A United States jury has ruled against Elon Musk in his lawsuit against OpenAI, finding the artificial intelligence (AI) company not liable to the world's richest person for having allegedly strayed from its original mission to benefit humanity. In a unanimous verdict on Monday, the jury in Oakland, California US federal court said Musk had brought his case too late. Following the verdict, Musk's lawyer said he reserved the right to appeal, but the judge suggested he may have an uphill battle because whether the statute of limitations ran out before Musk sued was a factual issue. "There's a substantial amount of evidence to support the jury's finding, which is why I was prepared to dismiss on the spot," US District Judge Yvonne Gonzalez Rogers said. Musk was a co-founder of OpenAI, the company that launched in 2015 and went on to create ChatGPT.

After three weeks of testimony and not much deliberation, a jury has ruled against Elon Musk, finding that Sam Altman and Greg Brockman were not liable in the case. The jury found that the statute of limitations had already passed when Musk sued the two executives. Musk filed his lawsuit in 2024, accusing them of stealing a charity following his departure from the AI lab in 2018. Though the jury in the case served only an advisory role, Judge Yvonne Gonzalez Rogers agreed with the jury's ruling. Musk's claims of breach of charitable trust and unjust enrichment are dismissed as untimely, she said according to Though Musk could still appeal the ruling, Rogers told his lawyer she would dismiss an appeal on the spot.

The nine-member panel took only two hours to return a verdict in favor of OpenAI on Monday, which the judge quickly adopted as her own final decision. Elon Musk suffered the worst defeat possible in his legal battle against OpenAI as a federal jury and a judge ruled he waited too long to bring his claims against the AI startup and its top executives, Sam Altman and Greg Brockman. The jury's decision was a nonbinding recommendation sent to US district judge Yvonne Gonzalez Rogers, though she immediately accepted it on Monday as her own, making it final. The nine-member panel delivered the unanimous verdict in an Oakland, California courtroom after deliberating for under two hours. They found that statutes of limitations expired well before Musk filed his lawsuit in 2024.

ChatGPT Pro users can now connect banking and investment accounts from over 12,000 financial institutions through Plaid integration for personalized financial insights. PCWorld highlights that while the feature offers read-only access and 30-day data deletion, it raises significant privacy and security concerns for users. The AI-powered dashboard provides financial summaries and answers queries, but users must weigh convenience against potential data risks. If there's one area where LLMs excel, it's plowing through reams of data and teasing out patterns, trends, and insights. So it's not surprising that OpenAI is zeroing in on personal finance, with ChatGPT now capable of delving into our banking, checking, and investment accounts .

Inside Anduril and Meta's quest to make smart glasses for warfare It's been a year since the duo entered the US Army's troubled augmented-reality contest. Here's what it looks like so far. The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it's prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands. Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army's Special Operations Command, says his fundamental goal is to optimize "the human as a weapons system." The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one. Anduril actually has two such projects in the works.

Musk v. Altman week 3: Musk and Altman traded blows over each other's credibility. Now the jury will pick a side. In the final week of the Musk v. Altman trial, lawyers attacked the credibility of the two tech leaders. Sam Altman was accused of lying and self-dealing, while Elon Musk was portrayed as a power-seeker trying to control artificial general intelligence. The case unearthed new details about the two arch-rivals and OpenAI's contested nonprofit status, as well as a golden trophy of a donkey's ass awarded to an employee who challenged Musk. Michelle Kim, who's also a lawyer, has been in court throughout the Musk v. Altman trial.

Once-speculative concerns about the technology have now become pressing matters. AI has ascended to the role of main character. When Donald Trump traveled to Beijing for an historic summit last week, AI was one of the central topics of his discussions with Xi Jinping. As the two nations remain locked in a technological arms race, the president brought along some of the United States' most powerful AI executives, including Elon Musk and Nvidia's Jensen Huang. A continent away, the European Union has been unsuccessfully petitioning Anthropic to grant access to its advanced cybersecurity model, Mythos. Back in the United States, millions of students and teachers are dealing with the fallout of a devastating ransomware attack on the software platform Canvas--a hack that was likely aided by AI tools.

Jill Lepore chronicles the rise of machine-generated writing, from a Hollywood plot-writing grift and Cold War computer poetry to the age of ChatGPT.

Joint-embedding predictive architectures (JEPAs) propose that a model should learn more useful abstractions when trained to predict latent representations rather than observed outputs. For autoregressive language-model fine-tuning the principle entails a stricter requirement: the induced hidden-state geometry must reach the language-model head \emph{and} improve the decoded task metric. We test that requirement under a fixed Llama-3.2-1B-Instruct LoRA harness on natural-language-to-regex generation, comparing twenty-two training-time auxiliaries across trajectory-shape regularisation, distributional constraints, predictor/target asymmetry, Fisher-metric Jacobi residuals, and a decoder-visible JEPA objective constructed to lie in cross-entropy's positive cone. The empirical answer is a structured null: several auxiliaries clear single-cell paired $α= 0.10$ without correction (T3-Local at $Δ= +2.53$~pp, $p = 0.003$ being the strongest), but none survives Bonferroni or Holm--Bonferroni at the relevant family-wise threshold, even though many change curvature, anisotropy, variance, and gradient direction. Decoder-visible JEPA yields the first positive auxiliary--cross-entropy gradient cosine in the study, yet exact match remains inside seed noise; a full-fine-tuning replication of the same auxiliary at $n = 5$ seeds reproduces the null on both benchmarks (TURK: $Δ= +0.04$~pp, $p_{\text{paired}} = 0.96$; SYNTH: $Δ= +0.52$~pp, $p_{\text{paired}} = 0.28$), so the null is robust across LoRA and full fine-tuning for the decoder-visible construction. Hidden-state representation work and decoded-task accuracy are therefore weakly coupled in this regime; we accordingly reframe LLM-domain JEPA evaluation as a coupling problem, in which the operative question is under which metrics useful hidden geometry becomes decoder-visible task signal.

Mixture-of-Experts (MoE) models rely on balanced expert utilization to fully realize their scalability. However, existing load-balancing methods are largely heuristic and operate on noisy mini-batch assignment statistics, introducing bias relative to population-level objectives. We propose $ϕ$-balancing, a principled framework that directly targets population-level expert balance by minimizing a strictly convex, symmetric, and differentiable potential of the expected routing distribution. Using convex duality, we derive an equivalent min-max formulation and obtain a simple online algorithm via mirror descent, yielding an efficient EMA-based routing adjustment with negligible overhead. Across large-scale pretraining and downstream fine-tuning, $ϕ$-balancing consistently outperforms prior Switch-style and loss-free baselines, demonstrating more stable and effective expert utilization.