Meta Platforms, Inc. Q2 FY2025 earnings call

Management Statement and Operational Highlights

• AI Initiatives: Meta has established Meta Superintelligence Labs, with progress on Llama 4.1 and 4.2, and a talent-dense team led by Alexandr Wang, Nat Friedman, and Shengjia Zhao. Investments in compute clusters like Prometheus and Hyperion are underway.
• Business Opportunities:
  • Advertising: AI-powered ad recommendations drove 5% more ad conversions on Instagram and 3% on Facebook, with generative AI features contributing to ad revenue.
  • Engaging Experiences: AI improved content discovery, with a 5% increase in time spent on Facebook and 6% on Instagram. AI video editing tools launched.
  • Business Messaging: Testing of business AIs shows product market fit, integrated into ads and e-commerce sites.
  • Meta AI: Over 1 billion monthly active users, focus on deepening the experience with next-generation models.
  • AI Devices: Ray-Ban Meta sales accelerated, with Oakley Meta HSTN launched.
• Financials: Q2 total revenue $4７.5 billion, up 22%; operating income $20.4 billion, 43% operating margin; net income $18.3 billion; CapEx $17 billion; free cash flow $8.５ billion; $9.8 billion in stock repurchased; $1.3 billion in dividends paid.

Segment Performance

• Family of Apps: Q2 total revenue was $47.1 billion, up 22% year-over-year. Family of Apps ad revenue was $46.6 billion, up 21% year-over-year, with the online commerce vertical being the largest contributor to growth. Family of Apps other revenue was $583 million, up 50%, driven by WhatsApp paid messaging revenue and Meta Verified subscriptions. Family of Apps expenses were $22.2 billion, representing 82% of overall expenses, and operating income was $25 billion, with a 53% operating margin.
• Reality Labs: Q2 revenue was $370 million, up 5% year-over-year due to increased sales of AI glasses, partially offset by lower Quest sales. Expenses were $4.9 billion, up 1% year-over-year, resulting in an operating loss of $4.5 billion.

Guidance

• Q3 2025: Total revenue expected $47.5 billion to $50.5 billion.
• Full-Year 2025: Total expenses expected $114 billion to $118 billion, narrowed from prior outlook.
• 2026: Expense growth expected to be faster due to infrastructure and employee compensation increases. CapEx expected to grow significantly in 2026 to support AI efforts.

Risks

• Regulatory: Increasing legal and regulatory headwinds in the EU, particularly with the LPA offering, which could negatively impact European revenue.
• AI Uncertainty: Uncertainties around the pace and impact of AI progress on business outcomes.

Question and Answer

Q: Mark, when you think about where the AI parts of your business have been evolving over the last 3 to６ months, I wanted to know what your key learnings were as you went deep into that strategy that informed some of the shifts in both talent, acquisition and compute, coupled with some of the blogs you put out recently in terms of how that strategy might have evolved based on those key learnings? And Susan, building on Mark's comments on scaling talent and compute, I want to know if you go a little bit deeper on how we should be thinking about those two components driving some of the commentary you've given around OpEx and CapEx over the next１２ to１８ months.

A: Mark discussed observing AI progress trajectory and the importance of elite talent and leading compute. Susan talked about 2026 expense growth driven by infrastructure (depreciation, operating costs) and employee compensation from AI talent hiring.

Q: Brian, on the sort of forward-looking road map for the core recommendation engine. There are a handful of shorter-term things that we're focused on in the near term. One is we're focused on making recommendations even more adaptive to what a person is engaging with during their session so that the recommendations we surface are the most relevant to what they're interested in at that moment. And we're making optimizations to help the best content from smaller creators break-out by matching it to the right audiences sooner after it gets posted. And we're also working on improving the ability for our systems to discover more diversified and niche interest for each person through interest exploration and learning explicit user preferences. We're also planning to scale up our models further and incorporate more advanced techniques that should improve the overall quality of recommendations. But we also have a lot of long-term bets in the hopper around areas like developing foundational models that will support recommendations across multiple services.Incorporating LLMs more deeply into our recommendation systems. And a big focus of this work is going to be on optimizing the systems to make them more efficient. So that we can continue to scale up the capacity that we use for our recommendation systems without eroding the ROI that we deliver.

A: Mark talked about research on self-improvement and team configuration for frontier research. Susan detailed near-term and long-term focus on recommendation engine improvements.

Q: Doug, on open source AI, has your thinking changed here at all, just as you pursue superintelligence and push for even greater returns on your significant infrastructure investments? And then, Susan, your comments on '26 CapEx suggest more than $１００ billion of spend next year potentially. Do you continue to expect to finance all this yourself? Or could there be opportunities to partner here?

A: Mark said they will continue open sourcing work but consider practicality. Susan discussed exploring partnerships for data center development.

Q: Justin, I'll ask another one on infrastructure. Mark, your spend is now approaching some of the biggest hyperscalers out there.Do you think of all this capacity mostly for internal uses? Or do you think there's a way to share or even come up with a business model where leveraging that capacity for external uses? And then Susan, when you think about the ROI on this CapEx, I'm sure you have internal models, I'm sure you can't share all that, but how are you thinking about the ROI? And are you optimistic about the long-term returns?

A: Susan said current focus is internal use for AI work. ROI on core AI is strong; genAI is early but optimistic on long-term monetization.

Q: Mark, as you go after the superintelligence vision, especially for those of us on the outside, what are kind of some of the markers or KPIs that you're tracking on whether you're on track and making progress? Is it really against kind of those five pillars you outlined above? Or should we be thinking more broadly? And Susan, obviously AI delivering great ROI today, all those investments and also building towards kind of longer-term goals, just curious, has there just been any change or adjustment to how you think about the relationship between revenues or core business performance and the cadence of investment?

A: Mark mentioned tracking model quality, team quality, and AI system improvement across the company. Susan said focus is on consolidated operating profit growth, with investments in AI setting up future growth.

Q: Ron, I wanted to ask you on Meta AI, and I think you talked about in the call just growing engagement overall, particularly on WhatsApp. And now we have 1 billion users on the platform and the focus is now on driving personalization. So I want to understand a little bit more how these next-gen models can help drive adoption here, particularly with Behemoth coming online at some point. And then as people are using that Meta AI with WhatsApp, thoughts on search and queries and potentially monetizing that.

A: Q: Youssef, I have two.So Mark, the Ray-Ban initiative has been a [ hallmark ] for you guys so far. Where are we on the development of glasses? And has that new computational platform that you've talked about in the past, is it moving faster or slower than you thought? And as you leverage Meta AI, do you believe glasses ultimately replace smartphones? Or do you need the new form factor that's AI first? And then, Susan, just quickly, how do you guys see SBC progressing over the next couple of years? Is it fair to assume it will grow materially faster than the revenue and OpEx? And how do you minimize shareholder dilution?

A: Mark talked about Ray-Ban and Oakley Meta progress, seeing glasses as key AI form factor.Susan said SBC impact is factored into expense outlooks, with focus on buybacks and dividends to manage dilution.