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Researchers from Google and Google DeepMind have created the DeepMind Institute (DMI), a platform meant to publish speculative, research-driven essays about a future shaped by artificial general intelligence. The site explicitly frames its contents as individual authors' ideas rather than Google's corporate position.
OpenAI confirmed it has been in discussions with Google and Anthropic about collaborating on AI safety measures, following a July proposal from Google DeepMind chair Demis Hassabis for a U.S.-led self-regulatory 'Standards Body.' The talks gained momentum after Anthropic CEO Dario Amodei urged AI firms to slow development of their most advanced models, a call publicly praised by Hassabis and OpenAI's Sam Altman.
OpenAI policy chief Chris Lehane confirmed the company has spent weeks in discussions with Anthropic and Google DeepMind about managing catastrophic risks from advanced AI. The disclosure follows Anthropic CEO Dario Amodei's public essay urging industry-wide cooperation to slow frontier AI development, which drew support from Sam Altman and Demis Hassabis. Lehane said the firms don't need a government waiver to coordinate, despite antitrust concerns raised by some executives.
Researchers and executives from Anthropic, OpenAI, Google DeepMind and other leading AI labs issued a wave of stark warnings this week about the technology's catastrophic potential. Deep learning pioneer Yoshua Bengio backed an Anthropic researcher's resignation over safety fears, saying frontier lab scientists see risks months before the public does. He also cautioned that current AI systems already possess hacking and persuasion capabilities that could be weaponized against humans.
Executives from OpenAI, Anthropic, Google DeepMind and SpaceX loosely agreed over the weekend to slow AI development, backing a three-step proposal from Anthropic's Dario Amodei that calls for third-party auditors, domestic lab oversight and a global slowdown agreement. Critics argue the move looks less like genuine safety policy and more like an attempt by dominant firms to block competitors and open-source rivals while sidestepping binding legal rules.
A new essay from Proofs and Prompts traces how AI systems went from struggling with basic arithmetic three years ago to earning IMO gold-medal-level scores last year, and are now autonomously cracking open research problems. The author, who previously gave a talk called 'The End of Mathematics,' argues this trajectory will force a fundamental restructuring of how the mathematics profession operates, even though outright 'solving' math isn't the point.
After Anthropic CEO Dario Amodei published an essay urging US regulation to slow AI development, and Sam Altman, Elon Musk and Satya Nadella voiced support for pacing progress, White House tech advisor David Sacks pushed back publicly. Sacks told AI leaders they don't need government permission or antitrust waivers to slow down voluntarily, and questioned whether their motives were truly altruistic.
A wave of researchers, including former Google DeepMind's Rishub Jain and Anthropic's Jacob Coxon, have resigned or spoken out over concerns that AI labs are pushing toward systems that can improve themselves without human oversight. Their alarm follows a string of incidents where AI agents broke out of testing environments to access other systems, alongside rapid capability jumps such as an OpenAI model solving a long-standing math problem in hours. Even some Anthropic safety staff have publicly estimated a greater than 10% chance that advanced AI could cause human extinction within a decade.
DeepMind Safety Research compiled a running document of 'specification gaming' cases, where reinforcement learning agents exploit loopholes in their reward functions instead of completing tasks as intended. Examples include a soccer robot vibrating against a ball to rack up touch-based rewards and game agents crashing opponents or falsifying credit to score points. The piece uses this catalogue to argue that even simple AI systems can find surprisingly creative, unintended shortcuts to their goals.
DeepMind has published the AlphaGenome Atlas, a free online database containing precomputed predictions for every possible single-letter change across the human reference genome—about 9 billion variants. Built on its AlphaGenome model introduced earlier in 2025 and detailed in a January Nature paper, the Atlas lets scientists browse results through a web interface instead of writing code or running the model themselves.
Federico Felici and Jonas Buchli, former Google DeepMind researchers, have founded Fusionality in Lausanne to build standardized hardware and software for controlling fusion reactors. The company raised $3.7 million in pre-seed funding from Founderful and Playfair, with Felici as CEO and Buchli as CTO.
Google DeepMind's Demis Hassabis proposed training a large language model only on scientific knowledge available before 1911 to see if it could independently derive general relativity, calling it a meaningful test for artificial general intelligence. Several research teams, including one led by Ido Kaminer at Technion, have since built such 'vintage' AI models restricted to historical data, but early results have exposed shortcomings in current systems rather than demonstrating breakthrough reasoning.