Lately, I’ve been seeing more discussions about a possible AI bubble. From rising tech valuations and massive investments in computing infrastructure to questions about whether AI companies can eventually deliver the profits investors expect, concerns about the industry’s future seem to be growing. Some commentators have even started suggesting that 2027 could be a turning point for the current AI investment boom.
Whenever I come across these discussions, I find myself wondering what people actually mean by an AI bubble. Are we overestimating the technology itself, or are investors simply too optimistic about how much money it will generate? And beyond the stock market and the technology companies attracting billions of dollars in investment, could there be another kind of bubble forming in the everyday lives of people who believe AI will help them become wealthy?
I started thinking about this not because I doubt AI’s usefulness. Quite the opposite. I use AI almost every day, and I’ve experienced firsthand how much it can change the way we work. But over the past few years, my social media feeds have been filled with people explaining how they built businesses with AI in just a few months, how they run entire companies on their own, or how a particular AI tool helped them earn tens of thousands, sometimes even hundreds of thousands, of dollars a month.
After watching enough of these stories, I occasionally find myself wondering whether I’ve somehow missed the secret to making money with AI.
I’ve been using AI for quite a while, and I have made money with its help. Still, compared with the numbers I keep seeing online, my results look rather modest.
AI Has Changed the Way I Work, but Making Money Hasn’t Become That Easy
Before AI became part of my daily routine, most of my work revolved around areas I was already familiar with, including B2B international trade, supply chains, market research, and brand operations. As AI tools became more accessible, I gradually started experimenting with things I had little experience in and, in some cases, had never imagined doing on my own.
Building websites is one example. I’m not a programmer, and I don’t have a formal background in software development, but AI has helped me build and maintain my own websites. The process hasn’t always been smooth. Sometimes a feature that looks simple takes several attempts to get working properly, and there are still technical problems I can’t solve without additional research. AI hasn’t suddenly turned me into a professional developer, but it has made it possible for me to explore an area that previously felt beyond my abilities.
The same applies to content creation, industry research, and market analysis. Tasks that once required hours of gathering and organizing information can now be completed much faster, at least in their early stages. When I encounter an unfamiliar subject, AI can help me understand the basics before I decide what deserves further investigation.
It has saved me a considerable amount of time, expanded the range of work I can handle, and yes, helped me earn money. But my experience is still a long way from the effortless high incomes that some social media videos seem to promise.
I believe there are people who have built genuinely successful businesses using AI. What I find harder to accept is the suggestion that their results can be easily replicated. We rarely hear much about the years of experience they may have had before AI, their existing customer relationships, advertising expenses, or the projects that failed along the way. Sometimes the impressive figures shown in videos represent revenue rather than actual profit.
Even when every number is accurate, it tells us very little about how likely someone else is to achieve the same result.
Eventually, I started asking myself whether we were learning to use AI or becoming increasingly fascinated by the stories of people who had already made money from it.
When Teaching People to Make Money With AI Becomes a Business
Not long ago, a friend asked me about learning AI image generation. She had come across training programs claiming that people could earn good money by creating AI images, producing videos, or offering related services. She wanted to know whether learning these skills would actually help her make money and whether those institutions could really teach her how to do it.
I responded with a question: “How many wealthy people do you think would go out of their way to publicly teach everyone exactly how they make their money?”
Looking back, that was probably a little too absolute. Successful entrepreneurs can genuinely enjoy sharing their knowledge, and good training programs can certainly help people develop useful skills. Still, when a business opportunity is advertised as both highly profitable and easy for almost anyone to replicate, I think it’s reasonable to ask what makes it work and why the people who discovered it are so eager to sell the method to thousands of strangers.
Over the past few years, the opportunities being promoted around AI have changed almost as quickly as the technology itself.
When AI writing tools became popular, some people began selling courses on generating articles, running blogs, and providing content services using a handful of prompts. As AI image generation attracted more attention, training programs appeared around selling AI-generated designs and taking commercial illustration orders. Then came increasingly capable AI video tools, followed by courses on producing short videos, running social media accounts, and monetizing content. More recently, AI-generated animated dramas have attracted interest, bringing another wave of tutorials and programs promising to explain how creators can profit from this emerging format.
Each time a new AI application becomes popular, someone seems ready to package its potential into a course for people hoping to increase their income.
I don’t doubt that real commercial opportunities exist in these areas. AI has lowered some of the technical barriers to content production, allowing people without traditional design or filmmaking experience to participate in markets that were previously harder to enter.
But I keep wondering about the people who paid for those courses. How many eventually earned a stable income? How many recovered the money they spent on training? And how many completed their courses only to discover that knowing how to operate an AI tool didn’t automatically tell them where to find paying customers?
We don’t have enough reliable, representative public data to determine how successful AI training students are overall, so I wouldn’t claim that most of them fail to make money. What we can say is that the revenue earned by a training provider and the income eventually earned by its students are two very different things.
For students, purchasing a course is only the beginning. They still have to invest time, compete for customers, and accept the possibility that their business efforts won’t succeed. For the institution selling the course, however, the transaction is already generating revenue.
A case involving the US Federal Trade Commission (FTC) in 2026 offers an example of why income claims deserve closer examination.
Publishing.com, a company selling online publishing training products that included its AI Publishing Academy, promoted ways for consumers to earn money through publishing e-books and audiobooks. According to the FTC’s complaint announced in April 2026 , the company’s marketing misled consumers about the income they could expect, while most purchasers did not achieve the advertised earnings. The company and its owners agreed to pay $1.5 million to settle the allegations.
The case doesn’t represent the entire AI training industry, but it illustrates why consumers should pay attention to the information that rarely appears in promotional success stories, especially when a course is marketed less around the skills it teaches and more around the income students might earn.
Sometimes I wonder whether certain training businesses really have found a reliable way to make money from AI, just not necessarily through the business model they’re teaching their students.
Their most successful business might actually be selling courses to people who want to make money with AI.
When AI Makes Writing Easier, What Kind of Content Is Still Worth Paying For?
Of all the income opportunities associated with AI, writing is one of the areas I know best.
I run my own independent website, Flowanriver, and regularly use AI to help with research, organizing ideas, and writing in English. It has saved me a great deal of time, particularly when I’m exploring subjects outside my usual areas of expertise. But the more frequently I use it, the more aware I become of certain problems with AI-generated writing.
Some articles are perfectly grammatical and logically structured, yet they feel strangely familiar. The paragraphs follow similar patterns, transitions sound almost interchangeable, and certain words or expressions appear repeatedly. Even the way an article introduces a question and arrives at its conclusion can feel predictable.
There may be nothing technically wrong with these articles, and they may contain useful information. But after reading enough of them, I sometimes struggle to remember anything distinctive about the person supposedly behind the words.
For me, writing has never been only about arranging information into polished sentences. I’m interested in why someone cares about a particular subject, what experiences made them question something, and how they arrived at their own understanding of it. Those details often matter more to me than whether every sentence sounds impressive.
Take this article, for example. I could ask AI to gather recent news and financial data about the AI bubble and produce a reasonably professional-looking analysis within minutes. But that’s not why I wanted to write it.
The subject interested me because I use AI regularly, have made money with its help, and still find myself puzzled by the number of people online who apparently earn extraordinary amounts of money from it. A conversation with a friend about AI training, combined with my own experiences of using AI for content creation, made me question some of the income claims surrounding the technology.
Those questions came from things I’ve experienced and observed, not from deciding that a trending keyword might make a good article.
Google’s approach to AI-generated content is also worth understanding. As generative AI has become more widely used, questions about originality, accuracy, and the usefulness of online content have become increasingly important.
According to Google Search Central’s official guidance on generative AI content , using AI to create or assist with content does not automatically make that content low quality. AI can be useful for research, organizing information, and content creation. However, producing large amounts of content primarily to manipulate search rankings, without providing meaningful value to users, may violate Google’s policies on scaled content abuse.
I find that distinction important. The question isn’t simply whether AI was involved in writing an article, but whether the finished work offers something worth reading.
Of course, human writers can be repetitive and unoriginal too, while AI-assisted writing can still reflect a person’s experiences and perspective. But if someone can generate an article in a few minutes using the same prompts and tools available to everyone else, it’s worth asking why customers would continue paying substantial fees for that service alone.
If a company can already use AI to produce basic marketing copy, what additional value does a writer offer by using exactly the same technology without bringing stronger research, industry knowledge, or editorial judgment to the work?
I don’t think this means professional writing will lose its value. But the skills clients are willing to pay for may be changing. Writers who understand an industry, know their audience, verify information, and contribute original thinking can still benefit enormously from AI.
Simply knowing how to generate words, however, may not be enough to build a sustainable source of income.
The Value of AI and the Price People Are Willing to Pay for It
In financial markets, concerns about an AI bubble are largely focused on a different question: whether the money being invested in AI, and the valuations assigned to companies involved in its development, can eventually be justified by their revenue and profits.
In its July 2026 Financial Stability Report , the Bank of England discussed the growing financing needs of AI-related companies and the high expectations surrounding their future earnings. The report acknowledged AI’s potential to improve productivity and contribute to long-term economic growth, while emphasizing uncertainty about how large those gains might be, how quickly they could materialize, and how effectively businesses could turn them into revenue.
A company might develop an impressive AI product while still facing enormous operating costs. Its user base could grow rapidly without generating enough paying customers to cover those expenses. Even if some companies eventually become extremely profitable, that doesn’t mean every investment made during the AI boom will produce an attractive return.
It reminds me of the dot-com bubble.
Around 2000, the share prices of many internet companies collapsed, and some businesses disappeared entirely. Yet the internet itself didn’t lose its usefulness. Over time, it transformed how people communicate, shop, work, and run businesses, becoming an essential part of the global economy.
I don’t know whether AI will experience a similar market correction, and I don’t believe anyone can reliably predict exactly when such an event might occur. But the history of the internet reminds us that a technology can have enormous long-term value while the market becomes excessively optimistic about its near-term financial returns.
When I look at the income promises being made to ordinary AI users, I see a somewhat similar tension.
Investors put money into AI companies because they expect those businesses to generate future profits. Individuals may purchase training programs, subscribe to software, or spend their savings on new projects because they believe AI will create income opportunities for them.
Buying a course is obviously not the same as investing in a stock. The economic mechanisms and risks are different. Still, both decisions can involve spending money today based on expectations about financial benefits that have not yet materialized.
When the eventual returns fall far short of those expectations, disappointment and financial losses can follow.
When Everyone Can Use AI, Who Actually Benefits Financially?
In the past, technical skills prevented many people from entering certain markets. Building a website required development knowledge, producing commercial videos involved specialized equipment and experience, and product design often depended on professional software and training.
AI has made some of those activities much more accessible. But the market doesn’t automatically produce more customers simply because more people can build websites, nor does the audience’s attention or advertisers’ budgets expand endlessly as content becomes cheaper to produce.
Lower production costs may give consumers more choices, while also creating more competition among the people producing those goods and services.
There’s another question I find particularly interesting: who ultimately captures the economic value created by AI-driven productivity improvements?
Imagine a task that previously took three days but can now be completed in a few hours with AI. For a business, that might mean lower operating costs. For clients, it could create an expectation of lower prices. For a freelancer, it means the work takes less time, but it doesn’t necessarily mean the client will continue paying the same amount.
And if every competitor has access to similar tools, the advantage gained from using AI may gradually become less distinctive.
Some people will certainly benefit by taking on more projects, offering new services, or creating business models that weren’t previously practical. Others may find that the market value of certain tasks changes as those tasks become easier to perform.
That is why, whenever someone promises that learning a particular AI tool can lead to a high income, I want to know more about how the proposed business actually attracts customers and whether it can remain profitable once many other people learn to do the same thing.
I’ll Still Be Using AI Tomorrow
Despite all these questions about a possible bubble, I still believe AI is worth learning and will continue changing how people work. For someone like me, who runs independent projects and handles different kinds of work, its practical benefits are already part of everyday life. A change in stock market sentiment won’t suddenly make those benefits disappear.
Even if AI company valuations fall one day, some once-popular startups close, or certain AI money-making courses lose their appeal, I’ll probably still open my AI tools the following morning to research something, solve a problem, or learn a new skill.
And yes, I hope AI will help me earn more money in the future. I’m running my own businesses and looking for new opportunities, so naturally, I want the skills I’m developing to contribute to better financial results.
I’ve simply become less interested in using someone else’s exceptional success as the standard for judging whether I’m using AI correctly.
Some people have built AI-related businesses generating hundreds of thousands of dollars a year. Others have increased their freelance income, while some simply use AI to make their existing jobs less time-consuming. These experiences can all be genuine, and I don’t think the people earning the most money are necessarily the only ones who understand the technology.
Perhaps I still haven’t discovered the most profitable way to use AI. That’s entirely possible. But impressive success stories shouldn’t automatically become income promises for everyone else.
So what exactly is the AI bubble?
In financial markets, it may involve expectations about future corporate earnings running far ahead of what companies will ultimately deliver. In everyday life, what concerns me more is how easily the possibility of making money with AI can be presented as an opportunity that almost anyone can achieve with relatively little effort.
A technology can be genuinely valuable while the expectations surrounding it become unrealistic. I don’t think we need to deny one to acknowledge the other.
As for whether the AI bubble will burst in 2027, I don’t have an answer, and I’m not interested in making predictions simply because a particular date has become part of the conversation.
But the next time I come across someone explaining how they make tens of thousands of dollars a month with AI, or another training company advertising a new method for earning money with the latest AI tool, I’ll probably still be curious.
I won’t just wonder how much those successful people earned. I’ll also wonder what happened to everyone who bought the same courses, spent time learning the same skills, and never appeared in the promotional videos.
After all, the number of extraordinary success stories a technology produces tells us something very different from how much it actually improves the working lives and incomes of ordinary people.
And I’m far more interested in the latter.
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