Is HTML Dying? The Rise of AIDI and the Web’s Future

The growing reliance on artificial intelligence for quick answers, product comparisons, and decision-making highlights a critical issue: the web’s underlying architecture was never designed for machine interpretation. As AI agents become more sophisticated, the methods of information delivery and the relevance of conventional webpages may undergo profound transformation.

Change is a constant in technology, even when it catches us off guard. During a recent discussion, the notion that the familiar web might eventually fade sparked reactions from curiosity to disbelief. One person firmly declared, “The web will always be there.” Yet history repeatedly shows that terms like “always” and “never” rarely apply to technology. Major shifts often seem disruptive only in retrospect. Consider the World Wide Web’s debut in 1991, few could have predicted its global impact. Similar patterns emerged with innovations like the steam engine, the printing press, and smartphones, each initially dismissed as impractical or unnecessary. We tend to evaluate emerging technologies by comparing their early, unrefined versions against established systems, rather than envisioning their mature potential. This habit obscures our view of what lies ahead.

Take the process of buying a smartwatch. Where do you typically look for details? Most people start with manufacturer sites, retailers, or search engine results. Suppose you want to understand the distinctions between Samsung’s Galaxy Watch8, Classic, and Ultra models, and whether the price differences align with your needs. Visiting Samsung’s website often proves frustrating. Each product page describes the watch as “super” but lacks clear, comparable specifications. You might find yourself jotting down notes by hand, trying to decipher terms like “fabric band,” “3nm processor,” or “One UI.” If sleep-tracking details are explained only in English, you may need to copy and translate the text. The built-in comparison tool tends to raise more questions than it answers, highlighting marketing phrases like “quick release” or “timeless design” while omitting practical differences. Comparing across brands becomes even more challenging, often leading to SEO-heavy articles that push affiliate links rather than offering genuine insights.

Contrast this with asking an AI assistant. Posing the question, “What are the main differences between the Galaxy Watch8, Watch8 Classic, and Ultra?” yields a concise overview in seconds, similarities, differences, and value assessment included. Follow-up queries receive clear, specific answers. For instance, if you mention using a Pixel phone, the AI can instantly note that certain health features, like blood pressure monitoring, won’t function without a Samsung device. This example underscores how traditional web research can be both time-consuming and inadequate. Companies typically present products in isolation, emphasizing strengths and downplaying weaknesses. Consumers, however, are “delta thinkers”, we want to compare options and understand the gaps.

Try this experiment: pause and ask your preferred AI to explain, compare, or evaluate any product or service. If you haven’t done this regularly, you may be surprised by the depth and relevance of the answers. For topics where source reliability matters, you can specify prompts like “Only use well-known institutions” or “Provide all sources.” Advanced AI tools can even conduct multi-minute research sessions, delivering comprehensive reports tailored to your query. Students already use these capabilities to draft assignments, raising questions about learning outcomes, but undeniably saving time.

HTML, the markup language behind web pages, was created for human consumption. It arranges text and images in browsers, defining layout and presentation. Humans intuitively grasp that “€9.99” indicates a price and “90409” is a postal code. But HTML conveys formatting, not meaning. As machines increasingly analyze web content, this became a problem. How does a machine recognize “9.95” as a price, or determine if tax is included? The workaround was structured data, hidden markup within HTML that helps machines interpret content. Yet adoption remains low, with estimates suggesting only 10–30% of sites use it. Search engines like Google have developed workarounds, using pattern recognition and external databases to infer meaning, but these are imperfect solutions. For AI, which processes language statistically, the lack of semantic clarity is a significant hurdle.

Looking ahead, chatbots like ChatGPT and Gemini are not yet replacing websites. Current AI lacks genuine comprehension, operating largely on statistical associations. But consider the progress made in just two to three years. From the first ChatGPT in late 2022, we now have AI that generates images, plans vacations, and summarizes complex topics. In five years, personal AI assistants could manage schedules, handle emails, conduct research, and provide summaries, proactively, without constant prompting. Platforms like n8n already allow users to build automated agent systems that, for example, coordinate meeting times by interacting with other digital assistants. Such scenarios are increasingly feasible.

Today’s AI models often function as “stochastic parrots,” generating responses based on statistical likelihoods rather than true understanding. Training data gaps can lead to hallucinations or inaccuracies. Some systems now pull real-time data from websites to improve reliability, but this runs into the same interpretation issues. A better approach would involve serving data in a structured, machine-readable format, similar to an API. Imagine an AI data interface (AIDI) where servers provide information in labeled fields, eliminating guesswork for AI. This would enable faster, more accurate machine processing.

Most websites monetize through two primary models: direct sales or traffic-based revenue (ads, affiliate commissions). AI-driven answers are already reducing visits to information-focused sites, impacting the traffic model. While Google faces criticism for diverting clicks through AI Overviews, the broader shift stems from user preference for quick, tailored answers. AI’s ability to deliver precise information in seconds is reshaping how people seek knowledge. For transactional queries, like purchasing a smartwatch, challenges remain, comparing delivery times, warranties, or retailer trust requires nuanced judgment. Here, too, AIDIs could help by providing structured data that personal agents use to make informed recommendations.

In B2B contexts, systems like SAP already use structured data exchanges for inventory and logistics. Expanding this concept would require standardized, accessible data interfaces. Just as domain name servers (DNS) help locate websites, AIDI registries could help AI agents find suppliers. Your personal agent could query these interfaces to check availability, compare prices, and place orders, streamlining what we now call “silent commerce.”

Publishers relying on ad revenue might transition to credit-based systems for AI content retrieval. Instead of subscriptions, users could pay small fees per query, with bots handling micropayments seamlessly. This approach could support quality content while moving away from intrusive ad models.

Looking further, personal agents might eventually replace browsers altogether. Websites could persist, but AIDIs would allow agents to handle tasks directly, booking services, purchasing goods, or generating custom informational displays. AI could assemble visually rich, personalized summaries from multiple structured sources, even showing how a smartwatch looks on your wrist rather than just listing dimensions. If we imagine a world without the web, starting fresh with advanced AI, we likely wouldn’t invent HTML. We’d build systems optimized for machine intelligence.

The web as we know it may gradually fade, not because of a single decision, but because personalized AI offers greater speed, convenience, and relevance. If history is any guide, systems that are easier, faster, and more tailored tend to prevail. Corporate leaders have long benefited from human assistants; soon, technology may extend that advantage to everyone.

When exactly this transition will occur is uncertain. Breakthroughs often arrive unexpectedly. One looming question: if the web declines, what will train future AI models? Much existing training data has already been consumed, and the web is increasingly saturated with AI-generated material. Some startups claim to produce thousands of automated podcasts weekly, content meant for machines more than humans. Future AI training may need to incorporate multisensory learning from robots and real-world interaction, moving beyond text alone.

Whatever the timeline, the direction is clear. As AI and structured data interfaces evolve, the ways we access information and conduct commerce will continue to transform, potentially leaving today’s web behind.

(Source: Search Engine Land)