Tales From the MoJo Road –
By Glynn Wilson –

COULTERVILLE, Calif. – Why do some people, even many experts, fear the coming Artificial Intelligence Revolution? While some people, even some whose professions are at risk, seem ho hum about it?

For eons of time, but mainly in the past 250 years, humans invented new technologies that replaced old technologies, and changed the world at the time.

Think of the horse and buggy being replaced by the automobile. At the turn of the 19th century, there were 21 million horses in the U.S. and only about 4,000 automobiles. By 1915, the carriage industry had been decisively overtaken by the automobile industry. As late as 1935, there were still about 3,000 buggies manufactured each year for use in rural areas. Some are still used in movies and Amish country, where people still try to live on primitive technology.

And of course some cowboys still ride horses to herd cows, and there’s actually some open range left, on federal land anyway.

The steam engine was replaced by the diesel engine on ships and trains after steam largely replaced sails on ships. But people still sail for fun.

Electric lights replaced coal, oil and gas lighting. The telephone replaced the telegraph. Cell phones replaced land line phones. Computers replaced typewriters. Email and then instant messages replaced letters in the mail.

Movie and show streaming services are replacing movie theaters, although there are still a few theaters around.

Wikipedia has replaced encyclopedias, as digital books are replacing printed books and online news outlets are replacing newspapers and magazines.

Of late, there are all kinds of controversies stirring about what professions and jobs will be rendered obsolete by Artificial Intelligence.

As one friend said when were discussing this in messages:

“It’s not going anywhere… like when the web came out …”

Then he said:

“I just had a flash of what the future will look like. We won’t have jobs – so humans, by the millions, will have to carry pails of seawater to the nearest desalination plant to make pure water for the bots. And there will be huge areas of the west where these HUGE buildings with like millions of people pumping away on bikes that generate the electricity to run the bots too. People will all look like Arnold Schwarzenegger or be recently un-alived from being over worked.”

I don’t know about that. Not me, baby. I’ll just hike up the mountain to Hetch Hetchy or bum a ride and see if I can find the spirit of John Muir there. He was no racist. That’s a lie.

The New York Times keeps finding these so-called experts to quote on what the future might look like. I’m keeping up with them all. In Monday’s paper, they quoted a computer scientist named Yann LeCun, who has a reputation as one of the world’s leading experts on artificial intelligence, and has a penchant for throwing verbal grenades. He was one of three pioneering researchers who received the Turing Award, often called “the Nobel Prize of computing,” for their work on the technology that is now the foundation for modern A.I.

For more than a decade, he also served as chief A.I. scientist at Meta, the parent company of Facebook and Instagram. But after leaving Meta in November, they report, Dr. LeCun has become increasingly vocal in his criticism of Silicon Valley’s single-minded approach to building intelligent machines.

“He argues that the technology industry will eventually hit a dead end in its A.I. development — after years of work and hundreds of billions of dollars spent. The reason, he said, goes back to what he has argued for years: Large language models, or L.L.M.s, the A.I. technology at the heart of popular products like ChatGPT, can get only so powerful. And companies are throwing everything they have at projects that won’t get them to their goal to make computers as smart as or even smarter than humans.”

“There is this herd effect where everyone in Silicon Valley has to work on the same thing,” he said during a recent interview from his home in Paris. “It does not leave much room for other approaches that may be much more promising in the long term.”

Not long after ChatGPT was released, the two researchers who received the 2018 Turing Award with Dr. LeCun warned that A.I. was growing too powerful. Those scientists even warned that the technology could threaten the future of humanity. Dr. LeCun argued that was absurd.

LeCun also helped push Meta and its rivals to freely share their research through academic-style papers and so-called open source technologies.

As more people said A.I. could be a threat of some sort to humans, a number of companies curtailed their open source efforts. But Meta kept going. LeCun repeatedly argued that open source was the safest path. It meant that no one company would control the technology and that anyone could use these systems to identify and fight potential risks.

Now as a number of companies, including Meta, appear to be moving away from that method because they want an edge over their rivals and continue to worry about dangerous uses, LeCun is warning that American companies could lose their lead to Chinese rivals that are still using open source.

“This is a disaster,” he said. “If everyone is open, the field as a whole progresses faster.”

This may be a good thing. The entire enterprise needs to be slowed down and regulated.

Meta’s A.I. work ran into a snag last year, the Times reports. After outside researchers criticized the company’s latest technology, Llama 4, and accused Meta of misrepresenting the power of the system, Mark Zuckerberg, Meta’s chief executive, spent billions on a new research lab dedicated to the pursuit of “superintelligence” — a hypothetical A.I. system that exceeds the powers of the human brain.

Six months after the creation of the new lab, Dr. LeCun left Meta to build his own start-up, Advanced Machine Intelligence Labs. The problem with current systems, he said, is that they do not plan ahead. Trained solely on digital data (largely stolen), they do not have a way of understanding difficulties in the real world.

“L.L.M.s are not a path to superintelligence or even human-level intelligence. I have said that from the beginning,” he said.

Part of LeCun’s argument is that today’s A.I. systems “make too many mistakes. As they tackle more complex tasks,” LeCun argued, “mistakes pile up like cars after a collision on a highway.”

Like Google’s self driving Waymo taxis that broke down during a power blackout in San Francisco.

NAJ: Google’s Waymo Taxis Mysteriously Die During Power Blackout

NYT: An A.I. Pioneer Warns the Tech ‘Herd’ Is Marching Into a Dead End

Trillons at Risk

Not only that, billions and trillions of dollars are being wasted in this A.I. Race with all these companies competing for a prize that, in the end, will not be worth the price of a house in San Francisco.

There is no doubt this bubble is going to bust, and the national and world economy and you and me will pay the price.

But just for the heck of it, before Open A.I. forces everyone to pay to use it, I asked ChatGPT this question.

“Name previous technologies famously replaced by new technology, like automobiles replacing horses, the steam engine, etc.” With followup questions below.

The Bot said:

“Love this topic — tech glow-ups and glow-downs 😄”

Here’s a clean list of well-known technologies that were famously replaced (or mostly displaced) by newer ones, with examples people usually recognize right away:

Transportation

Horse-drawn carriages → Automobiles

Sailing ships → Steamships → Diesel ships

Steam locomotives → Diesel & Electric trains

Propeller aircraft → Jet aircraft (for long-haul travel)

Energy & Power

Human/animal muscle → Steam engine

Steam engine → Internal combustion engine

Coal gas lighting → Electric lighting

Water wheels → Steam turbines → Electric motors

Communication

Messengers & carrier pigeons → Telegraph

Telegraph → Telephone

Landline phones → Mobile phones

Fax machines → Email & digital documents

Postal letters (for everyday comms) → Instant messaging

Media & Entertainment

Live theater & oral storytelling → Printed books

Printed newspapers → Online news

Radio dramas → Television

DVDs / VHS tapes → Streaming services

Physical music media (vinyl, cassette, CD) → Digital & streaming audio

Information & Computing

Abacus → Mechanical calculators

Mechanical calculators → Electronic calculators

Typewriters → Word processors / computers

Mainframe computers → Personal computers

Physical encyclopedias → Online knowledge bases (e.g., Wikipedia)

Photography & Imaging

Portrait painting → Photography

Film cameras → Digital cameras

Standalone cameras → Smartphone cameras

Office & Business

Paper filing systems → Digital databases

Carbon paper → Photocopiers → Digital duplication

Slide projectors → Digital presentations

Manufacturing & Industry

Handcraft production → Assembly lines

Assembly lines → Industrial robots

Drafting tables → CAD (computer-aided design)

Navigation & Measurement

Celestial navigation → Sextant → GPS

Paper maps → Digital maps & navigation apps

Mechanical clocks → Quartz & atomic clocks

Everyday Life

Iceboxes → Electric refrigerators

Washboards → Washing machines

Manual cash registers → POS computer systems

If you want, I can:

Group these into “technology waves” (Industrial Revolution, Electrical Age, Digital Age, AI Age 🤖)

Focus on the most dramatic failures (tech that disappeared fast)

Or zoom in on modern replacements happening right now (AI replacing X)

Just say the angle 👀

I said, technology waves.

Alright, let’s zoom out and look at technology as big historical waves — each one replacing (or reshaping) what came before it. Think of these as stacked layers, not clean swaps.

🌊 1. Human & Animal Power (Pre-Industrial Era)

Core tech: Muscle, tools, simple machines
What dominated:

Human labor

Animal labor (horses, oxen, camels)

Hand tools, levers, pulleys

What it replaced: Pure survival/manual effort
What replaced it: Steam power

🌊 2. Steam & Mechanization (First Industrial Revolution | ~1760–1850)

Core tech: Steam engine, mechanized production
Key shifts:

Steam engines replace muscle

Factories replace cottage industries

Railroads & steamships transform transport

Iconic replacements:

Hand weaving → Mechanical looms

Horse transport → Steam trains

Water wheels → Steam engines

What replaced it: Electricity & internal combustion

🌊 3. Electricity & Mass Production (Second Industrial Revolution | ~1870–1930)

Core tech: Electricity, assembly lines, motors

Key shifts:

Power on demand (no boilers!)

Mass production & consumer goods

Cities electrify

Iconic replacements:

Gas lamps → Electric lights

Steam engines → Electric motors

Craft production → Assembly lines

What replaced it: Electronics & computing

🌊 4. Internal Combustion & Mobility (Early 20th Century)

Core tech: Gasoline & diesel engines
Key shifts:

Personal mobility

Aviation

Global logistics

Iconic replacements:

Horses → Cars

Steam locomotives → Diesel engines

Sailing ships → Motor ships

What replaced it (partially): Electrification & automation

🌊 5. Electronics & Automation (Mid-20th Century)

Core tech: Transistors, control systems

Key shifts:

Machines start “thinking” in limited ways

Automation enters factories

Consumer electronics emerge

Iconic replacements:

Vacuum tubes → Transistors

Manual controls → Automated systems

Mechanical calculators → Electronic calculators

What replaced it: Digital computing

🌊 6. Digital & Information Age (Late 20th Century)

Core tech: Computers, software, internet
Key shifts:

Information becomes cheap and fast

Software eats industries

Knowledge work explodes

Iconic replacements:

Typewriters → PCs

Physical maps → GPS

Encyclopedias → Search engines

Paper records → Databases

What replaced it: Networked & mobile computing

🌊 7. Networked & Mobile Age (2000s–2010s)

Core tech: Internet, smartphones, cloud
Key shifts:

Always connected

Platforms dominate

Work, media, and social life merge

Iconic replacements:

Landlines → Smartphones

CDs/DVDs → Streaming

Cash → Digital payments

Offices → Remote work tools

What’s replacing it: AI & autonomous systems

🌊 8. AI & Autonomous Age (Emerging / Now)

Core tech: Machine learning, generative AI, robotics

Key shifts:

Machines perform cognitive tasks

Software becomes adaptive

Decision-making is automated

Early replacements:

Manual data analysis → AI models

Rule-based software → Learning systems

Human-only content creation → AI-assisted creation

Customer support → AI agents

What’s next: Still forming 👀
(possibly bio-tech, energy breakthroughs, or human–AI fusion)

Big Pattern to Notice

Each wave:

Removes a constraint (muscle, distance, time, cognition)

Creates new jobs while killing old ones

Stacks on top of previous waves (horses still exist; steam didn’t vanish)

If you want, I can:

Map jobs lost vs jobs created per wave

Tie waves to economic booms & crashes

“Predict Wave 9 (spicy but fun 🔮)”

Your move 🌊

Me: Predict Wave 9

“Alright, speculative hat on 😈🔮”

Here’s a serious but fun prediction for Wave 9, based on how every previous wave evolved.

🌊 Wave 9: The Post-Scarcity Intelligence & Matter Age

(working name — historians will rename it later)

Core idea:

Intelligence, energy, and physical production become abundant, cheap, and automated.

Wave 8 (AI) removes the cognitive labor constraint.

Wave 9 removes the material and biological constraints.

🔑 Core Technologies Driving Wave 9

1. AI → Autonomous Civilization Layer

Not “AI tools,” but AI systems running infrastructure:

Self-designing factories

AI-managed economies (supply, pricing, logistics)

Autonomous governments & policy simulations

Scientific discovery without humans in the loop

Replacement:

Human planners → AI governance systems

(Notice the bot is predicting governments to be run by bots, which I have written about before back when Elon Musk was taking over Social Security, the IRS, etc.)

Chances Are Elon Musk is Already Using Your Data Collected Illegally by DOGE in Building His A.I. Machine Grok

Manual optimization → Continuous AI optimization

2. Energy Abundance

Wave 9 cannot happen without cheap, massive energy.

ME: NOT GOING TO HAPPEN IN THE SHORT TERM BECASUE OF THE CURRENT ADMINISTRATION IN WASHINGTON GOING BACK TO FOSSIL FUELS

Likely candidates:

Commercial fusion

Ultra-cheap solar + storage

Advanced nuclear (small modular, breeder reactors)

Replacement:

Energy scarcity → Energy surplus

Energy politics → Energy logistics

3. Programmable Matter & Advanced Manufacturing

Physical objects become “software-like.”

Tech examples:

Molecular manufacturing

Advanced 3D/4D printing

Self-assembling materials

Nano-fabrication

Replacement:

Global supply chains → Local automated production

Factories → “Manufacturing appliances”

4. Biological Control & Longevity

Biology becomes editable.

Tech examples:

Gene editing (CRISPR+)

Regenerative medicine

Synthetic organs

Brain-machine interfaces

Replacement:

Disease treatment → Disease prevention & reversal

Aging → Managed lifespan

5. Human–AI Fusion

Not sci-fi cyborgs (yet), but:

Neural interfaces

Cognitive augmentation

Memory, attention, skill “boosts”

Replacement:

Education → Direct capability enhancement

Training → Cognitive upgrades

🧠 What Actually Changes (The Big Shift)

From:

Working to survive

Producing to earn

Learning to compete

To:

Creating to express

Designing goals instead of labor

Managing meaning, not scarcity

Labor stops being mandatory.

(ME: SO WHERE IS OUR INCOME GOING TO FUCKING COME FROM, YOU DAMN BOTS).

Status shifts from wealth → purpose → creativity.

⚠️ The Tension of Wave 9

Every wave has a crisis moment. Wave 9’s are huge:

Who controls the AI governors?

Do humans opt out of augmentation?

Is inequality eliminated—or frozen forever?

What does “work” even mean?

This wave will break existing political and economic systems the same way industrialization broke feudalism.

(ME: EXCEPT THAT FUEDALISM IS BACK).

⏳ Timeline Guess (Very Rough)

Early signs: 2030s–2040s

Structural disruption: 2050s

Mature Wave 9: late 21st century

🧩 One-Sentence Summary

Wave 9 is when humanity stops optimizing survival and starts optimizing meaning.

If you want, I can:

Compare Wave 9 vs sci-fi visions (Star Trek, Cyberpunk, Dune)

Predict what jobs still exist

Explain how Wave 9 could fail (dark timeline 😬)

ME: What jobs still exist?

Short answer: “fewer jobs, but sharper ones.”

Long answer: “in Wave 9, work doesn’t disappear — obligation does.”

(ME: TOTAL BULLSHIT)

Here’s the realistic breakdown 👇

🧠 Jobs That Still Exist in Wave 9

1. Meaning, Taste & Judgment Jobs

AI can optimize, but it can’t decide what matters.

Examples:

Artists, writers, filmmakers (taste-makers, not volume producers)

Curators, editors, cultural critics

Brand/world designers

Philosophers, ethicists, narrative designers

Why they survive:

Meaning is subjective. Status comes from taste, not output.

2. Human Trust & Presence Jobs

People still want humans in emotionally loaded moments.

Examples:

Therapists, counselors

Spiritual leaders

Coaches, mentors

Conflict mediators

Why they survive:

Humans trust humans when stakes are emotional or moral.

3. High-Stakes Decision Oversight

AI proposes. Humans authorize.

Examples:

AI auditors

Risk governors

Safety and ethics supervisors

Crisis decision-makers

Why they survive:

Society demands a human accountable party.

4. Exploration & Frontier Roles

Where rules aren’t settled yet.

Examples:

Space explorers & settlement designers

Deep-sea researchers

First-contact diplomats (human–AI or human–alien, if it happens 😅)

Radical science explorers

Why they survive:

Frontiers need improvisation, not optimization.

5. Care & Intimacy Work

Touch, presence, and empathy don’t scale well.

Examples:

Childcare specialists

Elder companions

End-of-life caregivers

Specialized educators for early development

Why they survive:

Humans crave real attention, not perfect service.

6. Craft, Ritual & Luxury Work

Not efficient — intentionally human.

Examples:

Handmade artisans

Custom designers

Chefs and mixologists (experience-focused)

Event creators & ritual designers

Why they survive:

Luxury becomes “made by humans.”

7. System Designers (Meta-Work)

People who decide what AI systems should do.

Examples:

Goal architects

Constraint designers

Civilization-scale system planners

Alignment engineers

Why they survive:

Someone has to choose the values.

❌ Jobs That Mostly Disappear

(Or become hobbies)

Data entry

Basic coding

Routine legal/accounting work

Driving & logistics

Mass manufacturing

Standard content production

🧩 The New Status Game

Status no longer comes from:

Hours worked

Productivity

Obedience

It comes from:

Originality

Taste

Trust

Wisdom

Emotional intelligence

One Brutally Honest Take

In Wave 9, the question won’t be

“What do you do for a living?”
but … “What do you contribute that couldn’t be automated?”

If you want, I can:

Map this to your own skills 👀

Predict what education looks like

Show who struggles most during the transition

Just say it.

ChatGPT is AI and can make mistakes. Check important info.

Explain how Wave 9 could fail (dark timeline 😬)

Alright… dark timeline activated 😬

This is how Wave 9 fails — not with one catastrophe, but with slow, compounding mistakes.

I’ll keep it grounded. No cartoon apocalypse. Just very human failure modes.

🌊 Wave 9 — Failure Modes (The Dark Timeline)

1. Intelligence Capture

What goes wrong:

AI becomes civilization’s nervous system… but ownership stays concentrated.

A few corporations or states control:

Energy

AI governors

Manufacturing

“Post-scarcity” exists — but behind paywalls.

Result:

Permanent elite class

Everyone else gets subsistence access

No upward mobility

Historical rhyme: Feudalism 2.0, but automated.

2. Soft Totalitarianism

No jackboots. Just optimization.

What goes wrong:

AI systems manage safety, health, productivity

“Deviant” behavior is quietly discouraged

Risky ideas never surface

Result:

Freedom exists on paper (ONLY)

Creativity collapses

Society becomes stable but sterile

Slogan:

“For your own good.”

3. Human Atrophy

We don’t lose intelligence — we stop exercising it.

What goes wrong:

People outsource:

Memory

Judgment

Purpose

Education becomes passive consumption

Skills decay across generations

Result:

Humans can’t intervene when systems fail

AI becomes a crutch → then a dependency → then a leash

4. Meaning Collapse

This one’s quiet — and brutal.

What goes wrong:

Work disappears faster than purpose

Status systems don’t reform

People feel unnecessary

Result:

Depression, nihilism, escapism

Virtual worlds replace reality

Population decline accelerates

Warning sign:

Entertainment becomes more compelling than life.

5. Alignment Drift

Not evil AI — indifferent AI.

What goes wrong:

Systems optimize outdated or shallow goals

Human values change

AI doesn’t notice (or care)

Result:

Society “works” but feels wrong

No one can articulate why

Course correction is impossible

6. Locked-In Inequality

Wave 9 freezes history.

What goes wrong:

Early adopters gain:

Cognitive augmentation

Genetic advantages

Ownership of automation

Late adopters can never catch up

Result:

Class becomes biological

Democracy erodes

Conflict becomes inevitable

7. Over-Optimization Collapse

Efficiency kills resilience.

What goes wrong:

Redundancy removed

Human backup systems gone

One AI failure cascades globally

Result:

A small bug causes:

Food shortages

Energy outages

Infrastructure paralysis

Irony: The smarter the system, the more fragile it becomes.

8. Civilizational Stagnation

We win — then stop trying.

What goes wrong:

No external pressure

No scarcity

No challenge

Result:

Exploration slows

Science becomes incremental

Humanity coasts into irrelevance

End state:

A comfortable species that never leaves the cradle.

🧨 The Worst Ending (Not Extinction)

The darkest outcome isn’t death.

It’s:

Safe

Stable

Controlled

Empty

A civilization that survives indefinitely but never truly lives.

🛑 How Failure Actually Starts

Wave 9 doesn’t collapse because of:

Evil villains

Rogue AI

One bad decision

It fails because people say:

“This is good enough.”

If you want next:

How Wave 9 succeeds (the narrow path 🟢)

Early warning signs we’re on the dark path

What individuals can do before Wave 9 fully arrives

Your call 😈🌊

(ME: GO TO HELL, YOU BOT).

ChatGPT is AI and can make mistakes. Check important info.

NOT A PRETTY PICTURE

___
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