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My bus rolls into Port Authority. I’ve got 10 minutes to get across town for my first meeting. I sprint down the escalator, run through droves of people, and arrive at a subway turnstile. I swipe my MetroCard through the magnetic reader, step forwardonly to get crotch-checked by a locked metal bar and flipped the finger by a screen that displays PLEASE SWIPE AGAIN. I give it another swipe. INSUFFICIENT FARE. To refill my MetroCard, I power walk toward the kiosk. It refuses to read my credit card. I swipe a few more times. Nothing. I sift through my back pocket, discover a crumpled ten-dollar bill, and slide it into the machine. It won’t accept my cash. I waffle-iron the bill flat with my hands and feed it back in. [Photo: Luiz C. Ribeiro for NY Daily News/Getty Images] The kiosk spits out my refilled MetroCard. Baked into its awful blue and yellow design is this same awful experience, on repeat. The MetroCard has been a defining artifact of New York City’s subway system for more than three decades. In that time, some might argue, it has become an icon of design. I respectfully disagree. Design is inextricable from experience. The MetroCards design is as outdated as its technology. Fortunately, after years of poor MetroCard experiences like mine, the MTA has made its final update to the swiping technology. In 1993, the MetroCard was introduced as a replacement for subway tokens. It existed for decades as New Yorkers dominant method for accessing the subway. But in 2019, the MTA announced they were introducing a tap-and-go system called OMNY. That year, they installed it on Staten Island buses and across 16 subways as part of a pilot program. Over the next four years, they installed OMNY machines throughout all five boroughs. Manhattan and Brooklyn were early adopters. By November 2024, 60% of riders were using OMNY, according to Shanifah Rieara, the MTAs chief customer officer. Running two systemswith their duplicative costsmeant we had to set a certain date, she says. But that date was continually delayed due to slow installation and technical issues with the remaining vending machines. Now, with an OMNY reader and vending machine at nearly every transit location, the MTA will say goodnight to the MetroCard. And theyll save at least $20 million in operational costs. A Design That Wouldnt Go Away The MetroCard design remained more or less the same since the ’90s. Why? Were wedded to the nostalgia and the brand, says Rieara. We had no interest in changing it. [Photo: MTA] When it was redesigned in 1997, the look of the MetroCard was novel. There were new gradient and perspective tools at the designer’s disposal. Someone at the MTA had a field day: they created a glowing yellow sunset, a reflection, and a skewed MetroCard logo, which mimicked a train. This design looked fast. Riders would have expected a frictionless swiping experience, not a constant PLEASE SWIPE AGAIN.” In contrast, the original MetroCard design from 1993 was less ambitious. It was also more honest. The gradient was pure utility: it directed the rider to swipe left. And that MetroCard logo? It floated in a vague 3D space. The design didnt mimic. It didn’t overpromise. [Photo: MTA] Transit card design shouldn’t put you to sleep. In Hong Kong, they have the Octopus card, which features a dynamic yellow, green, and blue infinity loop. Paired with a small typographic Octopus logo, the cards modernist design looks like something out of Chermayeff & Geismar & Havivs studio. Its confident. And since 1997, the cards functionality has delivered upon the designs promise with mostly reliable tap-and-go service. [Image: Octopus] One of my favorite parts of the Octopus card? It embraces being a collectible item. Riders can customize their cards with ornaments like Pokémon keychains and plastic googly eyes from the movie Minions. This level of customization creates the perception of quality serviceyou wouldn’t chuck your tricked-out card in the trash next week. David Bowie collector’s edition Metro Cards, 2018. [Photo: Eduardo MunozAlvarez/VIEWpress/Corbis/Getty Images] Over the years, MetroCard riders would receive special cards, but the design was a half-measure: a partial print on the back of the card. It looked like an ad. These cards featured a range of icons, from artist Barbara Kruger to baseball player Jackie Robinson to musician Olivia Rodrigo. For a plastic card that was often reissued, the MTA could’ve treated each of these heavy-hitters to a full redesign of the card. Other countries do it. [Photo: Eye Ubiquitous/Universal Images Group/Getty Images] Londons transit card, the Oyster, will occasionally trade in its signature two-tone blue for a special design on the front of the card. Theyve celebrated the royal wedding of William and Kate, the Queens Diamond Jubilee, the 150th anniversary of the Underground, and even the 20th anniversary of the Oyster card itself, which debuted in 2003. These designs arent anything to write home about, but at least they create a shared celebratory moment for the rider. [Photo: John Phillips/UK Press/Getty Images] Looking Ahead Oysters parent company, Transport for London, licensed its scanning technology to the MTA for the OMNY. So far, Ive had a solid experience with the new card. Every Thursday afternoon, I rush downtown to my office after teaching a class at School of Visual Arts in Gramercy Park. I need to catch up with three hours of missed work and meetings, and unlike my Port Authority MetroCard nightmares, the OMNY taps without a hitch. That keeps me sane. This functional experience is reflected within OMNY’s design. That black and white card is straightforward, no b.s. It uses Neue Haas Grotesk, aligning with the utilitarian typography of the MTA’s graphics system. The inline cutaway of the letters signal road lanes and railroad tracks, the barcode highlights the card’s scanning technology. This design isn’t overly dramatic like the MetroCard of yore. [Photo: Schvaxet/Wiki Commons] But is a functional design enough for New Yorks transit card of the future? Design is culture. The comedian Kareem Rahma turns a MetroCard into the microphone for his podcast. The store OnlyNY sells MTA-licensed merch, like metal subway signs and mini-lampposts. To others, those objects are utility. To New Yorkers, they’re identity. The OMNY card is a real opportunity to intertwine culture and design. This year, the MTA proved they truly care about design: they unveiled an animated movie by designer Giorgia Lupi, titled A Data Love Letter to the Subway. Their new subway mapthe first update in 50 yearsnods to a classic design by Massimo Vignelli. And most subway stations finally have digitized schedules with slick typography. If the MTA continually updates the OMNY card, in print and digital form, it will become a cultural artifact. New York is full of designers with pride whod love to create a special edition OMNY. Champions Design could give the card attitude. Collins could celebrate civic glory. Center could give it a sports flair. These special designs would create a shared moment among New Yorkers. But, those designs need to hit at the right moment. When Zohran Mamdani takes the NYC mayoral office in January, design shouldnt sit at the bottom of his to-do list. He’s got audacious ideas. If they go well, great design will cement the experience in our minds. A free bus that runs on time? A special-edition OMNY card would floor us with a sense of New York pride.
Category:
E-Commerce
As employers have wrested back control of the job market, it has been a sharp contrast to the post-pandemic years when workers seemed to hold the power. In 2025, employees fretted about their job security and the sweeping impact of artificial intelligence on their work livesnot to mention corporate Americas continued commitment to keeping them in the office for longer. Here, weve compiled some of the most popular Work Life stories from this yearon the issues that consumed you most. The 996 schedule This year saw the return of hustle culture in Silicon Valley, as AI startups popularized a grueling work schedule that became popularized in China. The 996 schedule refers to a 72-hour workweekin other words, working from 9 a.m. to 9 p.m., six days a weekand has grown more common in Silicon Valley as founders and tech leaders scramble to outrun the competition. But experts say this could stoke burnout at a time when workers are already stretched too thin. The steady drumbeat of RTO The return to office is here to stay, despite how workers may feel about it. Business leaders like Jamie Dimon have been among the most vocal supporters of in-office work, dismissing employee concerns and the concept of work from home Fridays. Hes not alone: Amazon employees were forced to return to the office five days a week, while the federal government put an end to remote work this year. The truth behind quiet quitting Were still talking about quiet quitting. While older generations might think Gen Z workers are lazy or lack motivation, Fast Company contributor Jeff LeBlanc argues quiet quitting is a rational response to workplaces that lack fairness, structure, and alignment with employee values. Leaders who cant retain Gen Z talent should wonder whether theyre the problem, he writes. The question isnt whether Gen Z is willing to work hard. The real question is: Are leaders willing to evolve? The rise of job hugging In a tough job market, many employees are actually job hugging rather than quiet quitting. But doing so can actually hurt workers who are unhappy with their job situationor speed up their burnout. Cognitive reframing can helpfocusing purely on the positive aspects of a draining role, such as a friendly team, and tuning out the rest, writes Alex Christian. Sometimes, however, the only solution is to wait it out and hope that the economy turns around. The fractional leadership boom In the years since the pandemic, many senior leaders have been reevaluating what they want out of work. Enter the fractional role, which has enabled experienced C-suite leaders to set their own schedule and work across multiple companies. Fractional leaders have become more common at companies that dont need someone in the position full-time, allowing people in these roles to find more balance. The plight of middle managers Middle managers have had a challenging few years. As the pressures on them mount, many are headed for a crash, according to meQuilibriums Jan Bruce. With Gen Z increasingly rejecting the manager track, there could be a shortage of qualified leaders in the years to come, she argues. So what can companies do differently? Explicit policy decisions can help managers protect and promote their own mental and physical well-being, Bruce writes. This might look like mandatory disconnect periods, sabbaticals, or easing access to acute mental healthcare resources. Making sure managers have consistent, supportive check-ins with their own supervisors can help reduce isolation. The importance of office friends Workplace friendships are not what they used to beand its not good for business. Friendships at work can help boost employee performance and well-being, writes Fast Company contributor Mark C. Crowley. In fact, leaders should create an environment that encourages connection and invests in those friendships. Creating a culture where connection is valued doesnt just improve employee moraleit strengthens retention, creativity, and performance, he writes. By fostering friendships, leaders dont just build better teams; they create desirable workplaces. The productivity gains from AI We all know AI is reshaping how we work. But as the technology permeates the workplace, it might just be revealing how much of what we do is busywork. Were witnessing a productivity revolution without a purpose revolution, write Fast Company contributors Tomas Chamorro-Premuzic and Alexis Fink. Tools are improving, but the work remains hollow. Instead of using AI to invent better ways of working, many companies are simply using it to churn out more of the same, only faster.
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E-Commerce
Theres bad news for those using digital surveys to try to understand peoples online behavior: We may no longer be able to determine whether a human is responding to them or not, a recent study has shownand there seems to be no way around this problem. This means that all online canvassing could be vulnerable to misrepresenting people’s true opinions. This could have repercussions for anything that falls under the category of information warfare, from polling results, to misinformation, to fraud. Non-human survey respondents, in aggregate, could impact anything from flavors and pricing for a pack of gum, to something more damaging, such as whether or not someone could get government benefitsand what those should be. The problem here is twofold: 1) humans not being able to tell the difference between human and bot responses, and 2) in instances where automation is regulating action based on these responses, there would be no way to use such polling and safeguard against potentially dangerous problems as a result of this indistinguishability. The study by Dartmouths Sean J. Westwood in the PNAS journal of the National Academy of Sciences, titled The potential existential threat of large language models to online survey research, claims to show how we can no longer trust that, in survey research, we can no longer simply assume that a coherent response is a human response. Westwood created an autonomous agent capable of producing high-quality survey responses that demonstrate reasoning and coherence expected of human responses. To do this, Westwood designed a model-agnostic system designed for general-purpose reasoning, that focuses on a two layer architecture: One that acts as an interface to the survey platform and can deal with multiple types of queries while extracting relevant content, and anothercore layer that uses a reasoning engine (like an LLM). When a survey is conducted, Westwoods software loads a demographic persona that can store some recall of prior answers and then process questions to provide a contextually appropriate response as an answer. Once the reasoning engine decides on an answer, the interface in the first layer outputs a mimicked human response. The system is also designed to accommodate tools for bypassing antibot measures like reCAPTCHA. Westwoods system has an objective that isnt to perfectly replicate population distributions in aggregatebut to produce individual survey completions [that] would be seen as reasonable by a reasonable researcher. Westwoods results suggest that digital surveys may or may not be a true reflection of peoples opinions. There is just as likely a chance that surveys could, instead, be describing what an LLM assumes is human behavior. Furthermore, humans or AI making decisions based on those results could be relying on the opinions of simulated humans. Personas Creating synthetic people is not a new concept. Novels, visual media, plays, and advertisers use all sorts of creative ideas to portray various people in order to tell their stories. In design, the idea of Personas have been used for decades in marketing and User Interface design as a cost-cutting and timesaving trend. Personas are fictional composites of people and are represented by categories like Soccer Mom, Joe Six-pack, Technophobe Grandmother, or Business Executive. Besides being steeped in bias, Personas are projections of what the people creating them think these people would be and what the groups they might belong to represent. Personas are a hidden problem in design and marketing, precisely because they are composites drawn from real or imaginary people, rather than actual people — the values ascribed to them are constructed by other peoples interpretations. When relying upon Personas instead of people, its impossible to divine the true context of how a product or service is actually being used, as the personas are projected upon by the creator, and are not real people in real situations. Thus, the problems with using Personas to design products and services often arent identified until well after such products or services come to market and fail, or cause other unforeseen issues. This could be worse when these human-generated Personas are replaced with AI/LLM ChatBot personas with all the biases that these entailincluding slop influences or hallucinations that could make their responses even more odd or potentially even psychotic. Quant versus qual Part of the larger problem of not understanding peoples needs with surveys started when research shifted to statistical data collection based on computation, also known as quantitative methods, rather than contextual queries based on conversations and social relationships with others, or qualitative methods. As Big Data came online, people began to use quantitative methods such as online surveys, A/B testing, and other techniques to understand customer/user behavior. Because machines could quickly compile results, quantitative research seems to have become an industry standard for understanding people. It is not easy to automate qualitative methods, and replacing them with quantitative methods can forfeit important context. Since almost a generation has gone by with the world focused on computational counting, its easy to forget about the qualitative data methodsfound in social sciences such as Anthropologythat use contextual inquiry interviews with real people to understand why people do what they do, rather than trying to infer this from numerical responses. Qualitative research can give context to the quantitative data and methods that rely upon machines to divine meaning. They can also work outside of big data methods, and are grounded in relationships with actual people, which provides accountability to their beliefs and opinions. The process of talking with real people first contextualizes that content, leading to better outcomes. Qualitative methods can be quantified and counted, but quantitative methods cannot yet easily be made to be truly broadly contextual. One difference between using qualitative and quantitative methods has to do with transparency and understanding the validity of peoples responses. With older human-made Personas, there are obvious assumptions and gapsits crude puppetry and projection. But when people become manufactured by Chatbot/LLMs that utilize a corpus of knowledge mined from massive volumes of data, there can be fewer ways to separate fact from fiction. With chatbots and LLMs, the artificial entity is both the creator of the person, potentially the responder to te person, and either the interpreter of that fake chatbot persons responses, or being interpreted by an LLM. Thats where it can get dangerous, especially when the results of this type of slop tainted research are used for things like political polling or policing. Westwoods research has shown that: Rather than relying on brittle, question-specific rules, synthetic respondents maintain a consistent persona by conditioning answers on an initial demographic profile and a dynamic memory of previous responses. This allows it to answer disparate questions in an internally coherent manner,generating plausible, human-like patterns It can mimic context, but not create it. Back to the basics When GenAI is moving towards conducting the surveys, acting as respondents, and interpreting the surveys, will we be able to tell the difference between it and real people? A completely automated survey loop seems fictional, until we see how many people are already using Chatbots/LLMs to automate parts of the survey process even now. Someone might generate a persona, then use that to answer surveys that AI has designed, that someone else will then use a Chatbot to access AI to interpret results. Making a complete loop could be terrible: someone may then use AI to turn the Chatbot created, Chatbot answered, and AI interpreted survey responses into something that impacts real people who have real needs in the real world, but instead has been designed for fake people with fake needs in a fake world. Qualitative research is one path forward. It enables us to get to know real people, validate their replies, and refine context through methods that explore each answer for more depth. This type of work AI cannot yet do as LLMs currently base answers on statistical word matching, which is unrefined. Bots that replicate human answers will mimic a type of simulated human answer, but to know what real people think, and what things mean to them, companies may have to go back to hiring anthropologists, who are trained to use qualitative methods to connect with real people. Now that AI can falsely replicate human responses to quantitative surveys, those who believe that both quantitative methods and AI are the answers to conducting accurate research, are about to learn a hard lesson that will unfortunately, impact all of us.
Category:
E-Commerce
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