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In-depth Analysis of the Nine Major Schools of Economics: Understanding the Underlying Logic of How the World Operates Many people find economics obscure, dull, and full of formulas, seemingly far removed from everyday life. But the truth is quite the opposite. Every price fluctuation around you, every round of policy adjustment, every trade competition hides the underlying logic of different economic schools. The world we see today is the result of centuries of collision, iteration, and competition among the nine major economic thoughts. Today, we will explain these nine schools clearly in one article. Once you understand them, you will see society’s operation, market rules, and human nature from a completely different perspective. 1. Classical Economics: The Golden Rule of the Free Market The starting point of modern economics is Adam Smith’s "The Wealth of Nations." It established the core tone of classical economics: freedom, spontaneity, and self-healing. Imagine a city without government control or regulations, yet everyone has a job, food to eat, and a stable life. This seemingly contradictory picture is the core belief of classical economics: everyone pursues self-interest, ultimately benefiting the whole society. A baker bakes bread not to do charity but to make a living. Yet this selfish choice allows the whole street to enjoy warm bread. Merchants seek profit, workers labor for wages, and while everyone works for themselves, they silently support the operation of society. This is Adam Smith’s famous "invisible hand." Classical economists firmly believe the market has perfect self-regulation and needs no external intervention. When bread prices rise and profits increase, more bakers enter the market; competition intensifies, and prices naturally fall. When wages soar, many newcomers seek jobs; supply and demand balance, and wages return to reasonable levels. All fluctuations are automatically corrected by the market; any human intervention disrupts the balance. Building on this, David Ricardo further perfected the system and proposed the core theory defining global trade: comparative advantage. Even if a country outperforms others in all industries, trade is still necessary. England is more efficient than Portugal in producing both cloth and wine, but England is better at cloth, and Portugal is relatively better at wine. Each country abandons its weaker side and focuses on its strength, then trades to maximize mutual benefits. The ultimate conclusion of classical economics is that the market has a perfect order, and intervention is destructive. 2. Behavioral Economics: Overturning the "Everyone is Rational" Myth Classical and neoclassical economics are based on an idealized premise that humans are perfectly rational and calculate costs and benefits precisely. But in reality, humans are never cold calculators. In the 1970s, Daniel Kahneman overturned traditional views with extensive experiments and data, giving birth to behavioral economics. It revealed a truth: human thinking is biased, flawed, and habitual; our irrationality is predictable, summarizable, and exploitable. Everyday life confirms this. People impulsively buy expensive gym memberships at the start of the year, knowing they won’t persist. Losing 100 yuan hurts more than gaining 100 yuan feels good. People will cross half a city to save 5 yuan but ignore a 5 yuan price difference on a several-hundred-yuan item. They buy unnecessary goods just because of a 50% discount. Our consumption, decisions, and judgments are always driven by emotions, biases, and habits, not absolute rationality. More importantly, behavioral economics not only exposes problems but also offers practical solutions, already integrated into modern social rule design. Organ donation uses default registration, requiring no active sign-up but voluntary opt-out, greatly increasing donation rates. Pricing items at 9.9 yuan instead of 10 yuan uses visual illusions to reduce consumer resistance. Hotels inform guests that 80% reuse towels, leveraging social proof to guide behavior. The core of behavioral economics is to accept human irrationality and optimize choices through rules rather than idealize human nature. 3. Marxist Economics: Seeing Through Exploitation and Class Behind Capital While classical economics indulges in a harmonious, self-balancing market vision, Marx saw the harshest truth of the market. Consider a simple calculation. You work 8 hours in a factory, producing 10 items daily, each sold for 50 dollars, creating 500 dollars of value, but your wage is only 80 dollars. What happened to the remaining 420 dollars? Marx believed this was not accidental or an error but systemic appropriation, the core concept of surplus value. Workers create most value; capital owners appropriate the surplus profit. Society is divided into two opposing classes: workers and capitalists. This silent class struggle is the underlying logic of capitalism and the root of growing wealth inequality. Marx compared capitalism to a pressure cooker. The faster the economy grows, the more monopolized capital becomes, the greater the wealth gap, and the stronger the social contradictions and tensions. When pressure reaches a critical point, the old order inevitably collapses. Thus, he proposed historical materialism to change the world. Feudalism was replaced by capitalism as an inevitable era. Similarly, capitalism will eventually be surpassed by a fairer system, ultimately eliminating private capital, class antagonism, and exploitation. The core of Marxist economics is that the market is never harmonious; capital’s essence is harvesting labor value. 4. Game Theory: The Survival Game of Mutual Checks and Balances Previous economics mostly studied isolated markets and rational individuals. Game theory studies the checks and balances and confrontations between people and opponents. The classic Prisoner’s Dilemma reveals the absurdity of human nature and competition. You and your accomplice are held separately, unable to communicate, and can choose to betray or stay silent. Rationally, each prioritizes self-preservation and chooses betrayal, but the result is both get heavy sentences, a lose-lose outcome. Individual rationality leads to collective disaster. This is game theory’s most disruptive insight: success or failure depends not on what you do but on what your opponent does. Life, business, and international affairs are full of Prisoner’s Dilemmas. Competitors could cooperate to maintain prices and profit together but fear price cuts and market loss, leading to cutthroat price wars and shrinking industry profits. Countries could cooperate on environmental protection and development but fear free-riding and losses, resulting in delays and shirking. Arms races, business competition, and public opinion confrontations share the same logic. Game theory’s core breakthrough is the Nash equilibrium. At this balance point, as long as others don’t change strategies, no one can benefit by unilaterally changing their choice. Everyone is stuck in fixed choices, knowing the outcome is bad but no one dares to break the deadlock first. Today, game theory has transcended economics, deeply influencing politics, military, business, and social interactions, becoming a universal tool to interpret cooperation, competition, and conflict. 5. Public Choice Theory: Breaking the "Government Serves the Public" Illusion Our ingrained belief is that businesses pursue profit selfishly, while governments are neutral, fair, and serve the public interest. Public choice theory shatters this idealized assumption. James Buchanan and Gordon Tullock proposed the most realistic view: politicians and bureaucrats are also self-interested ordinary people. Politicians seek votes and terms; bureaucrats seek bigger budgets and more power. Most policies are driven not by public interest but by personal and group interests. This is the most common social imbalance: concentrated benefits, dispersed costs. A universal subsidy policy costs each citizen only 20 dollars annually but benefits a few industries and interest groups by millions. The public barely notices or opposes, but interest groups lobby fiercely to maintain policies. Ultimately, a small group hijacks the interests of the vast majority. Many regulations, subsidies, and industry rules appear to protect the public but actually protect monopolies, nurture interest groups, and eliminate market competition. Public choice theory offers a remedy: limit government power and introduce competition among governments. Power cannot expand indefinitely; people can "vote with their feet" by moving to fairer regions if taxes are too high or rules unreasonable. The core of public choice theory is not to overestimate human nature or sanctify power; all actors pursue self-interest. 6. Neoclassical Economics: Redefining Value with Marginal Utility At the end of the 19th century, to solve the value paradox of classical economics, neoclassical economics emerged, completely reconstructing human understanding of value. The classic water-diamond paradox puzzled scholars for years. Water is essential for life, irreplaceable in value, yet cheap and abundant. Diamonds have no practical use, are luxury goods, yet are as expensive as gold. Classical economics couldn’t explain this; neoclassical economics provided the answer. Value is not determined by the item itself but by people’s subjective feelings and marginal utility. Marginal utility means the value of an item depends on the satisfaction the next unit brings. The first slice of pizza is delicious and brings great happiness; the third slice is bland; the seventh makes you feel sick. Water is everywhere; marginal utility diminishes infinitely; one more or less cup makes no difference, so it’s cheap. Diamonds are extremely scarce; each additional one brings high satisfaction and value, so they are expensive. Neoclassical economics thus established the underlying logic of the modern market. Consumers seek to maximize satisfaction; businesses seek to maximize profit; supply and demand compete, automatically forming price equilibrium. Its core model is perfect competition, which rarely exists in reality but serves as a compass to judge market imbalance, monopoly, or failure. Since then, supply-demand relations and marginal utility have become the universal language of economics. 7. Keynesianism: Markets Can Fail, Government Must Backstop Classical economics believed markets always self-heal, but the Great Depression shattered this myth. Economic collapse, factory closures, millions unemployed, famine spreading; the market not only failed to self-repair but fell into a vicious cycle and continued decline. Keynes proposed a revolutionary theory: markets can fail; laissez-faire cannot save the economy; human intervention is needed. He proposed the famous paradox of thrift. Individuals saving money and living frugally is rational self-protection. But if everyone saves simultaneously, avoids consumption and investment, societal demand collapses, and the economy halts. Individual rationality causes collective destruction. Keynes likened government to firefighters: no intervention needed normally, but when the market is on fire or the economy recesses, active firefighting is necessary. When businesses hesitate to invest, the government supports infrastructure and creates jobs. When residents hesitate to consume, policies stimulate demand and revive the market. Through the multiplier effect, every government dollar circulates through wages, consumption, business income, and economic gears, gradually resolving the crisis. For Keynes, austerity during recession worsens the situation; counter-cyclical stimulus is the solution. This is the core theoretical basis for modern countries’ counter-cyclical regulation, infrastructure-driven growth, and interest rate cuts to stimulate the economy. 8. Supply-Side Economics: Activating Producers is the Core of Growth Keynesianism focuses on stimulating demand, while supply-side economics, born in the 1980s, advocates activating supply and empowering producers. Its core view is that economic downturns stem not from lack of consumption but insufficient production incentives and weak enterprise vitality. The iconic Laffer Curve was born on a napkin. Excessively high tax rates shrink corporate profits and residents’ incomes; people are unwilling to work, invest, or even evade taxes, causing total tax revenue to fall. Moderate tax cuts reduce burdens, activate investment, entrepreneurship, and employment, leading to overall economic growth, expanding the tax base, and increasing total tax revenue. This gave rise to the trickle-down effect. Tax cuts increase corporate profits, expand production and employment, raise residents’ incomes, upgrade consumption, and promote overall prosperity. Theoretically, this is a perfect positive cycle. In the 1980s, the U.S. implemented large-scale tax cuts, boosting short-term growth but causing soaring fiscal deficits and widening wealth gaps, forcing the government to raise taxes again, shattering the myth of tax cuts’ omnipotence. Supply-side economics proves activating production is the foundation of growth, but practice shows tax cuts alone cannot solve all economic problems. 9. Development Economics, Austrian School, New Institutional Economics: The Ultimate Code to Understand Wealth Gaps 1. Development Economics: Poverty’s root is never lack of money Many think poor countries are poor because of lack of funds and factories. But reality offers counterexamples. Argentina was wealthy a century ago but stagnated into poverty. Singapore, starting from scratch and lacking resources, rose to developed status. Development economics reveals the truth: wealth gaps stem not from capital but from institutions, education, infrastructure, rules, and social structure. Farmers can’t afford fertilizer, yields are low, savings scarce, and poverty persists—a poverty trap. Without education, healthcare, infrastructure, and with corrupt institutions and chaotic rules, no amount of investment or aid can achieve sustainable growth. Breaking poverty cycles requires more than money; it needs microcredit, education subsidies, basic livelihood improvements, and institutional reforms. True prosperity is built bit by bit on rule of law, security, education, and fair rules. 2. Austrian School: Rejecting formulaic economics, respecting market spontaneous order While mainstream economics obsesses over formulas, curves, and data models, the Austrian School states economics is not algebra; human hearts and choices cannot be quantified or predicted. Its core economic cycle theory overturns conventional wisdom. Economic crises are not market failures but consequences of human market interventions. Central banks artificially lower interest rates and release easy money; markets seem prosperous; businesses blindly invest and expand. When money tightens and rates normalize, bubbles burst, projects collapse, and crises arrive as expected. This school firmly opposes central planning and excessive regulation. No bureaucrat or system can predict billions’ needs or plan everyone’s life. Market prices are the truest signals, aggregating everyone’s demands, preferences, and expectations, forming spontaneous order. Any human manipulation disrupts balance. Less intervention means more freedom and efficiency. 3. New Institutional Economics: Institutions are the ultimate dividing line between rich and poor nations Two countries with identical resources, climate, and population can diverge over decades, one rich and strong, the other poor. New institutional economics says the difference is institutions. All market transactions have costs: negotiation, contracting, enforcement, supervision consume resources. Laws, companies, rules, and social systems exist to reduce transaction costs and improve efficiency. Without institutions, making a movie requires separate contracts with hundreds of workers, making it costly and unfeasible. Without rule of law, private property is unprotected, contracts unenforceable, rules change arbitrarily; capital won’t invest, and industries can’t root. It also proposes path dependence: once an inefficient institution forms, it gains inertia and is hard to reform despite flaws. This explains why Silicon Valley leads tech long-term and why some countries remain poor despite aid. The real gap is not money or resources but trust, rules, rule of law, and stable institutions. Conclusion The nine major economic schools have no absolute right or wrong; they fit different eras, scenarios, and social stages. Classical economics shows us the power of the free market. Behavioral economics makes us face human irrationality. Marxist economics reveals the essence of capital and class. Game theory helps us understand the logic of competition and checks and balances. Public choice theory breaks the illusion of power. Neoclassical economics defines the underlying rules of value and supply-demand. Keynesianism teaches us that crises need backstops and regulation. Supply-side economics points out that production vitality is the foundation of growth. Development economics, Austrian School, and new institutional economics ultimately show us that institutions, order, and human nature are the ultimate answers to economics.

Top 20 Must-Have Skills in the World, Young People Use AI More 1. superpowers (by obra), starred 302K at the time It breaks development into four steps: brainstorming, planning, test-driven, code review, forming a complete development process. Install it before working on medium to large features; AI won’t just start writing code randomly. 2. skills (by mattpocock), starred 298K at the time When requirements are unclear, it will ask you questions to help clarify the architecture. Best used for debugging and refactoring legacy projects. 3. andrej-karpathy-skills (by multica-ai), starred 221K at the time A set of project rules: AI thinks carefully before acting and only changes what needs to be changed. Place it in the project root directory; it takes effect while coding. 4. skills (by anthropics), starred 182K at the time Official skill library that works directly with Word, PPT, spreadsheets, and PDFs. To write your own Skill, just modify its template. 5. ponytail (by DietrichGebert), starred 168K at the time Makes AI think like the laziest senior engineer: avoid unnecessary abstractions. After coding, let it review to identify over-design and technical debt. 6. ui-ux-pro-max-skill (by nextlevelbuilder), starred 138K at the time Includes complete color schemes, font pairings, and interaction guidelines. Install before making landing pages, backends, and app interfaces to make pages look more like a designer’s work. 7. graphify (by Graphify-Labs), starred 129K at the time Converts code, documentation, and database structures into a knowledge graph. When taking over unfamiliar projects, build the graph first, then ask "Who calls this function?" to quickly understand. 8. caveman (by JuliusBrussee), starred 114K at the time Makes AI answer with the fewest words, cutting out verbose explanations. Install when running agents for a long time to save a lot of tokens. 9. agent-skills (by addyosmani), starred 109K at the time Engineering standards compiled by Google engineers covering interface design, context management, and quality checks. Use it for consistent coding style in team projects. 10. taste-skill (by LeonxLnx), starred 99K at the time Adds aesthetic sense to AI: typography, reference images, and animations are all considered. Use it for homepage and portfolio design to avoid templated looks. 11. Understand-Anything (by Egonex-AI), starred 87K at the time Turns code into clickable interactive graphs and explains business processes clearly. Saves time for newcomers onboarding projects or reading open-source code. 12. impeccable (by pbakaus), starred 83K at the time Specifically enhances AI’s design skills by checking spacing, hierarchy, and details. Let it review pages first, then polish according to suggestions. 13. awesome-claude-skills (by ComposioHQ), starred 77K at the time A skill collection with over 800 Skills categorized by scenarios. If unsure what to install, start here. 14. last30days-skill (by mvanhorn), starred 65K at the time Gathers discussions from Reddit, X, and YouTube from the last 30 days. Use it for topic selection, trending research, and competitor analysis. 15. humanizer (by blader), starred 59K at the time Removes AI traces from text, such as clichés and fixed phrases. Use it to polish public account posts, voice scripts, and emails. 16. diagram-design (by cathrynlavery), starred 58K at the time Supports over 40 chart types and outputs directly as web files. Use it to draw architecture and flowcharts for technical documents and presentations. 17. i-have-adhd (by ayghri), starred 58K at the time Makes AI give conclusions and next steps first, then details, avoiding answers buried in long paragraphs. Install it for technical questions when you want quick results. 18. marketingskills (by coreyhaines31), starred 56K at the time Covers marketing skills like copywriting, SEO, and data analysis. Use it for writing promotional copy and improving website search rankings. 19. academic-research-skills (by Imbad0202), starred 53K at the time Links the whole process from literature review, paper writing to peer review simulation. Useful before writing theses or submitting to journals. 20. scientific-agent-skills (by K-Dense-AI), starred 53K at the time Turns AI into a research assistant by connecting to databases and tools in biology, chemistry, and other fields. Use it for experiment analysis and professional data lookup.

Every year there are scary stories about the US stock market crashing. Will this time also be a close call without disaster? 01) Scary stories every year: A chronicle of crashes in the past five years If you stay in the market long enough, you’ll notice a pattern: every year the market tells a terrifying ghost story. The stories change every year, but the endings are surprisingly similar. Looking back over the past five years, there’s almost one major hurdle each year: 2022 was the worst year, with violent interest rate hikes plus the Russia-Ukraine war. The outbreak of the Russia-Ukraine war coincided with aggressive Fed rate hikes—a geopolitical black swan combined with a total liquidity tightening, a classic "Davis double whammy" on a macro scale. The Nasdaq fell more than 30% from its peak, global stocks and bonds both took a hit, and countless balance sheets were permanently reshaped that year. The pit dug in 2022 was the deepest in five years. In 2023, the scary story shifted to Silicon Valley Bank. A bank with $200 billion in assets collapsed in just three days, Signature Bank followed suit, and Credit Suisse was urgently sold off at a discount. The market briefly believed the "Lehman moment 2.0" had arrived, with autopsy reports on regional banks flying everywhere. But what happened? The Fed overnight launched the BTFP tool, opened the liquidity valve, and the panic vanished within three months. In 2024, it was the turn of the yen interest rate hike. The Bank of Japan ended the negative interest rate era, triggering massive unwinding of carry trades. On August 5, the "Black Monday," the Nikkei index plunged 12% in a single day, and global markets bled heavily. The panic index surged at a speed only comparable to March 2020 and October 2008 in history. Yet three weeks later, the market acted as if nothing had happened. In 2025, the scary story came in two parts. The first half was Trump's tariff war, with "reciprocal tariffs" emerging suddenly and doomsday theories about global supply chains rampant; the second half exposed an internal AI financing scam, revealing tricks like computing power leasing and recurring revenue, causing the market to question the financial authenticity of the entire AI narrative. Both times made the Nasdaq shudder, but neither ended the bull market. This year, 2026, the story is again in two parts. The first half is the US-Iran war—decapitations, retaliation, the shadow over the Strait of Hormuz, and oil prices soaring; the second half is the midterm election shift, with Democrats controlling both chambers, starting to crack down on data centers, undermining the faith in computing capital expenditures. The US stock market is once again pushed underwater. 02) Why does every scare end without disaster? Every ghost story leaves a big pit in the market. Some pits are deep and long-lasting, like in 2022, which took nearly two years to fill; some are shallow and short, like in August 2024, where the market climbed out in three weeks. But without exception—the US stock market always recovers, continues to rise strongly, repeatedly hits new highs, and extends the bull market. This is not luck; there are two solid logics behind it. The first logic is the new global investment and innovation cycle brought by the AI revolution. This is not an ordinary inventory cycle or equipment upgrade cycle, but a superposition of the Juglar cycle and Kondratiev wave—a decade-level capital expenditure wave nested within a once-in-five-or-six-decades technological revolution long wave. In human history, every round of general-purpose technology diffusion—steam engine, electricity, internet—has triggered epic investment and growth, and AI is the culmination. Productivity is improving, consumption is expanding, and corporate profits are genuinely rising step by step—this is the strongest foundation for the bull market. As long as this underlying narrative is alive, every crash is just a deep squat in the middle of the bull market. The second logic is the Fed’s massive liquidity injection since the pandemic, which has raised the overall water level of global asset pricing. Huge liquidity has settled in the financial system with nowhere better to go. Every panic seems like money fleeing risk, but in reality, it’s just money briefly repositioning within risk assets. When the water is deep enough, the boat won’t sink. 03) Will this time also be a close call without disaster? So, the core question: will the market, like the past five years, have a close call and then resume the bull run? My answer is: it depends on the market; it cannot be generalized. The probability of the US resuming a bull market is very high. There are two reasons. First, the AI technological revolution itself is real; what was killed off were valuation bubbles and disorderly capital expenditures, not the big trend of technology diffusion—data center construction will slow and become more regulated but will not stop, because the real growth in computing demand is there. Second, the Democrats’ crackdown is "for high-quality development," not to bury the market—the average American invests in stocks, and Democratic elites also invest in stocks; both parties have a fundamental common interest in maintaining capital market stability. Historically, every "left-wing correction" pit turned out to be a golden opportunity afterward. The 1929-level exception required the technology cycle to have already declined, which is clearly not the case today. But for other markets, the outlook is less optimistic. Markets like South Korea and A-shares will likely gradually bottom out and oscillate upward—after all, the global environment of easing liquidity, war ending, and tariff rollback is improving. But note, they are experiencing a fierce bear market, and after a fierce bear, usually only a weak bull market follows. Breaking previous highs will face considerable pressure. The reason is not hard to understand. The US bull market has the "global pricing power of the technological revolution" as an anchor, so when it falls, there are buyers; while valuations in other markets largely parasitize the US AI capital expenditure chain—Korea’s memory, China’s computing hardware export chain, essentially all "supplying US data centers." When the chain leader starts cutting spending and clearing bubbles, the profit expectations of chain members must be systematically revised downward. Valuation pits can be filled by liquidity; profit pits can only be filled by time. Returning to the title question: will this time also be a close call without disaster? For the US, very likely yes—the long cycle of the technological revolution is not over, the flood of liquidity has not receded, and after panic clearing, the mid-bull whistle will sound again. For other markets, it’s "there will be scares, but the danger won’t fully disappear"—they will bottom out and warm up, but jumping directly from a fierce bear to a raging bull is unrealistic. For five years, the market has told ghost stories every year, with people selling out every year, only to regret it every year. What this teaches us should not be "mindless bottom-fishing," but to distinguish what is a ghost story at the cycle bottom and what is a death knell at the cycle top. There is only one standard for identification: has the underlying technological revolution and profit cycle died? As long as the answer is no—then every ghost story is an opportunity disguised in a terrifying outfit. But if one day, the protagonist in the ghost story is no longer liquidity, politics, or war, but the technological revolution itself has gone out—then the real bear market will begin.

Trading U.S. stocks requires understanding political calculations The most common misconception when playing the U.S. stock market is treating it as a calculation problem composed solely of earnings, interest rates, and liquidity. As if an upward revision in earnings should automatically lead to a stock price increase. However, the real ledger is not just one book. Companies calculate profits, traders calculate volatility, and a higher level of power calculates seats, agendas, and votes. Several ledgers overlap, and the final price that emerges may not align with any earnings forecast but often matches another, larger report. November 5th is a case in point. The S&P 500 dropped 4.36%, the Philadelphia Semiconductor Index fell 12.25%, Nvidia alone lost $447 billion, and the entire market evaporated about $3 trillion. Looking only at earnings, this is hard to understand. AI demand hasn’t changed, capital expenditure plans haven’t changed, what changed was the election two days earlier. The Democrats took both houses of Congress and also won Texas. The incoming Texas governor publicly demanded an immediate halt to data center development, and the newly elected legislators and governors all campaigned on slowing AI infrastructure. The market realized one thing overnight. Half of AI’s valuation story is built on the rapid expansion of data centers, and half of the expansion switch is held by politicians. Many things that are hard to understand from an earnings perspective become straightforward when viewed politically. Building data centers has never been just a technical issue. Where computing power is built determines whose land it occupies, whose power grid bears the electricity cost, and whose local government grants tax incentives. After production starts, stock price gains go to shareholders, while electricity cost pressures fall on local residents. No one accounted for this ledger in the past two years because rising stock prices masked cost allocation during the bull market. Once earnings expectations waver, cost allocation immediately becomes a public issue. The governor’s call to pause is not a lack of technical understanding but a reading of voter sentiment. This is not the end of capital expenditure. It is a fixed pattern in the middle stage of a technological revolution. Railroads were like this, electricity was like this, and the internet was like this. The first half focuses on efficiency, with regulation making way for innovation. The second half focuses on distribution, politics enters the scene, demanding a redivision of the bill. Recognizing this rhythm prevents mistaking a single shout for the end of the world or naively thinking that technological narratives can forever avoid political pricing. Brothers, let’s be clear. This reckoning is not here to bury the bull market. Politicians’ own pensions rise and fall with the market, and both parties have a strong interest in maintaining market operations. The goal of political calculations is not to kill this bull. It only wants a bigger share, exchanging some profits for votes and fiscal revenue. Meanwhile, liquidity calculations must also be watched. The Fed restarted rate hikes in September, and two regional Fed presidents recently hawkishly hinted at possible continuation in December. Political calculations determine the discount rate, liquidity calculations determine the denominator. When both tighten simultaneously, earnings optimism no longer holds exclusive explanatory power. What truly determines investment difficulty is this misalignment between ledgers. Most people study P/E ratios; a few study constraints. Most watch earnings calendars; a few watch hearing schedules. During windows when political variables dominate pricing, the latter at least know when not to mistake luck for a pattern. Thinking a big enough drop means cheap, and thinking a long enough rise means safe, are two sides of the same naive coin. Retail investors’ situation can also be viewed through this relationship. Formal market access does not automatically bring substantive information equality. Policy signals first reach lobbying firms’ briefings, then financial media headlines, and only finally the comment sections of market software. By the time the public narrative becomes clear, prices have often completed most of their adjustment. Watching U.S. stocks ultimately cannot be just about economic calculations. Computing power superficially is demand, but deep down it’s the power grid. Valuation superficially is earnings, but deep down it’s discounting. Elections superficially are seats, but deep down it’s distribution. The two often share a language but serve different goals. Being able to distinguish this doesn’t guarantee profit but at least reduces naivety. Knowing some pullbacks are not risk clearings, some rebounds are not trend recoveries, and some big pictures are ultimately bought by countless small accounts pooling money.

How exactly is big money made? 1. Big money comes from calming down and focusing on doing just one thing; wealth does not come through haste. You can learn anything, but mastering one method leads to mastering all methods. You must deeply learn the essence of one field, understand its methods and principles, and immerse yourself obsessively to become an expert in a small niche. Then everything connects—it’s the universality of methodology. 2. The human brain has a characteristic: it focuses best over long periods, continuously learning, which also means researching and pondering daily. Adults often burden themselves with many constraints, thinking they must do this and that at the same time. As you age, you should calm down. 3. Focus on one thing, one direction, and think about it every day, ponder it daily. Don’t waste energy on unrelated matters like brain-computer interfaces or the world’s richest person—why bother? 4. If you don’t know anything, then become a boss. 5. If you have no money, become a middleman; let others do the time-consuming, laborious, and costly work while you take the middle cut. 6. No matter how big the business, it’s not related to your capital size but to your ability and mindset! 7. The essence of the lottery is fiction—something gained without labor, enticing those who want to get rich without working, thereby supporting those who truly get rich without working. 8. This applies to any business: to be a boss, you must know how to set the stage. No matter the industry, there’s money to be made. Without knowing how to set the stage, whatever industry you’re in, you’ll just be cannon fodder. 9. Wealth breaks the ruthless, emotion traps the soft-hearted. Without love, you can break emotional traps; without emotion, you can break the whole game. 10. Healthy debt is leverage. Reasonably using low-interest bank funds to amplify cash flow is safer than hoarding cash and offers greater chances to leverage bigger returns. 11. 90% of wealthy people’s first pot of gold isn’t "glamorous"; most have ridden the waves of their era’s dividends and walked through gray areas, though they never admit it publicly. 12. When in a place, follow local customs and respect the rules. Building good relations with local power players isn’t being slick; it’s finding survival and development gaps outside the rules. 13. Don’t go into business with beautiful women. Emotion and profit naturally conflict; beauty easily distracts and invites unnecessary trouble. 14. The rich mostly earn money from ordinary people. The information gap in lower-level cognition is the easiest to be marketed to and exploited. 15. The essence of making money is competition. Past, present, and future won’t change this: if you’re weak, others will be strong; if you don’t fight for it, you’ll be taken advantage of. 16. People who wear luxury brands to talk business mostly lack real strength. True big shots rely on strength, not logos to impress. 17. Those who can’t let go of face will never make big money. The top adult skill is to bow, show weakness, and negotiate cooperation. 18. Big money is never made slowly. Real class leaps often come from seizing one or two windfalls and opportunities, making in one or two years what others make in a lifetime. 19. Never believe "others will teach you how to make money." The core of making money is profit; no one will hand you their livelihood. The real path can only be explored by yourself. 20. If you understand the first nineteen but still can’t do it, there’s one last simple way: control spending, invest regularly in the Nasdaq, and hold for ten years. No need to set the stage, be ruthless, or exploit information gaps. Compound interest is the only stable way for ordinary Chinese people to turn the tables.

Why do Democrats dislike AI so much? The U.S. has been quite wild lately. Silicon Valley is frantically building data centers, Nvidia is busy selling chips, and OpenAI, Google, and Anthropic are competing to train smarter models. According to the tech industry, humanity is standing at the doorstep of the next industrial revolution. On the other hand, some heavyweight Democrats have started to put the brakes on AI. Bernie Sanders advocates pausing the construction of new data centers, AOC showcased polluted water samples around data centers in Congress, and Elizabeth Warren proposed taxing AI companies and data centers. In the 2026 U.S. midterm elections, opposing data center expansion has even become a campaign weapon for some Democratic candidates. A Pew Research Center survey in September this year showed that 56% of Democrats are increasingly worried about the spread of AI, and 75% of Democrats believe AI will reduce jobs. Why are Democrats so worried about AI? What’s the big deal?? Traditional Democratic supporters include union workers, teachers, doctors, government employees, as well as many college-educated white-collar and professionals. AI is precisely starting to affect these people's livelihoods. Previously, factory automation mainly replaced repetitive tasks on assembly lines. Now AI can write code, design, organize contracts, write reports, even participate in research, and perform surgeries on living humans. A job that used to require ten people in a company might gradually be done by three or four people working with AI. For ordinary employees, when the boss announces that AI has improved productivity, it sounds like good news, but when layoffs start in the department, the feeling is completely different. AI is very smart, but it has a particularly big drawback: it consumes a lot of electricity. Those data centers that look like warehouses are packed with servers that require power to run and water for cooling. Tech companies gain computing power, but local residents may start worrying about electricity bills, water resources, and the environment. This is easy to understand: a community originally wanted job opportunities, but instead got a huge server warehouse, and the first thing residents feel might be the electricity bill. This is also why opposition to data centers crosses party lines; even some Republican voters do not welcome them. Among this, the conflict between new money and old money is no secret. The "old money" here does not refer to a few wealthy families but the social order represented by the traditional elite class and the Democratic Party’s value system. Old money usually cherishes scarcity. Prestigious school diplomas, professional qualifications, artistic cultivation, and social networks are all thresholds that require long-term accumulation. A person's social value largely comes from these hard-to-replicate assets. AI happens to excel at lowering these barriers. Previously, hiring someone to write a decent report cost money, creating a professional image required skills, and entering certain knowledge fields required long learning. Now, ordinary people can open a computer and get professional assistance that used to be expensive. This makes some people in the Democratic camp who rely on knowledge barriers and professional qualifications uncomfortable. More troublesome is that behind AI stands a group of new wealthy individuals who own computing power, capital, data, and platforms, rapidly gaining the ability to influence social rules. Progressive Democrats see a familiar picture here: a few billionaires take the technological dividends while ordinary workers bear the costs of transformation. So, anti-AI sentiment includes both worker protection and old elites’ vigilance against new power. It’s time to pour out the milk. So, what would happen if AI development really stopped? In the short term, chip orders, data center investments, and power infrastructure would be affected, and the valuation of the AI industry chain would need to be recalculated. In the long term, scientific research, drug development, industrial design, and improvements in corporate productivity might slow down. Stanford University's 2026 AI Index already shows AI entering natural science research, with over 90% of important cutting-edge models developed by industry. Stopping development would impact far more than just losing a chat tool; many industries would be directly affected. If the U.S. stops but China and other countries do not, the U.S. might hand over its talent, capital, and technological leadership. Of course, a pause does not mean AI will immediately disappear. Existing models can still be used today, and companies can still deploy current technologies. The most worth observing aspect of this debate is that American society is beginning to realize that technological progress itself does not automatically solve the problem of benefit distribution. AI can make the pie bigger, but who cuts the pie remains a matter of power distribution, a political issue, the core issue, and the greatest problem human society has faced for thousands of years. What Democrats dislike may not be AI itself, but a future where a few tech giants set the rules and everyone else can only passively accept the results. However, those trying to hit the pause button on AI must also answer another question: if others keep running forward, are you really willing to stop the whole society with them?