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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.
Category:
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":""}}
Category:
E-Commerce
The way consumers search is changing faster than the industry expected. This holiday season, many shoppers are looking for gifts inside AI platforms, rather than retailer sites or traditional search. They are asking natural questions like: Find me a cruelty-free skincare gift for sensitive skin under $100. What are good gift ideas for a three-year-old that are safe and durable? What are the safest, nontoxic treats for my Golden Retriever? This shift is already measurable. Adobe Digital Insights reports a 4,700% year-over-year increase in retail visits driven by AI assistants between July 2024 and July 2025. At the same time, click-through rates from SEO have dropped 34% as users bypass the search results page entirely. eMarketer reports 47% of brands have no idea whether they appear in AI-driven discovery at all. The platforms know this shift is accelerating. Googles recent decision to add conversational shopping and AI-mode ads just weeks before the holidays shows how quickly consumer behavior is moving. Brands must adjust too. Despite the complexity behind AI systems, three simple signals determine which products get recommended: trust, relevance, and extractability. These signals are the backbone of how AI decides what to surface, and matter as much as packaging, price, or placement. 1. Trust: The models instinct about which information is dependable AI systems develop a sense of which sources to believe during training. Domains with consistent verification signals gain more weight because the model has learned they usually publish accurate information. This is why leading retailers, including Ulta, Sephora, Target, Amazon, and Bloomingdales, rely on independent verification partners for the claims displayed on their digital shelves. Verified domains act as trust anchors. When a model must choose, it selects the product backed by clearer and more reliable sources. Trust often determines whether you are included in the answer at all. 2. Relevance: How well your product matches the shoppers question AI assistants answer based on meaning, not keywords. When a shopper asks for eczema-safe moisturizer or gluten-free protein bars, the system retrieves products whose attributes clearly map to those concepts. Relevance depends on using consistent claims across every channel you sell inconsistency is heavily prioritized. When multiple sources concur, this repeated confirmation strongly reinforces your product is the right choice. Missing or inconsistent attributes keep your product of the candidate pool. 3. Extractability: How easy it is for AI to read and use your product data Even accurate information gets ignored if its hard for AI to parse. Clean structure, consistent formatting, and machine readability significantly increase the likelihood your product will be selected. Brands improve extractability by adding structured markup for details like ingredients, materials, and benefits so retrieval systems can interpret it without ambiguity. Clear structure anchors the attention of the large language model, giving your product an advantage. Extractability is often the deciding factor when competing products meet the same need. AI RECOMMENDATIONS SHAPE BEHAVIOR Algorithms do more than respond to consumers. They influence them. We see this in language, where content moderation has led millions of people to adopt new vocabulary. The same pattern is emerging in commerce. If AI consistently recommends a certain moisturizer, probiotic, or baby product, shoppers begin to trust those recommendations and carry those preferences into stores. Optimizing for trust, relevance and extractability goes beyond improving digital performance. It shapes real-world buying behavior. A PRACTICAL PLAYBOOK FOR THE HOLIDAY WINDOW Even with peak season here, brands can still make meaningful progress with these four steps: 1. Structure your data for machine and human audiences Fix blocked pages or missing product schemas, and use standard formats like JSON-LD that AI can parse reliably. Keep consumer-facing PDPs simple while storing deeper technical details, ingredients, and safety information in underlying schemas. Clean up formatting and refresh retailer feeds weekly, since AI systems prioritize recency. Example: A candle brand can keep the PDP simple for shoppers while storing allergen, VOC, and material data in structured markup that AI can read. 2. Align product claims everywhere you sell Match titles, claims and benefits across DTC sites, retailer PDPs, and marketplaces. Remove conflicting or outdated language that can weaken trust. Example: If one PDP says cruelty-free and another says not tested on animals, unify the phrasing so AI sees one consistent claim. 3. Map your data to real shopper intent Identify the attributes consumers care about most in your category. Encode those attributes in machine readable fields; add supporting evidence where possible. Example: For baby toys, encode safety standards like ASTM or CPSC in your structured data so AI can confirm the claim. 4. Build machine-readable authority with credible certifications and verification signals Encode ingredients, materials, certifications, and testing outcomes in structured fields so AI can verify your caims without guessing. Keep claim language consistent across channels to strengthen authority. Use references to third-party standards, testing, or retailer badges. AI gives more weight to claims it can trace back to trusted sources. Example: A sensitive skin serum should encode fragrance-free, eczema-safe, dermatologist testing details, and any third-party certifications directly in schema. 5. Use a tool that monitors, optimizes, and implements the work end-to-end Choose a tool that goes beyond generic visibility tracking, looks at each SKU individually, and helps you implement structured data improvements. Prioritize systems that strengthen your authority signals product by product, not just surface-level optimizations. Look for tools that measure real outcomes, like increased visibility in AI or higher conversion, so you can measure ROI. Consumer discovery is changing faster than most brands are prepared for. But there is still time. By reinforcing trust, relevance, and extractability now, brands can stay visible in AI-driven search this season and build a long-term foundation for every channel where AI shapes consumer decisions. Kimberly Shenk is cofounder and CEO of Novi.
Category:
E-Commerce
European Union regulators on Friday fined Elon Musk’s social media platform X 120 million euros ($140 million) for breaches of the bloc’s digital regulations that they said could leave users exposed to scams and manipulation.The European Commission issued its decision following an investigation it opened two years ago into X under the 27-nation bloc’s Digital Services Act, also known as the DSA.It’s the first time that the EU has issued a so-called non-compliance decision since rolling out the DSA. The sweeping rulebook requires platforms to take more responsibility for protecting European users and cleaning up harmful or illegal content and products on their sites, under threat of hefty fines.The Commission said it was punishing X, previously known as Twitter, because of three different breaches of the DSA’s transparency requirements. The decision could rile President Donald Trump, whose administration has lashed out at digital regulations, complaining that Brussels was targeting U.S. tech companies and vowing to retaliate.The company did not respond immediately to an email request for comment.EU regulators had already outlined their accusations in mid-2024 when they released preliminary findings of their investigation into X.Regulators said X’s blue checkmarks broke the rules because on “deceptive design practices” and could expose users to scams and manipulation.Before Musk acquired X, when it was previously known as Twitter, the checkmarks mirrored verification badges common on social media and were largely reserved for celebrities, politicians and other influential accounts.After he bought it in 2022, the site started issuing the badges to anyone who wanted to pay $8 per month for one.The means X does not meaningfully verify who’s behind the account, “making it difficult for users to judge the authenticity of accounts and content they engage with,” the Commission said in its announcement.X also fell short of the transparency requirements for its ad database, regulators said.Platforms in the EU are required to provide a database of all the digital advertisements they have carried, with details such as who paid for them and the intended audience, to help researches detect scams, fake ads and coordinated influence campaigns. But X’s database, the Commission said, is undermined by design features and access barriers such as “excessive delays in processing.”Regulators also said X also puts up “unnecessary barriers” for researchers trying to access public data, which stymies research into systemic risks that European users face.“Deceiving users with blue checkmarks, obscuring information on ads and shutting out researchers have no place online in the EU. The DSA protects users,” Henna Virkkunen, the EU’s executive vice-president for tech sovereignty, security and democracy, said in a prepared statement. Kelvin Chan, AP Business Writer
Category:
E-Commerce
FIFA has invited more teams than ever for a World Cup priced largely for fans in the 1%. The process of figuring out which teams in the expanded 48-nation field will play where begins with Friday’s draw at the Kennedy Center for the Performing Arts.Cape Verde, Curaçao, Jordan and Uzbekistan will appear in soccer’s premier event for the first time when next year’s tournament is played from June 11 to July 19 at 16 sites in the United States, Mexico and Canada.“I’m quite optimistic because to qualify you need to beat the other teams of your confederations, and that’s a sign of quality,” former Arsenal manager Arsene Wenger said Thursday as red carpets were installed at the Kennedy Center. “The teams are not there by coincidence.”President Donald Trump of the U.S. and Claudia Sheinbaum of Mexico are expected along with Canada Prime Minister Mark Carney. Instead of soccer gear, the Kennedy Center gift shop still was filled with socks of Shakespeare, Beethoven and Verdi along with shelves of red and white holiday nutcrackers.The world’s top 11-ranked teams have all qualified, with No. 12 Italy among 22 nations competing in playoffs for the final six berths to be decided March 31.Led by captain Lionel Messi, who turns 39 during the tournament, Argentina seeks to become the first nation to win consecutive World Cups since Brazil in 1958 and 1962. Messi will look to extend his record of 26 games played and enters with 13 career goals, three shy of Miroslav Klose’s record.Games will be played at 11 NFL stadiums along with three in Mexico and two in Canada, where construction is underway to add 17,000 temporary seats to BMO Field, raising capacity to around 45,000. Attendance will top the record 3.59 million in 1994.“We basically set the new tone in terms of attendance, in terms of surrounding the tournament with a lot of entertainment and glamor,” said Alan Rothenberg, head organizer of the 1994 World Cup in the U.S. “We did a lot of things that kind of broke the ice with respect to how you present the tournament as something other than just a soccer tournament.”FIFA announced initial ticket prices of $60-$6,730, saying they would be dynamic, up from $25-$475 for the 1994 tournament in the United States. It has refused to release a complete list of prices, as it had for every other World Cup since at least 1990. The governing body also is selling parking passes for up to $175 for a single match, a semifinal in Arlington, Texas.FIFA spokesman Bryan Swanson did not respond to a request for FIFA President Gianni Infantino to discuss ticket prices.Sixty-four nations will participate in the draw, 30% of FIFA’s members, but just 42 countries are assured of sports. Among the playoff teams, Albania, Kosovo, New Caledonia and Suriname are trying to reach the World Cup for the first time.With the expansion, the top two teams in each of 12 groups advance along with the eight best third-place teams. Some nations could reach the new round of 32 with three points.“I think we’re going to be in pretty good shape,” said former U.S. midfielder Tab Ramos, who during his playing days mapped out permutations for advancement. “We have a good team, so I’m not worried as much as I’ve been in the past about about this draw.”Opta Analyst’s computer projects the U.S. has a 0.9% chance of winning the Americans haven’t reached the semifinals since the first World Cup in 1930. Spain tops the forecast at 17%, followed by France (14.1%), England (11.8%), Argentina (8.7%), Germany (7.1.%), Portugal (6.6%), Brazil (5.6%) and the Netherlands (5.2%).In a new twist, FIFA said the top four teams in the rankings Spain, Argentina, France and England will avoid each other until the semifinals if they finish first in their first-round groups.Specific sites for most matchups and kickoff times won’t be announced until Saturday. In 1994, there were just seven night games. A team’s group play sites will be restricted to an Eastern, Central and Western regional. The 1994 World Cup draw in Las Vegas was apolitical, featuring performances by Stevie Wonder, Barry Manilow, James Brown and Vanessa Williams plus comedian Robin Williams, who called the draw screen “the world’s largest keno board,” yelled “Bingo!” when Greece was selected.This draw figures to be more akin to the ceremony for 2018 tournament in Moscow, opened by Russian President Vladimir Putin. Trump, who has campaigned for a Nobel Peace Prize, is expected to be awarded FIFA’s own peace prize that Infantino established after traveling to several events with Trump.But the main event is the pulling of balls from bowls to create groups. Retired tars Tom Brady of the NFL, Shaquille O’Neal of the NBA and Wayne Gretzky of the NHL along with three-time AL MVP Aaron Judge will assist in a ceremony to be run by former England captain Rio Ferdinand.“There is the angst and the looks of sheer terror and disappointment and/or joy and elation from the coaches and from the staffs,” said former U.S. defender Alexi Lalas, now Fox’s lead soccer analyst. “It really gets kind of real for people.” AP soccer: https://apnews.com/hub/soccer Ronald Blum, AP Sports Writer
Category:
E-Commerce
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