Slides
Page 1
Meta Earnings Presentation Q4 2025 investor.atmeta.com
Page 2
$17,784 $15,451 $16,593 $17,389 $20,982 $18,259 $20,045 $21,331 $25,643 $9,159 $8,327 $9,135 $9,358 $11,154 $9,527 $11,366 $12,072 $14,198$7,316 $7,338 $7,721 $8,050 $9,012 $8,224 $9,148 $10,020 $10,893 $4,447 $4,519 $4,880 $5,088 $5,635 $5,382 $6,004 $6,659 $7,403 Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Advertising Revenue by User Geography In Millions Our revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a revenue-generating activity. This allocation differs from our revenue disaggregated by geography disclosure in our consolidated financial statements where revenue is geographically apportioned based on the addresses of our customers. 2 Rest of World Asia-Pacific Europe US & Canada $38,706 $35,635 $58,137 $38,329 $39,885 $46,783 $41,392 $46,563 $50,082
Page 3
$18,585 $15,824 $16,847 $17,609 $21,783 $18,605 $20,371 $21,751 $26,472 $9,441 $8,483 $9,300 $9,492 $11,503 $9,680 $11,532 $12,268 $14,480$7,512 $7,481 $7,888 $8,220 $9,245 $8,439 $9,366 $10,272 $11,182 $4,573 $4,667 $5,036 $5,268 $5,854 $5,590 $6,247 $6,951 $7,759 Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Revenue by User Geography In Millions Our revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a revenue-generating activity. This allocation differs from our revenue disaggregated by geography disclosure in our consolidated financial statements where revenue is geographically apportioned based on the addresses of our customers. 3 Rest of World Asia-Pacific Europe US & Canada $40,589$40,111 $39,071 $36,455 $48,385 $59,893 $42,314 $47,516 $51,242
Page 4
Segment Results In Millions, Except Percentages We report our financial results based on two reportable segments: Family of Apps (FoA) and Reality Labs (RL ). FoA includes Facebook, Instagram, Messenger, WhatsApp, and other services. RL includes our virtual and augmented reality related consumer hardware, software, and content. Q4’23 Q1’24 Q2’24 Q3’24 Q4’24 Q1’25 Q2’25 Q3’25 Q4’25 Advertising $ 38,706 $ 35,635 $ 38,329 $ 39,885 $ 46,783 $ 41,392 $ 46,563 $ 50,082 $ 58,137 Other 334 380 389 434 519 510 583 690 801 Family of Apps Revenue 39,040 36,015 38,718 40,319 47,302 41,902 47,146 50,772 58,938 Reality Labs Revenue 1,071 440 353 270 1,083 412 370 470 955 Total Revenue $ 40,111 $ 36,455 $ 39,071 $ 40,589 $ 48,385 $ 42,314 $ 47,516 $ 51,242 $ 59,893 Family of Apps Operating Income $ 21,030 $ 17,664 $ 19,335 $ 21,778 $ 28,332 $ 21,765 $ 24,971 $ 24,967 $ 30,766 Reality Labs Operating (Loss) (4,646) (3,846) (4,488) (4,428) (4,967) (4,210) (4,530) (4,432) (6,021) Total Income from Operations $ 16,384 $ 13,818 $ 14,847 $ 17,350 $ 23,365 $ 17,555 $ 20,441 $ 20,535 $ 24,745 Operating Margin 41 % 38 % 38 % 43 % 48 % 41 % 43 % 40 % 41 % 4
Page 5
19% 18% 19% 18% 18% 18% 18% 18% 18% 26% 27% 27% 28% 25% 29% 27% 30% 29% 8% 7% 7% 7% 7% 7% 6% 6% 6% 6% 9% 9% 5% 2% 5% 6% 7% 6% Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Expenses as a Percentage of Revenue 5 General & Administrative Marketing & Sales Research & Development Cost of Revenue
Page 6
Effective Tax Rate In Millions, Except Percentages Q4’23 Q1’24 Q2’24 Q3’24 Q4’24 Q1’25 Q2’25 Q3’25* Q4’25 Income before provision for income taxes $ 16,808 $ 14,183 $ 15,106 $ 17,822 $ 23,553 $ 18,382 $ 20,534 $ 21,663 $ 25,354 Provision for income taxes $ 2,791 $ 1,814 $ 1,641 $ 2,134 $ 2,715 $ 1,738 $ 2,197 $ 18,954 $ 2,586 Effective Tax Rate 17 % 13 % 11 % 12 % 12 % 9 % 11 % 87 % 10 % 6 *Provision for income taxes includes a one-time, non-cash income tax charge of $15.93 billion accrued in the third quarter of 2025, related to the implementation of the One Big Beautiful Bill Act. Excluding this one-time tax charge, our third quarter 2025 effective tax rate would have decreased by 73 percentage points to 14%, compared to the reported effective tax rate of 87%.
Page 7
Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25* Q4'25 Net Income In Millions 7 $2,709 $16,644 $20,838 $15,688 $13,465 $12,369 $14,017 $18,337 $22,768 *Net income includes a one-time, non-cash income tax charge to provision for income taxes of $15.93 billion accrued in the third quarter of 2025, related to the implementation of the One Big Beautiful Bill Act. Excluding this one-time tax charge, our third quarter 2025 net income would have increased by $15.93 billion to $18.64 billion, compared to the reported net income of $2.71 billion.
Page 8
Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25* Q4'25 Diluted Earnings Per Share $5.33 $8.88 $6.03 $5.16 $4.71 $1.05 $7.14 $6.43 $8.02 8 *Net income includes a one-time, non-cash income tax charge to provision for income taxes of $15.93 billion accrued in the third quarter of 2025, related to the implementation of the One Big Beautiful Bill Act. Excluding this one-time tax charge, our third quarter 2025 diluted earnings per share would have increased by $6.20 to $7.25 compared to the reported diluted EPS of $1.05.
Page 9
Q4'24 Q4'25 2024 2025 Capital Expenditures In Millions 9 Quarterly YTD Capital expenditures for periods presented were related to purchases of property and equipment and principal payments on finance leases. $72,215 $39,225 $22,137 $14,836
Page 10
Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Family Daily Active People (DAP) In Billions 10 We define a daily active person (DAP) as a registered and logged-in user of Facebook, Instagram, Messenger, and/or WhatsApp (collectively, our "Family" of products) who visited at least one of these Family products through a mobile device application or using a web or mobile browser on a given day. The numbers for DAP do not include users on our other products unless they would otherwise qualify as DAP based on their other activities on our Family products. We do not require people to use a common identifier or link their accounts to use multiple products in our Family, and therefore must seek to attribute multiple user accounts within and across products to individual people. Our calculations of DAP rely upon complex techniques, algorithms, and machine learning models that seek to estimate the underlying number of unique people using one or more of these products, including by matching user accounts within an individual product and across multiple products when we believe they are attributable to a single person, and counting such group of accounts as one person. As these techniques and models require significant judgment, are developed based on internal reviews of limited samples of user accounts, and are calibrated against user survey data, there is necessarily some margin of error in our estimates. For additional information, see "Limitations of Key Metrics and Other Data" located in the Appendix of this presentation. 3.19 3.583.543.43 3.27 3.353.293.24 3.48
Page 11
Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Family Average Revenue per Person (ARPP) 11 Beginning in the first quarter of 2024, we define average revenue per person (ARPP) as our FoA revenue during a given quarter, divided by the average of the number of DAP at the beginning and end of the quarter. We have recast ARPP in prior periods for comparative purposes. $16.56 $13.65 $14.46 $12.36 $14.25 $12.29$11.89 $11.20 $12.33
Page 12
Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Ad Impressions Delivered YoY Percentage Change 12 Worldwide Our ad impressions growth by user geography is geographically apportioned based on our estimation of the geographic location of our users when an ad impression is delivered. Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 US & Canada Europe Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Rest of WorldAsia-Pacific 14% 6% 13% 8% 18% 14% 11% 5% 4% 9% 6% 13% 9% 6%5%5% 1% 3% 23% 16% 9%10% 24% 9% 7%
Page 13
Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Average Price Per Ad YoY Percentage Change 13 Worldwide Our average price per ad growth by user geography is geographically apportioned based on our estimation of the geographic location of our users when an ad impression is delivered. Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 US & Canada Europe Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Q4'24 Q1'25 Q2'25 Q3'25 Q4'25 Rest of WorldAsia-Pacific 15% 14% 9%13% 6%10%9%10% 14% 11%12% 12% 19%17% 9% 16% 17% 23% 1%2%3% 12% (2)% 20%15%
Page 14
Appendix
Page 15
Free Cash Flow Reconciliation In Millions Q4’23 Q1’24 Q2’24 Q3’24 Q4’24 Q1’25 Q2’25 Q3’25 Q4’25 Net cash provided by operating activities $ 19,404 $ 19,246 $ 19,370 $ 24,724 $ 27,988 $ 24,026 $ 25,561 $ 29,999 $ 36,214 Less: Purchases of property and equipment 7,592 6,400 8,173 8,258 14,425 12,941 16,538 18,829 21,383 Less: Principal payments on finance leases 307 315 299 944 411 751 474 545 754 Free Cash Flow $ 11,505 $ 12,531 $ 10,898 $ 15,522 $ 13,152 $ 10,334 $ 8,549 $ 10,625 $ 14,077 15 Free cash flow (FCF) is a non-GAAP financial measure that has limitations as an analytical tool, and you should not consider it in isolation or as a substitute for analysis of other GAAP financial measures, such as net cash provided by operating activities. FCF is not intended to represent our residual cash flow available for discretionary expenses. Some of the limitations of FCF are: (i) FCF does not reflect our future contractual commitments, and (ii) other companies in our industry present similarly titled measures differently than we do, limiting their usefulness as comparative measures.
Page 16
Limitations of Key Metrics and Other Data 16 Family Metrics The numbers for our key metrics are calculated using internal company data based on the activity of user accounts. We report our estimates of the numbers of our daily active people (DAP) and average revenue per person (ARPP) (collectively, our "Family metrics") based on the activity of users who visited at least one of Facebook, Instagram, Messenger, and WhatsApp (collectively, our "Family" of products) during the applicable period of measurement. While these numbers are based on what we believe to be reasonable estimates of our user base for the applicable period of measurement, there are inherent challenges in measuring usage of our products across large online and mobile populations around the world. The methodologies used to measure these metrics require significant judgment and are also susceptible to algorithm or other technical errors. In addition, we are continually seeking to improve our estimates of our user base, and such estimates may change due to improvements or changes in our methodology. We regularly review our processes for calculating these metrics, and from time to time we discover inaccuracies in our metrics or make adjustments to improve their accuracy, which can result in adjustments to our historical metrics. Our ability to recalculate our historical metrics may be impacted by data limitations or other factors that require us to apply different methodologies for such adjustments. We generally do not intend to update previously disclosed Family metrics for any such inaccuracies or adjustments that are within the error margins disclosed below. In addition, our Family metrics estimates will differ from estimates published by third parties due to differences in methodology or other factors such as data limitations or other challenges in measuring large online and mobile populations. For example, our methodologies include measurements of our user base that have in some instances exceeded estimates of addressable online and mobile populations that are based on data published by third parties. Many people in our community have user accounts on more than one of our products, and some people have multiple user accounts within an individual product. Accordingly, for our Family metrics, we do not seek to count the total number of user accounts across our products because we believe that would not reflect the actual size of our community. Rather, our Family metrics represent our estimates of the number of unique people using at least one of Facebook, Instagram, Messenger, and WhatsApp. We do not require people to use a common identifier or link their accounts to use multiple products in our Family, and therefore must seek to attribute multiple user accounts within and across products to individual people. To calculate these metrics, we rely upon complex techniques, algorithms and machine learning models that seek to count the individual people behind user accounts, including by matching multiple user accounts within an individual product and across multiple products when we believe they are attributable to a single person, and counting such group of accounts as one person. These techniques and models require significant judgment, are subject to data and other limitations discussed below, and inherently are subject to statistical variances and uncertainties. We estimate the potential error in our Family metrics primarily based on user survey data as described further below, which itself is subject to error as well. While we expect the error margin for our Family metrics to vary from period to period, we estimate that such margin generally will be approximately 3% of our worldwide DAP. At our scale, it is very difficult to attribute multiple user accounts within and across products to individual people, and it is possible that the actual numbers of unique people using our products may vary significantly from our estimates, potentially beyond our estimated error margins. As a result, it is also possible that our Family metrics may indicate changes or trends in user numbers that do not match actual changes or trends.
Page 17
Limitations of Key Metrics and Other Data 17 To calculate our estimates of DAP, we currently use a series of machine learning models that are developed based on internal reviews of limited samples of user accounts and calibrated against user survey data. We apply significant judgment in designing these models and calculating these estimates. For example, to match user accounts within individual products and across multiple products, we use data signals such as similar device information, IP addresses, and user names. We also calibrate our models against data from periodic user surveys of varying sizes and frequency across our products, which survey questions are based on monthly usage, and which are inherently subject to error. The timing and results of such user surveys have in the past contributed, and may in the future contribute, to changes in our reported Family metrics from period to period. In addition, our data limitations may affect our understanding of certain details of our business and increase the risk of error for our Family metrics estimates. Our techniques and models rely on a variety of data signals from different products, and we rely on more limited data signals for some products compared to others. For example, as a result of limited visibility into encrypted products, we have fewer data signals from WhatsApp user accounts and primarily rely on phone numbers and device information to match WhatsApp user accounts with accounts on our other products. Any loss of access to data signals we use in our process for calculating Family metrics, whether as a result of our own product decisions, actions by third-party browser or mobile platforms, regulatory or legislative requirements, or other factors, also may impact the stability or accuracy of our reported Family metrics, as well as our ability to report these metrics at all. Our estimates of Family metrics also may change as our methodologies evolve, including through the application of new data signals or technologies, product changes, or other improvements in our user surveys, algorithms, or machine learning that may improve our ability to match accounts within and across our products or otherwise evaluate the broad population of our users. In addition, such evolution may allow us to identify previously undetected violating accounts (as defined below). We regularly evaluate our Family metrics to estimate the percentage of our DAP consisting solely of "violating" accounts. We define "violating" accounts as accounts which we believe are intended to be used for purposes that violate our terms of service, including bots and spam. Our violating accounts estimation is based on an internal review of a limited sample of accounts, and we apply significant judgment in making this determination. For example, we look for account information and behaviors associated with Facebook and Instagram accounts that appear to be inauthentic to the reviewers, but we have less visibility into WhatsApp user activity due to encryption. In addition, if we believe an individual person has one or more violating accounts, we do not include such person in our violating accounts estimation as long as we believe they have one active account that does not constitute a violating acc ount. In the fourth quarter of 2025, we made certain updates to the methodology we use for this estimation, including to incorporate updated data signals as a result of improvements in our ability to identify activity we believe to be violating our policies, as well as to focus on the most recent account activity when determining whether to include a person in our violating accounts estimation. Accordingly, in the fourth quarter of 2025, we estimated that less than 5% of our worldwide DAP consisted solely of violating accounts. We believe the increase compared to our prior estimation was a result of the methodology update described above. From time to time, we disable certain user accounts, make product changes, or take other actions to reduce the number of violating accounts among our users, which may also reduce our DAP estimates in a particular period. We intend to continue to disclose our estimates of the percentage of our DAP consisting solely of violating accounts on an annual basis . Violating accounts are very difficult to measure at our scale, and it is possible that the actual number of violating accounts may vary significantly from our estimates. User Geography Our estimates for revenue by user location, as well as year-over-year percentage changes in ad impressions delivered and the average price per ad by user location, are also affected by data limitations and other challenges in measuring user geography. Our data regarding the geographic location of our users is estimated based on a number of factors, such as the user's IP address and self-disclosed location. These factors may not always accurately reflect the user's actual location. For example, a user may appear to be accessing our products from the location of the proxy server that the user connects to rather than from the user's actual location. The methodologies used to measure our metrics are also susceptible to algorithm or other technical errors.
Page 18
Meta Earnings Presentation Q4 2025 investor.atmeta.com