In 2026, to Make Money with AI, First Find Where AI Can’t Help You

A-Zhe had been running a food channel for two years and went all in on AI by the end of 2025. He let AI handle topic selection, script writing, and cover design. He could produce five videos a day—more than he used to make in a week.

The numbers were decent for the first month. Then they started dropping. By early spring 2026, single-video views fell from tens of thousands to just a few hundred. He tweaked everything he could: posting times, tags, cover colors, title formulas—nothing worked. The completion rate curve in his backend looked like a slowly flatlining ECG.

One day, too lazy to fire up AI, he casually shot a video of himself cooking instant noodles in his rented apartment at midnight, ranting about how tough the industry was. The footage was blurry, no script, no timed cuts—he almost deleted it. The next morning, that video had over two million views, with thousands of comments like “this feels real” and “this broke me.”

He stared at those numbers for a long time. An uncomfortable thought crept in: everything he’d spent two years learning to “professionalize,” all the efficiency of producing five pieces a day—together they weren’t worth as much as a single raw moment he thought wasn’t good enough.

What exactly separated those five AI-polished videos from this one trash clip he almost deleted? If the difference was what truly mattered, what did that make the last two years—and all the AI subscriptions he’d bought?

This isn’t just A-Zhe’s dilemma. In 2026, 91% of creators are using AI to scale output, and most have hit the same wall. On the other side of that wall lies a counterintuitive answer: in a world where everyone can produce with AI, your ability to earn money hinges precisely on whether you’ve found the space where AI can’t help you.

Every Wave of Content Windfall Follows the Same Script

This feeling—“a tool becomes worthless once everyone has it”—isn’t unique to the AI era. It’s played out several times before, each time following an almost identical script.

When printing technology first spread, those who could print books held the key to dissemination. Once printing presses became ubiquitous, the ability to print was no longer the differentiator—content quality once again became paramount. The blog era followed the same pattern. Around 2005, just having “a blog you update consistently” could attract a crowd, because so few people were doing it. But when platforms reduced publishing to a one-click action, the blog itself stopped being valuable—what you wrote and who wrote it mattered.

WeChat official accounts are a textbook case. Between 2013 and 2015, people who could run accounts, design layouts, and craft clickable headlines rode the first wave of success—casually hitting 100,000+ reads. Try reading those articles today, and you’d likely struggle to finish them. It’s not that the writing declined; readers were overfed with similar content until their tolerance skyrocketed. When ten thousand accounts use the same title formulas, emotional templates, and “story-then-epiphany” structures, each piece contributes almost zero novelty—and attention shifts to the next unexploited frontier.

Short video repeated the story. Early creators who could shoot, edit, and nail the rhythm were scarce, and they profited enormously. Once editing software became foolproof and templates proliferated, “making short videos” collapsed into a basic skill.

Why does this keep happening? Shannon offered a counterintuitive definition in information theory: the value of a message equals the degree of surprise it carries. If everything is expected, the information content is zero. Apply this yardstick to content, and it’s clear. When ten thousand accounts write using the same methodology, they carry virtually no surprise relative to each other. Reading one feels like reading a thousand. Readers haven’t become pickier; these contents objectively no longer carry anything new.

In the past, each wave—from windfall to cutthroat competition—took a few years. AI has eliminated that gap. ChatGPT reached 100 million users in two months. A content production capability can shift from rare advantage to standard equipment in just a few months. With 91% of creators already using generative AI, we’re already at the tail end of the cycle—the window for profiting from AI production capabilities has essentially closed.

Yet in every cycle, those who survive and continue to earn aren’t the ones best at using the current tool, but those who have something beyond the tool. Tools depreciate wave after wave; what lies beyond them does not. The only question is: what exactly lies beyond the tool for you?

When Everyone Can Produce, No One Is Valuable

Let’s confront the uncomfortable numbers. In inBeat’s 2026 creator survey, 91% of creators are already using generative AI to scale output. In other words, “knowing how to use AI for content” no longer gives anyone an advantage. It’s gone from a skill to a default setting—on par with knowing how to use Word.

When 90% of people have a capability, it ceases to be a moat. This is common sense. Yet in 2026, many still cling to the 2023 illusion that “learning prompt engineering, buying subscriptions, and getting AI to write ten articles a day” equals finding a money-printing machine.

Ahrefs sampled nearly a million new web pages and found 74.2% contain AI-generated content. This means what you produce with AI likely looks identical to hundreds of thousands of pieces being created simultaneously. Readers’ feeds are flooded with such content, pushing their tolerance higher and higher. Herbert Simon clarified this mechanism back in 1971: the more information is abundant, the scarcer attention becomes. AI can produce limitless content, but humans still have only 24 hours a day. Supply can surge infinitely, but demand is capped by a wall called attention.

In another article, I broke down the token economics: prices have dropped 400-fold in three years, while consumption has risen 1,800-fold. But content creation falls in the layer with the lowest token elasticity—you won’t read ten times more articles just because writing gets cheaper. With supply exploding and demand capped, only one outcome is possible: price collapse. This is why so many feel that despite multiplying their output tenfold, their income hasn’t moved—or has even dropped.

The twist is this: at the same time, the creator economy pie is still growing. Precedence Research values the 2026 market at roughly $254 to $314 billion; Goldman Sachs predicts it will approach $480 billion by 2027. In the U.S. alone, sponsored content ad spending is projected to hit $43.9 billion in 2026, up 26% year-over-year. Money isn’t just present—it’s accelerating.

Money is growing while most people’s income is falling. How can both be true? The answer lies in distribution. Uscreen data shows the top 10% of creators earn an average monthly income of about $48,500, or roughly $582,000 annually. But averaged across all creators, the annual income is only $36,000 to $58,500. Further down, nearly half earn pocket change. Over 200 million people identify as creators, but only about 2 million make a full-time living from it. This isn’t a normal distribution; it’s an extreme long tail. AI hasn’t flattened this curve—it’s steepened it. As production barriers hit rock bottom, more people pour in. Meanwhile, the top creators, who already have trust and recognition, use AI to amplify output and capture an even larger share.

So what about Maor Shlomo? He doesn’t know how to code, yet he used AI alone for six months to build Base44, which Wix acquired for about $80 million. He and the person “using AI to write ten articles a day that no one reads” are using the same tools. The difference isn’t in the tools—it’s in the layer of work they delegate to AI. The ten-article writer uses AI to produce what anyone can produce, and thus what’s worthless. Shlomo offloaded execution entirely to AI, focusing only on what AI cannot do: judging which problems are worth solving and how a product should look.

So the real question was never “whether to use AI to make money,” but a sharper one: when the part AI can help with is becoming worthless, what are you charging for? What about you remains unreplicable by AI and unstealable by others?

Slice Your Work into Three Layers

To answer that, you must first break any creative work or business into three layers.

At the bottom is the execution layer: writing copy, editing videos, generating graphics, translating, formatting. These are the parts of “getting the job done given instructions.” AI has crushed the cost of this layer to near zero, so its price is collapsing. No matter how fast or well you perform here, you’re merely scavenging residual value in a depreciating market.

In the middle is the judgment layer: deciding which topics to write about, which angles to take, which direction to bet on, selecting the right piece from a pile of material. A PwC analysis covering nearly one billion job postings found that design skills now surpass pure technical skills as the most desired quality by employers. Here, “design” doesn’t mean drawing—it means judgment, knowing what’s worth doing and what’s noise. The wage premium for AI skills rose from 25% to 56% annually, but what’s actually rising isn’t knowing how to use AI—it’s knowing how to command AI to do the right things.

At the top is the connection layer: why readers trust you, why they’re willing to pay, why they distinguish you from ten thousand similar accounts. AI cannot penetrate this layer because its core is you—the specific human being.

The typical path to failure in gaining followers or income is to pour all energy into the bottom layer, competing with AI on volume, and losing inexplicably. Those who earn money are people who hand the entire execution layer to AI, focusing only on the judgment and connection layers. So what exactly in the judgment and connection layers can AI not take from you? I count four things.

First, your own experiences. AI runs on the statistical average of everything everyone has ever written. What it generates is essentially “what people usually say.” This gives it a natural blind spot: it doesn’t know why you lost sleep last night, doesn’t know your palms were sweating when you made a certain decision, doesn’t know how your project unraveled step by step. These are exactly what hold the highest long-term value. Diaries have survived millennia; Marcus Aurelius’s Meditations were personal notes never intended for publication, yet they’re still read two thousand years later. Samuel Pepys recorded trivial details of 1660s London, creating the most precious firsthand account of that era. They survived not because they were beautifully written, but because they were too specific to be replicated. The same applies to self-media: a product manager detailing how they killed a feature with a million daily active users is harder to replace than any AI-generated “product methodology.” AI can write methodologies more neatly than you, but “I tried Plan A and it failed because I overlooked X; I switched to Plan B and hit Y; finally I compromised with C”—that process only you can write. Write the process, not the conclusion. The conclusion is the average; the process is you.

Second, picking the right one from a pile. AI makes production infinite, so scarcity flips. The hard part used to be making something; now it’s picking the one worth making from a hundred options. This is taste. A valuable creator is becoming more like a curator than a producer. They let AI generate twenty titles and ten topics, then do what AI cannot: based on their understanding of their audience, select the only right one and discard the other nineteen. AI can give you options; it can’t give you the click of “this one is right, that one isn’t,” because that click requires a judgment benchmark rooted in your real experiences.

Third, others’ trust in you as a person. A line in PrometAI’s 2026 report cuts deep: “The best product without an audience will lose to a mediocre product with 100,000 followers.” Now that production capability is universal, the only thing still scarce is “why you?” Readers follow you not because your content is much higher quality than AI’s, but because they trust your judgment and stance, believing you won’t sell them out for a buck. This trust cannot be manufactured by AI—it can mimic tone but not a real relationship. Even Google is adjusting in this direction: it’s not just penalizing AI content, but increasingly rewarding content with human expert signals. Multiple analyses also predict that in 2026, platforms will further separate and label human versus AI content. When synthetic content floods the market, credible humans become the scarce resource platforms must protect most.

Fourth, discovering needs no one has yet voiced. The first three lean content; this one leans money-making. AI crushes product creation costs to the floor, but it won’t tell you what to make. The most blunt yet effective approach starts from “where people get stuck in what situations” rather than “what AI can do.” Clayton Christensen’s Jobs to Be Done framework is about this: don’t ask what business AI can help you build; ask who, in what situation, wants to accomplish what but is blocked by what, and would pay to solve it. Reddit, Zhihu, industry groups, product review sections—they’re full of unmet pain points waiting to be translated into products. For example, a non-native English-speaking small business owner wanting to write a polished business email to overseas clients without losing deals due to language barriers—this is a need that’s existed stably for decades. Building around it is far more resilient than building around a hot AI concept.

String these four together, and you have the correct posture for using AI: don’t let AI think for you—let AI handle volume while you do the judging. Step one: let AI scan signals, mass-scraping forum comments from target demographics for pain-point clustering. One person plus AI can complete in 48 hours what a research firm takes two weeks to do. Step two: human judgment. AI gives you a hundred signals; you pick three to five truly worth pursuing, based on criteria like frequency, how much users currently pay to work around the problem, and whether AI can slash costs by an order of magnitude. Step three: let AI rapid-validate. Build a minimum viable version in 48 hours, throw it at users. If it fails, pivot; if it works, double down. Validating an idea used to take three months and $100,000; now it takes two days and $300. The number of trials can multiply fiftyfold.

In the end, if you treat AI as a cheaper writing tool, you’re in the layer with the lowest elasticity, and your ceiling is your own time. If you treat it as fuel for agents—as infrastructure to enter markets previously inaccessible—you’re in the layer with the highest elasticity, and your ceiling is how many worthy problems you can define. The only scarce resource left is your judgment.

The Gap Between Course-Seller Data and Reality Is Quite Wide

Open any platform, and you’ll find tutorials on “earning $100,000 a month with AI” to the point of overwhelm. These narratives share one thing in common: they always cite a handful of names at the pyramid’s peak, never showing you the denominator.

Add the denominator, and the picture chills. In the U.S., there are 29.8 million solopreneurs, collectively generating $1.7 trillion in annual revenue—that sounds impressive. Look closer: 77% turn a profit in their first year, but only 20% earn between $100,000 and $300,000 annually. The other 80% earn subsidies or unstable small change. Indie Hackers data shows a median monthly income of about $3,000 for independent founders, or $36,000 annually. The reason you see those “million-dollar-a-month” names daily is pure survivorship bias—the failures don’t post. AI has indeed cut startup costs by up to 98%, and solo entrepreneurship’s share has risen from 23.7% in 2019 to 36.3%. The barrier to entry is genuinely lower. But a lower barrier means more people flooding in and fiercer competition—it doesn’t mean everyone who enters will make money. Low barriers and high income are two separate things; course sellers deliberately conflate them.

Platforms are also shifting in a direction unfriendly to pure AI content. Multiple agencies predict that in 2026, platforms will further distinguish human from synthetic content, labeling and adjusting weights for AI content. This addresses a life-or-death issue for platforms: when 74% of new web pages contain AI content, platforms fear their feeds will be overwhelmed and users will leave due to distrust. The Columbia Journalism School tested eight mainstream AI search tools, finding over 60% of queries returned wrong answers. In such an environment, trustworthy humans become the scarce resource platforms are most willing to amplify, while pure volume-riding AI accounts face squeezing from both algorithms and readers.

There’s a more insidious risk happening within you. MIT Media Lab EEG monitoring found that people completing tasks with ChatGPT showed significantly lower cognitive engagement. Randomized controlled experiments revealed that AI-assisted learners had notably worse knowledge retention after 45 days. The more you outsource thinking to AI, the more your own thinking muscles atrophy. This is especially ironic in the context of earning money and gaining followers: the only thing still appreciating in value in the AI era is judgment. But if your daily work is letting AI mass-produce content while you only hit “publish,” you’re rapidly degrading the only valuable asset you have. Short-term, output doubles and efficiency peaks; long-term, you train yourself into a megaphone for AI—and megaphones are the most oversaturated role in this market.

Those who actually make money are the ones who keep AI firmly in the execution layer while fiercely defending the judgment layer. They let AI handle the grunt work, channeling all the time saved into clarifying what really needs to be done. This isn’t the opposite of efficiency maximization—this is what true efficiency maximization looks like in 2026.

Three Questions to Find Your Value

If you truly want to earn money or gain followers using AI in 2026, don’t start with “What can I do with AI?” That’s where 90% begin—and where 90% get stuck. Start with these three questions.

First, what about you does AI lack? AI runs on the statistical average of everyone; the only thing it lacks is specificity. List out: a project you’ve done, a pitfall you’ve fallen into, a niche group you’ve served, a judgment you’ve formed about something that others don’t have. The more specific, the better. “I understand operations” is useless; “I grew a parenting account from zero to 50,000 followers and then killed it myself” is useful. This list is your raw material. AI processes it, but only you can supply it.

Second, whom do you really serve, and what blocks them? Don’t try to serve everyone. Writing for everyone forces you to grind content into the greatest common denominator—which is exactly what AI does best and what’s least valuable. Lock onto a specific group. Lurk in their forums, groups, and comment sections. Find the two or three things they repeatedly complain about. Find comments like “If someone could solve this, I’d pay.” That’s your entry point.

Third, can you bear to hand off the execution layer entirely? Many can’t, because writing and editing by hand gives a satisfying feeling of “working.” But in 2026, that feeling is a trap—it burns your time on a depreciating layer and hinders the honing of judgment. Offload production to AI. Bet your most precious asset—your time—on clarifying what to do and maintaining that trust with your audience.

Compress these three questions into one: your pricing power equals your specific experiences multiplied by the specific group you serve multiplied by your judgment and taste. If any of the three is missing, the rest can’t support a price. Experience without an audience is self-talk; an audience without judgment will inevitably drown in homogeneity; judgment without experience is hollow—and AI will expose it with one question.

The direction is counterintuitive. The more vertical, niche, and trust-dependent the space, the greater the leverage from the combination of AI and you. The more generic, standardized, and universal the space, the faster it gets flattened. So don’t squeeze into what looks like the biggest-traffic lanes. Find the corners so small that AI can’t be bothered to optimize, yet where people genuinely pay. Your community, your local knowledge, your quirks—there, they’re all moats.

Back to A-Zhe. That video of cooking instant noodles went viral not because of high quality, but because it carried something all five AI videos lacked: a specific person, in a specific late night, showing a sliver of real fatigue. What he’d spent two years accumulating shouldn’t have been “skill with tools”—it should have been this.

Tools will depreciate wave after wave. You—the human—are the only thing that won’t be flattened by the next price drop.

Appendix: Sources Cited in This Article

Creator economy market size and AI adoption (inBeat / Precedence Research / Outfame): https://inbeat.agency/blog/creator-economy-statistics
Creator income distribution (uscreen / demandsage): https://www.uscreen.tv/blog/creator-economy-statistics/
Solopreneur statistics (founderreports / autofaceless): https://founderreports.com/solopreneur-statistics/
One-person company valuation and “distribution beats production” (PrometAI / Forbes): https://www.forbes.com/sites/elainepofeldt/2026/01/28/as-more-founders-aim-to-build-billion-dollar-one-person-businesses-new-research-points-to-high-potential-niches/
Google and platform separation of human/AI content (Pangram / One Day Agency): https://www.pangram.com/blog/does-google-penalize-ai-content ；https://oneday.agency/blog/how-platforms-will-separate-human-and-ai-content-in-2026
Jobs to Be Done framework (Christensen / Ulwick): https://jobs-to-be-done.com/jobs-to-be-done-a-framework-for-customer-needs-c883cbf61c90
The following data reuses existing final drafts from this series with traced sources: Token price and consumption (a16z LLMflation, Robonomics), 74.2% web pages contain AI content (Ahrefs / Originality.ai), AI search accuracy (Columbia Journalism School Tow Center), cognitive debt (MIT Media Lab EEG study), Herbert Simon’s attention economy, Shannon information theory, Indie Hackers median income, Base44 acquisition.