The Ignored Continent

Here is what happened.

A while ago, my friend Lawted met a boss who runs a traditional manufacturing and transportation business. After they talked through the business, Lawted casually helped him install DouBao on his phone.

The boss opened it and tried a few things. Face reading, fortune telling, chatting.

Then he said one sentence: “Holy shit, this is fucking insane.”

This boss runs a company with annual revenue in the tens of millions and dozens of people under him. But he had never used any AI product before. He did not know what ChatGPT was, did not know what Claude was, did not know what a prompt was. His entire business runs on WeChat groups, Excel handoffs, and people brute-forcing the workflow.

What did he use DouBao for?

Face reading and fortune telling.

And it was not the kind of thing where he tried it once and put it down. He was using it every day. Completely hooked. Almost possessed. It was honestly wild.

When I heard this story, I sat there stunned for a while.

Not because I thought it was funny. Because I suddenly realized something: those of us soaking in the AI world every day and the bosses actually running businesses on human labor live in two completely different worlds.

We are discussing Harness Engineering, multi-agent orchestration, whether Codex or Claude Code is stronger. Codex ships on mobile and some people think it is convenient while others complain it is slow. Our feeds are full of this stuff every day.

But outside our field of vision, there is an entire continent we cannot see.

On that continent, a boss making tens of millions a year sees AI for the first time through a face-reading app and is stunned speechless.

Just picture that scene.

This is not a joke. It is a signal. A huge signal that almost everyone has ignored.

Honestly, I used to think the market for AI was inside internet companies, inside big tech, inside teams that already understand technology. I thought AI deployment meant making better tools, stronger agents, and smoother workflows for people who already knew how to use AI.

But ever since we started doing HA7CH and actually began touching traditional industries, my view has been completely changed.

The largest market is not there at all.

The largest market is on every industrial belt in China that you cannot see.

Take the shipping-doc logistics business that boss is in. In Shenzhen alone, there are more than 8,000 companies doing this line of work.

More than 8,000.

And across these companies, the workflows are almost the same. Orders come in through WeChat, documents are made in Excel, PDFs get passed around, people manually check, manually enter, manually chase payments, manually track progress. A company may have twenty or thirty people whose daily work is basically moving something from one system to another, from one spreadsheet to another, from a sentence in a WeChat group into a cell in Excel.

What the boss says is one thing. What the employees do is another. What is written in the system is one thing. How it actually works is another. Many of the key rules are not in any document. They live inside the head of one old employee who has been there for more than a decade. A lot of the information is not structured data. It is a voice message in a WeChat group, a tiny line in a PDF, an abbreviation in the notes column of an Excel sheet.

And this is just one industry in one city.

Multiply that number across the whole country and across every traditional industry: logistics, education, manufacturing, trade, freight forwarding, building materials, restaurant supply chains. You get a number that makes your scalp go numb.

Most of these bosses do not use Jike, do not follow AI news, do not know the difference between ChatGPT and DouBao. The only thing they know is: I spend a huge amount on labor every month, it gives me a headache, I want to cut costs and boost efficiency, but I do not know who to call.

This is the real PMF for AI.

Not helping people who already know how to use AI use it better. Helping people who have never seen AI see it for the first time and feel their heads explode.

Some people may wonder: if the market is this large, why are the big companies not doing it? Why has nobody eaten it?

To be blunt, it is not that they do not want to. It is that they cannot.

When big companies do FDE, companies like Palantir, OpenAI, and Anthropic have mature platforms behind them: Foundry, Claude Enterprise, entire delivery systems. They face Fortune 500 companies and clients with budgets in the millions or tens of millions. They cannot send a dedicated team to deeply customize software for a logistics company with thirty or forty people, a local education business, or a small traditional factory.

Domestic companies like MiniMax and Zhipu follow a similar logic. Enterprise customers do not want “I chat with AI for a bit.” Enterprises want models that can enter the intranet, connect to existing systems, and handle concrete business scenarios. Delivery costs are high, so the FDE teams at model companies can only prioritize large clients.

VCs do not fund this kind of business either. It is too scattered, too dirty, too non-standardized. It does not tell a clean exponential-growth story.

But the problem is, the opportunity is exactly hidden in these places.

The more local, messy, and labor-heavy something is, the more room there is for AI to transform it.

Traditional outsourcing companies do not do this well either. Communication costs are high, delivery cycles are long, pricing is not cheap, and the final product often does not work well. Outsourcing also charges by feature: you tell me what you want, I build it. But these traditional companies do not have the problem “I need a feature.” Their problem is “my entire workflow is chaotic, and I cannot even explain what I need.”

This is the gap. Big companies cannot enter, outsourcing cannot do it well, and the boss cannot figure it out alone.

And what HA7CH wants to do is get inside this gap.

Back to the DouBao story. That boss was blown away by a face-reading app, but what he actually needs is not fortune telling. What he needs is someone to help him rewrite all those repetitive, labor-stacked workflows with AI.

What he needs is a young person with a MacBook and a $200 Claude Code plan to walk into his company, sit beside his employees, and watch how they actually work every day. Then, over two or three months, turn the most painful workflows into a system that actually runs.

That person is the FDE: Forward Deployed Engineer.

But our kind of FDE is different from big-company FDE.

Behind a big-company FDE is a massive model platform. Behind our kind of FDE there may just be a MacBook, a $200 Claude Code plan, a few APIs, a WeChat group, and one person brave enough to walk into the company on-site.

It sounds very local.

But precisely because it is local, it can enter places the big companies cannot enter.

And why does this work now? Because AI coding tools have amplified the delivery ability of an individual by too much. In the past, a small enterprise system needed a small team working for two or three months. Now, a sufficiently strong builder using Claude Code, Cursor, Codex, and tools like these may be able to ship an MVP in two weeks. In the past, juggling five or six projects at once was basically impossible. Now, if the method is right, it really can be done.

This turns “enterprise solutions” from an organizational capability into a strong-individual capability.

This is also why we say HA7CH is not an outsourcing company, not a bootcamp, and not a startup community.

HA7CH is an FDE accelerator.

We send AI-native builders into traditional companies on-site. During the day, they interview operations people and watch how they work, how they fill forms, how they reconcile orders, how they copy and paste, how they move back and forth between WeChat and Excel. At night, they go back and code what they saw into a system. The next day, they bring it back on-site and let the employees try it.

If it is wrong, they change it. If it is right, they keep pushing forward.

The boss pays based on “how many people did this save,” not based on “what technology did you use.”

This logic is very simple and very real. If a company has 10 operators, each making 8,000 RMB a month, that is nearly 1 million RMB in labor cost a year. If your system can turn the work of 10 people into something 3 to 5 people can handle, charging 10% to 20% of the saved cost, 100,000 or 200,000 RMB is not exaggerated at all.

And the more important point is: this system is not something you can only sell to one company. In the same industry, workflows are largely similar. After you finish the first company, you take the case study to its peers. You do not need to preach the future of AI again. You just say, this company is already using it. This workflow used to take this many people, now it saves this many people. They used to process this many orders per day, now they can process this many.

The first company is the show apartment. The ones after that can become a repeatable industry product.

If the first boss is smart enough, he may even become your angel investor. Because he understands very clearly that if this system works, there is no way you will only sell it to him. You will definitely sell it to his competitors. So he will think: can I get a seat first?

That is the part of FDE that is genuinely sexy.

At the beginning, it may look like you are working for free, like you are doing outsourcing. But if you pick the right industry, the right first customer, and the right repeatable workflow, what comes after is not outsourcing at all. It becomes SaaS. It may even become an industry-level AI system.

At this point, some people may ask: what kind of people are you actually looking for?

I will say it directly. Two traits. You need both.

Ability and time.

First, ability. You need to be able to build. You do not have to be the strongest engineer, but you must be able to make something from zero. You know how to use Claude Code, Cursor, Codex, and tools like these; how to quickly set up a system; how to connect APIs; how to build a page; how to deploy. If you are already using vibe coding to make small projects in daily life, you probably already have the basic ability.

Then, time. This is extremely important. FDE is not remote coding work. You have to actually go on-site, to the company, the office, maybe even the warehouse, and stand beside front-line employees watching how they work. You need to be able to talk to the boss and also to the operations people. You need to understand the very local industry language they use. This takes at least several continuous weeks, and sometimes two or three months.

So I genuinely think college students are a very suitable group.

Not only college students. If you are a freelancer, a developer who has left a job, or someone on a gap year, as long as you meet the two conditions above, you can do it. But college students have several natural advantages that are hard for other groups to match.

They have time. A winter or summer break of two or three months is just enough to go on-site and complete one project.

They have momentum. They have not yet been trained by big-company process. They are willing to try, willing to run, willing to throw themselves into an unfamiliar industry.

And most importantly, this generation of students already naturally knows how to use AI tools. They know how to use Claude Code, how to use Cursor, how to open issues, how to send PRs, how to quickly fork something and modify it.

Think about it: a sophomore spends two months in the summer helping a logistics company build a system. That system later sells to the second and third companies in the same industry. Every month, he can still receive some revenue share from it. That money might cover living expenses, buy equipment, fund the next project.

That feeling is completely different from having a job. It lets you feel, for the first time, that something you built can actually make money in the real world.

Not salary. Cash flow you created yourself.

This experience cannot be taught by school, and big-company internships may not teach it either. Taking ten classes in school is not as good as actually going on-site to a company, watching how a boss makes money, how an operations person works, how a system goes from zero to one and starts running. You learn product, engineering, sales, delivery, business, industry, and human nature all at once.

This is the best kind of learning.

Of course, I do not want to make this sound too beautiful. To be blunt, doing FDE is hard.

You do not have a proper desk. You do not have a proper work environment. Everyone in the office may be smoking. The boss may pour tea for you, or he may ask you to drink with him. Your first project will probably only be paid after delivery, maybe with not even one yuan of deposit. You may put your own time into it, go on-site every day, and write code until midnight.

And what you face is not standardized requirements. Real business is not written in a PRD. The boss will not write user stories for you. Employees will not tell you the full workflow either. You have to watch, ask, break it down, and judge by yourself. You will run into a mess of fields, historical baggage, and endless cases of “this customer is special.”

This is not something everyone can do.

But if you can do it, and you dare to do it, the return is also very real.

Because in traditional industries, you suddenly become a very scarce person. When you tell them AI can help reconcile orders, organize spreadsheets, automatically generate files, analyze customer data, and take over the daily copy-paste work, they will really think: holy shit, this person has something.

Inside a big company, you may just be an ordinary engineer. At Stanford, you may just be an ordinary research assistant. But when you arrive in a traditional industry that truly lacks AI, automation, and technical understanding, you suddenly become a key person.

Technology has completely different value depending on where you put it.

I am not sure where HA7CH will ultimately go. Maybe a few months from now we find out some assumptions were wrong. Maybe Hatch House is a false premise. Maybe the business model needs a major adjustment. All of that is possible.

But there is one thing we are certain about.

That ignored continent is real.

Those bosses who have never seen AI truly need someone to walk in.

Those workflows stacked on human labor are truly waiting to be rewritten.

And every company can only be AI-ified once. Whoever gets in first owns that workflow. Distilling it later becomes extremely difficult.

This window will not stay open forever. Big companies will enter within 12 months, but they will start with large clients. The market of small and mid-sized “local bosses” may still have a 12-month window.

So, back to the opening story.

A boss making tens of millions a year uses DouBao for face reading and fortune telling, and thinks it is incredible.

He does not know what else AI can help him do. He does not know that half of the labor cost he spends every day can be taken over by a system. He does not know that his company is waiting to be distilled.

But we know.

And what we need to do is find those young builders who have ability, have time, and dare to walk into the field, then send them to the side of these bosses.

Let them become the first person to walk into these companies.

HA7CH, BUILD IN THE FIELD. HATCH INTO IMPACT.

If you feel like you are this kind of person, or you want to learn more, come to ha7ch.com. Our first batch is already running.

See you next time.