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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
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