 |
|
|
|||||
Should I take this project? Say yes to the new job offer? Stick with this plan or walk away? Every choice we make can feel huge. And every path has its own set of risks and rewards. There are always more questions for every life-changing decision. Sometimes the pros-and-cons lists feel more like busywork than progress. You check off the boxes, stare at the lists, and still end up confused, stuck in the same mental loop. Thats why I rely on the rule of 3 framework to make tough decisions. I hope it helps you clarify your life-changing choices. How it works Whenever youre stuck, force yourself to create three paths: B, C, and D. Why not A? A is usually the default for most people. The thing youre already doing. The path of least resistance. It doesnt need your help. What you need are alternatives. Then comes the second step, and this is where most people stop thinking too soon. Now, for each path, think through: First-order effects Second-order outcomes And third-order consequences And then, and this matters, choose the path with the most meaningful but least life-changing consequences. Why the two-option path doesnt work When you only have two options, your brain keeps going back and forth. Right vs wrong. Safe vs risky. Smart vs stupid. You stop being logical. Theres a term for it: binary bias or black-and-white thinking. We do it all the time. Two choices feel better. But they are not. Theyre restrictive and create a lot of unnecessary pressure. Most decisions are not binary, and there are usually better answers waiting to be found if you do the analysis and involve the right people, Jamie Dimon, the CEO of JPMorgan Chase, says. Three options open things up. Adding a third option reduces your emotional load and improves perceived control. You feel less trapped. And more capable. For example, if you are thinking about changing jobs. This is how it usually goes. Option 1: Quit and leap.Option 2: Stay and suffer. Now try the Rule of 3. Path B: Quit and take a new role in a similar field.Path C: Stay for six months and skill up aggressively.Path D: Go part-time or freelance while testing something new. Of course, none of these options is perfect. Thats why the next stage of the process is even more important: the consequences. 1st, 2nd and 3rd order effects It simply means keep asking, and then what? First-order effects are immediate. What happens right away when you make the decision? Second-order effects come next. What does that lead to? Third-order effects are longer-term. Who do you become if this path continues? I will now apply the effects to the job-changing example. Path B: Quit and take a similar role. First-order: New environment. Relief. You may stop dreading Mondays. Second-order: You become more confident. Now, you know youre employable. You can actually change jobs. Third-order: You might stay on the same path longer than you want. Now Path C: Stay and upgrade your skills First-order: You may feel frustrated for a while. You will need a lot of discipline for this path. Second-order: You will get leverage to open your options. Third-order: You redefine yourself from stuck to building a career. You may become indispensable to your employer. The mistake most people make Most people pursue the best outcome. Thats a trap. The future is uncertain. Youre probably guessing what could work. Everyone is. Once you are done with the effects, choose the path with the least life-altering effects. The one that teaches you something. Keeps doors open. And doesnt completely make your life worse if youre wrong. Its my risk psychology approach. People regret irreversible decisions more than bad ones. We hate closing doors we didnt mean to close. Thats why picking the path that means a lot to you but wont burn bridges matters. Make better decisions with the least panic. This framework works when you are emotionally attached to the decision you are about to make. When youre stressed, your brain throws logic out of the window. The rule of 3 gets you back on the rational path. It takes you from reacting to responding to life. It helps you answer the most important question. Which future can I live with? You can use this rule anywhere. Money decisions. Relationship decisions. Creative decisions. A big purchase. Even small ones. Do I say yes to this commitment? What are the effects, and what are my options? And what path can I live with and still function? Force the three paths. Pursue the consequences in places most people ignore. Then, opt for the choice that makes life better without disrupting your entire life. Use it to pick a path with tolerable unknowns The rule of three doesnt remove uncertainty. Nothing does. Youre never picking certainty. Youre picking a path with tolerable unknowns. Good decisions come from better processes. The 3 rule takes away the emotional attachment that drains the life out of you. Most of our hard decisions become unbearable because we want a perfect choice. The one that proves we are smart and avoids regret. So you panic. Or overthink. Some people let time decide for them. Which is still a decision, by the way. I use the rule of three to pick a direction. Adjust where necessary. And keep moving. I want forward motion without self-destruction. You dont need to outsmart the future. Just stop putting so much pressure on yourself. Most choices dont need courage. They need structure. Three paths. Three consequences. It makes overthinking your options almost impossible.
Category:
E-Commerce
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.
Category:
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.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||