Apple gained 6.3 points in installed-base share among 18-to-29-year-old US smartphone users from Q3 2025 to Q1 2026. Exclusive loyalty in the same cohort is 12.3 percent, 23 points below the 60-plus reading.

In the US smartphone installed base, Apple commands the 18-to-29 cohort at 73.7 percent share across the combined Q3 2025 to Q1 2026 window, more than three times Samsung’s combined 19.5 percent. Behind that combined reading, the trend is also moving in Apple’s favor. Apple’s 18-to-29 share rose from 68.9 percent in Q3 2025 to 75.2 percent in Q1 2026, a 6.3-point gain in nine months that is well above the margin of error.

The data come from Recon Analytics’ Consumer Pulse Survey, covering 248,279 US smartphone respondents tracked from Q3 2025 through Q1 2026, with respondents’ device brand and model identified via passive detection at survey entry rather than self-report. Apple’s installed base is 93.2 percent in the premium tier and 6.8 percent in the value tier. Samsung’s installed base splits roughly 50-50 between Samsung Flagship and Samsung Non-Flagship.

The age-cohort gap closes sharply by 45-plus and then reverses. Samsung’s combined book reaches 44.7 percent of the 45-to-60 cohort and 44.1 percent of the 60-plus cohort, its highest installed-base concentrations across the four cohorts tracked. Apple’s combined share in those same cohorts falls to 40.2 percent and 42.6 percent. At the brand-total level, Samsung’s installed base exceeds Apple’s at 45-plus, an inversion of the 18-to-29 reading.

Chart 1: US Smartphone Installed-Base Share by Age Cohort

Inside the comparable premium tier, the gap compresses even more sharply than the brand-level numbers indicate. Apple Flagship leads Samsung Flagship across all age cohorts. In the 18-to-29 cohort, Apple’s flagship installed-base share is 69.8 percent, compared with Samsung’s 9.3 percent, a 60.5-point gap. By the 60-plus cohort, Apple Flagship is 38 percent compared with Samsung Flagship at 23.2 percent, a 14.8-point gap. The Apple Flagship lead in the youngest cohort is more than four times that in the oldest cohort. The premium tier is closely contested at 45-plus in a way the youth-cohort dominance does not predict.

When Samsung’s combined installed base is plotted against Apple’s, Samsung Total runs 4.5 points above Apple in the 45-to-60 cohort and 1.5 points above in the 60-plus cohort. That convergence reflects portfolio coverage rather than head-to-head premium competition. Samsung addresses every price band in the US smartphone market, from sub-200-dollar Galaxy A devices to the 1,900-dollar Z Fold. Apple addresses only the premium tier, with a value-tier presence at the SE and 16e price points that does not extend below $599. The combined-book comparison sums two different addressable markets. Inside the comparable premium tier, no cohort crossover exists.

Within the same nine-month window, the premium-tier picture varies across cohorts. Apple’s lead over Samsung Flagship in the 18-to-29 cohort widened from 57.6 to 66 percentage points across the window, the most decisive premium-tier movement in the dataset. In the 45-to-60 cohort, the same lead narrowed from 16.7 to 15 percentage points. In the 60-plus cohort, the compression was sharper: from 19.4 to 16 points. The premium tier is sorted by generation. Apple is consolidating among young buyers; Samsung’s flagship is gaining ground among older buyers.

The combined Q3 2025 to Q1 2026 window includes the iPhone 17 launch quarter (Q4 2025), which produced a sharp Apple gain across all cohorts, partially reverting in Q1 2026. The within-window quarterly cuts isolate that effect: Apple’s 18-to-29 share peaked at 76.3 percent in Q4 2025, then settled at 75.2 percent in Q1 2026. The Q1 2026 reading is the most recent post-launch quarter and remains 6.3 points above the Q3 2025 entry.

The 18-to-29 cohort that is moving most decisively toward Apple is also the iPhone segment that cross-shops the most in the dataset. Exclusive Apple loyalty (don’t consider any other brand at all) among 18-to-29 iPhone users is 12.3 percent, the lowest of any age band tracked. Among consumers over 60, exclusive Apple loyalty reaches 35.2 percent, almost three times higher. The young Apple user holds an iPhone today and is also the most open to switching brands for the next purchase.

The combination is unusual. Rising share with low exclusive loyalty indicates Apple is acquiring new young users at scale rather than locking in the ones already there. Position is rising; lock-in is not. Cross-brand consideration data confirms the openness: among 18-to-29 Samsung Flagship users, 30.6 percent considered Apple before their most recent device purchase, the highest cross-Apple consideration rate of any cohort and brand combination in the report. Older cohorts narrow their consideration sets dramatically. Among Apple users over 60, exclusive loyalty at 35.2 percent means more than one in three did not consider any other brand at all. The same shopper behavior that drives high cross-shopping in the young cohort also yields a stable share among older cohorts. Apple is winning the conversion contest in this cohort. Cross-shopping consideration runs both ways, but the realized installed-base share movement of 6.3 points net runs to Apple.

Google’s installed-base share never exceeds 4.3 percent in any age cohort. Motorola peaks at 9.1 percent in the 45-to-60 cohort. Neither brand’s age curve approaches the spread shown by Samsung Flagship or Apple Flagship in the premium tier. The mass-market US smartphone story is an Apple-Samsung story; Google and Motorola compete inside narrower segments.

What this picture suggests for OEM and carrier strategy is asymmetric. Apple’s 18-to-29 share gains arrive without the loyalty buffer that the 45-to-60 and 60-plus shares carry. Samsung Flagship’s natural addressable opportunity is not in the cohort it has lost most ground in, but in the 60-plus cohort, where the premium-tier gap is narrowest and where the brand’s combined book already exceeds Apple’s. Carrier-led upgrade campaigns converting Samsung Non-Flagship users to Galaxy S face different population profiles across target cohorts. The young cohort that Apple is gaining is also the cohort most likely to consider switching.

The Galaxy S26 launched in January 2026, within the most recent quarter in the data window. Q2 2026 will be the first full post-launch quarter to confirm whether Samsung Flagship’s 18-to-29 decline is a structural pattern or a launch-cycle artifact.

Apple’s 38 percent Flagship installed-base share among 60-plus consumers represents the brand’s smallest cohort lead. The structural question is whether today’s 18-to-29 cohort, which has the lowest level of exclusive Apple loyalty in the market, stays with Apple as it ages. The share gains in 18-to-29 are real. So is the openness to change.

This article draws on the demographic findings of Recon Analytics new report, US Consumer Device Purchase Journey Part 4: Demographic Segmentation and the Upgrade Pipeline. The full report covers gender, ethnic community, geographic, consideration, and feature-priority segmentation across all four major US smartphone brands, plus quarterly trend cuts from Q3 2025 to Q1 2026.

If you are interested in the full report, you can find it here:
Digital Products – Recon Analytics

By Joe Salesky, Head of AI Research

Based on interviews with 193,266 Americans conducted between March 2025 and February 2026

A massive and largely quiet transformation is underway in the American workforce. Recon Analytics tracks AI adoption through weekly pulse surveys of over 300,000 respondents annually. The data reveals that 42% of employed AI users now direct those tools toward work tasks, generating an estimated $420 billion in annual productivity value. Only half of these workers say their adoption came through any formal company program. The other half found their own way.

The Workforce Picture

33% of employed Americans now use AI for work tasks. Among knowledge workers in financial services, professional services, scientific and technical fields, communications, media, law, healthcare, education, and government, the rate is 37%. Among employed people who already use AI in any capacity, 42% use AI tools in their jobs.

Workers Are Leading, Companies Are Following

Work AI users who answered questions about their adoption pathway (n=20,934 since July 2025), roughly half report either using AI independently or working at a company with no formal AI initiative. 20% say they adopted AI through a deliberate, self-directed choice with no company involvement at all; another 38% report their company has no formal AI initiative or they are unsure whether one exists. The workforce is pulling AI adoption forward while many organizations are still debating governance frameworks.

For those who can identify initiative drivers, executive leadership accounts for 33% and IT departments for 47%. These figures overlap because respondents can select multiple drivers. The gap between the 47% who credit IT and the 50% operating outside any formal program is a governance challenge.

Who Pays for AI

29% of work AI users pay for a premium subscription to their primary tool. Employer or team funding covered 40% of paid subscriptions in August 2025. That figure has climbed to 50% in February 2026. Corporate procurement is catching up, while half of all paid work AI subscriptions still come out of workers’ own pockets.

Workers spending $20-30 per month of their own money on AI tools they believe make them more productive, without being asked and often without their employer’s knowledge, is a revealed preference. These workers have already conducted the cost-benefit analysis that many enterprise procurement processes are still debating.

Retention Is Strengthening, Not Weakening

For work AI users on paid subscriptions, cancellation intent has declined steadily from 26% in August 2025 to 20% in February 2026. The trajectory is consistent: Four in five paid AI subscribers plan to keep paying.

A 20% cancellation rate in a product category barely two years old compares favorably to mature SaaS categories that typically see 25-35% annual churn in their first 18 months. The downward trajectory suggests that workers who survive the first few months of paid use are settling into habitual usage patterns. Paid AI has crossed from experiment to infrastructure.

The Productivity Signal

Among 18,117 work AI users surveyed over the last 90 days, 97% report at least one tangible benefit and 94% report measurable time savings. The top benefit is increased productivity and efficiency at 45%, followed by quick generation of actionable information at 34%, enhanced decision-making at 28%, and automated repetitive tasks at 24%.

Workers who report hours saved (n=16,154) average 6.3 hours per week. Some of AI’s productivity value does not register as “hours saved”, a developer who writes cleaner code the first time, a marketing analyst who produces three campaign variants instead of one, a sales rep who personalizes 50 outreach emails instead of sending one generic blast: none of these show up as recovered hours. They show up as better output. Self-reported time savings captures one dimension of a multi-dimensional productivity gain.

Based on 161.3 million employed Americans (BLS, 2024 annual average), with 33% using AI and 42% of those directing it toward work, approximately 22 million workers are using AI on the job. At the self-reported mean of 6.3 hours per week and BLS total compensation of $48.05 per hour for civilian workers (Employer Costs for Employee Compensation, June 2025), the annualized productivity value is approximately $340 billion. Adjusted for the occupational skew of AI work users toward professional, technical, and financial roles that carry above-average compensation, the estimate reaches approximately $420 billion in annual productivity value.

What This Means

For enterprise IT leaders, internal AI adoption metrics almost certainly undercount actual usage. Dashboard-tracked deployments of Copilot, Gemini for Workspace, or other enterprise-licensed tools capture only the formal channel. Workers using personal ChatGPT Plus, Claude Pro, or Perplexity subscriptions on their own devices are invisible to those dashboards. The opportunity is to channel demonstrated demand into governed platforms.

For AI platform vendors, employer-funded subscriptions grew from 40% to 50% of paid work AI users in six months. That trajectory points toward corporate procurement absorbing what workers started on their own. The conversion trigger for free users considering paying is integration with existing productivity applications: Microsoft 365, Google Workspace, Salesforce. Prior reports have noted that Enterprise data infrastructure expansion will clearly grow both enterprise paid usage and business impact.

The comprehensive report providing deeper analysis, conclusions, and recommendations is available on ReconAnalytics.com.

Methodology: Recon Analytics AI Pulse Survey. Continuous weekly data collection, March 2025 to present. Total respondents: 193,266 (n=16,154 for hours-saved analysis, last 90 days). Monthly sample sizes range from 944 (March 2025, early panel) to 34,253 (October 2025, full panel). Quality controls reject unqualified responses at 4-10x the industry standard. Productivity value calculation: BLS Employer Costs for Employee Compensation (ECEC), June 2025 release, civilian workers total compensation $48.05/hour; BLS Current Population Survey, 2024 annual average, 161.3 million employed.

Contact: [email protected]

AI Revenue: Paid Adoption Requires Data, Not Better Models

February 17, 2026 | Joe Salesky, Analyst & Head of AI Research

Eight breakthrough model releases in seven months produced four percentage points of daily usage growth. 75% of Americans have tried AI, but only 25% use it daily. The bottleneck is not intelligence. It is trust and context. Privacy scores -30 cNPS across the entire AI ecosystem, the lowest attribute by 28 points, based on more than 120,000 respondents surveyed over 38 weeks. Users who connect AI to their private data convert to paid subscriptions at 3x the rate of those performing basic tasks. The model capability race has hit diminishing returns. The next phase belongs to whoever solves the data infrastructure problem for consumers and enterprise.

The 50-Point Gap Between Trial and Habit

Three out of four Americans have tried an AI tool. One in four uses it daily. The 50-point spread between trial (76%) and habitual use (26%) is not a marketing problem. It is a utility problem.

The middle of the funnel defines the conversion opportunity. 19% of respondents use AI weekly, suggesting it occupies a place in their routine but has not become essential. Another 10% use it monthly. Combined with the 21% who tried AI and walked away, these cohorts represent half the population: consumers who engaged with AI and did not find sufficient value to stay.

The demographic surprise: Millennials, not Gen Z, are the heaviest users. Daily usage peaks in the 30-44 age bracket at 36%, five points ahead of 18-to-29-year-olds at 31%. The explanation is context and compensation. Mid-career knowledge workers face more tasks that align with AI capabilities. The inflection arrives at 60, where daily usage drops to 11%. Seniors have not rejected AI. Nobody has shown them what it can do.

Privacy Is Both the Lock and the Key

45% of Americans remain outside the AI economy. Among those who have never tried AI, 44% cite lack of interest, 25% cite ethical concerns, and 24% cite distrust of AI answers. Cost ranks last in both the never-tried and tried-and-quit segments. Only 5-9% cite expense as a barrier. Free versions exist for every major platform. Price is not the obstacle.

Privacy is the top barrier and the top motivator. 21% of never-tried respondents say knowing their data would be safe and private would motivate them to try AI, the only response exceeding 20%. The same consumers who say they do not trust AI say privacy assurance would bring them in. This is not a contradiction. It indicates that trust is the gate through which new users must pass.

Among active users, the picture is worse. Privacy scores -30 cNPS, nearly 28 points below complete experience (-2). Every platform in the dataset reports negative privacy cNPS, from Perplexity at -18 to Meta AI at -43. Zero for twelve. Apple Intelligence scores -25, underperforming its privacy-centric brand positioning. Payment helps: paid users report -9 versus -34 for free users, a 25-point lift. But no platform achieves positive territory.

ChatGPT Dominates. Satisfaction Tells a Different Story.

ChatGPT leads primary platform selection at 50%. Google Gemini follows at 20%. Microsoft CoPilot (8%), Apple Intelligence (6%), and Meta AI (6%) form a competitive middle tier. The top four platforms represent 83% of primary selections.

Scale inversely correlates with satisfaction among free users. Perplexity, at less than 1% share, posts cNPS of +8. ChatGPT, at 48% of free users, registers -1. Google Gemini scores -6. The pattern is monotonic: larger user base, lower satisfaction. Smaller platforms attract self-selected enthusiasts who chose the tool deliberately. Larger platforms accumulate casual users through distribution advantages and brand awareness.

Paid users report higher satisfaction across every platform without exception. The aggregate cNPS gap: +18 for paid versus -7 for free, a 25-point differential. ChatGPT moves from -1 to +25. Payment is not a gamble. It is a reliable upgrade across the board. The challenge is getting users past the trust barrier to that first payment.

The 3x Conversion Divide

The use case hierarchy is steep. Web search leads adoption at 43%, followed by writing assistance at 33% and topical research at 26%. These information retrieval tasks require no private data, no account connections, no infrastructure beyond the prompt itself. Transformation use cases cluster at the bottom: data analysis at 20%, coding at 9%, automation at 9%.

The conversion gap tells the real story. Coding assistance converts at 41% to paid subscriptions. Automation follows at 37%. Data analysis converts at 31%. Information use cases trail: web search at 17%, topical research at 21%. The 2x gap between transformation and information use cases is not about features. Both tiers offer the same use cases. This is about user profile. Users who need transformation already have infrastructure that makes AI valuable.

Crossing work context with subscription status creates four segments with dramatically different satisfaction profiles. Work + Paid users report productivity cNPS of +23. Home + Free users report -20. The 43-point gap defines the challenge for consumer AI. A Home + Paid user (-1) is no more satisfied than a Work + Free user (-1). Work context provides more satisfaction improvement than payment alone. The difference between +23 and -1 is the combination of subscription plus infrastructure, not the subscription by itself.

The Second Inning Is About Data, Not Models

Data-dependent users, the 18% who employ AI for data analysis or automation, report productivity cNPS of +9 versus -13 for non-data users. They show 61% daily usage versus 28%. They convert to paid at 32% versus 9%, a 3x advantage. This minority demonstrates the engagement and monetization patterns every platform seeks. When users connect AI to their private data, they use it more, value it more, and pay for it more.

The entities with access to private data are not primarily AI companies. Apple holds photos, messages, health data, and financial transactions for over a billion users. Google holds Drive documents, Gmail archives, and location history. Microsoft holds OneDrive files and Outlook correspondence. These companies have the Organize and Unify layers that AI platforms lack. The strategic landscape creates natural partnership opportunities: AI platforms have models but need data access; device and cloud providers have data but need AI differentiation.

The shift is from “ask me anything” to “help me with my data.” The former is a party trick that saturates quickly. The latter is a utility that compounds with use. The stronger engine is ready. It needs better tires for traction.

Report Details

Genius Myopia: Why Smarter Models Aren’t Enough

How Trust and Context Unlock the Next Stage of Adoption | March – December 2025

The complete 23-page report includes detailed analysis of:

• Usage frequency distribution and demographic breakdowns across age and income segments
• Barrier analysis for the reluctant 45%, including never-tried and tried-and-quit segments
• Platform-by-platform market share, satisfaction, and subscription dynamics
• The O/U/T Framework: Organize, Unify, Transform as the value creation model for consumer AI
• Connectivity as infrastructure proxy: fiber converts at 2x the rate of cable for paid AI
• Privacy cNPS across all twelve platforms and the payment improvement effect
• The Second Inning Playbook for AI Platforms, Device OEMs, Cloud Providers, and Investors

📊 View Report Details & Purchase →

For licensing inquiries: [email protected]

AI Choice 2026: Why Licenses Don’t Equal Adoption

February 3, 2026 | Joe Salesky, Analyst & Head of AI Research

Despite Microsoft’s enterprise distribution advantages and Office 365 integration, Microsoft Copilot lost 7.3 percentage points of paid subscriber share in seven months while Google Gemini gained 2.9 points, based on more than 150,000 respondents. Distribution advantages do not lock in market position. Employees receiving enterprise AI tools evaluate options and select based on experience. The platform that delivers the most reliable results wins, regardless of vendor seat licenses.

The 39% Market Contraction

Copilot’s decline from 18.8% in July 2025 to 11.5% in January 2026 represents a 39% contraction in market position among U.S. paid AI subscribers. This occurred during a period when Microsoft actively invested in enterprise distribution and deepened Office 365 integration. The platform accesses the same OpenAI models as ChatGPT, so underlying capability is comparable. The divergence in user perception points to product experience and integration execution rather than model quality.

ChatGPT maintained dominant share near 55.2% with modest erosion from 60.7% in July. Gemini climbed from 12.8% to 15.7%, crossing Copilot in late November to claim the number-two position. The two platforms now separated by more than 4 percentage points, with Gemini’s trajectory continuing upward while Copilot stabilized in the 10-12% range.

Exhibit 1: Primary Platform Share Among U.S. Paid AI Subscribers

Source: Recon Analytics U.S. AI Survey, July 2025 – January 2026. U.S. paid subscribers only.

The Workplace Conversion Gap

When Copilot is the only AI platform an employer provides, 68% of workers adopt it as their primary tool. This demonstrates meaningful uptake when alternatives are absent. The competitive dynamics shift when employers offer multiple platforms.

Among workers with both Copilot and ChatGPT available, Copilot’s adoption falls to 18% while ChatGPT captures 76%. When all three major platforms are available, only 8% choose Copilot while 70% choose ChatGPT and 18% choose Gemini. The pattern is consistent: as worker choice expands, Copilot adoption collapses.

ChatGPT converts 83.1% of U.S. paid subscribers who have workplace access. Copilot converts 35.8%. Gemini converts 34.0%. The 47-point gap between ChatGPT and Copilot quantifies the adoption challenge. Microsoft’s Office 365 distribution creates exposure. That exposure does not automatically translate to preference when workers evaluate alternatives.

Exhibit 2: Workplace Conversion Rates by Platform

Source: Recon Analytics U.S. AI Survey, July 2025 – January 2026. Workers with paid AI subscriptions only.

Quality Perception Drives Share Movement

Gemini’s rise correlates with quality leadership. The platform posts the highest accuracy satisfaction scores among major competitors, 23 points above Copilot and 9 points above ChatGPT. Copilot’s decline correlates with the lowest accuracy perception in the market. Quality drives share movement, not deployment volume.

Copilot’s accuracy NPS remained persistently negative throughout the measurement period. July showed -3.5, September declined to -24.1, and January finished at -19.8. The gap is not closing. Users who tried Copilot and stopped using it cited distrust of answers at 44.2%, exceeding comparable figures for Gemini (42.8%) and ChatGPT (40.6%).

The correlation between accuracy perception and market share movement is direct. Platforms investing in quality gain share. Those relying on ecosystem integration without matching quality lose share.

The Enterprise Market Remains Contestable

Multi-platform enterprise deployment is common. Among U.S. paid subscribers with Copilot available at work, over half also have ChatGPT available. Workers evaluate options and select based on experience rather than defaulting to whatever platform their employer provisions.

The enterprise AI market is not winner-take-all. Every renewal cycle presents an opportunity for challengers to displace incumbents based on demonstrated superiority. Microsoft’s enterprise dominance in productivity software does not foreordain Copilot’s dominance in AI. Google’s Workspace position does not guarantee Gemini’s success. The platforms that execute on accuracy, integration, and use case demonstration will capture enterprise spend in 2026.

ChatGPT’s 83.1% workplace conversion rate and 55.2% market share reflect entrenched product-market fit. Competitors are not displacing ChatGPT. They are competing for second position. Gemini demonstrated that share can shift when product experience improves. Copilot demonstrated that share can decline when it does not.

On January 16, 2026, OpenAI announced plans to test advertisements in ChatGPT’s free tier and the new $8/month “Go” tier in the United States. The move was widely anticipated: advertising funded the scaling of Google Search and Facebook and has been expected as the monetization path for consumer AI services. With OpenAI reportedly losing over $11.5 billion in Q3 2025 and projecting infrastructure spending of $1.4 trillion over the next eight years, the decision reflects economic necessity rather than strategic pivot.

Commerce Secretary Lutnik signaled in an interview with Broadband Breakfast on March 5th, 2025 that the US government will rethink the BEAD program so that Americans “get the benefit of the bargain.” He elaborated that it could mean that homes get broadband through satellite instead of fiber. “We want the lowest cost broadband access to Americans,” he said.

Secretary Lutnik gets it. Connecting a location to broadband for $10k-$30k makes more sense than spending $60k to do the same thing. By approaching the issue from a technology-neutral perspective, we can connect a lot more people for a lot less money while improving connectivity and satisfaction with the connection.

From February 28th, 2024, to February 28th, 2025, Recon Analytics surveyed 160,848 people in the United States and asked them about their broadband experience with their current provider.

To determine satisfaction with their service, we asked consumers a standard net promoter question as developed by Bain, but modified to ask about specific components of the customer experience. Below you see a heatmap of Recon Analytics’ component NPS scores for connectivity. I omitted the customer interaction part of the heatmap for readability.

The 1,113 Starlink subscribers we interviewed over the last year were the most satisfied, followed by consumers of fixed wireless (FWA) with cable, and DSL customers being the least happy. Why are Starlink customers and FWA customers happier than cable and DLS?

While Starlink and FWA can be slower than speeds over cable, most consumers are not engineers or economists who make decisions based solely on technical merit or price. In the case of both Starlink and FWA, our data confirms that customers value the fact that the technology solution is easy to install and easy to get rid of if the consumer is not happy. In Starlink’s case, we have about three times as many former Starlink customers as current Starlink customers. This indicates that there are quite a few people who were unhappy with the solution and swapped out for a different one.

At the same time, the people for whom Starlink or FWA works are very happy with it, especially in comparison to what they had before. Interestingly, we consistently find in our polling data that the higher the cost for a service, the lower consumer satisfaction is, all other things held equal. The low satisfaction scores for cable, even though cable internet service is substantially cheaper than fiber, is a clear indication that cable needs to do a much better job in serving its customers. As confirmed in the quarterly reported financial data, customers leave services with low satisfaction and join services with high satisfaction. In the home internet case, customers choose a more expensive service because, in their experience, the cheaper service is not worth it.

We are also seeing in the data that the Starlink and FWA routers are state-of-the-art equipment. Investment in good routers leads to better scores for how well existing devices stay connected to the Wi-Fi network and how easy it is to connect to Wi-Fi. It also aids in every other connectivity and customer issues metric. This is demonstrated again in the table below.

We are also asking all of our home internet respondents how often they have subjectively experienced internet issues in the last 90 days. The data below is again from the 160,848 people we surveyed over the last year.

What is striking is how well Fiber, FWA, and Starlink are performing when it comes to reliability. When looking at reliability from a customer perspective, it is the interplay between the connection, which is determined by network technology; the router used or supplied; and the end user equipment. The end user equipment is the same for every customer – a mix of smartphones, laptops, desktops, and other connected devices. While Verizon FiOS and AT&T Fiber customers report fewer instances of their internet connection going down, the FWA and Starlink routers are able to mitigate a lot of the more difficult connection technology challenges.

When comparing the data presented in this research note with what was presented six months ago, the Starlink scores for connectivity increased as the company launched more satellites during that period. As Starlink continues to launch more satellites, its scores will change depending on what increases faster – the number of satellites or the number of customers.

Furthermore, it is interesting to see where Starlink customers came from. Eighty-five percent of Starlink respondents are from rural areas, consistent with Starlink’s reporting of where it sells. Almost 31% came from small ISPs. For more than 11% of respondents, Starlink is the first home internet provider that they have, followed by CenturyLink, Charter, Frontier and Comcast, who provide a lot of internet coverage in rural areas.

Recon Analytics data shows that a technology-neutral approach is the right way to go for allocating federal dollars to get affordable broadband out to as many Americans as will take it. There are many Americans, especially in very rural areas, who are very happy with Starlink’s service. Fixed wireless is also solving the broadband rubric for many customers in a satisfactory way. Fixed wireless is especially valuable in less densely populated areas, where ample spectrum and thereby speed and capacity is shared among fewer people resulting in higher speeds. Fiber, without a doubt, is the workhorse technology for more densely populated areas, where satellite and FWA do not have sufficient capacity given the current licensed full-power spectrum constraints to serve customers well.

What we need, and what Secretary Lutnik rightfully alluded to, is a technology-neutral approach where the Americans can choose how they want to be connected at the lowest price considering the circumstances.