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Foldable phones have spent years trying to justify themselves. Some were too fragile, others too bulky, and most felt like solutions in search of a problem. The Galaxy Z TriFold is Samsungs clearest attempt yet to answer a more reasonable question: Can one device replace the phone-tablet combo without becoming a chore to carry? Coming to the United States later this month, the TriFold folds twice, opens into a 10-inch screen, and closes back into a pocketable form. Its an assertive design, but not a novelty play. Samsung seems very aware that this kind of device only makes sense for a specific kind of user. [Photo: Emily Price] The double fold is the trick, but the software does the real work The headline feature is the dual hinge. Closed, the TriFold behaves like a premium smartphone. Open it fully, and it becomes a genuinely usable tablet-size workspace. That space matters. You can run three apps side by side, resize them, and keep them anchored even when calls or notifications interrupt. Samsungs task bar lets you jump back into complex layouts without rebuilding them, which is a small thing until youve lost your place mid-task one too many times. We had a chance to try the phone first hand at a Consumer Electronics Show (CES) preview. The first time you open the device, the folding mechanism, in particular, stands out. Fully open, you might not even notice youre holding a phone rather than a tablet. The three separate screens blend together seamlessly. Samsung has also added guardrails. The phone will warn you if youre folding it the wrong way when you go to put it awaywhich feels less like hand-holding and more like protecting an expensive mistake. Editing photos is where the bigger screen actually shows off The TriFolds size gives Samsungs photo tools room to breathe, especially its generative editing features. Blake Gaiser, head of smartphone product management, says the difference is immediately obvious once you start using them. We’re really well known for what we call generative editingbeing able to remove things from a photo, Gaiser told me during a demo this week. He took a photo that included a person, and then was able to select and remove that person from the photo in seconds. It understands everything that I want to pick out here, and I’m able to take all the pixels out of that. He points to something thats easy to miss on smaller screens: cleanup details. Not only did it take the person out, but it took their shadow out as well, he said. So now I can look at both side by side each other, and you can see the shadow that she had there is gone. Being able to zoom in on before-and-after images simultaneously sounds minor. But for people who actually edit photos regularly, its the difference between trusting the result and hoping for the best. [Photo: Emily Price] This is very much not meant for everyone The TriFold is not designed for everyone. Samsung isnt pretending otherwise. Gaiser is blunt about the intended audience. It is for your top productivity people, he says. That philosophy shows up most clearly in DeX (short for desktop experience), Samsungs desktop-style interface. On the TriFold, DeX treats the device like a full monitor. You can resize windows freely, stack them, snap them into place, and even create multiple desktops that remember their layouts. So if I’m consistently looking at news articles and Samsung apps because I’m working on a piece or whatever, I could set those up in their own desktop, Gaiser said. Even when I clear the memory and everything, it remembers that setup. Gaiser has been using the TriFold as part of his own daily setup, and not always as the primary device. The two key things that I’ve done with this personally, in the three months that I’ve had this device: I have just a portable stand that I put it on, wireless keyboard, mouse, use it like a PC, he said. Or in my hotel room, I had my PC and I had this set up as a second monitor. The TriFold supports wired and wireless display output, including 4K when wired, making it less of a stretch to imagine it replacing a second screen for travel or temporary setups. Built sturdier than it looks Triple-folding phones raise obvious durability questions. Gaiser acknowledges the complexity. Because we have two different hinges on here. You have two different pivot points, he said. The phone uses magnets to keep it shut, but also to give the third screen a gentle pop after you open the first, making it easier to lift. Samsung also leaned heavily into materials, using ceramic glass fiber, a titanium lattice, and carbon fiber reinforcements to protect the folding display. Gaiser was candid in comparing it with competitors. [Photo: Emily Price] Power without cutting corners Under the hood, the TriFold runs on a customized Snapdragon 8 Elite chip, includes a 200-megapixel camera, and uses a 5,600 milliamphour battery spread across its three panels. That complexity is invisible to the userwhich is the point. The phone lasts through a full day of heavy use and charges quickly enough not to feel precious. Samsung also worked with Adobe to create a subscription-based Lightroom-specific app that behaves like its desktop counterpart, reinforcing the idea that this device is meant for people who actually produce things on their phones. The phone will come with a free trial. How it stacks up against other foldables Huawei Mate XT Huawei was the first with the Mate XT, proving that trifold hardware was possible. Availability is limited, software support is complicated outside certain markets, and it feels more like a statement piece than an everyday device. Concept triple-folds from other brands Several manufacturers have shown trifold concepts at trade shows. Most trifold devices are still prototypes, and thats fine. Building one is hard. Making one that survives daily life, and the bumps that come with it, is even harder. Samsungs advantage isnt that it folded a phone twice. Its that its spent years figuring out hinges, software behavior, durability testing, and what users actually tolerate. The TriFold feels like the result of that learning curve rather than a shortcut. So who should even consider this? Samsungs own answer is narrow. Gaiser calls the target audience the top 1% heavy users. Productivity tools, multi-window users, your ultra-top users, he said. Its not for everyone. That honesty helps. The Galaxy Z TriFold isnt trying to convince casual users to upgrade. Its aimed squarely at people who already push their devices hard and want fewer things in their bag. Its not flawless, not cheap, and not subtle. But its also the clearest signal yet that foldables are moving out of the experimental phase and into something more practical, even if only for a small slice of users.
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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.
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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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