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Being tired is practically a personality trait in corporate Americaespecially in 2025. Everybody is exhausted, it seems. Folks are doing fiftyleven jobs. Youre always juggling tasks, always late for the next meeting because the last one ran long. But when youre one of the few Black employees at the gig, theres a subconscious fear of looking like youre in over your head, especially with the looming fear of layoffs. So you push through, even when youre running on fumes. You go harder, telling yourself youll rest once you get through the busy patch. But thats a lie. The job is a perpetual busy patch.For months, I kept telling myself I was just tired. Regular tired. The kind of tired you fix with a good nights sleep and maybe a WFH power nap between meetings. But one random Tuesday, as I stared blankly at my laptop trying to decipher a three-sentence Slack message like it was hieroglyphics, it hit me: This wasnt normal fatigue. My mind was cooked. The exhaustion hit back in the spring, but it was nothing like the dramatics you see in movies. There were no panic attacks in the bathroom or conference room crashouts on Kyle. It showed up subtly, in little ways that I dismissed. Id reread emails multiple times because the words refused to connect in my mind. I had the attention span of a goldfish. Id get irrationally annoyed by people asking me perfectly reasonable questions. I was just . . . over it. I chalked it up to adulting, the natural byproduct of ambition and bills. This too shall pass, I thought.The breaking point wasnt cinematic. I was in a brainstorming session when I realized my mind felt blank. I managed to offer a few contributions to the meeting, but they were all cliché rehashes, none of the outside-of-the-box ideas Id usually bring to the table. I felt like Charles Barkley in Space Jam after the Monstars stole the NBA players skillslike a whole scrub. Shortly after, I took a week off. Booked a trip. But a change of scenery didnt fix anything. I came back just as fried, which was more depressing. I tried damn-near all of the things Solange sings about in Cranes in the Sky. Then I realized I required a factory reset. I began to make some real changes to improve my work-life balance. It wasnt just that I needed time away from the office; I needed better boundaries and mental-health maintenance. I began closing my laptop at a designated time, and keeping it closed until it was time to clock in the next day. I blocked off meeting-free focus time during workdays. I got a biweekly gym routine going. I stopped thinking of myself as a machine that could operate nonstop. Somewhere along my come up, I had convinced myself that I needed to treat my job like I was back in college. In those undergrad days, I felt the need to pile on electives and explore diverse fields of study. I wanted to be well-rounded and sure of my career path. But once I was in the workplace, it became about being marketable. I took on fringe projects outside of my job description to open myself up to new opportunities and, ideally, more moola. The game plan served me well until it didnt. Ive been taking it easy since then. I have nothing to prove to anyone else, or to myself. So I stay in my lane. I delegate more. I turn down things that arent my responsibility. Ive unlearned the foolish idea that rest is a reward, something I had to earn by pushing myself to the brink. Doing the most is a thing of the past. It took burning out for me to learn a simple truth: Nothing at work is worth losing yourself over. Not the project, not the promotion, not the pat on the back. Protect your energy like its finite, because it is. If youre feeling the kind of tiredness that sleep cant fix, follow the sage guidance of Ice Cube: Check yourself before you wreck yourself.
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E-Commerce
Every year, open enrollment forces Americans to confront a familiar dilemma: Pay more for coverage that delivers less, or gamble on going without it. This year, that choice has become even starker. Employers are shifting more costs to workers, marketplace premiums are poised to rise, fewer prescription drugs are covered by insurance, and 3.8 million people could lose insurance annually if Affordable Care Act subsidies arent extended. Together, these developments represent a structural break in the U.S. healthcare system. Its a perfect storm that will price many Americans out of health insurance altogethermany involuntarily, but some voluntarily. Fed up with skyrocketing premiums and deductibles that offer little protection, they’ll instead pay out-of-pocket for medical needs, hoping that they won’t face catastrophic expenses. Whats emerging is not a temporary coverage gap. Its a permanent coverage squeeze. One that will fundamentally reorder consumer behavior and redefine what access means. The implications for healthcare organizations are profound, and those who fail to adapt will struggle to stay relevant. SHIFT FROM COVERAGE TO CONTROL For decades, the U.S. healthcare model has been built on the assumption that insurance is the gateway to care. But when premiums and deductibles reach levels that rival a second mortgage, consumers start to ask a different question: What am I actually getting for this? Increasingly, the answer feels out of step with consumer expectations. High deductibles mean many people pay full price for most of their care anyway. Network limitations constrain choice. Surprise bills erode trust. And the complexity of benefits makes it nearly impossible to be an informed consumer. As a result, were seeing a quiet but significant reorientation. Consumers are moving from a coverage-first mindset to a control-first mindset. They want to understand costs upfront. They want to choose where they go for treatment. They want the ability to pay in ways that fit their budgets. And when the value equation breaks, theyre willing to bypass the system entirely. THE CONSUMER HEALTHCARE MARKET WILL EXPAND If current trends hold, 2026 could mark one of the largest expansions of the uninsured and underinsured population in more than a decade. But instead of disengaging from the healthcare system, these consumers are building a parallel path through it. They are demanding the same things they expect from the best retail and digital experiences: clarity, predictability, immediacy, and trust. This creates a massive opportunity, and a significant responsibility, for the industry. Companies that can simplify access, make pricing transparent, and deliver affordable pathways to care will become essential partners. Those that cling to legacy models built around opaque reimbursement flows will watch consumers go elsewhere. We already see evidence of this shift. People are embracing subscription-based care for predictable costs, using telehealth for speed and convenience, and relying on platforms like GoodRx to access lower prescription prices. Services like my company GoodRxs newly-launched telemedicine subscriptions for erectile dysfunction, hair loss, and weight loss are examples of how companies are meeting this demand, offering affordable, accessible healthcare options outside traditional insurance frameworks. WHAT HEALTHCARE LEADERS MUST DO NOW Healthcare has historically been built around the needs of institutions, not individuals. That era is ending. The organizations that thrive in the next phase will redesign around consumer agency and economic reality. Three shifts are essential: Make cash pricing a standard, not a contingency. If people are paying out-of-pocket, they need to see the cost clearly, consistently, and upfront. Transparent pricing should be a baseline expectation across providers, pharmacies, and manufacturers. Embed affordability into clinical decision making. Cost isnt a back office issue. It should be integrated into prescribing tools, clinical workflows, and patient conversations. Providers need real-time insights into cash prices and savings options so they can help patients make informed choices before they reach the pharmacy counter. Build care models that meet consumers where they are. Telehealth, retail clinics, asynchronous care, and hybrid models represent the way consumers want to access routine, preventive, and even chronic care. Healthcare companies must expand their presence in these channels or risk losing relevance. BUILD A CONSUMER-CENTRIC FUTURE The coverage squeeze is exposing something important: Consumers are demanding value, not just benefits. They want care that feels intuitive and affordable. They want to make decisions with clear information rather than insurance complexity. And they want healthcare that adapts to their lives. If we meet that demand, we have a chance to rebuild trust and deliver a healthcare experience that works for more people, regardless of their coverage status. If we dont, consumers will continue to chart their own path, with or without the traditional system. The next chapter of American healthcare wont be defined by the rise or fall of insurance premiums. It will be defined by whether we, as industry leaders, embrace a radically simple idea: When we design for the consumer first, everyone benefits. Wendy Barnes is president and CEO of GoodRx.
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E-Commerce
Weve been here before. At so many pivotal moments in our adoption of digital technology, people and businesses mistake a companys walled garden for the broader, more powerful network underneath. In the 1990s, many people genuinely believed AOL was the internet. When I left Facebook in 2013, hundreds of people asked how I would function without the web. Over and over, packaged productsoperating systems, app stores, streaming serviceseclipse quieter, less expensive, bottom-up alternatives like Linux or torrents. We forget they exist. Today were making the same mistake with large language models. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/adus-labs-16x9-1.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/anduslabs.png","eyebrow":"","headline":"Get more insights from Douglas Rushkoff and Andus Labs.","dek":"Keep up to date on the latest trends on how AI is reshaping culture and business, through the critical lens of human agency.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/www.anduslabs.com\/perspectives","theme":{"bg":"#1a064b","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420531,"imageMobileId":91420530,"shareable":false,"slug":""}} To many of us, AI now means choosing among a handful of commercial LLMs such as ChatGPT, Claude, Gemini, or Grokand perhaps even choosing the one that matches our cultural or political sensibilities. But these systems share important structural limitations: they are centralized, expensive, energy-intensive operations that depend on massive data centers, rare chips, and proprietary data stores. Because theyre trained on roughly the same public internet, they also tend to generate the same generalized, flattened results. Companies using them wholesale often end up substituting their own expertise with recombinations of whatever is already out there. This is how AI will do to businesses what social media did to publications, and what the early web did to retailers who went online without a strategy. Using the same generic tools as everyone else produces the same generic results. Worse, outsourcing core knowledge processes to a black-box service replaces the long-term development of internal capacityespecially junior employees learning through real practicewith cheaper but future-eroding automation. The limits of centralized AI Commercial language models are optimized for generality and scale. That scale is impressive, but it creates real constraints for organizations. Centralized LLMs require: Large volumes of training data scraped from the open web Expensive server infrastructure and power consumption Constant external connectivity Business models built around subscription, token fees, or upselling For many companies, these models become another outsourced dependency. Every time a commercial LLM updates itselfwhich can happen weeklyyour workflows change underneath you. Your proprietary data may be exposed to third-party APIs. And your differentiation erodes, because the models knowledge is drawn from the same public corpus available to your competitors. Meanwhile, the narrative surrounding AI has encouraged businesses to believe that this centralized path is the only viable onethat achieving meaningful AI capability requires enormous data centers, billion-dollar training runs, and participation in a global race toward Artificial General Intelligence. But none of this is a requirement for using AI productively. A practical alternative already exists You do not need frontier-scale models to benefit from AI. A growing ecosystem of open-source, locally deployable language models provides organizations with far more autonomy, privacy, and control. A $100 Raspberry Pior any modest home or office servercan run a compact open-source model using tools like Ollama or GPT4All. These models dont learn on the fly the way people do, but they can produce high-quality responses while remaining completely contained within your own environment. More importantly, they can be paired with a private knowledge base using retrieval systems. That means the model can reference your own research library, internal documentation, or curated public resources like Wikipediawithout training on the entire internet, and without sending your data to an external provider. These systems build on your own data instead of extracting it, strengthen your institutional memory instead of commoditizing it, and run at a fraction of the cost. This approach allows an organization to create an AI system aligned with its actual priorities, values, and domain expertise. It becomes a private assistant rather than a generalized product shaped by the incentives of a trillion-dollar platform. And the alternative doesnt have to be a solitary effort. Neighborhoods, campuses, or company departments can form a mesh networka set of devices connected directly through Wi-Fi or cables rather than through the public internet. One node can host a local model; others can contribute or withhold their own data stores. Instead of a single company owning the infrastructure and the knowledge, you get something closer to a community data commons or a digital library system. Projects like the High Desert Institutes LoreKeepers Guild are already experimenting with this approach. Their Librarian initiative envisions local libraries acting as the data hubs for mesh-networked AI systemsresilient enough to function even during connectivity disruptions. But their deeper innovation is architectural. These systems give organizations access to powerful language capabilities without subscription costs, lock-in, data extraction, or exposure of proprietary information. Local or community models enable organizations to: Curate their own data Maintain complete privacy by keeping computation on-site Reduce latency to near zero Preserve and strengthen internal expertise Avoid recurring token or API costs And they do so using energy and computing resources that are orders of magnitude lower than those required by frontier-scale models. Why decentralized AI matters now The more institutions adopt localized or mesh-based AI, the less they are compelled to fund the centralized companies racing toward AGI. Those companies have made an effective argument: that sophisticated AI is only possible through their services. But much of what organizations pay for is not their own productivityit is the constrution of massive server farms, procurement of rare chips, and long-term bets on energy-intensive infrastructure. By contrast, in-house or community-run systems can be deployed once and maintained indefinitely. A week of setup can eliminate a decade of subscription payments. A small rural library has already demonstrated the feasibility of operating a self-hosted LLM node; a Fortune 500 company should have no trouble doing the same. Still, history suggests that most organizations will choose the convenient option rather than the autonomous one. Few people accessed the early Internet directly; they chose AOL. Today, many will continue to choose centralized AI services, even when they offer the least control. But what social media companies did to businesses that mistook them for the Internet will be mild compared to what comes when companies mistake these proprietary interfaces for AI itself. Decentralized AI already exists. The question now is whether well choose to use it. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/adus-labs-16x9-1.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/anduslabs.png","eyebrow":"","headline":"Get more insights from Douglas Rushkoff and Andus Labs.","dek":"Keep up to date on the latest trends on how AI is reshaping culture and business, through the critical lens of human agency.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/www.anduslabs.com\/perspectives","theme":{"bg":"#1a064b","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420531,"imageMobileId":91420530,"shareable":false,"slug":""}}
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