Are AI Apps Profitable? (28 Data to Understand)

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Despite 1.8 billion people using AI apps and explosive revenue growth, the economics are broken. Only 3% of users pay for premium features, even market leaders lose billions annually, and 90% of startups fail within their first year.

The data reveals why: customer acquisition costs jumped 222% over the past decade, infrastructure expenses consume 47-67% of total budgets, and switching costs are practically zero. ChatGPT generates $2.7 billion in revenue but loses $5 billion per year, while even $200/month premium subscriptions can't cover costs for heavy users.

Yet some AI wrappers thrive. Midjourney hit $500 million in revenue with just 40 employees and zero marketing spend, while Replika converts 25% of users to paid subscribers (8x the industry average) through emotional attachment that creates real switching costs.

This analysis examines 28 concrete data points from 2024-2025 to show what actually works, what fails, and where the real opportunities hide in consumer AI. Read our 200-page report covering everything you need to know about AI Wrappers for the complete picture.

In our 200+-page report on AI wrappers, we'll show you the real user pain points that don't yet have good solutions, so you can build what people want.

28 brutal data points on consumer AI profitability

• 1. Only 3% of 1.8 billion AI users pay anything

  What the numbers show:

  Out of 1.8 billion people globally using generative AI services, only 54 million pay for premium features. That creates a $12 billion market when the theoretical addressable market at $20/month average would be $432 billion annually. The gap between usage and payment is $420 billion.

  Why this matters:

  This represents one of the largest monetization failures in consumer tech history. The 97% of users staying on free tiers shows that people find value in AI tools but won't pay when free alternatives exist. High engagement doesn't translate to revenue without clear differentiation or switching costs.

  Source: Menlo Ventures

• 2. ChatGPT loses $5 billion yearly on $2.7 billion revenue

  What the numbers show:

  OpenAI generated $2.7 billion in ChatGPT revenue in 2024 (a 285% increase from 2023) but lost over $5 billion annually. The company won't reach profitability until 2029 according to internal projections. This happens despite having 800 million weekly active users and first-mover advantage.

  Why this matters:

  If the market leader with the most users and brand recognition can't make money, it reveals fundamental problems with consumer AI economics. Revenue growth that looks impressive masks catastrophic unit economics where infrastructure costs grow faster than pricing power.

  Sources: Business of Apps, Fortune

• 3. ChatGPT converts only 5% into paying subscribers

  What the numbers show:

  Despite 800 million weekly active users, ChatGPT converts approximately 40 million into paying subscribers. That's a 5% conversion rate, barely better than the 3% industry average. Even with massive brand awareness and network effects, 95% of users never pay a dollar.

  Why this matters:

  First-mover advantage and dominant market share don't automatically solve the monetization problem. Users have trained themselves to expect powerful AI for free, making it extremely difficult to convince them to upgrade even when they use the product heavily.

  Source: The Register

• 4. Midjourney hit $500M revenue with 40 employees

  What the numbers show:

  Midjourney reached $500 million in revenue in 2025 (up from $200M in 2023 and $50M in 2022) with zero marketing spend and only 40-107 employees. The company converts 6.7% of users to paid (1.4 million paying subscribers from 21 million Discord members), more than double the industry average.

  Why this matters:

  Specialized tools focused on specific high-value use cases can achieve exceptional unit economics. When quality differences are immediately visible (like image generation), users will pay premium prices. Extreme operational efficiency matters more than user acquisition at massive scale, which we explore in depth in our market report about AI Wrappers.

  Sources: Sacra, Latka


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• 5. Replika converts 25% of users to paid subscribers

  What the numbers show:

  Replika converts 25% of its 30+ million users into paying subscribers. That's 5-10x higher than typical AI apps and 8x better than ChatGPT's 3% industry average. The AI companion app achieves this through emotional connections and relationship-building features.

  Why this matters:

  Emotional attachment creates genuine switching costs in a market where they're otherwise non-existent. When users form bonds with AI companions, they can't easily replace them with ChatGPT or other generic alternatives. This proves that the right use case can overcome the monetization crisis.

  Source: BBN Times

• 6. $200/month AI subscriptions still lose money per user

  What the numbers show:

  OpenAI CEO Sam Altman admitted the company is "currently losing money" on $200/month ChatGPT Pro subscriptions because "people use it much more than we expected." The new o3 reasoning model can cost $1,000+ per query on high compute settings, while even the "low compute" version costs around $20 per task.

  Why this matters:

  Even premium pricing 10x higher than traditional consumer software ($200 vs $20/month) fails to ensure profitability when users expect unlimited usage. Consumer AI has reintroduced significant marginal costs to software, creating a race between efficiency improvements and user consumption that companies are currently losing.

  Source: Digital Trends

• 7. AI companion users spend 2 hours per session

  What the numbers show:

  Character.AI users spend an average of 2 hours per session, which is 18x longer than ChatGPT's 6 minutes 25 seconds. Yet Character.AI generates only $32.2 million in annual revenue from 20 million monthly active users while ChatGPT generates $2.7 billion.

  Why this matters:

  High engagement doesn't automatically equal high revenue. Character.AI proves users will spend massive amounts of time with AI companions, but converting that time into dollars requires the right monetization model. The company generates only $0.16 per visitor annually despite exceptional stickiness.

  Source: Demand Sage

• 8. AI app acquisition costs surged 222% in a decade

  What the numbers show:

  Average customer acquisition costs increased from $19 to $29 per user over the last decade, a 222% jump. For AI apps specifically, average cost-per-install reached $5.11 for iOS and $4.61 for Android in 2023. Apple's iOS 14.5 privacy changes in 2021 caused Facebook Ads cost-per-install to spike from $3.75 in 2020 to $15 in 2021.

  Why this matters:

  Rising acquisition costs combined with 3% conversion rates create brutal math. Companies need customers to stick around 18+ months just to break even, but most churn within 3-12 months. The narrow window to demonstrate value and convert free users makes profitable growth extremely difficult.

  Source: Business of Apps

• 9. AI apps achieve only 42% one-month retention

  What the numbers show:

  AI apps retain only 42% of users after one month compared to 63% for consumer entertainment, social media, games, and education apps. That's 33% lower retention than traditional consumer apps. ChatGPT itself retains only 56% after one month, meaning 44% uninstall within 30 days.

  Why this matters:

  Poor retention amplifies the monetization crisis. If users abandon apps before forming habits or seeing enough value to upgrade, companies can't recover acquisition costs. The 58% who churn within 30 days never had a realistic chance to convert to paid, making customer acquisition spending largely wasted.

  Source: Voicebot.ai


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• 10. ChatGPT's DAU/MAU ratio is only 14%

  What the numbers show:

  ChatGPT's daily active users to monthly active users ratio sits at just 14%. That means 86 out of every 100 monthly users don't open the app daily. The median across all generative AI services is also 14%, compared to 51% for established consumer apps.

  Why this matters:

  Most people who try AI apps fail to make them a daily habit. Without daily usage patterns, these tools remain occasional utilities rather than essential services. Low daily engagement makes it harder to justify subscription pricing and reduces opportunities to demonstrate ongoing value.

  Source: Voicebot.ai

• 11. Anthropic expects $3 billion loss on $5.3B revenue

  What the numbers show:

  Anthropic forecasts $5.3 billion in revenue for 2025 but expects to lose $3 billion. Even with strong revenue growth and a premium product (Claude), the company operates at a 57% loss rate relative to revenue.

  Why this matters:

  The second-largest player in consumer AI faces the same profitability challenges as OpenAI. This pattern shows the problem isn't execution or product quality, it's fundamental economics where infrastructure costs outpace revenue growth even for well-run companies.

  Source: Where's Your Ed At

• 12. Perplexity spent 164% of revenue on infrastructure

  What the numbers show:

  Perplexity AI reached $80 million in annual recurring revenue by end of 2024 but spent 164% of that revenue on infrastructure costs (AWS, Anthropic, and OpenAI APIs). That means for every dollar earned, the company spent $1.64 just on compute and API access.

  Why this matters:

  Building on rented infrastructure without owning the underlying models creates impossible unit economics. Companies become margin-squeezed middlemen where providers capture most value. This explains why most AI wrappers struggle to achieve profitability regardless of user growth, something we address extensively in our report covering the AI Wrapper market.

  Source: Where's Your Ed At

• 13. ChatGPT costs fell 97% but still 3x Google Search

  What the numbers show:

  ChatGPT's operating cost dropped from approximately $0.36 per query in early 2023 to around $0.01 per query by 2024 (a 97% reduction). However, this remains 3x higher than Google Search at roughly $0.0106 per query. Daily operating costs fell from $700,000 to around $50,000.

  Why this matters:

  Even with dramatic efficiency improvements, AI search remains fundamentally more expensive than traditional search. As frontier models improve and become more complex, costs may rise again rather than continue falling. The 3x cost difference versus Google Search limits how low pricing can go while maintaining profitability.

  Source: SemiAnalysis

• 14. OpenAI's API margins collapsed from 75% to 55%

  What the numbers show:

  OpenAI's API business operated at approximately 75% gross profit margin in June 2024 but dropped to an estimated 55% following August 2024 price cuts. The company reduced GPT-4o pricing by 90% in just 16 months, from $30 per million input tokens (March 2023, GPT-4) to $3 per million tokens (August 2024).

  Why this matters:

  The aggressive price war reflects intense competition but comes "straight from OpenAI's profit margins rather than reflecting another breakthrough cost reduction." Even the market leader feels pressure to sacrifice margins to maintain market share, making profitability harder for everyone in the ecosystem.

  Sources: Future Search AI, Nebuly

• 15. Fast-growth AI apps achieve only 25% gross margins

  What the numbers show:

  Fast-ramping AI "Supernovas" average only 25% gross margin early on, with many showing negative gross margins. This compares to 60-80% for traditional SaaS companies and 77% average for cloud software stocks. Anthropic reported gross margins between 50-55% in early 2024, far below software norms.

  Why this matters:

  Low gross margins leave little room for sales, marketing, and product development after covering infrastructure costs. Traditional SaaS built businesses on 70%+ margins that funded growth, but AI companies must solve profitability challenges with half the financial cushion.

  Sources: Tanay Jaipuria, Mostly Metrics


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• 16. Infrastructure costs eat 47-67% of AI budgets

  What the numbers show:

  Infrastructure costs typically consume 47-67% of total AI implementation budgets. Organizations spent $47.4 billion on AI compute and storage in H1 2024 alone, up 97% year-over-year. Global AI infrastructure spending is projected to surpass $200 billion by 2028.

  Why this matters:

  When half or more of spending goes to infrastructure before considering development, marketing, or support, the path to profitability becomes extremely narrow. These fixed costs create high baseline burn rates that require massive scale to overcome.

  Source: IDC

• 17. H100 GPU cloud pricing dropped 64-75%

  What the numbers show:

  H100 GPU cloud pricing fell from $8-10 per hour in Q4 2024 to $2.85-3.50 per hour in Q3-Q4 2025, a 64-75% decrease. Purchase price for H100 GPUs is around $25,000 per unit, with full multi-GPU systems costing $400,000. Demand still exceeds supply despite the price drops.

  Why this matters:

  While prices are falling, costs remain substantial and create ongoing burn. A 1 megawatt data center (enough for roughly 10 racks of high-end GPUs) costs approximately $1 million annually just for power and cooling. Infrastructure remains a major barrier to profitability even with improving economics.

  Source: Jarvis Labs

• 18. AI company CAC payback period increased from 14 to 18 months

  What the numbers show:

  Median customer acquisition cost payback period increased from 14 months in 2023 to 18 months in 2024 for SaaS companies broadly. AI-powered marketing tools show monthly churn rates of 3-7% (translating to 31-58% annually), while AI customer support tools face even higher churn at 6-12% monthly.

  Why this matters:

  Companies need customers to stick around longer just to break even while actual retention is getting worse. If 71% of users churn within 90 days (typical for mobile apps), companies have an extremely narrow window to recover acquisition costs before users disappear.

  Sources: Drivetrain, LiveX.ai

• 19. 71% of AI app users abandon within 90 days

  What the numbers show:

  For mobile apps broadly, 71% of users abandon apps within the first 90 days, with only 25% using an app after one use. If this pattern holds for AI apps (which show even worse retention than category averages), the vast majority of acquired users never generate meaningful revenue.

  Why this matters:

  The death spiral becomes clear. Rising acquisition costs plus 71% churn within 3 months plus 3% conversion rates equals burning money to acquire users who leave before paying anything. Only exceptional products that quickly demonstrate unique value can break this pattern.

  Source: Business of Apps


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• 20. Character.AI's users dropped 29% from peak

  What the numbers show:

  Character.AI peaked at 28 million monthly active users in mid-2024 before declining to 20 million by 2025, a 29% drop. This happened despite the app achieving the highest DAU/MAU ratio (41%) among AI apps, nearly 3x better than ChatGPT's 14%.

  Why this matters:

  Even the stickiest AI apps with exceptional engagement face retention challenges. Character.AI proved emotional attachment works for monetization (25% conversion) but couldn't maintain user growth against free alternatives and competition from tech giants. The company was eventually acquired by Google for $2.7 billion.

  Source: Demand Sage

• 21. ChatGPT session time fell 57% from peak

  What the numbers show:

  ChatGPT users now spend an average of 6 minutes 25 seconds per session, down from nearly 15 minutes at its October 2023 peak. That's a 57% decline in average session duration over roughly one year, suggesting the novelty factor is wearing off.

  Why this matters:

  Declining engagement threatens monetization even for the market leader. Shorter sessions mean fewer opportunities to demonstrate value and convert users to paid tiers. The drop suggests users are either finding what they need faster (good for utility, bad for stickiness) or losing interest (bad for everything).

  Source: Elfsight

• 22. ChatGPT captures 70% of consumer AI spending

  What the numbers show:

  ChatGPT captures approximately 70% of the total $12 billion consumer AI market and 86% of spending specifically on general AI assistant tools in 2024-2025. In the chatbot category alone, ChatGPT generated nearly $230 million in the first 8 months of 2024 while the global AI+Chatbot category reached $580 million total (40% market share).

  Why this matters:

  Winner-take-most dynamics leave scraps for everyone else. The remaining 30% of the market gets divided among thousands of competitors, making it extremely difficult for startups to achieve meaningful scale. Being first and biggest creates reinforcing advantages that competitors struggle to overcome.

  Sources: Menlo Ventures, Sensor Tower

• 23. 90% of AI startups fail within year one

  What the numbers show:

  Between 90-92% of AI startups fail within their first year, dramatically higher than the 63% failure rate for traditional tech startups. In Q1 2024 alone, 254 venture-backed startups filed for bankruptcy, a 60% surge from the previous year, with the AI sector leading this wave.

  Why this matters:

  The brutal failure rate reflects fundamental challenges, not just execution problems. Most AI startups "don't survive their first birthday" because building on rented infrastructure without defensible differentiation makes them unable to compete against free alternatives from tech giants. The market punishes undifferentiated products ruthlessly, which is exactly why we built our report to build a profitable AI Wrapper.

  Sources: AI4SP, Edge Delta, Generative AI Pub

• 24. Over 4,000 new AI apps launched in 2024

  What the numbers show:

  More than 4,000 new AI apps launched in 2024, with total downloads reaching 1.49 billion. Yet only 3% of users pay for anything, creating a brutally competitive landscape where thousands of apps fight for the small percentage of paying customers.

  Why this matters:

  Massive app proliferation dilutes the already small pool of paying users. The flood of new entrants keeps pushing monetization rates down as users can easily find free alternatives. This creates a race to the bottom where differentiation becomes nearly impossible for most players.

  Source: Sensor Tower

• 25. 91% reach for favorite AI tool first for every job

  What the numbers show:

  While 60% of AI users report using both general AI assistants and specialized tools, 91% reach for their favorite general AI tool first for nearly every job as default behavior. Users "only look for alternatives when it comes up short," and with switching costs "practically zero," retention depends entirely on continuous superiority.

  Why this matters:

  Multi-homing behavior offers limited relief for competitors because users default to the same tool repeatedly. ChatGPT's position as the default choice creates massive advantages. Specialized tools only get considered after the general tool fails, putting them in a reactive rather than proactive position.

  Source: Menlo Ventures


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• 26. Jasper's revenue collapsed 54% in one year

  What the numbers show:

  Jasper AI's revenue dropped from $120 million in 2023 to $55 million in 2024, a 54% collapse. The company, once valued at $1.5 billion, cut its internal valuation by 20% and conducted layoffs. Jasper had initially projected $250M ARR by end of 2024 but revised forecasts down by 30%+ in summer 2023.

  Why this matters:

  Jasper's dramatic reversal illustrates the existential threat that general-purpose AI tools pose to specialized applications. Building on OpenAI's GPT-3 created vulnerability when OpenAI's ChatGPT directly competed with superior models. The company has no proprietary data moat or network effects, making it replaceable.

  Sources: Latka, Sacra

• 27. Inflection AI needed $2B more after raising $1.5B

  What the numbers show:

  Inflection AI raised $1.5 billion (including a $1.3B round in 2023) but concluded it needed "$2 billion more merely to fund ambitions through 2024." Microsoft eventually paid $620 million in licensing fees plus $30 million in non-poaching fees, hiring founder Mustafa Suleyman as CEO of a new Microsoft AI division along with about 65 of 70 employees.

  Why this matters:

  Even with elite founding team (DeepMind co-founder) and massive capital, standalone consumer AI economics proved impossible. The capital intensity required to compete at the frontier exceeds what most startups can raise, forcing acquisition or shutdown as the only viable exits.

  Sources: Fortune, TechCrunch

• 28. Artifact shut down after winning Google Play award

  What the numbers show:

  Artifact, built by Instagram co-founders Kevin Systrom and Mike Krieger, shut down in February 2024 after just 12 months despite winning Google Play's "everyday essential app of the year" award. The founders concluded "the market opportunity isn't big enough to warrant continued investment." Yahoo eventually acquired it in April 2024 for technology integration.

  Why this matters:

  Elite founders with proven track records and award-winning products still couldn't find viable economics. When Instagram co-founders decide the market isn't big enough, it signals fundamental problems beyond execution. Recognition and quality don't automatically translate to sustainable business models in consumer AI.

  Source: TechCrunch


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