Consider the contrast between Amazon’s product pages and Apple’s iPhone product page.
Amazon presents a dense amount of information: specifications, pricing tiers, customer reviews and comparative data points. Apple offers sleek, visually immersive experiences built around images, video and minimal text.
This isn’t merely aesthetic, this could be the future of the internet. It may reveal something fundamental about how digital interfaces will need to evolve. Amazon’s data-rich structure, while not explicitly designed for AI, could be readily adapted for LLM bot consumption. Conversely, Apple’s beautiful human-centric design would prove difficult for an AI agent to parse efficiently making it a much more enjoyable and easy-to-process experience for humans.
If agentic AI fulfills its promise of handling routine purchases and research on behalf of users, we may be witnessing the early stages of a significant split in the way the internet is presented: digital experiences optimized for human engagement versus those structured for machine interpretation.
The purchase consideration spectrum
The delegation of purchase decisions to AI agents will likely follow predictable patterns based on what behavioral economists call the consideration spectrum.
High-consideration purchases such as cars, homes, banking, insurance and investment products, involve countless variables: safety ratings, financing options, aesthetic preferences, status signaling. These decisions will remain firmly in human hands for the foreseeable future.
Low-consideration purchases present a different calculus entirely. Commodity products like household supplies or routine travel bookings could become prime candidates for AI agent-driven commerce.
The challenge lies in the fact that “low consideration” varies dramatically based on individual income levels and personal preferences. A $50 purchase might require deliberation for some consumers, while others wouldn’t think twice about delegating anything under $500 to an AI assistant.
This creates a complex matrix where income level intersects with consideration requirements. Understanding where products and services fall within this matrix becomes crucial for determining which aspects of digital presence require bot optimization versus human-focused design.
The shift toward AI-driven commerce will likely begin at the lowest end of the consideration spectrum. We should expect to see commodities like groceries, household supplies and basic personal care products where convenience is valued over brand loyalty as the first AI-based purchases. As consumers grow comfortable with agentic-generated transactions in these categories, the boundary will gradually expand upward, testing what the majority will accept as “low consideration.” Of course, the pace of this expansion will depend primarily on how well AI agents minimize errors and build trust.
The agentic commerce challenge
Early agentic AI systems reveal both the promise and the problems. Anthropic’s Claude and similar agent frameworks include explicit “stop” checkpoints where the AI requests permission before executing actions. These safeguards exist because hallucination carries consequences in agent-driven commerce.
An AI booking a flight to Beirut instead of Barbados isn’t merely inconvenient; it represents a failure that undermines trust in the agentic model. The permission stops between actions help mitigate these risks. But they also introduce friction that defeats the purpose of delegation.
Companies like Etsy and Shopify are addressing this challenge by uploading structured product feeds directly into ChatGPT. (Sounds a bit like Google merchant data feeds, no?) They’re creating data environments where AI agents can accurately interpret product specifications, compare options and theoretically execute purchases without misinterpretation. The success of these experiments will likely determine how quickly agent-driven commerce scales.
The underlying technical challenge remains, which is why these data feeds and text-content rich formats matter so much for agentic web. LLMs function as transformers on natural language. They convert words into mathematical representations, and then process those as probabilities, and generate responses.
This entire architecture is predicated on processing human-readable text. Introducing protocols designed specifically for machine interpretation raises fundamental questions about whether existing LLM architectures can effectively serve both purposes or whether we’re heading toward genuinely separate systems.
The data density vs. visual engagement trade-off
The potential split manifests most clearly in interface design requirements.
Bot-optimized interfaces would prioritize data density and structured information: clear product specifications, unambiguous taxonomies, explicit markers indicating relationships between data points. Visual aesthetics become not just irrelevant but actively counterproductive, as design flourishes interfere with machine comprehension.
Human-optimized interfaces serve opposite objectives. They prioritize emotional engagement, visual storytelling and aspirational messaging. These interfaces guide users through choreographed experiences designed to build desire and trust, where raw specifications take secondary importance to the feelings the interface generates.
Amazon’s existing interface — though certainly not designed with AI agents in mind — could be adapted for bot consumption with relative ease. Product specifications, comparison charts and detailed reviews provide exactly the structured data an AI agent would need for purchase decisions. Apple’s full-screen hero images and minimal text would require fundamental restructuring to support agent-driven commerce.
Strategic implications
If this bifurcation, as typified by the respective Amazon and Apple user experiences, materializes, then the implications for marketers are significant but not yet fully clear.
Brands offering low-consideration products and services would need to develop bot-friendly infrastructure: structured data, clear specifications, integration with emerging agent frameworks.
The question becomes how an AI shopping assistant will evaluate products against competitors. Can it parse pricing, features and availability? Can product data integrate cleanly with agent protocols? It all still needs to be defined.
High-consideration purchases would still require compelling human experiences. Visual storytelling and emotional engagement that drives desire and builds trust can’t be outsourced to the AI. These interfaces would need to delight, inspire and convert human visitors actively engaged in decision-making processes.
Most brands would likely need both approaches. The challenge wouldn’t be choosing between human optimization and bot optimization. Rather, it’s a matter of developing parallel strategies serving fundamentally different audiences.
Whether the internet bifurcates into distinct human and machine-readable systems remains an open question. The trajectory suggests we’re moving in that direction. What is clear is that brands should be considering these possibilities now rather than waiting for the split — if it comes — to force their hand.
Opinions expressed by SmartBrief contributors are their own.
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