Lately I've been seeing a lot of videos like this: three minutes, a person types a few sentences at a screen, and an admin dashboard appears. The titles say, "You won't need to hire an outsourcing team anymore," "Software isn't worth money anymore," "Build any system you want in three days."
Half the comments are amazement, and the other half are "Then I'll make one too."
Most people saying this aren't lying. What they demo is real: AI really can generate a working interface in minutes. The problem is that the video ends there—and the real bill only starts adding up from this second.
One, the half-sentence "development has become cheaper" is true.
Let's first get clear on what's right.
AI has genuinely smashed down the barrier to development. What used to take a team months can now be turned into a usable prototype by one person in a few days; writing code, tweaking styles, chasing down errors — efficiency is far higher than it was three years ago.
We admit this ourselves — we use it every day, and our delivery speed is noticeably faster.
But between "development got faster and cheaper" and "software is worthless" lies a long stretch that goes unsaid. Development is only the beginning of this, not the whole of it.
II. Why So Many Bosses Believed It
Because what’s visible on the quote is exactly the part that gets discounted.
When you ask for a price, they quote you how much the design and development cost; what you see in the video is also just those few minutes of “getting it made.” Everyone’s attention stops at the same place: when will the thing come out.
The rest never appears on any piece of paper: after launch, who backs it up, who upgrades it, who fixes it when the API changes, who answers the phone when something goes wrong in the middle of the night, who updates the content, and three years from now, when you want to add a feature, who still understands this codebase.
Invisible costs appear not to exist. That is the most critical part.
III. Do it yourself; accounting has three time periods.
The first stage is exploration.The boss wants a customer management system, but when asked what fields it should have, he can't say clearly; halfway through, he remembers, "I need something that reminds salespeople when to follow up"; by the seventh revision, he realizes what he really wants is "which customers have had no activity for three months." Everyone has to walk this path no matter who builds it, but if you walk it alone, no one helps you ask the questions in advance.
The second stage is rework.Things built by AI seem to run fine; the problems come after they're up and running. We've seen a self-built inventory system where the return logic was reversed—not that it wouldn't open, but that it was completely invisible in normal use and only failed to reconcile at month-end closing, off by several thousand yuan, and finance spent three days investigating. We've also seen a backend take five minutes to import tens of thousands of records, so salespeople simply stopped using it and went back to working in Excel.
None of these throw errors. The interface looks perfectly normal; the error is hidden in the numbers.
The third stage is taking over.Once the person who wrote this thing leaves, the code becomes a black box. No one dares to change it for fear of breaking it; no one can add features because they can't understand why it was written that way at the time. The company is left with something that runs but can't be touched.
IV. Why is the cost of trial and error in doing it yourself more expensive than hiring someone to do it?
This is what this article is really trying to say.
First, mistakes surface late. When you hire someone to build it, errors are exposed at acceptance, and you pay only after they're fixed. When you build it yourself, errors often only become visible in the second year—by then the system is already in use, there's already data inside it, and fixing it once means halting the business, migrating data, and explaining things to a whole crowd of people.
Second, you can't change it. You can get AI to change it, but after the third round of changes, it no longer knows what the original logic was, and the more it changes, the messier it gets. With an outsourced team, on the other hand, breaking it is their problem, and the contract covers you.
Third, and the most expensive one: you have no reference point. You don't know whether what you built is good or bad, because there's nothing to compare it against. It will keep "running normally" until one day you find the numbers don't reconcile, or the client never actually placed a single order on it—and you've already spent half a year making decisions based on its figures.
What money can calculate is the development cost. What it can't calculate is that during all this time, the business has been stuck in a place that isn't quite right.
Fifth, responsibility is one thing AI cannot provide.
Problems that arise after something goes live are mostly not about technology but about responsibility: the account entity was not changed and got suspended, the certificate expired and external services were interrupted, an API was shut down and the business could not continue, data was accidentally deleted, or the system was breached by a scanner.
At such times, what the boss wants is not a piece of code but a sentence: “We’ll handle it.”
When buying a service, there is one entity in the contract: someone answers the phone, there is a response time, version update notifications, and a fallback when problems occur. Even if the thing is very ordinary, at least there is someone accountable you can track down.
AI-generated things have no entity; they only have one sentence: “I did what it told me to do.”
For internal use, where if it breaks you can just do it again, that is fine. But once orders, money, customer data, or external service commitments are involved, an accountable entity is essential. This is not conservatism; it is division of labor.
VI. Four Questions to Determine Whether to Do It Yourself or Have Someone Else Do It
- Does anyone else in the industry do this? (If yes, it’s a common model—hire someone to do it.)
- Does the company have a dedicated person who can maintain it long-term? (No—hire someone to do it.)
- Is the scale large enough to spread development and maintenance costs? (Dozens of people, hundreds of customers—hire someone to do it.)
- When something goes wrong, does someone need to be accountable for the results? (Yes—hire someone to do it.)
It is worth building in-house only when all four conditions are met at the same time. Most traditional enterprises meet none of them—not because it cannot be built, but because no one will maintain it once it is built.
Seven, this line of work will not disappear; it will split into two halves.
Half of it is commodity: templates, generation, a few hundred to a few thousand yuan, deployable, presentable, usable. This half will get cheaper and more crowded.
The other half is someone accountable for it: requirements analysis, structural design, performance, continuous iteration, and taking responsibility when problems arise. This half won't be cheap, because what clients are buying was never development hours, but the years that follow.
The middle layer is the most dangerous: neither cheap enough nor professional enough. Clients will try it themselves with AI, and once they succeed, they won't come back.
8. "Can't I just do it myself?" Here is how we answer
This question will come up more and more often, and there's no point avoiding it. Our answer is layered:
An internal small tool, a campaign page, something for a quick demo—you can build those yourself. AI really is good enough for that, and we'll even tell you how to do it.
But as long as it is a company's public face, meant for customers, needs long-term updates, and carries responsibility for data and compliance, writing the code is only the down payment. The real cost comes later: who maintains it, who upgrades it, and who still understands this system when you need to add a feature.
We've been in this industry for ten years, doing websites, mini programs, digital platforms, and AI customer service, and the pattern we've seen is always the same—there are far too many projects that peak the day they launch: they are at their best on delivery day, and then get older every year after that.
Short videos capture the first half beautifully, and there's nothing wrong with that. It's just that nobody films the second half, because it isn't fast, it isn't flashy, and it doesn't produce a result in three minutes—but that is the real cost of software.
AI changes "who can build it"; it does not change "who maintains it after it's built."