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As someone who has tried almost every workout class on the planet, there is nothing like SoulCycle, said TikTok creator Matt Trav in a video posted in early January. The average human being cannot understand what blowing out the candle during the soulful song can do to the human psyche. @mattatrav1 SoulCycle Renaissance 2026 you heard it here first original sound – Matt Trav Nearly 40,000 people who liked the video seem to agree. Absolutely nothing can beat NYC SoulCycle circa 2016, one commenter wrote. Literally like going to church, another added. Across social media platforms, that renaissance is already underway. Devotees have been sharing nostalgia-laced photos from years past, unearthing old merchandise, and swapping stories in comment sections. Some have been documenting their first-time experiences, while longtime riders welcome newcomers with open arms. View this post on Instagram Come with me to soulcycle as someone who was severely addicted . . . after a 6 year hiatus, one TikTok user wrote, adding, Soul cycle is back btw. Once at the center of the fitness zeitgeist in New York City, the 45-minute workout class was branded a cult during its 2010s heyday. Its clientele included a number of A-list celebrities, and its instructors became bona fide stars, building obsessive fan bases who followed them across the country for a chance to snag a front-row bike in one of their classes. At SoulCycle, we very quickly became the club you cant get into, and that has a lot of appeal to a lot of people, cofounder Ruth Zukerman said on the Wharton Business Daily podcast in 2019. The same exclusive culture that fueled its rise also helped drive its fall. How SoulCycle lost its soul, a 2020 Vox headline read. The next year, a New York Times article reported allegations of sexual harassment, racism, and fat-shaming by members of SoulCycles elite ring of master instructors. In 2022, the chain shuttered about 25% of its locations, a ripple effect from the COVID-19 pandemic. A decade after its peak, SoulCycle is back, and its return reflects the cultural moment. The revival coincides with a broader 2016 nostalgia takeover on social media. Alongside a renewed fascination with so-called millennial optimism, hyper-filtered, grainy throwback images have flooded feeds as millennials and older Gen Zers reminisce about the music, fashion, and even workouts of a decade ago. It was the era of Snapchat filters, skinny jeans, and SoulCycle. @tumblr goodbye 2025 hello 2016 #2016 #2016vibes #2016core #2016makeup #aesthetic Let me love you – High-intensity workouts have since fallen out of favor, replaced by reformer Pilates and hot girl walks. But as saunas and ice baths become the new social clubs, and wellness-focused Gen Zers trade alcohol-fueled nights out for early morning workouts, its no surprise that a candlelit class set to blasting music and bound by a cultlike sense of community is being embraced by a new generation. As one TikTok user suggested: Out of all the things to bring back from 2016, I vote to bring back SoulCycle.
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E-Commerce
AI isnt eliminating human work. Its redistributing human judgment, away from routine tasks and into the narrow zones where ambiguity is high, mistakes are costly, and trust actually matters. This shift helps explain a growing disconnect in the AI conversation. On one hand, models are improving at breathtaking speed. On the other, many ambitious AI deployments stall, scale more slowly than expected, or quietly revert to hybrid workflows. The issue isnt capability. Its trust. The trust gap most AI strategies overlook AI adoption doesnt hinge on whether a system can do a task. It hinges on whether humans are willing to rely on its output without checking it. That gap between performance and reliance, the trust gap, is what ultimately determines where AI replaces work, where it augments it, and where humans remain indispensable. Two factors shape that gap more than anything else: ambiguity and stakes. Ambiguity refers to how much interpretation, context, or judgment a task requires. Stakes refer to what happens if the system gets it wrong: financially, legally, reputationally, or ethically. When ambiguity is low and stakes are low, automation thrives. When both are high, humans must stay firmly in the loop. Most real-world work lives somewhere in between and thats where the future of labor is being renegotiated. A simple way to see where AI fits Think of work along two axes: how ambiguous it is, and how costly errors are. Low ambiguity, low stakes tasks, basic classification, simple tagging, routine routing, are rapidly becoming fully automated. This is where AI quietly replaces human labor, often without much controversy. Low ambiguity but high stakes tasks, such as compliance checks or identity verification, are typically automated but closely monitored. Humans verify, audit, and intervene when something looks off. High ambiguity, low stakes work: creative labeling, sentiment analysis, exploratory research, which tends to use AI as an assistant, with light human oversight. But the most important quadrant is high ambiguity and high stakes. These are the tasks where trust is hardest to earn: fraud edge cases, safety-critical moderation, medical or financial interpretation, and the data decisions that shape how AI models behave in the real world. Here, humans arent disappearing. Theyre becoming more targeted, more specialized, and more on demand. When the human edge actually disappears Interactive voice response systems refine the rule. The stakes were not low, IVR is literally the companys voice to its customers. But ambiguity was. Once synthetic voices became good enough, quality was easy to judge, variance was low, and the trust gap collapsed. That alone was sufficient for AI to take over. When trust keeps humans in the loop Translation followed a different trajectory. Translation is inherently ambiguous, as there are multiple ways to translate a sentence. As a result, machine translation rapidly absorbed casual, low-risk content such as TikTok videos. However, in high-stakes contexts, such as legal contracts, medical instructions, financial reporting, and global brand messaging, trust is never fully transferred to the machine. For these tasks, professional translators are still required to augment the AI’s initial output. Since AI now performs the bulk of the work, full-time translators have become rare. Instead, they increasingly operate within expert networks, deployed just-in-time to fine-tune and verify the process, thereby closing the trust gap. The same shift is now playing out in how data is prepared and validated for AI systems themselves. Early AI training relied on massive, full-time human labeling operations. Today, models increasingly handle routine evaluation. Human expertise is reserved for the most sensitive decisions, the ones that shape how AI behaves under pressure. What this means for the future of work The popular narrative frames AI as a replacement technology: machines versus humans. The reality inside organizations looks very different. AI is becoming the default for scale. Humans are becoming the exception handlers, the source of judgment when context is unclear, consequences are severe, or trust is on the line. This doesnt mean fewer humans overall. It means different human roles: less repetitive labor, more judgment deployed just in time. More experts working across many systems, fewer people locked into single, narrowly defined tasks. The organizations that succeed with AI wont be the ones that automate the most. Theyll be the ones that understand where not to automate, and that design workflows capable of pulling human judgment in at exactly the right moment, at exactly the right level. The future of work isnt humans versus machines. Its AI at scale, plus human judgment delivered through expert networks, not permanent roles. Translation and model validation show the pattern; white-collar work is next. And that, quietly, is what companies are discovering now.
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E-Commerce
AI can do incredible things. So far, though, most of those things have been virtual. If you want a killer article for your bichon frise blog or an expertly crafted letter disputing a parking ticket you probably deserve, chatbots like ChatGPT and Gemini can deliver that. All those things are locked into the nebulous world of information, though. Theyre helpful, but the products of todays large language models (LLMs) and neural networks arent actually doing much of anything. AIs silicon-bound status, however, is beginning to change. The tech is increasingly invading the real world. 2026 is the year that AI gets physical. And that shift has huge implications for the future of the technologyand for the impact when it fails. Call Me a Robot The change started with cars. The idea of a self-driving car goes back to the 1950s. But the technology always felt like it was decades away. Now its here. Robotaxi companies like Waymo and Zoox give more than 450,000 rides per week to paying customers. I ride in Waymo vehicles all the time, and I love calling a robot from an app and having it drive me across town. Self-driving cars finally arrived because of a whole slew of things, including cheap lidar scanners and better batteries. But the rise of deep learning and AI played the most pivotal role. The AI models that power Waymo vehicles are much better at driving than humans. And they can learn and improve on the flyhere in San Francisco where I live, Waymos have gotten more assertive as theyve learned the roads better. Self-driving AI is getting so good that its increasingly able to navigate roads without the need for the fancy (and expensive) sensors you see atop first-generation Waymos. Tesla uses simple cameras, and is getting closer to true self-driving. Fold My Laundry, Siri Self-driving cars are an incredible application of physical AI. But theyre hardly the only one. Driving is a great initial test case for the tech, because it has fairly clear rules and limits. Cars need to stay on the road, recognize red lights, and minimize cat fatalities. Other physical tasks are harder to automate with AI. But they have potentially even bigger upsides. Companies are increasingly pairing artificial intelligence with humanoid robots, teaching the robots artificial brains about the physical world so they can navigate it capably. The ultimate dream is to put these robots to work. They could perform a wide variety of jobs in factories or warehouses, for example. Generally speaking, current industrial robots need to be specifically built for a single task, but an AI powered one could learn multiple onesassembling a product and then placing it on a shelf, for example. But AI-powered robots could also fill gaping holes in the human labor market. Caretaking for the elderly is incredibly important as the world gets older on average. Yet finding enough people for caretaking roles is nearly impossible. Especially in countries like Japan, robots are beginning to fill the gaps. Dexterous, AI-powered robots may soon work well enough for tasks like doing dishes, folding laundry, or even cooking to be automated. These robot companions could help elderly people live on their own more independently. With advanced LLMs, they could even form relationships with their real-world charges, helping with loneliness or reminding a person with memory challenges to take their meds on schedule. The Parable of the Raunchy Bear Of course, all of this comes with risks. When an LLM hallucinates in a virtual space, its annoying but rarely damaging. If your ChatGPT-generated recipe for meatballs sucks, you probably wont die. And if the chatbot writing your blog post confuses a bichon for a poodle, your dog will be very angry with you, but otherwise the consequences are minor. Physical AI is different. Clearly, if Waymos technology goes awry, it could accidentally steer a 5,000-pound object into a building or a bystander. And youve read enough science fiction that I dont need to remind you about robot uprisings. Many of these risks are well understood, though, and thus well controlled. Power outages aside, Waymos rarely run into serious challenges on the road, and industrial robots rarely injure people. The bigger risks start to creep in when AI is applied haphazardly to the physical world without a lot of oversight or planning. As physical AI expands and LLMs get cheaper, this will happen more often. Take the case of an AI teddy bear with a built-in LLM. It was supposed to chat with kids, and perhaps read them bedtime stories. Instead, it started instructing them on BDSM and other raunchy topics, as well as how to pop pills and where to find knives. The bear was quickly pulled from the market. But the lesson is clear: Unlike traditional computer code, LLMs are nondeterministicyou cant predict their outputs from the inputs you feed them. In 2026 and beyond, this will mean cars that avoid accidents better than human drivers, robots that can easily learn work theyve never done before, and AI embedded in physical systems (like power and utility grids) that can instantly respond to damage or outages. But it will also mean lots of failuresand perhaps a few catastrophic ones. LLMs unpredictability is their power. But as AI gets physical, that unpredictability will also lead to a faster, less tractable, more chaotic world.
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E-Commerce
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