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AI is no longer just a cascade of algorithms trained on massive amounts of data. It has become a physical and infrastructural phenomenon, one whose future will be determined not by breakthroughs in benchmarks, but by the hard realities of power, geography, regulation, and the very nature of intelligence. Businesses that fail to see this will be blindsided. Data centers were once the sterile backrooms of the internet: important, but invisible. Today, they are the beating heart of generative AI, the physical engines that make large language models (LLMs) possible. But what if these engines, and the models they power, are hitting limitations that cant be solved with more capital, more data centers, or more powerful chips? In 2025 and into 2026, communities around the U.S. have been pushing back against new data center construction. In Springfield, Ohio; Loudoun County, Virginia and elsewhere, local residents and officials have balked at the idea of massive facilities drawing enormous amounts of electricity, disrupting neighborhoods, and straining already stretched electrical grids. These conflicts are not isolated. They are a signal, a structural friction point in the expansion of the AI economy. At the same time, utilities are warning of a looming collision between AIs energy appetite and the cost of power infrastructure. Several states are considering higher utility rates for data-intensive operations, arguing that the massive energy consumption of AI data centers is reshaping the economics of electricity distribution, often at the expense of everyday consumers. This friction between local resistance to data centers, the energy grids physical limits, and the political pressures on utilities is more than a planning dispute. It reveals a deeper truth: AIs most serious constraint is not algorithmic ingenuity, but physical reality. When reality intrudes on the AI dream For years, the dominant narrative in technology has been that more data and bigger models equal better intelligence. The logic has been seductive: scale up the training data, scale up compute power, and intelligence will emerge. But this logic assumes that three things are true: Data can always be collected and processed at scale. Data centers can be built wherever they are needed. Language-based models can serve as proxies for understanding the world. The first assumption is faltering. The second is meeting political and physical resistance. The third, that language alone can model reality, is quietly unraveling. Large language models are trained on massive corpora of human text. But that text is not a transparent reflection of reality: It is a distillation of perceptions, biases, omissions, and misinterpretations filtered through the human use of language. Some of that is useful. Much of it is partial, anecdotal, or flat-out wrong. As these models grow, their training data becomes the lens through which they interpret the world. But that lens is inherently flawed. This matters because language is not reality: It is a representation of individual and collective narratives. A language model learns the distribution of language, not the causal structure of events, not the physics of the world, not the sensory richness of lived experience. This limitation will come home to roost as AI is pushed into domains where contextual understanding of the world, not just text patterns, is essential for performance, safety, and real-world utility. A structural crisis in the making We are approaching a strange paradox: The very success of language-based AI is leading to its structural obsolescence. As organizations invest billions in generative AI infrastructure, they are doing so on the assumption that bigger models, more parameters, and larger datasets will continue to yield better results. But that assumption is at odds with three emerging limits: Energy and location constraints: As data centers face community resistance and grid limits, the expansion of AI compute capacity will slow, especially in regions without surplus power and strong planning systems. Regulatory friction: States and countries will increasingly regulate electricity usage, data center emissions, and land use, placing new costs and hurdles on AI infrastructure. Cognitive limitations of LLMs: Models that are trained only on text are hitting a ceiling on true understanding. The next real breakthroughs in AI will require models that learn from richer, multimodal interactions from real environments, sensory data and structured causal feedback, not just text corpora. Language alone will not unlock deeper machine understanding. This is not a speculative concern. We see it in the inconsistencies of todays LLMs: confident in their errors, anchored in old data, and unable to reason about the physical or causal aspects of reality. These are not bugs: they are structural constraints. Why this matters for business strategy CEOs and leaders who continue to equate AI leadership with bigger models and more data center capacity are making a fundamental strategic error. The future of AI will not be defined by how much computing power you have, but by how well you integrate intelligence with the physical world. Industries like robotics, autonomous vehicles, medical diagnosis, climate modeling, and industrial automation demand models that can reason about causality, sense environments, and learn from experience, not just from language patterns. The winners in these domains will be those who invest in hybrid systems that combine language with perception, embodiment, and grounded interaction. Conclusion: reality bites back The narrative that AI is an infinite frontier has been convenient for investors, journalists, and technologists alike. But like all powerful narratives, it eventually encounters the hard wall of reality. Data centers are running into political and energy limits. Language-only models are showing their boundaries. And the assumption that scale solves all problems is shaking at its foundations. The next chapter of AI will not be about who builds the biggest model. It will be about who understands the world in all its physical, causal, and embodied complexity, and builds systems that are grounded in reality. Innovation in AI will increasingly be measured not by the size of the data center or the number of parameters, but by how well machines perceive, interact with, and reason about the actual world.
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E-Commerce
In the world of social impact and sustainability, 2025s word of the year could have been headwinds. It became a euphemism for everything from political pressure and regulatory changes to economic uncertainty, AI disruption, and social upheaval. But in many ways, headwinds is an understatement for what impact and sustainability leaders across the corporate and nonprofit sectors navigated in a year of budget cuts and evolving risk factors. For much of the past year, leaders across the corporate and nonprofit sectors have been recalibrating approaches to advancing their missions against these trends. In 2026, well start to see those new approaches in action. Based on interviews with dozens of experts, here are five big shifts to pay attention to over the next year in social impact and sustainability. 1: Evolving the sustainability narrative One of the most visible shifts to note is that social impact and sustainability are becoming much less, well, visible. For years, companies have been making bold commitments, setting lofty goals, and engaging in the kind of storytellingbut not always following through, a trend that finally led Merriam-Webster to add greenwashing to its dictionary in 2022. 2025 felt like a correction, as companies reacting to a changing landscape of risk and political attention ushered in a period of greenhushing, where companies were reluctant to talk about their sustainability initiatives. As Andrew Winston of Winston Eco-Strategies puts it, “The biggest issue in the U.S. is the very strong desire of leadership teams to keep their heads down and say nearly nothing about sustainability. The work seems to be mostly continuing, but it’s certainly not great for morale or moving at speed and scale if your bosses are telling you to hide out.” Thats why 2026 is likely to bring another narrative correction that grounds sustainability storytelling in business performance and operational rigorwhich has always been where sustainability is heading. The best companies arent just making pledges, theyre building and executing solutions that scale, measure, and return value, says Dave Stangis at Apollo. Seeing capital, innovation, and outcomes align always gives me optimism. 2: Adopting a new leadership mindset An organization laser-focused on delivering results also requires a laser focus from its leaders. As Alison Taylor of Ethical Systems notes, the rapid-fire disruption of 2025 made this focus hard to find: Many of sustainability’s core assumptions no longer apply, and there is a need for a reframe of the profession. The practitioners I talk to are struggling with terminology, legal risk, and threats to their roles. While it is true that much great work is going on behind the scenes, it is difficult for most leaders I speak to to maintain organizational momentum, simply because there is so much fire fighting to do. 2026 will bring new fires to fight, but the demand for results and focus will give rise to a new mindset for leaders. Kristen Titus of the Titus Group predicts that leaders will emerge from this period of uncertainty and paralysis with a renewed willingness to engage: Clients, customers, and employees are hungry for engagementand they’re craving moral leadership. Those that step forward with clarity and courage will help define the next chapter of impact and sustainability. 3: Aligning rapid response with long-term goals One strategy that helps impact leaders maintain their focus involves finding ways to connect their communities immediate needs with long-term business strategy. Uncertainty demands agility, as Laura Turner, VP and Head of Community Impact at TIAA points out: Most companies hold flexible funding that can be adapted for unexpected needs. When the government shutdown hit, TIAAs first-generation college student program pivoted quickly, redirecting funds to local food banks. That flexibility isn’t just nice to have anymore, it’s essential for navigating uncertainty. For many organizations, balancing immediate and long-term needs also means AI-proofing their impact strategy. Royal Bank of Canada, for example, is leveraging business expertise and resources around AI adoption to support nonprofit partners in keeping pace with innovation. There is a broad consensus that AI and digital innovation can drive the biggest economic transformation in a generation. And yet, at this very same moment, the non-profit sector faces unprecedented strain and ongoing barriers to funding and technical training. Without intentional support, the sector risks falling behind. said Kara Gustafson, President of the RBC Foundation USA. 4: Putting well-being first All of this uncertainty and disruption has taken a toll on professionals in this space in 2025. In 2026, well-being will become a core function of impact strategyboth as a response to workforce and community needs. Haviland Sharvit, Executive Director of Susan Crown Exchange (Susan Crown Exchange and TIAA, above, are clients of mine), predicts that more companies and nonprofits will meet the moment with an impact strategy focused on youth well-being in the age of AI: Rapid advances in technology and AI offer powerful opportunities for learning and connection. Yet impact leaders face rising youth mental health strain, widening digital inequities, advancements that have outpaced youth protections, and the erosion of real human connection. Well see a shift toward promoting and safeguarding youth wellbeing in an AI-driven world, more attention on responsible tech, and greater investment in human connection. 5: Investing in community Amid all of this disruption, we asked leaders what gives them hope, and a common refrain emerged: we find hope in each other. Community is, and will continue to be, everything. In real and virtual rooms all over the countryand across impact networks like Trellis, UN Global Compact, NationSwell and many moreleaders spent 2025 reflecting, commiserating, and charting a new course forward. The last prediction Ill offer is one of my own: impact networks and convening spaces will grow rapidly in 2026, as new communities of practice emerge and existing communities grow. With all of the growth and learning 2026 has in store, finding safe spaces for reflection, knowledge sharing, and collaboration is a top priority for impact leaders.
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E-Commerce
Severance is the hit sci-fi show about office workers who sever their consciousnessslipping into another mode the moment they arrive at the office, then forgetting everything about their 9-to-5 as soon as they leave. The concept was inspired by the creators own monotonous desk job before he found success in television. Part of the shows appeal lies in how familiar the premise feels: a dull, repetitive workday that people cant wait to escape. In the real world, employees dont have a mental switch to flip, but theyve found subtler, and potentially more insidious, ways to disengage. The latest trend, dubbed task-masking, has taken over Instagram and TikTok. Its all about looking busy without actually being productive: charging around the office with a laptop, pretending to be on an urgent call, or typing furiously with no real purpose. According to recent research, more than a third of U.K. workers admit to faking productivity. Task-masking doesnt just waste timeit slows career growth and hurts company performance. Employees miss out on meaningful progress and promotion opportunities. Leaders lose engagement and confidence in their teams. In short, task-masking is a problem no leader can afford to ignore. Here are some strategies to stop it. Be clear on the companys values Task-masking isnt born of laziness or lack of ambitionits a fear-based response to productivity pressure and always-on work cultures. Research from Workhuman found that strict time-tracking exacerbates the problem: When workers strongly agree they are expected to respond immediately to Slack, Teams, or other instant messages, the rate of fake productivity shoots up to 51%. To free employees from the sense that their time card matters most, leaders should clarify what the company truly values. Face time or hours logged at a desk shouldnt be measures of successmeaningful productivity should. What that looks like will vary by organization, but at Jotform, for example, it means advancing projects and meeting reasonable deadlines. It also includes less-measurable but equally valuable behaviors like showing curiosity, supporting teammates, and helping create a more engaged work culture. Leaders should also be explicit about what doesnt count: busywork, unnecessary meetings that could be handled asynchronously, and burning the midnight oil just to give the impression of busyness and commitment. Break down projects into more manageable tasks As AI and automation boost efficiency and productivity, theyve fundamentally transformed workloads. In many ways, thats a positive change. Employees can devote more time to meaningful, higher-impact work. For example, you can spend more time on strategizing and creative writing, and fewer hours sifting through your inbox and searching through meeting notes. But it also brings a challenge: When technology accelerates what you can accomplish in a day, leaders expectations often rise in tandem. The slope to burnout becomes slippery. One of the best antidotes to that pressure, especially when facing large, intimidating projects that can leave employees feeling paralyzed or faking productivity, is to break them into smaller tasks. For starters, this helps people identify steps that can be automated, eliminated, or delegated. It also makes progress more tangible. Ticking off one item at a time, with restorative breaks in between, keeps momentum steady. When a daunting to-do list is broken down into a sequence of manageable tasks, employees can work efficiently and stay on track toward deadlines without burning out. Make psychological safety a priority If task-masking is rooted in fear, a quick fix wont eliminate it. Economic downturns, global pandemics, and rapid technological change have all contributed to a heightened sense of workplace anxiety, especially among the younger generations. More than one-third (37%) of Gen Z workers fear losing their jobsmore than any other generationaccording to research from Edelmans Gen Z Lab. Creating an environment where psychological safety is a priority can help assuage career-related fears and the pressure to appear productive all the time. When employees feel safe admitting theyre stuck or uncertain, theyre less likely to mask their struggles with performative busyness. At Jotform, we have multiple channels where employees can voice their concerns, ranging from all-hands meetings and dedicated chat threads to a general management open-door policy. I make a point to share the challenges Im facing, too, in hopes that my candor will encourage others to speak openly about their own doubts and setbacks. Ultimately, leaders must be explicit about the resources available to support employees and model the transparency they want to see. A bit of vulnerability from the top can help promote psychological safety throughout an organization. Employees shouldnt fear work so much that they want to escape itthrough severance or through task-masking.
Category:
E-Commerce
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