Meta Tests AI-Powered Shopping Research Tool to Compete with ChatGPT and Gemini

Meta Platforms Inc. is expanding its artificial intelligence ambitions by testing a new shopping research feature within its Meta AI chatbot. The move signals a stronger push into AI-driven commerce, placing the company in direct competition with tools offered by OpenAI and Google. As AI chatbots evolve beyond conversation and productivity tasks, Meta is positioning its platform as a personalized shopping assistant designed to deliver tailored product recommendations.

Meta Introduces Shopping Research Feature in Meta AI

Meta has begun testing a shopping research capability integrated into its Meta AI web experience for selected users in the United States. The feature allows users to request product suggestions directly within the chatbot interface. Instead of providing only text-based recommendations, the tool presents a visually engaging carousel of product images.

Each product listing includes a caption detailing the brand name, website source, and price. Alongside the visual display, the chatbot provides concise bullet-point explanations outlining why certain products were selected. This structured response format aims to simplify product discovery while offering context behind the recommendations.

A Meta spokesperson confirmed that the feature is currently in testing but declined to provide additional information regarding its broader rollout or monetization strategy.

Competing with AI Rivals in the E-Commerce Space

Meta’s new tool places it in direct competition with shopping features already introduced by ChatGPT and Gemini. Both platforms have gradually incorporated commerce-related capabilities, enabling users to explore products, compare options, and access merchant links without leaving the chatbot environment.

As AI assistants become more integrated into daily life, companies are increasingly exploring e-commerce as a revenue opportunity. AI-powered shopping tools represent a natural extension of chatbot functionality, blending conversational AI with product discovery and digital retail.

Meta’s approach suggests a strategic effort to capture user attention at the research stage of online shopping — before customers visit dedicated retail websites.

Zuckerberg’s Vision of ‘Personal Superintelligence’

Meta’s Chief Executive Officer, Mark Zuckerberg, has emphasized the company’s ambition to develop what he describes as “personal superintelligence.” During a January earnings call, Zuckerberg highlighted plans to introduce new products that demonstrate Meta’s ability to deliver uniquely personalized experiences.

According to Zuckerberg, the next generation of Meta AI products will leverage users’ interests, browsing behavior, relationships, and historical activity across Meta’s ecosystem. The goal is to create a highly individualized assistant capable of anticipating user needs and offering tailored suggestions.

The shopping research tool appears to be an early example of this broader strategy. By combining AI recommendations with data already available through Meta’s platforms, the company aims to enhance relevance and engagement.

Personalized Recommendations Based on User Data

Testing of the new feature revealed that Meta AI’s product suggestions can reflect a user’s location and inferred gender. For example, when asked for puffer jacket recommendations, the chatbot referenced New York as the user’s location and displayed women’s jacket options based on name inference.

This level of personalization underscores Meta’s strength in leveraging user data gathered through platforms like Facebook and Instagram. By integrating AI with its extensive data ecosystem, Meta can potentially deliver more targeted recommendations compared to standalone AI tools.

However, the use of inferred attributes also raises questions about transparency and user control, particularly regarding how demographic information influences search results.

No In-App Checkout — Yet

Currently, Meta’s shopping research tool does not support direct purchases within the chatbot. Instead, users can click on merchant links to browse products on external retail websites. This suggests that the feature is primarily focused on product discovery rather than full transaction management.

Meta has not clarified whether it earns referral commissions from merchants featured in chatbot recommendations. Additionally, the company has not addressed whether advertisers on its social media platforms receive preferential placement in AI-generated suggestions.

The Future of AI-Driven Commerce

Zuckerberg’s earlier comments provide potential insight into Meta’s long-term vision. He noted that while Meta’s advertising tools already help businesses reach targeted audiences, future “agentic shopping tools” will enable users to find highly specific products from businesses within Meta’s catalog.

If expanded, the shopping research feature could become a bridge between Meta’s advertising infrastructure and AI-powered product discovery. This would allow the company to integrate commerce more deeply into its AI ecosystem while offering users a streamlined shopping experience.

As AI chatbots increasingly become gateways to online information and services, the competition among Meta, OpenAI, and Google in AI-powered commerce is likely to intensify. The ability to deliver highly personalized, transparent, and trustworthy recommendations may ultimately determine which platform gains a competitive edge.