The number of businesses using both standard and Same Day ACH grew significantly from 2023 to 2024, a new Federal Reserve report found. 60% said they use standard ACH, up from 48% a year earlier. And 56% reported using Same Day ACH, an increase from 45% in 2023. Additionally, 47% of businesses said they encourage using ACH. One study respondent, identified as a “very large diversified service business,” told researchers, “We are using Same Day ACH more—it’s a good value for the price.” Still, even as both forms of ACH continue to gain usage, checks use in fact rose from 68% to 73%. It was highest among small (83%) and very small (78%) firms. “One key takeaway is that checks are unlikely to be disappear completely in the near future—a trend to monitor,” researchers noted. “Nacha’s own figures show that ACH volume is rising,” said Michael Herd, Nacha Executive Vice President, ACH Network Administration. “Given this widespread use and acceptance of ACH, plus the increasing amount of check fraud, the industry needs to focus on why businesses of any size are still writing and receiving checks.” When it comes to pain points for business payments, high costs/fees was the top issue cited at 48%. Speed was tied for a distant second with security issues, cited by 32%.
Loans to nonbank entities like buyout firms and private credit outfits topped $1 Trillion, up 20% from last year raising systemic risk
Lending to nonbank entities like buyout firms and private credit outfits has topped $1 trillion. This trend is happening amid concern by regulators that the connections between banks and their nonbank counterparts could present a systemic risk. The report, citing data from Fitch Ratings, said loans from banks to nonbank financial institutions (NBFIs) totaled roughly $1.2 trillion at the end of March, up 20% from last year and driven by lending to private credit firms. That data shows that, since the pandemic’s start, bank loans to NBFIs have gone from approximately $600 million at the end of 2019 to over $1 trillion when this year began, as businesses increasingly seek private credit funding. However, borrowers who look to private credit and direct lenders for funding tend to be riskier and more levered. As some of these loans are made with funds borrowed from banks, there are concerns that bad credit could infect the wider financial system. Another report from Fitch saying that a downturn in the private credit sector is “unlikely to have widescale financial stability implications for the largest banks,” at least in the short term. Still, Fitch said it’s hard to fully assess the risks and that “second-order effects are more difficult to quantify.”
Capital One is among Top 10 for GenAI patent filings alongside Google, Microsoft, IBM and Nvidia
Google has overtaken IBM to become the leader in generative AI-related patents and also leads in the emerging area of agentic AI, according to data from IFI Claims shared first with Axios. Patent filings, though they’re not a direct proxy for innovation, indicate areas of keen research interest — and generative AI patent applications in the U.S. have risen by more than 50% in recent months. “The surge in applications for AI related patents is a sign companies are actively seeking protection for their AI technologies, leading to an increase in grants as well,” IFI Claims spokesperson Lily Iacurci said. In the patents-for-agents U.S. rankings, Google and Nvidia top the list, followed by IBM, Intel and Microsoft. Globally, Google and Nvidia also led the agentic patents list, but three Chinese universities also make the top 10, highlighting China’s place as the chief U.S. rival in the field. In global rankings for generative AI, Google was also the leader — but six of the top 10 global spots were held by Chinese companies or universities. Microsoft was No. 3, with Nvidia and IBM also in the top 10. IFI Claims identified only a single patent tied to China’s DeepSeek, one for a method of constructing training data. In U.S. rankings for generative AI, Google and Microsoft topped the list of U.S. patent applications, surpassing previous leader IBM. Also in the top 10 were Nvidia, Capital One, Samsung, Adobe, Intel and Qualcomm. Many of the same names cropped up in the list of overall AI-related U.S. patent applications, with Google in the top spot, followed by Microsoft, IBM, Samsung and Capital One. Globally, Google topped the list, followed by Huawei and Samsung. Neither Meta nor OpenAI ranked in the top 10, though OpenAI has stepped up its patent efforts over the past year, IFI’s analysis found. Overall, the number of U.S. patent applications related to generative AI surged 56% last year, to 51,487. Granted patents in the U.S. also rose 32%.
Agentic AI could change customer acquisition channels and prompt retailers to shift ad dollars from pay-per-click campaigns, consolidating search, selection and checkout into the same dialogue
“What consumers really want is for commerce to happen immediately,” Scott Hendrickson, chief revenue officer of the agentic AI merchant network firmly, said. Hendrickson and his co-founder Kumar Senthil argue that the evolution from search to suggestion to settlement is the next logical step in eCommerce. It’s agentic AI at its most promising. That subtle shift in search behavior is forcing merchants to rethink where, and how, they meet shoppers. If an AI agent can compress browsing, selection and checkout into the same dialogue, retailers that sit outside the conversation risk ceding both visibility and sales. For Hendrickson, the technical plumbing enables a strategic shift in how merchants think about customer acquisition. “Right now, the search experience is you search for a product and then you immediately leave,” he told. “With the new LLMs you might start your research, refine what you’re looking for, and make the purchase in the same place. You don’t have to leave the site to complete that full funnel anymore, and that’s what’s ultimately going to drive better performance.” Consolidating the funnel could also upend traditional advertising economics. If the agent closes a sale within the first interaction, retailers may decide to shift dollars from pay-per-click campaigns toward deeper catalog and logistics integrations. Conversely, platforms that mediate the sale, such as chatbots, voice assistants or augmented-reality overlays, gain new leverage to capture a slice of the transaction rather than bill for impressions. That model works only if the agent can reliably see a broad, up-to-date range of products. Lifestyle labels in particular worry that ceding the front-end experience to an agentic AI could erode brand storytelling. Hendrickson counters that many customers now encounter products through a third-party channel anyway from an influencer’s livestream, a buy button inside Instagram, or a same-day-delivery marketplace. “We’re not eliminating brand,” he said. “We’re shortening the distance between intent and conversion.”
Nvidia’s blueprint for AI factory digital twins allows developers to design, simulate and optimize entire AI factories in physically accurate virtual environments by aggregating detailed 3D and simulation data representing all aspects of the data center
Nvidia announced a significant expansion of the Nvidia Omniverse Blueprint for AI factory digital twins, now available as a preview. The expanded blueprint will equip engineering teams to design, simulate and optimize entire AI factories in physically accurate virtual environments, enabling early issue detection and the development of smarter, more reliable facilities. Built on reference architectures for Nvidia GB200 NVL72-powered AI factories, the blueprint taps into Universal Scene Description (OpenUSD) asset libraries. This allows developers to aggregate detailed 3D and simulation data representing all aspects of the data center into a single, unified model, enabling them to design and simulate advanced AI infrastructure optimized for efficiency, throughput and resiliency. Siemens is building 3D models according to the blueprint and engaging with the simulation-ready, or SimReady, standardization effort, while Delta Electronics is adding models of its equipment. Because these are built with OpenUSD, users get accurate simulations of their facility equipment. Jacobs is helping test and optimize the end-to-end blueprint workflow. Connections to the Cadence Reality Digital Twin Platform and ETAP provide thermal and power simulation, enabling engineering teams to test and optimize power, cooling and networking long before construction begins. These contributions help Nvidia and its partners reshape how AI infrastructure is built to achieve smarter designs, avoid downtime and get the most out of AI factories. The OpenUSD-based models within the blueprint are inherently SimReady, designed from the ground up to be physics-based. This is especially valuable for developing and testing physical AI and agentic AI within these AI factories, enabling rapid and large-scale industrial AI simulations of power and cooling systems, building automation and overall IT operations. A key enhancement to this blueprint is the SimReady standardization workflow.
Google is transforming the Gemini app into a universal AI assistant that can make plans and imagine new experiences by understanding and simulating aspects of the world, and take action on your behalf across any device
Google has been working on building the foundations for the modern AI era, from pioneering the Transformer architecture to developing agent systems that can learn and plan. They are now working to extend their best multimodal foundation model, Gemini 2.5 Pro, to become a “world model” that can make plans and imagine new experiences by understanding and simulating aspects of the world, just as the brain does. This is a critical step in developing a universal AI assistant that is intelligent, understands the context you are in, and can plan and take action on your behalf across any device. The ultimate vision is to transform the Gemini app into a universal AI assistant that will perform everyday tasks, take care of mundane admin, and surface delightful new recommendations, making us more productive and enriching our lives. This includes capabilities like video understanding, screen sharing, and memory. Over the past year, they have integrated these capabilities into Gemini Live, and are gathering feedback from trusted testers to bring them to Gemini Live, new experiences in Search, the Live API for developers, and new form factors like glasses. Safety and responsibility are central to their work, and they recently conducted a large research project exploring the ethical issues surrounding advanced AI assistants. Project Mariner, a research prototype that explores the future of human-agent interaction, includes a system of agents that can complete up to ten different tasks at a time. It is available to Google AI Ultra subscribers in the U.S. and will be brought into the Gemini API and Google products throughout the year.
Catena Labs aims to be the first fully regulated AI-native financial institution enabling AI agents to transact with regulated stablecoins offering near-instant settlement, minimal transaction costs, and easy integration with AI workflows
Catena Labs announced its plan to establish the first fully regulated AI-native financial institution (FI) designed to serve the unique needs of the emerging AI economy. The company released a new open-source project defining protocols and patterns for agentic commerce. The company also confirmed an $18 million financing round led by a16z crypto, with participation from Breyer Capital, Circle Ventures, Coinbase Ventures and others. The company aims to address the shortcomings in legacy financial systems that make them poorly suited to the needs of AI agents and agentic commerce. “AI agents will soon conduct most economic transactions, but today’s financial systems are unprepared and resistant to interactions with automated intelligence,” said Sean Neville, CEO and co-founder of Catena Labs. “That’s why we’re building an AI-native financial institution that will give AI agents, and the businesses and consumers they serve, the ability to transact safely and efficiently.” The company is building upon protocols, patterns, emerging standards, and open source components to address new requirements AI agents create for identity and payments. Today, the company released the open source Agent Commerce Kit (ACK), which defines several of these open source building blocks. The company is building on ACK and other emerging standards to offer a broad suite of licensed financial services addressing new risk, security, and compliance challenges that arise from AI systems working as independent economic actors.
Klarna’s first-quarter consumer credit losses rose 17% compared to the January-March period of last year, suggesting growing stress in BNPL market
More Klarna customers are having trouble repaying their “buy now, pay later” loans, the short-term lender said. The disclosure corresponded with reports by lending platforms Bankrate and LendingTree, which cited an increasing share of all “BNPL” users saying they had fallen behind on payments. The late or missed installments are a sign of faltering financial health among a segment of the US population, some analysts say. This concern is consistent with previous research that has shown consumers spend more when BNPL is offered when checking out and that BNPL use leads to an increase in overdraft fees and credit card interest payments and fees. Industry watchers point to consumers taking out loans they can’t afford to pay back as a top risk of BNPL use. Without credit bureaus keeping track of the new form of credit, there are fewer safeguards and less oversight. Justine Farrell, chair of the marketing department at the University of San Diego’s Knauss School of Business, said that when consumers aren’t able to make loan payments on time, it worsens the economic stress they’re already experiencing. “Consumers’ financial positions feel more spread thin than they have in a long time,” said Farrell, who studies consumer behavior and BNPL services. The Consumer Federation of America and other watchdog organizations have expressed concern about the rollback of BNPL regulation as the use of the loans continues to rise. “By taking a head-in-the-sand approach to the new universe of fintech loans, the new CFPB is once again favoring Big Tech at the expense of everyday people,” said Adam Rust, director of financial services at the Consumer Federation of America.
Dell leverages ‘customer zero’ model—using internal teams as first adopters—to refine AI services in real time and accelerate scalable enterprise deployment
Customer zero is becoming a strategic advantage in the age of AI-powered services. Enterprises deploying artificial intelligence at scale are learning that the real advantage isn’t just in new tools, it’s in being their own first customer. This “customer zero” approach lets them test AI in-house, fine-tune it in real time and apply those insights externally. By embedding intelligence into workflows from day zero, they gain speed, precision and a repeatable model for real-world impact, according to Doug Schmitt, chief information officer of Dell Technologies Inc. and president of Dell Technologies Services Inc. Acting as customer zero gives Dell greater control over AI’s impact while grounding innovation in real business needs, not theory. By testing and refining AI internally, Dell builds credibility and sharpens its services-led approach, guiding customers from strategy to deployment. That firsthand experience helps reduce friction and deliver smoother, faster AI transformations across the enterprise, according to Scott Bils, vice president and general manager of product management, professional services, at Dell Technologies. “When you take a look at what we’re doing from a professional services standpoint, it’s really to help customers on their end-to-end journey and helping them drive AI transformation. It’s around helping them deploy,” he said. “We provide the consulting services and manage services around it all the way from day zero to day two plus. As we go through the journey internally at Dell, [it] provides us a tremendous amount of insight that we can then take to our customers and help accelerate their journey.” This internal-first mindset has also enabled Dell to digitize and refine its own processes, creating a blueprint that customers can follow. AI is layered into workflows that are already disciplined and well understood, allowing for both rapid experimentation and reliable results. That strong foundation of data, automation and process discipline provides fertile ground for scalable AI and LLM deployment, Schmitt noted. Customer zero is more than a model; it’s a mindset that blends internal accountability with innovation, giving organizations the confidence to build, test and deliver real-world AI outcomes. As AI factories mature and agents begin to automate decisions across the enterprise, that feedback loop between internal use and external delivery will be essential.
UnGPT.ai offers specialized tools for refining AI-generated text to appear more human-like, focusing on tone and flow enhancements
In the book “More Than Words,” writer-educator John Warner makes the case for renewing the concept of writing as a fundamentally human activity. Warner has argued before that much of what LLMs will destroy deserves to be destroyed. We should, he argues, take the advent of chatbots as “an opportunity to reconsider exactly what we value and why we value those things.” In education, writing has become performance rather than communication, and if we want students to simply follow a robotic algorithm to create a language product–well, that is exactly the task that a LLM is well-suited to perform. Warner encourages us to resist “technological determinism,” the argument that AI is inevitable and therefore we should neither resist or regulate it, as well as the huge hype, the manufactured sense that this is the future and you must get on board. Warner also points out the constant tendency to anthropomorphize AI, even though it is a machine that does not think, understand, or empathize, folks are constantly projecting those qualities onto the AI. Warner encourages renewing the sense of and appreciation for the human. And he calls on readers to explore their understanding of the field, in particular finding guides, people who have invested the time and study and thought to provide deeper insights into this growing field. At one point Warner responds to the notion that AI somehow improves on human work, noting that LLMs are machine. “To declare the machines superior means believing that what makes humans human is inherently inferior.” To those who argue that chatbots teamed up with humans will be able to create more, better, faster writing, Warner says no. “I’ll tell you why not. Because ChatGPT cannot write. Generating syntax is not the same thing as writing. Writing is an embodied act of thinking and feeling. Writing in communicating with intention. Yes, the existence of a product at the end of the process is an indicator that writing has happened, but by itself, it does not define what writing is or what it means to the writer or the audience for that writing.”