You open the exact same ChatGPT, dump a report into it, and tell it to “make a PPT.” It spits out 30 slides packed wall-to-wall with text. Your boss flips through three pages and asks: What exactly do you want me to remember?
Someone else opens it, asks AI one upfront question, then has it design the deck slide by slide. The result is a clean, conclusion-driven deck with actual pacing. They list it on Xianyu and Upwork, charging $50 to $150 per slide, and pull in thousands for a full deck.
The tools are identical. The gap comes down to one single step: locking in the conclusion first, or just formatting text first.
A lot of people think “PPT ghostwriting” is a decade-old side hustle that’s been squeezed to death. The reality? AI has crushed the barrier to entry for this legacy business to the floor, which ironically leaves a massive profit margin for those who actually know how to use it. This post breaks it down for you: why PPT ghostwriting is actually easier to monetize in the AI era, how much you can really make, how to nail the entire delivery process with a single prompt, and how to land your first client today.
When most people use AI to make PPTs, they get the fundamental approach wrong from the very start.
The common move is to dump a massive wall of text into an AI and tell it to “paginate.” The AI dutifully slices the article into 20 chunks—one page per chunk. The result? A bloated deck packed with information, but the audience has no earthly idea what your actual point is. The problem isn’t the AI; it’s your prompt. A killer presentation should make the audience remember exactly one thing. Cramming every detail onto the slides is a guaranteed way to fail.
A truly high-impact pitch deck works in reverse: lock down your conclusion first, then map out the evidence and sequence. Nail the logic first, sweat the layout later.
The entire difference comes down to a single prompt. After you feed your source material to the AI, don’t rush to ask for slides. Drop this block in first:
First, determine the single most critical takeaway the audience needs to remember from this presentation, then design the entire deck around that objective. Do not paginate sequentially based on the source text, and do not mechanically compress the content. Provide the title, core message, suggested charts, page structure, and speaking points for each slide. Each slide must carry only one specific task, and there must be a clear causal, progressive, or transitional relationship between slides.
(You don’t need to tweak this—just copy and paste it straight to the AI. Don’t worry if terms like “page structure” or “speaking points” feel vague; the AI knows exactly what they mean.)
It does exactly one thing: it forces the AI to shift from “formatting in source-text order” to “designing for the audience’s cognitive flow.” Each slide covers a single point, and the pages push forward one by one, just like telling a gripping story. That’s all you need. At this point, you’ve got a logically sound, presentation-ready skeleton. Filling in the content, adding visuals, and tweaking the styles from here is just pushing on an open door.
Don’t buy the hype that “PPT design is dead,” and don’t fall for the “easily make $15K a month” BS either. The real market in 2026 is a clear, tiered ladder (based on publicly available domestic PPT freelancing rates—just look at the order of magnitude):
A 20-page business report billed at 80 yuan/page nets you 1,600 yuan. Take on two of these a week, and clearing five figures a month is no exaggeration. But you need to understand that hitting that “five figures” requires a dozen-plus hours of manual labor every week. I’ll break down the math for you below. And your overhead? A Gamma Pro subscription costs $18/month (just over 100 RMB), and ready-made templates in domestic tools like iSlide and WPS get the job done just fine. Your monthly tool subscription costs the price of a cup of coffee, but your average order value is over a dozen times that. This “low-cost tools + high-ticket delivery” structure is the real dividend AI offers side-hustlers.
Let’s make a direct comparison and the picture becomes clear: some folks use AI to design logos, set up a Xianyu store, and pull in 5,000+ a month; others use AI to write copy for merchants, taking freelance gigs on Xiaohongshu to break five figures monthly. But the average order value for PPT outsourcing blows both of those out of the water. Why? Because clients aren’t paying for “a few slides.” They’re paying for “I have to present this on stage next week, and I’ll be dead meat if I bomb it.” The closer you get to high-stakes anxiety, the more ruthlessly you can charge.
Prompt engineering isn’t enough—you need a bulletproof end-to-end system. I’ve broken it down into 4 exact steps. Even a complete newbie can execute this right out of the gate:
Players who know the game never rely on just one channel. Run both tracks in parallel and you can double your revenue:
Domestic Volume Play: List “PPT Commissions / AI-Generated PPTs” on Xianyu, post portfolios of “AI-Made Business Pitch Decks” on Xiaohongshu to drive traffic, and open a Taobao store to sell standardized packages. Domestic order values are lower, but the demand is dense—perfect for dialing in your workflow and stacking case studies.
Global USD Cash Grab: Search Upwork and Fiverr for “PowerPoint presentation” and “pitch deck.” Overseas order values are way higher: a single pitch deck project typically quotes between $500–$2000, with four-figure payouts being incredibly common (based on Upwork’s public rates and third-party quote aggregations). On Fiverr, the average PPT gig goes for $60–$90. As for platform cuts, Upwork scrapped its flat 10% commission in May 2025, replacing it with a floating 0%–15% fee. If you know how to negotiate, you can push that take rate even lower. Fiverr holds a flat 20% cut.
But the real barrier to going global isn’t your English. Letting AI handle the translation for your English PPT is a no-brainer; what foreign clients actually scrutinize is your business logic, your storytelling structure, and your design aesthetics. AI can’t replace you on those three fronts. Throw in the hassle of registering on Upwork and figuring out how to receive USD payments, and it’s the absolute norm for newbies to land zero orders in their first two weeks. That’s why going global is a move you should only make after you’ve successfully closed a few domestic deals and built a solid portfolio. Don’t bet the farm on overseas markets right out of the gate.
Stop waiting until you “finish learning.” The only real barrier to this side hustle is whether you’re willing to take action:
Run it for two weeks. If you land orders, your workflow is validated—time to pivot toward premium and high-end custom pricing. If you don’t, you’ve only burned one weekend. Pick a tighter niche (like “teacher lecture decks” or “founder pitch decks”) and run it back.
The brutal truth about the PPT ghostwriting hustle is this: Everyone is using AI to build decks, but the vast majority stop at “let the AI split my text across slides.” The ones actually making real money are the few willing to ask one extra question: “What does the audience actually need to remember?” That single gap is the difference between kidding yourself and charging $150 a slide.
]]>You open ChatGPT to write weekly reports, polish emails, or draft a few jokes for social media. Someone else opens the exact same tool, posts a fine-tuned prompt on the marketplace, gets it downloaded over 700 times, takes on custom prompt orders, and rakes in $5,800 a month.
The tool is identical. The gap boils down to one word: “prompts.”
A lot of people think selling prompts is a 2023 relic. Back then, the market was indeed flooded with garbage—slapping together a “you are a senior copywriting expert” prompt, listing it for $4.99, and claiming to make five figures a month. But here’s the reality: by 2026, this business isn’t just alive; it’s growing at an annual rate of roughly 30% (according to market sizing for the “prompt engineering market” by firms like The Business Research Company, the scale has already hit the multi-billion-dollar level). The money hasn’t disappeared—it has just migrated. It shifted from “selling single prompts” to “selling prompt packs and workflows.” If you’re still running the 2023 playbook, you’re only burning your own time.
This article breaks it down for you: why this “smallest business of the AI era” is still worth doing, how much you can actually make, what the new playbook for 2026 looks like, and how a total beginner without a single prompt to their name can get started today.
Why Pay for AI-Generated Content?
The most pressing question is: why would someone who can type themselves pay for a few lines of text written by someone else? The answer is the same as why people buy Photoshop presets, Canva templates, or Lightroom filters - they’re buying quality and convenience, not just the text itself.
For an ordinary person, getting AI to generate a usable image or a safe-to-publish text often requires trial and error, with 10-15 attempts before getting it right. A well-crafted set of prompt words is like packaging “50 hours of trial and error” into a single copy-paste operation.
From a business perspective, prompt words are almost a perfect digital product:
What’s even more insidious is the demand side - people who pay for prompt words are not just buying efficiency; they’re also buying a psychological comfort. “I’m not using AI badly, I just haven’t found the right prompt words yet” - this mindset once established, makes paying for prompt words seem like a no-brainer. In essence, many people buy prompt words not for efficiency, but for the “I’m using AI” peace of mind; this is the most easily monetizable aspect.
Action Call:
Don’t just sit there - start creating and selling prompt words today!
Don’t buy the BS that you can “just list it and make five figures a month,” and don’t buy the doom-and-gloom that “the gold rush is over.” The real income landscape in 2026 is a clear-cut ladder (based on PromptBase platform rules and public teardowns from overseas sellers—focus on the scale here):
Here’s the brutal truth: Marketplaces like PromptBase take a 20% cut, leaving you with 80%. Listing is completely free, and they only take a cut when you make a sale—meaning your cost of trial and error is practically zero. The only capital you’re burning is your time.
Here are two samples for you (both are seller self-reports, platform-undisclosed—treat them as ballpark scale references, not guarantees). The first: someone listed prompts on PromptBase at an average price of $3, selling them over 700 times—grossing roughly $1,600+ from this alone (after the platform’s 20% cut). But the real bulk of their profit came from using that exact same skillset to take custom orders at $25 an hour, pushing their total monthly income to around $5,800. Pay attention to the structure here: the prompt itself only made pocket change; its real function was acting as a business card, funneling people into high-ticket custom work—this is exactly the “three-stage rocket” we’ll break down later. The second: an average price of $4, with 1,000+ cumulative sales, netting about $3,200 on the platform, stacked with custom orders and multi-platform distribution across Etsy and Ko-fi, hitting $6,000–$8,000 a month.
So that $5,800 in the title isn’t from “just selling prompts.” It’s the combined result of “prompt-driven lead gen + custom delivery”—you need to get this straight before you start, or you’re just going to be disappointed out of the gate.
This is the most critical judgment call in the entire article. In 2026, not a single seller making real money got there by “slapping a $4.99 price tag on a few lines of text.” The game has leveled up three tiers:
Tier 1: Upgrade from “selling a single prompt” to “selling a system.” What consistently moves the needle right now are niche prompt packs priced at $29–69. This isn’t just a lazy “You are a copywriting expert” line. It’s a full system: the prompt + a user guide + example outputs + swappable variables + common pitfalls to avoid. You’re no longer selling mere text; you’re selling a plug-and-play methodology that actually gets results for the buyer.
Tier 2: Upgrade from “relying on marketplaces” to a “three-stage rocket.” If you only list on marketplaces, you’ll always bleed revenue to platform fees and remain at the mercy of their algorithms. The players who actually win build it like this:
That $5,800 case study from earlier? It runs exactly on this playbook. The prompts on the marketplace pull in pocket change and build trust; the custom work in the private channel brings in the heavy revenue. Stack these three tiers together, and your income jumps from “a few hundred bucks” to “thousands, even tens of thousands.”
Tier 3: Upgrade from “selling prompts” to “selling workflows.” This is the biggest shift in 2026—as models get smarter, standalone prompts are depreciating. But a complete system built on “prompts + tool chaining + automation” is skyrocketing in value. Here is a concrete example: take a “write product copy” prompt, run it through tools like Coze, Dify, and n8n, hook an automated competitor-title scraper to the front end, and plug an auto-publishing output to Xiaohongshu / Shopify to the back end. Boom—you’ve built a self-running machine. You are no longer selling copy; you are selling the machine itself. Sellers are already listing these workflow templates on marketplaces, pulling in thousands of dollars a month in download royalties. You don’t need to build this today, but you need to know the direction: prompts will depreciate, but “turning prompts into reusable processes” is becoming the new hard demand—this is the real lane worth betting on in 2026.
Don’t jump straight into trying to build Agent workflows—that’s Tier 3. Start with a minimum viable test:
Run it for two weeks and watch the data. If people pay, you’ve nailed the niche—double down and scale into deeper layers. If no one buys, you’ve only burned a single weekend. Pivot to a new niche and run it back.
The brutal truth about the prompt business is exactly what makes it so intoxicating: the barrier to entry is so low that almost anyone can jump in, which is exactly why the vast majority quit after launching a couple of listings. But the ruthless few who stick around? They take a $4.99 test prompt and slowly forge it into a steady niche business pulling in thousands of dollars a month.
The tools are in everyone’s hands. The difference isn’t knowing how to use AI. It’s whether you have the guts to take the one prompt you’ve mastered, spend a weekend packaging it up, and putting it on the market—secure that first sale first, then we can talk about leveling up.
]]>You’ve definitely had this maddening moment: Last week you saw a robotics company raise a massive round. Today, you want to bring it up in a conversation, but you can’t dig up the exact amount, the investors, or the valuation to save your life. You comb through a dozen Substacks, three or four English sites, and two or three industry group chats. The info is scattered everywhere, the numbers contradict each other, and just “fact-checking” eats up your entire evening.
Here’s the real gut punch—the spreadsheet you spent all night putting together gets tossed into a group chat, only to receive a row of “thanks, man” reactions. You saved everyone else time, and you didn’t make a single dime.
The opportunity to make money is never in “getting more information.” It’s in “organizing scattered information and selling it to people who don’t have the time to organize it themselves.” In every fast-moving sector, someone is doing the exact same thing: building an intelligence vault you can search, compare, and dig deeper into—and then charging for access. You used to need a whole team for this. In 2026, one person + a stack of AI can get it done.
This article breaks it down for you: how this “paid intelligence product” business actually works, how much you can make, how to build it from scratch, and where the biggest pitfalls lie.
Let’s look at three real-world cases first to understand just how high the ceiling is for this “information curation” business:
See the pattern? They aren’t selling “information”—they’re selling “saved time.” What investors and founders lack isn’t data; it’s a ready-to-use spreadsheet they can actually make decisions from. Compress hundreds of reports into one sortable, comparable table, and they’ll gladly pay you a monthly retainer.
And the biggest game-changer in 2026 is this: AI has taken over the heavy lifting of “aggregating hundreds of sources.” You used to have to hire someone to monitor feeds and copy-paste all day; now, one person who knows how to use AI can run the whole operation. But let’s be brutally honest—the cost hasn’t vanished, it just shifted from “paying for manual data entry” to “babysitting the AI so it doesn’t hallucinate.” We’ll dive deep into this trap later. The barrier to entry has undeniably collapsed, yet most people lack the discipline to list 50 sources and extract 100 rows of data before giving up on day three to doomscroll short videos. That’s exactly why this business is still wide open.
Whether this business succeeds depends 80% on the niche you choose. Not every field is worth building an intelligence database for. A winning niche must meet three conditions simultaneously:
Run those three filters, and embodied AI / humanoid robots emerge as the ultimate textbook track right now:
This track is a perfect bullseye on all three criteria: there’s fresh funding, new products, and new papers dropping every single day (high information volume); that intel is scattered across dozens of English media outlets, research papers, and official sites (fragmented information); and the investors and operators tracking it have zero time to spare and an exceptionally high willingness to pay (highly lucrative).
If you feel like robots are too far out of your reach, grab something right within your arm’s reach: a cross-border e-commerce winning product database (structuring last week’s viral products, sales volumes, pricing, and supply chain data from TikTok Shop / Shopee), an AI tool monetization case study vault (who made how much using which tool, and exactly how they pulled it off), or a plugin/theme ecosystem database for a specific vertical SaaS. The logic is exactly the same—as long as the people in that niche are pulling their hair out every day over “incomplete data and misaligned metrics,” that is your golden opportunity.
Conversely, here is what you must avoid: entertainment gossip (tons of information but zero monetary value), niches where someone has already built a free, high-quality database (you have zero differentiation), and slow-moving industries (updating once a week won’t retain subscribers).
Don’t write a single line of code yet. Spend a week listing every reliable information source in your niche: industry media, company official websites, career pages (which often hide massive signals), academic paper repositories, GitHub, key KOLs on X / Twitter, and overseas newsletters. First, compile 20–50 high-quality sources. This is the bedrock of your intelligence database. The quality of your sources directly dictates the credibility of your entire vault.
This is where AI truly flexes its muscles. Use a tool like Firecrawl to scrape the webpages (open-source alternative: Crawl4AI), then feed the data to Claude or GPT for field extraction—company name, product, funding amount, round, investors, tech stack—and auto-populate a structured spreadsheet.
Don’t know how to code? Don’t panic. Start with the “Weekend Foolproof Edition”: dump the full text of 20 news articles straight into the chat window of Kimi / 豆包 / ChatGPT. Hand it a predefined field template (Company Name / Funding Amount / Round / Investors / Date), tell it to extract the data based on that template, manually verify it once, and grind your way to 50 rows first. Once you’ve validated that “people actually want to read this table,” then you can start thinking about automation.
Here’s the detail that separates the pros from the amateurs: standardize the numbers onto a single yardstick. In the raw data, funding amounts might read as “tens of millions of RMB,” “$5 million,” or “undisclosed.” You use an LLM plus your own custom conversion rules to normalize everything into a single USD figure, and clearly flag whether it includes strategic investments. Users hate it when numbers clash. You turn “messy” into “unified”—and that is exactly why they’ll pay you.
This is the dividing line between paid and free. Content regurgitators just churn out “another company raised funding today”—that stuff is free everywhere, and nobody pays for it. What you need to build is a database: when users search for a company, they can see its funding history, tech roadmap, key players, and competitors; they can horizontally compare the valuations and product specs of ten companies in the same lane; they can follow a single thread all the way up and down the supply chain—like starting with a robotics company, digging up its core suppliers, and then uncovering those suppliers’ customer structures.
Remember this: Users don’t pay for “fast”—they pay for “comprehensive, accurate, and searchable.”
How do you build the V1? No coding required. Use Feishu Bitable / Notion / Airtable to build the database, set up a few views for filtering and comparison; collect payments by hooking up a subscription via Xiaobot / AiFaka / Stripe. Once people actually start paying, then consider building a standalone website. Don’t get bogged down in front-end, hosting, or logins right out of the gate—that stuff comes after you’ve validated the market.
Reference IT Juzi’s proven revenue mix, and pick what fits your current stage:
The cold-start cheat code: funnel in highly targeted traffic with free content first. Drop one deep-dive analysis based on your database every week (WeChat Official Account / newsletter / X). Let your readers experience the sheer value of your database firsthand, then lock “full data + real-time updates” behind the paywall. Stratechery paved this exact path—free articles build trust, deep-dive content drives subscriptions.
But here’s the brutal truth: 90% of the battle in this business is customer acquisition, not building the database. An intel hub nobody knows about will easily sink without a trace in the attention scarcity of 2026, no matter how many “weekly posts” you publish. You either already have deep connections in the industry, or you’re willing to grind it out on X / Jike / a niche community for six months to build your first few hundred highly targeted users. You could build the most gorgeous database in the world, but without eyeballs, it’s worth zero—this step is exponentially harder than writing the code.
Trap #1: AI will “hallucinate” funding amounts—you have to verify the numbers yourself. When large language models extract structured data, their accuracy ceiling typically caps out at 70% to 85%. The rest is pure garbage data. Even worse, it will fabricate with a straight face—turning “rumors” into “confirmed facts,” reporting RMB as USD, and rounding “nearly 100 million” up to “100 million.” AI can do the heavy lifting to scrape the data, but it cannot replace you in the verification step—which happens to be the most exhausting part. In the early stages, every single row of critical numbers must be cross-checked against two independent sources. If you aren’t certain, explicitly label it “estimated based on public reports, not independently verified.” Don’t fake authority.
Trap #2: The true Valley of Death is Month 2–3 retention. Building a database isn’t a one-and-done deal; it requires relentless, long-term maintenance week after week, month after month. That’s the real invisible barrier. Free users churn the second they feel they’ve “seen enough,” and paid users cancel their subscriptions the minute they think “updates are getting slow.” The “one person + AI” model sounds breezy, but the grind lies in relentless, continuous updates. NomadList has survived for a decade not because of its launch day hype, but because Pieter Levels maintains that data day in and day out.
Trap #3: Don’t buy the “set it and forget it” myth. Scrapers get blocked, data sources overhaul their layouts, APIs hike their prices, and API keys expire—waking up to a database flooded with NULLs overnight is just Tuesday in this business. The operational grind is far more brutal than the initial data collection. So don’t go hiring people or burning cash on premium data sources before you’ve nailed down a working paid model. Start by running a minimum viable version using free or dirt-cheap sources paired with AI. Your leverage lies in AI and your own hustle—not in burning capital.
Stop waiting until you “figure it all out.” This weekend, do exactly three things:
Shoot that spreadsheet to 10 people in that specific circle and gauge their reaction. If even one person asks, “Can you keep this updated? How much?”—congratulations, you’ve just sniffed out your first subscription.
AI has completely demolished the barrier to entry for organizing information, but this window won’t stay open forever. By the time you see the third guy in your feed making money with this exact playbook, you’re already too late. This used to require a whole team; now it’s a solo hustle powered by a stack of AI. This weekend—make your move.
]]>Look at these three sets of numbers first.
Right out of the gate for its new 2026 fiscal year, Microsoft slashed roughly 4,800 jobs—2.1% of its global workforce. According to Reuters, Meta is prepping another massive round of layoffs that could exceed 20% (Meta has not officially confirmed this). TrueUp’s stats are even more brutal: in just the first 5 months of 2026, the global tech sector has already axed over 100,000 employees.
Meanwhile, OpenAI’s Sam Altman keeps repeating the exact same prediction: “Soon, there will be a one-person company worth a billion dollars.” Anthropic CEO Dario Amodei is betting on an even more aggressive timeline—claiming it could happen as early as 2026.
On one hand, Big Tech is shattering “iron rice bowl” job security one by one. On the other, the smartest minds in Silicon Valley are betting that “one person can take down an entire team.”
These two phenomena are actually the exact same trend: corporations are getting leaner through layoffs, while solo operators who can hold their own are raking in more cash than ever. The only thing standing between you and the latter is knowing how to leverage AI.
So this article answers just one question: as an ordinary person, what exactly must you do right now to stop handing your fate over to Big Tech? The answer boils down to a single sentence: Don’t wait for the axe to fall. Use AI to build a “one-person company” first.
Let’s shatter the biggest piece of startup BS first: “The most important parts of starting a business are finding the right people, money, and resources.”
That only holds true for serial entrepreneurs or trust-fund kids. For ordinary folks, “assembling a team” is a massive trap—you can’t attract reliable talent, you can’t secure funding, and you’re left with nothing but a chaotic mess.
Historically, this was a dead end. But AI has slashed the cost of “finding people, money, and resources” to a level the average person can actually afford:
This isn’t a pipe dream. Data from the venture capital platform Carta reveals: nearly one-third of startups founded today are built by solopreneurs—almost double the rate in 2015. There’s only one reason for this surge: AI tools are finally good enough.
So, the real secret to a “one-person company” was never “doing everything entirely by yourself.” It’s this: you + a stack of AI agents + an outsourced infrastructure, doing the work that used to require a whole small team.
I’ve compressed this roadmap to the absolute minimum. You can start executing it today.
Step 1: Pick a hyper-niche you actually understand. Avoid the mass market.
Going “mass market” is just feeding yourself to the tech giants. Your only edge as an everyday person is to find a niche audience the giants ignore, but one where you know the inside baseball. If you’re a mom, tackle “sleep anxiety for second-time moms.” If you’ve done cross-border trade, build “used phone recycling in a specific Southeast Asian country.” The tighter the niche, the bigger the information gap, and the harder AI can amplify your leverage.
Step 2: Use AI to build a “minimum viable product” and launch it in a week. Stop sitting on your “masterpiece.”
Stop obsessing over making your product perfect. Use ChatGPT and Claude for copy and code, Midjourney for assets, and plug into ready-made storefront and sales tools (Shopify, 小报童, 爱发卡) to get your stuff listed and selling. Land your first sale first—optimize later. The sole goal of your V1 is to validate one thing: will anyone actually pay you for this?
Step 3: Outsource all the repetitive grunt work to AI agents.
Customer support, email replies, daily social media posts, competitor price tracking—these tasks are massive time sinks and the easiest to replace with AI. Automate these workflows with an agent tool, and you can pour 100% of your time into the high-leverage work only you can do: finding users and making strategic decisions.
Step 4: Assemble existing infrastructure. Never build from scratch.
This is where so many people faceplant. Compliance, logistics, payments, warehousing—there are already mature service providers dominating these spaces. For payments, you have Stripe, PayPal, and 连连; for cross-border logistics, 店小秘 and 芒果店长; for storefronts, Shopify. Your job is to “wire the cables,” not “build the components.” Plug your product into existing ecosystems, and you alone can run an entire full-stack company.
Methods mean nothing without data. Let’s look at the numbers.
Case Study 1: Dan Koe—One guy, zero employees, $4.2 million a year.
Dan Koe is the most unavoidable name in the overseas “one-person business” space. Through writing, courses, and digital products, he scaled a company consisting of exactly one person—himself—into a business pulling in roughly $4.2 million in annual revenue at a 98% profit margin (he self-reported this figure during a 2023 podcast interview; given the inherently low costs of digital product businesses, this margin is credible but should be taken with a grain of salt).
What does a 98% profit margin actually mean? It means almost zero employee overhead—nearly every dollar of revenue drops straight to the bottom line as pure profit. The formula he publicly preaches is dead simple: pick a niche where you can consistently produce → build an audience through content → convert that audience into paying customers for your digital products. Today, armed with AI tools, the barrier to entry to replicate this exact playbook is an entire order of magnitude lower than it was back then.
Case Study 2: Medvi—Two people, $20K to start, hitting $400 million in revenue in one year.
Even more savage is Medvi (telehealth), founded by Matthew Gallagher. According to a 2026 Forbes report (with its financials reviewed by The New York Times), he launched in September 2024 with roughly $20K, backed by a team of just two—himself and his brother. By 2025, revenue skyrocketed to $401 million, with projections exceeding $1.8 billion for 2026.
How did he pull it off? His entire tech stack is pure AI: ChatGPT, Claude, and Grok for code and copywriting, Midjourney and Runway for creative assets, and AI agents for customer service. The heavy lifting—compliance, logistics, pharmacy operations—was entirely outsourced to ready-made providers like CareValidate and OpenLoop. He didn’t manufacture a single component; he just connected the wires. Even harder to swallow is the profit margin: industry giant Hims & Hers employs 2,442 people and hits a net profit margin of just 5.5%. Medvi? Two people, net profit margin of 16.2%—nearly three times higher. Fewer people, way harder profits.
Back to domestic markets and Southeast Asia: this exact playbook is completely transferable.
You don’t have to tackle Western telemedicine. Let’s look at a more grounded example: pick an obscure, niche sub-category on TikTok Shop Indonesia (like some niche beauty tool), use AI to mass-produce Indonesian-language short video assets, deploy an agent for 24/7 automated inquiry responses, and pass the orders to a third-party overseas warehouse for fulfillment. You don’t need to hire a single person for inventory, logistics, or customer service. Dan Koe proved the “content + digital product” model; Medvi proved the “AI + assembled infrastructure” model. Both paths work flawlessly when applied to Southeast Asian cross-border e-commerce.
I’ve talked up the upside, but I have to put the biggest pitfalls right on the table—otherwise, I’m just selling you a pipe dream. When you’re going solo, the vast majority of people don’t die because they “couldn’t build a product.” They die on these three hurdles.
Hurdle 1: You Die on Customer Acquisition, Not on the Product.
For a solo founder, the scarcest resource is never product capability—it’s distribution. Your product can be incredible, but if no one sees it, it’s a fat zero. Too many people spend three months polishing a “perfect product,” only to launch on day one and realize they have absolutely no idea how to get customers. That is the real cause of death for 90% of one-person businesses. So don’t build the product first. Figure out where your first 100 customers are coming from first.
Hurdle 2: You Die on a Cash Flow Gap.
Medvi was started with $20,000, but that’s in the US, and that’s telehealth. For the average person, the most realistic risk is this: your side hustle hasn’t even closed its first deal yet, and your day job paycheck disappears first. Don’t get hyped up, quit on impulse, and go all in. The right playbook for a one-person business is “keep your day job as a safety net, test ideas on the side.” Once your side hustle’s stable income crosses half of your day job’s, it’s not too late to consider going full-time.
Hurdle 3: AI Makes Your Moat Shallower.
When AI agents are available to everyone and assets can be mass-generated by anyone, “knowing how to use AI” is no longer a moat. Your true competitive moat is the hard-won know-how you’ve accumulated in a specific vertical—others might have AI, but they don’t know the space like you do. This is exactly why Step 1 relentlessly emphasizes “choosing a vertical you genuinely understand”: AI is an amplifier, and it amplifies you. Without “you,” the amplifier is just an empty shell.
Once you’ve wrapped your head around these three hurdles, look back at the 4-step path from earlier—only then will you truly understand what each step is actually protecting you against.
Stop waiting until you’re “ready”—that day will never come. Do these three things today:
The tech layoff lists are only going to get longer, and Sam Altman’s “one-person billion-dollar company” might become a reality as early as next year. The window between these two events is the ultimate opportunity for ordinary people to rewrite their destiny using AI.
You don’t start a side hustle because you got laid off; you fear layoffs no more because you started a side hustle. Build your “one-person company” first. Turn it into your safety net, then turn it into your way out.
]]>Most people who want to monetize on YouTube die at the exact same bottleneck: you need to be on camera, edit footage, write scripts, and do voiceovers. From scripting to final cut, a single video eats up at least half a day, if not two or three. You grit your teeth and push out 4 videos a month, only to pull a few hundred views per piece. Three months in, you lose hope and quit.
But those philosophy and psychology channels you’ve been scrolling past on YouTube lately? The voices are AI-generated, the visuals are AI-rendered, and even the scripts are AI-written. The quality is insanely high, and they’re dropping one or two videos a day. That’s not the typing speed of one or two savants. This is a whole faction of operators mass-producing videos using a “factory” model.
Let’s lay the cards on the table: this playbook genuinely prints money, but by 2026, the “fully automated passive income” fantasy is dead. YouTube’s “unreal content” policy has mass-demonetized lazy channels that just scrape articles, slap on stock footage, and offer zero original perspective. The survivors who are still cashing checks are the ones who use AI as leverage and act as the director. How to build this pipeline, how much you can actually earn, and where the landmines are—I’m breaking it all down for you right now.
The trail starts with an observation from inside the circle (inside1024 channel): the players behind these channels have fully cracked the code on “industrial-scale, rapid production of high-quality videos.” The analogy from the original post hits the nail on the head—it feels like the shift from “backyard farm chickens” to “industrial farm chickens”: cheap, stable, and massive volume.
First, look at the size of the pie:
The money-making logic is dead simple: YouTube splits 55% of long-form ad revenue with creators. AI industrializes the “scriptwriting → voiceover → visuals → editing” pipeline, crushing the cost per video from hundreds of dollars down to a few bucks. You win by stacking three variables: production volume × views per video × RPM.
But here’s the dividing line: YouTube only pays the “directors.” The tools are available to everyone. What determines whether you can monetize long-term is whether you inject human perspective and original storytelling into the content. Keep this in mind—the entire article revolves around it.
Most people think “views equal money.” Wrong. Depending on your niche, the exact same view count can earn you 5x more.
Let’s clarify what RPM is. RPM is your take-home revenue per thousand views (after YouTube’s cut is deducted). It isn’t set by YouTube—it’s a byproduct of advertiser bidding. In finance and business, advertisers are willing to pay over ten bucks for a single ad click; in comedy, they might only pay a few cents. Your cut reflects that massive gap. In other words, when you choose a niche, you’re essentially choosing how deep the pockets of the advertisers willing to pay for that audience are.
The ranges below are compiled from public analyses by VidIQ, StudioBinder, and others:
| Niche | Typical RPM (USD per 1K views) |
|---|---|
| Personal Finance / Investing | 10–25 |
| Education / Learning | 9–14 |
| Business Documentaries | 8–18 |
| Philosophy / Psychology | 4–8 |
| History / True Crime | 4–10 |
| Entertainment / Comedy | 2–5 |
Take 100,000 views: the finance niche banks $1,000–$2,500, while entertainment barely scrapes $200–$500. Pick the wrong niche, and all your effort goes down the drain. This also explains the massive glut of philosophy/psychology channels—AI writes the scripts effortlessly (evergreen content, zero fact-checking required), and the RPM is decent enough. But if you’re playing to actually get paid, finance and education are where the real money is.
Here’s the ultimate multiplier: US audiences pull an RPM 3–5 times higher than Southeast Asian audiences. For Chinese creators, this is a hidden goldmine. Your English accent used to be the bottleneck; now, AI voiceover tools (like ElevenLabs) have completely demolished that wall. You can serve up flawless American English and cash in on premium US advertiser RPMs. This is the “earn in USD” global arbitrage that everyday creators can actually grab right now.
Real case study (a creator who publicly shared their post-mortem on Medium, @Non_Conformist): He runs a faceless English education channel targeting Americans aged 25–34, pulling an RPM of $12–$14, accumulating $253,000 (approx. 1.8 million RMB) over two years. 70% of that revenue came from 10 viral hits, with a single video peaking at $57,000. But he keeps it real—he had his monetization suspended for 5 months. The volatility in this game is very real.
Here’s the exact pipeline you can start building today:
Step 1: Pick your niche. Find the intersection of High RPM × AI-friendly × Evergreen. Philosophy, psychology, historical trivia, meditation, personal growth, and basic financial literacy are all proven sweet spots. Stay away from entertainment and comedy.
Step 2: The Script (AI writes, humans lead—this is the most valuable step in the entire process). Use Claude / GPT to draft the script. Structure it as “Hook → Argument → Story/Data → Punchline.” Aim for 1,200–1,800 words to yield an 8–12 minute video (longer videos mean more ad breaks and a higher RPM). Why is this the most valuable step? Because your topic angles, personal perspectives, and exclusive examples are the only “human touch” YouTube’s review algorithm recognizes, and they are exactly what separates you from the mass-produced factory slop.
Step 3: Voiceover. Use ElevenLabs for English, and Volcano Voice / CosyVoice for Chinese—they’re so natural now that even native speakers can’t tell the difference. ⚠️ One massive trap: don’t use cloned celebrity voices. YouTube has zero tolerance for unauthorized synthetic voices. At best, you’ll lose your monetization; at worst, you’ll get your channel permanently banned.
Step 4: Visuals. Generate static images with Midjourney / 即梦, then add Ken Burns effects (slow pan and zoom) for an instant cinematic feel. If you want motion, use 可灵, Runway, or Vidu to generate AI video clips. 10–15 high-quality images paired with camera movements are more than enough to carry a 10-minute video.
Step 5: Editing. Let 剪映 / CapCut handle auto-captions, beat syncing, and transitions. Batch processing is the key to massive output. Once you templatize your scripts, voiceovers, and image generation, seasoned players can batch-produce nearly 20 videos in a single weekend. That’s a volume faceless personal channels (averaging 4–8 videos a month) simply can’t touch. But let’s be brutally honest here: the real bottleneck isn’t the editing itself; it’s the detail work like aligning B-roll, lip-syncing, and syncing subtitles. So when we say “two to three hours per video,” that’s the averaged time for a templatized batch workflow, not the hours spent grinding out a single documentary from scratch.
Step 6: Scale and Monetize. First, stack up around 50 videos so the algorithm locks in your niche (the widely accepted tipping point for traction). The monetization threshold: 1,000 subscribers + 4,000 watch hours (or 10 million Shorts views in 90 days) → join the YouTube Partner Program (YPP—meaning you only get a cut of the ad revenue inserted into your videos once approved, with reviews typically taking anywhere from a few days to a month) → turn on ad revenue sharing. Then stack affiliate marketing, brand sponsorships, and digital products to max out the leverage on every single video.
Let’s be brutally honest: odds are, you will lose money. According to industry blogs like autofaceless, only about 3% of YouTube automation channels actually crack the code to monetization. The most common way people flop is by quitting in months 4 through 6—right before the algorithm’s compounding effect is about to kick in. The cold hard cash you have to burn is real, too: one creator did a retrospective showing they burned through $26,000 over 150 days (mostly tool subscriptions + outsourced editing + stock footage) before seeing a single dime of revenue; building out a library of 50 videos via outsourcing will run you around $5,000.
And the biggest new variable in 2026 is YouTube systematically purging the “lazy versions.” Here is the timeline: In July 2025, YouTube rebranded its old “repetitious content” policy as “inauthentic content” and began mass-demonetizing a specific type of channel—scraping articles → AI voiceovers → slapping on stock footage → zero perspective, zero narrative. The industry calls these cash cow channels. In July 2026, they tightened the screws further, explicitly defining several categories of content that will be flagged as “inauthentic.”
Pay attention to this detail—it determines whether you survive: What triggers the ban isn’t AI voiceovers themselves, but the lack of “human perspective, original insight, and narrative structure.” YouTube’s official stance: content that uses AI as a tool and is driven by human creativity will monetize as usual; fully automated, cookie-cutter mass production gets the axe.
So, players in this space face only two destinies:
My verdict: Starting in the second half of 2026, the window for purely automated, mass-produced faceless channels is already half-closed. The remaining opportunities are reserved exclusively for those willing to step up and be the director.
Don’t try to build a massive production line on day one. Validate yourself with a minimum viable loop first:
On the cost side, the free tiers of these tools are enough to run this minimum viable loop. If you want to get serious, start with a monthly budget of $30–$50 (primarily for tool subscriptions)—don’t blow cash on heavy outsourcing right out of the gate.
One last hard truth: if you only plan to try it for 3 months and aren’t ready to invest, stay out—you’ll likely end up as part of that 97%. But if you’re willing to use AI as leverage, act as the director yourself, and hold your nerve through month 6, getting in now still gives you a seat at the table where YouTube distributes tens of billions of dollars to creators every year. Once even “AI voiceover + stock footage” gets completely killed by the algorithm, there won’t be a seat left for you. Write your first script today. Don’t wait.
]]>Data Sources: Official YouTube Partner Program documentation (YPP thresholds, 55% revenue split, unrealistic content policies); market and RPM data compiled from public industry analyses by Hollywood Reporter, VidIQ, StudioBinder, Frameloop, Mixcord, Clippie, Unkoa, and autofaceless; creator revenue case studies sourced from public Medium breakdowns (@Non_Conformist, $253,000 in two years). Industry percentages, success rates, and investment figures mentioned herein are largely estimates from analytics firms or vendor blogs and do not represent official YouTube data.
When people talk about “expanding to the US,” the usual playbook comes to mind: building a brand, grinding SEO, burning cash on mainstream ad channels, and fighting tooth and nail against established Western giants in a hyper-competitive red ocean.
But if you look at the cross-border veterans quietly making a killing, they stopped fighting for the white middle-class a long time ago. In US e-commerce, the highest profit margins and the easiest path to scale right now have nothing to do with the white demographic—it’s the 68 million Hispanic (Spanish-speaking) market.
This is a classic information arbitrage opportunity. Today, we’re going to break down exactly why the Hispanic demographic is the ultimate target audience, and how you—as an individual with zero capital and no background—can leverage AI tools to penetrate this market in just 30 days.
Let’s look at some hard data that will completely shatter your current perspective:
Beyond the sheer scale of the data, looking at their commercial attributes, this demographic is an absolute goldmine for white-label e-commerce—especially for Chinese sellers:
In this arena, the early movers will gorge on the fattest traffic dividends.
In the past, cracking the Spanish-speaking market meant paying top dollar to hire Spanish-speaking operators. Today, with AI in your arsenal, a solo founder with a laptop can run the entire closed-loop operation.
Stop guessing what Hispanic consumers want. Go straight to Mercado Libre, the largest e-commerce platform in Latin America, and see exactly what they are already buying.
AI-Assisted Action:
Fire up Perplexity or ChatGPT (with web browsing enabled) and drop in this prompt:
“Analyze the top 50 best-selling products on Mercado Libre over the past 30 days. Exclude well-known brands. Extract unbranded (white-label) everyday household goods with high repurchase rates and strong family-use appeal. Output the results in an analysis table.”
Take these battle-tested, locally-proven smash hits from Latin America and reverse-engineer them back to the US Hispanic market via TikTok Shop or your own independent storefront. This “reverse-export” strategy has an insanely high success rate.
If you’re selling to the Hispanic market, absolutely ditch the stiff, corporate, translated English vibe. You need the street-level Spanish they actually scroll through on TikTok every day.
AI-Assisted Action:
Feed your product images or English descriptions into Claude 3.5 Sonnet using the following prompt:
“You are now a Hispanic mom living in Los Angeles—budget-conscious but fiercely devoted to her family. Write 3 short TikTok ad scripts for this product using highly persuasive, street-level American Spanish. Requirements: The tone must be highly conversational, highlight the insane value-for-money, include emojis, and keep it under 50 words.”
You can score dozens of high-quality localized ad copies at zero cost, throw them straight onto Facebook or TikTok for A/B testing, and scale whatever crushes it.
Hispanic communities in the U.S. are deeply rooted in community and family trust (Family First). Rather than blowing your budget on platform ads, partnering with Spanish-speaking KOCs (Key Opinion Consumers) who only have a few thousand followers will drive insanely high conversion rates.
AI-Powered Action:
Leverage AI tools (like ChatGPT) to bulk-scrape and filter micro-influencers using Spanish hashtags (like #LatinaMom, #Familia), and have AI craft highly sincere Spanish outreach DMs.
Offer free product samples in exchange for their review videos. These creators charge next to nothing—you can often close a deal with just a few pairs of socks or some clothes. But the Mexican-American followers behind them? They convert without hesitation.
In an era where AI has completely flattened language and information gaps, the so-called “barriers to entry” exist only in your head.
An audience of 68 million and $2.7 trillion in spending power is sitting right there. You don’t need to speak a word of Spanish, and you don’t need to fly to the U.S.
Today: register an account, use AI to write your very first Spanish promo copy, and run a $10 test.
Remember, in the global expansion game, your biggest risk is still staring only at the markets everyone else is staring at.
Source: yunying23/10971
]]>“One person, one computer, a stack of AI tools—selling $19 million worth of socks in 30 days. This isn’t luck; it’s ‘running a system.’ Let me break this system down for you: using AI to scan trends for product selection, mass-producing ad creatives with AI, optimizing ad spend with AI… There are at least 3 steps you can replicate and set up today.”
In an era where traffic is getting increasingly expensive and profit margins are razor-thin, many traditional e-commerce sellers and side-hustlers are busy complaining: “E-commerce is too cutthroat!” “Traffic costs are way too high!” “Is it still possible to make money now?” They stare at their screens every day, grinding it out with competitors over microscopic profit margins and racking their brains to write a few viral ad copies.
Here’s the real pain point: the vast majority of people are still running their e-commerce businesses using a purely manual, brute-force traditional model. One person doing the work of ten, working themselves to the bone, only to earn hard-earned, sweat-equity money.
But under the radar, people are already using AI to build automated money-making systems that completely obliterate the competition.
According to the source post (not independently verified), an absolutely mind-blowing case recently went viral: one person, relying purely on AI to sell socks, raked in $19 million in just 30 days.
It sounds like a fairy tale, or even an unscrupulous exaggeration. But if you’re only fixating on the words “selling socks” and “$19 million,” you’re completely missing the core wealth code behind this case: this isn’t about selling goods; it’s about running a system.
Once you grasp this model, you realize it can be infinitely replicated and scaled. Today, we’re going to break down this widely overlooked AI money-making playbook.
In traditional business, hitting eight-figure revenues requires a team of dozens, if not hundreds: a sourcing crew, a design team, copywriters, media buyers, customer support… every single link in the chain demands bodies, and every headcount adds costs and management friction.
But the biggest opportunity of the AI era is that tools have compressed the entire organizational structure of a “company” down to the absolute极限 of “one person.”
You no longer need to grind against people; you need to grind against systems. When you use GPT to nail your copy, Midjourney to crank out product shots, Coze to build smart customer service workflows, and various automation tools to handle product sourcing and data monitoring, you essentially command a tireless, emotionless, zero-payroll super-team of a hundred.
The underlying business logic of that so-called “$1900万美金” (whether the figure is inflated or not) absolutely holds up: razor-thin marginal costs + extreme testing efficiency + global traffic distribution = terrifying explosive power.
This isn’t about getting lucky and stumbling upon a viral hit. Statistically speaking, when you can test 1,000 creatives a day at rock-bottom costs, hitting a winner is inevitable. This is the “BUG” of our era.
Don’t let this system intimidate you. Strip away the mystique, and replicating this exact model on domestic or Southeast Asian platforms (like Taobao, Temu, TikTok Shop, or Shopee) boils down to executing three core steps.
How does traditional e-commerce handle product selection? Stalking competitors, relying on “experience,” and wild guesses.
How does AI-automated product selection work? Crunching data, spotting trends, and finding blue-ocean opportunities.
You don’t need to rely on your own intuition to guess which socks will sell. Your job is to deploy a scraper—or leverage off-the-shelf data analytics tools (supercharged by GPT’s data processing capabilities)—to crawl the entire web for product features that are seeing a surge in social buzz but still lack adequate supply.
For instance, by scraping social media data and feeding it to Claude or GPT for semantic analysis, you might discover a rising demand for socks that are “breathable for summer, feature specific pop-culture patterns, and are odor-resistant and antibacterial.” The system instantly locks onto these keywords and generates a precise product profile.
Action Plan:
Leverage existing product-scouting software APIs to regularly feed category trend data from TikTok and Shopee directly into GPT. Have the AI spit out the top 10 potential blue-ocean products for you every single day.
You’ve picked your winning product. Now, how do you sell it? This is the exact phase where the real gap is forged.
It used to mean hiring models and photographers, renting a studio, shooting for a day, and retouching for three—just to squeeze out 10 usable photos. But now?
While your competitors are testing 3 creatives a day, you’re testing 300. You don’t need to guess which image will go viral—you just dump all 300 into the system and let the algorithm give you the answer.
Action Plan:
Build an automated workflow on Coze or a similar platform: Input product link or selling points -> GPT generates 50 short-video scripts -> Call text-to-speech and image generation APIs to produce the assets. Fully automated, hands-off.
With your creatives locked and loaded, it’s time to flood the market on platforms like TikTok Shop or Pinduoduo. This is where the most labor-intensive parts kick in: customer service and data review.
Once this system is up and running, your daily grind is simply checking the dashboard and tweaking parameters—like playing a business sim game.
You might think “$19 million” is too far out there or abstract. So let’s look at a rock-solid vertical case study from overseas.
Recently, a grip socks brand targeting the Pilates audience used a pure AI workflow to hit $1 million (approx. 7 million RMB) in revenue in a single month, annualizing straight into the eight-figure stratosphere.
Their core playbook completely tosses out the traditional e-commerce product-testing logic. Instead, they fed thousands of hours of YouTube Pilates follow-along videos and fragments of comment sections into ChatGPT to extract “Demand Signals.” AI told them: the real pain point isn’t that socks lack grip, but the absence of socks that match the “Pilates Princess” aesthetic identity and come in colors that complement yoga outfits.
Next, they used AI to mass-generate low-saturation blue-toned, minimalist visual assets, churning out 30-second short videos like crazy for viral distribution. This funnel—capturing data insights + mass AI-rendered visuals + identity-driven marketing—took a pair of socks costing $3 to $5 and sold them for over $20, hitting a gross margin of 70%.
This is the power of the “system.” Selling socks, selling cups, selling digital products—what you sell no longer matters. What matters is this funnel model of “AI product selection + mass AI creative assets + automated outreach.”
Stop arguing in group chats about whether AI is a bubble. Stop agonizing over whether some large language model has gotten dumber.
The people making money on this wave already got their hands dirty and started using AI to drive traffic and monetize.
What you can do today isn’t fantasize about making $10 million tomorrow. It’s getting your hands dirty right now and building your very first Minimum Viable Product (MVP).
Your First Action Checklist:
Stop viewing today’s world through a traditional lens. Start acting like the CEO of a multinational empire with an infinite workforce. The keyboard in your hands is the baton that commands an army of thousands.
Take action right now and get your first monetization system up and running!
]]>“One person, one computer, a stack of AI tools—selling $19 million worth of socks in 30 days. This isn’t luck; it’s ‘running]]>