AI browsers aren’t smart enough yet to take over the internet

Core Insights - Current AI browsers, such as OpenAI's Atlas and Perplexity's Comet, are not yet capable of replacing legacy browsers like Chrome, as they still exhibit bugs and struggle with straightforward tasks [1][22] - The goal of AI developers is to integrate chatbots into browsers and mobile operating systems, which could enhance ad targeting and create new revenue streams [1][22] - User behavior is shifting, with a growing need for tools that cater to both human and AI interactions, prompting developers to rethink their design strategies [2][22] User Behavior and Preferences - A survey indicated that 60% of users are only comfortable using generative AI for low-stakes tasks or topics they can verify, highlighting a cautious approach to AI adoption [4][22] - Users are increasingly combining traditional search methods with generative AI to ensure accuracy while saving time [5][22] - AI browsers are popular for summarizing content, such as long YouTube videos, and users are asking significantly more questions compared to traditional chatbots [6][22] Feature Requests and Expectations - There is a demand for advanced features like task scheduling, which would allow users to automate repetitive tasks [7][22][24] - Users express a desire for browsers to handle more complex tasks, such as filling out government forms and managing financial reports [8][24] Technical Challenges and Limitations - The current web infrastructure is primarily designed for human users, which hampers the performance of AI browsers in executing advanced features [13][14] - AI browsers often struggle with complex visual elements and can become stuck in loops or take excessive time to process tasks [15][16][17] - Legacy browsers have a head start in optimizing resource usage, while AI browsers tend to consume more computing power during advanced operations [17][22] Competitive Landscape - The AI browser market is evolving, with Google's Gemini 3 model outperforming OpenAI's systems, prompting a competitive response from AI developers [18][22] - Efforts to encourage online providers to create AI-specific versions of their services have faced challenges, with some companies hesitant to adopt [20][22] - Legal disputes have arisen as high-traffic firms seek to protect their content from being leveraged by AI companies, indicating a contentious environment [21][22] Trust and Adoption Barriers - Concerns about the trustworthiness of AI systems remain a significant barrier to wider adoption, particularly for tasks involving financial transactions [22][22] - Users are wary of potential conflicts of interest, such as whether an AI agent is providing the best deals or acting in its own interest [22][22]