OpenAI says its new model GPT-2 is too dangerous to release (2019)

Lol. The vast majority of content has always been low-quality. Those who believe that things were better before LLMs have selective memory.

I'm somewhere between frustrated and baffled why people raise this as an example of overselling. This was clearly a reasonable call! Not all the experts quoted in the source article agree that the model should have been held back, but they all agreed that the risks were real and it's understandable why OpenAI would do it.

Someone needs to make a compilation of all these classic OpenAI moments. Including hits like GPT-2 too dangerous, the 64x64 image model DALL-E too scary, "push the veil of ignorance back", AGI achieved internally, Q*/strawberry is able to solve math and is making OpenAI researchers panic, etc. etc.

I use Codex btw, and I really love it. But some of these companies have been so overhyping the capabilities of these models for years now that it's both funny to look back and tiresome to still keep hearing it.

Meanwhile I am at wits end after NONE OF Codex GPT-5.4 on Extra High, Claude Opus 4.6-1M on Max, Opus 4.6 on Max, and Gemini 3.1 Pro on High have been able to solve a very straightforward and basic UI bug I'm facing. To the point where, after wasting a day on this, I am now just going to go through the (single file) of code and just fix it myself.

Update: some 20 minutes later, I have fixed the bug. Despite not knowing this particular programming language or framework.

We got extremely, extremely lucky that society is as resilient as it's proven to be against fake news. I don't think very many people predicted that it simply wouldn't matter when photorealistic compromising images of whoever you don't like became available for $5.

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:

https://www.nature.com/articles/s41586-024-07566-y

To jump to the last output sample, C-f Gen 9

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.