The Next AI Infrastructure Battle Is Over The Web

A new infrastructure layer is emerging to help AI agents search, retrieve, understand and interact with the web. What agents learn along the way is becoming just as important as the information they retrieve.

The web was built for people. Search engines help people find websites. Web pages are designed for people to read, and browsers allow people to navigate those pages, fill out forms and interact with online services. Advertising, analytics and access controls have also been built around the assumption that a person is somewhere in the loop.

AI agents are changing that assumption. An agent does not want ten blue links from a search engine. It needs information it can consume directly. It needs to find relevant sources, retrieve data from them, determine what matters, compare different sources and decide whether it has enough information to complete a task or continue searching.

Agents are becoming a new class of internet users. They need much of the same information as people, but they can retrieve and process it at a much greater scale. This shift is driving the development of two distinct layers of the internet: one optimized for human interaction and another designed to deliver information directly to AI agents. The result is an emerging infrastructure stack between AI models and the internet.

For CIOs and CTOs, this changes how agent infrastructure should be evaluated. Model selection and inference performance still matter, but an agent’s effectiveness increasingly depends on the quality, cost and reliability of the information infrastructure supporting its agent harness. Enterprises must also consider what information agents can access, which services handle that information and how those interactions are governed.

Search Is Already Changing

At the same time, a new group of companies is building web infrastructure specifically for AI agents, rather than adapting traditional human-oriented search systems to meet their needs.