Meta reported strong Q4 2025 with total revenue of $59.9B (+24%), guided Q1 2026 revenue to $53.5-56.5B, and announced a massive FY2026 capex plan of $115-135B while still guiding absolute operating income above 2025. The call emphasized AI-driven ad performance gains, persistent compute capacity constraints through 2026, and deliberate silicon diversification across NVIDIA, AMD, and in-house MTIA. Q4 total revenue $59.9B (+24% YoY); Q4 capex $22.1B; FY2026 capex guided to $115-135B.
Meta reported strong Q4 2025 with total revenue of $59.9B (+24%), guided Q1 2026 revenue to $53.5-56.5B, and announced a massive FY2026 capex plan of $115-135B while still guiding absolute operating income above 2025. The call emphasized AI-driven ad performance gains, persistent compute capacity constraints through 2026, and deliberate silicon diversification across NVIDIA, AMD, and in-house MTIA. Q4 total revenue $59.9B (+24% YoY); Q4 capex $22.1B; FY2026 capex guided to $115-135B.
Management framed 2026 as a major AI acceleration year: agents are starting to work, Meta is shifting toward personalized AI experiences, and AI is being fused with recommendation and ad systems to improve both organic content and monetization. The strategic goal is 'personal…
Q4 total revenue $59.9B (+24% YoY); Q4 capex $22.1B; FY2026 capex guided to $115-135B.
Q1 2026 revenue guide of $53.5-56.5B implies continued acceleration, helped by a ~4% FX tailwind.
Guidance · revenue to $55B
Management framed 2026 as a major AI acceleration year: agents are starting to work, Meta is shifting toward personalized AI experiences, and AI is being fused with recommendation and ad systems to improve both organic content and monetization. The strategic goal is 'personal super intelligence,' with glasses and business AI as forward-looking product surfaces, though model releases are expected t
Meta is guiding to a major capex step-up to $115-135 billion while still expecting absolute operating income to grow, reflecting confidence that AI-driven ad performance and revenue growth can outpace the investment surge.
Capex is being raised sharply again, with 2026 guidance of $115–135 billion, and compute capacity is still expected to be constrained through much of the year until new facilities come online. Management emphasized infrastructure flexibility, silicon diversity, and efficiency gains as strategic priorities, and expects operating income to remain above 2025 despite the step-up.
Executives repeatedly described 2026 as an inflection point for AI, cited strong demand and ad performance, and reaffirmed the ability to grow operating income while making a major infrastructure push.
“On your first question, we do continue to be capacity constrained.”
“we expect over the course of 2026 to have significantly more capacity this year as we add cloud, but we'll likely still be constrained through much of 2026 until additional capacity from our own facilities comes online later in the year.”
“To that end, in Q4 we extended our Andromeda ads retrieval engine, so it can now run on NVIDIA, AMD, and MTIA. This, along with model innovations, enabled us to nearly triple Andromeda's compute efficiency.”
| Metric | Period | Range | Midpoint | Status |
|---|---|---|---|---|
| Capex | FY2026 | $115B–$135B | $125B | GUIDED |
| Revenue | FY2026 Q1 | $53.5B–$56.5B | $55B | GUIDED |
| Issued | Metric | Target | Guide | Actual | Outcome |
|---|---|---|---|---|---|
| FY2026 Q1 | Revenue | FY2026 Q2 | $58B–$61B | $60.801B | Met / beat |
Meta's Andromeda ads retrieval engine now runs on NVIDIA, AMD, and in-house MTIA, and Meta nearly tripled Andromeda's compute efficiency; MTIA will also expand into core ranking/training workloads in Q1. — Meta is intentionally reducing reliance on any single GPU vendor for ad ranking/inference, giving AMD more hyperscale credibility while NVIDIA's pricing power is incrementally contested by MTIA.
“To that end, in Q4 we extended our Andromeda ads retrieval engine, so it can now run on NVIDIA, AMD, and MTIA.”
… our core business in 2026. We plan to continue to prioritize investing in the business to support these opportunities while also positioning us for an entirely new and exciting product cycle over the coming years powered by our AI models. Procuring sufficient infrastructure capacity is central to these initiatives. And we're working to meet our silicon needs by deploying a variety of chips that optimally support each of our different workloads. To that end, in Q4 we extended our Andromeda ads retrieval engine, so it can now run on NVIDIA, AMD, and MTIA. This, along with model innovations, enabled us to nearly triple Andromeda's compute efficiency. In Q1, we will extend our MTIA program to support our core ranking and recommendation training workloads, in addition to the inference workloads it currently runs. More broadly, as we invest in infrastructure to meet our business needs, we continue to prioritize maintaining long-term flexibility so we can adapt to how the market develops. We're doing so in several ways, including changing how we develop data center sites, establishing strategic partnerships, contracting cloud capacity, and establishing new ownership structures …
Meta says it remains compute capacity constrained through much of 2026, with cloud capacity added before its own new facilities come online later in the year.
Thanks so much for taking the question. Maybe two, if I could. In prior periods, you've talked about being capacity constrained internally and not having enough compute to sort of achieve the goals you have on a platform and a product standpoint. I want to know if we get any update on currently how you think about your own internal needs for compute against that roadmap. And the second part of the question would be, as we continue to see the ads business sort of scale, especially in terms of dollar growth year on year. Have we yet seen the full first order effects of scaling the business against applying more compute to it? Or how should investors think about the direction of relationship…
On your first question, we do continue to be capacity constrained. Our teams have done a great job ramping up our infrastructure through the course of 2025, but demands for compute resources across the company have increased even faster than our supply. So we expect over the course of 2026 to have significantly more capacity this year as we add cloud, but we'll likely still be constrained through much of 2026 until additional capacity from our own facilities comes online later in the year. With that said, I think we have done a good job internally mitigating the impact of compute constraints on our business. I expect that will continue to be the case in 2026. We're continuing to focus on increasing our infrastructure efficiency in several ways, including by optimizing workloads, improving infrastructure utilization, diversifying our chip supply, and just investing in efficiency improvements as part of our core technology development efforts in areas like content and ads ranking. So that was your first question. The second question about how the ads business scales. I don't have an extremely precise answer to this question. What I'd say is one of the ways that we are working to drive ads performance improvements is by improving our larger scale models along with our lighter weight ones that we use for ads inference at runtime. You know, we don't typically use our larger model architectures like GEM for inference because their size and complexity would make it too cost prohibitive. So the way that we drive performance from those models is by using them to transfer knowledge to smaller lightweight models used at runtime. But I would say that we think that there is room for our larger models to benefit from having more compute. And I think as we scale up the compute available to those models and the foundational models in different areas that power the different stages of ads ranking and recommendation, we expect that we will see gains coming from that.