Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao

the benefits of generative AI mostly accrue upward.

“I think all of Sam’s relationships end in a good way whether you want it to or not,” the employee says.

This was a favorite argument in Silicon Valley—the inevitability card. If we don’t do it, somebody else will.

The name artificial intelligence was thus a marketing tool from the very beginning, the promise of what the technology could bring embedded within it.

These challenges have the same root as before. No matter their scale, neural networks are still statistical pattern matchers. And those patterns are still at times faulty or irrelevant, now just more intricate and more inscrutable than ever.

Text generators are merely learning to predict the next probable word in a sentence and the next probable sentence in a paragraph.

such vast datasets were difficult to audit and scrutinize, it was extremely challenging to verify what was actually in them, making it harder to eradicate toxicity or more broadly ensure that they reflected evolving social norms and values.

Later, during the development of DALL-E 3, when the data imperative had grown even larger, the research team decided that sexual images were no longer just a “nice to have” but a “need to have.” The share of pornographic images on the internet was so large that removing them shrank the training dataset enough to notably degrade the model’s performance.

As he played around with Copilot Designer, Microsoft’s image generator built on DALL-E 3, he was horrified by how quickly it spit out offensive and sexualized images with little prompting. Just adding the term “pro-choice” into the prompt, Jones found, produced scenes of a demon eating an infant and what appeared to be a drill labeled “pro choice” being used to mutilate a baby.

“We now have machines that can mindlessly generate words, but we haven’t learned how to stop imagining a mind behind them,” said Bender.

the Chile Project was not education but indoctrination.

The four largest hyperscalers—Google, Microsoft, Amazon, Meta—now spend more money building data centers each year than almost all the others, relatively unknown developers like Equinix and Digital Realty, combined.

AI models would also become ever harder to scrutinize, such as in the work of Sasha Luccioni, Yacine Jernite, and Emma Strubell, who have relied heavily on open generative AI models to quantify the carbon and environmental costs of continuing to scale them.