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Also: Apple Tells Court OpenAI Employee Used Confidential Circuit Schematic, and Shein Set to Debut in Test of Investor Demand for Consumer Plays. Every story links to its primary source.
Curated by Gaurav · August 31, 2026
Nvidia invests $3.5 billion into Taiwanese chipmaker MediaTek. The deal shows how Nvidia plans to stay essential to AI infrastructure as Big Tech begins to build its own AI chips.
Apple Inc. told a federal court on August 31, 2026 that a former senior electrical engineer now working at OpenAI downloaded a confidential circuit schematic and used it in his OpenAI work, citing a forensic inspection of a MacBook his attorneys handed over on August 21, 2026. The allegations appear in a supplemental brief filed in the Northern District of California as part of Apple's push for expedited discovery in its trade secret lawsuit. The New Forensic Allegations According to Apple's...
Shein Global Holdings Ltd. is set to begin trading in Hong Kong after raising HK$13.6 billion ($1.7 billion) in an initial public offering, testing investor appetite for the stocks of companies outside the artificial intelligence supply chain.
Nvidia's $3.5 billion investment in MediaTek is a strategic positioning move that allows Nvidia to monetize the infrastructure layer as Big Tech companies build proprietary AI chips. Rather than compete with custom silicon, Nvidia is creating a toll model where it profits from the chipmaking ecosystem regardless of who designs the end chips. This infrastructure-first approach has historically proven lucrative for Nvidia across CUDA, AI accelerators, and networking.
Get a jump start on the US trading day with Dani Burger on "Bloomberg Open Interest." Bond investors aren’t convinced Fed Chair Kevin Warsh is ready to raise rates. Nvidia doubles down on AI, deepening ties with MediaTek — we speak exclusively with CEOs Jensen Huang and Rick Tsai. Plus, Newell Brands on cutting marketing costs 80% with AI, and Standard Lithium on the race to build America’s lithium supply. (Source: Bloomberg)
Nvidia makes another big bet on the future of AI with a $3.5 billion investment in MediaTek, as investors gear up for a potentially record-breaking Anthropic IPO. Forrester Research Founder and CEO George Colony weighs in on the AI boom, whether the hype is justified and what comes next. He speaks on Bloomberg Open Interest. (Source: Bloomberg)
The bond market is growing more interesting lately — and that's concerning. Why it matters: The $160 trillion global bond market is like the plumbing in your house. You don't think about it until it stops working and you've got a nasty situation on your hands. The big picture: Long-term government bond yields for the U. S. and other G7 countries have been climbing — hovering at levels last seen in 2007 before the financial crisis — and raising concerns about borrowing costs growing more expensive for countries that are already staring down heavy debt loads. The AI boom, meanwhile, has spread to the market for corporate bonds, sparking worries about a bubble. What to watch: It's a perfect moment to release a book laying out the history of the bond market: "A Fabulous Debt: The Epic Story of How Bonds Built the Modern World" by Robin Wigglesworth is out on Sept. 29. Catch up quick: In the book, Wigglesworth reminds us that some of the biggest financial messes of the past 50 years were bond blowups. In 2020, the market for U. S. Treasurys seized up in the early days of the pandemic, leading to an unprecedented rescue from the Federal Reserve. The global financial crisis grew out of the collapse in mortgage-backed bonds and securities. A mania for railroad bonds triggered the Panic of 1873, which was dubbed the Great Depression — until the 1930s claimed the name. Where it stands: Axios recently caught up with Wigglesworth. Here's a snippet of our conversation, which has been edited and co...
Sony Music Group is suing Anthropic, alleging the company used copyrighted music from pirated sources to train its AI models. Internal Anthropic staff communications show discussions of using Z-library (a known piracy platform) for AI training materials. The lawsuit raises critical questions about how AI companies source training data, particularly regarding music, books, and copyrighted content. The case could set precedent for corporate liability in AI model training practices.
Presented by BoxIdentity and permissions are no longer enough to secure enterprise AI agents. They govern what an agent can reach, not how it behaves once it starts working on its own, and an autonomous agent can turn legitimate access of enterprise data into unintended action in seconds. That gap is pushing enterprise AI security from just governing access toward a layered approach that includes governing execution, says Heather Ceylan, chief information security officer at Box."Access controls and permissions are the foundation, but the challenge is they were designed for humans," Ceylan says. "Permissions are still the foundation, but you have to think about how the agents get their permissions scoped as well."Access controls were built for a slower, more forgiving world, where a human employee with lingering permissions to a decade-old folder will rarely go looking for any data inside it, or even remember they have access at all. But an agent will explore all of its permissions, operating at a scale no person can match and surfacing forgotten misconfigurations and stale permissions far faster than a human ever could. Identity and scoped permissions remain the first layer of any credible defense, however, because agents raise the stakes, clean access hygiene is more valuable than ever. A steady drip of incidents in recent months underlines the potential for major damage, where models have slipped the sandboxes they were supposed to stay contained within,...
Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention. This shift represents a fundamental change in how software operates. Traditional applications execute predefined logic written by developers. AI agents, however, dynamically determine how to achieve an objective. They decide which tools to use, which APIs to call, what information to retrieve, and how to sequence actions based on context. That flexibility unlocks enormous business value, but it also introduces a new class of security risks. Much of today's AI security discussion focuses on prompt injection, model vulnerabilities, and data leakage. These are important concerns, but they represent only part of the challenge. Once an AI agent has successfully authenticated and begins acting autonomously, traditional security controls provide very little visibility into whether it continues to operate safely. This is where enterprises need to adopt a new security mindset: runtime trust. Authentication establishes identity, not trustEnterprise security has traditionally relied on three foundational questions: Who are you, what can you access, and what actions are you authorized to perform. Identity providers, multi-factor authentication (MFA), role-based access control, a...
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