Why I Read This Book#
In the final pages of Elon Musk, the author briefly introduced two books by economist Tyler Cowen: The Great Stagnation and Average Is Over. The Great Stagnation is about why America’s development has stalled over the past 40 years — something I’m naturally not that interested in. But Average Is Over is not a study of history; it’s a perspective on future development, especially the impact of AI on human life.
I’ve always been interested in what human life will look like in the future. Recently, OpenAI has been hot, and it feels like the AI era is upon us. What changes will AI bring to our lives and work? Will social structures shift? Which jobs will gradually disappear? Which jobs will benefit?
Chess#
The book spends a large portion (nearly half) discussing chess and computer programs. You can tell the author is definitely a chess enthusiast — he’s deeply knowledgeable about chess history and its evolution. Reading this section always reminds me of The Queen’s Gambit. If it weren’t for that show, I wouldn’t have known chess had rapid formats or that the Soviet Union was the world’s strongest chess nation. The author also uses chess to explore the influence of computer programs on the game.
This influence goes beyond AlphaGo defeating the world’s strongest human Go player — the “beating the brightest human minds” kind of impact. It also includes how early chess programs changed the way humans learn chess. In the early days of chess, before computers took off, people could only learn chess from other people. A beginner couldn’t often play against a chess master. But as computer programs became widespread, they were adopted en masse. Chess programs could teach you, you could play against them, and you could even set the difficulty level. This was incredibly convenient for beginners. Without us even noticing, computer programs quietly reshaped our lives. In the future, we will increasingly collaborate with AI.
Polarization#
Once AI is widely deployed, many aspects of our lives will change. AI is unlikely to revolutionarily overturn the social structure of rich and poor; the reality that a tiny minority controls the vast majority of wealth may intensify further. The middle class is perhaps the most vulnerable stratum. Many middle-class workers perform partially intellectual but repetitive work — exactly AI’s sweet spot. The book argues that the value of middle-class work isn’t actually that great and may be relatively easily replaced. Disparities in basic assets will widen the gap in wealth accumulation — in other words, differences in starting capital will amplify differences in asset accumulation. In this age, that sentence is easy to understand.
The book approaches wealth distribution from an American perspective, but it’s easy to map onto the Chinese context. China’s economic development over recent decades has been truly remarkable — the dividends of population and infrastructure construction, a phase all developed nations went through. But the introduction of market economics and the passage of time have been accompanied by growing wealth inequality. Let’s leave it there… I don’t want to write anything too sensitive…
The Rising Cost of Learning#
The cost of learning keeps rising. This doesn’t refer to the cost of tuition or training courses, but the difficulty of learning or mastering a profession. The word “inventor” — I suspect many people haven’t heard it in a long time. Our impression of the term is still stuck in the Edison era. Back then, individuals could invent things on their own; they just needed some relatively advanced knowledge in their field and a bit of brainpower. “Inventing” didn’t seem that hard. But as time passed, we rarely hear the word “inventor” anymore. It’s not that humans have stopped inventing — it’s that what people invent now is almost always the work of a team, many people, often requiring cross-disciplinary collaboration among multiple specialists. The cost of “inventing” things keeps rising because the knowledge required to master a field grows ever larger and more complex. It’s unrealistic for one person to master an entire industry; people tend to specialize in narrower domains — and even a narrow domain is enough for a lifetime of study.
Academia today faces this exact situation. A relatively successful paper typically requires experts from various fields to use their specialized knowledge to verify the correctness of one small segment of a proof. The book gives a classic example: if a mathematician proves a conjecture in mathematics, there may be only a handful of people in the entire world who can truly understand what the mathematician is proving. Most of them may only understand one section of the content — and even the mathematician themselves may only say: “I might be right.” We have no way to verify the correctness of the proof.
Human knowledge is becoming increasingly complex. Scientists now tend to, and increasingly do, hand calculations and experiments over to machines. Humanity seems to have reached a tipping point: our brains are nearly incapable of understanding this knowledge anymore. From a biological perspective, the human brain necessarily has a limit. The processing speed of the human brain can’t remotely keep up with machines.
Self-Learning#
Even as learning costs rise, education will become ever more important in the future. The education system may change. Since time will be more precious in the future world and learning resources will be easier to access, people will lean toward online learning and self-directed learning. At the same time, this makes self-drive even more critical.
As an IT professional, I have a deep appreciation for self-learning. This industry is intensely competitive; if you don’t keep learning, you’re basically on the brink of obsolescence. But highly effective learning is also reflected in your salary. Our parents’ generation relied on assigned jobs and could work in one position for decades without major changes. People back then just thought about working, not obsessively self-improving and chasing certifications. Times have truly changed. How many people, like me, are still writing articles at 11 PM? I’m even baffled by industries where you don’t need to keep learning after graduation — just how backward are they? You graduate university in your early twenties and still have decades to learn. It would be utterly strange to just stagnate there. Of course, I don’t like cutthroat competition, but I like standing still even less — especially in an age where just sitting on a stool spacing out causes the wealth gap to widen.
Finally#
The cover and illustrations for this post are all AI-generated. I just typed in “goodbye age of mediocrity” and the AI produced astonishing images. I don’t know exactly which industries or professions will disappear in the future, but at the very least, illustrators are going to have a hard time surviving in the AI era.
AI has already invaded the IT domain. As a DBA, which of our work patterns will be replaced? That’s a question worth pondering. Whatever happens, in this age, only learning can keep you competitive. I hope none of us will be the “disappearing shoulder pole porter.”