On Wednesday, the White House released "Winning the Race: America's AI Action Plan," a 25-page document that outlines the Trump administration's strategy to "maintain unquestioned and unchallenged global technological dominance" in AI through deregulation, infrastructure investment, and international partnerships. But critics are already taking aim at the plan, saying it's doing Big Tech a big favor.
Assistant to the President for Science and Technology Michael Kratsios and Special Advisor for AI and Crypto David Sacks crafted the plan, which frames AI development as a race the US must win against global competitors, particularly China.
The document describes AI as the catalyst for "an industrial revolution, an information revolution, and a renaissance—all at once." It calls for removing regulatory barriers that the administration says hamper private sector innovation. The plan explicitly reverses several Biden-era policies, including Executive Order 14110 on AI model safety measures, which President Trump rescinded on his first day in office during his second term.
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On Thursday, OpenAI launched ChatGPT Agent, a new feature that lets the company's AI assistant complete multi-step tasks by controlling its own web browser. The update merges capabilities from OpenAI's earlier Operator tool and the Deep Research feature, allowing ChatGPT to navigate websites, run code, and create documents while users maintain control over the process.
The feature marks OpenAI's latest entry into what the tech industry calls "agentic AI"—systems that can take autonomous multi-step actions on behalf of the user. OpenAI says users can ask Agent to handle requests like assembling and purchasing a clothing outfit for a particular occasion, creating PowerPoint slide decks, planning meals, or updating financial spreadsheets with new data.
The system uses a combination of web browsers, terminal access, and API connections to complete these tasks, including "ChatGPT Connectors" that integrate with apps like Gmail and GitHub.
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How many Google AI researchers does it take to screw in a lightbulb? A recent research paper detailing the technical core behind Google's Gemini AI assistant may suggest an answer, listing an eye-popping 3,295 authors.
It's a number that recently caught the attention of machine learning researcher David Ha (known as "hardmaru" online), who revealed on X that the first 43 names also contain a hidden message. "There’s a secret code if you observe the authors’ first initials in the order of authorship," Ha wrote, relaying the Easter egg: "GEMINI MODELS CAN THINK AND GET BACK TO YOU IN A FLASH."
The paper, titled "Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities," describes Google's Gemini 2.5 Pro and Gemini 2.5 Flash AI models, which were released in March. These large language models, which power Google's chatbot AI assistant, feature simulated reasoning capabilities that produce a string of "thinking out loud" text before generating responses in an attempt to help them solve more difficult problems. That explains "think" and "flash" in the hidden text.
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There's a curious contradiction at the heart of today's most capable AI models that purport to "reason": They can solve routine math problems with accuracy, yet when faced with formulating deeper mathematical proofs found in competition-level challenges, they often fail.
That's the finding of eye-opening preprint research into simulated reasoning (SR) models, initially listed in March and updated in April, that mostly fell under the news radar. The research serves as an instructive case study on the mathematical limitations of SR models, despite sometimes grandiose marketing claims from AI vendors.
What sets simulated reasoning models apart from traditional large language models (LLMs) is that they have been trained to output a step-by-step "thinking" process (often called "chain-of-thought") to solve problems. Note that "simulated" in this case doesn't mean that the models do not reason at all but rather that they do not necessarily reason using the same techniques as humans. That distinction is important because human reasoning itself is difficult to define.
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Remember when teachers demanded that you "show your work" in school? Some new types of AI models promise to do exactly that, but new research suggests that the "work" they show can sometimes be misleading or disconnected from the actual process used to reach the answer.
New research from Anthropic—creator of the ChatGPT-like Claude AI assistant—examines simulated reasoning (SR) models like DeepSeek's R1, and its own Claude series. In a research paper posted last week, Anthropic's Alignment Science team demonstrated that these SR models frequently fail to disclose when they've used external help or taken shortcuts, despite features designed to show their "reasoning" process.
(It's worth noting that OpenAI's o1 and o3 series SR models were excluded from this study.)
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