This comprehensive research report examines the quantifiable improvements in page loading speed that result from blocking advertisements and tracking scripts. Recent studies demonstrate that ad blockers produce substantial performance gains across various websites and network conditions, with measured speed improvements ranging from modest gains of approximately 50 milliseconds on fast-loading sites to dramatic 3.5 times speedups on content-heavy pages. The evidence indicates that while ad blocking delivers consistent data reduction benefits of 25 to 40 percent, the translation of these savings into perceived page speed improvements varies significantly based on website architecture, network conditions, device capabilities, and the specific configuration of blocking tools employed. This report synthesizes existing academic research, industry benchmarks, and performance metrics to provide a detailed understanding of how to accurately measure and interpret page speed gains from ad and tracker blocking, while highlighting the methodological challenges and limitations that researchers and practitioners must navigate when evaluating these improvements in real-world scenarios.
Understanding the Relationship Between Advertisements and Web Page Performance
The fundamental premise underlying ad blocking’s impact on page speed rests on a well-documented phenomenon: advertisements and tracking scripts constitute a substantial portion of downloaded content and computational overhead on modern websites. Research utilizing data from the HTTP Archive and Chrome User Experience Report reveals that advertisements, tracking pixels, and associated third-party scripts frequently represent between 18 and 60 percent of total page load data, depending on the specific website and content category. When these elements load successfully, they consume network bandwidth, require browser processing power to render, and demand CPU cycles for JavaScript execution—all of which contribute to measurable delays in page rendering and interactivity.
The presence of ads on websites serves legitimate purposes within the digital advertising ecosystem, providing revenue streams that enable publishers to sustain content creation and distribution. However, the mechanics of modern online advertising introduce complexity that directly impacts performance. Advertisement content originates from multiple third-party servers, each requiring separate network requests and TCP connections. These requests often contain heavy JavaScript files that execute in the browser’s main thread, blocking other critical operations during execution. Video advertisements, in particular, impose substantial computational demands on both CPU and GPU resources, as these elements require continuous rendering and decoding operations. Additionally, tracking scripts embed functionality designed to monitor user behavior across websites, collect behavioral data, and transmit this information back to advertising and analytics servers—operations that impose persistent overhead throughout a browsing session.
Understanding this relationship between ad delivery mechanisms and page performance provides essential context for interpreting the speed gains achieved through ad blocking. When ad blockers intercept and prevent these resources from loading, they eliminate not only the downloaded bytes but also prevent the associated JavaScript execution, rendering overhead, and network latency. However, the magnitude of performance improvement depends critically on how these elements would have loaded under normal conditions, what proportion of the total page load they represented, and whether blocking these elements introduces additional overhead through the ad blocking process itself.
Methodologies for Measuring Page Speed Improvements From Ad Blocking
Accurate measurement of page speed gains from ad blocking presents substantial methodological challenges that researchers have approached through multiple complementary techniques. These approaches broadly fall into two categories: synthetic performance monitoring conducted in controlled laboratory environments, and real user monitoring that captures actual browsing experiences across diverse network conditions and device types.
Synthetic testing methodologies typically employ automated tools that load websites under standardized conditions, capturing detailed network waterfall data and performance metrics before and after enabling ad blocking. This approach offers significant advantages in terms of reproducibility and control. Researchers can eliminate confounding variables such as network fluctuations, cache states, and background system processes by conducting tests under precisely specified conditions with disabled caching. Tools like WebPageTest, Google Lighthouse, and GTmetrix enable researchers to compare page load metrics such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, and Total Blocking Time (TBT) with and without ad blockers enabled. The resulting data often includes detailed waterfall charts showing the timing of individual network requests, allowing researchers to precisely attribute performance improvements to specific blocked resources.
However, synthetic testing approaches have notable limitations. Laboratory conditions typically feature fast network connections and high-powered test devices, which may not represent the conditions experienced by many real-world users who browse on slower connections with less capable hardware. Additionally, synthetic tests capture only a single page load in isolation, whereas real users experience multiple page navigations, varying network conditions, and interaction patterns that may yield different performance characteristics. Cache behavior, which can substantially influence page load times on subsequent visits, is artificially manipulated in synthetic tests rather than naturally developing as it does during actual browsing.
Real user monitoring represents the complementary approach, capturing actual browsing performance data from deployed websites and aggregating this data across diverse users, devices, and network conditions. Google’s Chrome User Experience Report (CrUX) and similar real user monitoring platforms provide statistical distributions of Core Web Vitals metrics observed across millions of actual page loads. This approach inherently captures the performance impact of ad blocking as experienced by real users in production environments, reflecting genuine network variability, device capabilities, and user interaction patterns. However, real user monitoring typically provides less granular visibility into which specific page elements contribute to performance variations, and attributing performance differences to ad blocking specifically requires careful analysis to control for other confounding factors.
Many rigorous studies have employed a hybrid approach, combining synthetic testing to understand detailed mechanisms and resource attribution with real user monitoring to validate findings under real-world conditions. Researchers conducting synthetic tests typically perform multiple independent runs (often 3-5 runs per test condition) to capture natural variability, discard outlier results, and report averaged performance metrics. This methodology acknowledges that even under laboratory conditions, network routing, server processing time, and operating system scheduling introduce measurable variance in page load times.
The specific comparison methodology employed by researchers also substantially influences reported results. Some studies disable all caching to measure pure page load performance without any browser cache benefits from previous visits. Others conduct testing with cache disabled on the first page load but enabled on subsequent loads, better representing typical user browsing patterns where most pages are visited with cached resources available. The choice of comparison baseline also matters: comparing pages with and without ad blockers enabled provides different insights than comparing pages that have been specifically configured to minimize ads (such as through subscription-based ad-free experiences) versus pages with normal ad loads.
Performance Metrics and Measurement Approaches
Measuring page speed improvements requires careful attention to which metrics are captured and how they are interpreted, as different metrics reveal distinct aspects of the user experience and respond differently to ad blocking. Modern web performance measurement has converged on Core Web Vitals as the primary metrics that correlate most strongly with user experience and search engine rankings, while supplementary metrics provide additional diagnostic insights.
First Contentful Paint (FCP) measures the time from when a user initiates page navigation until the browser renders the first piece of content to the screen—typically text, an image, or another visual element. For ad blocking’s impact assessment, FCP holds particular importance because advertisements often load asynchronously and do not block initial page rendering. Therefore, FCP frequently shows modest improvements from ad blocking, particularly on well-optimized sites where critical content loads before ads are fetched. However, on poorly optimized pages where large advertisement elements block rendering or where ads load synchronously, FCP improvements from blocking can be substantial.
Largest Contentful Paint (LCP) measures when the largest visual element within the user’s viewport completes rendering, providing a metric more closely aligned with user perception of page readiness. LCP frequently shows more substantial improvements from ad blocking than FCP, particularly on pages where large advertisement tiles, video ads, or sponsored content constitute significant page elements. Research from Brave indicates that LCP improvements directly from blocking can range from tens to hundreds of milliseconds, though the magnitude depends on whether the blocked advertisements constituted the page’s largest element.
Cumulative Layout Shift (CLS) quantifies visual stability by measuring the extent to which page layout changes after the initial rendering. Advertisements, particularly those that load asynchronously, frequently cause layout shifts as they push other content around the page to make room for ad content. Ad blocking eliminates these layout shift events, typically yielding CLS improvements that are quite dramatic—ad blocking often reduces CLS from poor values (indicating substantial layout instability) to excellent values, though the magnitude depends on the specific advertisement placement and behavior.
Total Blocking Time (TBT) measures the cumulative duration for which the main JavaScript thread is blocked by long-running tasks after the initial content renders, directly affecting user ability to interact with the page. Advertisement scripts frequently create long tasks that block the main thread during their execution. Ad blockers eliminate these blocking tasks, yielding TBT improvements that particularly benefit user interactivity on pages that previously loaded heavy JavaScript advertisement code. Studies examining TBT improvements from ad blocking have documented reductions ranging from dozens to hundreds of milliseconds, depending on ad JavaScript complexity.
Beyond Core Web Vitals, researchers also measure several supplementary performance metrics that provide diagnostic value. Speed Index quantifies the average time at which visible page content appears during the loading process, aggregating the visual completeness across the loading timeline. Data consumption measurements track the total bytes downloaded for page assets, allowing quantification of bandwidth savings from preventing ad downloads. Request count metrics enumerate the number of separate HTTP requests required to load a page, revealing how ad blocking reduces HTTP connection overhead. Some studies also measure specific resource-type breakdowns—distinguishing video content, scripts, images, and fonts—to understand which content categories contribute most substantially to performance gains.
Research Findings on Page Speed Improvements From Ad Blocking
Empirical research into ad blocker performance improvements has produced consistent findings across multiple independent studies conducted over several years, with varying methodologies and website samples. These studies collectively demonstrate that ad blocking delivers meaningful but heterogeneous performance improvements, with the magnitude varying substantially based on website characteristics, ad loading practices, and network conditions.
A foundational study published in 2017 measured performance across the top 150 websites and a diverse dataset of news websites, finding that ad blockers achieved average data consumption reductions of 25 to 34 percent, with uBlock Origin providing the most substantial data savings across different website types. The same study found that while data consumption decreased significantly, improvements in wall-clock page load times were more modest, with average improvements ranging from 50 to 263 milliseconds depending on the specific ad blocker employed. This finding highlighted a crucial insight: data reduction does not automatically translate into proportional page speed improvements, as blocking resources that load in parallel with main content may save bandwidth without reducing the critical path of page rendering.
More recent benchmarking conducted by Magic Lasso Adblock in 2024 reported substantially larger performance improvements, with pages loading on average 2 times faster with ad blocking enabled, and specific pages such as The Verge displaying 3.5 times speedups (71 percent reduction in load time). This substantial divergence from earlier findings suggests several possibilities: modern websites may contain heavier advertisement content than those tested in earlier studies, ad loading patterns may have shifted toward more blocking or render-delaying approaches, or the specific websites tested in recent benchmarks may be particularly ad-heavy. The Magic Lasso findings also noted that The Verge’s advertisement and tracking code represented over 60 percent of the page’s total load, suggesting that on advertising-saturated sites, ad blocking can produce dramatic improvements.
A large-scale study analyzing page load times across approximately 100,000 websites found that the distribution of page load time improvements from ad blocking was highly skewed, with the majority of websites showing modest improvements (averaging 174 milliseconds) but a long tail of websites displaying very substantial improvements. Critically, this study also identified a subset of faster-loading websites where ad blockers actually slightly increased load time (by approximately 7-13 milliseconds), likely due to the overhead introduced by the ad blocking process itself outweighing the resource savings from preventing ad loads. This finding reinforces that ad blocker performance impact is not uniformly positive across all websites but rather depends on the interplay between resource savings and blocking overhead.
Data consumption improvements appear more consistent across studies than load time improvements. Research from Simon Fraser University in 2022 found that Adblock Plus reduced data consumption by 25 percent on average, with video traffic data usage dropping by 40 percent through prevention of video advertisement delivery. Similar findings from academic studies consistently report data reductions of 25-40 percent, with variation reflecting differences in website categories (technology and news sites typically show larger absolute data reductions than entertainment sites, though percentage reductions may be similar).
CPU and power consumption improvements from ad blocking have been extensively documented in recent research. A 2025 study examining ad blocker impacts on system power consumption found that the Brave browser with built-in ad blocking reduced CPU usage by 44 percent compared to Chrome without ad blocking, with GPU usage dropping 68 percent on media-heavy sites like Dailymotion. These substantial power consumption reductions, which translate to approximately 20 percent battery life improvements on mobile devices through more efficient resource management, highlight a substantial user benefit beyond simple page speed metrics. Studies specifically examining uBlock Origin and uBlock Origin Lite found these tools were most effective at reducing power consumption, with uBlock Origin reducing CPU power consumption for some websites by over 26 percent on Windows and 50 percent on Ubuntu.
Comparative Performance Analysis of Different Ad Blockers
Different ad blocking solutions exhibit heterogeneous performance characteristics, with measurement research revealing meaningful differences in both blocking effectiveness and performance overhead. These differences reflect variations in blocking rule implementation, filter list selection, performance optimization techniques, and underlying architectural approaches.
uBlock Origin consistently emerges as the top performer across multiple independent studies examining both blocking effectiveness and performance efficiency. In a 2017 study comparing multiple popular ad blockers, uBlock performed best in terms of both data savings and user tracking prevention, achieving near-perfect performance in identifying tracking resources while incurring minimal overhead. A 2019 performance study specifically examining matching speeds found that Ghostery outperformed other libraries in terms of request matching speed, but uBlock Origin maintained excellent performance with median decision times in the sub-millisecond range—far below the threshold at which users would perceive performance degradation. More recent 2025 studies examining power consumption continue to identify uBlock Origin and uBlock Origin Lite as most effective for reducing CPU and GPU power consumption while maintaining high blocking coverage.
Adblock Plus, one of the most widely installed ad blockers, presents a more complex performance profile. While widely used and effective at blocking many ads, Adblock Plus’s implementation of the “Acceptable Ads” program—which allows certain non-intrusive ads to load by default—means that its users experience smaller performance gains than would be achieved through complete ad blocking. Research examining the Acceptable Ads program found that if Adblock Plus blocked the acceptable ads by default, the percentage of blocked requests would increase by 20 percent. On pages with substantial acceptable ad content, this architectural decision reduces performance improvements compared to more aggressive blocking approaches. Additionally, studies have identified that Adblock Plus introduces greater overhead on fast-loading websites (under 1 second) compared to other ad blockers, potentially degrading performance on optimized pages.
Ghostery presents an interesting case as a hybrid tracker and ad blocking tool. Because Ghostery does not block all content by default but rather allows users to choose which trackers to block, its default performance gains are more modest than more aggressive ad blockers. However, when configured to block comprehensive tracker and ad lists, Ghostery performs comparably to other tools while offering advantages in user control and transparency regarding which parties are being blocked.
Brave’s built-in Shields adblocking implementation, which uses a Rust-based blocking engine integrated directly into the browser rather than operating as an extension, shows strong performance characteristics. As a native browser feature, Shields avoids the overhead and limitations imposed on extensions by Chrome’s Manifest V3 restrictions, and studies indicate it achieves 96-99 percent effectiveness in blocking ads even under these constraints. The architectural advantage of being built into the browser allows deep optimization of blocking rule matching and allows Brave to continue supporting full WebRequest API capabilities that extensions no longer possess.
AdGuard offers a comprehensive filtering solution across multiple devices and browsers, with performance characteristics generally falling between the top performers like uBlock Origin and less optimized solutions. Research testing AdGuard’s effectiveness found it to be broadly effective, though specific performance measurements show results aligned with other major ad blockers rather than demonstrating particular efficiency advantages.
A critical consideration in ad blocker comparison relates to blocking rule completeness and the relationship between list size and performance. Research examining the EasyList filter list—the most widely used community-maintained filter list underlying most major ad blockers—found that 90.16 percent of blocking rules in EasyList provided no benefit to users in common browsing scenarios. This finding suggests that blocking rule optimization offers substantial opportunity for performance improvements without reducing blocking coverage, as demonstrated by an optimized version of EasyList that achieved 99 percent of the full list’s coverage while performing 62.5 percent faster.
The Complexity of Real-World Performance Measurement
Measuring page speed improvements from ad blocking in real-world conditions introduces substantial complexity that laboratory tests cannot capture, as actual user experiences vary across dimensions including network speeds, device capabilities, browser cache states, and website content variations. Understanding these real-world measurement challenges is essential for properly interpreting reported performance improvements and avoiding misleading conclusions about ad blocker benefits.
Network conditions fundamentally shape the performance impact of ad blocking. On fast broadband connections (10+ Mbps), where network bandwidth is abundant, the primary benefit of ad blocking comes from reduced computational overhead—preventing JavaScript execution and rendering operations rather than primarily saving bandwidth. In such conditions, ad blocking typically produces modest load time improvements (perhaps 50-200 milliseconds) while delivering substantial data savings (25-40 percent). Conversely, on mobile or slow connections where bandwidth is constrained, the percentage impact of preventing large ad downloads becomes far more substantial. A user on a 3G connection downloading a page with 500 KB of ad content receives substantially different benefits than a user with gigabit connectivity, as the large data transfer creates a material bottleneck in the first scenario but not the second.
Device capabilities influence ad blocking performance impact in ways laboratory tests often underrepresent. Desktop computers with powerful processors and ample RAM can process advertisement JavaScript with minimal user-perceptible delay, even on complex ad scripts, whereas lower-powered mobile devices experience substantial responsiveness improvements when ad-related JavaScript overhead is eliminated. Studies examining mobile performance have documented that ad blocking benefits are often more pronounced on lower-capability devices, where the CPU can struggle with heavy JavaScript loads.
Browser cache behavior creates divergence between first-visit and repeat-visit performance. On a user’s first visit to a website, ad blocker performance benefits reflect the full impact of preventing ad downloads and processing. However, on subsequent visits within the same browsing session or within the cache expiration period, the browser typically caches static advertisements along with other content. The performance differential between blocked and unblocked ads on repeat visits depends on cache hit rates and whether changed advertising requires fetching new ad content. Many benchmarking studies specifically note that they disable caching to measure pure load performance, which may overstate the differences between ad-blocked and non-blocked pages in real-world scenarios where caching is prevalent.
Website content variation introduces substantial measurement complexity. Different pages on the same website may feature vastly different quantities of ads and tracking scripts. Homepage benchmarks often feature different ad densities than interior pages, article pages may contain inline ads while navigation pages do not, and category pages may feature different ad arrangements than product detail pages. Performance research must carefully consider whether findings from homepage testing generalize across the full range of pages users visit.
Additional confounding factors affecting real-world measurements include variations in server response time (which ad blockers do not influence), variations in Content Delivery Network (CDN) performance between tests, different rendering behaviors in various browser versions, and the presence of other browser extensions that may interact with ad blockers. Some studies have documented that the presence of other extensions can occasionally interfere with ad blocker functionality or introduce additional overhead, creating measurement challenges.
Impact on User Engagement and Experience
Beyond the narrow metrics of page load speed, understanding how ad blocking affects broader user engagement provides important context for evaluating its real-world value. Research examining user engagement patterns following ad blocker installation reveals that ad blocking produces not only faster pages but also measurably increased user engagement with websites, presenting a nuanced picture of the relationship between ad blocking and browsing behavior.
A comprehensive study published in 2018 examined the effect of ad blocker installation on user web engagement using large-scale telemetry from millions of browser users. The research found that installing an ad blocker led to approximately 28 percent increase in active browsing time and 15 percent more page loads (URIs visited), controlling for baseline activity. These substantial increases in engagement suggest that users are not simply browsing faster with ad blockers; rather, they are spending more time on the web and visiting more pages. The study authors interpreted these findings as evidence that ad blocking removes enough friction and annoyance from the browsing experience that users are motivated to engage more deeply with online content.
The mechanism underlying these engagement increases likely involves multiple contributing factors. First, the faster page load times associated with ad blocking reduce user frustration from waiting for pages to become interactive. Second, the absence of disruptive ads (pop-ups, autoplay videos, expanding banners) likely reduces the frequency of accidental clicks and navigation away from intended content. Third, the visual clarity resulting from ad removal may make content more readable and engaging. Finally, reduced tracking overhead may improve overall system responsiveness, creating a subjectively faster browsing experience that encourages continued use.
These findings align with user surveys showing that 71 percent of users block ads to remove annoying banners and 41 percent cite page speed improvements as a motivation, alongside 31 percent citing privacy concerns about tracking and 40 percent mentioning reduced data usage concerns. The convergence of these multiple user motivations—speed, privacy, distraction reduction, and data efficiency—suggests that users value ad blocking for holistic improvements to their browsing experience rather than any single dimension.
However, it is important to note that improved engagement with individual websites does not necessarily benefit publishers or advertisers, as the increased page views might come from users who would have encountered fewer ads, generating lower advertising revenue despite higher traffic volumes. This represents a fundamental tension in the ad blocking ecosystem: features that improve the user experience and encourage broader web exploration may simultaneously reduce publisher revenue, creating competing incentives in the system.
Measurement Challenges and Limitations
Despite consistent findings across multiple studies, measuring page speed improvements from ad blocking faces numerous technical and methodological challenges that researchers continue to grapple with. Awareness of these limitations is essential for properly contextualizing research findings and avoiding overconfident conclusions about ad blocking benefits.
First, the attribution challenge complicates measurement in scenarios where websites implement counter-blocking measures or where websites vary their ad delivery based on ad blocker detection. Some websites identify when visitors are using ad blockers and respond by either serving alternative ad experiences (such as server-side inserted ads that bypass ad blockers) or requesting that users disable ad blockers as a condition of accessing content. These scenarios create situations where simple before-and-after ad blocker measurements may not reflect the true performance differential, as the website’s architecture changes in response to ad blocker presence.
Second, different ad blockers may block different subsets of content, creating measurement challenges when trying to attribute performance improvements to “ad blocking” as a general category versus specific blocking rules or tools. Some ad blockers prioritize aggressive blocking and may block content that other tools classify as acceptable, while some specialized tools focus on tracking prevention rather than ad blocking per se. Research findings from one ad blocker tool may not generalize to other tools with different filter lists.
Third, the question of what constitutes a fair or accurate performance test remains contested. Some researchers argue that testing with caching disabled better represents the worst-case scenario or the true overhead of ads, while others contend that disabled caching artificially inflates ad blocker benefits since most users experience subsequent page loads with cache hits. Some researchers test across website samples weighted toward ad-heavy news and technology sites, while others test more representative website samples; these choices substantially influence reported averages.
Fourth, measurement tools themselves introduce artifacts. Synthetic testing using headless browsers or specialized test machines may execute advertisement JavaScript differently than real browsers with various plugins and configurations. The very process of performing network analysis (through HAR file capture or similar tools) introduces overhead that may distort the measured performance characteristics of the pages being tested.
Fifth, determining whether an observed speed improvement should be attributed to ad blocking or other confounding factors requires careful experimental design. If a website is simultaneously optimizing for speed, updating its ad network partners, or migrating to a faster content delivery network, observed speed improvements might reflect these other factors rather than ad blocking specifically. Rigorous studies attempt to control for these confounds through statistical methods, but perfect isolation remains challenging.
Sixth, the temporal dimension of ad blocker performance creates measurement challenges. Advertisement content, network conditions, and ad blocker filter lists all change continuously. Findings from research conducted in one year may not accurately reflect performance in subsequent years as these systems evolve. A study showing modest page speed improvements from ad blocking in 2017 may reflect a different reality than current conditions if websites have subsequently shifted toward heavier advertisement content or if ad blockers have improved their efficiency.
Advanced Measurement Approaches and Predictive Models
Recent research has begun developing sophisticated approaches to measure and predict ad blocker performance impacts more accurately than simple before-and-after comparisons. These advanced techniques offer promise for understanding the mechanisms through which ad blocking improves performance and for identifying which website characteristics are most strongly associated with large performance gains.
Machine learning approaches to performance prediction have emerged as a particularly promising development. Brave and academic collaborators have constructed machine learning models trained on performance characteristics of thousands of websites, learning to predict which page features correlate most strongly with ad blocking performance improvements. These models incorporate features such as the number of blocked requests, the size of blocked resources, the prevalence of specific third-party domains known to serve ads or tracking code, JavaScript task statistics, and DOM complexity metrics. The research found that the single strongest predictor of ad blocking performance benefits is the number of blocked requests—pages with more ad network requests see larger benefits from blocking. However, the predictive models also revealed that JavaScript execution characteristics, page complexity, and the presence of specific known ad domains also contributed meaningfully to predicting performance improvements, enabling more nuanced understanding than simple correlations.
Network-level analysis approaches have also advanced measurement capabilities. Some researchers have employed instrumented browsers that capture detailed network behavior, allowing precise attribution of which resources and request types contribute most substantially to loading time. ADGRAPH, a graph-based machine learning system for identifying ad and tracker resources, demonstrated 95.33 percent accuracy in replicating manual labels of ad and tracking resources and identified additional advertising and tracking resources that simpler rule-based approaches missed. These more sophisticated detection approaches enable more accurate measurement of which resources are actually providing blocking benefits versus false positives where ad blockers block resources that don’t actually impact performance.
Real user monitoring platforms have advanced beyond simple metric reporting to provide detailed analysis of performance variance across user segments. By segmenting performance data by device type, network condition, geography, and browser, researchers can now observe how ad blocking performance benefits vary across different user populations. This segmentation reveals that benefits are often most pronounced for users on slower connections and lower-capability devices—precisely the populations that benefit most from reduced computational overhead.
The Role of Content Type and Website Categories
Performance improvements from ad blocking vary substantially across different website categories, a finding that sophisticated researchers carefully consider when interpreting and generalizing their results. News and technology websites, which often contain extensive advertisement inventory, typically display larger absolute performance improvements than entertainment or social media sites, though percentage improvements may be similar or even smaller if those categories also contain heavy ad content.
Testing conducted on The Verge, a technology news website, demonstrated 3.5 times speedup with ad blocking enabled, reflecting the site’s substantial advertisement content. In contrast, testing on other website categories might show substantially smaller improvements. Video-heavy sites present a particular case, as video advertisements can impose either substantial or minimal overhead depending on whether the video autoplays or requires user interaction. Sites where advertising appears primarily in sidebar locations or in standardized display ad placements typically show moderate improvements, while sites that employ aggressive advertisement strategies (such as interstitials, mid-content ads, or popup overlays) often show larger improvements.
The distinction between different website content types also influences which performance metrics show the most substantial improvements. News sites featuring primarily text and static images may show moderate FCP improvements but substantial LCP improvements if large advertisement elements block rendering of main content. E-commerce sites where product images constitute the largest visual elements might show different performance patterns than media sites where article text dominates. Understanding these category-specific patterns is important for avoiding overgeneralization of findings from particular website samples.
Future Directions and Evolving Measurement Challenges
The landscape of ad blocking measurement continues to evolve as web technologies, advertising practices, and blocking techniques advance. Several emerging developments will likely influence how researchers measure and interpret page speed gains in coming years.
Server-side ad insertion (SSAI) represents a technical development that challenges traditional measurement approaches. By inserting advertisements at the server level and embedding them directly into video streams before delivery to users, SSAI bypasses client-side ad blockers entirely. As this technique sees increased adoption on streaming video platforms and other sites, it may reduce the population of ads that client-side ad blockers can prevent, potentially diminishing measured performance gains over time. Measurement approaches will need to account for the distinction between ads blocked through network-level prevention versus ads delivered through SSAI.
Manifest V3 implementation in Chromium-based browsers represents another evolving challenge affecting measurement. By imposing restrictions on what browser extensions can do and removing support for the declarativeNetRequest API’s extensive blocking capabilities, Manifest V3 will likely reduce the effectiveness of extension-based ad blockers in Chrome and related browsers, though built-in browser ad blocking (such as Brave’s Shields) remains unaffected. This fragmentation may create divergence where different browsers achieve different ad blocking effectiveness, complicating measurement comparisons.
The rise of AI-powered content generation and personalized ads delivered through proprietary mechanisms may also influence measurement approaches. As advertising becomes more dynamically generated based on user data and rendered through JavaScript rather than delivered as static creative assets, ad blocker performance measurement may need to shift toward measuring JavaScript execution overhead rather than simple resource blocking metrics.
Core Web Vitals evolution represents another measurement consideration. As Google continues to refine which metrics most strongly correlate with user experience, and as new metrics like Long Animation Frames (LoAF) receive increased emphasis, researchers may need to expand their measurement focus beyond the current standard Core Web Vitals to capture aspects of performance that evolving metrics prioritize.
The Bottom Line on Blocking’s Speed Benefits
Measuring page speed gains from ad and tracker blocking represents a nuanced research domain where consistent empirical evidence establishes meaningful performance improvements across multiple studies and methodologies, yet substantial variation exists in the magnitude of improvements across different websites, network conditions, and user populations. The evidence overwhelmingly demonstrates that ad blockers achieve consistent data consumption reductions of 25 to 40 percent, which translate into variable but generally meaningful page speed improvements that range from modest tens of milliseconds on optimized sites to multiple seconds on advertisement-saturated pages. Research further demonstrates that these speed improvements, while important, represent only one dimension of ad blocking’s benefits, as reduced computational overhead improves battery life, enables faster user engagement with content, and reduces visual distraction and layout instability.
The most reliable findings emerge when researchers employ multiple measurement approaches—combining synthetic testing for mechanistic understanding with real user monitoring for ecological validity, varying test conditions to understand sensitivity to network and device factors, and measuring multiple performance dimensions rather than relying on single metrics. The evidence suggests that ad blocking produces the most substantial benefits for users on slower connections and lower-capability devices, on advertising-saturated websites where ads comprise 30 percent or more of page load, and when blocking Javascript-heavy advertisement code rather than simply preventing image downloads. Conversely, ad blocking produces minimal or occasionally negative effects on fast connections, well-optimized websites with minimal ad overhead, and situations where ad blocker overhead exceeds blocked resource savings.
Sophisticated measurement approaches incorporating machine learning prediction models, network-level resource analysis, and segmentation across user populations offer promising directions for more accurate quantification of ad blocking performance benefits. Future measurement challenges will emerge from evolving advertising technologies including server-side ad insertion, personalized dynamic ad delivery, and JavaScript-rendered advertisements, requiring researchers to continuously adapt measurement methodologies to reflect actual technological reality rather than relying on outdated assumptions about ad delivery mechanisms. Practitioners seeking to understand ad blocking performance benefits would be well-served by conducting site-specific measurements reflecting their actual audience characteristics and network conditions rather than relying on generic benchmarks, while researchers should carefully attend to methodological choices regarding caching, website sample selection, and performance metrics to ensure findings accurately reflect the systems being studied and translate appropriately to other contexts.
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