The Browser Revolution in the Age of AI: From Search Engines to Intelligent Agents

The third browser war is quietly unfolding. Looking back at history, from the Netscape and IE of the 90s to Firefox and Chrome, the browser competition has always been a microcosm of platform control and shifts in technological paradigms. Chrome has become the dominant player due to its rapid updates and ecosystem integration, while Google has formed a closed loop of information access through its dual oligopoly structure of search and browser.

But today, this pattern is shaking. The rise of large language models (LLM) is enabling more and more users to complete tasks on the search results page with "zero clicks," leading to a decrease in traditional web click behavior. At the same time, rumors about Apple possibly replacing the default search engine in Safari further threaten Google's profit foundation.

Browsers themselves are also facing a role reshaping. They are not just tools for displaying web pages, but rather a container of various capabilities such as data input, user behavior, and privacy identity. While AI Agents are powerful, to complete complex page interactions, call local identity data, and control web elements, they still need to rely on the trust boundaries and functional sandboxes of browsers. Browsers are transforming from human interfaces into system call platforms for Agents.

What can truly break the current browser market pattern is not another "better Chrome", but a new interaction structure: not the display of information, but the invocation of tasks. Future browsers need to be designed for AI Agents - capable of not only reading but also writing and executing. Projects like Browser Use are attempting to semantically structure page layouts, transforming visual interfaces into structured text that can be invoked by LLMs, achieving a mapping from pages to commands.

Mainstream projects in the market have begun to experiment: Perplexity is building a native browser called Comet, using AI to replace traditional search results; Brave combines privacy protection with local reasoning, enhancing search and blocking functions with LLM; while Crypto-native projects like Donut target a new entry point for AI and on-chain asset interaction. The common feature of these projects is: attempting to reconstruct the input end of the browser rather than beautifying its output layer.

For entrepreneurs, opportunities lie in the triangular relationship between input, structure, and agency. The browser, as the interface for future Agent calls to the world, means that whoever can provide structured, callable, and trustworthy "capability blocks" will become part of the new generation of platforms. From SEO to AEO (Agent Engine Optimization), from page traffic to task chain invocation, product forms and design thinking are being restructured.

The third browser war occurs in "input" rather than "display"; what determines the outcome is no longer who captures the user's attention, but who earns the trust of the Agent and gains access to the call.

Insights for Entrepreneurs

1. Standardization of interface structure: Make your product a "callable" API component. Consider abstracting key operations into clear schemas to support scriptable restoration of user behavior.

2. Identity and Access: Becoming the intermediary layer for AI agents to overcome trust barriers. Especially in the Web3 scenario, consider building a "MCP (Multi Capability Platform) for the blockchain world."

3. Reunderstanding the traffic mechanism: shifting from SEO to AEO/ATF. Design products as "callable capability units", optimize the Agent calling process, and adapt to the calling syntax of different LLM frameworks.

The future entrepreneurial projects are not about recreating browsers, but rather making existing browsers serve Agents. What you need to build is the "interface syntax" for Agents to call your world, striving to become a part of the trust chain of intelligent agents, and constructing the "API castle" in the next generation search model. In the era of AI Agents, the key to success lies in capturing the execution intent of the Agents, rather than the attention of users.