Can You Really Make Money With AI? (30 Data to Understand)

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You've probably heard the hype: anyone can make money with AI now.

Just build a ChatGPT wrapper, launch in a weekend, and watch the revenue roll in.

But when you look at the actual data, a completely different picture emerges. Check out our 200-page report covering everything you need to know about AI Wrappers to see what really works.

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.

Can Everyone Easily Make Money with AI? Here's What the Data Shows

• 1. 85-92% of AI startups fail within 3 years

  Explanation of the data:

  Research analyzing over 1,000 artificial intelligence companies found that 92% of AI and tech startups fail overall, with 85% failing within their first three years. The primary reasons include poor product-market fit (34%), no market demand (38%), and running out of cash.

  How to interpret:

  This exceptionally high failure rate means nine out of ten people who try to build AI businesses will fail. It indicates significant barriers exist beyond just having an idea or basic technical skills, making money with AI far from easy for most attempts.

  Sources: AI4SP, Edge Delta

• 2. Only 23% of AI startups get Series A funding

  Explanation of the data:

  Analysis of nearly 1,000 generative AI companies founded between 2015-2025 shows that fewer than 1 in 4 startups that receive initial seed funding can secure Series A rounds. Most AI startups die at this stage.

  How to interpret:

  Even among AI ventures that get initial investment, 77% fail to get their next round of funding. Getting started with AI doesn't mean you'll succeed long-term. Most AI businesses struggle to prove they can actually make money.

  Source: Towards AI

• 3. 80% of AI projects fail to deliver value

  Explanation of the data:

  A RAND Corporation study surveying 65 data scientists found that more than 80% of AI projects fail, wasting billions in capital and resources. A separate MIT study found that 95% of companies fail to accelerate revenue with GenAI.

  How to interpret:

  Most AI projects don't make money. Having access to AI tools or even building AI solutions doesn't mean you'll profit. The technical side is hard, but making it work as a business is even harder.

  Source: Tom's Hardware

• 4. 254 AI-backed startups went bankrupt in Q1 2024

  Explanation of the data:

  First quarter 2024 saw 254 venture-backed startups file for bankruptcy, up 60% from the previous year. Analysis shows AI startups "burn twice as fast, crash twice as hard" compared to traditional tech startups. Most don't survive their first year.

  How to interpret:

  Even AI startups backed by professional investors with lots of money fail constantly. Having funding doesn't make it easy to succeed. The AI market is brutal and most companies die quickly.

  Source: Generative AI

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• 5. 81% think they have AI skills but only 12% do

  Explanation of the data:

  Surveys show a huge gap between what people think they know and what they actually know about AI. 81% of IT professionals believe they can use artificial intelligence, but only 12% actually have the skills. Meanwhile, 75% of companies are adopting AI, but only 35% of workers got any AI training in the last year.

  How to interpret:

  Most people think they're better at AI than they really are. This leads them to try making money with AI and fail. The gap between thinking you can do it and actually being able to do it is massive.

  Sources: Randstad, InformationWeek

• 6. 66% of leaders won't hire without AI skills

  Explanation of the data:

  Microsoft and LinkedIn surveyed 31,000 people across 31 countries. Two-thirds of leaders now say AI skills are required for hiring. But only 39% of workers have received AI training from their companies. You need the skills to get hired, but companies won't teach you.

  How to interpret:

  This creates a trap. Employers want AI skills but won't train you. You have to learn on your own time and spend your own money. The barrier to getting AI jobs is high because you can't get the training you need to qualify.

  Source: Microsoft

• 7. Learning AI properly takes 9 months of intensive study

  Explanation of the data:

  Learning AI from scratch requires about 9 months of hard work. Months 1-3 cover math, statistics, and programming basics. Months 4-6 cover data science and machine learning. Months 7-9 cover AI tools and specialization. You can learn basic AI tool usage in under 10 hours, but that won't help you build anything serious.

  How to interpret:

  There's a huge gap between using AI tools and actually being able to make money from AI. 10 hours gets you basic skills. But building AI solutions or getting AI jobs requires 9 months of intense study with lots of technical prerequisites. That's a massive time barrier for most people.

  Sources: Coursera, Google

• 8. Only 0.7% of professionals have real AI engineering skills

  Explanation of the data:

  Only 7 out of every 1,000 LinkedIn members globally are AI engineers. That's just 0.7% of LinkedIn's professional network. This number increased 130% since 2016, but it's still tiny. There's also a 74% gender gap (men are 74% more likely than women to have these skills).

  How to interpret:

  Very few people have true AI engineering skills. This shows how hard it is to get into technical AI development roles. AI literacy skills grew 600% in one year, but the gap between using AI tools and being able to build AI products is enormous. We break down these skill gaps extensively in our report covering the AI Wrapper market.

  Source: OECD.AI

• 9. 40% of workers need AI reskilling within 3 years

  Explanation of the data:

  World Economic Forum data shows that 40% of the global workforce will need to learn new AI skills over the next three years. That's nearly half of all workers. This is a huge barrier for people hoping to make money with AI.

  How to interpret:

  Nearly half of all workers must learn new skills just to keep their jobs as AI changes everything. You have to keep learning just to stay employed. This makes it extremely hard for average workers to get ahead and make money with AI when they're constantly trying to catch up.

  Sources: IBM, World Economic Forum

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• 10. 95% of successful AI founders have college degrees

  Explanation of the data:

  Kauffman Foundation and Zippia analyzed thousands of entrepreneur profiles. 95.1% of successful founders have at least a bachelor's degree. 47% have Master's or PhDs. 75% were in the top 30% of their high school class. 52% were in the top 10%.

  How to interpret:

  Your education level strongly predicts whether you'll succeed with AI. This creates huge barriers for people without college degrees. The data proves that "anyone can make money with AI" is false. Educational credentials matter a lot for getting access to profitable AI opportunities.

  Sources: Entrepreneur, Zippia

• 11. Building custom AI solutions costs $50,000-$500,000+

  Explanation of the data:

  Market studies show AI development costs vary a lot. Simple chatbots cost around $5,000. Advanced solutions cost $20,000. Custom AI development typically costs $50,000 to over $500,000. AI consulting services cost $200-$350 per hour.

  How to interpret:

  Building serious AI solutions requires huge amounts of money. Most people can't afford $50,000-$500,000 to develop custom AI products. This limits AI business creation to people with lots of funding or technical skills to build things themselves.

  Source: Coherent Solutions

• 12. AI companies spend 2x more on computing than SaaS

  Explanation of the data:

  Analysis of 800+ startups shows AI companies spend twice what traditional SaaS businesses spend on servers and infrastructure. Compute costs went from 24% to 50% of revenue for AI companies. That's growing at 300% per year versus 53% for SaaS. Medium-sized NLP projects cost $23,622/month ($283,464/year) just for infrastructure.

  How to interpret:

  Even after you launch your AI business, you face way higher costs that keep growing. Growing your business means your costs grow too. This makes it super hard to actually make profit. The challenge isn't just starting an AI business but keeping it alive without burning all your money.

  Source: Kruze Consulting

• 13. Training frontier AI models costs over $10 billion

  Explanation of the data:

  Meta's current top AI system uses 350,000 Nvidia H100 processors at about $30,000 each. That's over $10 billion just for the hardware. Only 6 tech giants (Google, Amazon, Microsoft, Meta, Apple, Nvidia) have enough infrastructure and money to train the largest models.

  How to interpret:

  The costs to train big AI models are now so high that policy researchers say it's "completely out of reach of public funding." This kills competition from anyone except the richest tech companies. AI capabilities and money-making opportunities are concentrated in the hands of a tiny number of massive players.

  Source: Bruegel

• 14. AI training programs cost $500 to $250,000

  Explanation of the data:

  Professional AI education varies a lot. Individual courses cost $500-$15,000. Corporate training packages cost $12,000-$250,000. Specific programs include MIT's $2,500-$4,700 per course, DataCamp at $25/month for basics to $20,000 for complete programs, and role-specific training from $5,000-$50,000 depending on your job level.

  How to interpret:

  Learning AI skills requires spending a lot of money, not just time. If your employer won't pay for training, you need to spend $5,000-$15,000 out of pocket. That's months of savings for average workers. This makes AI education inaccessible for many people who might otherwise learn these skills.

  Sources: TechTarget, Bizzuka

• 15. AI freelancers earn 22% more than traditional roles

  Explanation of the data:

  Upwork research shows AI-specialized freelancers get paid 22% higher hourly rates than traditional AI/ML roles. Generative AI skills grew 220% year-over-year. Median freelancer income reached $85,000 versus $80,000 for full-time employees. AI-related work grew 60% year-over-year.

  How to interpret:

  AI skills do pay more, but 22% isn't life-changing for most workers. AI creates moderate income advantages for people who successfully learn and market these skills, but it's not the massive income boost that would make "easy money" possible for everyone. The real barrier is getting and proving those skills, which we explore in detail in our market clarity report covering AI Wrappers.

  Sources: Upwork, GlobeNewswire

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• 16. Companies using AI get 29% higher revenue growth

  Explanation of the data:

  Gong surveyed 600+ revenue leaders. Companies implementing AI in 2024 got 29% higher revenue growth than companies not using AI. They also got 11% better efficiency. But only 48% of revenue leaders say their teams currently use AI.

  How to interpret:

  AI helps organizations that successfully use it, but only 48% are actually using it. That shows how hard implementation is. The 29% revenue boost goes to companies, not individual workers. It requires organizational resources and expertise that average individuals don't have, which limits how most people can make money from AI.

  Source: PR Newswire

• 17. AI investments return 3.5X-3.7X on average

  Explanation of the data:

  Microsoft and IDC studies found that AI investments return an average of 3.5X per dollar invested. The top 5% of companies report returns as high as 8X. Top GenAI leaders get $10.30 per dollar invested. But Forrester found 330% ROI with under 6 month payback for best performers, while IBM reported most enterprise AI initiatives only achieve 5.9% ROI.

  How to interpret:

  Returns vary massively depending on how well you execute, from barely breaking even (5.9%) to 8-10X returns for top performers. This shows that successful AI money-making requires expertise, resources, and execution skills that average people and most companies don't have. "Easy money" is highly unlikely for typical attempts.

  Sources: Coherent Solutions, Microsoft

• 18. AI startups hit $1M revenue 4 months faster than SaaS

  Explanation of the data:

  Stripe data shows AI startups reach $1M in yearly revenue about 4 months faster than traditional SaaS companies. Top performers scale to $30M in revenue 5 times faster. Examples include Cursor reaching $100M in revenue, Lovable hitting $17M ARR in 3 months, and Bolt getting $20M ARR in 2 months.

  How to interpret:

  Successful AI startups scale faster than traditional software, but this data only shows extreme outliers (the tiny fraction that survive when 85-92% fail). The faster timeline also makes competition more intense and creates "winner-take-all" dynamics. Well-funded, experienced teams win. Average individuals trying to make money with AI lose.

  Source: Salesforce Ben

• 19. 70,717 AI startups compete worldwide

  Explanation of the data:

  As of 2024, there are 70,717 AI startups worldwide competing for customers, with 17,500 (25%) in the United States. This includes 214 AI unicorns valued over $1 billion. The market got $100 billion in venture funding in 2024, which is 33% of all global venture capital.

  How to interpret:

  The huge number of AI startups means extreme competition. With over 70,000 companies competing and only 214 becoming unicorns (0.3%), the odds of building a successful, profitable AI business are extremely low. The market is crowded, which makes it harder and harder for new people to stand out and get customers.

  Sources: Edge Delta, Crunchbase News

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• 20. Only 6 tech giants can train frontier AI models

  Explanation of the data:

  Policy research shows only 6 tech companies (Google, Amazon, Microsoft, Meta, Apple, Nvidia) have enough infrastructure and computing power to train the biggest AI models. Just 100 companies, mostly US and Chinese, accounted for 40% of global AI R&D in 2022.

  How to interpret:

  AI capabilities are extremely concentrated among a handful of massive corporations. This creates an impossible barrier for individuals and small companies trying to compete. Most AI money-making opportunities exist as service providers dependent on these platforms, which limits profit margins and makes you vulnerable when platforms change.

  Source: Bruegel

• 21. 63% say AI talent is harder to hire than other tech roles

  Explanation of the data:

  Market research shows 63% of recruiting leaders say AI talent is way harder to hire compared to other technical positions. This talent scarcity is "made worse by the fast-paced evolution of AI, which moves faster than schools can train people."

  How to interpret:

  The extreme talent shortage helps the small number of people with genuine AI skills. But it also shows how hard it is for average people to get marketable AI expertise. Companies with money hire the scarce AI talent. Individuals without credentials struggle to compete in a market full of people who think they have AI skills but don't.

  Source: Next Move Strategy Consulting

• 22. 33% of all venture capital goes to AI companies

  Explanation of the data:

  In 2024, AI companies raised over $100 billion globally. That's about one in three venture capital dollars invested worldwide. This was 80% growth from $55.6 billion in 2023. By mid-2025, 48% of all global venture funding went to AI companies.

  How to interpret:

  Massive money flows suggest opportunity, but they also mean intense competition and inflated valuations that create unrealistic expectations. The concentration of investment in AI attracts more competitors while raising the bar for what counts as success. This makes it harder for unfunded or bootstrapped individuals to compete against well-funded rivals burning investor money to get customers. This dynamic is something we cover extensively in our report to build a profitable AI Wrapper.

  Source: Crunchbase News

• 23. Average successful AI founder is 45 years old

  Explanation of the data:

  MIT Sloan analyzed 2.7 million founders. The average age for the top 0.1% highest growth ventures is 45.0 years. Founders aged 50 are 2.8 times more likely to succeed than 25-year-olds. 60-year-olds are 3 times more likely to succeed than 30-year-olds.

  How to interpret:

  Success in AI entrepreneurship is strongly linked to age and experience. This directly contradicts the "anyone can do it" story. Younger people and career switchers face big disadvantages against middle-aged founders with decades of industry experience, networks, and resources. This age advantage creates a barrier that only time can fix.

  Sources: MIT Sloan, Zippia

• 24. Only 22% of AI professionals worldwide are women

  Explanation of the data:

  Interface-EU analyzed 1.6 million AI professionals worldwide. Only 22% are women. At senior executive levels, this drops to less than 14%. Major tech companies show even lower numbers: women are only 15% of Facebook's AI research staff and 10% of Google's.

  How to interpret:

  The huge gender gap shows major structural barriers that stop women from accessing AI money-making opportunities. This suggests that factors beyond skills and interest (like bias, workplace culture, networks, and educational pipeline problems) create obstacles. AI income generation is far less accessible for half the population.

  Sources: Interface EU, MIT Sloan Management Review

• 25. 67% of AI entrepreneurs are White, only 6.3% are Black

  Explanation of the data:

  Zippia analyzed 30 million profiles and found big racial gaps in entrepreneurship: 67.1% White, 15.4% Hispanic or Latino, 6.4% Asian, and 6.3% Black or African American. In AI specifically at major tech companies, Black workers are only 2.5% of Google's workforce and 4% at Facebook and Microsoft.

  How to interpret:

  Major racial gaps show that AI money-making access is far from universal. Systemic barriers related to funding access, educational opportunities, networks, and discrimination create obstacles that prevent equal participation. The data proves that "everyone" can't easily make money with AI when certain demographics are dramatically underrepresented.

  Sources: Zippia, MIT Sloan Management Review

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• 26. US produces nearly 50% of all AI unicorns

  Explanation of the data:

  The US dominates the AI startup world, producing about half of all AI unicorns globally (China has 24). Top AI talent hubs show extreme concentration: San Jose has 25 AI professionals per 1,000 workers (highest globally), followed by San Francisco (11 per 1,000) and Seattle (10 per 1,000).

  How to interpret:

  Where you live creates massive barriers. Access to funding, talent networks, mentorship, and customers concentrates in a handful of cities. This makes it way harder for people in other locations to successfully make money with AI skills. The "anyone can do it" story ignores how critical location-based advantages are.

  Sources: Edge Delta, Interface EU

• 27. College student hit $64K monthly revenue in 6 months with AI

  Explanation of the data:

  Yasser Elsaid, a college student, built Chatbase (a ChatGPT wrapper for custom website chatbots). He reached $64,000 in monthly recurring revenue within 6 months of launching in January 2023. He said "being really early in the market" was the key advantage. He worked full-time alongside university studies.

  How to interpret:

  This success story seems to support accessibility, but it actually shows barriers: the founder had programming skills, launched during peak ChatGPT hype for first-mover advantage (timing), worked full-time hours while in college (privilege). Competitors like InsertChatGPT reaching $30K MRR show the market got saturated fast and eliminated easy wins. This window closed quickly.

  Source: Indie Hackers

• 28. Jasper AI revenue dropped 54% from $120M peak

  Explanation of the data:

  Jasper AI, an AI copywriting tool, grew from $0 in 2020 to $120 million in revenue by 2023. They raised $131 million at a $1.5 billion valuation. But 2024 revenue dropped 54% to $55 million. This led to CEO/CTO leaving, staff cuts, and 20% valuation cut as competition got intense and ChatGPT added native features.

  How to interpret:

  Even highly successful AI companies with massive funding and initial growth face existential threats from market changes and platform competition. The dramatic revenue collapse shows that early AI success doesn't guarantee long-term survival. Competitive advantages are extremely hard to maintain when larger platforms can copy features instantly.

  Sources: Latka, Sacra

• 29. 18-year-old's AI app makes $1.4M/month but spends $770K on ads

  Explanation of the data:

  18-year-old CEO Zach Yadegari's Cal AI calorie tracking app makes $1.4 million monthly (after app store cuts). But he spends $770,000 per month on advertising alone, plus costs for 30 employees. This leaves only $274,000 in net operating income before taxes. The app has 8.3 million downloads.

  How to interpret:

  Headline revenue numbers hide challenging unit economics. Despite impressive gross revenue, the 55% customer acquisition cost and operational expenses show how hard profitable AI money-making is, even for viral consumer apps. The founder also started coding at age 7 and had a 4.0 GPA while building. That's hardly representative of "everyone."

  Source: CNBC

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• 30. ChatGPT adding native features killed wrapper businesses overnight

  Explanation of the data:

  Many "chat with PDF" startups and GPT wrapper businesses failed when ChatGPT, Microsoft, and other platforms added similar features directly. Examples include Tome (hurt when Microsoft added Copilot to PowerPoint), and countless generic wrappers that saw their business models disappear overnight. Builder.ai raised $450M+ and filed for bankruptcy in 2025.

  How to interpret:

  Platform risk creates deadly threats for AI money-making attempts that depend on third-party APIs without unique technology. Individuals building AI businesses on someone else's infrastructure face constant risk that platform changes can instantly kill their competitive advantage and entire business model. This makes sustainable income generation extremely risky. This is one of the biggest risks we analyze in our market report about AI Wrappers.

  Sources: DEV Community, Futurism

In our 200+-page report on AI wrappers, we'll show you what successful wrappers implemented to lock in users. Small tweaks that (we think) make a massive difference in retention numbers.