PCWorld reports Anthropic's Fable AI model will no longer be fully included in cheaper Claude plans, requiring usage credits for Pro and Team Standard subscribers. Claude Max and Team Premium users retain Fable 5 access, while affected users receive a one-time $100 credit as compensation. This shift toward tiered AI access may influence competitors like OpenAI and signals premium models becoming exclusive to expensive plans. So, there's good news and bad news when it comes to Fable, the most powerful Claude model. Good news first: Fable won't be yanked from all Claude plans, Anthropic announced late Friday.

Plus: SpaceX is negotiating to sell the Pentagon AI compute. The next time you apply for a job, AI may screen your résumé before any human sees it. But there's good reason to question whether AI will judge you fairly. We already know that LLMs pick up human biases from their training data. New research suggests they can also develop their own biases from experience--and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.

Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility implies a solution space that strictly supersets the fixed autoregressive trajectory, theoretically unlocking superior reasoning potential. Indeed, for specific constraint satisfaction tasks (e.g., sudoku puzzles), this capability has proven to be highly advantageous. However, in this paper, we reveal that for general reasoning tasks (e.g., mathematics and coding), arbitrary order generation may in fact limit the reasoning potential of dLLMs. We find that dLLMs tend to exploit this order flexibility to bypass high-uncertainty tokens that are crucial for exploration, leading to a premature collapse of solution coverage. This observation motivates a rethink of RL approaches for dLLMs, where considerable complexities, such as handling combinatorial trajectories and intractable likelihoods, are often devoted to preserving this flexibility. We demonstrate that effective reasoning can be better elicited by simply forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead. Our approach, JustGRPO, is minimalist yet surprisingly effective (e.g., 89.1% accuracy on GSM8K) while fully retaining the parallel decoding ability of dLLMs.

The next time you apply for a job, AI may screen your résumé before any human sees it. But there's good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience--and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.

'Nobody is falling for a toucan sighting in Siberia' This image is AI-generated, using ChatGPT. 'Nobody is falling for a toucan sighting in Siberia' This image is AI-generated, using ChatGPT. For many birdwatchers, recording a species outside its normal range is the holy grail. In the UK, the discoveries often make national headlinesThe western reef heron, for example, usually found in Africa and southern Europe, spotted in a seaside town in north Wales in June, which was widely celebrated on birding forums. But a new scourge is threatening to disrupt the fun: AI slop.

Experts believe it's at least possible. I n January, the AI company Anthropic published a new constitution for Claude, its most advanced large language model (LLM), which contained the comment: "We are caught in a difficult position where we neither want to overstate the likelihood of Claude's moral patienthood nor dismiss it out of hand." A month later, Anthropic's CEO Dario Amodei went on a podcast and said his company couldn't rule out the possibility that Claude was conscious. Philosopher David Chalmers, who coined the phrase "the hard problem of consciousness", has said there is a significant chance of conscious LLMs within a decade. When asked during testing to estimate the probability that it is a, meaning that its wellbeing matters in its own right, it gave numbers ranging from 5% to 40% and stressed how uncertain it was.

Moonshot AI's Kimi upends conventional wisdom on U.S. lead over China People walk past the Moonshot booth promoting its Kimi K3 AI model, during the World Artificial Intelligence Conference in Shanghai on Friday. At an event in Beijing earlier this year, some of China's top artificial intelligence leaders warned that the country remained meaningfully behind the U.S. in developing cutting-edge AI models, with one executive arguing that "the gap may actually be widening." Leading U.S. firms also appeared confident they were significantly ahead. As recently as last week, one executive at Anthropic, who spoke on condition of anonymity, mused that the Claude maker's technology was roughly six to 12 months ahead of Chinese rivals. The Chinese AI lab released Kimi K3, a more advanced open-weight model that it said outperforms all rivals except for Anthropic's Claude Fable 5 and OpenAI's GPT-5.6 on overall capability.

In the data-center age, the business of tech companies is more like oil-refining than coding. The AI boom has showered some of the nation's most prominent companies in market value. OpenAI and Anthropic are now the two most valuable private companies in the world. Google, Microsoft, and Nvidia have become larger than ever. But among the biggest winners has been Caterpillar, a purveyor of yellow trucks and cranes.

"Context bombing" tricks malicious AI agents into shutting down before they can do harm. Prompt injections, the malicious commands attackers embed into content to entice large language models to follow them, have been attackers' go-to tool for turning AI platforms against their users. A well-phrased command sneaked into an email or calendar invitation is often all it takes to cause the LLM to exfiltrate sensitive data or follow other harmful actions. Now, defenders are embracing the prompt injection, too. Researchers from Tracebit on Monday said they found that placing prompt injections alongside passwords, cryptographic keys, and other secrets stored on Amazon Web Services was often all that was needed to shut down attacks from AI hacking agents.

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