Inflation is a concept that everyone has direct experience with. The same 100 yuan that could buy more things in the past can now buy fewer things. The money hasn't disappeared, and the goods haven't disappeared either - what has actually changed is the yardstick used to measure value. When the entire price system shifts upward, the money that was enough yesterday is no longer enough today.
So, what inflation truly changes is not just prices, but the standard of what is **"enough"**. In the past, 5,000 yuan could support a certain lifestyle, but today it may require 8,000 yuan; a project that could be undertaken with 100,000 yuan in the past may now require 150,000 yuan. Although the numbers are increasing, people are not naturally becoming more affluent as a result.
And as AI enters enterprises on a large scale, we may be experiencing another phenomenon that is highly similar to inflation, yet rarely discussed seriously. I would rather call it efficiency inflation.
It's not a strictly academic economic term, nor is it a traditional price increase, but rather an increasingly evident competitive phenomenon:
When technology universally improves everyone's efficiency, the efficiency that was once a competitive advantage will gradually be absorbed by the entire system and eventually become the new minimum standard.
As people work faster, companies produce more, and organizations respond more promptly. However, it is strange that people have not become significantly more relaxed as a result, and companies have not necessarily earned more money. Instead, we have collectively entered a new, faster competitive paradigm.
The reason why the 650 points we had when we were young may become 700 points today
There's a very intuitive question worth considering first: why is it that in many people's memories, a score of 650 was once enough to determine one's fate, while today in some highly competitive regions, the threshold for top universities seems to be getting higher and higher?
Strictly speaking, it's not possible to make simple horizontal comparisons between different years, provinces, and exam papers. However, if we temporarily set aside these technical differences and look at the underlying competitive logic, we'll see a thought-provoking fact: students have become stronger, learning methods have improved, and educational resources have become more abundant, but Tsinghua remains Tsinghua, and Peking University remains Peking University. The scarcity of top-tier universities has not expanded with the improvement in the overall efficiency of the education system.
In the past, there was no mature question bank, no systematic training industry, no rich online courses, no highly summarized methodology, and no such complete information dissemination system. Later, everyone became more skilled at taking exams. The result was not that it became easier for everyone to get into top universities, but rather the competition standards were raised together - the excellence represented by 650 points yesterday may just be one of many competitive positions today.
What's truly interesting about this matter is:
When the rewards themselves are still scarce, the overall improvement in participants' abilities will eventually become a universal increase in the threshold.
Your learning efficiency has improved, and so has everyone else's; you're completing exercises faster, and so is everyone else. Everyone's absolute abilities are rising, but what ultimately determines the outcome is still relative position. As a result, everyone is getting stronger, but also more exhausted. This is the most counterintuitive aspect of the competitive system: technology and methods can enhance a person's absolute abilities, but they cannot automatically improve their relative position.
It's worth noting that this logic is not unique to the exam scene, it just happens to be particularly pronounced in the context of the gaokao.
AI Repeats the Same Story in the Corporate World
Today, almost all enterprises are discussing AI-driven efficiency gains, and these gains are indeed real: programmers can write code faster, marketing teams can generate content more quickly, sales teams can manage more clients simultaneously, customer service can respond 24 hours a day, and analysts can process information in minutes that previously took days to complete. Looking at these numbers alone, AI is undoubtedly a massive productivity revolution.
The problem is that what companies ultimately care about isn't "can we do things faster today than yesterday," but rather "can we make more money because of it." And there is no natural equals sign between those two things.
If only one company possesses AI, the increased efficiency can certainly be converted into excess profits: a business that originally required 100 people can now be completed with 60, and customers still pay the original price, so costs decrease and profits naturally rise. This is the most beautiful stage of technological dividends, but it usually does not last too long - because AI will not belong to just one company, competitors will also use it, and the entire industry will start to improve efficiency.
When all companies can accomplish with sixty people what previously required a hundred, having sixty people do the job is no longer an advantage, but just the normal standard. Then the market starts to reprice: since costs have decreased, customers naturally demand lower prices; since delivery is faster, customers naturally demand shorter cycles; since one person can serve more customers, companies naturally redefine the amount of work one person should handle. The surplus originally created by technology is gradually absorbed by the competitive system, ultimately leading to an absurd yet real situation:
Every company is more efficient than it was five years ago, but none of them feel like business is getting any easier.
Everyone has become faster, so "fast" has lost its value; everyone has become cheaper, so "cheap" has become the default requirement; everyone can mass-produce content, so content itself has become less valuable; everyone can respond 24 hours a day, so customers have come to think that 24-hour responses are a given. This is almost identical to the logic of the gaokao: in the past, 650 points was an advantage, but now it's just a ticket to entry. Today, being able to use AI is perhaps still considered a skill, but in a few years, not being able to use AI may simply mean you're not qualified to compete.
Efficiency Redefines What's Considered 'Normal'
If the efficiency dividend was simply absorbed by prices, things would be relatively straightforward. A deeper change occurs at the level of expectations.
The technological revolution is initially always exciting, because what people see first is the time saved: what used to take a day can now be done five times a day. The first reaction of people is often - we can finally take it easy.
But competing systems don't usually work that way. When one person can complete five tasks in a day, the organization will soon ask: if five tasks can be completed in a day, why are only one task scheduled? Customers will also ask: if technology can complete tasks in real-time, why do I have to wait three days? Capital will ask similarly: if unit costs have decreased, why hasn't the growth rate increased? The new productivity is quickly written into new expectations, and yesterday's "excess capacity" becomes today's "normal capacity".
This is the deepest layer of efficiency inflation:
Efficiency is not just about making people work faster, it will ultimately redefine what constitutes a reasonable workload.
So, historically, many technological advancements have not directly resulted in more leisure time, but instead have initially led to higher work density. Computers have not eliminated offices, email has not reduced communication, instant messaging has not eliminated meetings, and mobile internet has not truly freed people from work - they have simply allowed more tasks to be accommodated within a unit of time.
AI will likely advance this trend by an order of magnitude - previously, one person could handle ten issues in a day, but in the future, one person can handle one hundred. The real question is: will the additional ninety quotas ultimately become ninety units of free time, or ninety new tasks? The answer is not determined by technology, but by the competitive structure.
Why 'Productivity Growth' Doesn't Equal 'Profit Growth'
Once it is acknowledged that the competitive structure is the distributor, it is necessary to distinguish between two concepts that are often confused.
Productivity describes how much you can accomplish with fewer resources, whereas profit describes how much value you can retain after all competitors have joined in. The former is an absolute capability, while the latter is a relative outcome. If a company doubles its efficiency while its competitors remain stagnant, it gains a significant advantage; however, if the entire industry doubles its efficiency, it may ultimately only lead to a shift in the industry's prices, delivery speeds, and customer expectations, with competition rebalancing at a new equilibrium.
The newly generated efficiency gains will not disappear, they are just being redistributed: some go into corporate profits, some become lower prices for consumers, some become better products, some become faster services, and a large part directly becomes new competitive requirements. So the question entrepreneurs should really be asking is not just "How much efficiency has AI helped us improve," but:
The increased efficiency brought about by AI ultimately ends up on whose balance sheet?
This is a strategic issue of a completely different level. If all efficiencies are ultimately quickly repriced by the market, then even if a company becomes extremely efficient, it may only avoid being eliminated, rather than actually gaining access to a new profit pool.
The Most Dangerous Thing Is to Participate in Old Games with New Productivity
This is the biggest misconception many companies have when using AI today: they gain a new productivity, but use almost all of it to complete things that already existed in the past. Previously, ten people wrote code, now five people do; previously, one week was needed to write ten articles, now ten articles are written in one day; previously, one salesperson served fifty clients, now they serve three hundred; previously, a product was released four times a year, now it is released forty times a year.
These things are valuable, but they are essentially optimizations within the same competitive system. You're just replacing the engine and continuing to run on the same track. And once all competitors have the same engine, the result will be very familiar: everyone accelerates together, the average speed increases, the race doesn't end, and it even becomes more brutal.
Since accelerating on the old track cannot solve the problem, the only direction worth discussing seriously is how to leave this track.
Six, the real breakthrough is not about doing things faster, but about doing things that would not have happened in the past.
If the productivity gains materialize, the truly important question emerges: where should the additional productivity be applied?
My answer is: don't use all of it to make yesterday's tasks faster. Instead, allocate the most important portion to things that simply weren't viable under the old productivity conditions. Here, "not viable" doesn't necessarily mean humanity never imagined it. More often, it means: technically possible in the past, but not economically worthwhile; there was demand, but the cost of serving it was too high; it could be customized, but couldn't scale; it could be done manually, but marginal costs couldn't be driven down; only a privileged few could access it. A truly massive productivity revolution rarely comes from making old products a little faster. It comes from turning these "uneconomical" things into economical ones for the first time.
We have always known that one-on-one education is more personalized than large-class education, but the problem is that an excellent teacher cannot provide continuous one-on-one services to 100,000 people at the same time - the demand has always existed, but the cost structure does not allow it. If AI enables every student to have a private tutor that understands their learning status in real-time, remembers their weaknesses over the long term, and continuously adjusts the teaching content, then it's not just about "teachers preparing lessons faster", but rather creating a service that could not be scaled up in the past.
Similarly, companies would of course like to provide each client with an exclusive research team, but it's impossible to assign 100,000 analysis teams to 100,000 clients. If AI enables each client to have access to a continuous information service that's close to having an independent research team, it's not about "researchers writing reports faster," but rather a level of service that didn't exist before. This is especially true in the software industry: each user's needs are different, but software must be standardized, otherwise the cost of customization would be unbearable; if future software can generate interactions, logic, and processes in real-time based on the user's current task, then the software may not even be what we consider fixed software today.
This is where the productivity revolution truly unlocks its potential.
True Wealth Creation in Tech Revolution Often Lies Beyond Traditional Boundaries
The value that lies "beyond boundaries" is often overlooked because humans tend to use the language of the old world to understand new technologies. Cars were initially understood as "horseless carriages", computers were initially used to simulate paper and spreadsheets, and many early websites simply moved offline information online.
AI is no exception. Today, we ask: Can AI help programmers write code, help marketers write copy, help customer service answer questions, and reduce the number of employees? These questions are certainly important, but if we look back in ten years, the things that truly change the economic structure may go beyond this, because they all occur within the boundaries of old production. The truly huge value often occurs in another place:
New productivity is making it possible for economic activities that previously did not exist to emerge for the first time.
The most interesting part of the productivity revolution is not just moving the original curve up a bit, but rather pushing out the boundaries of "what is worth doing". If a service that used to require a cost of 100 yuan can now be provided for 1 yuan, then the key issue is not just reducing the price of a service from 150 yuan to 120 yuan, but rather asking: what things were previously not worth buying due to the 100 yuan cost, but now can become a huge market after the cost drops to 1 yuan?
This is where the new productivity is most worth looking for, and it has also drawn a clear distinction between two completely different paths for enterprises.
Efficiency Competition and Boundary Innovation: Two Distinct Corporate Strategies
In the future, companies using AI may become increasingly divided into two categories.
First-tier companies will constantly ask themselves: how much more can we cut costs, how many more people can we reduce, and how much faster can we deliver. This is an efficiency competition. It is necessary, of course, because if others improve and you don't, you will be eliminated. However, efficiency competition is essentially defensive in nature: it allows companies to stay in the game, but it doesn't necessarily create a new game.
The second type of enterprise asks another set of questions: with the emergence of new productivity, what products that did not exist in the past can now exist? What services that were only available to a few people in the past can now be popularized? What things that could not be scaled up in the past can now be scaled up? What demands that did not have a viable unit economic model in the past are now viable for the first time? This is boundary innovation.
The former is about running faster in the old world, while the latter is about finding a new world. If efficiency inflation indeed becomes a universal phenomenon in the AI era, true excess profits are likely to become increasingly concentrated in the second type of company - the efficiency premium in the old world will be quickly diluted by competition, and only when a company brings new productivity into a space that has not yet formed a stable competitive standard can it truly retain the value created by technology.
Don't Just Use AI as a Cost-Cutting Tool
Thus, for today's enterprises, there may be a question that is more important than "how AI can improve efficiency":
AI gives us the ability to do something for the first time that we would never have done before.
This is the real strategic issue. If the answer is just to hire a few less people, write more code, generate more content, and complete projects faster, then AI will likely just become a new type of office software. It will be extremely important, but the efficiency it brings will ultimately be written into the cost structure of all companies, becoming a basic infrastructure that everyone has, and therefore no one has an advantage.
Just like no company today can gain a long-term competitive advantage simply by using Excel. Excel is important, but everyone has it. AI will likely be the same in the future. The companies that truly create new profits will not just "have AI", but will be able to use AI to redefine a product, service, organizational method, or market structure that was not economically viable in the past.
Lesson 10: Don't Waste Productivity on Yesterday
Humans always hope that technology will make their lives easier, but history has time and again given the opposite answer: increased productivity does not automatically bring freedom, it may instead bring more production, more consumption, more competition, more expectations, and a higher threshold.
When everyone achieves the same level of efficiency, efficiency will experience a "decline in purchasing power" similar to currency. The competitive advantage that one unit of efficiency could buy yesterday now requires two units today. Yesterday, 650 points meant excellence, but today it may require 700 points; Tsinghua is still Tsinghua, and Peking University is still Peking University, what has changed is the ability cost required to enter the competition.
Companies are facing the same situation. AI can make all companies smarter, faster, and more efficient, but if all enterprises simply use this increased productivity to repeat what already existed yesterday, what we ultimately get is likely not a more relaxed business world, but an old world that operates faster, with higher demands, and more intense competition.
So, in the AI era, what is truly worth exploring is not more efficiency, but the next step after efficiency.
The real dividend of every productivity revolution lies not in doing yesterday's tasks faster, but in making it worthwhile to do things today that were not worth doing yesterday.
Perhaps this is the real way for companies to break through the efficiency expansion: not to continue rolling 650 to 700, but to find an exam room that never existed before.