They were more than right. They were correct in an intentional, precise manner. This is what OpenAI actually stated[0]:
> Synthetic imagery, audio, and video, imply that technologies are reducing the cost of generating fake content and waging disinformation campaigns.
> ‘The public at large will need to become more sceptical of text they find online, just as the ”deep fakes” phenomenon calls for more scepticism about images.
Yeah, I find it a bit odd how at the time everyone was pointing and laughing at OpenAI for being obviously wrong about this. Now in 2026, AI slop is very obviously a serious problem - it inundates all platforms and obscures the truth. And people are still saying OpenAI in 2019 were wrong?
I think people today are more focused on how OpenAI released a model "too dangerous to release", not that they were right or wrong, as part of the general trend of criticizing OpenAI for not following any of its stated principles.
Exactly. The (real) issues were ultimately disregarded even if they were correctly identified.
My assumption is that it was too expensive to actually release at the time. It wasn't good enough for anybody to pay to use it yet, and it surely was very expensive to run, especially for a (fake, granted) non profit.
Both crowds are right because two messages were spread. The researchers spread reasonable fears and concerns. The marketing charlatans like Altman oversold the scare as "Terminator in T-4 days" to imply greater capacity in those systems than was reasonably there.
The problem is the most publicly disseminated messaging around the topic were the fear mongering "it's god in a box" style messaging around it. Can't argue with the billions secured in funding heisted via pyramid scheme for the current GPU bonfire, but people are right to ridicule, while also right to point out warnings were reasonable. Both are true, it depends on which face of "OpenAI" we're talking about, researchers or marketing chuds?
Ultimately AGI isn't something anyone with serious skill/experience in the field expects of a transformer architecture, even if scaled to a planet sized system. It is an architecture which simply lacks the required inductive bias. Anyone who claims otherwise is a liar or a charlatan.
I think it's important to consider that OpenAI's qualms weren't with making the dangerous models usable, they were with making the model usable without paying them. They're perfectly fine with any harm, as long as they get money out of it and can't be held liable.
It's the same with the Mythos stuff, I appreciate their concern/work on safety, but if it's "too dangerous", it should be unavailable until it is less dangerous.
Now imagine all that low quality AI slop is being posted online and a new generation of AI will "learn" from it, output it's own version of AI slop, that will eventually end up online again for a new generation of AI to "learn" from.
This leads to a well-documented phenomenon known as model collapse. You know how if you blur and sharpen an image repeatedly you eventually end up with just a rectangle of creepy, wormy spaghetti lines? You lose information on each blur, and then ask it to reconstitute the image with less information on each sharpen, until there's nothing recognizable left.
Training a model is like the blur and generating from that model is like the sharpen. Repeat enough times and enough information is lost that you're just left with "wormy spaghetti lines"—in an LLM's case, meaningless gibberish that actually pretty closely resembles the glitchy stuff said by the cores that fall off GLaDOS in Portal. I dunno, you read the paper and be the judge:
Of course you may be talking about the human aspect of this. Gods willing, we'll realize that our LLMs are spewing gibberish and think twice about putting them in all the things, all the time. But the scenario I fear isn't Idiocracy—it's worse: a community of humans who treat the gibberish as sacred writ, Zardoz style.
The actuality is, anyone with pre-slop data still has their pre-slop data. And there are endless ways to get more value out of good data.
Bootstrapping better performance by using existing models to down select data for higher density/median quality, or leverage recognizable lower quality data to reinforce doing better. Models critiquing each other, so the baseline AI behavior increases, and in the process, they also create better training data. And a thousand more ways.
Managed intelligently, intelligence wants to compound.
The difference between human and AI idiocracy, is we don't delete our idiots. I am not suggesting we do that. But maybe we shouldn't elect them. Either way, that is one more very steep disadvantage for us.
Maybe that's true, But I think before LLMs became common, people had more distinct ways of expressing themselves, low-quality for not. Now, a lot of online writing feels uniform and I think that is worse.
The quality hasn't changed. The volume has. It used to take real human time to create garbage. There was value in that. Someone though "Hmm, what worthless thing can I do? I know! I'll make people online mad." And then they spent the time getting someone else's goat. It was great. A good balance, spreading lies took some minimum effort. Now we have automated garbage. And the flavor of the garbage is: gaslighting people with an illusion of community. We've empowered the trolls with an infinite meme-o-rater while ignoring the real human time spent unwillingly sifting through the ever increasing pile of worthlessness.
The world does not have to get worse. We're letting it though.
It would be nice if “we” had anything to do with it. Just think about the next campaign trail for any superpower, it’s going to be a disaster of fake news and slop coming from all over the globe.
