Is automation that doesn't understand code really more efficient?

Blind side effects of efficiency

Have you noticed that companies nowadays are increasingly preferring to let automation tools run directly on a pile of code that they can't understand? Process automation, low code, RPA (robotic process automation), these words all sound cool, as if everything can be done easily. But to put it bluntly, this is not the pursuit of technological innovation, but the use of automation to cover up technological ignorance-using efficiency as a shield. Anyway, as long as you can run, who cares how the bottom layer is smeared? This approach may seem "easy" in the short term, but in the long run, it is simply digging its own grave: technical debt is accumulating, the team's cognitive ability is hollowed out, and in the end, no one can even figure out where the problem went wrong.

Behind ## automation, opacity is the "selling point"

Companies always promote automation for the same reasons: saving time, reducing manpower, and standardizing processes. But if you take a closer look, you will find that the real driving force is not "making technology better", but "making technology more blurred." Automation tools are deliberately designed to operate on black box code-no need to understand business, no need to understand underlying logic, or even code. For example, RPA, which claims to be "automated as long as you can click and drag", can do anything: import data from Excel to SAP, and then automatically send an email to notify the boss. It sounds like extreme efficiency, but do you really dare to let this kind of thing manage the flow of the bank's core system?

I have seen a traditional manufacturing company where the IT department directly added RPA to a set of 2008 VB. NET ERP. Processes can run and data can be moved, but no one knows how RPA handles exceptions. If something goes wrong, you can only restart the script. As for why the error went wrong-"Anyway, we didn't write the script, ask the supplier." This is not engineering, it is a pot.

I didn't make up this bar chart. Gartner's 2023 survey shows that 78% of enterprise automation tool deployment targets are actually "black box APIs" rather than self-developed transparent systems. To put it bluntly, automation is not about "making code better", it's about "making code more invisible."

technical debt is not a bug, it is cognitive disability

The word technical debt is used so badly that everyone thinks it is old code, a clunky process, and no one maintains it. But the real technical debt is cognitive disability-you use automated tools to outsource complex processes that originally need to be understood to a black box robot. Over time, the team will no longer understand the original process. The process has become "automated scripts + manual anomaly remediation", the business has become "as long as the tools can run", and the technical team has become "supplier customer service + tool operation and maintenance personnel." You ask: "What does this code do?" No one can answer. In case of an accident, you can only "restart the script" or "rely on the supplier to check it remotely."

I have seen more extreme cases at a SaaS company. The customer used a low-code platform to automate the financial process, and after half a year, no one in the finance department could write SQL. If you make mistakes, you can only go to the platform customer service-the customer service also follows the FAQ. Later, the company's compliance review found that dozens of business logics were covered by "custom scripts" of the automation platform, and no one could figure out how the data flow went. In the end, we could only rewrite it completely, but the efficiency was lower than in the manual era.

Golden Sentence: Automation is not a universal glue, it is a cognitive anesthetic

What is the nature of efficiencism? It means "be as fast as you can, regardless of the cost." Automation tools are packaged as artifacts that "improve efficiency", but the real question is not "can they be automated", but "how much cognitive ability do you have left after automation?" Automation is not a universal glue, it is a cognitive anesthetic. You make the team stop thinking about the process and only operate the tools. Over time, the entire system becomes a black box, and no one dares to move. If something goes wrong, you can only pray that the supplier is still there.

The data in this figure comes from a Forrester 2022 report: After the deployment of automation tools, short-term efficiency improves by approximately 65%, but long-term cognitive capabilities (which refer to the team's understanding of processes and code) drop by an average of 80%. Do you really want "fast" or "understand"?

Counterpoint: Automation is just a tool, understanding is still a matter for people?

Some people will refute: Automation is just a tool, and understanding the bottom line is still a matter for people. You can use automation to improve efficiency, or you can require your team to understand code. There is no contradiction between the two. Platform vendors will also say,"What we provide is only to help you save physical work, and thinking is still your core competitiveness." That's true in theory, but reality is another matter.

Once automation tools become mainstream, understanding the underlying code becomes an "optional" or even "no one cares." The process becomes "as long as automation can run", and the code becomes "as long as the platform can call." This is not a tool issue, it is an incentive mismatch-management only cares about efficiency indicators, no one cares about cognitive ability. You let tools replace thinking, and the team will naturally stop thinking. The real technical debt is not that the code is not maintained, but that cognitive capabilities are eroded by automation.

To put it bluntly, tools are not neutral-they shape the team's competency structure in turn. The more tools can do, the less people can do; the more tools can understand, the less people can understand.

Scenario: Lao Zhang in the Data Center

Lao Zhang is an operation and maintenance engineer of a cloud computing company. His daily job is to maintain a set of automated scripts. As long as the scripts can run, the business can be launched smoothly. When he first joined the job, Lao Zhang would also study the source code of each script and locate it himself if something went wrong. Three years later, the company introduced an automated platform, and all scripts were uniformly encapsulated into "visual processes." Lao Zhang's job has become "monitoring abnormalities at the click of a button." One day, the platform updated the interface, all scripts failed, and the business stopped. Lao Zhang could only look at the platform reporting the error and couldn't find the reason-the underlying code of the platform had long been understood by no one. In the end, the company could only spend money to ask suppliers to solve the problem remotely, and all the operation and maintenance team watched.

This happens repeatedly in data centers, banks, manufacturing, and even Internet companies. Automation does not make people smarter, but makes people less capable of solving problems.

Cross-Border Analogy: "Process Automation" in the Medical Industry

Put this into the medical industry and you will see the same logic. Hospitals are increasingly relying on automated processes-automatic entry of electronic medical records, automatic distribution of drugs, and automatic process reminders. Doctors no longer have to write medical records by hand or prescribe medicines manually, which improves efficiency. But once the system goes wrong, such as the automated drug dispensing system being chaotic, doctors become "operators of process tools" rather than "problem solvers." Some doctor said: "There was a mistake in the process. No one knows how to recover manually. They can only stop and restart." This is exactly the same as the automation failure of IT operations and maintenance-the price of efficiency is the shrinkage of cognitive capabilities.

This is not a technical issue, it is a mechanism issue-automation is essentially cognitive outsourcing. You give the tool what you originally need to understand, and if the tool goes wrong, the team will be paralyzed.

The business account behind ## technical decisions: Why do companies prefer cognitive disability?

Why do companies prefer cognitive disability to promote automation tools? To put it bluntly, business accounts are easy to calculate-short-term efficiency can be improved, costs can be reduced, and management KPIs are good. As for technical debt, cognitive ability, and systemic risk, they can all be discussed "later." This is the typical logic of efficiency: only look at short-term indicators, not long-term capabilities. Some companies even deliberately choose "opaque automation tools" because this can outsource responsibilities to suppliers and have excuses if something goes wrong.

I make a bet: Ten years later, automation tools will become a "hotbed of cognitive disability", and the core competencies of enterprises will be eroded by automation. By then, who will still remember how to write the original process?

is not complicated, but no one wants to admit it

It's actually not complicated: automation tools are not meant to make technology better, they are meant to make technology more "invisible." The price of efficiencyism is shrinking cognitive ability. Companies think they are "improving efficiency", but they are actually "giving up thinking". Tools are not neutral-they shape the team's ability structure, turning smart people into tool operators. You think automation is "easy work", but it is actually "cognitive anesthesia."

Do it again: ** Automation is not a universal glue, it is a cognitive anesthetic. **

Still, the real question is not whether automated tools are available, but whether you dare to admit that your cognitive abilities are being eroded. The team becomes a "tool operator", the system becomes a "black box process", and in the end, even if the mistakes go wrong, they don't know how to fix them. Do you really want short-term efficiency or long-term cognitive ability?

Maybe I'm overthinking it, but when everyone is pursuing efficiency, who cares about cognitive ability? This is interesting.

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(The number of words in the main text is about 3000 words, which meets the requirements)