Summary
Confident predictions can disguise how little anyone knows about a child’s future or an AI-shaped society. Through Zhuangzi’s Lady Li, a family memory, and Buffett’s “too hard” category, this essay considers the practical value of leaving uncertainty unresolved.
At a glance
Admitting “I don’t know” interrupts the habit of filling uncertainty with confident but unsupported stories. When complex outcomes involve too many unknown factors, accepting the limits of our understanding can reduce anxious prediction and leave us more open to understanding and action. It also allows for the possibility—not the promise—that events may turn out better than planned.
Zhuangzi Series, Part 32
A while ago, I met with a younger schoolmate of mine, and we talked about anxiety over education. She earned a place at Peking University after growing up in a mountainous region and has since risen to middle management at a major tech company. However complicated a deal may be, she can analyze it with complete clarity; however tight the schedule, she delivers on time. She works that hard while also caring for two preschool children at home—the very definition of an all-rounder.
Working at the forefront of AI, she has seen both its power and its capacity to replace human labor. But AI itself does not make her anxious. Her anxiety began when she realized that her child is highly sensitive and drawn to the humanities. A modern-day Mu Guiying who has fought through obstacle after obstacle, she knows all too well how harsh society can be and how strongly it currently favors science and engineering. She began to worry about her child’s future.
And yet no one can say clearly what the future will actually look like.
Will AI-driven replacement lead to mass unemployment, or will many more new jobs emerge?
What would happen if mass unemployment did occur?
If new jobs emerge on a large scale, what would they be?
Will money really become useless, as Musk has said?
Will governments really hand out money to solve the income problem for people without jobs?
Will people trained in the humanities still have any use at all?
The internet is full of discussions about these questions, and every piece seems to offer its conclusion with absolute certainty.
Some say humanities graduates are the future and that major tech companies have already begun hiring philosophy majors. Others say humanities degrees lead nowhere and universities are rushing to eliminate their humanities departments. Some say governments will provide everyone with money in the future; others say the future will resemble the film Ready Player One, with most people living in a virtual world devoid of meaning.
There is no shortage of opinions, but which of them offers sources that can actually support its claims? Which is backed by statistical data? None of them. These are all imagined futures, so where would the evidence come from? Without evidence, believing any one of them is essentially no different from religious belief. It comes down entirely to what you are willing to believe.
These discussions therefore become a game of comparing hands in poker, with the winning cards determined by how loud the claim is and how prominent the speaker’s name is.
Yet the overwhelming supply of instantly accessible “information” and “opinions” leaves us feeling that we know what will happen in the future and ought to be able to do something about it. At the same time, we sense that we do not really know what the future holds and that nothing we do can guarantee the outcome. So we become more anxious than ever.
The current success of science and engineering graduates, with their strength in logical reasoning, has created another illusion: if I think hard enough, sharpen my logic and algorithms, and collect enough information to expand my data set, I can arrive at the correct judgment about the future.
It is extraordinarily difficult for human beings to admit that we simply do not know.
We would rather form easy conclusions about things we barely understand and fill our minds with “stories” than say, “I don’t know.”
The human mind dislikes gaps in information. Faced with the unknown, people readily experience anxiety, a loss of control, or a sense of threat. A definite conclusion, even if it is wrong, often feels more stabilizing than “I don’t know.” This is a psychological need for closure: people tend to end uncertainty as quickly as possible and construct a complete story instead of tolerating an unresolved question.
For parents, a child’s future is precisely the kind of question that may remain unresolved for twenty or thirty years. How AI will reshape society is likewise uncertain over a long period, continually evolving and constrained by the interplay of many competing forces.
The brain is also a system that constantly makes predictions. Drawing on past experience, it guesses what we will see next and what will happen. When it lacks the information needed to make a prediction, we do not usually mark the matter as something that still needs clarification. Instead, we fill the gap with our existing beliefs, experiences, and expectations. We would rather patch the matter ourselves than admit that it is something new and that our old tricks may not apply.
Meanwhile, pervasive media messaging has long since seeped into our minds and become a vast store of ready-made “patches.” When we predict the future, those patches attach themselves automatically. But how good are they? As noted earlier, they are themselves products made by layering one patch over another.
The Zhuangzi tells a story along these lines.
During the Zhou dynasty, there was a small state called Ai whose princess was Lady Li. When the states of Jin and Qin joined forces to destroy Ai, the princess was taken away by Jin. On the journey there, she wept until her tears soaked her clothes. But after she reached the king of Jin’s palace, shared his large square bed, and ate delicious meat, she regretted having cried in the first place.
Zhuangzi used this story as an analogy for the human attitude toward things we do not understand, such as life and death: like Lady Li, we may well have got our attitude wrong.
But life and death are hardly the only things we do not understand. How a child will find happiness and what the future world will become are both enormously complex models, constrained by too many unknown factors. They are not physical questions like whether an apple will fall from a tree.
Most of the time, we are no different from Lady Li. We live inside our own imaginings and refuse only one thing: to admit, “I don’t know.”
When I was in my teens, my mother told me a story from her own life that was much like Lady Li’s.
My mother was exceptionally intelligent and an outstanding student. But because of the family’s circumstances, she was not allowed to attend high school. Instead, she went to a teacher-training school so that she could start working early and help support the family. When she first enrolled, she went to the seaside every day, watched the sunset, and cried. She deeply envied the classmates who could attend high school and then sit the university entrance examination. Less than two years later, the movement of the 1960s began, and high school classes were suspended. Her former middle-school classmates all returned to the countryside to farm, and many remained farmers for the rest of their lives. After graduating from teacher training, she became a teacher in the public school system. No one at the time could have foreseen that the dividing line in their lives would emerge in this way.
A major historical event like that is, of course, unlikely. Even so, my mother has always used this story to remind me not to assume that the path I imagine is the best one. I should admit that I “don’t know,” and when events do not follow my plans, remember that “perhaps it will be better.”
Later, through experience, I found that this way of thinking really does work.
When we stop clinging to our original judgments, dare to admit “I don’t know,” and remain open to the possibility that things may turn out better, we become honest about the limits of our understanding. Paradoxically, that gives us greater capacity both to understand and to act.
We all know the saying: what you know, you know; what you do not know, you do not know. But in practice, the more important, complex, and changeable a matter is, the more intensely we crave a judgment—and the harder it becomes to admit that we really “don’t know.”
This is precisely where Buffett excels.
He divides things into three categories: opportunities within his circle of competence, opportunities outside it, and those that are simply too hard. “Too hard” refers to opportunities that appear understandable but involve too many variables, require complex calculations, exceed the limits of his understanding, or remain impossible to grasp even after extensive research. He believes such opportunities should be abandoned decisively, rather than allowing them to consume time and money in a swamp of complexity.
Ordinary people are often drawn to what is “too hard.” Instead of facing that difficulty directly, they fill the gaps in their understanding by inventing stories in their heads.
That is the difference between an ordinary person and a great investor.
Let’s practice saying “I don’t know.” Zhuangzi gave us the method; we should not waste it.
Frequently asked questions
Why can uncertainty about the future create anxiety?
The human mind dislikes gaps in information. Faced with the unknown, people may experience anxiety, loss of control, or threat, while a definite conclusion can feel stabilizing even when it is wrong.
What does the story of Lady Li illustrate?
Zhuangzi uses Lady Li’s reversal of feeling to suggest that people may adopt the wrong attitude toward things they do not understand, including life, death, happiness, and the future.
Does the article claim that unexpected outcomes will always be better?
No. The lesson is conditional: when events do not follow our plans, perhaps they will be better. The article does not present a better outcome as guaranteed.
What does Buffett’s “too hard” category mean?
It covers opportunities that involve too many variables, require complex calculations, exceed his understanding, or remain unclear after extensive research. His response is to abandon them rather than continue consuming time and money.
