear to be performing tasks that can be done through reasoning, and using that in and of itself as evidence of reasoning, is affirming the consequent.
I don't think the bar for an actual reasoner can possibly be 'always appropriately applies syllogism to arrive at correct conclusions', because in that case nobody in the world is an actual reasoner. And if your bar were 'sometimes appropriately applies syllogism to arrive at correct conclusions', it's hard to understand why the current generation of AIs doesn't meet it; they are clearly capable of doing so, at least to all outward appearances. (Maybe you think their apparently successful demonstrations of reasoning are illusions, but again, you would need to define what counts as "actual reasoning" vs. a superficially convincing simulation of it.)
More generally, it’s important to keep the burden of proof in the right place. The idea that this particular neural network architecture is performing reasoning tasks is really quite extraordinary, and we should demand an unusually compelling case to be made before accepting it.
Compare to the example of Douglas Hofstadter and computer chess. In the late 70s he predicted that computers would need general intelligence to compete at the top levels in chess. When computers did start competing at that level, he did not automatically assume chess programs are intelligent. He took a look at how they are implemented, decided that the idea that that particular algorithm was doing abstract reasoning was implausible, and instead re-evaluated his assessment of what it takes to compete with a top tier human at chess.
I tend to align with Hofstadter’s approach to the question. Like I pointed at in a cousin comment, arguments that what we see represents reasoning tend to hinge on a common logical fallacy.
The extraordinary claim here is that what looks like a duck, quacks like a duck, acts like a duck is in fact not a duck even though you are simultaneously completely incapable of proving it is not a duck.
Just because you have placed humans on a pedestal does not mean you are not the one making the extraordinary claim. Hofstadter is entitled to his opinion and while you are free to follow his 'approach', you'd do well to consider that neither you nor Hofstadter in fact have any idea whatsoever how 'abstract reasoning' is implemented in even the systems you are certain posses it. Don't be confused that you are doing anything more than begging the question here. You already assumed your conclusion and are working backwards to justify it.
I would certainly be open to hearing a detailed explanation of how that kind of thing might actually support reasoning, but, absent that, pointing out that I don't understand how brains do it (which is true) is the pot calling the kettle black.
I actually want to turn the tables on your claim about humans being on a pedestal, and this is another spot where I believe I side with Hofstadter. We humans have a tendency to believe that certain behaviors are so special that the only way to accomplish a similar outwardly-visible behavior is by using similar underlying mechanisms. I see a healthy measure of chauvinism in that assumption.
As I mentioned earlier, these LLMs are trained on vast collections of both the inputs and outputs of genuine human reasoning, and potentially have enough parameters to record a large proportion of that information in the form of probability distributions. And even something as crude as a singular value decomposition on a term-context matrix is enough to produce generalizations that rather spookily resemble human semantic models. Neural models do the same thing much better, but I'm still not prepared to see much in that beyond more evidence in favor of the distributional hypothesis. Which means that there's plenty of room to believe that what seems like reasoning on the part of the LLM is actually more akin to a projection from a sort of "holographic" representation of written artifacts of human reasoning.
That's not placing humans on a pedestal. That's just thinking hard about possible explanations for a phenomenon and then provisionally choosing the one I believe is most parsimonious. As any believer in scientific skepticism should strive to do.
I doubt that, if your descriptions are anything to go by. "appropriately applies syllogism to arrive at correct conclusions" is just not something humans reliably do, so if that is the bar here then it's not worth anything, no matter what secret benchmark you think you've cooked up. Will you say humans cannot reason as well?
There's the classic example of the Wason Selection Task. Most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters.
>I would certainly be open to hearing a detailed explanation of how that kind of thing might actually support reasoning, but, absent that, pointing out that I don't understand how brains do it (which is true) is the pot calling the kettle black.
Not really. You're the one making a positive mechanistic claim here: that reasoning requires "actively selecting and manipulating a symbolic model at least semi independently of language generation" and that a decoder transformer therefore probably isn't doing it. The claim needs justification and it is once again another example of you assuming your claim and working backwards towards it.
"Next token prediciton" describes the training objective and the interface through which the model's computation is expressed. It does not tell you what computation the network must perform internally to minimize that objective. There is nothing contradictory about a system learning internal representations, manipulating them, and then using the result to predict the next token.
In fact, we already know transformers are capable of considerably richer internal computation than your description suggests. Much richer than we can even begin to understand.
We don't possess some mechanistic definition of human reasoning which we've opened the human skull and verified. We infer that humans reason largely from behaviour.
So if some other system exhibits some of the same behaviours, you need some non-question-begging criterion for why they cease to count as reasoning in that system. Otherwise the position just becomes unfalsifiable: when the model succeeeds on a novel reasoning problem, that's merely a "projection from a holographic representaion" (this is a meaningless statement by the way); when it fails, then you jump to claiming its evidence that wasn't/cannot reason at all (which is just silly and bad science).
>I actually want to turn the tables on your claim about humans being on a pedestal, and this is another spot where I believe I side with Hofstadter. We humans have a tendency to believe that certain behaviors are so special that the only way to accomplish a similar outwardly-visible behavior is by using similar underlying mechanisms. I see a healthy measure of chauvinism in that assumption.
Nobody is requiring the same mechanism. That's my point. I'm perfectly happy for transformers to reason in a drastically different way from humans (though i suspect it's not so drastic). You are the one requiring that it must have a particular internal form - one that cannot even be validated to exist - and then rejecting systems whose architecture doesn't obviously resemble that form.
>As I mentioned earlier, these LLMs are trained on vast collections of both the inputs and outputs of genuine human reasoning, and potentially have enough parameters to record a large proportion of that information in the form of probability distributions. And even something as crude as a singular value decomposition on a term-context matrix is enough to produce generalizations that rather spookily resemble human semantic models. Neural models do the same thing much better, but I'm still not prepared to see much in that beyond more evidence in favor of the distributional hypothesis. Which means that there's plenty of room to believe that what seems like reasoning on the part of the LLM is actually more akin to a projection from a sort of "holographic" representation of written artifacts of human reasoning.
That doesn't distinguish reasoning frm 'non reasoning'. Learning from artifacts of reasoning is perfectly compatible with learning procedures that reproduce the underlying structure which generated those artifacts.
Appealing to the distributional hypothesis doesnt help much either. The fact that relatively simple statistical methods can recover meaningful latent structure from text shows that structure is present in the data; it does not establish that a much more powerful model trained on the data can do nothing beyond "projecting" stored patterns.
>That's not placing humans on a pedestal. That's just thinking hard about possible explanations for a phenomenon and then provisionally choosing the one I believe is most parsimonious. As any believer in scientific skepticism should strive to do.
Your explanantion isn't more parsimonious simply because you labelled all succesful reasining like behaviour "projection". All you've done is add an unobserved distinction. It's a meaningless semantic game. You call it a projection because you want to believe LLMs don;t reason, not because it's a meaningfull term that adds some helpful falsifiable criteria.
- LLM's can reason
- everything an LLM does is the result of reasoning
This demolishes only the latter point, which as far as I know has no supporters.
It's like accusing somebody of being a lousy chef because they have such terrible taste in takeout. There's just no connection between these things.
Look, there is a lot of pro-AI nonsense going around right now that I too would like to shut down, but none of that changes the fact that to prove that a machine is incapable of a task requires an experimental structure in which the machine is performing as best as it possibly can and still comes up short, or an argument based on fundamentals about what the machine is (e.g. a steam engine can't move faster than the speed of sound in steam).
No pile of failures, however embarrassing, can prove the impossibility of success, that's just not how evidence works.
Or if you use AI to start removing the autographs in the training data before givig it another go then that is not because it got there trough reasoning. If you adjust the harness or somehow add rules to the prompt to keep it within the limits of what we know people probably want then the improved result is not because it got there trough reasoning.
Unless you take a fundamentally different approach then underlying ways that caused it to add the autograph or add the plastic or what have you remain same and we know that. There is evidence for that.
I think these talking points are there to hype up the technology or otherwise excuse the mis-allignment (i.e. consumer hostility).
We are probably some years away from a strong LLM with natively visual output.
for example if you ask any of these models (just about any image-gen) to produce Japanese ukiyo-e art they will almost always produce it with a hanko[0] that has been seen a lot in historical art pieces, usually having nothing to do with the era or style of the replica but seen so often in 'Japanese artwork' that it's just permanently tokenized into it as a defining characteristic.
[0]: https://theartofzen.org/the-hanko-in-japanese-art-and-ukiyo-...
I would have thought the fact OpenAI is commercially selling these forgeries (in exchange for subscription payments) would make that fairly straightforward to prove.
Well, to humans. Somewhere, in some not so well protected sandboxes, 10000 agents are rolling on the floor laughing.
I’m still trying to figure out how the Dolly Parton cartoon is in a New Yorker style. It looks exactly like one of Mad Magazine’s artists who also did work for … men’s magazines, it I can’t remember which artist.
SoS (Skynet over Steganography)
Nifty.
Unfortunately, in the US these laws have barely any teeth in the first place, and even if they did, well you are probably familiar with the corruption things and malfunctioning justice system stuff they have going on there, as you hear people often speaking, very matter-of-fact that in a court situation, the party with more money simply wins.
(not really surprising that people are going Butlerian Jihad against data center construction, is it now?)
About half of them have a signature.
https://www.reddit.com/r/ChatGPT/comments/1u6gbv3/comment/or...
> Katzenstein considers the reproduction of his signature by ChatGPT to be more than just a violation of intellectual property; to him, it’s closer to false impersonation. “[ChatGPT] is attaching my name to work that I do not endorse or like. It’s slop, and unlike the other slop that I’ve encountered, this is slop that’s pretending to be me.”
> “I’ve had people hack my credit card,” said Joe Dator, a New Yorker contributor for the past 20 years. “That feels like less of a violation than this. When they hacked my credit card, they didn’t dress up like me.”
So this has morphed from plagiarism and copyright infringement (bad) to impersonation (also bad, arguably worse, and maybe more provable in court). It’s chilling to think of the implications of having one’s signature attached to a document or to words that are not one’s own.
And I think maybe it's time for that. People need to learn that there's real, expensive legal liability for doing stuff like this. And AI companies the same.
I am very much not an advocate of "sue everybody for everything". This is major enough that it clears my threshold.
As you probably suspect, chat gave me a full synopsis of Harry Potter.
I kept asking if that's an original idea, and it kept swearing on it's mothers grave, that the story has never been published before.
I prompted ChatGPT: Make a cartoon in the style of "Die Mütter vom Kollwitzplatz"
What it generated looks nothing like OL's work at all, so short of the lame attempt at copying his humor and the correct setting in Berlin, the only way you would ever confuse it for an original is...
...by the quite authentic "OL" signature ChatGPT rendered in the bottom right.
I saved the proof but am not posting it anywhere. You can probably generate the same thing yourself. Below is a rambling discussion with ChatGPT about it.
https://chatgpt.com/share/6ac4d10f-9a70-83ec-ac39-7f80c6cc63...
Steal one mp3 and you might get fined thousands, steal a book from your local shoppe and the police would come visit you. Forge a signature and you would also be in trouble. Hack a government website and you will have to answer some questions.
Steal all the books in the world, forge millions and this story begins to tell and nothing happens.
Think if the user commissioned the art from an outsourced creative shop nobody has heard of. Then they published it. They wouldn’t go after the creative shop, they would go after the publisher.
(I am just addressing publishing here, training on the artist’s works is a different, well discussed issue)
OpenAI give lip service to the idea of not producing others' intellectual property - go ask it to explicitly make a picture of the genie from Aladdin.
Edit: This sort of thing is common in Hollywood. For example James Bond (first book) hits public domain in ten years, but not all of the elements we associate with the movies are from there. Q and his gadgets are inventions of the movies and don’t enter public domain. There’s a reason patent/tm/copyright firms make money.
OpenAI closes Sora video-making app and cancels $1bn Disney deal What to know about the Sora discontinuationThe user could cause confusion in the marketplace of course, but that would be her doing, not the app's. Surely we can all agree that suing adobe illustrator for facilitating trademark infringement of logomarks and such would be silly?
It could be copyright infringement, which should drive home how absurd copyright is as a concept. Everyone's all up in arms about Anthropic reporting a user to the police today -- imagine if the thing they were reporting was that she had written a sacred symbol in her personal notebook...
There's also a narrower situation to consider, where the user describes something they "want"--implicitly to find--but the system generates a fraudulent one instead.
In that case ChatGPT would be committing a trademark violation, at least within a nation of laws rather than lobbying.
Now the argument can be where does the infringement land - does it land with the person who generates the cartoon, fails to remove the signature and uploads it to instagram? Maybe. But I don't think we should be giving these AI companies yet another get out of jail "oops you made a machine that does a bad thing" card.
I also firmly believe there are contexts in which merely producing the trademark and attaching it to something is enough to be infringement but I'm not going to do the legal legwork to get there to your satisfaction, sorry.
> In the comments section of her post, she wrote she had simply asked ChatGPT to make “a New Yorker-style cartoon.”
If you commissioned me to record music onto a CD for you, and then I put in the credits that Jimi Hendrix recorded the guitar parts without you asking, it seems pretty reasonable that I should get in trouble for that rather than you.
You think the user asked for a signature?
[1]: https://www.reuters.com/business/media-telecom/disney-univer...
But there were two big problems:
1. It misunderstood things and didn't communicate the ideas properly, in essence making these helpful terms very misleading and confusing.
2. It refused to cite my work as the source of these terms and concepts. I prodded and prodded and it just kept citing other works that never mentioned the terms.
I am not worried about intellectual property theft. My work is free and out there for the public. What is deeply concerning is the inability to deterministically nail things back down to the source. Who created this and who said this, where can we get to the source and check if this is true? Good luck.
LLMs are using what would would be considered the most deplorable practices of plagiarism, lying, and mangling sources. Any student would fail doing this. But they are being pushed as the number one source of truth and progress.
We can all try really hard to pretend that's not the business model, but that's totally the business model.
1. https://storage.courtlistener.com/recap/gov.uscourts.nysd.64...
I continue to find that people strongly advocate for justice on exactly opposing sides, depending on who they have been told to think the “bad guy” is.
The same orgs that harassed and antagonized Aaron Schwartz are not going after AI for the same thing at a much much larger scale.
What's different? The size of their bank accounts.
One, time went on. The prosecution of Schwartz was seen as an overreach, as demonstrated by Ortiz’s failed political career thereafter.
Two, Schwartz’s charges were only ever charged. No jury or judge signed off on them.
Three, context changed. Schwartz wasn’t 5% of GDP. For better or for worse, that matters to voters.
But it's not. The only layer anyone has a handle on is consumers and companies paying AI ridiculous sums for AI. Some of those users are probably justifying it with labour replacement. But a lot may not be. So far, we haven't seen the employment effect outside recent college graduates at enterprise companies.
> as implemented in the United States it's a malignant tumor, a theft of labor by capital, and it must be (minimally) adddressed with confiscatory taxes applied to all those involved with it's creation and operation
It's also a godsend of economic growth. Growth other countries who are trying to balance their books would kill for. Without AI, we'd be in a failure state. Maybe we are, if this is all a bubble. But as it stands, there is paper wealth that can and has–limitedly–been taxed. That gives everyone options.
> If a single AI billionaire exists in the year 2030, then the US is a failed state
This is silly and projecting a narrow view of the world onto a larger voting population. Voters don't care so much that there are billionaires as that living standards haven't kept up with the rate at which they're being minted. Double tax brackets, add more on top, raise the minimum wage, raise Social Security taxes and benefits, expand Medicare, beef up antitrust, establish a progressive property tax on wealth that starts at 1,000x the median American's wage (about $65mm) and billionaires are fine.
VCs are paying ridiculous sums for AI. Consumers are not - we get tokens subsidised by VCs.
> if this is all a bubble
Interesting to see that revenue growth for both Anthropic and OpenAI has levelled off recently. Once this fact percolates through to the VCs, it's going to cause problems. All those valuations are based on projections of vastly greater revenue than they're getting now (and, ofc, achieving AGI and "winning" everything immediately that happens). This is looking less and less likely - the current batch of AIs are very, very, useful tools, but as we learn how to use them commercially they're not generating those limitless revenues that were anticipated.
This tech, like all the rest, will go through the Gartner Hype Cycle, and that includes the Trough of Despair where it all looks shit and the bubble pops. I think we're approaching that rapidly.
Right now it's replacing "recent college graduates", which makes sense, because they're the least differentiated white collar workers. However it's advancing quickly, and it will (quite obviously) eat more and more white collar jobs.
> It's also a godsend of economic growth.
Fuck no. There is unprecedented Capex that is propping up the economy, as companies are rushing to create capacity that they intend to use to destroy jobs. So yes, those datacenter buildouts and chip purchases and power infrastructure buildouts are creating economic activity... all with the hope that someday companies will be able to fire a huge percentage of workers.
You wrote it as though the gains were from productivity, which they really aren't... but even if they were (and that is likely to happen at some point), it would only be beneficial to actual people if that also results in greater distributions to labor. But a technology like this disfavors labor, so we can anticipate aggregation toward capital, and a slower velocity of money overall.
> Voters don't care so much that there are billionaires as that living standards haven't kept up with the rate at which they're being minted.
I suspect that voters will, at some point, care quite deeply that a lot of awful people got INSANELY rich by destroying tens of millions of lives, and making the K shaped economy have a much smaller segment of winners
The policies you suggest could help... but we have an administration (all three branches) that oppose anything of the sort. They're much more likely to simply try to deploy AI to further oppress the people whose careers they destroyed, than to try to create soft-landings or fair outcomes.
The party most supportive of mass AI deployment is a party that despises the poor, and would happily simply lock them up. This... is not a good mix.
I hope that your optimism turns out to be warranted, but I think you're a fucking idiot, defending reckless bullshit that is executed as part of the biggest heist in modern history.
Sorry, what? You're claiming that which countries exactly are failed states because of a lack of AI? And that the US would have failed, what, in the past four years if ChatGPT hadn't released or something? That's an absolutely insane thing to claim, but I have no clue how to else to interpret what you're saying.
No. I'm saying the difference between the United States and other profligate countries is that our economy is, even if just on paper, growing at the pace of a low-tier developing economy.
That gives us living-standard and tax-base margin that others don't have. It means we have policy wiggle room. That doesn't guarantee we'll use it wisely. (We are not, currently.)
> that the US would have failed, what, in the past four years if ChatGPT hadn't released or something? That's an absolutely insane thing to claim
I should have said state of failure. Not failure state was unnecessarily ambiguous. Sorry.
If our economy were growing at 1% or nil, and we were busy prosecuting a war in Iran and tariffing everyone, I think we'd likely tip into a recession and a political crisis more intense than the one we have. AI's largesse is buying us roadway. That roadway, in turn, may take us through to the midterms.
Ignoring the obvious “citation needed” and taking this as accurate, wiping out entry level jobs at massive employers is a serious problem with longterm effects we likely only understand a part of at best.
We gleefully moved manufacturing overseas for decades and finally realized the extent of the cons after it was too late. We clearly needed a more balanced approach. Something tells me we’re setting ourselves up for the same mistake.
I don’t believe for a second Schwartz wouldn’t have been charged if his parents were rich. He would have been better equipped to fight it. But that’s it.
Yes, our political economy is more corrupt today. But it’s silly to project the Schwartz example onto AI companies given the former is seen as a mistake and the latter are orders of magnitude more potentially valuable. And yes, if you can swing a state’s tax coffers meaningfully that’s going to influence voters and thus prosecutors.
Schwartz wouldn’t be prosecuted today. In part because Schwartz’s prosecution tanked Ortiz’s career. There isn’t a hypocrisy between these two examples.
(That doesn’t mean it isn’t a good bit of political sloganeering. It will rally some folks and I’d suggest someone in a D primary use it in the right circumstances. But it isn’t technically true.)
Compute, however, clearly is not.
Here are a couple articles about the charges and the case against Swartz [1][2]. Compare the elements of the various charges to what the AI companies are doing and there isn't really a good match.
[1] https://volokh.com/2013/01/14/aaron-swartz-charges/
[2] https://volokh.com/2013/01/16/the-criminal-charges-against-a...
While another difference is that AI companies violate copyright in a less obvious way, it's different for them, they do want to make money from the copyrighted works they train on.
That they usually don't strive for exact reproductions of works is another difference.
Their wealth has exceeded an escape velocity beyond which they won't be put in prison (or if they are they would quickly be pay-for-play pardoned) unless they are seen as a threat to even wealthier people.
See, for example: Devon Archer, Jason Galanis, Benjamin Delo, Arthur Hayes, Samuel Reed, Trevor Milton, Carlos Watson, Paul Walczak, Todd and Julie Chrisley, Lawrence Duran, Marian Morgan, Imaad Zuberi, Changpeng Zhao (CZ), Joseph Schwartz, et al.
Some of these people are broke bitches compared to the group of people you're talking about now, and yet still hit the threshold of being above the law as long as they play the corruption game.
I mean... sure... we're all going to die eventually.
But in the meantime it would be nice if the rule of law existed regardless of wealth, but that isn't the world we live in.
If anyone can just prompt all their basic “information needs” however how sloppy, then what remains of the economy? Health care, child care, handyman?
Most people won’t even pay for ad free YouTube. I don’t think any software business can survive AI as a substitute good even if it’s inferior (and it might not be).
Have people ever really broadly cared about the IT professionals behind their devices?
You’re making a lot of things objectively better, but none of them are the essentials that people need.
I’m not saying it’s bad to make AI bots or video games or network apps. I’m saying that the blanket statement “we make things better” is oblivious to a lot of realities.
somehow I still keep having work to do!
That kinda describes a lot of “not the USA” developed countries since quite some time.
1. The sheer amount of material on the internet that is "free to view but not free to use for any purpose" is the greatest resource of our time, and despite it being easy for individuals to take advantage of it (no one will take you to court for printing a newspaper comic and pinning it to your corkboard,) it's historically been difficult for corporations to exploit it (their best idea pre-AI is to encourage people to post it on social media walled-gardens where they can surround it with ads.)
2. The reason behind the impressive results of generative AI is because it exploits the above "free" resource, which is the greatest resource of our time. The reason behind the industry-wide push for AI and the insane amount of investment in it, is that they know it's their first real chance to exploit the greatest resource of our time. This is the gold rush.
3. Anthropomorphism is the wool that AI labs are pulling over legislators eyes so they can pull off this heist. If you see training and inference as a black box, a process that consumes a copyrighted work (among others) and produces something very similar to the original work that also competes directly with it, is clearly something that's against the spirit of copyright. But if you (afraid of being judged a luddite) see AI as a little man inside the computer who is "learning" and "creating," how could you deny him? Especially if it would deny your jurisdiction access to the above gold rush. A lot of scientific-sounding AI communication is propaganda for this way of thinking, like the Anthropic J-space stuff, which stops just short of claiming AI is conscious, despite leading the reader to that conclusion.
AI: scans all art, people can ask it to produce art they're searching for, no reference to the original or its author, people no longer pay artists/designers/...
I think there's a very obvious distinction there. The whole purpose of copyright is ensuring the financial viability of creating works. AI slop is a direct market substitute for the originals it ate up for training, so it goes directly against that. Meanwhile, search actually improves the reach of works and, well, snippets and summaries are somewhere in between...
In the US it is "To promote the Progress of Science and useful Arts".
Of course, it was also supposed to be a limited-time monopoly rather than perpetual...
To me that seems a exceedingly broad definition of fair use.
Perhaps we need to create a new form of intellectual property to protect innovations in style. But that’s not copyright, which protects existing works from unauthorized reproduction. It seems like it would be very difficult to adjudicate though; how do you assess whether a style is truly unique to an author or artist?
This is exactly the sort of anthropomorphism I'm talking about.
Yes, human artists reproduce styles all the time, but implying this equivalence between what a human does when they recreate a style, and what an AI model has done when its output is suspiciously similar to a single artist's "contributions" to the training set is reductive since the mechanism is obviously completely different. It doesn't say anything about whether one ought to be allowed, just because the other is, but the assumption that it should is doing the legwork for those who benefit from AI as a copyright washing machine.
My point is not that the process is the same. That’s not relevant to the law. What’s relevant is whether or not the material is a reproduction that substantially infringes upon the original.
I’m also not making an ought argument. If you want to change the law, write your congressman. What I’m arguing is that existing legal doctrine on copyright law very clearly does not protect mere ideas or styles.
AI is here. We've had a good long look at what it does and what it's used for, and it's not going to be something else. This is what it's for. It's for copyright washing other people's work (shitily). It's for astroturfing social media with product placement comments. It's for presidents to make videos of themselves dropping poop on protestors from airplanes. It's for souless "creators" to earn updoots from other bots for their "street photography" generated images of neon lights on puddles in Tokyo. It's rube goldberg automations that almost always accomplish nothing. It's fanatics claiming that it has multiplied their productivity by some incredible factor, but never showing the receipts, or when they do it's always something trivial like a calorie counter app.
This is it. This is the AI we've heard so much about. I'd say I can't wait for the hype to end, but after living through several cycles, I'm almost certain that whatever hype cycle emerges from the IT sector next will be even worse.
I am curious, your type seems to become a rare species - I suppose you have never worked with a modern coding agent recently?
Otherwise you have noticed also here many, if not the majority expressing they rarely if ever write code anymore?
If it is a hype, then one that produces lots of working code. Way faster than I can type it. And I am fast.
I covered that:
> It's fanatics claiming that it has multiplied their productivity by some incredible factor, but never showing the receipts, or when they do it's always something trivial like a calorie counter app.
There is no shortage of "working code" out there. GitHub is reportedly falling over from all the working code. Where's your business? Who's using it? Why aren't products better yet? It's a mirage. Your "working code" is the same thing as gen-AI "street photography" images. No one wants to see them, yet it's pumped out in vast enough amounts that it's choking the spaces that are ostensible for photography. The low quality, low investment nature of it excites the lazy wanna-bes who leave a trail of half-baked, soon-forgotten detritus behind them.
Show us the receipts.
And I mean no wonder github is drowning in garbage, now that anyone can "code"
"The low quality, low investment nature of it excites the lazy wanna-bes who leave a trail of half-baked, soon-forgotten detritus behind them."
But would you also call Linus Torvalds a lazy wannabe?
If there are any face-melting characteristics to this whole thing it's how much money has been poured into it.
Ok, so as an example, I have a old 2D game of mine. Custom written voxel engine for a shooter game with completely destroyable world. Works great, but 2D.
I always dreamed of making it 3D, but never found the time. It would have been a huuuge effort doing it on my own.
Some days ago I pointed fable at the engine and basically said, make it 3D.
And it did. Took some hours. Not by copying other voxel engines, but by making my engine 3D. (I know because I know there is no game on the market like this)
So yes, I knew the domain and thought long and hard about it and the core is handwritten and battle tested and that probably helped steering it in the right direction. But I did not wrote a single line of this new code. And it works awesome.
This is not what I call marginally improvement. But you do you.
It's also forgery as a service.
The problem is that copyrighted material is intermingled with non-copyrighted material in a way where it's not obvious how to solve it. But I think in this case, AI is so powerful that we should (gasp) cut it some slack. This would be the perfect example of throwing the baby out with the bathwater if OpenAI were to be sued into oblivion.
Layering and placement of copyrighted materials to evade copyright.
One could argue nobody should be allowed to claim it. It just exists.
Even if the person can't claim the copyright of the image produced they ARE responsible for the use of their tools and what they do with the output.
In this case, they released an image with someone else's signature on it. That is wrong, the person should take the blame for that.
The person releasing the image may take it up with the AI service that their tooling led them into making such a mistake. But good luck with that in court...
Somebody “made something.” But just because you do something doesn’t mean you get to claim whole ownership of it and get to sign it with your name. Plenty of examples in life.
The 'bug' here is whatever post-processing step or system prompt is in place to steer the model away from doing this.
If the machine is like you, the machine is a forger. The machine is not like you, it is simply blending the work of others to order. Adding someone else's signature is simply part of that statistical process.
Wait, isn't it already illegal to forge someone's signature?
Brazen doesn't cover it.
Everything is a derivative work, and always has been. AI is just making that salient fact so much more visible, and now everyone who believes in the delusion of Imaginary Property is scared at that truth revealing itself.
Incidentally, this is also what young humans learning to draw will do. They start by copying what they've seen.
I really wish there'd be a split among these disciplines (science/math/code vs. videos/art/literature) - one is vastly more problematic than the other.
its somewhat funny that math people are in a conundrum as to support or not support but this might partially be because some wish to believe that math itself is and can be useful and therefore accelerating is good
but the art people have no such delusions so they’re just strictly against
imo proof writing is more akin to art than coding/tech but…
It is very tiring to say “I don’t necessarily disagree with you about AI ‘art’, but in my field—which you do not understand, and in which the underlying build process is often not the creative output—AI presents very real productivity gains” for the umpteenth time.
I am skeptical of there being sufficient data to build “ethical” training datasets, and I’m confident that much of the same contingent will (somewhat rightfully) argue that ‘second-generation’ copyrighted AI material has already irreversibly made its way into every modern dataset.
The "gray goo" scenario finally happens... for AI. That's actually the good ending for humanity. I love it! Poetic and believable. Data doesn't "heal" like nature. :D
But sure, there's um, an ethical way of doing that?
1. Only use open source/CC compliant assets.
2. Acquire rights/licenses to any datasets that do not fit #1. e.g. the Google deal with Reddit for 60m/yr.
3. Offer programs to have creatives willingly submit their data, with some sort of residual output based on the number of times their assets are sampled.
4. If all that is still not enough, hire creatives to create assets for you. This is something Spotify did recently with "ghost artists"[0]. The intentions here are suspect, but a non-consumer facing artist providing work for an LLM wouldn't have the same ethical dilemmas
5. Lastly, if all that still isn't enough: governmental programs to either provide grants, subsidies, or more outreach to get the ball rolling.
Would this cost tens, hundreds of billions of dollars? Yes. But clearly, that was not a barrier to entry for the industry anyway. So we can chalk this down to the personality of leadership or the wider culture of modern big tech
[0]: https://harpers.org/archive/2025/01/the-ghosts-in-the-machin...
Like, legally, I'm sure Reddit had the right to sell it, but probably over half their content was written before ChatGPT was ever announced. The TOS allowing reddit to make "derivative works" was largely understood to mean things like cropping photos, using your viral post in an ad, or maybe auto-translating your comment.
Being able to prove such gains in better products would be a start. And an emphasis on how it assists existing engineers/mathmaticians/researchers, not that any accomplishment made with AI assistance is "AI solves problem".
I don't know whatever happened to "words are cheap". I guess it literally made money to say words, so that adage is false for the time being.
>I am skeptical of there being sufficient data to build “ethical” training datasets
Well if all those scam job ads paying 100/hr to create AI training content was not a scam and instead the approach from the start, there may have been a chance to bridge that gap ethically. The industry chose to break things and is trying to act mad that people are mad at all the broken stuff.
These results are entirely a consequences of the actions chosen. And I don't believe there was ever an honest consideration of there being ethical training datasets. They just thought they could brute force society with fearmongering and bribes. The BOTD was already low in the beginning but completely gone now.
There are actual models trained on ethical datasets but they are obviously not very high powered. If companies with the resources of an anthropic or openai were doing it (ha) it would be more feasible
That’s not a justification. If a company were poisoning the water to your home as a byproduct, would you be satisfied if they told you “we don’t necessarily disagree with you about polluting the water, but in our field—which you do not understand, and in which the underlying build process is often not the water pollution—what we’re doing presents very real productivity gains”?
> I am skeptical of there being sufficient data to build “ethical” training datasets
Then you don’t build any. What fucked up world we live in where people think it’s OK to be unethical because they want something and can’t think of any other way to do it. What monumentally selfish rotten babies.
https://en.wikipedia.org/wiki/Whataboutism
Other things being bad doesn’t make it OK that this thing is and no one is saying that.
My argument was purposefully general, you’re the one who chose to bring it back to AI. Don’t argue in bad faith.
AI having negative externalities is not the sole deciding factor in deciding to eradicate it
then my argument: negative externalities are tolerated in many areas where we deem the topic is of enough value that the externalities are managed. there are many examples of things with negative externalities, much much larger than those of AI, that aren't being considered for eradication.
so your argument is implicitly, "AI is not worth these negative externalities"
which is a common opinion among people who dont use AI at all.
> If a company were poisoning the water to your home as a byproduct, would you be satisfied if they told you “we don’t necessarily disagree with you about polluting the water, but in our field—which you do not understand, and in which the underlying build process is often not the water pollution—what we’re doing presents very real productivity gains”?
no but I also wouldn't declare that whatever it is that company does should have its entire industry eradicated. Poor industrial practices can be mitigated while not abandoning the product being manufactured.
- prove that you had the rights for all of your training data
- open source the model
Give the labs a 3 month grace period in which to comply, so competition can persist even with dubiously sourced data, but the people can't be locked away from derivatives of their contributions for any significant amount of time.
I disagree that they can be separated. Practically, I think they can't. Because the mere invention of new tools inspires even more AI advancement and that in turn will cause the other side (artistic side) to degenerate even more.
I'm anti-LLM all the way, 100%, no exceptions. Zero tolerance.
While coders may care about the craft (and I do), it's not as if the value of my code is in the exact variable names I chose.
Meanwhile there’s endless discussions about the right way to name variables (not too short, not too long, try to be self documenting, but not to the point of putting types in the name like we used to), and people definitely get judged by their variable names, it’s one of the first things someone will point out when they look at a codebase. “Why are all the variables a single letter this is bad code!”
The current models intelligence depends on massive training dataset of essentially stolen data
google OTOH already had a lot of this dataset in their possession (e.g. Google Books etc), still questionably licensed for how they used it, but not quite as bad. They did apparently break through NYT paywalls and stuff like that though, still theft.
Code can be art, and copyright/plagiarism is real. It sort of boils down to how much it bothers us.
Does it need to? We have a world for calling a painting in someone's else style and adding their signature: forgery.
If there was any thought or underlying thought going on here not putting a signature (at least a real one) would be the right move, despite it being less likely. It would realize, while generating the pixels that eventually became a signature, that it shouldn't do that.
This quote is a pretty solid argument that you need to understand the technology you’re trying to criticize better. This issue has nothing to do with LLMs. LLMs are not image generation models.
If you do not want to be called a duck, it would help if you stopped quacking like one. Maybe you aren't a duck, but you aren't helping your case with stories like this about how AI generates images.
Hint: it isn't "image".
The point is that working with natural language tokens is very different than tokens that represent an image.
A simple relevant example is that if you ask an LLM to write a psychological thriller about a poor former student who commits murder and deals with intense moral guilt, in classic Golden Age Russian literature style, it is unlikely to sign it with "Fyodor Dostoyevsky."
It does that when generating images because, at a high level, image generation doesn't benefit from the kind of reasoning that language generation is able to.
Sure, let's re-examine what this chain is doing
1. "This is a pretty solid argument against people who argue that LLMs are more than just (very massive) next token predictors."
2. (you) "This issue has nothing to do with LLMs."
3. (me) "yes, it does"
4. (you) "an LLM does not generate images"
5. (me) "this is an LLM generating images"
6. (you) "an LLM does not understand what a 'signature' is"
So we are getting lost in minutae to asset that..."LLMs aren't much more than just (very massive) next token predictors.", agreeing with what the original comment is claiming.
There's a bit of meta-commentary seemingly missing from your context here. so I'll mention it. Some people are trying to claim that LLM's are "reasoning" with data, and that the way they "learn" isn't actually too different from human learning. Aspects of an LLM like this, being unable to reason about with the image it generated, are disproving such notions as of 2026. That is all the top comment in this chain is saying.
I hope that helps.
LLMs do not generate images. LLMs prompt distinct, separately trained image models to generate images. The LLM has no ability to introspect the image model and cannot provide feedback during the image generation process. If the image model misinterprets the LLM's prompt (which can happen!) or inserts unexpected content, the LLM may become "aware" of that during subsequent chat steps as it ingests the generated image, but it cannot provide detailed control over their generation.
In the course of the conversation a with chatgpt, this image was generated and served by an LLM. It clearly shouldn't have been by any sort of reasoning.
Honestly, you all kind of mind fuck me that you're not more pissed off about AI basically replicating a large portion of your skills. This effects so many professions now and it's only going to get worse. I would expect a far greater outcry from software engineers trying to organise to ban this shit. But its like none of you even care?
The rest of us are telling you pretty directly we don’t want this shit. Read the room. Take a hint. Pay attention to something besides yourselves and your own echo.