Lots across the U.S. are using systems that let customers park their cars without swiping a credit card or paying an attendant. AI company Metropolis bought SP+ Parking, one of the biggest parking companies in the world, and added its AI-recognition technology to thousands of parking lots across the United States. The system requires a driver to enter their credit card information the first time they park, using a QR code, and then an AI camera system recognizes their vehicle by its license plate and charges them for the duration of their stay. Drivers have to provide their credit card information only the first time they use the service. On subsequent parking trips, they just drive into the lot and the system charges them appropriately. This method of parking will become more ubiquitous as consumers become accustomed to the ease and convenience, said Andrew Radlow, an industry consultant and former executive for the now defunct cashierless payment provider Grabango, who advises companies like Metropolis. “You literally have the ability to just drive in,” Radlow said. “The experience of waiting to get in, or waiting for someone to take your ticket is all obviated by AI.” The payment method is also used at toll booths and now accounts for millions of transactions daily, Radlow said. The AI-assisted parking system is one of the ways the payments industry is embracing embedded methods of payment, according to industry insiders.
Overlooked Gen X is the core spending engine through 2033 and is projected to increase spending across three key categories: Food & Non-alcoholic Beverages, Beauty and Beverage Alcohol
NielsenIQ in collaboration with World Data Lab issued a comprehensive generational spending report focused on Gen X. The report found that despite being smaller in size than Millennials or Gen Z, Gen X would form the world’s second-largest consumer market—second only to the U.S. and roughly twice the size of China’s total spending—in 2025. When buying well-known, large brands, nearly three-quarters (72%) of Gen X respondents say they usually buy “name brands” made by a big national or international manufacturer rather than store-branded private label products. 35% of Gen X respondents allow smart devices to automatically order new products, 39% accept product recommendations from an AI assistant, and 40% leverage AI to automate and speed up daily tasks. 58% of Gen X respondents say they avoid sharing details in virtual interactions because they don’t trust AI data privacy, but more than one-third say they are likely to purchase a product or service they have experienced solely through an augmented or virtual reality platform. Often called “the sandwich generation,” Gen Xers influence purchasing by their parents and their dependent children. Gen X women control 50% of global consumer spend, influencing 70–80% of household purchasing decisions. North America: Gen X is the core spending engine through 2033. In the US, Gen X households spend more per household than Boomers or Millennials in most CPG categories. Wealthier Gen Xers are concentrated in city centers. Western Europe: Gen X dominates in the UK and Germany, with strong spending in Health & Beauty, Frozen Food, and Travel. Asia Pacific: Gen X spending is peaking in China, while Millennials and Gen Z lead in India. Latin America: Brazil will become a Gen X-led economy by 2028, while Mexico is already Millennial- and Gen Z-driven. Key highlights: In the next five years (2025-2030), Gen X is projected to increase spending across three key categories: Food & Non-alcoholic Beverages (+$507 billion); Beauty (+$80B); Beverage Alcohol (+$42B). Gen X is comprised of tech-savvy decision-makers who influence purchases across generations and embrace omnichannel shopping. Gen X behaviors and needs are nuanced, highlighting the need for brands and retailers to examine regional and local data to maximize incrementality from this cohort.
Layoffs and AI are causing “paranoid attribution,” where employees read negative meaning into regular workplace occurrences
AI and white-collar layoffs have office workers feeling paranoid about job security: the threat of AI taking jobs, stricter return-to-office pushes, and a new hardcore culture that’s eroding work-life balance. There’s also the hollowing out of middle managers, and the Great Flattening has left some with fear that they’ll be next. “Workers are feeling disempowered,” Michele Williams, a professor of management and entrepreneurship at the University of Iowa, said, adding that this trend reared its head during the 2008 recession and is now back again. It’s what experts call “paranoid attribution,” where employees read negative meaning into regular workplace occurrences. The paranoia may be more psychological than based in reality. Overall, layoffs are still low and concentrated in white-collar sectors, especially at big-name companies that dominate headlines. While hiring has slowed in the last year, the unemployment rate is still relatively low, as well. However, it has gotten much more difficult to get a new white-collar job, and promotions have slowed way down. Attention on layoffs, as Business Insider’s Tim Paradis writes, might bite into productivity amid worker unease. Williams said that workers become less engaged as their energy shifts from actually getting work done toward worrying and becoming hypervigilant. On the other hand, employees might also cling harder to old adages about becoming indispensable at work. This is what some Big Tech companies are hoping for when they place more emphasis on performance reviews in a shift toward a more “hardcore” management style.”But if you push it to the extreme, you’re going to have workers hoarding information and knowledge because then they become indispensable,” she said. “But the sharing of that knowledge is what the organization needs to increase collaboration and innovation.”
JPMorganChase to charge data aggregators like Plaid to offset the costs of maintaining a secure system for protecting customer data
The largest U.S. bank, JPMorganChase, has been in conversations with data aggregators — companies like Plaid that draw customer data from banks and feed it to their fintech clients — to charge them for this data. The bank said the new fees are intended to offset the costs of maintaining a secure system for protecting customer data; it did not say how much it will charge. “We’ve invested significant resources creating a valuable and secure system that protects customer data,” said JPMorgan spokeswoman Emma Eatman. “We’ve had productive conversations and are working with the entire ecosystem to ensure we’re all making the necessary investments in the infrastructure that keeps our customers safe.” The conversations between JPMorgan and the data aggregators have been constructive, according to a person familiar with those discussions. “There is an understanding and agreement that there is value that [the bank has] created through significant investments in building up infrastructure for data sharing,” this person said. “The aggregators have been leveraging that, and they built businesses off of that. The bank should be getting compensated for the value of the significant investment it made.” For data aggregators, “the cost of goods sold has been zero. They charge their customers, the fintechs, and they have not had to pay in any way to actually get any of that data,” the person familiar with the conversations said. The move, which was first reported in Bloomberg, drew an immediate backlash. The Financial Data and Technology Association, a group that represents data aggregators including Plaid, Intuit and Trustly, objected on several grounds. “JPMorganChase is exploiting regulatory uncertainty to levy an arbitrary and punitive tax on competitive offerings,” FDATA said in a statement shared with American Banker. “This is a blatant effort to curtail innovation and undermine a stronger American financial system.” It is assumed that data aggregators will pass bank fees on to fintechs. PayPal’s stock price dropped 2.8% after the news came out, a hit that Yahoo Finance attributed to the data access fees. “This is huge,” Todd H. Baker, senior fellow at the Richman Center for Business, Law and Public Policy at Columbia Business and Law Schools, wrote in a LinkedIn post. “By some accounts, 25% of U.S. individuals are customers of JPMorganChase. Many U.S. fintechs require that data for their apps to work. If they have to pay for it, watch out. JPMorgan’s plan to charge fees to aggregators comes after a decade of rancor between banks, fintechs and data aggregators over the sharing of consumers’ bank account data. Banks historically have seen their customer databases as information they need to protect and use to provide their customers with products and services. Fintechs see their own products and services as more innovative and popular than banks’ offerings; many of them depend on bank account data to function properly. Data aggregators like Plaid originally established themselves as middlemen by getting consumers to provide them with their bank account login data, accessing banks’ online banking sites with the consumers’ credentials and siphoning out that data, then passing it on to fintech clients, without telling the banks what was going on. To banks, these high-volume, automated hits on their core systems looked like denial of service attacks or malware and sometimes overloaded their servers. Over the years, data aggregators established relationships with banks whereby they pulled customer data out of the banks through application programming interfaces; JPMorgan was one of the first banks to come to such agreements.
J.P. Morgan Growth Equity Partners takes stake in security startup behind an “enterprise browser,” that allows baking security controls, data-loss prevention and productivity tools directly into the browser layer, making remote work and bring-your-own-device policies easier to police
J.P. Morgan Growth Equity Partners has taken a strategic stake in Island, the Israeli-US company behind the “enterprise browser,” becoming the latest blue-chip backer in the start-up’s $250 million Series E financing at a valuation of roughly $5 billion, Island said on Monday. The investment, made through J.P. Morgan’s $1 billion growth fund, follows the March round led by Coatue Management and brings Island’s total capital raised to about $730 million. Other investors include Sequoia Capital, Insight Partners, and Cyberstarts. “Cybersecurity is at the top of the priority list for the world’s largest organizations, and Island is exactly the type of company we aim to support,” Paris Heymann, co-managing partner at J.P. Morgan Growth Equity Partners, said in the release. The arrival of the Wall Street bank “is testimony to the value we bring,” added Island co-founder and CTO Dan Amiga, noting that “many of the world’s largest banks have already chosen Island.” Launched from stealth only in February 2022, Island says its secure browser now runs at more than 450 enterprises, including eight of the 10 largest US banks. The software lets corporate IT teams bake security controls, data-loss prevention and productivity tools directly into the browser layer, making remote work and bring-your-own-device policies easier to police. The company employs roughly 500 people—about 200 of whom are based at its R&D hub in Tel Aviv—and is co-headquartered in Dallas. The company was founded in 2020 by Amiga, a former Unit 8200 officer and serial entrepreneur, and CEO Mike Fey, the onetime president of Symantec and CTO of McAfee. For Island, the J.P. Morgan check delivers both fresh capital and a marquee customer reference in the financial services sector—a vertical that has accounted for the company’s earliest adopters and remains its fastest-growing segment. Heymann’s fund has been making late-stage bets on enterprise software and cybersecurity since its launch last year; its move on Island “underscores how critical browser-level security has become to regulated industries,” the company said. With the new money, Island plans to accelerate hiring in engineering and go-to-market roles, expand its Dallas and Tel Aviv sites, and double down on product integrations aimed at large financial institutions.
Capital One’s Head of Enterprise AI opines bank CIOs with an obsession for calculating ROI in AI projects “are making irreversibly bad decisions” in an environment of dramatically changing costs of inference
Prem Natarajan, EVP and head of enterprise AI at Capital One, says the economics of gen AI costs are changing so dramatically that attempts to use traditional financial tools to calculate and project AI ROI is the wrong way to go about it. “In the last 22 months, the cost of inference has come down by more than a factor of 1,000 on a performance equivalent basis,” Natarajan tells CIO.com. “Something that cost you $10 to do inference on [two years ago] is now costing you one cent. In that environment of dramatically changing costs, any focus on near-term robust prediction of ROI as a justification for investing in gen AI” is likely to fail. Natarajan, who described the economic changes as being “on the throes of a generational inflection,” believes that CIOs taking that approach to projecting ROI “are making irreversibly bad decisions that will make them fall behind” given their “obsession of calculating ROI in the face of transformative technologies.” Natarajan joined Capital One in March 2023 from Amazon, where he spent almost five years as vice president for Alexa AI. He estimates that at Capital One he oversees “several hundred petabytes of data that will approach exabyte scale” in “a couple of years.” That data trove is a key asset for Capital One in making the most of AI, according to Natarajan, who sees data governance and accessibility as additional keys to AI success. Jason Andersen, a vice president and principal analyst tracking AI for Moor Insights & Strategy, says Natarajan’s take on the ROI issue for CIOs is valid. “Enterprises [such as Capital One] are starting to get really smart about how they are deploying AI and building AI applications,” Andersen says. “The reality is that we haven’t seen a trend in technology ever move this fast — ever.” That speed has caught many IT executives off guard as techniques that have always worked for them stop working, Andersen adds. “With this absolute velocity, you are seeing the old norms of trying to figure out how much to invest, those are no longer useful tools,” he says. “If you use traditional methods, you just don’t get it.” With almost any form of AI, he says, “your data advantage is your AI and ML advantage.” “The amount of proprietary data we had was an important asset to be brought to life in building generative AI applications and capabilities that would be differentiating for us,” Natarajan says, stressing that their evaluations showed that they “could not use closed-source models, because you cannot meaningfully customize those models.” The Capital One AI team eventually opted to use Meta’s open-source Llama LLM and set about building AI solutions atop its public cloud foundation, established prior to Natarajan’s tenure at the financial services company. “Capital One was the first bank — and to date, the only bank — that is all in on the public cloud. They shut down all their data centers over a period of two years and moved everything to AWS,” Natarajan explains. “We became cloud-native developers.” At Capital One, which employs roughly 14,000 IT specialists, talent is critical but so too is data — perhaps more so, Natarajan says.
AWS model distillation feature transfers intelligence from a larger model to a smaller, more specialized model by generating 10X more synthetic data based on customer prompts
AWS is preparing for the upcoming AWS re:Invent later this year, with a series of product updates based around intelligent automation and agentic AI. “We are seeing employability for some of these cloud models. We also have been busy launching some of our first party models with Nova,” said Atul Deo, director of product, AWS Bedrock, at AWS. Since announcing a new generation of foundation models at the last re:Invent, AWS has made intelligent prompt routing generally available. This tool enables users to combine the advantages of cheaper and larger, more capable models. Another product that offers the best of both worlds is Bedrock’s model distillation feature, which transfers intelligence from a larger model to a smaller, more specialized model. “We’ll generate additional data for the distillation process based on the prompts that a customer provides,” said Deo. “It can give a few indicator 30, 40 prompts of what it wants kind of generally for the distillation purpose. Then behind the scenes we can generate 10 times more data, which is basically synthetic data, then that synthetic data response of that larger model then gets used to essentially kind of make the smaller model more targeted and focused.” Two of the hottest areas for generative AI have been code generation and sales and marketing. Part of making AI a good assistant for customer service, code or even real estate is providing agents standardized access to relevant context through Model Context Protocol, according to Deo.
New energy-based transformer (EBT) model architecture enables building cost-effective AI applications that can generalize to novel situations without the need for specialized fine-tuned models
Researchers at the University of Illinois Urbana-Champaign and the University of Virginia have developed a new model architecture that could lead to more robust AI systems with more powerful reasoning capabilities. Called an energy-based transformer (EBT), the architecture shows a natural ability to use inference-time scaling to solve complex problems. For the enterprise, this could translate into cost-effective AI applications that can generalize to novel situations without the need for specialized fine-tuned models. EBTs are trained to first verify the compatibility between a context and a prediction, then refine predictions until they find the lowest-energy (most compatible) output. This process effectively simulates a thinking process for every prediction. The researchers developed two EBT variants: A decoder-only model inspired by the GPT architecture, and a bidirectional model similar to BERT. The architecture of EBTs make them flexible and compatible with various inference-time scaling techniques. Crucially, the study found that EBTs generalize better than the other architectures. Even with the same or worse pretraining performance, EBTs outperformed existing models on downstream tasks. The benefits of EBTs are important for two reasons. First, they suggest that at the massive scale of today’s foundation models, EBTs could significantly outperform the classic transformer architecture used in LLMs. Second, EBTs show much better data efficiency. This is a critical advantage in an era where high-quality training data is becoming a major bottleneck for scaling AI.
US regulators provide blueprint for lenders’ crypto custody – requiring a risk-governance framework that appropriately adapts to relevant risks
US regulators gave fresh guidelines for how banks can offer crypto custody services and not run afoul of rules. Banks that contemplate providing safekeeping for crypto-assets should consider the evolving nature of the crypto market, including the technology underlying the cryptoassets, regulators said. The Federal Reserve, Federal Deposit Insurance Corp. and Office of the Comptroller of the Currency said firms must also implement a risk-governance framework that appropriately adapts to relevant risks. The statement outlined key risk areas and warnings for banks to consider: Potential risks prior to offering crypto safekeeping; Being held liable for customers’ losses in cases of possible compromise or loss of cryptographic keys or other sensitive information; Crypto safekeeping relationships are subject to applicable Bank Secrecy Act/Anti-Money Laundering laws; Risks from contracting with a third-party; Appropriate audit coverage — especially assessing management and staff expertise.
JPMorgan has established a new unit within its commercial and investment bank focused on creating bespoke financing structures that span public and private markets
JPMorgan Chase & Co. has established a new unit within its commercial and investment bank focused on creating bespoke financing structures that span public and private markets. Named Strategic Financing Solutions, the unit will integrate efforts across banking, markets, and sales, according to an internal memo circulated Monday. Initially, the unit will concentrate on structured private solutions, infrastructure finance, strategic asset-backed securities finance, merchant banking, and direct lending. “Financing needs are becoming increasingly complex while investors are seeking exposure to new markets,” wrote Doug Petno and Troy Rohrbaugh, co-heads of the commercial and investment bank. The memo stated that the new team “will focus on delivering alternative solutions to our corporate and sponsor clients.” Warfield Price, head of general industries leveraged finance, and Masi Yamada, global head of corporate structuring, will co-lead the group while retaining their current roles. They will report to Kevin Foley, global head of capital markets, and Brad Tully, global head of private side sales and corporate derivatives. The initiative targets the convergence of public and private markets, noting that many U.S. firms are choosing to remain private longer and are demanding more complex financial solutions. A notable example is JPMorgan’s advisory role in 3G Capital’s $9.4 billion acquisition of Skechers USA Inc., a deal involving cash, debt, two term loans, two notes, and a revolving credit facility. The new unit will also encompass JPMorgan’s direct lending operations, for which the bank allocated an additional $50 billion this year after deploying over $10 billion across 100 deals since 2021. In a February interview, Foley emphasized the bank’s “product-agnostic” approach to serving clients. The move mirrors Goldman Sachs Group Inc.’s recent formation of a capital solutions group, which also reflects the rising significance of private markets.