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The White House cannot lapse in its funding of the Consumer Financial Protection Bureau, a federal district court judge ruled on Tuesday, only days before funds at the bureau would have likely run out and the consumer finance agency would have no money to pay its employees. Judge Amy Berman ruled that the CFPB should continue to get its funds from the Federal Reserve, despite the Fed operating at a loss, and that the White House’s new legal argument about how the CFPB gets its funds is not valid. At the heart of this case is whether Russell Vought, President Donald Trump’s budget director and the acting director of the CFPB, can effectively shut down the agency and lay off all of the bureau’s employees. The CFPB has largely been inoperable since President Trump has sworn into office nearly a year ago. Its employees are mostly forbidden from doing any work, and most of the bureau’s operations this year have been to unwind the work it did under President Biden and even under Trump’s first term. Vought himself has made comments where he has made it clear that his intention is to effectively shut down the CFPB. The White House earlier this year issued a reduction in force for the CFPB, which would have furloughed or laid off much of the bureau. The National Treasury Employees Union, which represents the workers at the CFPB, has been mostly successful in court to stop the mass layoffs and furloughs. The union sued Vought earlier this year and won a preliminary injunction stopping the layoffs while the union’s case continues through the legal process. In recent weeks, the White House has used a new line of argument to potentially get around the court’s injunction. The argument is that the Federal Reserve has no combined earnings at the moment to fund the CFPBs operations. The CFPB gets its funding from the Fed through expected quarterly payments. The Federal Reserve has been operating at a paper loss since 2022 as a result of the central bank trying to combat inflation, the first time in the Fed’s entire history its been operating at a loss. The Fed holds bonds on its balance sheet from a period of low interest rates during the COVID-19 pandemic, but currently has to pay out higher interest rates to banks who hold their deposits at the central bank. The Fed has been recording a deferred asset on its balance sheet which it expects will be paid down in the next few years as the low interest bonds mature off the Fed’s balance sheet. Because of this loss on paper, the White House has argued there are no combined earnings for the CFPB to draw on. The CFPB has operated since 2011, including under President Trumps first term, drawing on the Feds operating budget. White House lawyers sent a notice to the court in early November, where they argued that the CFPB would run out of appropriations in early 2026, using the combined earnings argument, and does not expect to get any additional appropriations from Congress. This combined earnings legal argument is not entirely new. It has floated in conservative legal circles going back to when the Federal Reserve started operating at a loss. The Office of Legal Counsel, which acts as the government’s legal advisors, adopted this legal theory in a memo on November 7. However, this idea has never been tested in court. In her opinion, Berman said the OLC and Vought were using this legal theory to get around the court’s injunction instead of allowing the case to be decided on merits. A trial on whether the CFPB employees’ union can sue Vought over the layoffs is currently scheduled for February 2026. It appears that defendants new understanding of combined earnings is an unsupported and transparent attempt to starve the CPFB of funding and yet another attempt to achieve the very end the Courts injunction was put in place to prevent,” Berman wrote in an opinion. Were very pleased that the court made clear what should have been obvious: Vought cant justify abandoning the agencys obligations or violating a court order by manufacturing a lack of funding, said Jennifer Bennett of Gupta Wessler LLP, who is representing the CFPB employees in the case. A White House spokeswoman did not immediately respond to a request for comment on Berman’s opinion. Ken Sweet, AP business writer
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In a year defined by companies pouring shocking sums of money into AI, one more deal squeaked in just before 2026. Meta just made a play on Manus, the buzzy Singapore-based company with Chinese roots that turned heads earlier this year when it showed its AI agents executing complex tasks, like hunting for real estate and sorting through resumes. The deal is sure to turn heads too. Manus and its parent company Butterfly Effect are now based in Singapore but were founded in China a country with a fraught relationship to the U.S tech industry and maintain operations there. Facebooks parent company will reportedly pay more than $2 billion to acquire the startup, which it hopes will bolster its own lagging AI capabilities. In a crowded field of soaring chipmakers, nimble startups laser focused on AI, and ancient tech giants like Microsoft making themselves freshly relevant with big AI bets, Meta is far from leading the pack a fact the company seems well aware of. The acquisition will bring the startups agentic AI tech on board, allowing Meta to potentially integrate it into its vast suite of products, including Facebook, Instagram, WhatsApp and Metas AI chatbot. The Manus deal follows Metas $14.3 billion investment in AI training data startup Scale AI earlier this year. Joining Meta allows us to build on a stronger, more sustainable foundation without changing how Manus works or how decisions are made, Manus CEO Xiao Hong said in a blog post announcing the news. Metas (latest) course correction Metas AI spending spree is only accelerating. After renaming itself Meta and declaring itself all in on the metaverse less than five years ago, Meta abandoned its course and its massive investments to play catch up on AI. Mark Zuckerberg declared last month that Meta plans to invest a mind boggling $600 billion into U.S. AI tech and infrastructure by 2028. Meta Chief AI Officer Alexandr Wang, formerly of ScaleAI, welcomed the Manus team into the fold Tuesday in a post on X. Excited to announce that @ManusAI has joined Meta to help us build amazing AI products! Wang wrote, adding that the Meta Superintelligence Labs team will be hiring in Singapore. The Manus team in Singapore are world class at exploring the capability overhang of todays models to scaffold powerful agents. Manus is no DeepSeek, but the company is still notable as a prominent Asian AI company coming under the wing of an American tech giant. In April, Manus raised $75 million in a round of funding led by San Francisco venture firm Benchmark. The startup is also backed by Asian investors, including Chinese tech conglomerate Tencent and HongShan Capital Group, previously the China-focused wing of American venture capital firm Sequoia, which frequently invests in Chinese startups. Meta told Fast Company that it plans to wind down Manus business operations that continue in China. That process will include relocating remaining Manus employees and severing any Chinese business entanglements. The company also emphasized that Manus employees joining Meta wont have access to first party user data from Metas existing products. “Metas acquisition of Manus AI will enable us to provide the most advanced technology to our users with safeguards in place to eliminate areas of potential risk, a Meta spokesperson told Fast Company. There will be no continuing Chinese ownership interests in Manus AI following the transaction, and Manus AI will discontinue its services and operations in China.
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In artificial intelligence, 2025 marked a decisive shift. Systems once confined to research labs and prototypes began to appear as everyday tools. At the center of this transition was the rise of AI agents AI systems that can use other software tools and act on their own. While researchers have studied AI for more than 60 years, and the term agent has long been part of the fields vocabulary, 2025 was the year the concept became concrete for developers and consumers alike. AI agents moved from theory to infrastructure, reshaping how people interact with large language models, the systems that power chatbots like ChatGPT. In 2025, the definition of AI agent shifted from the academic framing of systems that perceive, reason, and act to AI company Anthropics description of large language models that are capable of using software tools and taking autonomous action. While large language models have long excelled at text-based responses, the recent change is their expanding capacity to act, using tools, calling APIs, coordinating with other systems, and completing tasks independently. This shift did not happen overnight. A key inflection point came in late 2024, when Anthropic released the Model Context Protocol. The protocol allowed developers to connect large language models to external tools in a standardized way, effectively giving models the ability to act beyond generating text. With that, the stage was set for 2025 to become the year of AI agents. AI agents are a whole new ballgame compared with generative AI. The milestones that defined 2025 The momentum accelerated quickly. In January, the release of the Chinese model DeepSeek-R1 as an open-weight model disrupted assumptions about who could build high-performing large language models, briefly rattling markets and intensifying global competition. An open-weight model is an AI model whose training, reflected in values called weights, is publicly available. Throughout 2025, major U.S. labs such as OpenAI, Anthropic, Google, and xAI released larger, high-performance models, while Chinese tech companies, including Alibaba, Tencent, and DeepSeek, expanded the open-model ecosystem to the point where the Chinese models have been downloaded more than American models. Another turning point came in April, when Google introduced its Agent2Agent protocol. While Anthropics Model Context Protocol focused on how agents use tools, Agent2Agent addressed how agents communicate with each other. Crucially, the two protocols were designed to work together. Later in the year, both Anthropic and Google donated their protocols to the open-source software nonprofit Linux Foundation, cementing them as open standards rather than proprietary experiments. These developments quickly found their way into consumer products. By mid-2025, agentic browsers began to appear. Tools such as Perplexitys Comet, Browser Companys Dia, OpenAIs GPT Atlas, Copilot in Microsofts Edge, ASI X Inc.s Fellou, MainFunc.ais Genspark, Operas Opera Neon, and others reframed the browser as an active participant rather than a passive interface. For example, rather than helping you search for vacation details, it plays a part in booking the vacation. At the same time, workflow builders like n8n and Googles Antigravity lowered the technical barrier for creating custom agent systems beyond what has already happened with coding agents like Cursor and GitHub Copilot. New power, new risks As agents became more capable, their risks became harder to ignore. In November, Anthropic disclosed how its Claude Code agent had been misused to automate parts of a cyberattack. The incident illustrated a broader concern: By automating repetitive, technical work, AI agents can also lower the barrier for malicious activity. This tension defined much of 2025. AI agents expanded what individuals and organizations could do, but they also amplified existing vulnerabilities. Systems that were once isolated text generators became interconnected, tool-using actors operating with little human oversight. The business community is gearing up for multiagent systems. What to watch for in 2026 Looking ahead, several open questions are likely to shape the next phase of AI agents. One is benchmarks. Traditional benchmarks, which are like a structured exam with a series of questions and standardized scoring, work well for single models, but agents are composite systems made up of models, tools, memory and decision logic. Researchers increasingly want to evaluate nt just outcomes, but processes. This would be like asking students to show their work, not just provide an answer. Progress here will be critical for improving reliability and trust, and ensuring that an AI agent will perform the task at hand. One method is establishing clear definitions around AI agents and AI workflows. Organizations will need to map out exactly where AI will integrate into workflows or introduce new ones. Another development to watch is governance. In late 2025, the Linux Foundation announced the creation of the Agentic AI Foundation, signaling an effort to establish shared standards and best practices. If successful, it could play a role like the World Wide Web Consortium in shaping an open, interoperable agent ecosystem. There is also a growing debate over model size. While large, general-purpose models dominate headlines, smaller and more specialized models are often better suited to specific tasks. As agents become configurable consumer and business tools, whether through browsers or workflow management software, the power to choose the right model increasingly shifts to users rather than labs or corporations. The challenges ahead Despite the optimism, significant socio-technical challenges remain. Expanding data center infrastructure strains energy grids and affects local communities. In workplaces, agents raise concerns about automation, job displacement, and surveillance. From a security perspective, connecting models to tools and stacking agents together multiplies risks that are already unresolved in standalone large language models. Specifically, AI practitioners are addressing the dangers of indirect prompt injections, where prompts are hidden in open web spaces that are readable by AI agents and result in harmful or unintended actions. Regulation is another unresolved issue. Compared with Europe and China, the United States has relatively limited oversight of algorithmic systems. As AI agents become embedded across digital life, questions about access, accountability, and limits remain largely unanswered. Meeting these challenges will require more than technical breakthroughs. It demands rigorous engineering practices, careful design and clear documentation of how systems work and fail. Only by treating AI agents as socio-technical systems rather than mere software components, I believe, can we build an AI ecosystem that is both innovative and safe. Thomas Şerban von Davier is an affiliated faculty member at the Carnegie Mellon Institute for Strategy and Technology at Carnegie Mellon University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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