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It’s well known that artificial intelligence has driven skyrocketing demand for electrical power, computing hardware, and network connectivity at data centers. But AI has also quietly shifted how consumers use their home internet service. AT&T reports a recent boost in the share of data that customers upload through its network as they communicate with AI systems. “In 2025, customers’ upload traffic grew two times faster than download traffic,” says Jenifer Robertson, executive vice president and general manager for AT&T Mass Markets. “And that’s driven by AI use.” Historically, home internet use has centered on downloading data—accessing websites, scrolling through social media, and watching streaming services—rather than uploading it. But Robertson says that pattern is beginning to change. People are uploading more of their own content and sending data back and forth with AI systems, whether that involves audio and video, photos, or other material like source code. To adapt to customers’ changing needs in the age of AI—and even respond proactively to issues that could cause outages—AT&T has turned to artificial intelligence itself. AI systems, built with a mix of industry-standard tools and proprietary AT&T data, now help the telecom company plan, design, and build new parts of its network. They also help model and forecast network traffic and capacity, test equipment, and configure systems across hundreds of thousands of network sites. Jenifer Robertson “We have to optimize all of those sites to deliver the best customer experience,” Robertson says. “And that obviously gets both much easier [and] much more efficient when we apply AI to that modeling and allow it to improve the customer experience.” Robertson declined to go into too many specifics about the algorithms or AI systems used, but says the company makes sure to respect its privacy policies around user data. AI can even help “auto-heal” network issues, sometimes even before customers detect them. It can also quickly help send outage notifications and, if needed under AT&T’s service guarantee program, credits to customers experiencing problems, Robertson says. “Building that trust with customers is going a long way in increasing our net promoter scores and . . . customer satisfaction, and decreasing churn,” she says. Because of its AI-bolstered confidence in its network, the company is expanding its AT&T Guarantee—which automatically grants customers credits for qualifying outages—to its Internet Air wireless home internet product. Versions of the guarantee already applied to AT&T mobile service and fiber-based internet as of early 2025. Last year, AT&T reported serving at least 10 million fiber internet customers, and the company recently gained more than 1 million subscribers through the acquisition of most of Lumen’s mass-market fiber business. The company says it surpassed 1 million Internet Air subscribers in summer 2025, including in areas not served by AT&T fiber lines. Robertson says the service’s rapid growth contributed to the decision to add it to the guarantee program. Additionally, AT&T customers who have both fiber and wireless service will now automatically receive Internet Backup at no extra charge. The program allows home internet connections to reroute through a linked AT&T mobile device if the fiber connection goes down, switching back to the wired connection once service is restored. After all, even with the most sophisticated AI modeling, it’s not possible to avoid all outages from causes like accidentally severed fiber lines, which Robertson says can be caused by errant backyard gardeners. “In the event that a fiber outage were to occur, the connectivity rolls over to their AT&T Wireless, and they never go without internet,” she says. “They have connectivity until we can get out there and repair it.” View the full article

The numbers on a new patriotic Pennsylvania license plate were designed to be easy to read, but they’ve actually introduced a new point of confusion. Pennsylvania Governor Josh Shapiro announced the “Let Freedom Ring” specialty license plate last summer to promote the commonwealth’s role in America’s founding 250 years ago. The cream-colored plate depicts a dark blue Liberty Bell in the background, along with the previously mentioned slogan and commonwealth’s name in red. None of that is at issue, though: The problem is the style of the zero. The number has a slash through its counter to prevent confusion with the letter O. Now, however, Pennsylvania toll cameras—not to mention locals—are confusing the zero for an eight. The mix-ups are occurring even though the lettering follows industry best practices to differentiate characters that can sometimes look alike. “The addition of the slash through the zero was intended to help differentiate between the zero and the letter O, which both the license plate readers and human eye have had difficulty differentiating on past registration plates,” the Pennsylvania Department of Transportation said in a statement to ABC affiliate WPVI, the Philadelphia network that first reported the new plate’s design problem. The diagonal slash was added in accordance with license plate recommendations from the American Association of Motor Vehicle Administrators (AAMVA), a Virginia-based trade group for state and province motor vehicle divisions in the U.S. and Canada. The design passed other tests, too. PennDOT says the plate met its production and legibility requirements, and that it was developed in consultation with Pennsylvania State Police and the Pennsylvania Turnpike, its toll roads commission. But those precautions didn’t prevent one motorist who contacted WPXI in Pittsburgh from receiving a toll-by-plate invoice for a car that wasn’t theirs. A camera mistook an “08” in their license plate for “88.” Easily confused characters Similar characters—like A and R; 8 and B; 0, O, and Q; 1 and 7; D and O; and Z and 2—can be misread by image processing tools, so AAMVA recommends characters be made distinct and identifiable. That’s easier said than done, apparently. One tolling authority found the misread rate of its automated license plate readers was 20%, per AAMVA’s 2012 recommendations. To reduce confusion, some states limit the use of the most easily confused characters. Arizona doesn’t print I, O, Q, or U on standard license plates, while Arkansas doesn’t even offer Q for personalized license plates. Pennsylvania solved one problem only to introduce another—but at least officials say misreadings are not widespread. They hope their systems will get better at distinguishing the new character over time. Turnpike Commission spokesman Crispin Havener told NBC affiliate WPXI that the agency is working with its software vendor to improve accuracy, but that machine learning is not perfect yet and improvements won’t be immediate. View the full article

I may have just seen the biggest interface breakthrough in years. Or not. But I think so? Things are moving so fast that it’s hard to tell. Ryo Lu, head of design at the white-hot coding tool Cursor has invited me to their charcoal-hued San Francisco studio. Before anyone says hello, I’m greeted by a pile of footwear in the entry of the no-shoes open office. I suddenly regret my choice to wear my New Balance loafers without socks. The softspoken Lu, donning the creative-approved uniform of flowy wide-legged pants and a button down, weaves me through desks—past half a sports bar’s worth of uptime monitors and a shelf of knicknacks including a New Jeans record and Bondi Blue iMac. Maybe you’re a normie and you haven’t even heard of Cursor. That’s okay. It’s an AI coding startup at the vanguard of this movement that many now believe will reshape software as we know it. Cursor is aimed at serious development teams, but as we sit at his desk, Lu acknowledges that strength is also a weakness. If you’ve signed up for Cursor to do some casual vibecoding, you’ll probably find yourself disoriented by the command lines and acronyms that live throughout the software. Lu proposes he can solve this tension with his new project, something he’s calling “Baby Cursor.” Lu imagines Baby Cursor as the next generation of the company’s software, which first launched in 2023. When he loads it, I see no scary boxes of code. I’m mostly just looking at a prompt. But with a tap, a designer can pull up an app and rearrange its components, which will spit out the updates as code. Or a product manager can load a project summary and translate goals into concrete workflows. Or anyone, really, can pull up a team of agents to coordinate and work while they grab a matcha. As Lu whirs around his creation, he demonstrates how Baby Cursor can ultimately unfurl to a massive workstation—not unlike how Cursor looks now—or shrink down into an assistant that lives in the corner of your screen. Lu is imagining the future of Cursor as something of an infinite Swiss Army Knife, where every window offers a different facet of the service: an AI with a dozen different faces that all plug into the same engine, offering the perfect interface for any audience. “All I’m thinking is make Cursor the most simple thing and the most crazy thing all at once,” he says. But the craziest part about Baby Cursor isn’t even the design. It’s that Lu built it in a single week, with just one other person. Read that again: A team of two rebuilt Cursor, currently valued at $29.3 billion, in a week. While for most of us, the AI revolution has meant little more than conversational search engines, auto-written emails, and endless streams of multimedia slop, in the Valley, it’s completely upending product development. The way software is built has not only changed; it’s hit an exponential acceleration. Now, the designer can be the coder who can be the product manager, in a development process that can go straight from concept to production in a single step. I’ve visited San Francisco countless times over my two decades of reporting, but during a trip three years ago, I felt the world shift a little. After ChatGPT exploded to the mainstream in 2023, I visited ground zero of the AI revolution, taking a 72-hour tour of startups in an attempt to untangle how AI was going to impact the future of design—and by proxy, the way people would experience this new technology in their lives. Just three years ago, designers waxed poetic in deeply philosophical discussions that unpacked ideas like: What, really, is an LLM? What might you do with an omniscient machine other than chat? With AI as the engine behind software, how could its touchpoints change into something we’ve never imagined before? Then in February, I returned as both a design journalist and AI tourist, and found people were now speaking in far more concrete terms. I took back-to-back meetings at AI giants including OpenAI and Anthropic, and I also checked in with the investors and startups chasing the next big thing. In several cases, I caught up with the same people three years later to see how their views had changed. The piece that follows is a synthesis of their perspectives and my own observations. Think of it as a snapshot of the AI zeitgeist, and a forecast into what happens when the designer is also the software developer. As Jason Yuan, a former Apple designer who founded the social AI startup Future Lovers tells me, “There’s never been a better time to be an auteur.” Behind the vibe(code) shift Whereas San Francisco in 2023 felt almost post-apocalyptic, the city has recently undergone a complete vibe shift. I was greeted by streets I hardly recognized, as countless venture capital dollars have wooed a new generation of young entrepreneurs to build anew. Parks are now teeming with people. Twenty-somethings line up outside once-abandoned storefronts for $7 croissants and $45 prix fix meals—wallet-friendly luxuries for pre-IPO life. Self-driving Waymos are so trusted that they command higher rates than human-driven Ubers. This is a city that’s mastering automation, using new AIs to build new AIs, while every entrepreneur is worried about taking a vacation, lest they be left behind. From what I saw in just a few days, those concerns seem valid. The new creative energy you can feel in San Francisco is fueled by the VC industry, which invested $122 billion in AI into Bay Area companies in 2025 alone. The greater VC industry itself is growing fast. In 1994, VC firms had just 150 general partners; now there are more than 33,000, according to James Currier, founding partner at the SF-based investment firm NFX. He says it’s a FOMO market, and so a company valued at $18 million for their series A in 2022 now commands a valuation of $140 million. When Currier and I first met three years ago, he had his eyes peeled for the startup that would leverage AI to change life as we know it, much like Uber used the smartphone to transform transportation. But instead, we’ve seen entrepreneurs largely gravitate toward one use case to rule them all: vibecoding. “[Investment success] is largely random at this point, because there’s so many startups, and there’s so many venture firms, and because everyone looks alike,” he says. After my 2023 visit, it seemed like nothing much happened in AI for a while. Yes, new models came out every week. Yes, they were each better than the last. But no one had really demonstrated how AI would make us live or work all that differently. That changed in November 2025, when Anthropic released Claude 4.5. While developers had been using AI tools to help them code for years, this update was an inflection point. It was far more reliable and promptable. For the first time, you could truly code complex projects simply by chatting with AI. “If AI didn’t evolve from now, we would see another 95% impact in the world,” insists Currier, speaking to not only the impact of vibecoding, but the untapped potential still lurking in modern LLMs. You could argue that vibecoding is that revolution, or that vibecoding is one of many tools that will get us there. But one thing that is certain? Here at ground zero of AI, vibecoding has already changed work in ways Middle America doesn’t see. Unlike earlier investment booms, many VC dollars need not go to funding large development staffs; they can simply be leveraged to buy more and more AI code. I had expected that to tap the power of AI, we’d need a suite of new modalities, like how the mouse introduced the GUI or multitouch made smartphones intuitive. As it turned out, the AI revolution of today has nothing to do with buttons or knobs or voice. AI changed work without changing much about the front-end UI. It provided the power of having a coding agent, cooking up hundreds of lines of reliable code at a time, coordinating with other agents to bring new software to life. Coding has been the most successful use case of AI, end stop. In retrospect, it makes sense. Machines naturally speak the language of machines. “I think what’s so amazing about code is it makes something useful for you. Like, it doesn’t come back with an answer or a sentence,” says Joel Lewenstein, head of design at Anthropic. “It actually creates.” Intelligence is the new materiality Sitting in a dimly lit cabaret, Abs Chowdhury places his iPhone Pro onto the table next to mine. I can tell he’s sizing up the color I chose (orange), which is fair because he designed the thing. The former Apple designer was on stage at Apple just last year, debuting his Pro and Air models. He was wooed away last November by an offer he couldn’t refuse, and the industrial designer started vibe-designing the UI of his new, secretive AI startup Hark (backed by $100 million in funding). While recruiting his team and building his design studio, he confesses that he transferred rough designs from Photoshop or Illustrator straight into AI code tools, and edited them via code prompts. No conceptual fantasies to be realized by some engineer required. Likewise, another former Apple designer, Yuan, muses that he raised too much money for his new startup after learning how capable vibecoding had become. Over Albarino and potato soup at a packed restaurant where we can barely hear one another talk, he details how his company Future Lovers is creating a sort of social AI where Pluribus meets Gossip Girl. He’s spent the five months since Claude 4.5 came out building his first product mostly on his own alongside AI, with a coding advisor and a few contractors—though he has since hired a full-time AI specialist. (Disclosure: I was briefly a consultant for Yuan’s last startup, New Computer.) “There’s a new reason to raise lots of money, which is compute,” Yuan says. “If you have lots of conviction, and you know exactly what you want, like, why would you hire another 20 other people right now to tell you what you’re doing? It’s a coordination cost.” Chowdhury and Yuan are two talented designers with prestigious professional pedigrees. They are true craftsmen who’ve mastered design tools to tweak details most of us can’t even perceive. But the fact that they’ve each embraced vibecoding or vibedesign, or vibeimplementation—whatever strange thing you want to call it—demonstrates a most certain evolution of practice. Yes, Chowdhury has since hired dedicated interface designers. But their enthusiasm for these workflows shows that once designers begin manifesting their ideas with AI as auteurs, it’s hard to go back. As Yuan has written, intelligence is the new material from which designers create. It’s a medium that’s becoming as natural for people to reshape as pixels or aluminum were in the last era. This evolution seems sure to pull power from engineering teams back toward designers and other product visionaries. As designers are in essence learning to code, in many cases, the professional coder is becoming more abstracted from the process. Such abstraction is creating tension for engineering teams to straddle new efficiencies alongside traditional expertise—which is easier said than done. Later in the week, I visited the video game vibecoding startup Moonlake (with $30 million in funding from NVIDIA and others). The two young Stanford graduate student founders tell me that all of their engineer hires have to code in front of them now as part of the hiring process, and observing specifically how they use AI is a significant criteria for the job. “It’s a very fine line. We find coders today who don’t really understand your code too well, and they end up breaking code bases,” says cofounder Sharon Lee. “We make sure half of our engineers now use [traditional] code, and the other half use a ton of tools.” No doubt, speed to market is driving many of these decisions to build with machines rather than people. Even craftsmen have embraced the “move fast and break things” era of design, which is at odds with the last 30 years of chasing perfection. “You can’t do the old school Apple thing of like, create lickable craft and interface,” says Yuan. “You can’t because, by the time you’ve done the best interface for ChatGPT 3, you’re on GPT 6.” The next great interface (doesn’t exist?) I’m perched in one of the many sun-filled conference rooms at Anthropic, clutching a fruity, light roast coffee that’s been handed to me in a half-glazed ceramic mug. The earthy sensation feels downright anachronistic as head of product design Joel Lewenstein speaks in a rapid fire, hyper optimistic cadence about his vision for the future of Claude. Three years ago, much of the design world pondered if there would be one great AI interface to rule them all—something that came after chatting in a prompt with an LLM. Experiments in hardware were abound (RIP Humane and Rabbit), while debates raged around whether the future of all interfaces would be generative, in which AI spun up the perfect new buttons for you at any given moment. “There’s a great irony. Obviously I hire and interview dozens of designers in AI, and everyone comes in [saying] ‘I want to do the next paradigm after chat! I have this idea!’ I’ve seen dozens of different directions, and none of them is the one after chat,” says Lewenstein—who notes that even Claude Code, as successful as it’s proven, is ostensibly an extension of chat. “So I don’t know the answer here. We’re not sitting on a prototype which I’m 100% sure is the paradigm after chat.” Much like its rival OpenAI, Anthropic is in an expansion period, as the major model providers are diversifying their product portfolio similar to how Microsoft and Google stretched their services in earlier decades. Instead of making Claude itself do more through a single hero portal or interface, it’s spinning off Claude into all sorts of different sub products that feel somewhat the same. “We have this Excel plug-in that finance people love. It kind of vaguely looks like our Chrome extension, which kind of vaguely looks like Claude AI, which kind of vaguely looks like Claude Code, but they’re all really bespoke for their different users,” says Lewenstein, noting that they’ve programmed a shared design language into their AI-fueled development process. “We would rather, at this point, have four really awesome products for four different types of people, and then figure out later what to do, because it just lets us learn faster, right?” Ship first, learn later: It’s this sort of mentality that let Anthropic build its new Claude Cowork platform in just five days. But it also means that Cowork is divorced from Claude itself rather than tightly integrated. It’s another thing that Anthropic’s got to sell to its own audience. “Things are moving so fast that we just have to experiment faster,” Lewenstein says. “Convergence is hard. Because you have to figure out what’s shared. You have to build that shared path. You have all of the fringe things that people loved on these other systems. And there’s too much changing too quickly.” After Waymoing across town, I arrive at OpenAI whose offices are housed in a tower formerly owned by Uber. While most security is stationed at the front desk or door, OpenAI’s spills right outside onto the sidewalk. I make my way to an enclosed porch jutting out from an upper floor, where I meet with perhaps the greatest living legend behind web browsing. Sitting casually at a picnic table, Darin Fisher explains why his own approach to design at OpenAI isn’t more radical. The mind behind the Netscale, Chrome, and Arc browsers now spends his days leading the design of OpenAI’s Atlas browser. One of his most pressing design debates? Which side of the interface gets the AI chat box, left or right? (Which, to be fair, is a more perplexing problem than it might first appear—but still not paradigm-busting work.) “I’m not that person who’s like, ‘how can we transform everything?‘” he says. “I’m much more thinking about, how do you take where people are [and] what’s the iteration? How does it get better? It doesn’t really surprise me that a lot of stuff ends up being where people already are centered, about things that optimize workflows they’re familiar with.” But then Fisher audits his own thoughts for a moment, offering a fair counterpoint. “The whole aspect of [AI] doing it for you, and you not having to be there in all the weeds, is a paradigm shift in UI, right?” Fisher isn’t wrong. But Anthropic and OpenAI—along with all the frontier model providers—still face a most certain risk. They are recreating a disparate suite of loosely connected services that made software giants dominant in the last era (think Microsoft Windows, Teams, Outlook, OneDrive, Excel, Word, etc). This tactic elbows out competition for highly specific applications like legal and healthcare, sure. But this isn’t the ’90s or aughts. Software development is easy now. And so being a 30-headed hydra of platforms actually makes frontier model builders vulnerable to startups that have the clarity and license to build more encompassing, clearer, thoughtful services that could become the primary touchpoint of AI. The strategy the AI industry never saw coming When I first met Barcelona cool kid Victor Perez a few years ago, he was building La Croix towers at his live-work condo. He was also building Krea, a sort of Photoshop for the AI age that incorporates the latest models into his software on an unrelenting cadence, promising new features every week. Now, a $500 million valuation later, he sits in Krea’s new digs in Fisherman’s Wharf, a second floor, brick-walled space with a glass-encased conference room and views of Alcatraz through its century-old arched windows. Despite the natural light, Perez refers to spending his last three years in a cave. Krea is profitable and valuable, but Perez (alongside his team of 37) is still grinding. He looks like he could use a sandwich. In 2023 when most AI companies were raising money in attempts to train massive AI models, Krea took a different approach. It built its own software, and then it plugged in the AI models of others. It offered the front end experience packaging many of the world’s leading AI models like Runway and Luma—models that appeared closed and closely protected at the time. “This idea of being an API wrapper was really not obvious to me. We were the first to do it, but I thought that we would get sued,” admits Perez. “How was it possible that all of these companies are spending so many millions of dollars on training these systems, and they don’t try to log these systems into their own products? That’s what I expected!” Instead of getting sued, Perez fielded requests to move models higher on the list to get more visibility. Krea was amongst the first companies to prove out an architecture that’s now commonplace, where a piece of software can serve as an interface for the AI models of others. Cursor takes a similar approach of owning AI through the application layer—like Krea, it runs some of its own AI models, but it also plugs in Claude and other third parties. Because AI models can be swapped in and out with a literal line of code, your Kreas and Cursors have some survivability even as better, newer models come and go. Their moat is their interface. Perez acknowledges that no strategy is a safe bet in AI right now. “People, including us, have been very successful putting together APIs and building products on top,” he says. “But it feels to me that in three years, we’re gonna have a conversation around how those API wrappers were very hyped in 2026 and how they were not hyped anymore in 2029.” Ironically, while Perez believes model generation is a dead-end for smaller startups in an era when no one can hope to compete with Google or OpenAI, he also sees Krea’s future as embracing it. He argues that you can’t build creative tools to manipulate AI without controlling the core levers of the model. That’s because frontier models are tuned in post-training for broad functionality across tasks rather than a specific POV—they’re built to be Wonder Bread to please the broadest generic audience. So in 2025, Krea worked with Black Forest Labs (creators of the popular model Flux) to help tune a custom Krea model, essentially giving the system taste across a diversity of styles. The text-to-image workflow creates images that shake off the obvious AI feel, creating photos that feel more photorealistic and illustrations that feel more painterly. This arrangement might sound technically confusing, but the partnership is familiar to the business world: It’s just a collab! Companies partner with external design teams all the time to take products, ranging from shoes to ice cream flavors, somewhere neither party could reach on their own. Perez compares the process of post-training the model to using Pinterest. You customize your experience of Pinterest by teaching the algorithm your preferences. However, the opportunity to customize train frontier models doesn’t really exist in the industry today. This leaves everyone creating media at the mercy of engineers rather than designers, and we generate a lot more slop as a result. “You cannot create a smarter model than Google, but you can create more taste—a model that is more tasteful,” says Perez. It’s a point echoed by Karina Nguyen, formerly a researcher at OpenAI and Anthropic, who is building her own company called Thoughtful. (We connect on the phone, as she’s just signing a lease on her company’s first space.) She estimates there are only 200 or so experts in post-training methods in the world, and because they are engineers, they optimize models around a mathematical and engineering mindset. But Nguyen imagines that Thoughtful, backed by an equal dose of engineering and design expertise, could post-train models for other companies, bringing specific AI sensibilities to areas like healthcare or legal otherwise lost in broadly optimized models. A lot of AI experiences feel the same because the AI they plug into is the same. Krea and Thoughtful are considering how to tune frontier models without building them from scratch—allowing them to create richer experiences than the quick-shipped features we’re getting from frontier model companies. “Every week there’s something new happening, they have to react. And so there’s no space [to really think],” says Nguyen. “You should allow people to just imagine, give them creative space to go off and imagine. I think that’s how the most transformational research came to be, and design is the same.” When I ask Perez why we haven’t seen more experimentation in the UI of AI from startups, his answer is two-fold. First, he notes that workflows have changed for a lot of creatives—echoing what I heard from OpenAI’s Fisher. For example, he says, designers can now take a product photo and generate more angles of that photo, or even a poster or a film. The UI here isn’t new, but the workflow is. Second, he says the new modalities that might unlock the next level of AI capabilities aren’t possible yet because AI simply isn’t fast enough to support them. “We’re still in the stage of gaining capabilities,” says Perez, “and after we finish the capability stage, there’s going to be performance optimization.” Krea has chased performance optimization. It was the first company to generate stylized videos in real time, but he says it’s pretty much turned out to be a proof of concept, because people prefer the vastly better output they can get by waiting. Give the machine time to render, and it will create a higher-fidelity AI video. But AI render times are irreconcilable with fluid tools. “You cannot build an interface with something that takes two to three minutes to generate,” Perez says flatly. But training models with Black Forest Labs gets them closer. Inevitably, Perez imagines a day when these AIs are running 100 to 1000 times faster, and at last, we will see more aggressive experimentation with how a new suite of mixed-modal GUI tools work. Until then, we have vibecoding. AI is everything everywhere all at once Blocks away from Cursor’s headquarters, I ascend a long staircase in North Beach as Lu discusses his greater vision for the company. He recalls that when he lived in China, he coded everything himself. But when he arrived in the Valley, he became a designer with a capital D. Suddenly touching code was divorced from his work. An early project in the U.S. was particularly spirit crushing, as he watched a vision project wither in development. Cursor has allowed him to come full circle, to be a designer who is, in essence, able to code again. In this new era, designing and development are no longer separate steps. Each concept can be almost instantaneously made real. And that’s brewing new expectations for software. “I think Figma still is useful for when I want to just play in 2D space. I want to do my artboards. I want to specify how my pixels look exactly how I want,” he says. “But then there’s a point where it doesn’t make sense to keep making these marks anymore. Like, you want it to happen in real life, right? If you…prototype in Cursor…it’s like, just really, really hard for me to go back to Figma.” It’s this ethos that’s driving his entire Swiss Army Knife of windows. Instead of buttons and tooltips, Cursor is evolving into an infinite browser of possibilities. It’s a myriad of tabula rasa, or digital putty, to be filled with your next creations. The challenge is really making sure that each window meets the user where they are, and takes them where they want to go next. But the engine under them? In the AI age, no matter the company, that engine is probably just a handful of shared models. The grand vision that Lu is teasing through Baby Cursor is actually largely the same as what Anthropic is chasing with its ever-expanding platforms and extensions—albeit they are coming at it from completely opposite directions. Developers are realizing that AI is an infinitely ergonomic machine. It’s not literally shapeshifting with generative-born UIs as some suggested, but it’s increasingly squeezing into every possible context. It can become any touchpoint that any particular user needs. That means AI will not be defined by one new or old modality—not buttons, not agents, not voice, not tooltips. It will be all modalities, all the time, all at once. Ever growing with new capabilities. Ever bending toward new demands. Ever in more control of the designer-auteur. But if all these touchpoints ultimately plug into the same AI backend, I do wonder how long it will make sense to have all that many different pieces of software to begin with. “My theory is, just like all software is pretty much the same thing. Some like wrappings of concepts and then data floating somewhere and then passing things around,” says Lu. “So the convergence is almost inevitable. And then it becomes like, whoever is creating the best interface and the best abstractions, the simplest ones that scale.” You could almost imagine a future, for the first time ever, where everybody is using one app, I tell Lu. We already have a version of that with iOS and Android. “But you know, the old OSs were built with this app model that doesn’t make sense anymore. Maybe we need to make an OS,” he says. “Cursor?” I ask. “Yeah,” says Lu. “I don’t know. It’s easy. Now, you just use the agent swarm, and then they just work on it for like a week.” View the full article

In 1994, Bernard Tschumi, then Dean of Columbia University’s Graduate School of Architecture in New York, launched an experiment that banned paper and hand drawings, requiring architecture students to use computers instead. Together with the rise of computer-aided programs, Tschumi’s “Paperless Studio” accelerated the profession’s embrace of digital tools and reshaped how architects conceived ideas. Now that AI has entered the picture, you’d be forgiven for thinking the architectural sketch as we know it is dead. Quite the opposite. “We are in a world that is now completely dominated by digital tools, but something strange is happening: The hand sketch is back,” says Andrew Holder. Holder, a practicing architect and chair of graduate architecture, landscape, and urban design at the Pratt School of Architecture in Brooklyn, recently curated an exhibition that examines the role of the sketch in contemporary architecture. The exhibition, titled Levers Long Enough, includes more than 200 sketches from over 60 architecture practices that sent in watercolors, pencil sketches, and even embroidered scribbles. It is both a rebuke to AI, and an ode to the physical experience in an increasingly digital world. Summary House 1 How the architectural sketch (temporarily) died… Definitionally, at least according to Holder, a sketch is quick, economical, and physical. Sometimes, he says, the sketch can be performed on a touch screen like an iPad, but only if “we can feel the contact between the hand and the image.” As it happens, this physical contact has been disappearing for decades. “Beginning the ’90s and through the early aughts, the sketch was obliterated from the classroom,” says Holder. Massing Study With the dawn of computer-aided design, the sketch took a step back in the architecture practice, and though it never disappeared, it has yet to reclaim its place in architectural pedagogy. While life drawing was once a cornerstone in architecture schools—architecture students at the Paris École des Beaux-Arts studied and sketched the fragments and plaster casts—few universities today have a class dedicated to sketching. London’s Bartlett School, as well as the AA School, are both famous for their emphasis on freehand drawing, but most architecture schools around the world, Pratt included, focus on more technical practices like drafting and perspective drawing. …And why it’s making a comeback Slowly, however, the sketch is returning to the spotlight. Holder first noticed its re-emergence in 2025, in the work of Hilary Sample and Michael Meredith of MOS Architects. “A whole section of their website popped up where they showed hand sketches for every project,” he recalls. Plant with Holes MOS, who, in the early 2000s, became known for their experimental use of custom-coded software to animate renders, had never stopped hand-sketching. “They just haven’t been showing it,” says Holder, who went on looking for similar patterns across the industry. Grids06 The breadth of work on display at the exhibition is the culmination of his search. From industry giants like Steven Holl and Weiss Manfredi, to emerging practices like Current Interests and Almost Studio, everyone, it seemed, had a hand-sketching practice. “Everywhere you look, these people are showing [sketches] right alongside images of finished buildings, as though they had equal weight,” he says. The willingness to show a hand-sketch right next to a photograph suggests a certain pride in this once-endangered art form. It also proves how much the narrative has shifted. “Pride is the word, but if we think back to what people were proud of 10 years ago, it would’ve been a polished photorealistic rendering,” says Holder. The unintended AI effect That the traditional sketch is coming back in the age of AI might seem surprising to some, but it becomes predictable once you understand what technology tends to do to the things it displaces. When digital cameras flooded the market, film photography was reborn as a deliberate practice. Similarly, in 2024, vinyl sales in the U.S. surpassed CD sales. Each time a new technology promises to render an older one obsolete, the older one re-emerges, stripped of its utility but charged with new meaning. MUDAC As Holder points out, the most common arguments in favor of AI have been about efficiency or speed, but for many architects, that is precisely what the sketch is for. As “AI slop” continues to creep into every nook and cranny of our digital lives, clients are also beginning to see the hand-drawn sketch as a sign of care, deliberation, and “actual thought,” says Holder. As it turns out, the sketch may well be the most primordial expression of the human experience. No algorithm can ever replace that. View the full article

Qualcomm's new Wi-Fi 8 SoC for phones is a huge leap forward in connectivity peformance and features. The post MWC: The FastConnect 8800 Wi-Fi 8 platform for phones is yet another tiny engineering marvel from Qualcomm appeared first on Wi-Fi NOW Global. View the full article

EU proposals presented on Wednesday require vehicles for corporate fleets and small EVs to be assembled within the blocView the full article

The controversy over the Pentagon’s use of Anthropic’s models has become a flashpoint in the national debate over military artificial intelligence, and sparked outrage from Washington to Silicon Valley. The Pentagon wanted to buy Anthropic’s AI models without any restrictions on their use; even as it scrapped its flagship safety rule, the company wouldn’t budge on two particular red lines. And so, just after a Friday evening deadline, the Secretary of War killed the company’s $200 million Pentagon contract and declared the firm was not just “woke” but a “supply chain risk,” banning it from working with defense agencies. Meanwhile, OpenAI CEO Sam Altman had been negotiating his own Pentagon deal, with some, but not all, of the contractual guardrails that Anthropic had wanted. (Anthropic’s CEO would later tell employees that OpenAI’s messaging around the deal was “mendacious.”) In any case, removing Anthropic won’t be easy: On Saturday the company said it would sue the government over the ban, just as its AI models—already deeply embedded in the Pentagon’s systems—were being used by the US military to carry out strikes on Iran. On an ethical and legal level, much of the headspinning spat has centered largely on one question: should AI be deployed in military settings autonomously, without a human “in the loop”? But that framing is narrowing the debate at a critical moment. The focus on human involvement in autonomous systems—while absolutely a critical issue that needs to be discussed—has drowned out broader questions. One is whether advanced AI should be embedded into military decision-making at all. Another is who should control its deployment, how oversight should be structured, and what constitutional processes are being bypassed as the Pentagon pushes forward with AI integration. The controversy is concentrating attention on one company and one question while minimizing a larger debate about responsibility and accountability, and all but shutting out the rest of the country—including voters and lawmakers. The resulting impacts on our information environment—agenda narrowing, issue substitution, and complexity reduction—are typically associated with outcomes of narrative warfare, not healthy, organic debate. But this is a debate we need to have. To do that, it helps to see what this information distortion looks like. Take the phrase “human in the loop,” which has become the shorthand for safety in many operational settings. Department of Defense Directive 3000.09 requires “appropriate levels of human judgment” over autonomous weapon systems, while international humanitarian law scholars often frame the issue around “meaningful human control.” This framing has political appeal because it offers a familiar safeguard and implies some degree of continuity with existing military doctrine. It also conveniently avoids confronting more disruptive questions. Yet decades of research complicate the reassurance supposedly provided by that phrase. Studies of automation bias show that humans supervising automated systems frequently defer to machine outputs, even when they’re wrong. One landmark study found that, when working with seemingly highly reliable automation systems, operators detected only about 30 percent of system failures. In another series of studies, researchers documented that roughly 65 percent of participants followed incorrect automated directives. A similar study found that 39 of 40 participants followed faulty automated recommendations, despite the ability to verify them independently. The human-in-the-loop debate, therefore, fails to resolve deeper questions about delegation of authority, acceleration of decision cycles, and institutional accountability. By centering the discussion on whether a human remains present, the current controversy sidelines the question of whether advanced AI systems should structure military decision pipelines in the first place. The integration of AI into military systems also alters the speed of decision-making. Research in strategic stability and crisis management has long emphasized the stabilizing role of deliberation and careful, slow review. Automation often encourages the opposite. A war-game simulation described recently by New Scientist found that large language models chose nuclear options (literally, nuclear strikes) in approximately 95 percent of test runs when objectives were loosely constrained, and the model was trying to choose a decisive action that would lead to a battlefield victory. The finding illustrates how AI models operating with ambiguous goals can produce extreme outputs while still following directions. Currently, the media and most of the public is focused on the question of whether humans will approve those outputs. Less attention is being paid to how AI integration changes the timing and framing of the options themselves. If AI systems generate rapid threat assessments and routinely recommend escalation at any cost, the menu of choices presented to decision-makers will be artificially narrowed before human review even begins. And even with a full range of options, existing research shows that human oversight doesn’t necessarily correct machine errors. Again, by focusing so much of our attention on questions about fully autonomous AI systems in military settings, we are implicitly and uncritically accepting the legitimacy of human-in-the-loop systems. That acceptance risks overlooking extremely important questions about what role—if any—AI should play in lethal decision-making at all. This dynamic mirrors a common feature of narrative warfare called issue substitution, which describes the process of substituting foundational issues or questions with narrower, more manageable proxies. The Constitution assigns Congress the power to declare war and regulate the armed forces. Yet the integration of frontier AI systems into military infrastructure is proceeding primarily through executive branch contracting and internal policy guidance. Meanwhile, the Anthropic standoff has focused attention on the Secretary of Defense and a single company’s leadership, which has resulted in a complete lack of substantive public debate about congressional authorization, statutory guardrails, and procurement oversight specific to autonomous AI systems. Congress has held hearings on AI and national security, but no comprehensive statutory framework governing autonomous lethal systems has been enacted. The National Defense Authorization Acts of recent years include AI funding and research directives, yet they do not establish detailed deployment constraints comparable to those governing, for instance, nuclear command authority. These gaps have resulted in an alarming amount of power being handed over to the executive branch by default, effectively allowing it to unilaterally make sweeping decisions about the current and future trajectory of the US military and foreign policy, with limited oversight. While we’re focused on negotiations between the executive branch and a private company, issues such as congressional authorization, legislative design, and oversight—as well as the will of the public—are being pushed out of the frame entirely. Another dimension receiving limited scrutiny concerns concentration of influence. A small number of AI companies—Anthropic, OpenAI, Google DeepMind, and others—are shaping the technical trajectory of systems that may be integrated into defense operations that will influence the direction of our military for years to come. The debate has thus far focused on whether Anthropic should comply with Pentagon requests. Left largely untouched is the question of whether any single private firm should have such leverage over the backbone of military AI capability, and whether a single unelected CEO or handful of CEOs should be given the power to shape current and future military strategy for an entire nation. (This is not to mention the political and financial influence these companies wield; before the Pentagon awarded a new contract to OpenAI, one cofounder had become one of President The President’s top donors.) In narrative warfare, the dynamics described above are categorized as agenda narrowing, which occurs when, out of many relevant questions or issues, only a small subset are given sustained attention or addressed at all. While the Anthropic controversy has largely centered on battlefield and targeting scenarios, AI integration into defense systems also vastly expands surveillance capabilities, and the Pentagon’s recent demands suggest that it has plans to use AI for both foreign and domestic surveillance. AI-driven pattern recognition, anomaly detection, and large-scale data analysis can dramatically increase the scope of surveillance. The Foreign Intelligence Surveillance Act (FISA) provides statutory guardrails for certain intelligence activities, yet AI-enhanced surveillance raises new questions about scale and inference capabilities. Scholars of surveillance technology have noted that advanced analytics can infer sensitive attributes from non-sensitive data, effectively expanding surveillance beyond explicit collection boundaries. This means that even if the raw data collected for surveillance purposes remains the same, the meaning extracted from it could expand dramatically with the use of AI. This is critically important, as current laws primarily govern the collection of data, but not things like inference capabilities or analytic tactics such as network restructuring, predictive modeling, or behavioral clustering—all of which have the potential to expand exponentially with the integration of AI into surveillance systems. Furthermore, since even those involved in developing AI models often don’t fully understand how these models work, this would introduce the potential for surveillance activities to produce inferences that are unexplainable, even if correct. In other words, an AI system could deliver accurate inferences yet not be able to explain how it did so. Hence, it would be effectively impossible to audit these systems to check for common problems like bias, hallucinations or privacy violations. Most importantly, given that AI is expected to completely transform surveillance capabilities beyond anything we can predict currently, there is an urgent need to formulate new laws and policies in order to ensure that our civil liberties are not sacrificed along the way. These safeguards will need to be developed and implemented on a continuing basis as the technology develops further and we learn more about its capabilities, but the process and framework for doing so should obviously be put in place before the AI is. I’m not hearing much, if any, discussion of that right now. There’s a reason that politicians often like controversies: they simplify things. Information ecosystems also tend to prioritize identifiable, explainable conflict (e.g., Secretary of Defense vs. CEO) over messier, more complicated dilemmas. Media coverage reinforces that over-simplification because two-sided disputes are easier to narrate than undefined governance questions. As a result of this complexity reduction, discourse tends to narrow around technical details, personality clashes, and narrative cliches (e.g., David and Goliath; good vs. evil). By comparison, broader questions—over congressional authorization, procurement transparency, war powers, surveillance expansion, etc—receive little or no sustained public scrutiny. Ultimately, this shapes public understanding in dramatic, yet often unseen ways. By artificially narrowing the boundaries of discussion, our discourse has gotten stuck on the question of whether humans should remain in the loop, rather than asking whether the loop itself is expanding beyond constitutional limits. We have seen these dynamics emerge in our information environment over and over again during critical periods of time throughout modern history, particularly in areas related to technology, the military, and abuses of power. For example, after the Edward Snowden revelations, public debate rapidly shifted from sweeping constitutional questions to technical details of specific programs like Section 215 metadata collection, resulting in a discourse focused on teasing apart nuances like metadata vs. content instead of questioning things like executive surveillance authority and the existence of secret courts. (Of course, these bigger picture discussions took place to varying extents, but that was in spite of, not because of, mainstream media coverage and public statements by politicians). Similarly, after 9/11, debates over the Patriot Act frequently centered on specific surveillance tools, while broader issues—like permanent expansion of executive emergency authority—received much less sustained public attention. More recently, during discussions about whether TikTok should be banned in the U.S. due to it being a national security risk, media coverage primarily emphasized concerns surrounding the company’s data storage location, the lack of algorithmic transparency, and the corporate ownership structure. Those conversations obscured questions about the broader precedent for executive authority to ban communication platforms and what that implies for the First Amendment, and the consequences of allowing that type of national security determination to be made without judicial review. All of these discussions are important and all of these issues should be laid out on the table, but right now that’s not happening with the current debate over the use of AI in military decision-making. Instead, we are seeing the terms of discussion set for us by the media, politicians, powerful corporations, and interest groups that stand to benefit immensely from you being unaware that there is even a debate to be had about not-fully-autonomous AI weapons systems. While the Anthropic controversy has illuminated a fracture line, it has also narrowed our field of vision. The question of whether a human should stay in the loop is clearly a very important one, and I don’t mean to suggest otherwise. However, the broader debate over military AI will shape the next generation of state power, and we cannot let this be collapsed into a single question, even if that single question is extremely critical. Expanding the conversation beyond a single company’s guardrails is necessary if democratic governance is to keep pace with technological capability. Yet that is the exact thing that the current discourse is discouraging us from doing, and it’s worth asking why—and who benefits. A version of this essay originally appeared at Weaponized. View the full article

When you visit the Samsung booth this week at the Mobile World Congress 2026—which, as always, is being held at the Fira Gran Via convention center in Barcelona—you can make your way past the array of brand-new devices to find a timeline of old Galaxy S phones mounted to a wall. It’s a neat piece of history, but I’m not sure it had the intended effect. Rather than demonstrating Samsung’s progress over the years, it highlights how the South Korean tech giant—still the No. 2 phone maker in the world, right behind Apple, according to data from Counterpoint—has been treading water at the top of its lineup. This year’s Galaxy S26 Ultra, announced a few days before the Mobile World Congress kicked off, is an extraordinarily iterative update in what has become a line of near-identical flagships. The design is almost indistinguishable from the previous three years’ models, and there isn’t much to shout about on the inside either. Nothing of note While the S26 Ultra does come with the expected upgrade to Qualcomm’s latest Snapdragon 8 Elite Gen 5 processor, it’s hard to find much else of note on the spec sheet. Samsung switched the frame from titanium to aluminum and shaved 0.3 millimeters off the thickness in the process. The screen is the same 6.9-inch 1440p OLED panel as before—that’s not a problem, since it’s still great. But the unchanged 5,000-milliampere-hour (mAh) battery is starting to fall behind, and an overdue boost to 60-watt wired charging speeds still leaves Samsung behind its Chinese competitors, most of which have adopted silicon-carbon technology and proprietary fast-charging systems. The camera system might be the most baffling example of inertia. All the sensors are the same except on the much-maligned 10-megapixel 3x telephoto camera, which has remained in place since 2002. Did Samsung finally swap it out for a better part? No—the S26 Ultra’s telephoto inexplicably uses an even smaller (1/3.94 inch) sensor. The main and 5x cameras did at least get slight bumps to their maximum apertures. Privacy update The biggest S26 Ultra feature this year, which I will say has actually gotten people talking about at the show, is the new Privacy Display mode. The screen essentially has two sets of pixels, with one reserved for rendering the image directly in front of the viewer—allowing parts of the panel to be switched off for anyone looking from an indirect angle. The implementation is clever, particularly in how the privacy masking can be applied to individual areas of the panel. But the basic concept isn’t new—built-in privacy screens have been commonplace in Japan ever since the days of flip phones. Samsung’s take might be more technically advanced, but the idea has been sitting there for a while. It never used to be difficult to point out what was new about a flagship Samsung phone. For better or worse, the company developed a reputation for putting out ideas just to see what might stick. The Galaxy Note line was probably the best example of that—what initially seemed like a gratuitously large design eventually became normalized to the point where the series has been absorbed into the regular Galaxy S lineup, stylus and all. And Samsung continues to innovate on the supply chain side, particularly with screen panels. Its Samsung Display subsidiary is known for producing breakthrough technology like flexible OLED screens, which led to the parent company inventing genuinely new form factors with the Galaxy Z Fold and Z Flip series. That unit is also responsible for neat new features like the Privacy Display. Losing its edge But in other areas, Samsung is losing its edge or falling far behind. Chinese display makers are quickly catching up to Samsung’s OLED manufacturing prowess. And when it comes to camera specs, there isn’t even a comparison. While Samsung chose to downgrade its telephoto lens this year, Xiaomi stole the show at MWC with its Leica-branded 200-megapixel 3.2x-4.3x zoom. Although it came out in the U.S. back in January, perhaps the biggest draw in Samsung’s MWC booth was the Z TriFold. This is a $2,900 double-hinged phone that folds out into a 10-inch wide-screen tablet. It’s the first device of its kind to be in the U.S. and does indeed have that typical gadgety Samsung appeal, albeit slightly diminished by the fact that Huawei did the same thing with the Mate XT in 2024 and is already on its second iteration. That might not be relevant to American shoppers, since Huawei phones generally aren’t available outside China. But it’s worth noting that Huawei had to completely rebuild its consumer business and supply chain after it got blitzed by U.S. sanctions, and it still managed to beat Samsung to market on this new form factor. None of this is to say that there’s no reason to go with Samsung if you want an Android phone and you live in the U.S. It’s a trusted brand that offers good local support and customer service, and its One UI software has matured to the point that it’s as usable as anything else out there. But Samsung is only holding onto an entrenched position in the U.S. market due to carrier support and a lack of competition. When looking at what’s available around the rest of the world, it’s clear that the company is rapidly losing the technological edge it built its reputation upon. Chinese companies would face stiff barriers to enter the U.S. market even if they wanted to, but it’s worth imagining what the landscape might look like if they did. View the full article

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Inc.com columnist Alison Green answers questions about workplace and management issues—everything from how to deal with a micromanaging boss to how to talk to someone on your team about body odor. A reader asks: Last fall, I left a beloved job and assisted them in hiring two people to replace me. One was an internal hire, the other required an outside interview process. We received over 50 applications, narrowed it down to 13 phone interviews, then seven in-person interviews, and finally made a very satisfying hiring decision. At each step along the way, I sent out polite rejection emails to those who didn’t make the next level. It was very professional, and all candidates but one reacted very well. However, one gentleman who was not granted an interview wrote back saying that since he was “clearly overqualified for such a position,” he “would have at least appreciated an interview.” In fact, he had no qualifications for the position: he’d never done the work of the role, worked in our industry, nor had any background in our field. I never responded, but he tracked me down and has asked me several times why he wasn’t interviewed. He is clearly well-educated and has an interesting work history, but nothing on his résumé was even remotely connected to our field, and frankly, he came across as condescending. That said, we are community-based and try to be friendly, kind, and helpful to all. I’m still peripherally involved in the organization, but no longer an employee. In fact, I moved across the country and took another job. Do I have any obligation to write him back? And, if so, how honest should I be? Apparently, he’s written to the organization, too, and they refuse to deal with him. If I write him back, might he leave them alone? A small part of me feels as if he’d benefit from knowing the truth, but I also feel like maybe it’s none of my business. I recognize that if I respond to him, it would not be in any way official. What should I do? Green responds: Block and ignore. You don’t work there anymore; there’s absolutely no reason you should have to engage with this guy and risk him getting more aggressive or angry. You didn’t do anything wrong in this hiring process. In fact, you did everything right! You sent out polite, timely rejection emails. You made hiring decisions that you feel good about. You don’t have anything to explain or defend or justify. You don’t even have to explain (here or to this candidate) why he wasn’t invited to interview. Employers regularly have more qualified candidates than they can interview, and there doesn’t need to be anything wrong with someone for them to be rejected; it can just be that others were stronger. While most candidates are professional and understand the nature of the hiring process, there are always a few who can’t believe they didn’t get an interview and think employers should have to justify that to them. You aren’t obligated to respond to them at all, but especially after they become rude or pushy. If you still worked there, I’d tell you to respond to him one time and say something like, “We received a tremendous amount of interest in the position, and the hiring process was very competitive. We weren’t able to offer interviews to many people with strong qualifications for the role.” Or, if you prefer, “We received a tremendous amount of interest in the position and we focused on candidates with experience in Skill X and Industry Y.” But you don’t even work there anymore! Not only do you not need to respond to this person, but you probably shouldn’t—the organization needs to be in charge of its communications with candidates (particularly since there can be legal ramifications if you word something badly). If they’re not responding, that’s their call. I’m not clear on whether the candidate somehow tracked down your personal email account and is messaging you there. If he’s emailing you at your personal account, that’s a real overstep (and a sign of some seriously inappropriate investment) and you absolutely should block him. If you want, you can email back once to say, “I no longer work for [organization’s name] and cannot answer any questions about their hiring processes. Please do not contact me at my personal account again” and then block him—but feel free to skip that and just block him. I do get that the organization is community-based and wants people to have warm feelings toward it. That’s an argument for them (not you) responding—once, and with the kind of language above. But after that, candidates who are rude or hostile don’t need to be indulged. It’s OK to decline to engage. —Alison Green This article originally appeared on Fast Company’s sister website, Inc.com. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy. View the full article

We’re only two months in, but 2026 is already shaping up to be the year of agents. The current surge began with Claude Code, which achieved critical mass over the holidays. That led to all kinds of lobster-themed software names (long story), which culminated in OpenClaw, an open-source agent creation and management system. It might also be a stealth marketing campaign for Apple to sell a ton of Mac Minis, but that’s neither here nor there. It’s too early to say what kind of productivity gains the current wave of agents will create, but the push to agents is undeniable. It’s also very exclusive. For all the talk of, “the only coding language you need to know is English,” there are technical barriers to joining this wave. You don’t necessarily need to know how to code in order to use OpenClaw, but it helps considerably. To help non-coders overcome some of the technical barriers to building and working with agents, AI companies have begun to release products that abstract away some of the more difficult parts. Anthropic released Claude Cowork—essentially Claude Code for the rest of us. More recently, Perplexity launched Computer, its “general-purpose digital worker” that users can prompt in natural language and watch it go to work. It all sounds magical, and if you squint, you might even see a near future where knowledge work, and especially editorial work, transforms: instead of pulling levers on various software menus and dashboards, you’ll just talk to agents. They’ll handle the hard stuff, and if they run into barriers, you’ll just ask another agent to build the solution. Where no code gets real Back in reality, it’s not that simple. Even if you use one of the no-code systems like Claude Cowork, creating tools and workflows still involves breaking down processes, finding API keys, navigating permissions, and iterating continually. And even though Anthropic markets Claude Cowork for non-coders, when I used it, the app gave me instructions that included using the Terminal on my Mac—an app that most people have no idea exists. And if you don’t, you probably shouldn’t mess with it. For builders, these don’t even qualify as barriers. Builders don’t need to be coders, but they do have characteristics that most workers don’t: They seek to understand the process beneath their tasks, and treat that process as modifiable and programmable. More importantly, they see failure and iteration as tolerable, even fun. They thrive in uncertainty. But the reality is most people don’t think that way. We’ve trained a generation of office workers to work within software with clear boundaries and reusable templates. If there’s an issue, they call IT. Any feature request gets filtered and, if you’re lucky, put on a roadmap that pushes it out 6-12 months. In short, most people don’t have a builder mentality to begin with, and expecting them to suddenly be comfortable working and creating with agents is unrealistic. In January, New York Times tech writer Kevin Roose pointed to a growing chasm between those fully in the AI bubble, who are building multi-agent teams to help them get work done, and those who aren’t, most of which have never even built a basic assistant like a Custom GPT or Gemini Gem. As someone who trains editorial teams on how to use AI, I can confirm this gap exists and is indeed massive. All of this is to say, for all the hype you might see on X, the percentage of workers who have actually adopted agentic tools is extremely small. However, they’re still massively influential. The catch is that agents, at least as they exist today, are hard to deploy safely inside organizations. They need access to files, email, calendars, internal systems, sometimes the ability to take actions automatically. That’s not a tooling problem. That’s a permissions problem, and it makes security teams nervous for good reason. You don’t need a sci-fi scenario to see why. A recent example involved an OpenClaw agent that appeared to run amok in a Meta engineer’s inbox, taking destructive actions despite attempts to stop it. Stories like that may be edge cases, but they point to a reality: delegating software access to agents can amplify ordinary mistakes into high-impact mistakes. Clawing through permissions Until there’s progress around security and fail-safes with these general-purpose AI agents, organizations will be slow to adopt them. That won’t keep builders—even those within those organizations—from developing or using them, however. They’ll just do it on their own time or elsewhere. This “capability chasm” between builders and users will eventually force solutions, and the systems those builders create will determine the workflows of the future. For non-builders, this isn’t a great place to be. Becoming a builder, though easier than ever from a technical standpoint, means a shift in mindset that many simply aren’t up for. The alternative seems to be to sit passively, wait for agentic systems to filter down to you, and hope you don’t get laid off in the meantime. There’s a third way, though. You don’t have to be a builder to understand how agentic workflows are changing your job. For journalists, that means identifying the parts of your work where human attention and judgment is paramount: the filtering of facts, the interviews, the writing (or maybe not), the cultivating of source and audience trust. You can help define what should never be delegated, and what can be automated without harming standards. You can push your organization—constructively—to adopt agents in bounded, defensible ways that match newsroom reality. In other words, you don’t have to build agents to matter in an agent-driven workplace. But you do have to understand the systems being built around you, because soon enough, your job will be defined by defaults someone else designed. Most professionals will not build agents. But everyone will work inside systems builders create. View the full article

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Job interviews can trip up even the most qualified candidates with verbal landmines. In this article, we discuss specific phrases candidates should avoid using in interviews and more effective alternatives recommended by experts. Discover common phrases that send the wrong message to interviewers, as well as stronger alternatives that demonstrate genuine value, so you can effectively communicate your skills and experience. Lead With Curiosity, Not Critique If you’re interviewing for a tech role, here’s a fast way to tank your chances: Walk in and immediately trash the company’s systems. You know the move. “Honestly, your system is outdated. I’d replace it with something better.” To you, it may feel confident. Like you’re demonstrating vision and technical chops. But to the people across the table? It usually just sounds arrogant. When you critique infrastructure without context, you’re not showing expertise, and you are showing you don’t understand how businesses actually work. That “outdated” system you’re eager to scrap? It might be running payroll for thousands of employees. It could be the backbone of compliance reporting that keeps the company legally covered. It represents years of investment and decisions made under constraints you know nothing about. The people interviewing you probably built it, maintain it, or depend on it. When you dismiss it out of hand, you’re not just criticizing code—you’re dismissing their work and their reality. Skip the unsolicited teardown. Lead with curiosity: “I’m curious how your team thinks about modernization, especially when balancing business continuity and innovation.” You’re still signaling strategic thinking. But now you’re inviting conversation instead of passing judgment. Two candidates. Same senior engineering role. • Candidate A: “This tech stack is old. I’d rebuild it using modern tools.” • Candidate B: “In previous roles, I modernized legacy systems without disrupting operations. How has your team approached that balance?” Same technical opinion. Completely different delivery. One sounds like a know-it-all. The other sounds like a leader. Companies aren’t just hiring for technical skill, they’re hiring for judgment and the ability to build trust. Great IT candidates don’t rush to fix. They ask to understand first. Because your insights only matter if people listen—and people only listen when they feel heard. Innovation isn’t just about having the right ideas. It’s about earning the credibility to implement them. —Alex Kovalenko, lead IT recruiter, Kovasys IT Recruitment Shift from Responsibility to Owned Results “I was responsible for . . .” It’s technically fine. But it’s forgettable. “Responsible for” describes a job description. It doesn’t describe impact. And in today’s environment—where companies are focused on disciplined execution, stronger customer relationships, and sustainable growth—impact is what matters. Instead, try this structure: “I led [initiative] that delivered [measurable result] by [specific action].” That shift moves you from passive to accountable. It signals ownership. It shows you understand how your work connects to business outcomes. Weak: “I was responsible for HR during a growth period.” Stronger: “I led a workforce redesign during a 30% growth phase, reduced time-to-fill by 22%, and strengthened coverage for our top revenue-generating customers.” Now you’re not just describing a function—you’re demonstrating how you drove performance. In my work advising boards and leading human capital strategy, I see this distinction constantly. Organizations aren’t looking for caretakers. They’re looking for operators who can deepen customer partnerships, execute relentlessly, and scale sustainably. Language shapes perception. When candidates speak in terms of outcomes, metrics, and enterprise value, they position themselves not as functional leaders, but as strategic growth partners. And that’s who gets hired. —Julie Catalano, GPHR, founder and chief human capital strategist, Blue Spruce Human Capital Advisory Prove You Learn Quickly With Metrics One specific phrase I advise candidates to avoid is “I am a quick learner.” Although being a quick learner seems like a positive trait, it is very vague and offers the interviewer no tangible proof of your abilities. It’s best to provide specific instances that show your ability to learn new ideas, skills, techniques, etc., relatively quickly. Instead of a generic claim like, “I am a quick learner,” you can tell a story where you demonstrate the ability. For example, you could say something like, “In my previous role, I was able to become proficient with our new CRM system in only two weeks, which allowed me to onboard new clients at a rate 30% faster than anyone else on the team. I started hosting office hours once per week to help my colleagues after a few were struggling with the transition.” Providing a clear, measurable example of how you applied a new skill will always make a stronger impression than a generic promise. —Phil Santoro, entrepreneur and cofounder, Wilbur Labs Open With Value, Not Titles I wish applicants would stop defining themselves by their current job title and years of experience. A lot of introductions still sound like this: “I’m a [job title] at [company], where I specialize in [field].” “I’m a marketing manager at a midsize SaaS company, where I’ve spent the last three years leading digital campaigns, analyzing performance data, and collaborating closely with sales and product teams.” Is it an immediate red flag? No. Stale and forgettable? Absolutely. In almost any interview, the employer is going to ask you, “Tell me a bit about yourself.” That’s your biggest opportunity to pitch your skills as the solution to the hiring manager’s needs. It’s hard to do that if you open with a self-focused summary of your role and area of expertise. Whether they’re an external recruiter or the employer themselves, the person sitting opposite you has already read your CV. They know what you do and how long you’ve been doing it. What they want from the interview is a clear sense of who you are and the value you’ll bring to the team. So don’t talk about your current job or proudest achievements—highlight what you can do for this specific employer: “I’m a [professional title], and I help organizations like yours achieve [meaningful result].” Before the interview, choose a professional title that aligns with the employer’s needs while accurately reflecting your experience. For example, I’m a professional CV writer and careers specialist, but I also have a background in marketing and journalism, so depending on the role, I could pitch myself as a career coach, a writer, or a digital strategist—and that title choice will have an immediate influence over how the interviewer perceives me. The same goes for your “meaningful result.” Reflect on the strengths you highlighted in your CV. Which one got you the interview? Turn those strengths into promises that make the employer lean in and take interest. For example: “I’m a careers writer, and I help organizations like yours produce unique resources that job seekers actually read and return to.” —Sebastian Morgan, senior career expert and content specialist, Resume Genius Clarify Your Impact With “I” It’s not one specific phrase but the importance of being mindful of how and when you use “I” versus “we” when describing your work. As leaders we naturally default to “we” because we value collaboration and want to recognize our teams. That matters. But in an interview, the hiring manager or recruiter is trying to understand your direct impact. Your decisions. Your influence. Your role in driving outcomes. If you continuously say “we,” it can unintentionally blur your contribution. For example, if you’re sharing the following, “We redesigned our onboarding program and saw great engagement.” You could consider saying, “I led the redesign of our onboarding program, partnering closely with my team and other hiring leaders. As a result we were able to increase engagement by 30% and we reduced our time to productivity.” This ensures you’re still giving credit to your team but you’re also clearly owning your role in the work. That clarity helps interviewers truly understand the value and contributions you will bring to their company. —Heidi Hauver, executive adviser and mentor | fractional VP, people and culture, Ranovus Stay Composed, Buy Time, Then Answer Skip saying “Sorry, I’m really nervous” in interviews. I’ve had candidates say this, and while I truly get it, it pulls focus away from what they’re good at. The people who stand out aren’t always the most confident, yet they know how to keep control of the moment. A better response would be, “That’s a great question. Give me a few seconds to think about it.” You sound calm, you buy yourself time, and you stay in control. I had a candidate use that exact line once. Paused, collected their thoughts, then gave a sharp answer. They got a second-round invite. In my experience, candidates don’t realize how much presence matters. Sometimes more than the actual answer. —Adam Czeczuk, head of consulting cervices, Think Beyond Trade Perfect Fit for Measured Enthusiasm I say this as an executive headhunter and recruiter for more than 20 years. Many candidates get very excited when the role and company are a great match for their profile and experiences. So they often fall into the trap of telling a recruiter or company interviewer that they are “a perfect fit for the role.” This demonstrates that a person may be too emotional and lose sight of rational logic, as no one is a perfect match. And specifically with executive recruiters, the clients are looking for a needle in a haystack, and it’s almost impossible to deliver on all the things that would make a candidate perfect. In fact, after more than 10,000 interviews with executives and about 500-plus candidates who have said they are a perfect fit, I have never found one to be, and worse, they are usually below average compared to the top 10 candidates I interview for the same role. A better way for a candidate to describe that emotional excitement toward a role would be the following: “I read the job description and know the industry and this company well. Although there is no perfect candidate, I check most of the boxes/requirements, and I’m excited to have you explore my experiences and background to see if I align with what you are looking for.” It shows enthusiasm as well as practicality. —John Betancourt, CEO, Humantelligence Offer Trainable Weakness With a Plan Any phrase that points to personality or character could be open for interpretation. For example, if the interviewer asks you to describe a weakness, a phrase like, “I’m a perfectionist” isn’t likely to land well. This is a trait one cannot change, so if the interviewer views perfectionism as a crutch, there’s little chance of advancing through the interview process. It’s always better to be well-versed in the particulars of the position description and choose a “weakness” that can be improved or corrected shortly after hire. For example, the position description might ask for proficiency in Salesforce. The interviewee might not have this experience but they used HubSpot in the past. Their answer might then be, “I’m unfamiliar with the Salesforce platform; however, I have deep experience with HubSpot and am a quick study, so give me 30 days and it will be a nonissue.” —Heather Maietta, coach for career professionals, Career In Progress Ask About Growth After Contribution “How soon can I get promoted?” This is a terrible question to ask in an interview because of the numerous red flags it raises in the interviewer’s mind. The applicant thinks they’re showing ambition, but the interviewer is thinking other things. They don’t want the job they’re applying for. They’re way ahead of the process. They’re conceited. They’re demanding. An alternative phrase for an ambitious job seeker is to ask about career development opportunities. For example, “If I work hard and do my best to contribute to the strength of the organization, are there opportunities for growth and advancement?” Notice the applicant first wants to make a contribution. That’s the type of mindset that will win over interviewers. —Michael Neuendorff, president, Bay Area Executive Coach Demonstrate Collaboration With Concrete Outcomes Stop saying “I’m a team player,” because every single candidate says that exact same thing. It says nothing to me about what you actually bring to the group or how you work with others. That is a phrase that is overused to the point that my brain tunes it out the second I hear it. What’s more effective is to show me a specific example of collaboration. Say “I coordinated with three departments to get our new enrollment system running in half the time we were supposed to,” or “I trained four new agents who are now consistently achieving their monthly targets.” Those kinds of statements demonstrate that you work well with others without resorting to the generic label. I had a candidate last year who said, “I’m great at teamwork” in her first interview. I asked her to give me an example, and she couldn’t think of one off the top of her head. Someone else came in and told me about dividing up her commission with a struggling teammate to keep up morale in a tough quarter. That person got hired. —Clayton Eidson, founder and CEO, AZ Health Insurance Agents Explain Misalignment and Focus Forward When asked why they left their last position, candidates should refrain from using the phrase “It wasn’t a good fit.” This always raises concerns for me as an interviewer of its vagueness and because it avoids accountability. It lacks insight into the candidate’s decision-making, self-awareness, or professional growth. A more effective approach is to name the specific professional misalignment and pivot toward what you’re seeking next, without assigning blame. For example, “I met and, in several cases, exceeded the goals of the role and was proud of the results we achieved. As organizational priorities evolved, the work gradually shifted away from some of the areas where I was able to contribute most effectively, so I made a thoughtful decision to pursue opportunities where my strengths and experience could have the greatest impact.” In this case, the language chosen demonstrates both judgment and self-awareness, and it frames the transition as intentional and future-forward. From an interviewer stance, the choice to focus on results instead of complaints also carries huge impact. —Laurie Carr, chief partnership officer, Generation Citizen Swap Passion Claims for Thoughtful Proof One phrase candidates should avoid using in interviews is “I’m really passionate about this role.” I still hear this general phrase all the time. Sure, it sounds positive, but it’s vague. Passion tells an interviewer how you feel, not how you think. It doesn’t help them understand your judgment, priorities, or how you approach real work. We don’t hire for passion. And if you think about it, does any interviewer want someone in front of them who doesn’t feel passionate about the role in some way? It’s a dull and useless phrase. A more effective alternative is to replace emotion with evidence of thinking and connecting dots. Instead of saying “I’m really passionate about this role.” Try something like this: “What drew me to this role is the focus on X. In my last position, I worked on Y, where I approached it by doing Z. That experience shaped how I think about this kind of work.” The shift is subtle but very important. The first phrase is generic and interchangeable. The second gives the interviewer something concrete to evaluate. It shows how the candidate connects experience to the role and how they make decisions. In interviews, impactful language isn’t louder or more confident. It’s specific enough to be trusted by the interviewer and the company. —Gina Dunn, founder and brand strategist, OG Solutions Ditch Generalities, Provide Specific Examples “In general.” When a candidate is being interviewed, we are looking for specifics, not generalities. Most good interviewers are using situational interviewing techniques—this sounds like, “Tell me about a time when . . .” Many candidates want to be general in their response, but I am always looking for a specific example. When I get a generality, I follow up with, “That’s great. Do you have a specific example you can reference?” This is because we can talk about how things could or should be all day long, but we need to know how you actually perform during a situation, circumstance, or on a certain type of tech or during a particular project. The more specific you can be, the more we can picture you sitting in that role, performing at our organization. —Kerri Roberts, founder and CEO, Salt & Light Advisors Signal Social Judgment Beyond Platitudes One of the things I tell my clients is to avoid the phrase “I’m a people person.” The phrase has been repeated so often it no longer signals anything. Every candidate claims it, but interviewers hear it as filler. What actually works is language that reveals how you think about working with others—not that you necessarily enjoy it, but that you’ve learned to navigate it when it’s difficult. Something like acknowledging you’ve developed the capacity to stay effective with people you don’t naturally align with. That signals maturity, self-awareness, and the kind of adaptability organizations desperately need. Interviewers are listening for evidence of judgment and critical thinking. Language that’s specific and slightly unexpected creates a different quality of attention. It suggests someone who reflects on experience rather than simply performs confidence based on platitudes. This shift may be small in words, but it’s very significant in impact. —Federico Malatesta, founder and executive coach, FM Transformational Coaching Drop Clichés, Share Honest Experience Any phrases that are canned or overused are absolutely a turn-off for me, including overly generic humor, even if it ends with “I’m just kidding.” Phrases such as “I go with the flow” or “Change management is crucial” won’t work with experienced hiring managers because they don’t communicate anything specific. Hiring managers need a candidate with strong experience and a strong conviction in their beliefs: what they believe in, what shaped that belief, where there might be gaps, and where they need help with their own personal growth. This helps us understand how deeply they would connect with the business and how well they would align their effort with business priorities. With this approach, we generally find A-players who, don’t complain much, never ask for more than business can afford, and are a delight to work with. An example of impactful language is leading with stories, which can be challenging for candidates at the start of their journey. But note: You don’t need to overthink stories. They’re examples of your experience, and you don’t need to oversell your experience. When you overpitch, experienced hiring managers can sense that from a mile away. Something like: “When I started in a leadership role, I had no clue what I was doing. Not even sure why I secured the position, what they saw in me. I had fears about what a leadership role would be. What would my colleagues reporting to me think of me as a leader? The initial days were challenging. But as I settled in, I learned that I don’t need to overthink leadership. I just need to humanize the experience a bit and be available when they need me.” Obviously, this would be an example of a leadership role where specific subject matter experience is not as critical. But this storytelling style can be applied across most roles that involve communication, transformation, learning, curiosity, attitude, or teamwork. If they have these skills down, it’s very likely that they’ll apply for positions where they are the best fit. A-players don’t overcommit, and they are very clear on what they want in their lives. —Sam Gupta, CEO, ElevatIQ Skip Work-Life Balance, Show Performance Candidates should avoid the phrase “work-life balance” in job interviews. Regardless of the good intentions, what the interviewer really hears is: • Limited availability/flexibility • Conditional commitment • Potential performance ceilings • Future negotiation before value is proven Even if none of the above is true, what does it really say: • It centers personal needs before business needs. • It signals constraints, not capability. • It raises risk in a hiring decision. • It forces the interviewer to wonder how flexible you will be under pressure. Interviewer’s internal reaction: • Will this person push back during crunch time? • Will deadlines turn into boundary conversations? • Will this become an issue six months in? The ruthless truth: Work-life balance is earned, not negotiated up front. Top performers get flexibility because they deliver. Average performers ask for it because they need protection. —Thomas Powner, executive career management coach, recruiter, résumé writer, career keynote speaker, Career Thinker Inc. View the full article

Christopher Harborne gave the party £3mn in November, on top of £9mn donated in AugustView the full article

My team often jokes about the “Vulcan Mind Meld.” They say I need to share a brain with a founder before we can write a check. They aren’t wrong, but the process isn’t science fiction. It’s usually just a walk. Specifically, it’s a walk to the Stanford Dish. The path is a 3.5-mile loop in the foothills above the university. It’s steep, exposed, and offers little place to hide. I don’t take founders here for exercise. I take them here because the controlled environment of a boardroom practically demands rehearsed answers. The trail does not. I don’t prepare a script for these walks. In fact, that’s the point. The pitch is already done; I know the metrics. Now I want to know the human. I want to build the trust required to hear the origin story—the raw, unpolished reason why a founder believes a worldview that everyone else thinks is misguided. As we hike, I’m listening for that drive, but I’m also mapping their story against my mental framework. The Map: Consensus vs. Contrarian For me, every founder sits somewhere on a mental grid defined by two axes: Is your view consensus or contrarian? And if it’s contrarian, how and when will the world come around to your worldview? How will you be proven right? Being in the consensus is a safe place. We can see this as we start the hike, looking down on the Valley. It looks safe, connected, and crowded. In market terms, investing in the consensus, assuming good execution, still yields a market return. But because everyone else is doing the same thing, the excess returns are competed away. And if the consensus turns out to be wrong, you fail safely, alongside everyone else. We are looking for the outliers. We want to find the founders starting in the lonely territory of contrarian. But the secret isn’t just standing there. The secret is movement. The Incline: The Energy to Move As the trail steepens, I’m thinking about movement. We don’t yet know if a contrarian founder is right or misguided. That’s the risk of my job. But the goal is to back founders whose conviction is strong enough to pull the rest of the world toward them—until what once looked contrarian becomes the new consensus. That journey, from lonely outlier to obvious winner, is where outsize returns are generated. The energy required to make that journey is enormous. A founder must survive a long stretch where nearly everyone is convinced they’re wrong. Even if they are ultimately right, they look foolish for a long time. They look stubborn. They look unreasonable. That’s why the incline matters. As we hit the steep part of the trail, the physical exertion acts as a kind of truth serum. You cannot maintain a polished persona when you’re breathing hard on a hill. I have seen brilliant executives with perfect pedigrees crumble here. In a conference room, their slide decks are flawless. On the hill, stripped of breath and polish, the camouflage falls away. I’m listening for obsession—the kind required to survive that lonely stretch. When a founder stops selling and starts agonizing over a specific customer pain point, ignoring the discomfort of the hike because they’re so consumed by the problem, that’s the fuel that moves a market. The Dish: Validating the Signal Finally, we turn a bend and face the Dish itself—a massive, 150-foot radio telescope listening to the sky. I have always thought of the Dish as a metaphor for the contrarian founder. It stands in silence above the buzz of Silicon Valley, tuned not to the noise below but to faint signals others miss—distant pulsars, quiet stars. We are in a hype cycle right now where consensus is loud and expensive. There are 20 firms chasing every inference chip company. That is the noise. To back a founder, I need to believe they have found a signal in the silence—that they’re not building for the current cycle but have identified a fundamental truth that will eventually force the market to come to them. The Return Loop By the time we loop back to the parking lot, the Vulcan Mind Meld has either happened or it hasn’t. The biggest risk in our business is not losing money on a failed startup; that downside is capped. The biggest risk is passing on a founder who successfully moves the world from contrarian to consensus. That lost opportunity is infinite. You cannot identify that kind of conviction in a conference room. You have to climb a hill with someone to see if they have the strength to move a market. View the full article

Every important endeavor in your life needs some kind of North Star to help you determine whether you’re succeeding. Fitness professionals recommend having an overarching goal when planning a workout regime. Similarly, it’s valuable to have strategic aims for your career. Professional goals are important, because they help you evaluate which of a variety of paths available to you is the ideal one to pursue. For example, if your aim is to play a leadership role in a company, then you might choose to get an advanced degree that hones your leadership skills. That time in school might slow your progress in getting promotions in your vertical in the short term, but will enable you to hone skills that will make you eligible for positions that weren’t an option without the degree. The thing is, long-term career goals are not something you can “set and forget.” There are several reasons why you need to check back on them periodically. Your understanding changes Think back to your childhood. Can you remember what 8-year-old you wanted to do when you grew up? While you might be one of those rare people who fell in love with a career path at that age, chances are the thing you do now is not something you even envisioned back then. And your understanding of the world of work does not stop evolving at 8, 18, 28, or beyond. As you learn more about what particular jobs entail, what you enjoy, and where you can make a contribution, your belief about the ideal role for you may also change. About once a year (perhaps aligned with your yearly HR evaluation), it’s useful to take stock of what you now think about the path you previously envisioned. Do you still feel like your goal is attainable and would allow you to have the impact you desire? If you have doubts that you articulated the right goal, engage with an adviser, mentor, or coach to rethink that path. Those conversations can help you turn your growing awareness of how your career may unfold into a more refined vision for what success would look like for you. Your values change Part of what drives your long-term career goals is what you value. Research by Shalom Schwartz and colleagues has identified a set of broad values that characterize factors that drive people’s sense of what’s important to them. Roughly, these values differ along two dimensions: whether a person prizes openness to change versus conservatism, and whether they are focused more on the self or others. Over the course of your life, your values may change. As a young person, you might focus on achievement (a combination of openness to change and self-development in which you value individual success). As a result, you might look for roles that will bring you accolades and attention. Over the years, you may shift to a focus that prizes the collective good rather than the individual. This shift in values may lead you to be less interested in visible leadership roles and more prone to seek opportunities to have a positive impact on your community. I encourage you to think about the ways your values may have shifted. Imagine yourself in what you thought would be your dream job. If that no longer feels like it would represent the culmination of your ideal career path, think about what that dream job is missing. That sense of mismatch between what you thought you wanted and what feels best now is likely to be a reflection of a change in values. To what degree do you think that reflects differences in how much you value change or tradition and how much you value individual versus group benefits? Use that insight to rethink your goals. The world changes Even if you feel like you have a good understanding of your industry and your values have remained consistent, the world around you continues to evolve. Technology advances in ways that influence the impact of particular jobs. The political climate can affect what ventures are poised for success. Economic shifts may privilege one sector over another. Sectors as diverse as entertainment, higher education, and automobiles have witnessed significant world changes over the past 15 years that affect what it means to succeed in these domains. It’s important to recognize that your career goals must adapt to many factors that are out of your control. Individuals who pursue outdated strategies are just as likely to see their efforts end in frustration as are companies that don’t remain agile in a dynamic world. It’s easy to express frustration over outside factors that may have blocked your progress. It takes courage to recognize that you need to pivot when circumstances change. One of the best ways to ensure that you stay a step ahead of changes in the world is to focus on improving competence around key durable skills like learning, reasoning, communication, and interpersonal interactions. When you feel like external factors are making your specific skills obsolete, consider getting more education in skills that are more resistant to economic, political, and technological shifts. View the full article

U.S. figure skating champion Alysa Liu captivated audiences during the 2026 Winter Olympics. Now, the young skater is offering some life advice. The 20-year-old won two gold medals in the recent Milan-Cortina games, charmed crowds with her style, cheered on her competitors, and offered her refreshing take on skating for joy, rather than medals. Liu told Today.com she had some nontraditional advice about pushing kids to continue to play sports, even when they want to quit. To put it simply, the Olympian said: “Don’t.” “It does not work,” she explained. “The kid knows himself pretty well, and it’s just never good to force anything.” While Liu’s advice is specifically for parents, it generalizes well to anyone struggling with their career: Just as she believes parents who have kids in sports shouldn’t force them to compete if they don’t want to—she’s also proven that she believes that the individual themselves shouldn’t feel compelled to keep competing if they don’t want to, and that breaks are not just welcome, but needed. The skater, who competed in the Olympics four years ago, quit skating at 16. In doing so, she reclaimed her freedom. During her time off from competing, she simply enjoyed being a teenager. “I was going to concerts, which I never could have done before,” she previously told NBC Sports. “I also got my driver’s license. I did a whole year at college. I went on vacation for the first time. I went skiing. I went snowboarding. I got to do so many different things that I never would have done had I stayed in the sport.” Ultimately, the star returned to skating, fresh, energized, and ready to skate—not to win—but for the pure love of the sport. While her journey didn’t follow the usual trajectory, it was her own, and in Liu’s case, following her instincts and tuning in to what she needed turned out to be the ultimate prize. It also led her to Olympic gold. Therefore, it’s no wonder Liu wants others to know that it’s okay to go off course. “If it’s really such a struggle, I would say definitely take that break,” she explained. “Don’t be scared to do that. Don’t be scared of failure.” Liu said, “Trying new things will definitely give you a different outcome.” It doesn’t have to be a gold medal, but if that outcome is finding renewed joy in something that has become drudgery, or joy elsewhere, then success has already been won. View the full article

It’s five answers to five questions. Here we go… 1. I’ve run out of a patience with a rude coworker I’ve run out of patience with a difficult coworker, Mary. I’m one of the few people who has to deal with Mary in person, and my work is closely tied with hers. She’s entry-level while I’m mid-level, but I’m not her manager or supervisor. She has difficulty completing her work, which causes many problems for her. I have tried mightily to be her friend and mentor for the past few years, but her struggles continue. We’re locked in a difficult dynamic where I have to sit back and watch her flail, and I bear the brunt of her complaints. On a personal level, most people find her to be entitled, high maintenance, and impossible to please. She lashes out at people frequently, and today she stormed into my office following a completely normal interaction to call me rude, offensive, and dismissive. This is very common. I’m not a confrontational person so I just take it on the chin and try to get on with my day. Over time, I’ve worked on being direct with her, setting boundaries, and learning how she wants to be communicated with. I’ve reported her to her manager and to HR multiple times, and she’s been put on performance improvement plans. Things improve for a time, then we’re right back in the same place. Any advice to improve this situation? It’s impacting my work and my mental health. I’m worried that one day I’m going to snap and unleash years of frustration on her. The biggest issue here is Mary’s manager, who apparently isn’t willing to deal with the situation in a way that gets it resolved. Putting someone on multiple performance improvement plans is ridiculous; the first one should have come with a clear statement that the improvement needed to be permanently sustained and if she backslid once it was over, they wouldn’t start the process all over again. You’re limited in what you can do yourself, but at a minimum you can cut Mary off from constantly complaining to you and can leave the room if she’s being rude to you — and you should give up on trying to be her friend and mentor, because that’s not working and apparently just gets you more exposure to her rudeness (along with storming into your office). Stop trying to help someone who doesn’t appreciate it and is abusive to you. You can also continue to report the issues you encounter with her to her boss and HR; make it less comfortable for them to keep ignoring the situation. And transfer the unpleasantness of dealing with Mary over to her manager as much as possible — meaning that if she’s not doing her work, rather than talking to her about it, take it to her manager (“I need X from Mary and don’t have it; can you please ensure I get it?”) and if she sends you rude messages, forward them to her boss with a note like, “Can you please address?” If you transfer the burden of dealing with Mary more to Mary’s boss, she might eventually be moved to act more decisively. Related: how to deal with a coworker who’s rude to you 2. “I forgive you” in a professional situation I teach part-time at a university with ties to a Christian denomination, although I’m not Christian. The administration is pretty laid back, but the students are required to attend religious instruction/events weekly. I made a remark in class within the context of the lesson that a student interpreted as meaning that I was applauding the fact that a police officer has been killed. In fact, I was indicating that the assailant had been caught. The student walked out of class but did not make an issue of it. He came back and after class, he spoke with me alone and said he was very upset by what he thought he’d heard me say because his father was a police officer. I explained what I had meant and apologized that it came out incorrectly and that he had been upset by it. He responded, “I forgive you.” I was taken aback by that and just thanked him. During the next class meeting, I apologized to the whole class and clarified what I had meant. No one else seemed to have noticed. Part of what we teach in the classroom is professionalism. If the student had said he forgave me in a work context, I would have felt that was out of line. At a Christian university, I still didn’t think it was appropriate, but should I have told him not to say that in a workplace? I talked with someone afterward who pointed out that “I forgive you” was heaps better than some other things the student could have said, which is true. He could done or said any number of other things that would have been problematic. Should I have instructed him — or the whole class without calling him out specifically — about how to accept an apology professionally? I’d let it go. “I forgive you” would be weird in a professional setting, but you’re better off leaving the entire incident in the past rather than reopening it and risking him making a bigger deal out of it. This incident is just not well suited for turning it into a teachable moment, because it could backfire on you in ways you don’t intend. For what it’s worth, I’m also not a fan of turning every small thing into a lesson about professionalism; sometimes the better part of professionalism is just giving people grace for not getting something quite right. You didn’t speak perfectly (it sounds like), he didn’t speak perfectly, and you can both allow for the other being a human who doesn’t always get things exactly right and just move on. 3. My old colleague recruited me for a job, then rejected me Last summer I had lunch with a former colleague with whom I worked successfully for many years. She revealed that a) she’d been promoted to vice president of my former division and b) she wanted me to come back. I agreed, contingent upon the conditions of the return. Months passed before she could create a position — this company is very bureaucratic — and when she did, it turned out the hiring manager was another former colleague with whom I worked successfully. He met with me privately to sell me on taking this new position, but there was a catch: I had to interview just like anyone else. I agreed. Four interviews later, I was rejected for the job, the reason being that it was felt I was not quite ready for the position. I felt a little blindsided, yes, but my husband was furious and wondered why I was not. He said, “They asked you to return, they persuaded you to take the job, then they rejected you? They knew your abilities when they asked — what is wrong with them?” He thinks I have been ill used, and I might agree. Is my husband right, or is this just a normal, unfortunate situation? I understand why you’re frustrated — they wooed you for the position — but it does sound like the hiring manager was straightforward with you that you’d need to compete for it and it wouldn’t just be handed to you. That said, their reasoning of “you’re not quite ready for the role” is pretty aggravating since that’s something they should have been able to figure out earlier on in the process or — if it really didn’t become apparent until a specific role was created and you were interviewing for it, which is possible — they should have given you different feedback, more along the lines of “we were hoping this position would be a good match because of ABC but as we went through the process, we realized that it’s going to require someone with more XYZ.” And ideally the vice president who originally said she wanted you to come back should have reached back out to you to say something like, “This turned out not to be the right role, but I’d still really love to get you over here so let’s talk about what could be a stronger match.” So I think fury is excessive, but it’s reasonable to be extremely irritated at how they handled it. 4. Applying for on-site jobs when I can’t drive at night What are your thoughts on applying for hybrid jobs or jobs that don’t advertise as being remote when the commute could be an issue? I can legally drive at night, but I won’t because my vision is so poor that I am no longer comfortable doing it. In my mid-sized city, public transit is awful, so I can’t easily get anywhere with it. The Job Accommodation Network seems to say the Americans with Disabilities Act (ADA) would cover the interactive process for your commute if you were already hired, but I’m not even 100% on that. I can find places I’d like to work that are across the city (and I own a house, so moving isn’t an option), and I don’t want people to think I’m ignoring the rules just to ignore their return-to-office mandate (even though I do think it is dumb), but for example, a 40-minute drive to cross the city takes 2.5 hours via two buses and an hour walk to a corporate location that I’ve heard is awesome to work for, and I can name a lot of places like that. Otherwise I’m stuck to the downtown corridor which is fine, but that’s all banking (yuck … been there, done it, and no). I’m currently fully remote for a local downtown law firm but trying to stomach working for the next 30 years and unsure how to handle it. Employers are required to make the same accommodations for potential hires that they’d make for existing employees; there’s no category of “yes, we have to do it if you’re already working here, but we don’t have to offer it before you start.” It’s something that would be appropriate to raise and negotiate as part of your offer. (And yes, the ADA does require them try to find an accommodation if it can be done without undue hardship; in this case, that might be a schedule that allows you to commute home before nightfall.) 5. Should I include union organizing work on my resume? I am looking to move out of my current organization and maybe make a bit of a career shift. A lot of the skills and experience that would make me a strong candidate for many of the jobs I’m looking at are not from my current job itself, but from the work I do here as a union organizer and steward. I was a lead organizer in the union effort and then, once the union was authorized, a part of the bargaining committee for our first contract — so I developed and exhibited lots of communication skills, leadership, project management, negotiation skills, you name it. I’m really proud of this work and would love to include it on my resume, but I imagine that most hiring managers wouldn’t be too keen on hiring a union organizer, especially if they thought I might try to also unionize my next workplace (and they wouldn’t necessarily be wrong to assume that). Is there a way to include this experience in my job applications? Maybe I save it for an in person interview, or mention just the bargaining committee work but not the organizing work, or somehow talk about the experience without mentioning that it was for my union…? Or is it safer to just leave it all off entirely, even if it means I may not appear as a of strong candidate? Yeah, the organizing work in particular will hurt you with some managers, who won’t want to invite a union organizer on to their staff. Others won’t care and will see the value in the leadership skills involved. All else being equal, I’d leave the organizing work off; the bargaining committee work is safer to include, especially if you can frame it as working collaboratively with management rather than adversarially. The other way to look at this is that maybe you’d be happy to screen out employers who’d have a problem with the organizing work … but that depends a lot on how in-demand you expect to be as a candidate. The post I’ve run out of a patience with a rude coworker, “I forgive you” in a professional situation, and more appeared first on Ask a Manager. View the full article

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In today’s competitive environment, enhancing customer service is vital for business success. By leveraging AI-powered tools, streamlining communication channels, and nurturing a customer-centric culture, companies can greatly improve their interactions with clients. Implementing continuous training programs and utilizing data analytics for decision-making can provide deeper insights into customer needs. Nevertheless, to truly stand out, you need to reflect on how to empower employees and optimize the overall experience. Explore the innovative strategies that can set your service apart. Key Takeaways Implement AI chatbots to efficiently handle basic inquiries, freeing human agents for complex issues and enhancing overall service efficiency. Utilize predictive analytics to anticipate customer needs, enabling proactive outreach and timely issue resolution to improve satisfaction. Foster a customer-centric culture by empowering employees to make decisions, enhancing service delivery and retention rates. Conduct regular training sessions focused on soft skills and product knowledge to boost team performance and customer satisfaction. Encourage feedback through quick surveys and follow-up calls, using insights to drive continuous improvements in service quality. Leverage AI-Powered Tools for Enhanced Support As you explore ways to improve customer support, leveraging AI-powered tools can greatly upgrade your operations. These tools provide real-time insights through AI-generated scorecards, allowing you to objectively assess agent performance and offer immediate feedback. Utilizing conversation intelligence, AI can analyze all contact center calls, eliminating manual scoring and enabling continuous coaching for your customer service representatives. AI chatbots can autonomously handle basic inquiries, freeing up your human agents to tackle more complex issues, which improves overall efficiency in customer service operations. Predictive analytics can likewise help you anticipate customer needs, enabling proactive outreach and swift issue resolution, nurturing greater customer trust and satisfaction. Furthermore, by leveraging AI-driven sentiment analysis, you can monitor customer emotions in real-time, allowing for customized responses that not just improve the customer experience but also build customer loyalty. These ideas to upgrade customer service can greatly transform how you engage with your clients. Streamline Communication Channels Improving customer service doesn’t just stop at utilizing AI tools; streamlining communication channels is equally important. By optimizing these channels, you can greatly improve client experience and how to increase customer experience. Here are some effective service tips: Implement Help Desk Software: Centralize customer interactions to manage inquiries efficiently and respond quickly. Utilize Omnichannel Support: Guarantee consistent service across platforms to avoid customer frustration and boost satisfaction. Integrate Communication Tools: Use chatbots and social listening software to automate responses to common inquiries, reducing pressure on your team. Adopt IVR Systems: Allow self-routing of calls to minimize unnecessary transfers and direct customers to the right department swiftly. Foster a Customer-Centric Culture Creating a customer-centric culture is essential for businesses aiming to improve customer satisfaction and loyalty. By empowering your employees to make decisions that prioritize customer needs, you greatly improve retention rates. Companies adopting a customer-centric approach report 60% higher profitability, showcasing the financial advantages of this strategy. Recognizing and rewarding exceptional customer service can motivate your team to consistently deliver high-quality interactions, enriching the overall customer experience. Furthermore, promoting collaboration among departments leads to improved service delivery, as teams work together to meet customer expectations effectively. Implementing regular feedback loops guarantees that customer insights are valued and acted upon, promoting a culture of continual improvement. This adaptability helps your company evolve alongside shifting customer preferences, ultimately reinforcing your position in the market. By prioritizing a customer-centric culture, you set the foundation for long-term success and customer loyalty. Implement Continuous Training Programs Implementing continuous training programs is crucial for developing ongoing skills in your customer service team. These programs should offer customized learning opportunities that address specific needs, ensuring agents stay equipped with the latest product knowledge and service protocols. Ongoing Skills Development As customer service environments evolve, ongoing skills development through continuous training programs becomes vital for maintaining a competitive edge. Investing in continuous training not only keeps your team engaged but likewise considerably improves performance. Here are key benefits to evaluate: Retention Rates: Continuous training can improve retention rates by 25%, keeping your knowledgeable agents on board. Profit Margins: Companies that invest in training see a 24% higher profit margin, showcasing the financial advantages. Response Times: Regular training sessions can reduce response times by up to 30%, boosting efficiency. Customer Satisfaction: Ongoing skills development can lead to a 10-15% improvement in customer satisfaction scores, as agents become better equipped to meet needs. Implementing these programs is vital for effective customer service. Tailored Learning Opportunities Customized learning opportunities in customer service training can greatly improve the effectiveness of your team, particularly when you consider that personalized approaches resonate more with individual learning styles. Implementing continuous training programs leads to better retention rates, boosting new hire success by up to 25%. Regular training sessions not only improve engagement but likewise increase productivity by 20%, ensuring agents meet customer needs effectively. Incorporating role-playing and real-life scenarios sharpens crucial soft skills, whereas technology-driven training provides real-time feedback for ongoing improvement. Benefits Impact Improved Retention Up to 25% Increased Productivity 20% Reduced Churn Rates Up to 15% Performance Assessment Integration Continuous training programs play a crucial role in elevating the performance of customer service representatives. By implementing these programs, you can boost effectiveness and reduce turnover rates, creating a seasoned team. Here are four key benefits of continuous training: Engagement: Ongoing education keeps agents motivated and informed about the latest customer service practices. Real-time Feedback: Utilizing AI-driven tools provides immediate insights into agent performance, promoting continuous improvement. Measurable Goals: Regular assessments help track individual progress and highlight areas needing development, leading to improved customer satisfaction. Adaptability: Routine reviews of training based on customer feedback guarantee that your programs remain relevant and effective, responding to changing expectations. Investing in continuous training will greatly improve your team’s ability to meet customer needs effectively. Utilize Data Analytics for Insightful Decision Making Utilizing data analytics for insightful decision-making is crucial for businesses aiming to improve their customer service. By identifying customer service trends, you can tailor improvements and create personalized experiences that resonate with your customers’ behaviors and preferences. Analyzing customer feedback provides actionable insights, highlighting your service’s strengths and weaknesses, which drives strategic upgrades. Tracking performance metrics through data analytics allows you to measure the success of your initiatives, enabling timely adjustments based on real-time insights. Regularly reviewing analytics reveals patterns that inform resource allocation, ensuring your customer service teams focus on areas with the highest impact on satisfaction. In the end, utilizing data-driven decisions boosts customer satisfaction by aligning your services with actual customer needs, leading to improved retention and loyalty rates. Encourage Customer Feedback and Act on It Gathering customer feedback is a crucial practice that enables businesses to improve their service quality. When you actively solicit feedback, you’ll uncover valuable insights into your strengths and weaknesses. About 70% of customers are willing to share their opinions if you ask, so take advantage of that willingness. Here are four effective strategies to encourage feedback: Use Surveys: Send quick surveys after customer interactions to gather immediate insights. Follow-Up Calls: Reach out personally to discuss experiences and gather nuanced feedback. Implement Changes: Analyze the feedback to make data-driven improvements; 60% of customers prefer brands that listen. Communicate Results: Share what you’ve changed based on feedback, as 77% of consumers value companies that act on their suggestions. Optimize the Omnichannel Experience How can businesses guarantee a seamless omnichannel experience for their customers? Start by ensuring smooth changes between communication channels, as 81% of customers prefer consistency across platforms. Integrating all channels into a centralized help desk software allows your representatives to access complete customer experiences, leading to informed responses. This cohesive service delivery can boost customer retention by 20-30%. Implementing AI-driven tools is another key strategy. These tools streamline interactions, providing personalized responses during reducing the need for customers to repeat information, addressing a pain point for 70% of consumers. Regularly auditing your omnichannel strategies based on customer feedback is crucial. This process helps improve engagement and satisfaction, as consumers expect brands to anticipate their needs. Focus on Quality Over Quantity in Interactions In today’s competitive market, focusing on quality in customer interactions is crucial for building loyalty. By prioritizing meaningful exchanges and employing personalization techniques, you can create customized experiences that resonate with customers. Furthermore, measuring experience quality metrics helps guarantee that your team consistently delivers high standards, reducing the risk of churn and nurturing long-term relationships. Prioritize Meaningful Interactions Prioritizing meaningful interactions in customer service is vital for creating lasting relationships, especially since each engagement offers a chance to cultivate emotional connections that boost loyalty. Focusing on quality over quantity can greatly improve customer satisfaction and retention. Here are some key strategies to implement: Engage Actively: Listen to your customers and respond thoughtfully to their needs. Train Staff: Empower your team with soft skills like empathy and active listening to strengthen interactions. Personalize Communication: Tailor your messages based on individual customer preferences to show you care. Value Each Touchpoint: Recognize that every interaction can make or break a customer relationship, so aim for excellence each time. Emphasize Personalization Techniques Building on the importance of meaningful interactions, it’s clear that personalization techniques can greatly improve customer service experiences. By using AI-driven data analytics, you can customize recommendations and support based on individual preferences and behaviors. This approach not only improves customer satisfaction but also drives a willingness to pay 16% more for better experiences. Implementing personalized notifications and offers boosts engagement, as 80% of consumers favor brands that provide customized experiences. Tools like Invoca’s PreSense give agents real-time insights, allowing them to offer informed interactions. Prioritizing quality over quantity guarantees each customer feels valued, which is crucial since 76% of consumers will stop doing business with a company after a single negative experience. Regular feedback analysis can refine your personalization strategies. Measure Experience Quality Metrics Even though many businesses focus on the number of customer interactions, it’s crucial to shift that emphasis toward the quality of those interactions. Prioritizing quality can greatly improve customer satisfaction, as 76% of consumers will stop doing business after just one bad experience. To effectively measure experience quality metrics, consider the following: Implement key performance indicators (KPIs) like customer satisfaction scores and resolution times. Regularly monitor and evaluate service interactions to identify strengths and weaknesses. Leverage AI-driven conversation intelligence for immediate feedback to agents. Cultivate a culture that prioritizes meaningful interactions over sheer volume to boost loyalty. Empower Employees to Make Customer-Focused Decisions Empowering employees to make customer-focused decisions is essential for improving service delivery and overall customer satisfaction. When you give your frontline staff the authority to resolve customer issues, it leads to quicker resolution times, reducing the need for escalations. This efficiency not merely improves the customer experience but also cultivates a culture of accountability. With 76% of consumers abandoning a company after a single bad experience, timely solutions are imperative. Furthermore, organizations that support employee autonomy often see a 21% increase in profitability and better customer retention. By equipping your team with the right tools and information, you guarantee they can meet customer needs effectively. Recognizing and rewarding those who take initiative further encourages a customer-centric culture, motivating others to follow suit. In the end, when employees feel empowered, you improve service delivery, leading to greater satisfaction for both customers and staff alike. Monitor and Measure Success Regularly To effectively improve your customer service, it’s crucial to monitor and measure success regularly. Establishing clear benchmarks allows you to evaluate your team’s performance against set goals. Regular assessments of key performance indicators (KPIs), like customer satisfaction scores and resolution times, help you track progress and pinpoint areas needing improvement. Here are four critical steps to take into account: Set Benchmarks: Define standards for evaluating performance to guarantee clarity in your objectives. Regularly Assess KPIs: Keep an eye on metrics such as customer satisfaction scores and resolution times. Utilize Customer Feedback: Leverage metrics like Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT) for actionable insights. Adjust Strategies: Use performance data to refine your approach, guaranteeing alignment with evolving customer expectations. Frequently Asked Questions What Are the Innovations in Customer Service? Innovations in customer service encompass several technologies that improve efficiency and interaction quality. AI-powered virtual assistants provide round-the-clock support, whereas predictive analytics anticipate customer needs, nurturing trust. Blockchain technology enables secure self-service portals for data verification, improving transparency. Furthermore, sentiment analysis tools offer real-time feedback insights, allowing businesses to adjust service strategies swiftly. Robotic Process Automation streamlines repetitive tasks, reducing response times and enabling agents to focus on complex inquiries, increasing overall productivity. What Are the 7 R’s of Customer Service? The 7 R’s of customer service are fundamental for meeting customer needs effectively. They include the Right Product, which means offering items that match preferences; the Right Time, ensuring timely responses; the Right Place, providing accessible support; the Right Price, reflecting competitive value; the Right Quantity, delivering appropriate amounts; the Right Quality, maintaining high standards; and the Right Customer, comprehending who your audience is to tailor services effectively. Each aspect plays an important role. What Are the 4 P’s That Improve Customer Service? The 4 P’s that improve customer service are people, processes, products, and personalization. First, investing in well-trained representatives guarantees they can effectively address customer needs. Second, streamlined processes, often aided by technology, improve efficiency and reduce wait times. Third, aligning product quality with customer expectations nurtures satisfaction. Finally, personalizing interactions using data analytics engages customers better, making them feel valued and increasing their likelihood to remain loyal to your brand. What Are the 5 R’s of Customer Service? The 5 R’s of customer service are Responsiveness, Respect, Relevance, Recovery, and Recognition. Responsiveness is about how quickly you address customer inquiries—aim for a reply within 60 minutes. Respect means treating customers with empathy, which nurtures loyalty. Relevance focuses on personalizing interactions to meet customer needs effectively. Recovery involves resolving issues swiftly, as most customers remain loyal when their problems are addressed. Finally, Recognition is about acknowledging customers’ contributions and loyalty. Conclusion By implementing these ten innovative ideas, you can greatly improve your customer service operations. Leveraging AI tools, streamlining communication, and nurturing a customer-centric culture will lead to more efficient and satisfying interactions. Continuous training and data analytics will empower your team to make informed decisions, whereas focusing on quality interactions creates deeper connections with customers. Regularly monitoring success will help you adapt and improve, finally driving customer loyalty and satisfaction. Prioritize these strategies to stay competitive in today’s market. Image via Google Gemini and ArtSmart This article, "10 Innovative Ideas to Enhance Customer Service" was first published on Small Business Trends View the full article