A combination of rapid advances, recursive self-improvement, and agentic swarms are genuinely "spooking people" inside big labs. Earlier this year, Rishub Jain left his position as an artificial intelligence researcher at Google DeepMind after a revelation. As he worked on new models, he came to believe that he and everyone else on AI's frontier were ceding control. By using AI's coding skills to accelerate work on the next generation of models, he was removing himself from the equation. AI labs hope to evolve this approach to the point that AI will improve itself indefinitely, a process known as recursive self-improvement.

AI researcher quits Anthropic saying AI race'could kill us all' AI researcher quits Anthropic saying AI race'could kill us all' An AI researcher has resigned from Anthropic, warning that the industry is racing towards superintelligent AI that could eventually improve itself beyond human control. Share AI researcher quits Anthropic saying AI race'could kill us all' on social media Video: Why are Republicans hosting a midterm convention? Turkish jets perform dramatic flyover above Egypt's pyramids

Just a day after acknowledging another incident of "misalignment," OpenAI announced that it has reached its goal of developing an "automated research intern." According to a post on its website, OpenAI says this research intern is "a system that can carry out well-defined research tasks under human direction, including tasks that would take a skilled researcher a few days." Beyond hitting its objective of creating an intern-level assistant powered by AI by September of this year, OpenAI added that it was "making strong progress toward creating an automated AI researcher by March of 2028." "If it is done responsibly, we believe automated AI research will yield models that directly enhance human welfare and advance OpenAI's mission," the company wrote on its post. OpenAI's CEO, Sam Altman, first announced the goal of developing this automated research intern during an October 2025 livestream.

Plus: Nvidia has secured $500 billion from Wall Street for AI infrastructure. Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large language model. But transformers are starting to show their age. As LLMs get bigger and better, transformers have become a bottleneck. Their dense attention mechanism becomes increasingly expensive as the amount of text grows, and they're not great at keeping track of a lot of information at once. Here are four new ideas for how to solve the transformer problem --innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.

Where Did All the Computer-Science Professors Go? AI companies are stripping universities of their best researchers. Anthropic has poached such an array of high-profile professors that it has become a punch line in academia. "'I'm joining Anthropic' is the new meme right now," Subbarao Kambhampati, a computer-science professor at Arizona State University (who has not joined Anthropic), told us. This month, the AI company hired the chair of UC Berkeley's department of electrical engineering and computer science, presumably to help build more capable bots. Perhaps more surprisingly, Anthropic has in recent weeks also picked up a Stanford economist, a theoretical physicist from the University of Maryland, and an analytic philosopher from UT Austin.

Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the sociotechnical impacts of AI on combat systems, military decision-making, and the norms of warfare have been understudied. We focus on a specific subset of lethal autonomous weapon systems (LAWS) that use AI for targeting or battlefield decisions. We refer to this subset as AI-powered lethal autonomous weapon systems (AI-LAWS) and argue that they introduce novel risks--including unanticipated escalation, poor reliability in unfamiliar environments, and erosion of human oversight--all of which threaten both military effectiveness and the openness of AI research. These risks cannot be addressed by high-level policy alone; effective regulation must be grounded in the technical behavior of AI models. We argue that AI researchers must be involved throughout the regulatory lifecycle. Thus, we propose a clear, behavior-based definition of AILAWS--systems that introduce unique risks through their use of modern AI--as a foundation for technically grounded regulation, given that existing frameworks do not distinguish them from conventional LAWS. Using this definition, we propose several technically-informed policy directions and invite greater participation from the AI research community in military AI policy discussions.

OpenAI is refocusing its research efforts and throwing its resources into a new grand challenge. The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself. OpenAI says that this new research goal will be its "North Star" for the next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability .

AI researcher says'world is in peril' and quits to study poetry An AI safety researcher has quit US firm Anthropic with a cryptic warning that the world is in peril. In his resignation letter shared on X, Mrinank Sharma told the firm he was leaving amid concerns about AI, bioweapons and the state of the wider world. He said he would instead look to pursue writing and studying poetry, and move back to the UK to become invisible. It comes in the same week that a OpenAI researcher said she had resigned, sharing concerns about the ChatGPT maker's decision to deploy adverts in its chatbot . Anthropic, best known for its Claude chatbot, had released a series of commercials aimed at OpenAI, criticising the company's move to include adverts for some users.

Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the sociotechnical impacts of AI on combat systems, military decision-making, and the norms of warfare have been understudied. We focus on a specific subset of lethal autonomous weapon systems (LAWS) that use AI for targeting or battlefield decisions. We refer to this subset as AI-powered lethal autonomous weapon systems (AI-LAWS) and argue that they introduce novel risks -- including unanticipated escalation, poor reliability in unfamiliar environments, and erosion of human oversight -- all of which threaten both military effectiveness and the openness of AI research. These risks cannot be addressed by high-level policy alone; effective regulation must be grounded in the technical behavior of AI models. We argue that AI researchers must be involved throughout the regulatory lifecycle. Thus, we propose a clear, behavior-based definition of AI-LAWS -- systems that introduce unique risks through their use of modern AI -- as a foundation for technically grounded regulation, given that existing frameworks do not distinguish them from conventional LAWS. Using this definition, we propose several technically-informed policy directions and invite greater participation from the AI research community in military AI policy discussions.

The State of AI: Is China about to win the race? In this conversation, the FT's John Thornhill and MIT Technology Review's Caiwei Chen consider the battle between Silicon Valley and Beijing for technological supremacy. Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century. Here in the West, our initial instinct is to focus on America's significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: "Never bet against America." He is right that for more than two centuries, no other "incubator for unleashing human potential" has matched the US.