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AI Is Not a Wishing Well ​

AI is not a wishing well for the old guard, and certainly not their fig leaf. Tossing a coin into a wishing well and expecting miracles is no different from tossing a prompt into AI and expecting instant efficiency gains — both are lazy governance.

🤔 What Is the "Wishing Well Mentality" ​

In plain terms: The logic of a wishing well is "wish and it shall be" — toss a coin, murmur a prayer, and expect fortune to rain down and problems to vanish. The true mindset of most traditional power holders toward AI is exactly this wishing well mentality: spend big on an AI system, toss in a "help me cut costs by 20%" prompt, then sit back and wait for miracles.

Specific manifestations:

  1. The absentee landlord: Buy an AI system, issue a command, wait for results, complain when disappointed
  2. One-shot deal: Assume plugging in AI is the end of it — no need for continuous tuning and iteration
  3. Blame-shifting tool: When things go wrong, blame the AI, then buy a more expensive model and wish again
  4. Fig leaf mentality: Use AI to dress up poor decisions — "the AI recommended this approach"

🔍 Why AI Is Absolutely Not a Wishing Well ​

1. Wishing Wells Demand Miracles; AI Delivers Probabilities ​

The logic of a wishing well is "make it happen." The underlying logic of large language models is "predict the next token by probability."

If a power holder simply tosses in the annual report and commands "help me cut costs by 20%," what AI spits out will be a pile of correct-sounding platitudes and generic advice. Because it lacks the company's real-time data streams, the unwritten rules of organizational politics, and the delicate trust relationships across the supply chain.

AI is not an oracle. It is a super-scholar exceptionally good at "continuing the conversation" — it gives "the most plausible answer," not "the most correct answer." Imagine someone who has lived in a reading room their entire life, having read every book, watched every film, consumed every online video. You say one line, and they most likely respond with another. You say "moonlight before the window," they say "frost upon the ground." But ask them what the moon is — they know too. They've never seen it, so how? They read about it in books. So they know everything, and even when they don't, they'll fabricate something plausible. But they don't understand. They have no feelings. They've never stepped outside, never felt the wind, never been caught in the rain. They are a merciless, script-reciting fitting monster — nothing more.

📖 Further reading: Probabilistic Prediction — Understand why AI is a probability model, not an oracle

2. Wishing Wells Don't Take Responsibility; AI Needs a "Guardian" ​

When a wish at the wishing well doesn't come true, you blame fate. But when you use AI for decisions, the responsible party is always the decision-maker themselves.

AI can generate 100 marketing plans in 10 seconds, but:

  • Which plan do you dare to fund and launch?
  • Which plan crosses a regulatory red line?
  • Which plan threatens the interests of entrenched internal stakeholders?

These decisions require human judgment, risk tolerance, and political skill. If a power holder treats AI's output as imperial edicts and executes them directly, when things go wrong it's not that AI failed — it's that the decision-maker was lazy.

AI is like a magnifying glass — it amplifies and materializes the cognitive blind spots and strategic fuzziness of power holders at light speed. Throw in a vague instruction, you get a vague plan. Throw in a wrong assumption, you get an elegantly crafted mistake.

📖 Further reading: Hallucination — Understand why AI "confidently fabricates"

3. Wishing Is a One-Shot Deal; AI Is Endless Tuning ​

Toss one coin into the wishing well and you're done. But AI's productivity gains depend on continuous, high-intensity "human-AI alignment."

  • Today you feed it past financial reports, and it helps with analysis
  • Tomorrow the market shifts, and you must redesign your prompt framework
  • The day after, management changes, and you must recalibrate AI's "value" parameters

This requires a cadre of "translators" in the organization — people who understand both the business and the logic, who can translate fuzzy strategic intent into discrete, executable instructions for AI. Without these people, AI is just an expensive electronic decoration.

4. Why Are "Old Guard" Power Holders Especially Prone to Treating AI as a Wishing Well? ​

Because in the era when their careers were formed, power = information asymmetry. They commanded by knowing things others didn't.

AI's essence is information democratization — it makes "knowing what" cheap, and makes "asking what" and "choosing what" expensive. When power holders discover that AI knows more than all their middle managers combined, their first reaction is often to "command AI" the way they "command subordinates" — issue orders, wait for results, complain when unsatisfied.

This old-era "command-and-control" circuitry and AI's "generate-and-iterate" logic operate on entirely different dimensions.

📖 The Solow Paradox: History's Echo ​

Definition in One Sentence ​

"You can see the computer age everywhere but in the productivity statistics." — Nobel laureate Robert Solow, 1987

What's the "Paradox"? ​

By common sense, in the 1980s-90s, American companies were pouring money into computers and systems — IT investment growing by tens of percentage points annually. By economic formula, technology investment = output gains.

But the U.S. Labor Department's statistics showed that total factor productivity (TFP) growth hadn't risen — it was actually lower than in the 1970s!

Trillions of dollars spent, and the national accounts couldn't show the economy had improved. That's the paradox.

Three Core Causes ​

Cause 1: Measurement Bias — The Way We Count Changed

In the old era, statistics counted "tons of steel" and "pounds of wheat" — quality was fixed. What computers brought was "quality improvement" and "consumer surplus." For example, a 1980 bank ATM let you withdraw cash 24/7, saving you 2 hours of queuing at the bank. But those 2 hours of "convenience" don't enter GDP accounting. Statisticians only count "how many transactions were processed," not "how much time you saved." So productivity was severely underestimated.

Cause 2: Technology Lags — A Permeation Period Is Needed

General-purpose technologies (GPTs) need an "installation period." Companies bought computers, and in the first year used them only for word processing — just converting printed reports into PDFs (the "paperless office illusion"), with no efficiency gain. It took 10-15 years of permeation — until the internet proliferated and ERP systems matured — for productivity to surge.

Cause 3: Organizational/Business Model Mismatch — The Most Fatal Factor

Companies used new tools but followed old paths.

When early companies adopted electricity, it wasn't enough to swap the "steam-engine power shaft" for an "electric motor shaft." If factories still arranged all machines in a circle around a central power source — the "steam-era" layout — electricity's flexibility couldn't be unleashed. Only when factories redesigned into "assembly-line layouts" did electricity ignite the Second Industrial Revolution.

Similarly, in the 1980s banks installed computers merely to save a few typists, but approval processes still required layer-by-layer signatures. Only in the late 1990s, when "hierarchical management" was replaced by "flat risk-control models," did IT's productivity finally release.

How Was the Paradox Resolved? ​

Not through technological improvement, but through "a generation's departure." When the CEOs who grew up in the "paper-and-pen era" of the 1980s retired, and the new managers of the 1990s who "grew up playing with computers" took over, they intuitively knew how to restructure business with data flows. Business models and organizational structures fundamentally changed, and the productivity curve surged vertically between 1995-2000.

⚡ The Solow Paradox Repeats in the AI Era ​

Micro Gains, Macro Zero ​

Many companies are now experiencing the bizarre phase of "micro-level productivity gains, macro-level productivity zero" — employees save 3 hours a day using AI, but company profits haven't risen, because the saved time gets consumed by more pointless meetings.

Old production relations are devouring new productivity.

This isn't a technology failure — it's that management circuitry hasn't synced with AI's "generative" logic.

AI Is Faster Than Electricity, But the Danger Is Also Here ​

AI is faster than the electricity revolution because AI comes with built-in "organizational restructuring" reasoning capability — unlike computers that merely provide data, AI directly offers "decision suggestions." So companies don't need to wait for the old generation to retire; young people using AI can bypass old management layers and produce directly.

But precisely because AI is so fast, companies have no historical experience to follow. Many are repeating the mistakes of the 1980s: bought AI, followed old paths.

🌪️ When Production Relations Lag Behind Productivity: The Storm Arrives ​

The Core Contradiction ​

When production relations fall behind productivity, you immediately enter the storm between darkness and dawn.

In the electricity era, production relations were "centralized control" (centralized power plant supplying energy); in the AI era, productivity is "emergent" (large models generating). If companies still use KPIs to evaluate every step of AI's output, and old approval flows to gate AI's results, the essence is using "industrial-era management logic" to drive "digital-era biological brains."

Specific Manifestations ​

  1. Using KPIs to evaluate AI: AI generates 100 plans, KPIs only measure "quantity," not "quality"
  2. Using approval flows to gate AI output: AI produces a plan in 10 seconds, the approval process takes 3 weeks
  3. Using hierarchical management to drive AI: Frontline employees discover AI can solve problems, but must report up layer by layer before they can use it
  4. Using information asymmetry to maintain power: AI democratizes information, but management still commands by hoarding it

The Way Out: From Tree to Network ​

What companies need is not "plug in AI," but to flatten the organization from a "tree structure" into a "network structure" — giving frontline employees the "resource allocation authority" to invoke AI.

Otherwise, even the most powerful computing is just an expensive typewriter — just like the banks of the 1980s that bought computers only for word processing.

Magnifying Glass + Double-Edged Sword ​

AI is more like a "demon-revealing mirror" + "double-edged sword":

  • It reveals not the golden light of the future, but the "demonic aura" of internal process blockages, strategic fuzziness, and power infighting
  • Whoever treats it as a wishing well will discover within half a year: AI hasn't brought profits, but has made frontline employees more exhausted (because they spend massive time proofreading AI-generated garbage for power holders), middle management more anxious (because AI has stolen their "messenger" function), and top leadership more confused (because too much data makes decision-making harder)

🎯 Those Who Truly Use AI Well Never Make Wishes ​

Those who truly use AI well are like "animal trainers":

  • They personally dismantle business processes
  • They redefine role KPIs
  • They even dare to cut the legacy channels they once prided themselves on for the sake of AI's effectiveness

They don't toss coins into wishing wells. They dig channels and divert the water themselves.

⚠️ Common Misconceptions ​

  • ❌ Misconception 1: Buying an AI system means you've used AI well

    • ✅ Fact: Plugging in AI is only step one. Continuous tuning and organizational restructuring are the key. Buying a computer just for typing doesn't count as "digitization."

  • ❌ Misconception 2: AI isn't working — just switch to a more expensive model

    • ✅ Fact: The problem isn't the model; it's the organization. Making the same wish with a more expensive model only yields more elegantly crafted platitudes.

  • ❌ Misconception 3: AI will automatically help companies cut costs and boost efficiency

    • ✅ Fact: AI is an amplifier — it amplifies your strengths and your flaws. Vague instructions only yield vague plans.

  • ❌ Misconception 4: If employees save time with AI, the company must be more efficient

    • ✅ Fact: Saved time can be devoured by old production relations — more pointless meetings, more approval forms, more reporting PPTs.

  • ❌ Misconception 5: In the AI era, just wait for the old generation to step aside

    • ✅ Fact: AI is faster than the electricity revolution — you don't need to wait for a generation to retire. But you must proactively restructure the organization, or the storm period will be longer and more painful.

📅 Timeliness Note ​

📅 Last updated: 2026-06-24

AI and organizational change are both evolving rapidly:

  • Whether the Solow Paradox repeats in the AI era, data is still accumulating
  • AI implementation effectiveness varies dramatically across industries
  • Organizational change moves far slower than technology iteration
  • The contradiction between production relations and productivity is intensifying

🔗 Further Reading ​

Prerequisites ​

Deep Learning ​

💡 Tip: AI is not a wishing well, not a fig leaf — it's a demon-revealing mirror. It illuminates the real problems inside organizations. Whoever treats it as a wishing well will be awakened by reality — or shift blame to AI and continue wishing with a more expensive model. That may be the key watershed determining whether a company survives or dies.

📝 Content Creation Checklist ​

  • [x] Cross-verified (Solow Paradox historical data, electricity revolution analogy, organizational change cases)
  • [x] Organized and distilled core points
  • [x] Only factual claims, verified
  • [x] Reviewed
  • [x] Revised and refined for accuracy and reliability
  • [x] English version created

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