A 1960s Farming Study Saw the AI Boom Coming | PYMNTS.com
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May 13, 2026
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For every technology that takes off and lasts, like desktops and iPhones, there’s a graveyard of ones that burst onto the scene only to flame out (cue Blackberrys and Segways).
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The question now is which artificial intelligence tools will cement their place as go-tos for consumers and their shopping and spending habits. A landmark study of Iowa corn farmers published decades before the emergence of ChatGPT, Claude and other AI models provides some clues.
The 1962 book-length study showed that on the eve of the Great Depression, a small number of farmers in two Iowa communities began scrapping old planting methods dating back to Native American practices in favor of recently invented hybrid strains. By 1941, after droughts had proved the hybrids resilient, nearly all the farmers had switched. Over roughly a decade, they showed an adoption pattern that followed the shape of the letter S, with slow initial uptake, then rapid acceleration as the majority climbed aboard, then a flattening of the upswing as the remaining holdouts joined in.
AI tools like Google Gemini aren’t popcorn. But they’re starting to show a pattern of acceptance similar to the one described by the influential sociologist Everett Rogers in his Iowa farmers study. Rogers argued that his “diffusion of innovations” model was a signature of how any new idea moves through a population. That’s why we’re talking here about a vegetable. With AI agents that carry out shopping orders autonomously estimated to handle 15% to 25% of all U.S. eCommerce purchases by 2030, according to JPMorganChase, how consumers adopt AI now and in the coming years has big stakes for the payments industry.
An April PYMNTS Intelligence report, “The AI On-Ramp: Data Shows How Everyday Tasks Build Consumer Habits,” suggested that consumer use of AI is following the same S-shaped curve as the Iowa corn farmers, crossing from early adopters (the small group trying hybrid seeds) into the majority. In Rogers’ framework, that transition is a highly consequential moment in a technology’s commercial life because it’s when the niche goes mass market and trials and experiments crystallize into lasting habits. It’s also when technologies that establish themselves as the default (hybrid corn seeds) for key tasks can gain structural advantages.
Seed of an Idea
Rogers argued that an innovation takes off with individuals when it has a clear advantage, is compatible with existing practices, is easy to understand, can be tried at low cost (financial and time- and asset-wise) and produces visible results. One of the leading scholars of how innovations take hold, he famously segmented his farmers into Innovators, Early Adopters, Early Majority, Late Majority and Laggards, which the PYMNTS report partially mirrored. Rogers found that with his farmers, the S-shaped curve took off when 10% to 25% of the population adopted a new technology as a result of “interpersonal networks” (“Hey! This seed works!”) becoming “activated.”
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In that vein, the PYMNTS report found that AI has taken off with young consumers. Generation Z adults, the oldest now 29, are firmly in the majority of AI users. Roughly 70% of Gen Z adults are already using the technology for tasks like finding product links, writing resumes and editing personal writing, looking up medical symptoms and health information, learning new skills, crafting social media content, and sourcing financial guidance. Boomers and seniors are another matter. More than 1 in 3 American consumers aged 60 and older don’t use AI tools, consistent with the “laggard” profile in Rogers’ thesis about older farmers resisting hybrid seeds.
The PYMNTS report also revealed that AI tools become an “on-ramp” to wide acceptance when they have certain features. One of them is delivering value immediately, such as allowing a consumer to find relevant product links faster through, say, Google Gemini, than through a conventional Google search or Amazon. That captures Rogers’ theory of relative advantage, meaning the degree to which an innovation is perceived as better than what it replaces. And it mirrors his observation that severe droughts across the Midwest in 1934, 1936 and 1939-40 convinced farmers that drought-resistant hybrid seeds were better than open-field pollinated ones.
Another feature for an on-ramp posited by the PYMNTS report was the ability to try AI without losing too much if it makes a mistake. If ChatGPT shows, say, purple king-sized 300-thread count duvet covers when you want a lavender gray, queen-sized 600 count one, you’ve wasted a bit of time but nothing more.
The PYMNTS report also partially echoed Rogers on another front. It used data to argue that tasks that require no prerequisite knowledge and no specialist context were the ones that spread most broadly. That’s another way of saying they’re compatible with the widest range of existing habits and values. Rogers argued that hybrid corn seeds took off in part because they were “perceived as consistent with the existing values, past experiences, and needs of potential adopters. An idea that is not compatible with the prevalent values and norms of a social system will not be adopted as rapidly as an innovation that is compatible.” Similarly, the PYMNTS report said that the two highest-adoption tasks with generative AI—finding product links and editing personal writing—succeed because they require no demographic preconditions and thus are compatible with broad societal norms. Among AI users of all ages and income levels, 30% use AI models to look for product links and writing tasks.
A ChatGPT prompt that lists peer-reviewed medical studies on Lyme disease can be a lifesaver. An agent that autonomously books a vacation on your behalf according to your specific requirements can be a time- and wallet-saver. Still, beneficial innovations don’t sell themselves. As Rogers wrote in a later edition of his original study, the British Navy took nearly 50 years to adopt rations of oranges, lemons and other citrus fruits. (He blamed competing alternatives, a negative signal from a credible source in Captain Cook’s Pacific voyage reports, and the low status of the British researcher who demonstrated the solution.)
In other words, it’s not the actual quality of an innovation that drives its diffusion. It’s what people perceive, based on what their peers tell them and show them. An AI model can have all the bells and whistles, but as the PYMNTS data showed, consumers just want to find the right product and information.
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Data Mobility Across the API Economy Is Rewriting Bank Security Playbooks
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Highlights
Banks face growing data governance risks as APIs, AI tools and FinTech integrations move sensitive information far beyond traditional banking perimeters.
A recent SEC filing showed how an unauthorized AI application exposed material customer data, underscoring the security challenges created by interconnected banking systems.
Financial institutions are replacing perimeter-based security with continuous monitoring, identity controls and AI-powered cybersecurity to manage constant data movement.
In the age of application programming interfaces (APIs) and artificial intelligence (AI), data governance is becoming harder for banks than perimeter defense.
After all, the infrastructure powering vital advances like instant payments and personalized financial services is also creating sprawling new security risks as banks connect to AI tools, FinTech solutions and third-party APIs for the thousands of financial software integrations on offer in today’s landscape. Information that once lived inside monolithic core banking systems now flows continuously across interconnected software layers designed for speed, personalization and real-time decision making.
A recent disclosure filed with the U.S. Securities and Exchange Commission (SEC) this month by U.S. commercial bank Community Bank illustrates the growing challenge of data sprawl for banks, particularly smaller and mid-size lenders looking to stand up digital innovation in order to compete with larger peers. The bank, a wholly owned subsidiary of CB Financial Services, voluntarily disclosed that an amount of sensitive customer information determined to be “material” had been exposed through an unauthorized AI application used within its environment.
The filing underscored an uncomfortable reality facing the industry: the modern banking perimeter is no longer clearly defined. The issue is not simply that banks are adopting more technology. It is that the architecture of modern banking increasingly depends on constant data mobility.
Why Banks Are Losing Sight of Their Data
For decades, banks operated on a relatively simple security premise: protect the perimeter, secure the core and tightly control access to customer data. Sensitive information largely stayed within institution-owned systems, moving slowly through carefully managed channels and governed by rigid internal protocols. That model no longer exists.
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Open banking frameworks, embedded finance partnerships and real-time payments have accelerated API adoption across the industry. Financial institutions now routinely integrate with FinTech providers for everything from fraud prevention and lending to customer onboarding and treasury management. At the same time, generative AI tools are rapidly becoming embedded inside employee workflows, customer service operations and internal analytics platforms.
Each integration creates value. Each integration also creates another potential exposure point. The challenge of defending, and even just governing, these exposure points is particularly acute for mid-sized and regional banks operating with leaner compliance and cybersecurity resources than the largest national institutions.
For example, across the credit union (CU) landscape, PYMNTS Intelligence research found that fraud now occurs across the full CU member life cycle, from account opening and onboarding to authentication and transaction activity. CUs must now defend every interaction point rather than a single stage, and 77% of CUs have experienced unauthorized network access in the past year.
The same technologies driving operational efficiency and customer personalization also increase organizational exposure. AI systems require data access to generate value. APIs require connectivity to function effectively. Modern banking infrastructure is inherently designed for openness and interoperability.
The End of the Closed-Core Era
The real question is whether banks can establish governance models sophisticated enough to match the complexity of the ecosystems they now depend on. What has changed is the scale, speed and opacity of modern data movement. As customer data becomes increasingly distributed across external systems, governance itself is emerging as a competitive differentiator.
Rather than attempting to seal off every endpoint, many smaller institutions are shifting toward continuous monitoring models built around identity management, behavioral analytics and real-time visibility into data movement. Increasingly, the focus is less about defending a fixed perimeter and more about understanding how information flows across interconnected systems.
Data in the report “Embedding Security: Designing Fraud Risk Out of Business Transactions,” a March PYMNTS Intelligence Business Payments Tracker Series report in collaboration with WEX, reveals that nearly a quarter of banking CEOs (24%) are prioritizing AI investments for cybersecurity.
The broader banking landscape is also hoping that a rising security and data governance tide can lift all boats. PYMNTS covered Tuesday (May 12) how JPMorganChase is making nearly $14 million in philanthropic investments to support seven organizations that are combating fraud and scams through consumer awareness and real-time prevention.
Ultimately, the institutions succeeding in this transition are generally not those attempting to halt technological change. They are the ones redesigning governance around the assumption that data mobility is now permanent. Because in the API economy, the most important security question is no longer whether data leaves the bank. It is whether the bank still knows where the data went.
Apple Supplier Foxconn Hit With Ransomware Attack
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Foxconn, which manufactures products for Apple and Google, is reportedly recovering from a ransomware attack.
A company spokesperson confirmed the incident for web publication BleepingComputer Wednesday (May 13), following claims earlier in the week by the Nitrogen ransomware operation that it had stolen 8 TB of data and upwards of 11 million documents.
“Some of Foxconn’s factories in North America suffered a cyberattack,” the electronic giant’s spokesperson told BleepingComputer via an emailed statement.
“The cybersecurity team immediately activated the response mechanism and implemented multiple operational measures to ensure the continuity of production and delivery. The affected factories are currently resuming normal production.”
The hackers have claimed to have stolen confidential instructions, internal project documentation and technical drawings for projects at Foxconn clients that include Apple, Google, Dell and Nvidia, according to a report by The Register.
As BleedingComputer notes, Foxconn has more than 900,000 employees at upwards of 240 campuses in 24 countries and reported revenues of over $260 billion last year.
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The incident is the latest in a series of high-profile cyberattacks. Last month, toymaker Hasbro revealed in a filing with the Securities and Exchange Commission (SEC) that it had discovered a breach March 28, causing it to take some of its systems offline.
Hasbro, whose properties include Games like Monopoly and toys such as Transformers and My Little Pony, said it has “implemented and continues to implement business continuity plans to enable it to continue to take orders, ship product and conduct other key operations while it resolves this situation.”
“The need to run these interim measures may continue for several weeks before the situation is fully resolved and may result in some delays,” the filing added.
Soon after, payday loan provider Check City notified 322,687 people about a March data breach that compromised names, Social Security numbers, government-issued ID numbers, financial account numbers, credit and debit card numbers, dates of birth and addresses.
“Ransomware has become a structured, global industry,” PYMNTS wrote recently “Organized cybercriminal groups now operate with business-like efficiency. Attacks are no longer limited to encrypting files; they often involve ‘double extortion,’ where attackers threaten to leak stolen data if payment is not made.”
Research from the PYMNTS Intelligence report “Vendors and Vulnerabilities: The Cyberattack Squeeze on Mid-Market Firms” shows that hackers are increasingly targeting middle-market companies, which rely on third-party cloud providers, software-as-a-service platforms, managed service and logistics providers, which can leave them open to attack.