This ranking evaluates how Fortune 500 companies are deploying AI, and how technology leaders value those investments relative to industry peers.

Fortune AIQ 75

The companies on the Global 500 employ 70.2 million people, and their revenue represents more than one-third of the world’s GDP.

Fortune Global 500

With a combined revenue of $21 trillion and $2.1 trillion in profits, the 2026 Fortune 500 is the definitive list of America's largest companies.

Fortune 500

AI is a major force shaping this year’s list—but it’s far from the only story.

World's Most Admired Companies

Fortune AIQ 50

The corporations on our annual list of the world's 500 largest companies combined to generate $41.7 trillion in revenue over the past year.

Almost $20 trillion in revenue—and a record-breaking $1.87 trillion in profits. Introducing the 2025 Fortune 500, the definitive list of the biggest companies in America.

Meta Platforms leaped eight spots to No.&nbsp;22 as its revenue rose 22% year over year, powered by advertising on Facebook and Instagram. Profits topped $62&nbsp;billion, even as Meta spent prodigiously on AI development.

The 300 companies on this year’s list brought in over $12.6 trillion in revenue for the latest 12 months, with the median revenue figure at $22 billion.

America's Most Innovative Companies

Leaders in all industries rely more than ever on Big Tech—and appreciate the giants that deliver, at scale, without drama.

These 50 companies exemplify the innovation, progress, and potential of AI.

AI Innovators 2025-Meta

Fortune 50 AI Innovators

The corporations on our annual list posted aggregate revenues of $41 trillion for the second year in a row.

Industries included in the sector are computers, internet services, IT services, semiconductors, and software companies.

Fortune Sector Leaders: Technology

The Fortune 500, in its 70th year in 2024, ranks the biggest U.S. companies by revenue.

The 26th edition of the list shows how much esteem Big Tech giants command among other business leaders.

The 25th Fortune World’s Most Admired Companies list suggests that tech giants’ peers respect them as much as ever.

The corporations on our annual list of the world’s largest companies delivered a record $41 trillion in aggregate revenues.

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The 300 companies on this list are transforming industries from the inside out.

The Fortune 500, in its 69th year in 2023, ranks the biggest U.S. companies by revenue.

F500 2023-Meta

The social media giant and metaverse pioneer formerly named Facebook posted a 1% decline in revenues in 2022, the first such decline in its history as a public company; it still brought in nearly $117 billion. 
<ul> 
 	<li>With a round of layoffs announced in late 2022 and another in spring 2023, Meta cut about 21,000 jobs, or roughly a quarter of its workforce. CEO Mark Zuckerberg described 2023 as a “year of efficiency” in which a leaner Meta can execute its highest priorities more quickly.</li> 
 	<li>Meta pivoted some of its attention and resources from the metaverse to generative A.I. In February 2023, the company announced an A.I. language model called LLaMA that it would use in its chatbots to compete with Google and Microsoft.</li> 
 	<li>In May 2023, the U.S. Federal Trade Commission accused Meta’s core Facebook platform of violating an earlier agreement in which the company had pledged to protect the data of users of its Messenger Kids app. The FTC proposed modifying the agreement to forbid Meta from monetizing minors’ data in any way. The company strongly objected to the proposal, calling it a “political stunt.”</li> 
 	<li>Later in May, the European Union ordered Meta to stop sending Europeans’ personal data to the U.S., and imposed a $1.3 billion fine on the company for its past data transfers, which it said violated European privacy laws.</li> 
</ul>

Aggregate sales hit $37.8 trillion last year—an increase of 19%, the highest annual growth rate in the list’s history.

This year’s Fortune 500 marks the 68th running of the list.

F500 2022 Meta

The company formerly known as Facebook climbed seven spots on this year’s Fortune 500; its revenue grew 37.2% in 2021, to $118 billion, and it generated nearly $40 billion in profit. But the name change to Meta Platforms, announced by CEO Mark Zuckerberg in October 2021, is a sign of some of the challenges the company now faces. Meta Platforms’ core social media product, Facebook itself, saw its number of daily active users decline for the first time over the last three months of 2021. Meta also acknowledged that recent changes in privacy settings on Apple’s iOS apps were hurting Facebook’s ad-sales revenue. That changing landscape adds momentum to Meta’s new commitment to the metaverse, the digital and virtual world that Citi predicts will be worth $13 trillion in 2030. In December, Meta disclosed that it had already spent $10 billion on metaverse-related projects. The company admits those will take a long time to pay off, however, and Wall Street is concerned: The company’s share price fell almost 40% over the first four months of 2022.

Total revenue for the world’s biggest companies fell 4.8% to $31.7 trillion this year—the first decline in half a decade.

G500-2021-086-Facebook

Facebook, it appears, can’t be hurt—not by major ad buyers boycotting its service, not by state and federal investigations, and not even by a pandemic. Despite the impact of COVID-19 on its ad sales, Facebook impressed Wall Street all year by beating expectations. The company ended 2020 with almost $86 billion in revenues, up 22% year over year, and with $29 billion in profits, up nearly 58%. But Facebook faced major backlash for lax handling of hate on its service, including from former President Donald Trump, whom the service eventually barred after the U.S. Capitol riot. And this year, Facebook risks a big financial hit owing to Apple’s new privacy controls that let users limit tracking of their data.

This year’s Fortune 500 marks the 67th running of the list.

F500-2021-34-Facebook

Collectively, the ranked companies produced a three-year average shareholder return of 33%, vs. 19% for the S&P 500.

FGC-2021-Facebook

100 Fastest-Growing Companies

For an astounding 13th straight year, Apple claims the top spot in Fortune’s annual ranking of corporate reputation, based on a survey of almost 3,800 executives, directors, and analysts.

This year's Fortune Global 500 generated $33.3 trillion in revenues and $2.1 trillion in profits.

G500-2020-144-Facebook

Facebook was the focus of increased government scrutiny in 2019. Regulators at the state, federal, and international level reviewed the ways the company has handled user data and permitted the dissemination of misinformation—investigations that sometimes led to hefty fines or other penalties. Despite the mounting heat, Facebook continued to rake in revenue in 2019. The company, led by CEO Mark Zuckerberg, had $70.7 billion in sales, up nearly 27% from the previous year. But 2020 will be a particularly challenging one for Facebook as it tries to proactively remove misinformation about the coronavirus pandemic while policing bad actors on its platform during the upcoming U.S. presidential election.

Explore this year's ranking of the 50 companies with the best long-term growth potential.

Future-50-2020-Facebook

Last but not least, the social media player’s underlying business remains strong. And new business opportunities continue to emerge: A global e-commerce strategy (including WhatsApp Pay) shows signs of promise, for example. Analysts project 17% to 20% future, long-term growth. At the same time, reputational risk is high—as is the very real threat of regulatory crackdowns across different regions, including the United States. Perhaps even more ominous, the Facebook core product is seeing declining engagement levels with younger demographics. In short: There is a reason the social network is on this list, but also a reason it comes in at No. 50.

The Future 50

This year's Fortune 500 marks the 66th running of the list.

F500-2020-046-Facebook

Facebook was the focus of increased government scrutiny in 2019. Regulators at the state, federal, and international level reviewed the ways the company has handled user data and permitted the dissemination of misinformation—investigations that sometimes led to hefty fines or other penalties. But despite the mounting heat, Facebook continued to rake in revenue in 2019. The company, led by CEO Mark Zuckerberg, had $70.7 billion in sales, up nearly 27% from the previous year, which helped it rise nearly a dozen ranks in the Fortune 500. But 2020 will be particularly challenging one for Facebook as it tries to proactively remove misinformation about the coronavirus pandemic while policing bad actors on its platform during the upcoming U.S. presidential election.

Collectively, the ranked companies produced a three-year average shareholder return of 19%, vs. 11% for the S&P 500.

Fastest Growing Companies 2020-52-Facebook

Here are the World's Most Admired Companies for 2019 as ranked in their industries. See who made the list.

This year's Fortune Global 500 generated $27.7 trillion in revenues and $1.5 trillion in profits.

Fortune's proprietary methodology allows us to zero in on the 50 global firms that are best poised to deliver—and continue delivering—above average gains.

This year's Fortune 500 marks the 65th running of the list.

Everybody loves an up-and-comer: 2019's top three-year performers in revenues, profits, and stock returns.

Here are the World's Most Admired Companies for 2018 as ranked in their industries. See who made the list.

Welcome to the inaugural Fortune Future 50, our new ranking of companies best positioned for breakout growth.

This year's Fortune 500 marks the 64th running of the list.

Everybody loves an up-and-comer: 2018's top three-year performers in revenues, profits, and stock returns.

Here are the World's Most Admired Companies for 2017 as ranked in their industries. See who made the list.

This year's Fortune 500 marks the 63rd running of the list.

Everybody loves an up-and-comer: 2017's top three-year performers in revenues, profits, and stock returns.

Here are the World's Most Admired Companies for 2016 as ranked in their industries. See who made the list.

This year's Fortune 500 marks the 62nd running of the list.

Too fast to be stopped: 2016's top three-year performers in revenue, profits, and stock returns.

Business insiders have spoken: Asked which companies they most admire, more than 4,100 executives, directors, and analysts chose these top 50 All-Stars.

This year's Fortune 500 marks the 61st running of the list. Wal-Mart claims the top spot again. See who made the list.

Nike, Cisco, MasterCard, SABMiller, Vodafone, Google—and 45 other companies that are doing well by doing good.

Change the World

This year's Fastest-Growing Companies marks the 19th running of the list. In our annual list of public companies with the most stellar three-year profit, revenue, and stock growth, Facebook makes its first appearance.

Wal-Mart stayed on top as its sales crept closer to half-a-trillion dollars. Apple moved into the top five. And UnitedHealth Group continued its steady climb. 

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Founded in 2004 as Facebook, Meta operates the world’s largest social media platforms and has heavily influenced the development of the internet over the past two decades through its pioneering use of personal data collection and targeted advertising. Meta’s products include Facebook, image-sharing app Instagram, and messaging platforms Messenger and WhatsApp; together, they enable users to keep in touch, share images and videos, and buy and sell goods. Meta’s Reality Labs segment develops virtual-reality-related consumer hardware and software, while Meta AI develops artificial intelligence tools including language-processing models. Facebook renamed itself Meta in 2021.

Q2 2026

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Meta Platforms (META): Company Profile, Stock Price, News, Rankings

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Meta Platforms

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My name is Krista, and I will be your conference operator today. At this time, I would like to welcome everyone to Meta's second quarter 2026 earnings conference call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question, please press star, then the number one on your telephone keypad. To withdraw your question, again, press star one. We ask that you limit yourself to one question, and this call will be recorded. Thank you very much. Chad Heaton, Meta's Vice President of Finance, you may begin.

Thank you. Good afternoon and welcome to Meta Platforms' second quarter 2026 earnings conference call. Joining me today to discuss our results are Mark Zuckerberg, CEO, and Susan Li, CFO. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today's earnings press release and in our quarterly report on Form 10-Q filed with the SEC. We undertake no obligation to update any forward-looking statement. During this call, we will present both GAAP and certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release. The earnings press release and an accompanying investor presentation are available on our website at investor.meta.com. Now I'd like to turn the call over to Mark.

All right. Hey, everyone. Thanks for joining today. We had a strong quarter for our community and business with 3.6 billion people using at least one of our apps each day. We reached several milestones. Instagram reached 2 billion daily actives. Threads crossed 500 million monthly actives, making it the fastest-growing conversation app ever. Facebook has reached more than 2 billion daily actives for a while now. WhatsApp just hit an all-time messaging record, peaking at 30 million messages sent per second during the World Cup final. Also, Kunal Shah just joined as our new Head of WhatsApp. He built one of India's most important payment companies and will be a great addition to the team. We also shipped some strong new models from Meta Superintelligence Labs and released new glasses.

Overall, the scale and reach of our community across our apps is pretty remarkable, and it gives us a strong platform for delivering new innovations to billions of people. The opportunity in front of us is massive. First, we are now at a point where our investments in AI are accelerating every major part of our core business. They're improving the experience for people using our apps, driving better performance for advertisers, and helping our teams build new experiences and ship faster. Second, we are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead. Third, we see a large enterprise opportunity to sell to businesses, including APIs, business agents, potentially selling Compute directly and other services that we're building for large customers.

I want to spend some time today laying out exactly how our investments are delivering results today and the opportunities that we see over time. For accelerating our core business, we are seeing strong and promising results in several areas. In Instagram and Facebook, I am very optimistic about our work to integrate large language models into our recommendation systems. LLMs add a first principles understanding of what the content is about and why it is compelling, as well as a deeper understanding of what people are interested in and what their goals are when they are using our apps. This means that we can show more relevant and engaging content that better reflects people's goals and interests. Our new Muse Image and Muse Video models will also dramatically expand the universe of content that people can discover across our platforms.

There are already two large sets of content to draw from. First, from your friends and the people you follow, and second, from creators that you don't follow. Now there's going to be a whole new and nearly infinite universe of personalized content. This is going to make our services a lot more useful and engaging for people. For ads, we are using LLMs to improve how our systems predict and rank the ads that we show. We've expanded the context that we can take into account around a person's organic and ads activity to determine an ad's relevance, driving significant increases in relevance and conversions on both Facebook and Instagram. On a dollar basis, our ads business is reporting faster year-over-year revenue growth than any other company's reported ad business. These AI investments are paying off.

We are also seeing a lot of demand for our new AI-powered creative tools. 9 million small businesses on our platforms are now using at least one of our AI ad creative tools, and we're rolling out new end-to-end creative solutions that help advertisers translate performance data into their creative decisions. Muse Image is going to supercharge this. The model can analyze images, improve its own work, and produce better ad variations based on advertiser input. We're getting great feedback on this so far. We also just launched Meta One, a new subscription offering that provides more tools and AI features across our apps. As demand grows, we're going to offer a variety of different tiers and pricing options there as well. I'm also excited about how AI is helping our teams speed up product development. Earlier this year, we shipped Instagram Instants.

We also just launched Forum, a standalone groups app, and Seller, a standalone marketplace app. I expect it to become a lot easier to ship new apps, we are planning to build out more ideas and use our recommendation systems to scale them to the people who will find them interesting, as we've done with Threads. It's been a little more than a year since we launched Meta Superintelligence Labs, our trajectory is strong. In the last month, we shipped Muse Spark 1.1 and Muse Image. Since we rebuilt Meta AI and integrated Muse Spark, we have seen a 60% increase in the number of people interacting with the assistant each day, that continues to grow quickly week-over-week. Muse Spark 1.1 is a strong agentic encoding model that is very efficient and excels at computer use, tool use, and multimodal understanding.

It's available through our new public API. We are ramping up distribution through partner channels and more coding agents over the coming weeks. We are also building out features to make it easier for enterprises to adopt Muse Spark. That is an area that we expect to continue focusing on. One reason that we are so focused on making Muse Spark great at agentic capabilities is that we think that there's a very big opportunity to ship a few types of agents that are aligned with our mission and business. The first is personal agents. Soon, we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want.

The first domain that agents have really taken off in is coding. Engineers are more technical and willing to spend time making those agents work. To build great personal agents, this needs to be a great consumer product that just works out of the box and is easy enough for billions of people to adopt and use. I'm very excited about this. We're going to have more to share soon. As we move towards a future where we're all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important. WhatsApp is already the leading surface where people engage with Meta AI. As we build out a platform for more agents across our messaging apps, we're going to innovate on how to deliver a private and secure AI experience.

We launched incognito mode this quarter on WhatsApp and the Meta AI app, allowing people to have private conversations with their assistant that even Meta can't see. We're planning to make strong privacy and security a fundamental part of the agents that we're building as well. I'm also very excited about our progress with business agents. We made Meta Business Agent available globally this quarter on WhatsApp and Messenger. There are already more than 1 million businesses using them to talk to their customers or complete sales every week. We're rolling business agents out on Instagram now, too. One interesting thing about having an agent talk to your customers every day is that it learns over time and can bring all of those insights back to you. We're building more agentic capabilities to summarize all these conversations, digest what happened overnight, and surface what customers are asking for.

Soon it'll go further, including suggesting ways to grow your business, giving you competitive intelligence and real-time insights into what's working and what's not. Over time, we'd like to build this into a business-in-a-box service that can help you start and run a whole business using Meta's platforms. In terms of how we will monetize these, we have a mix of subscriptions, volume-based pricing. I expect that we're going to continue to evolve more of these products to be like our ad systems, where businesses only pay us when we achieve results for them. Over time, that will let us run an efficient auction over our compute, similar to how we do that for advertisers today. As AI usage in our products and businesses continues to ramp, we continue to invest aggressively in infrastructure to meet the demand.

Yesterday, as part of our Meta Compute effort, we announced a new strategic venture with BlackRock to develop a new 1 GW data center in El Paso, Texas. Overall, we expect that a significant portion of our compute is going to go towards training our models, growing our core business, and delivering personal agents and new products. We also expect to grow a large business serving large customers as well. We've built our API. We're rolling out business agents. We're getting a lot of offers for compute at a significant premium over what we paid for it, and we have more coding and productivity tools on our roadmap as well. We'll have more to share on all of this soon. As we get closer to personal superintelligence, we are also going to need hardware that allows you to seamlessly interact with it.

Glasses are the ideal form factor since they can be with you throughout the day and they can assist you without pulling you away from the moment. Our glasses remain one of the fastest-growing consumer electronics of all time. We continue adding to the lineup. We just released our own line of Meta glasses in collaboration with EssilorLuxottica, including a style that we designed with Kylie Jenner. They're the first glasses to ship with Muse Spark out of the box so that they can understand what you're seeing and give even more helpful answers. Early sales have been strong, exceeding our expectations, and we're going to have more to share on our glasses lineup at this year's Connect conference on September 23rd. I encourage you to tune in then.

Before I wrap, I want to mention that I just published an op-ed about why I'm so optimistic that we are building a positive future for everyone. At Meta, we have always built technology to put power in people's hands, so that they can connect with the people they care about and shape the world in the ways that they want. It's why we've always focused on making our products affordable and accessible for everyone. It's a strategy that has served both our community and our business well. As we enter this next chapter, that same philosophy is guiding how we approach developing AI. We're the only major company building AI with the primary goal of putting superintelligence directly into people's hands. Rather than centralizing superintelligence, we are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them.

That's the way that society has always made progress. I think that these are the right values for building a positive AI future, and if we help build this, then I think that we will continue to build a very strong business as well. That's everything I wanted to cover upfront today. AI is improving our core business. It's making our apps more relevant and delivering better results for businesses. We're starting to deliver more novel products, and we'll have a lot more there soon as well. We are investing aggressively because the potential is huge, and we know that there are many ways to deliver value here. As always, I'm grateful to all of you for being on this journey with us. Now, here's Susan.

Thanks, Mark, and good afternoon, everyone. Let's begin with our segment results. All comparisons are on a year-over-year basis, unless otherwise noted. Q2 total Family of Apps revenue was $60.4 billion, up 28% year-over-year. Q2 Family of Apps ad revenue was $59.4 billion, up 27%, or 26% on a constant currency basis. In Q2, the total number of ad impressions served across our services increased 14%. Impression growth was healthy across all regions, driven by growth in engagement and users, as well as ad load optimizations. The global average price per ad increased 12% year-over-year, driven by ad performance gains, improvements in macro conditions relative to Q2 of last year, and currency tailwinds. This was partially offset by strong impression growth, particularly from lower monetizing surfaces and regions.

For the first time, quarterly Family of Apps other revenue reached $1 billion and grew 73% year-over-year, driven primarily by WhatsApp paid messaging and subscriptions revenue. Within our Reality Labs segment, Q2 revenue was $431 million, up 16% year-over-year due to strong growth in AI glasses revenue, partially offset by lower Quest headset sales. Moving now to our consolidated results. Q2 total revenue was $60.8 billion, up 28%, or 27% on a constant currency basis. Q2 total expenses were $42 billion, up 55% compared to last year, and included $2.4 billion in charges related to legal proceedings and $1.2 billion in severance expenses in connection with the May 2026 headcount reduction. Year-over-year growth was primarily driven by increases in employee compensation, infrastructure costs, legal-related costs, and third-party AI token costs.

Excluding the previously mentioned severance expense, growth in employee compensation was driven by technical hires we've added over the past year, particularly AI talent. The growth in infrastructure cost was driven by higher depreciation, data center operating costs, and third-party cloud spend. We ended Q2 with over 75,000 employees down 3% from Q1. This total includes approximately 8,000 employees impacted by the May 2026 headcount reduction. We expect the majority of impacted employees will no longer be captured in our headcount by the end of Q3 2026. Second quarter GAAP operating income was $18.8 billion, representing an 8% decline year-over-year and a 31% operating margin. Excluding the Q2 legal charges and severance expenses, our second quarter operating income would have increased 9% year-over-year. Our tax rate for the quarter was 16%. Net income was $15.8 billion, or $6.18 per share.

Capital expenditures, including principal payments on finance leases, were $31.1 billion, driven by investments in servers, data centers, and network infrastructure. Free cash flow was $784 million. We ended the quarter with $90.3 billion in cash and marketable securities and $83.7 billion in debt. Turning now to the business performance. There are two primary factors that drive our revenue performance, our ability to deliver engaging experiences for our community and our effectiveness at monetizing that engagement over time. On the first, we continue to see significant gains from our content recommendation initiatives. On Instagram, global time spent this quarter grew double digits year-over-year this quarter, largely driven by improvements to our Feed and Reels recommendations. On Facebook, video time spent increased 9% globally year-over-year and over 10% within the U.S. and Canada, where it was driven by ranking improvements.

We are finding that LLMs are increasingly capable of delivering ranking and recommendations gains. First, they make our existing systems smarter by understanding what the content is actually about and generating better training data. Second, LLM-powered agents are also helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes. Earlier this year, we reached a milestone of every public Reels and Feed post on Instagram being automatically processed through an LLM and analyzed across dimensions from topics to tone, and we're working towards including more surfaces on Facebook as well. These signals can then be passed to downstream applications across ranking, recommendations, and content policy enforcement, which is a key building block toward greater personalization. This quarter, we also began using our Muse family of models to conduct content understanding across signals like video topic classification and summarization, and we've seen positive early results.

Finally, our recommendations are also becoming more personalized, surfacing more fresh content while giving people more direct control over what they see. On Reels, we shipped our largest single-release ranking improvement to date, combining faster inference with a new architecture that draws on deeper user history to improve predictions. This drove a 15 basis point increase in sessions on Instagram, with particular strength in reshares and time spent, which are both strong indicators of better content to user matching. We are now bringing this to Feed, where early results look comparable. We are also getting new content to people more quickly. Investments we've made in more real-time infrastructure and modeling improvements on new videos are allowing our largest ranking models to now identify high-quality new Reels at creation.

On Instagram Feed, over half of all recommended content is now less than one day old, more than double from a year ago. We're also giving people more direct control of the content they see. Today, Instagram users can visit the Your Algo page, which lets users write natural language prompts to tune their recommendations. Similarly, on Facebook, we launched Shape Your Feed. Early results show over 80% retention among users who engage with it. Looking forward, we're executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that are designed to power organic content and ads recommendations simultaneously, as well as developing LLM native recommender systems. We hit our first research milestone this half by continuously pre-training a large-scale model with recommendations data, and observing healthy scaling laws in the process.

We're encouraged by this milestone and expect continued progress in the second half of the year. Turning to the second driver of our revenue performance, increasing monetization efficiency. The first part of this work is optimizing the level of ads within organic engagement. Here, we continue to enhance our systems to show ads at the optimal time and location. In Q2, we also expanded availability of ads on our newer surfaces, including completing our global ads expansion on Threads. On WhatsApp, we have introduced support for more types of ad destinations and advertiser performance goals in status, and continue on track toward our global rollout. Moving to the second part of increasing monetization efficiency, improving performance for the businesses who use our services. Within our ad systems, we're delivering performance gains as we deploy more complex and predictive models.

This quarter, we introduced Meta Generative Recommender, a paradigm shift in how our ad system works. Rather than scoring every possible ad individually, we are now using LLMs to reason about ad content and user preferences together and predict the best ad for each person. This makes our ad matching more intelligent and more precise, which compounds performance gains for advertisers. We deployed the first generative model into our ads retrieval system and saw notable improvements in ads performance. Early pilots using LLMs to better understand user preferences drove a 1% increase in app event conversions on Instagram. In Q2, we also advanced our user understanding models to analyze ads and organic activity, simultaneously improve both user experience and advertiser performance. Combined with our GEM model for ads ranking and sequence learning, these advancements generated an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook.

We're also leveraging AI to empower businesses to more easily manage their campaigns, develop ad creative, and engage with their customers. Our AI-powered Advantage+ end-to-end solutions continue to grow, reaching over $75 billion in annual revenue run rate this quarter. We're working to deepen adoption as advertisers who leverage multiple tools see compounding performance gains. I'll share one example of how Advantage+ is making performance marketing meaningfully easier and more effective for SMBs. Underneat, an online apparel brand in India, had been setting up each campaign manually across Facebook and Instagram. After adopting Advantage+ sales campaigns layered with Advantage+ audience placements and budget optimization, they saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions. Adoption of our gen AI ad creative tools continues to scale, with over 9 million small businesses using at least one AI creative tool.

Image generation, which now lets advertisers produce more creatives at scale from existing content, including a new ability to create images from video assets, saw adoption more than double this quarter. We also introduced a new end-to-end creative solution that gives advertisers the AI infrastructure to translate real performance signals into their next creative decision while preserving brand identity and tone. We're building with agency integrations from day one so teams can diagnose, generate, and scale high-performing creative without leaving their existing workflows. Looking ahead with the rollout of Muse Image, we expect to further expand advertisers' ability to generate high-quality, on-brand creatives at scale. With Meta Business Agent, businesses are better able to serve their customers through our messaging apps by responding to inquiries, recommending products, and handling support around the clock.

Earlier this month, we also introduced the Meta Business Agent Platform, which gives enterprises the infrastructure to build, customize, and deploy their business agent at scale on WhatsApp. The platform provides larger businesses with enterprise-grade controls, guardrails, and measurement built in so they can define rules and offer personalized experiences, starting within the messaging apps that their customers already use. Movida, one of Brazil's largest rental car companies with nearly 400 locations, deployed a business agent on WhatsApp to handle the entire booking flow, from vehicle selection and pricing to payment, in a single conversation. Returning customers could complete a reservation in as few as three messages. In a one-month period, Movida reported a 44% increase in daily bookings through WhatsApp when compared to the same period in the prior year, that 85% of conversations in the channel were resolved entirely by the AI agent without human assistance.

We're also building out additional ways to monetize our ecosystem. Two additional revenue streams are subscriptions and monetizing our competitive models through an API. Meta One is an evolution of our subscription portfolio to create more value for everyday users, businesses, and creators, so they get more features and AI tools to create, connect, and stand out. We are excited to bring this to more users and continue to build enhanced tools for our subscribers. We also recently launched a high-intelligence model API at a competitive price and are encouraged by the initial results. We recently made Muse Spark available on OpenRouter for U.S.-based developers, broadening its distribution and making it easier for developers to adopt the model. We soon expect to roll out the model API to more distribution channels, make it available in more countries, and open it up for enterprises.

Our approach to building capacity is strongly influenced by several key elements. First, the broad environment for building infrastructure is dynamic and uncertain in both near-term and longer-term time horizons. The industry has under-built historically for the wave of AI adoption, making existing capacity, including our own, extremely valuable. Longer-term, the supply chains need to be built out to support the capacity that we anticipate we and others will need for AI-powered experiences. Second, we have high confidence in our ability to utilize capacity to scale and build on top of our existing experiences, as well as continue to invest in foundational models that will create substantial new opportunities. Consequently, our current plans are geared towards maximizing 2026 and 2027 capacity.

When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels, and we are confident that this will be true in this timeframe as well. Longer-term, it's harder to predict the exact usage scaling curves. We believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses. This should be true regardless of whether our models are on the frontier. We believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute. Therefore, our longer-term capacity strategy aims to give us the flexibility to continue growing compute in 2028 and beyond by laying down data center and network foundations to accommodate future server decisions.

The long-lived nature of these assets inherently provides the flexibility that will make it possible to adjust our investment to the pace of AI adoption. In addition, we have been making strategic investments in areas like our internal custom silicon effort, which will provide long-term strategic flexibility and supply chain leverage. This will be helpful in driving better returns on those long-term investments. Finally, we believe that overall industry capacity is going to remain tight for the foreseeable future. As we've said earlier, we strongly believe that the models, consumer experiences, and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure. Those enterprise offerings have the potential to take multiple forms, as Mark mentioned. Agentic tools, our API, or monetizing compute directly, given outsized market demand.

We expect that remaining nimble about these opportunities will help us fund our build-out more efficiently while preserving our strategic flexibility to have the compute when we need it and provide us multiple pathways to generate returns on invested capital. In funding these infrastructure investments, the strength of our balance sheet gives us the ability to attract capital from a wide range of markets to supplement the cash flow generated by our business. Our announcement with BlackRock yesterday is an example of the partnerships that we can structure to complement our approach to building infrastructure capacity. Moving now to our financial outlook. We expect third quarter 2026 total revenue to be in the range of $61 billion- $64 billion. Our guidance assumes foreign currency is an approximately 1% headwind to year-over-year total revenue growth based on current exchange rates. Turning to the expense and CapEx outlooks.

We are raising the lower end of our expense outlook to incorporate the $2.4 billion charge related to legal proceedings recognized in Q2. We now expect full year 2026 total expenses to be in the range of $165 billion-$169 billion. We continue to expect to deliver operating income this year that is above 2025 operating income. We anticipate 2026 capital expenditures, including principal payments on finance leases, to be in the range of $130 billion-$145 billion, narrowed from our prior outlook of $125 billion-$145 billion. Absent any changes to our tax landscape, we expect our tax rate for the remaining quarters of 2026 to be between 15%-17%, an increase from our prior outlook of 13%-16%. Finally, we continue to monitor active legal and regulatory matters that could significantly impact our business and financial results.

For example, we continue to see scrutiny on youth-related issues in several markets and have a number of youth-related trials scheduled for this year in the U.S., which may ultimately result in a material loss. In closing, our business momentum continued in Q2 with strong execution across our core ads and engagement initiatives. We're also progressing in our efforts to bring personal super intelligence to everyone with exciting model releases, and we expect to build on that momentum over the course of this year with new products. With that, Krista, let's open up the call for questions.

Thank you. We will now open the lines for a question and answer session. To ask a question, please press star one on your touch-tone phone. To withdraw your question, again, press star one. Please limit yourself to one question. Please pick up your handset before asking your question to ensure clarity. If you are streaming today's call, please mute your computer speakers. Your first question comes from the line of Brian Nowak with Morgan Stanley. Please go ahead.

Thanks for taking my questions. I have two, one for Mark, one for Susan. Mark, I appreciate all the color on the big pipeline for new products across consumer and business agents, API tools, and compute rental. There's a lot of opportunities here. My question is, as you look at these opportunities and the state of the current offerings, the compute capacity, which of them do you expect to be able to scale first, sort of in 2026 and 2027, to sort of showcase quantifiable material ROIC for investors? Susan, there's been some public comments about capacity in 2027 and doubling capacity, and I appreciate your color about CapEx. Any early comments on 2027 CapEx, even the philosophy around sources of upside or sources of downward pressure as you sort of think through different ways to finance this multi-year build and have 2027 CapEx?

I can take the first one. In terms of the different opportunities and how we think about the compute, overall, a substantial amount of the compute goes towards training models to be a leading lab, and I think that's an important investment. The rest of it goes towards a set of different products and revenue opportunities, which spans from optimizing and improving our core business, to building new consumer products that we're releasing soon to the API, to the business agents work, to the developer tools work on the roadmap that I alluded to, and also the opportunity to sell compute directly. I mentioned that we have quite a number of offers at a meaningful premium over what we paid for the compute.

The question in thinking about this is, we believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly. We think that there's a big opportunity, obviously, to sell compute as well. I think you asked what one area would likely scale the most, but I'm actually quite optimistic that we're going to see meaningful growth in all of these areas. I think we'll have more to share soon on a number of them.

Brian, on your second question, we aren't providing a specific outlook for 2027 CapEx at this time. Infrastructure planning remains highly dynamic. Even this year, there are a range of outcomes embedded in our outlook. I alluded in my main remarks to our focus on gearing our current infrastructure plans towards maximizing capacity in 2026 and 2027, and giving us the flexibility to continue to grow in 2028 and beyond, but also giving us the ability to make server decisions when we come to being able to evaluate our actual needs in 2028 and beyond. We're still working through what our capacity needs are going to be over the coming years. Generally, we believe near-term capacity is more valuable than long-term capacity. It remains a very dynamic planning process.

Your next question comes from the line of Eric Sheridan with Goldman Sachs. Please go ahead.

Thanks for taking the question. Maybe two, if I can. When you frame up the enterprise opportunity, how much of that opportunity do you think is available to you today based on what you've built out in terms of go-to-market strategy as extensions of the advertising and the marketing business you have already, versus go-to-market strategies that might have to be built to capitalize on the opportunity? Then maybe Susan, if I could just squeeze a second one in. When you think about sources of capital for the business for the next couple of years, you referenced the deal that was announced yesterday as an example of looking at ways to finance forward obligations.

We continue to get questions from investors about how to think about the mix of debt and equity and sources of capital. Philosophically, how are you guys thinking about wanting to be ambitious on the spend, marrying that with the need for capital? Thanks so much.

Sure. I can talk about the first part. I think for enterprise, there's going to be a combination of extending the current business, which is effectively selling to marketers and businesses that are basically customer-facing, trying to reach customers and sell to them directly. Obviously, that's the vast majority of the business across Facebook and Instagram. We believe that there's an opportunity to extend this with business agents across messaging apps and other surfaces to continue to interact with customers. Just like the ad system, effectively, we will get paid when we deliver results for those businesses. We view this as an extension of the sales and the partnerships that we have with many millions of advertisers and hundreds of millions of small businesses that use our platforms. That one, I think, should be quite a natural opportunity for us.

We're focused on delivering it in a way where we're not trying to maximize the sale in the near term, we're trying to maximize the results for people and build out a robust auction, that's what we've seen has served the business well over time. There are other enterprise customers who I think we're increasingly going to serve too. We're building coding and developing an internal productivity tools partially because we need to build them ourselves, and we need to make sure that we have tools that are tuned for ourselves. Now that we have those, we feel like there's a large opportunity to serve, whether that's small businesses or larger businesses. That is a somewhat different muscle than we have historically had, and we will share more soon on how we're planning to build that out.

I think that there is just a very large opportunity on this. The way that I think about this is there's obviously been a bunch of news about the compute side, I think that the enterprise opportunity is kind of the sum of all of these different things. It's not just the selling compute, also the API services, the productivity services, the kind of business agents for other parts of the business beyond marketing, are all parts of the overall offering, I think that there's just a very, very large opportunity there. We're quite focused on that. That's going to be somewhat of a new muscle that we build as a company, I think it's a very important one that we build so that way we can make sure that we can maximize the opportunity ahead of us.

Eric, on your second question, in terms of sources of capital, this is something that we have been looking at thoughtfully as we think about financial planning for the future. Obviously, our strong operating cash flow certainly has put us in a position of strength as it pertains to funding our infrastructure build-out. We've also been evolving our capital structure in recent years to include a greater mix of debt as we work to bring down our cost of capital. We have generally found it prudent to continue adding cost-efficient, long-duration sources of capital as we make investments in initiatives that themselves have long time horizons, especially AI infrastructure projects. We've also broadened our aperture there to include partnerships like the one we announced with BlackRock yesterday, we'll continue to be thoughtful about evaluating the appropriate different sources of capital over time as we evaluate future projects.

Your next question comes from the line of Mark Shmulik with Bernstein. Please go ahead.

Yes, thanks for taking the question. Mark, everyone's got a story of someone coming back from Silicon Valley, deep writing code, building agents with AI, then they head home, and they kind of tell their parents they're using AI wrong. It's just kind of like a glorified search tool. Consumer behavior is always pretty difficult to predict, kind of reading your op-ed on "AI for Everyone," how do you think about whether consumer adoption can close this AI utility gap? Are we on the cusp of something kind of breaking through, or do we just need to be a bit more patient? Thanks.

Well, I think that some things have already broken through. I think one of the interesting things about AI compared to other technologies is that every year or so, the cycle may accelerate, every year or so, you get new capabilities that create new possible product lines. There's obviously the AI assistant market for consumers, where that's what we're doing with Meta AI, there's the other products that competitors have in that space. In the last year, I think the coding agent market has really grown very quickly, that's the first real agentic market, I think that there's a number of reasons why coding is first, right? You have technical customers who are willing to do the work to make it work.

Coding is an inherently digital and closed-loop activity, in some ways, it is somewhat of a kind of an easier system to train on. Our bet is that, one of the bets here that we're making is that we think that consumer personal agents is going to end up being an extremely important and massive market. I think that it's extremely unlikely if you look out five years from now, for example, that whatever period of time you want, that you don't have billions of people with a personal agent that understands your goals and that is just working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about, whether it's helping you with your health or your hobbies or your personal finances or your productivity in running your home better or improving and enhancing your relationships, helping with your career. Just all these different things. This is a very, very deep set of use cases. 

Like you said, I think that building for consumers is a little bit different than building for developers. If you're building for consumers, especially if you're trying to build something that isn't used by millions of people but is used by billions of people, it needs to just work. It needs to be simple. I think a lot of what we see with these agents today, a lot of the kind of proto-agents is it takes a bunch of fiddling. You have to get into a terminal to get it set up. You get a use case going. It kind of seems magical, maybe it breaks down over time.

I think that the companies that can deliver the personal agents that just work, I think that this is going to be one of the next major opportunities in AI. We are very excited about that in addition to what we're doing with Meta AI, in addition to what we're doing with business agents, in addition to all these other things, while also developing the models that we think are going to continue to advance new capabilities to create new product lines as well. This is why we're very optimistic. Now, I get we're going to ship this at some point soon. That's going to be very exciting. We haven't done that yet.

There's kind of only so much that I can say on an earnings call about this. I think this is a very large opportunity, one that I think is almost inevitable that someone does. I think it really plays to Meta's strengths as a company of we build consumer products that reach billions of people. We're great at once we get something working, scaling it to a large number of people. We're good at building the infrastructure to be able to support these intensive applications. I feel very good about this. I kind of understand that we need to deliver it for our community. That's what we're very focused on.

Your next question comes from the line of Doug Anmuth with JPMorgan. Please go ahead.

Great. Thanks for taking questions. One for Susan, one for Mark. Susan, you talked about how LLMs are increasingly capable of delivering ranking and recommendation gains. Can you just talk more about the roadmap here and how far along you are in just leveraging better models and more compute? Mark, just in terms of the number of offers to monetize your compute externally, you also at the same time are purchasing capacity from a number of third parties. Just hoping you can help us understand some of the differences here. Is it just timing and stopgap issues, or is it training versus inference and leveraging the chips that are best suited for each? Thanks.

Thanks, Doug. I'll take that first question about where we are on the recommendations roadmap. First of all, we certainly see further headroom to continue improving recommendations over the rest of the year and into 2027. We expect that will help us drive additional gains on both engagement on Facebook and Instagram. A couple things I would highlight. First, we'll continue to make recommendations even more personalized and relevant to user interests. That's in part by advancing our recommendation models and architectures to further capture user interest more precisely and respond faster to what people care about in a given moment.

Our AI investments are going to play a significant role in delivering on this vision, including the expansion of LLM-based content understanding to develop a deeper understanding of posts and creators that people value, to capture user interests more precisely and respond more quickly to what they care about in the moment, and using AI to surface high-quality, fresh, and trending content and reduce the share of low-quality content. Second, we're continuing to improve our data infrastructure to allow our models to train on more data and leverage that data more effectively. We're adding more detail to how we describe content that users have engaged with in the past and enriching past user interaction sequences with more granular content. That allows our models to more precisely learn which engagements are more or less valuable to users.

We're continuing to scale up both the length of user interaction sequences we use during training, as well as the complexity of our model architectures across Facebook and Instagram to take advantage of the larger datasets. We've already made significant strides leveraging LLMs for content understanding. We're going to further incorporate them into both our recommendations and content policy enforcement stacks, given their capability to more deeply understand content. I would say, broadly, I think we're very optimistic about that body of work. We've also invested in using LLM-based agentic approaches to transforming our recommendation system, and we grew the number of launches from our ranking agents this half. That also helps make our engineers more productive, and that's another path that we're very excited about as well.

I can answer the second part of that. I think your question was about how we think about offers that we're getting to sell the compute, we're also buying the compute. Look, the high-level observation is that there's just nowhere near enough compute for all the demand. That is why we see that basically, we are getting a large number of offers for the compute that we have, we have a lot of internal uses that we think are going to be quite valuable. Now, in terms of running the business, obviously, a common trade-off that we need to make is around how much do you monetize something today versus develop future assets for the future? I think that it's always a portfolio, right?

You don't want to only do long-term things and not kind of prove the markets out that exist in the near term. I also think it would be foolish to basically just sell all of the compute and take a short-term profit. When you have the opportunity to build intelligence on top of it, which will be a kind of multiple and that compounds the value of the compute on top of that. I think the answer is what we're doing, which is to basically use a lot of our capital to build out compute, having confidence that we have the ability to monetize the compute directly when that makes sense, but also knowing that we have quite a number of different use cases to monetize the intelligence on top of the compute, including the enterprise cases that we talked about and including some of the consumer and cases that we talked about, and including just the core business, which is not even necessarily new products that we haven't talked about, but just in terms of using that to be able to further add intelligence and improve the ranking and recommendations and ads in the core services.

I think all of that is true, it creates this dynamic where there is a lead time where we're investing in building out these data centers now. They come online at some point in the future. You obviously are not getting value out of them until they're online. We basically see a very large demand for all of this and want to go maximize the opportunity to build out all of these different businesses.

Your next question comes from the line of Justin Post with Bank of America. Please go ahead.

Meta Platforms (META) Q2 2026 Earnings Call Transcript

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