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Thank you once again for reading Fast Companys Plugged In. A quick programming note: We will be taking the next two Fridays off. Happy holidays to all, and I look forward to resurfacing in your inbox next year. For any number of reasons, 2025 has hardly been my favorite year. But if I were to make a list of things that went well, my relationship with AI would be on it. This was the year I went from being an AI dabbler to a daily user. And while some of that usage still amounts to messing aroundhello, Sora!even more involves tasks that make me more productive. More importantly, it brings me better results, a goal I hold dear. (Sadly, not every AI enthusiast agrees.) Here, then, is a look at how Im using AI as 2025 winds down. I covered some of this ground in a September Plugged In. But since I wrote that, the technology has become even more core to my workflow, and my AI A-team has shifted pretty dramatically to Google products for the first time. So a year-end update seemed worthwhile. First, Ive finally figured out how to use chatbots such as OpenAIs ChatGPT, Anthropics Claude, and Googles Gemini as research tools. I remain wary of accepting anything they say as the truth, since AI still has a devious knack for hallucinating fantasies that sound like fact. But its dawned on me that I dont need to take AI at its word. Starting a research quest with a detailed AI prompt is often more effective than trying to boil it down into keywords of the sort I would have typed into a search engine in the past. And every self-respecting chatbot now provides citations for its work, at least when I ask for them. They lead to web pages written by actual humans, which are far easier to assess than wordage extruded by an LLM. After spending most of 2025 weaving between ChatGPT and Claude as my chatbot of choice, I was (mostly) wowed by the new Gemini 3 Pro-powered version of Gemini that debuted in November. Its become my default bot. But the frenzied pace of competition in the category argues against long-term loyalty: I need to spend more time with the new GPT-5.2 version of ChatGPT, which arrived last week. More than any garden-variety chatbot, I have found Googles NotebookLM utterly essential this year. Instead of trying to be an expert on human knowledge in its entirety, it just digests files you feed to it. Then it lets you ask questions about them and responds with startlingly useful summaries and citations. They frequently lead to insights I wouldnt have managed if left to my own devices, and have never mischaracterized anything or otherwise led me astray. For me, NotebookLM is most valuable as I spelunk through transcripts of the interviews that provide raw ingredients for articles I write. (In the case of our five-part oral history of YouTube, there were dozens of them, about 168,000 words in total.) For you, the source material might be internal documents, white papers, or something else relating to whatever youre working on. Either way, this free tool, like most of historys best software, is a bicycle for the mind. (Disclaimer: Im not talking about NotebookLMs best-known featurepodcast-like audio overview synthetic conversations based on your sources, which are an astounding magic trick but have never left me feeling smarter about a topic.) Finally on the AI good news front, theres vibe codingcoming up with ideas for apps and having AI do nearly all the work of turning them into functioning software. When 2025 started, it didnt even exist as a thing, at least under that name. Now I cant imagine working without it. That started back in April, when I used a vibe coding tool called Replit to build the note-taking app of my dreams. The project required dozens of hours of effort and hundreds of dollars in usage fees. But eight months later, I use the app I created every day, and it still makes me unreasonably happy. Lately, I have been vibe coding with Googles AI Studio, which is powered by Gemini 3 Pro. So far, the results have been less quirky and buggy than Replits sometimes are, making whipping up my own apps even more irresistible. Case in point: Last month, I bought a ScanSnap document scanner and soon discovered that its cloud service gave the resulting PDFs incomprehensible names. With Geminis help, I constructed a smart PDF-naming utility. It reads the files and renames them with clearer descriptions than Id write myself. Problem solved, in about 20 minutes. Too much AI in all the wrong places For all the ways AI speeded my work in 2025, its been far from an unalloyed blessing. Notably, all the tools I praise above are newish and AI-first. When existing products are retooled to emphasize AI, the technology often feels bolted on. Its not just that it isnt dependably helpful; sometimes, its an obstacle to progress. For example, Google Docs, Microsoft Word, Gmail, and Outlook would all be delighted to compose text for me, a feature that has become as prominent an element of their user interfaces as the 58-year-old blinking cursor. I have no interest in turning that job over to them. And yet I cant ignore the various icons, widgets, and promos dedicated to these tools, which stare me in the face every time I sit down with these products. Its an ongoing mental tax levied for alleged benefits Id prefer to avoid. In other cases, its obvious that AI features have been rushed to market without sufficient quality control, as if the bragging rights for havng shipped them were all that mattered. I have learned to tamp down my expectations, or even assume that new functionality will perform as advertised at all. In August, for instance. I discovered that ChatGPTs new Agent feature couldnt perform some of the tasks in its own list of things I should try. It was also incapable of reliably determining the current date. A month later, I was intrigued enough by Perplexitys Email Assistant to briefly spring for a $200-per-month Perplexity Max account. I never got it up and running, in part because Perplexitys own explanation of its new tool was notably short on, you know, explanation. I might have felt less lost if it had just included a screenshot or two. Whether or not theres an AI bubble, the industry responsible for the technology is still in the process of confronting its legacy of overpromising and underdelivering. But with the good stuff getting really good, anything that fails to live up to its own hypeor simply meet reasonable standards of utilitywill only look more ridiculous. May the momentum recently seen in AI productivitys best products continue in 2026 and beyond. Youve been reading Plugged In, Fast Companys weekly tech newsletter from me, global technology editor Harry McCracken. If a friend or colleague forwarded this edition to youor if you’re reading it on fastcompany.comyou can check out previous issues and sign up to get it yourself every Friday morning. I love hearing from you: Ping me at hmccracken@fastcompany.com with your feedback and ideas for future newsletters. I’m also on Bluesky, Mastodon, and Threads, and you can follow Plugged In on Flipboard. More top tech stories from Fast Company These sites and apps will help you assemble the perfect holiday reading listEven in the AI era, bookstores and online reading communities still rely heavily on human expertise and personal recommendations. Read More The Warner Bros.-Netflix merger could doom Hollywood film workersFor media moguls, maximizing shareholder value is the only metric that counts. Read More With Apples help, storytellers are figuring out Vision ProThe headset opens up immersive new opportunities for dramas, documentaries, music videos, and beyond. Some filmmakers and developers are diving right in. Read More Robinhood knows you want to trade on everythingPrediction markets boomed in 2025. Now Robinhood wants to cash in. Read More This guys obscure PhD project is the only thing standing between humanity and AI image chaosA virtual watermark thats nearly impossible to remove. Read More Deepfakes are no longer just a disinformation problem. They are your next supply chain riskMost companies are woefully unprepared, and the traditional cybersecurity playbook isnt enough. Read More
Category:
E-Commerce
Pricing is one of the most powerful growth levers a business has, yet it is still one of the most overlooked. While teams spend months refining product and brand, pricing decisions are too often rushed, emotionally charged, or guided by instinct rather than insight. Under the pressure of rising costs and competitive pressures, many leadership teams resort to the fastest fix: promotions to meet short-term targets or price increases to plug a margin leak. The companies that consistently outperform take a different approach. They treat pricing as a strategic, evidence-led discipline. They ground pricing in how customers perceive value and make decisions to deliver growth that lasts. Take two companies facing the same rising costs. One applies a uniform 10% price increase to protect margin. It works briefly, until customers notice and push back. Sales slip and the business reacts with discounts that instantly undo the uplift they were counting on. The other takes a more thoughtful approach. They identify where value is strongest, redesign how options are packaged and presented, and adjust prices selectively. A year later, revenue and gross margin are up, and customer trust remains intact. This is what happens when pricing becomes a strategic capability rather than a quick fix. Here are six levers business leaders can use to make pricing a sustainable engine of growth. 1. Position: Where do you sit in the market? Positioning shapes how customers view your product or service when compared to the alternatives. That may be another version of your offer, a competitor offer, or a completely different option that your customers believe can get the job done. Where you sit among those options shapes what customers are willing to pay. Is your offer seen as premium or budget? A “want to have” or a “need to have”? Make your positioning clear by finding out why customers choose you over the alternatives, and what role price plays in that decision. 2. Perception: How do customers assess value? Perception is how customers judge the value of your product or service. Its how your solution meets their needs, solves a problem, or brings a moment of ease or delight. That perception forms before they buy through brand cues, third-party reviews and how clearly the benefits are communicated, and it continues to evolve based on how well the product performs in use. The mistake teams make is assuming customers see the value as clearly as they do. Instead, listen carefully to your customers to understand what they value most, and what they are willing to pay for that value. Use these insights to refine how you communicate value at every stage of the purchase journey. 3. Packaging: What choices are you offering? Packaging is the structure behind the choices you offer to customers: whats included, how different features are bundled together, and how customers compare options. For example, streaming services use goodbetterbest packaging, with tiers ranging from a basic with ads plan up to a premium option. Give customers too many choices, and its overwhelming. Give them too few, and it becomes a question of should I buy? rather than which should I buy? Guide customers toward better decisions by making options intuitive, with clear trade-offs and visible benefits as they move to more premium options. 4. Presentation: How is your offer presented? Presentation is how your prices are visually communicated. Customers rely on subtle cues such as color, size, language, and layout to interpret value and compare choices. Each of these cues implicitly shapes how the price feels and can nudge customers toward one choice over another. Test and refine how pricing information is framed and displayed to build confidence and improve conversion. Experiment to measure which changes drive the best outcomes. 5. Price: Are you charging the right amount? Price is the number customers see, but it should not be the starting point for pricing decisions. When companies skip straight to the number, they end up debating numbers, not value. The price needs to align with everything that comes before it: positioning, perception, packaging, and presentation. When customers see a price that mirrors the value they feel, it strengthens trust, confidence, and conversion. Think about your price point. Is it aligned with your value, your position in the market, and the choices on offer? 6. Promotion: When and how should you discount? Promotion is the lever you pull to spark a specific behavior: trial, urgency, repeat purchase, or upsell. The challenge is that discounts are often used to chase short-term targets, which risks eroding margins and teaches customers to wait for a deal. Discounts and promotions work best when they are intentional and anchored in a clear pricing strategy. Use promotions to drive specific customer behaviors without undermining value or long-term profitability. Shift the question from How much should we discount to hit this months number? to What behavior are we trying to drive, and is a discount the right lever to do it? Under pressure, leaders face a choice: rely on reactive decisions or treat pricing as a strategic capability. By pulling a broader set of levers and grounding decisions in real customer value, you turn pricing into a tool that can shape demand, signal value, and lead to sustainable growth.
Category:
E-Commerce
My work across decades has spanned sectors, geographies, and cultures, focusing on exploration, discovery, and innovation. My husband and I have defined our work across business, nonprofit, and philanthropy simply: “We invest in people and ideas that can change the world.” I spend much of my time exploring and sharing exciting developments that hold great promise. This work has taken me from building the Internet revolution, to working in villages and cities across the globe and America’s 50 states, to the boardroom of the National Geographic Society, where I just completed a decade of service as Chairman of the Board. It has been a true privilege to lead these efforts, and we have made a real impact in many ways. But this work can be difficultmy years of engagement in brain cancer research highlighted what an unknown frontier the brain represents. The work can also be complexlike rolling out initiatives across diverse geographies and communities, but it continues to energize and engage me. At nearly every turn, technology has been central to our quest to “find a better way,” and it has played an important part in every one of the success stories in our portfolio. But here, as we close out 2025, the reality is stark: while technology can still bring hope and promise on many fronts, the underbelly of its excessive use has become painfully clear. Americans now spend over seven hours a day looking at screens. Meanwhile, rates of anxiety, depression, isolation, and loneliness have skyrocketed, particularly among young people. Our brains are being rewired in ways none of us asked for, and the health and wellness of the population more broadly are seriously at risk. And sadly, the promise of technology to bring communities together that animated so many of us in our early tech careers has instead led to rising divisions between people and places. What can be done? So, what can be done here to address this worrisome trend? Well, it turns out a solution that might hold great promise was hiding plainly in sight: indeed, the answer doesnt lie in abandoning technology, but rather in the simple act of logging off and getting out in nature. Thats right. It turns out nature is a powerful medicine. Recent research validates what many of us intuitively know: a Stanford meta-analysis of 449 studies found that nature exposure significantly improves mental health outcomes, including mood, stress, and anxiety. Perhaps most encouraging, researchers found that just 20 minutes in a parkeven without exercisingpeople reported feeling better, while repeated nature exposure of as little as 10 minutes yields measurable benefits for those with mental illness. But the benefits extend far beyond individual wellness. These aren’t marginal improvementsthey’re prescription-strength results from the most accessible medicine on Earth. The barrier to entry is often just putting on a pair of sneakers or hopping on a bike. The beauty of outdoor engagement is its democratic accessibility. Unlike expensive gym memberships or specialized equipment, stepping outside costs nothing and requires no particular skill. So whether you walk around the block, walk for 20 minutes in your neighborhood, or find a way to hike in a city, state, or national park, walking delivers measurable health benefits. A fork in the road We stand at an inflection point. We can continue accepting digital isolation and declining physical and mental health as inevitable byproducts of technological progress, or we can recognize that the human experience began outdoors, in communities, solving problems togetherand that our health depends on experiences no app can replicate. This isn’t about returning to some romanticized past. It’s about balance. It’s about making outdoor, screen-free time as routine as checking email. It’s as simple as taking a walk, encountering neighbors or nature at a park or in your community. Where getting outdoors is the default, not the exception. The screen will always be there when you return. But the opportunity to rebuild America’s health and social cohesion by getting outdoors requires intention. We need individuals choosing strolling over scrolling, employers encouraging outdoor breaks as part of a productive workday, healthcare providers prescribing park time, and local leaders who prioritize walkable communities that enable us to meet and greet each other and Mother Nature. The question isn’t whether you have time for outdoor connectionit’s whether you can afford not to make time for the wellness program hiding in plain sight.
Category:
E-Commerce
AI is forcing every leader into a choice they cant dodge: do you believe your people are fundamentally creative and motivated, or lazy and in need of control? Most leaders wont want to answer that honestly, but their AI strategy already has. The AI mandates. AI-blamed layoffs. So-called AI-enabled bossware. The truth is in the tools: many leaders prefer synthetic employees they can control, and will treat human beings much the same way until they can be replaced. Sound hyperbolic? Just look at recent headlines. Klarnas CEO famously bragged about AI replacing his staff after the company fired or lost 22% of its workforce a year earlier (this blew up in his face, of course). Duolingo effectively announced a hiring freeze with the introduction of AI. Elijah Clark, a CEO who advises other CEOs on AI, quipped to Gizmodo, AI doesnt go on strike. It doesnt ask for a pay raise as he expressed excitement about laying off employees in favor of AI. A 2024 review found that more than two-thirds, 68 percent, of U.S. workers report experiencing at least one form of electronic monitoring on the job. There are actual billboards running that say, Stop hiring humans, while a new survey found that 37% of employers would prefer hiring a robot or AI over a recent college graduate. It isnt just that AI is replacing workers (it is), its that AI is reinforcing our dimmest view of workers in the process. Generation X Douglas McGregor was a social psychologist and MIT Sloan professor who, in 1960, argued that leaders dont just manage from goals and objectives; they manage from hidden assumptions about human nature. He called one cluster of assumptions Theory X: the belief that people dislike work, avoid responsibility, and need tight control and incentives to perform. The contrasting Theory Y assumed that, given the right conditions, people will seek responsibility, exercise self-direction, and bring far more creativity and judgment than most organizations ever tap. When leaders push AI in ways that amplify surveillance, shrink autonomy, or quietly replace judgment with automation, they arent just modernizing, theyre hard-coding Theory X into the operating system of work. Heres the thing about Theory X/Y: McGregor wasnt arguing which was right, whether employees were fundamentally lazy or capable, but that managerial beliefs become self-fulfilling. How you think about your employees determines how theyll act. Bossware, productivity scoring, keystroke tracking, sentiment analysis of employee chats, all of it sends the same signal: we assume you wont do the right thing unless were watching. These tools teach people that initiative is risky, creativity is irrelevant, and trust is conditional. And once those assumptions are embedded in tools, dashboards, and performance reviews, they stop being a management preference and start being the default culture. It doesnt matter that not every CEO or leader sees employees this way, enough vocal Theory X proponents will shape the narrative for everyone else. Ultimately, the more that human beings are placed in head-to-head competition with AI, the more that the workforce will respond with fear, mistrust, loafing, and even cheating. Y Not A Theory Y AI tool starts from the premise that people want to do good work when the system around them makes that possible. Unfortunately, the market isnt offering a lot of Theory Y AI right now. We need more tools here, more competition, more billboards blaring an alternative worldview. Imagine a tool, for example, that spots duplicated efforts early. Or one that learns from and simplifies decision-making and governance over time. That helps teams compare options, highlights trade-offs, and develops their strategic thinking muscles. That could create shared situational awareness by showing how changes in one team affect others in real time. Instead of secret dashboards used to police performance, Y-style tools could give workers ownership of their data and use it for growth, not punishment. They could make invisible contributions visiblementorship, relationship-building, problem-preventionso the whole texture of teamwork gets its due. In short, they could expand autonomy with guardrails, rather than constrict it with algorithms. Asking the Wrong Question The real question isnt how much productivity we can squeeze out by replacing people with AI or treating them like imperfect machines. Its how much potential weve never tapped because the modern workplace was built on bureaucracy, compliance, and risk-avoidance. For decades, weve constrained the very things that make humans extraordinarycreativity, judgment, curiosity, connection, the spark that happens when people riff on each others ideas. Those capacities have never been fully measured, let alone optimized, because most organizations designed them out of daily work. AI could help us reverse that. Not by automating humans out, but by clearing away the sludge that has buried human capability for a century: redundant approvals, performative documentation, meetings that exist because the calendar said so, processes created for a world that no longer exists. The opportunity isnt a marginal gain from policing employees harderits the exponential upside from finally unleashing the talent you hired in the first place. The leaders who will win the next decade arent the ones who solely bet on synthetic workers, but the ones who use AI to build the first truly human organizationsplaces where people can think, make, collaborate, and surprise you again.
Category:
E-Commerce
You can now read every article that has ever appeared in The New Yorkerfrom as early as February 1925with the click of a button. For the publications centennial anniversary, its editorial team has spent months painstakingly scanning, digitizing, and organizing every single issue it’s ever published, or more than half a million individual pages. Each issue is artfully arranged in a chronological display under a purpose-built archive section of the website; but the content has also been incorporated into The New Yorkers search algorithm so that readers can come across it organically. As the future of magazine journalism remains uncertain, a look back through this carefully archived material demonstrates the importance of preserving print media for the future. Digitizing a century-old archive The process of digitizing The New Yorkers full catalog actually started back in 2005. That year, explains Nicholas Henriquez, the publications director of editorial infrastructure, Random House published The Complete New Yorker, a book that came with DVD-ROMs (now retro tech) containing scanned pages from all the pre-digital issues. Then, in early 2024, Henriquezs team started to convert those scans into digital text. To start, this meant consulting with The New Yorker library, where the magazines physical archives are stored, to re-scan several hundreds of pages that required another pass for a number of reasonsincluding damage, a poor initial scan, or a corrupted file. Some of the older issues, from the first five years or so, were basically untouched, Henriquez says. I had to use a letter opener to open the pages to scan some of them. After the team had a complete collection of files, they then began the painstaking process of formatting and styling them for the web. There were the predictable challenges of making old magazine articles work online. Each needed a workable headline, description, and image. Bylines in particular were tricky, Henriquez says, as many early writers would use pseudonyms or humorous one-off pen namesor, in some cases, fail to sign their name at all. Thats part of the value of having, as The New Yorker does, a team of technologists who are part of the editorial staff: We can build these databases and apps and scripts, and we can also look at something in that database like Ogden de Sade and know, okay, thats Ogden Nash, and its funny, and we should figure out a nice way to keep that joke online, Henriquez says. There were many instances where our technological approach was informed by this deep understanding of the magazines history and its cultural context. Unearthing a treasure trove of early journalism Over the course of this process, Henriquez unearthed stories that he never could have expected. He came across a short, unsigned book review from 1935 of a memoir by a survivor of a Nazi concentration camp, and says he had to triple-check that we didnt have bad data somewhere, because that review was published in March of 1935, just two years after Hitler became chancellor. I didnt realize those stories were out there that early, much less being translated into English and published in America. On a lighter note, he also found a piece about going to the Newark airport at the dawn of commercial aviation in 1933, and a 1947 article thats one of the first examples of TV criticism ever published by The New Yorker. Along the way, he says, he rediscovered what makes magazine writing special. In a newspaper, most stories have the same framing: This happened, Henriquez explains. “But a magazine article can do something different: It can be told in a different tense or in a different wayThis could happen, This happened to this person. Examples of this distinct genre of analysis include a 1969 article, a few months before the moon landing, that lays out how it will happen, step-by-step; or a pre-Sputnik piece about American scientists trying to launch the first satellite; or a 1961 feature on the rollout of desegregation, as witnessed by author Katharine T. Kinkead and a group of Black college students driving around Durham, North Carolina. Henriquez says: These kinds of things, I think, make magazine journalism essential and unique.
Category:
E-Commerce
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