Meta Focuses on Personal AI Agents in Q2FY26 Earnings Call
Meta announced plans to develop and deploy personal AI agents during its Q2FY26 earnings call, aiming to make them a significant future market. The company is also expanding enterprise AI offerings and investing in infrastructure.
Meta is aggressively pursuing the development and widespread deployment of personal AI agents, which CEO Mark Zuckerberg described as the next major product category following coding assistants. During the company's Q2FY26 earnings call, Zuckerberg outlined a strategy to build these agents at scale, capable of working 24/7 on behalf of users to help achieve personal goals.
The company's vision extends to enhancing user lives, health, relationships, and finances through these AI agents. Meta is investing heavily in compute capacity to support these future products and is seeing strong growth across its platforms, with 3.6 billion daily active users across its apps. The development of new AI models, subscription services, and hardware designed for what it calls "personal superintelligence" are also key components of this strategy.
WhatsApp will serve as a primary interface for these personal AI agents, with Meta innovating on private and secure AI experiences within its messaging apps. The launch of "Incognito Mode" on WhatsApp and the Meta AI app are steps toward ensuring privacy, with Zuckerberg emphasizing that security will be fundamental to the agents being built.
Beyond consumer applications, Meta highlighted the adoption of its enterprise AI products, with over one million businesses already using Meta Business Agents on WhatsApp and Messenger weekly. The company aims to create a comprehensive "business-in-a-box" service leveraging its platforms. Additionally, Meta is enhancing its recommender systems using large language models (LLMs), which are driving improvements in content ranking and recommendations across Facebook and Instagram.
Meta plans to continue significant infrastructure spending, raising its 2026 capital expenditure guidance to $130-145 billion, citing high demand for compute power in the AI industry. This focus on infrastructure is crucial for training foundation models, supporting personal and enterprise agents, and developing APIs and coding tools.