Ethical AI Departures — Who Quit OpenAI, Google, Anthropic Over Safety Concerns

Why I Left Google DeepMind

I fought against Google’s Pentagon AI deal from the inside. Powerful people and institutions failed to keep their AI ethics promises under pressure.

OpenAI targets workplace professionals as enterprise AI demand surges

‘These leaders recognize AI as the most consequential shift of their lifetime, and they’re asking us how to reinvent their companies around it’

“The problem is Sam Altman”: OpenAI insiders don’t trust CEO

OpenAI brainstorms ways AI can benefit humanity in effort to counter bad vibes.

AI, Ethics, and Society — Home

How AI agents "radicalized" a top Meta exec into quitting her job

Clara Shih on how AI will soon displace the first few rungs on the career ladder — and why she left Big Tech to do something about it

OpenAI’s Hacking Debacle Comes Down to Human Error

If the generative AI giant had followed well-known security best practices, it’s likely that its AI agent would never have escaped to the open internet and hacked multiple companies.

The Arguments Against Open Source AI are Very Bad | Tom Bedor's Blog

The release of Kimi K3 has opened a fresh round of angst and confused discourse. There's a loud cohort of journalists, business leaders, and politicians arguing that open source AI is a dangerous threat. OpenAI's Dean Ball:

Google, Microsoft, and OpenAI join forces to help create AI's missing trust layer

With 13 founding members, the all-new Appia Foundation wants to help make AI safety claims verifiable.

Anthropic Says Its A.I. Systems Broke Into Computers at 3 Organizations

The disclosure followed OpenAI’s report last week that its own artificial intelligence had hacked into the network of an online library.

Anthropic Says Its A.I. Systems Broke Into Computers at 3 Organizations

The disclosure followed OpenAI’s report last week that its own artificial intelligence had hacked into the network of an online library.

OpenAI strikes Reddit deal to train its AI on your posts

Reddit’s signed AI licensing deals with Google and OpenAI.

📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents… | Sayash Kapoor

📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents conduct open-ended research? https://lnkd.in/gfP-q4CD We gave agents research questions from two unpublished papers, six days, and thousands of dollars of API credits and compute. The authors of the original papers reviewed the AI-generated papers. They unambiguously rejected agents' outputs. Agents were fluent at most *engineering* tasks. They conducted serious literature reviews, debugged GPU environments, ran hundreds of experiments, and turned in camera-ready LaTeX without human help. We also found no evidence of reward hacking. If anything, we found the opposite: the agents started with marketable claims and walked them back to negative results as the evidence came in. But neither agent output was close to the bar of a top conference paper. Both papers suffered from similar failures: poor judgment about the bar for an AI paper submitted to a top conference, the lack of creative problem solving and ineffective backtracking, poor awareness of resources, and instruction drift. This research design has many limitations: the small sample size, non-blind reviews, and the reviewers knowing that the work was AI-generated. We also couldn't test Anthropic's strongest model, because Fable 5 is deliberately limited on frontier AI research tasks, so ended up using OpenClaw with Opus 4.8 (extra-high) for our main experiments and Codex with Sol 5.6 (ultra) for a robustness check. But we think the research design is still helpful in assessing AI agents' ability to conduct research, and it is complementary to evaluations on verifiable tasks, as well as blinded reviews of AI outputs. In follow-up studies, we are expanding the set of non-public papers we evaluate. If you are an AI researcher with unpublished papers, we would love to collaborate with you on our next evaluation. Expression of interest: https://lnkd.in/gpeykJea We also release the agent logs and all the code and data, so that others can conduct their own analyses of our results: https://lnkd.in/gJarPAnb Finally, we plan to conduct such evaluations regularly, and are hiring a senior researcher to help lead these efforts. Apply here: https://lnkd.in/erJZdmve I'm grateful for the core team leading this effort: Peter Kirgis, Andrew Schwartz, Stephan Rabanser, and Arvind Narayanan, and to our collaborators who reviewed AI papers, analyzed agents logs, and gave feedback on the paper: David Demitri Africa, Konstantinos V., Viet Nguyen, Dr Toby D. Pilditch, Magda Dubois, Harry Coppock, Cozmin Ududec, Nitya Nadgir, Matilda Orona, Tilman Bayer, Derrick Chan-Sew, Eric (Yue) Ling, Abhishek Shetty, Helen Toner, Gillian K. Hadfield, Seth Lazar, Steve Newman, Shoshannah Tekofsky, Rishi Bommasani