{"id":10903,"date":"2026-04-16T09:48:27","date_gmt":"2026-04-16T09:48:27","guid":{"rendered":"https:\/\/wildgreenquest.com\/?p=10903"},"modified":"2026-04-16T09:48:27","modified_gmt":"2026-04-16T09:48:27","slug":"the-idea-that-the-internet-is-built-for-people-is-crumbling-that-has-huge-implications-for-your-business","status":"publish","type":"post","link":"https:\/\/wildgreenquest.com\/?p=10903","title":{"rendered":"The idea that the internet is built for people is crumbling. That has huge implications for your business"},"content":{"rendered":"


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For years, companies have assumed the internet was built for people. <\/p>\n

Websites were designed to attract human attention, explain, persuade, reassure, and eventually convert. Search engine optimization, user experience, digital merchandising, and checkout design all rested on the same basic premise: the user was a person sitting in front of a screen. <\/p>\n

That premise is beginning to crack. <\/p>\n

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Not because people are disappearing, but because they are starting to delegate. More and more often, the first system reading your site, comparing your offer, interpreting your policies, or even initiating a purchase will not be a human being. It will be a software agent acting on someone\u2019s behalf. That is the direction implied by Anthropic\u2019s Model Context Protocol<\/a>, by Google\u2019s Agent2Agent<\/a> protocol, its guide to agent protocols<\/a> and its Universal Commerce Protocol<\/a>, by OpenAI\u2019s Operator<\/a> and Agents SDK<\/a>, and by the growing work from companies such as Visa<\/a>, Mastercard<\/a>, and Cloudflare<\/a> to make agentic commerce trustworthy and operational at scale. <\/p>\n

This is not just a story about better chatbots or prettier interfaces. It is a story about the web acquiring a second interface: one for humans, and another for machines. <\/p>\n

From pages to actions <\/h2>\n

The old web revolved around pages. You published information, people found it, and then clicked through a sequence you controlled. The emerging web revolves more and more around actions. Agents do not care very much about your homepage, your visual hierarchy, or the emotional arc of your funnel. They care about whether they can understand your catalog, verify your policies, access reliable data, and complete a task without unnecessary friction. <\/p>\n

That is why the most consequential developments in AI are increasingly not just models, but protocols. Anthropic describes MCP as \u201ca universal, open standard for connecting AI systems with data sources,\u201d meant to replace fragmented integrations with a single protocol. Google\u2019s A2A describes a world in which agents advertise capabilities through an \u201cAgent Card,\u201d discover one another, and collaborate on tasks. Google\u2019s own commerce work goes one step further: UCP is explicitly designed to integrate checkout logic directly with Google AI Mode and Gemini, with \u201cnative checkout\u201d framed as the default path for unlocking \u201cfull agentic potential.\u201d In other words, the stack is moving from content to execution. <\/p>\n

The next SEO is not SEO <\/h2>\n

For two decades, companies learned that visibility depended on being legible to search engines. What is now emerging is more demanding. It is no longer enough to be indexable. You have to become usable. <\/p>\n

That is why ideas such as llms.txt matter. As I argued in a recent piece, websites were built for humans, while language models are better served by a concise, \u201cfat-free\u201d entry point that reduces ambiguity and strips away the noise of menus, scripts, repeated elements, and layout. The llms.txt proposal is simple<\/a>: place a markdown file at \/llms.txt that acts as a curated map for language models, exposing what matters, what is canonical, and where the useful resources live. The official proposal frames it as a way to \u201cprovide information to help LLMs use a website at inference time<\/a>,\u201d precisely because context windows are limited and converting complex HTML into useful plain text is often difficult and imprecise. <\/p>\n

That does not make llms.txt some magical ranking hack. It is not. It is closer to digital housekeeping for a world in which more and more discovery, summarization, and recommendation are mediated by AI systems. The point is not to game a ranking algorithm. The point is to reduce machine confusion. That distinction matters. <\/p>\n

The same logic applies to newer, more experimental ideas such as identity.txt<\/a>. The site describes it as \u201ca portable identity file that tells AI tools who you are, how you think, and on what terms,\u201d adding that \u201cllms.txt tells AI about websites. identity.txt tells AI about people.\u201d Whether identity.txt itself becomes broadly adopted is almost secondary. What matters is the direction of travel: the web is beginning to produce machine-readable self-descriptions on purpose, rather than leaving models and agents to infer everything from noisy HTML, metadata fragments, and guesswork. <\/p>\n

And this is unlikely to stop with those two examples. Google\u2019s agent protocol guide explains that each A2A agent can publish an Agent Card describing its name, capabilities, and endpoint. The point is obvious: systems are starting to announce themselves to other systems in standardized ways. Once that logic takes hold, it is easy to imagine a broader ecosystem of machine-readable files for policies, permissions, provenance, fulfillment, pricing logic, returns, and authenticated identity. <\/p>\n

Brands will still matter. But brands will no longer be enough <\/h2>\n

Many companies still treat AI as something layered on top of the web: a chatbot in customer service, some generated copy in marketing, an assistant in the app. That view is too shallow. <\/p>\n

What is actually happening is that a machine-facing layer is being added underneath the visible web and, in some contexts, in front of it. When a user asks an agent to find the best black blazer under a certain price, with quick delivery, decent return conditions, and a fit similar to previous purchases, the interaction does not begin with a homepage visit. It begins with machine interpretation. <\/p>\n

That changes the basis of competition. Strong brands will still matter because trust still matters. But trust will increasingly need to be expressed in forms machines can process: structured attributes, current inventory, transparent return rules, delivery promises, verified merchant identity, and payment systems that can distinguish a legitimate agent from a malicious bot. Visa says its aim is to \u201censure only approved AI agents transact,\u201d while Mastercard argues that protocols are essential to scaling agentic commerce because they support clear user intent, secure credentials, and verifiable agent identity. Cloudflare, working with the payment ecosystem, has made the same point more bluntly: merchants will need ways to grant access to legitimate AI agents while stopping fraudulent traffic at the front door. <\/p>\n

What this means for companies: the case of Inditex <\/h2>\n

A global leader such as Inditex makes this shift easier to understand because it sits right at the intersection of brand, logistics, e-commerce, and scale. <\/p>\n

Inditex started relatively late in e-commerce compared with digital natives, but it eventually built one of the most effective integrated retail systems in the market. In its FY2025 results<\/a>, the company reported sales of \u20ac39.9 billion, online sales of \u20ac10.7 billion, and explicitly highlighted that the integration of store and online operations enables a \u201cseamless global omnichannel experience.\u201d <\/p>\n

That gives Inditex a major advantage in an agent-mediated environment. Zara and the rest of the group already possess many of the things agents are likely to value: strong brand recognition, rapid inventory rotation, integrated logistics, broad geographic coverage, and operational coordination between physical and digital channels. <\/p>\n

But there is also a risk. Fashion has historically depended on presentation, aspiration, curation, and friction that was often commercially useful. Agents compress all of that. They reduce merchandising to a decision layer in which price, availability, size confidence, delivery date, returns, and trusted identity can become more visible than the atmosphere of the site itself. In that world, the question is no longer \u201cIs your site compelling?\u201d It becomes: \u201cCan an agent use you efficiently?\u201d For Inditex, the strategic response is not cosmetic. It is structural.<\/p>\n

So what should Inditex do? <\/p>\n