United States President Donald Trump and China's President Xi Jinping are set to meet in Washington, DC on Thursday for a summit that is expected to cover trade, artificial intelligence (AI), Taiwan and the US-Israel war on Iran. In advance of the talks, US Treasury Secretary Scott Bessent said Washington had proposed an AI " notification mechanism " with China - effectively a hotline to alert each other when AI incidents threaten national security. The meeting comes as leading AI companies warn about the technology's risks, while both superpowers push to accelerate their AI race. In this visual explainer, Al Jazeera compares the US and China's AI strength in computing power, AI models, spending and research. Who has more computing power?

What if the doomers and skeptics are both a little bit right? O ne 27-year-old quit his job, and everybody (including but not limited to the singer-songwriter Sheryl Crow) appears to have lost it. The past two weeks have been dizzying. The 27-year-old, Jacob Coxon, a former researcher at Anthropic, went public with an alarming allegation: That the people inside Silicon Valley's leading AI companies truly believe their products might pose an extinction level threat to humanity. AI safety advocates, often dubbed doomers, have been saying this for over a decade.

Anthropic's CEO says that safety hinges on understanding how AI "thinks." So far the evidence is disturbing. In early 2025 I was interviewing Anthropic CEO Dario Amodei when he explained why, despite the company's repeated acknowledgments that AI could yield catastrophic results, people seemed largely unperturbed. "There is compelling evidence that the models can wreak havoc," he said. But, he added, those dangers were still theoretical.

Mashable Selects Creator Playbook Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Say More Trending Now Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net All Series Microsoft's Mustafa Suleyman calls out Anthropic for chasing AI consciousness Timothy Beck Werth is the Tech Editor at Mashable, where he leads coverage and assignments for the Tech and Shopping verticals. Tim has over 15 years of experience as a journalist and editor, and he has particular experience covering and testing consumer technology, smart home gadgets, and men's grooming and style products. Previously, he was the Managing Editor and then Site Director of SPY.com, a men's product review and lifestyle website. As a writer for GQ, he covered everything from bull-riding competitions to the best Legos for adults, and he's also contributed to publications such as The Daily Beast, Gear Patrol, and The Awl. The AI industry is grappling with big questions this week -- Will AI kill us all?

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.