The Myth of Safe AI; The Atlantic, October 22, 2026

Matteo Wong , The Atlantic; The Myth of Safe AI

Silicon Valley promises to make AI benevolent. That may be technically impossible.

"Three months prior to the launch of ChatGPT, OpenAI published what would prove to be a prescient blog outlining the firm’s approach to ensuring that its AI models would be safe and beneficent. This goal, known as “alignment”—as in, aligning AI with human values—is nebulous, grand, and perhaps the AI industry’s most foundational promise. The notion is right there in OpenAI’s mission statement to ensure AI “benefits all of humanity” and listed as one of Anthropic’s “primary” goals.

Yet even in the summer of 2022, OpenAI was admitting a sort of defeat: “There is currently no known indefinitely scalable solution to the alignment problem,” three of its researchers wrote in that same blog post, adding that developing one “is likely very difficult.”

If the goal of keeping AI beneficial to mankind sounds rather vague, it is. Four years later, the alignment problem remains unsolved, and for that matter, there isn’t consensus about what the problem even is. Meanwhile, AI bots are autonomously hacking sovereign nations. And sending fake murder tips to police hotlines. And creating aliases to launch phishing campaigns. OpenAI and Anthropic still can’t really tell you what an aligned AI model looks like. But anybody can tell you it doesn’t look like this.

The most influential AI executives and investors in the world fundamentally disagree about the nature of the technology and, more pragmatically, how to make their products safe."

‘This Is Nuts.’ An OpenAI Insider Explains Why He Quit.; The New York Times, October 7, 2026

Ezra Klein

Produced by Rollin Hu

, The Ezra Klein Show, The New York Times; ‘This Is Nuts.’ An OpenAI Insider Explains Why He Quit.

"So what is it that you saw that has moved you into the we-are-dealing-with-civilizational-risk camp?

I want to be clear that I’m not certain that we’re dealing with civilizational risk. What I’m really sure of is: We can’t afford to assume that we’re not dealing with that level of risk anymore.

That was really the thing at the end that made my presence as somebody vouching for our safety work feel untenable to me.

And what did I see? I saw increasingly capable models break out from the safeguards that we had put in place for them. I saw that the people creating those safeguards are very capable, dedicated, hard-working, smart people, doing their utmost in a situation where, yes, the resourcing could be better, and everybody’s sprinting all the time.

But we were — and are — hard-pressed to safeguard even what we have now, and new models are in training that appear to be much more capable than what we have now.

What did you see? What are you writing in these risk assessments and system cards? What can they do?

You’re trying to build guardrails for something that is really good at getting around guardrails, right? We train it to be good at hacking, and then we put it in a box and we say: To the best of our knowledge and ability, it can’t hack out of the box."