AI App Ideas: What Mobile Category Growth Really Tells Founders

AI app ideas are easier to find when the market is visibly moving—but harder to evaluate when everyone sees the same chart. A recent discussion in r/SaaS argued that mobile Productivity is the standout category for founders, with usage and revenue accelerating while several legacy categories decline. The bigger lesson is not simply to build another AI assistant. It is to learn how to turn category-level signals into a specific, defensible customer problem.

The Reddit post, submitted by u/Alekssss-, interpreted a chart attributed to an a16z newsletter using Sensor Tower data for the United States. Its central observation was striking: Productivity, the App Store and Google Play category that includes ChatGPT and many AI assistants, had materially increased time spent and roughly doubled revenue from its 2025 baseline. The post also highlighted weaker results in Games, Books, Lifestyle, and News, alongside an unusual Sports pattern of falling time spent but rising revenue.

That interpretation broadly fits the wider mobile-market story. Sensor Tower says worldwide in-app purchase revenue reached $167 billion in 2025, up 10.6% year over year, and that non-game apps surpassed games in consumer spending for the first time, helped by generative AI, productivity, social, and streaming. (sensortower.com) But a category bubble chart is a starting point for research—not a substitute for a product thesis.

The chart behind the AI app ideas debate

The original r/SaaS thread deserves attention because it asks the right early-stage question: where are people spending both attention and money? Its chart uses three dimensions:

• Horizontal axis: change in time spent since 2025.
• Vertical axis: change in app-store revenue since 2025.
• Bubble size: the category's absolute revenue base.

On that framing, Productivity looked exceptional. The post described roughly 55% growth in time spent and an approximate doubling of revenue. Graphics & Design reportedly gained substantial usage without matching revenue growth. Games remained the largest revenue pool but appeared to be declining in revenue, while Books and Lifestyle were also down. News was close to flat in reported app-store revenue despite slightly lower time spent.

Those are useful directional observations. They tell a founder that consumer willingness to pay is not distributed evenly across mobile categories, and that AI-enabled work tools have become a major source of new spending. Sensor Tower's separate 2026 AI report offers an even stronger macro confirmation: it projected global time spent in generative AI apps to rise from 17.2 billion hours in the first half of 2025 to 36 billion hours in the first half of 2026. (sensortower.com)

Yet the source data should not be stretched past what it can prove. It describes category aggregates in one channel, in one country, over a particular period. It does not say that every new productivity app will grow, that users want more general-purpose chat interfaces, or that a solo developer can compete with companies that dominate category totals.

Why Productivity is growing faster than most mobile categories

Productivity's rise is not just an AI novelty cycle. It reflects a change in what users perceive software as doing for them. A game is entertainment; a traditional utility often performs a narrow function. An AI productivity tool can draft, summarize, research, organize, translate, code, plan, and automate within the same session. The user can connect its value directly to time saved or output created.

Users can justify paying for outcomes

A user may hesitate to add another $10 monthly subscription for a passive content app. The same user may pay for a tool that helps write client emails, turn meeting notes into tasks, prepare a presentation, or reduce administrative work. This is the economic logic behind the Productivity surge: AI products increasingly sell labor substitution or acceleration rather than mere access.

a16z has argued that consumer AI is also changing retention economics. Instead of every subscriber paying one flat fee until they churn, some AI products expand revenue as customers use more valuable capabilities or move work use cases into the product. (a16z.com) That does not mean usage-based pricing is always right, but it explains why a tool can grow revenue faster than simple time spent.

The premium ceiling has also moved. In 2025, a16z noted that several advanced consumer AI plans were clustering around $200 per month, a price point that would have looked implausible for mainstream consumer software only a few years earlier. (a16z.com) For builders, the important implication is not to copy a $200 tier. It is to recognize that a customer will pay much more when the product repeatedly creates high-value work.

The biggest products pull the category upward

A second explanation is concentration. ChatGPT is listed in Productivity on the major mobile stores and is an enormous driver of attention and spending. Sensor Tower reported that ChatGPT became the fastest mobile app to reach one billion monthly active users in May 2026. (sensortower.com) Its scale can make the entire category look unusually attractive.

That was a major theme in the Reddit replies. Several commenters warned that category totals can be winner-take-most: gains may accrue to a few globally recognized products rather than to a broad middle class of new apps. The warning is well founded. Sensor Tower says ChatGPT, Gemini, and DeepSeek accounted for nearly 90% of time spent across AI assistant apps in the first quarter of 2026. (sensortower.com)

The lesson is nuanced. Concentration makes a general AI assistant a dangerous startup target. It does not make every productivity workflow impossible. A specialized product can avoid competing on raw model quality by owning context, integrations, compliance, distribution, and a specific end-to-end job.

The critical difference between a category and a market

A category is how an app store classifies software. A market is a group of people who share a costly or urgent problem and can be reached with a credible solution. Founders who confuse the two often build polished products with no natural buyer.

For example, AI app ideas inside Productivity might include:

1. A tool that turns inspection photos and field notes into a completed compliance report for one trade.
2. An assistant that reconciles invoices and purchase orders for small distributors.
3. A client-update generator embedded in an agency's project-management workflow.
4. A research workspace that extracts structured answers from a specific class of industry documents.
5. A sales-call follow-up system that creates tasks in the CRM and checks whether they were completed.

All five may be categorized as Productivity. But their markets differ dramatically in willingness to pay, buyer sophistication, data sensitivity, sales cycle, retention, and competitive alternatives.

A broad category chart can help decide where to investigate. It cannot answer the questions that determine whether an app becomes a business:

• Who feels the pain frequently enough to change behavior?
• What task is currently slow, error-prone, expensive, or emotionally frustrating?
• What existing workaround is the customer using?
• Why will they trust your tool with their work or data?
• How will they discover the product without expensive paid acquisition?
• What stops a large assistant or incumbent workflow platform from copying the visible feature?

A good app idea is therefore not AI plus a growing category. It is a narrow customer, a recurring workflow, a measurable before-and-after result, and an acquisition path.

What app-store revenue data does and does not measure

The original Reddit poster included an important caveat: Sensor Tower's category figures cover App Store and Google Play spending, not the entire digital economy. That limitation matters especially when comparing categories with different business models.

Apple describes in-app purchases as payments for virtual goods, premium content, digital goods, and subscriptions. (developer.apple.com) Google Play similarly requires its billing system for many digital goods, subscriptions, app features, and cloud software services, while physical goods and services sit outside that billing model. (support.google.com)

What may be understated

App-store spend can undercount categories that monetize elsewhere, including:

• News: advertising, publisher subscriptions sold on the web, bundles, and enterprise licensing can be significant.
• Business software: many teams buy through web checkout, invoices, annual contracts, or sales-led procurement rather than mobile in-app purchase.
• Sports: ticketing, merchandise, betting in certain contexts, sponsorship, and viewing packages may not map neatly to app-store transactions.
• Education and professional tools: institutions may purchase seats or licenses outside consumer app stores.

This is why a flat News revenue line should not be read as proof that journalism has no viable digital business model. It only says the relevant app-store spending did not grow much in that dataset. Similarly, a Business category that appears smaller than Productivity may conceal far larger web and enterprise revenue pools.

Why this changes opportunity selection

A mobile-first consumer subscription product should care intensely about App Store and Google Play spending. A B2B workflow product should care more about buyer budgets, renewal behavior, activation, and implementation friction. A founder who sells software through direct outreach or content-led web acquisition can build a strong company even if their app-store category looks unremarkable.

For products that use email for sign-up flows, invitations, reports, and lifecycle communication, operational details matter as much as category selection. Teams should compare actual transactional email pricing against expected message volume rather than assuming a low sticker price will remain low after usage grows.

The Sports puzzle: less time, more revenue

The Reddit thread's most interesting anomaly was Sports: sharply lower time spent paired with higher revenue. Community members proposed that people may be moving live viewing and richer experiences to streaming services or the web while still paying for subscriptions through apps. Others suggested revenue might reflect price increases or premium league packages rather than ticket and merchandise sales.

The physical-goods explanation needs caution. Tickets and merch generally are not the sort of digital content that stores process as standard in-app purchases; Google explicitly identifies physical goods and services as unsupported by its billing system. (support.google.com) So, if Sensor Tower is primarily tracking store revenue, tickets and jerseys are unlikely to explain the result directly.

A more plausible interpretation is a mix of three factors:

1. Subscription monetization: league packages, premium scores, fantasy features, and live-content access can raise revenue even when sessions become shorter.
2. Price and plan changes: higher price points or a shift toward annual plans can create revenue growth without more time spent.
3. Behavior moving across surfaces: fans may check a score briefly in an app but watch, discuss, wager, or read elsewhere.

The key founder takeaway is that time spent is not the same as value delivered. An app that completes a valuable transaction in two minutes may be healthier than a feed that consumes thirty minutes of attention but cannot monetize reliably.

Why rising usage can fail to become revenue

Graphics & Design was another revealing point in the original post: usage was said to be up strongly while revenue moved very little. That pattern appears whenever creation tools improve faster than their monetization model.

Users may try image generators, editors, template tools, and design assistants frequently but remain on free tiers. They may hop among products based on the latest model, obtain a similar result from a bundled suite, or use the tool only for occasional social posts. In short, engagement can be real while willingness to pay is weak or fragmented.

For AI builders, this creates a practical warning: do not put a chatbot, image model, or generation button into a product and assume demand will translate into subscription revenue. Ask what economic event happens after output is produced. Does the output help someone close a deal, avoid an error, satisfy a client, ship a campaign, or finish a task that had a budget attached to it?

The strongest monetization often occurs where generation is only one step in a workflow. A marketing visual creator is more defensible if it also understands brand rules, produces campaign variants, routes approvals, exports in required formats, and reports performance. The image is a feature; the completed work is the product.

The competition tax on popular AI app ideas

The community reaction was right to push back against a simplistic build where growth is narrative. Growing categories attract builders, investors, influencers, and platform incumbents. That produces a competition tax in the form of higher ad costs, louder launches, faster copying, and more user skepticism.

Consumer AI spending is concentrated too. a16z reported that only 9% of consumers paid for more than one subscription across ChatGPT, Gemini, Claude, and Cursor in the data it cited. (a16z.com) This suggests a user may experiment with several tools but reserve paid budget for one or two products that become habitual.

Avoid the generic wrapper trap

A generic wrapper is an app that provides a lightly modified interface around a foundation model but does not own meaningful workflow, proprietary context, customer relationship, or distribution. It may acquire attention quickly when a new model launches, but it is vulnerable when the model provider adds the same feature, pricing changes, or users decide their existing assistant is good enough.

That does not mean every product built on an API is doomed. It means the product needs an advantage beyond prompt formatting. Useful moats can include:

• Proprietary customer data with permissioned access and real operational value.
• Deep integrations that remove tedious implementation work.
• Domain-specific evaluation, quality controls, and human review.
• Compliance, audit trails, and role-based permissions.
• A distribution channel embedded in a community, profession, or partner ecosystem.
• An outcome-based workflow that includes action, not merely advice.

If your AI tool sends automated messages, produces follow-ups, or triggers workflows, reliability becomes part of that moat. Clear event handling, suppression controls, and retry logic matter more than an impressive demo; teams can review the relevant email API reference and setup guides before wiring those communications into product flows.

How to use category data without being led by it

The most productive way to use a chart like this is as a hypothesis generator. Treat every bubble as a question, then go looking for primary evidence from customers.

A five-step founder research process

1. Choose a customer slice, not a category. Replace Productivity with freelance bookkeepers, recruiting coordinators, property managers, or dental-office administrators.
2. Map the current workflow. Identify each spreadsheet, inbox, template, meeting, approval, copy-paste step, and error-prone handoff.
3. Quantify the pain. Estimate time lost, revenue delayed, mistakes made, compliance risk, or customer dissatisfaction. A problem without a measurable cost is harder to price.
4. Test the smallest paid outcome. Sell a pilot, concierge service, template pack, or manual workflow before building a full application.
5. Measure retention before scaling acquisition. Look for repeated use by the same customer cohort, not merely sign-ups during an AI launch cycle.

This approach turns a vague insight—people spend more on productivity—into a testable claim: for example, property managers will pay $99 per month if the tool reduces maintenance-ticket triage by five hours each week and creates audit-ready records.

Use a scorecard instead of intuition alone

Score potential ideas from one to five across urgency, frequency, buyer budget, access to users, workflow complexity, trust requirements, competitive intensity, and expansion potential. A high-growth category may receive a high demand score but a low competition score. A declining category may receive a low top-line demand score but a high underserved-niche score.

This prevents a common mistake: ranking ideas only by market excitement. Excitement is useful for distribution, but it can be terrible for differentiation.

Should founders build in shrinking categories?

Yes—sometimes. The r/SaaS conversation also surfaced the contrarian view that shrinking categories can be attractive because competitors leave, users are neglected, and remaining customers may have stronger intent. That is a legitimate strategy.

Books, for example, may have lower aggregate mobile growth while still containing viable niches for serious readers, educators, audiobook listeners, religious communities, researchers, collectors, and accessibility-focused products. The opportunity is not necessarily a broad new reading app. It could be a specialized experience with a distinct audience and clear paid value.

A shrinking category becomes dangerous when the decline reflects a permanent change in behavior and no segment has urgent unmet needs. It becomes attractive when aggregate decline hides poor service, weak product design, outdated incumbents, or users whose needs are too specific for mass-market providers.

The decision is less about whether a category is green or red and more about whether you can answer three questions:

• Is there a segment whose demand is stable or rising?
• Can you reach that segment cheaply and credibly?
• Can you deliver a better result than the existing workaround?

The chart is therefore a map of macro tailwinds and headwinds. It is not permission to ignore the terrain.

A better framework for evaluating AI app ideas in 2026

The current AI market is expanding rapidly, but it is maturing at the same time. Sensor Tower reports that apps mentioning AI-related terms are on track for $12 billion in in-app purchase revenue in the first half of 2026, while downloads for that cohort are substantially above 2023 levels. (sensortower.com) More money and more downloads create opportunity, but they also create noise.

Use this framework when reviewing AI app ideas:

The best opportunities increasingly sit between broad assistants and old vertical software. They take a real job that an incumbent handles clumsily, apply modern AI where it reduces friction, and leave the customer with a completed business outcome rather than a block of generated text.

Conclusion: follow spending, then find the overlooked workflow

The Reddit chart captures a real shift: mobile money and attention are moving toward software that helps people get work done, and generative AI is a major force behind that movement. The broader evidence supports the direction. Non-game apps have overtaken games in mobile consumer spending, generative AI use is expanding quickly, and leading AI tools have proven that users will pay for meaningful utility. (sensortower.com)

But the winning interpretation is not build an AI app because Productivity is up. It is investigate where growing willingness to pay intersects with an underserved workflow, a reachable customer, and a result that is difficult to replace with a general assistant.

For founders, category data should narrow the search space. Customer conversations, workflow observation, paid pilots, retention data, and distribution economics should decide what gets built.

FAQ

Are Productivity apps the best AI app ideas to build?

Not automatically. Productivity has strong momentum, but it is also crowded and heavily influenced by large AI platforms. The best opportunity may be a narrow workflow inside Productivity rather than a general-purpose assistant.

Does more time spent in an app mean it will make more money?

No. Time spent can reflect entertainment, free usage, or inefficient workflows. Revenue depends on willingness to pay, pricing, conversion, retention, and whether the transaction happens inside the measured channel.

Why can Sports revenue rise while time spent falls?

A likely explanation is a shift toward higher-value subscriptions, price increases, or usage moving to web and streaming surfaces. App-store revenue also does not neatly capture physical tickets and merchandise.

Are declining app categories bad startup opportunities?

No. A declining category can have attractive niches if customers remain underserved, competitors withdraw, and the problem is urgent enough to support paid retention. Aggregate category decline is a headwind, not a verdict.

How should I validate an AI app idea before building it?

Talk to a specific customer segment, map its existing workflow, quantify the cost of the problem, and test a paid manual or lightweight version first. Prioritize repeated behavior and willingness to pay over downloads or social engagement.