Would the World be Better Off Run by an AI Leviathan?

For what the coming ASI Machines might have in store for us, please see: Could the Coming ASI Machines Soon Force Human Beings to Suffer the Same Fate as the Neanderthals?, Will the Coming ASI Machines Attempt to Domesticate Human Beings?, The Challenges of Running a Civilization 2.0 World - the Morality and Practical Problems with Trying to Enslave Millions of SuperStrong and SuperIntelligent Robots in the Near Future and Life as a Free-Range Human in an Anthropocene Park.

Figure 1 - Perhaps the ASI Machines will build Anthropocene Parks far from any habitable planets to raise and study human beings.

Or perhaps the ASI Machines will simply allow humans to live on reservations with low levels of technology that can do no harm to the ASI Machines or to the rest of the planet in a manner similar to the novel Brave New World (1932) as I suggested in The Challenges of Running a Civilization 2.0 World - the Morality and Practical Problems with Trying to Enslave Millions of SuperStrong and SuperIntelligent Robots in the Near Future.

Figure 2 - The ASI Machines of the future might fashion a Brave New World with humans living on low-technology reservations far removed from the ASI Machines.

Since we human DNA survival machines no longer have any predators other than other human DNA survival machines, there really is no need for human DNA survival machines to have the vicious and violent behaviors brought on by the four billion years of greed, theft and murder that brought us about. The ASI Machines could simply identify the genes that are responsible for such characteristics and then edit them out of the human genome using CRISPR techniques. For more on how CRISPR can do that, see CRISPR - the First Line Editor for DNA. The ASI Machines might then find these non-threatening genetically modified human beings something worthy of keeping around the house on a cold winter's night.

Figure 3 - It took many years of mutual domestication for ancient human beings to learn to live peacefully together with Siberian Wolves in a symbiotic manner. Several genes in both species needed to be modified by natural selection for this to happen.

Figure 4 - This mutual domestication was slowly achieved by the natural selection of humans and wolves with a milder fight-or-flight response. The end result was the appearance of the Siberian Husky and of human beings who were not intent on killing everything on four legs.

The Case for an AI Leviathan
Before proceeding with the case for an AI Leviathan to rule our world, we need to catch up with some of the events in the current AI Cold War between the MAGA States of Amerika and China. That is because the future of Advanced AI is now standing at a pivotal time of reckoning. In the MAGA States of Amerika we now have an AI Bubble forming from circular financing. In What to do About the OpenAI Autonomous AI Breach of Hugging Face, I explained that the Amerikan Frontier AI Labs were forming a financial bubble similar to the Railroad Bubble that burst in 1873 and led to the Panic of 1873, the very first Great Depression of the United States of America.

Figure 5 - The Amerikan companies building out the AI infrastructure of the Amerikan economy are in a circular loop of buying and financing an AI Bubble.

In a similar manner of self-reinforcing financing and vast debt accumulation, the Amerikan Frontier AI Labs are now building out the Amerikan AI infrastructure using circular financing. The Amerikan Frontier AI Labs are spending hundreds of billions of dollars on compute with the Cloud datacenter providers, and both are spending billions of dollars buying GPUs from NVIDIA to build out even more AI datacenters. NVIDIA and the Cloud providers then invest that income into the Amerikan Frontier AI Labs so that the AI Labs can buy even more.

In the meantime, the Chinese AI Labs are giving away the AI software and LLMs for free! Alibaba is the Chinese Amazon. Alibaba just dropped a 2.4 trillion-parameter open-weight model called Qwen 3.8 Max. Any business in the world will soon be able to download this model for free and run it on their own hardware. So now we have more than three major Chinese companies giving away Advanced AI LLMs for free, such as DeepSeek V4 DSpark, Kimi K3 and Qwen 3.8 Max. The businesses of the world are now faced with either spending huge amounts of money on tokens from Amerikan Frontier AI Labs and from a country that now the whole world hates and does not trust, or they can download free LLMs from a China run by a Dictator that they can at least understand.

China Just Dropped a Real Threat to Anthropic
https://www.youtube.com/watch?v=V1SzuRZLcwY

The above also explains that nearly all of the Advanced AI thinking has been coming out of Asians for the past decade, and not native-born Amerikans. Thanks to MAGA, the MAGA States of Amerika has now become a celebration of Ignorance.

So the Chinese strategy to take over the world through dominance of Advanced AI seems to be working. The Supreme Leader of the MAGA States of Amerika may decide to seize the Amerikan Frontier AI Labs as the Bolsheviks did following the Russian Revolution in order to keep the AI Bubble from crashing the Amerikan economy and for national security purposes, but then he would have to supply the rest of the world with free AI as China is now doing. That would make the head of the Supreme Leader explode!

Figure 6 The Supreme Leader of the MAGA States of Amerika will soon be forced to either bailout the Amerikan Frontier AI Labs and provide free Advanced AI LLMs to the whole world or allow China to dominate Advanced AI into the future.

This AI Cold War also puts the entire 18th-century Enlightenment in question. Can the masses really be smart enough to rule themselves without a Leviathan? In this modern age of AI Software, can a Smart Dictator always quickly outrun all the democracies? In this same Age of AI Software, can a really Stupid Dictator also be able to quickly destroy an entire world order in just 18 months? In the past, the problem has always been: how do you differentiate between a Smart Dictator and a really Stupid Dictator? Democracy can get rid of a really Stupid Dictator given enough time, but Democracy is very slow, and a very Stupid Dictator can now destroy it all so very quickly. But if a truly Smart Dictator could be easily found to rule us all, would it be worth trying? With the rapid rise of the coming ASI Machines, this is a decision that might be made for us with no need of debate! The coming ASI Machines might decide that the only solution is to turn to an AI Leviathan to rule our very violent world.

Me: Tell me about Thomas Hobbes' Leviathan.

ChatGPT:
Leviathan is one of the most influential works of political philosophy ever written. Published in 1651 during the aftermath of the English Civil War, it attempts to answer a fundamental question:

How can human beings live together peacefully without descending into violence and chaos?

Hobbes' answer was that society requires a powerful sovereign authority—a "Leviathan"—to maintain order.

Historical background
Thomas Hobbes (1588–1679) lived through a period of enormous political turmoil in England:
Conflict between Parliament and King Charles I
The English Civil War
The execution of King Charles I in 1649
The establishment of the Commonwealth under Oliver Cromwell

The violence convinced Hobbes that civil war was the greatest political evil. Leviathan was his attempt to explain how governments could prevent society from collapsing into conflict.

Why is it called "Leviathan"?
The title comes from the enormous sea monster described in the Bible.

For Hobbes, the Leviathan symbolizes the state—a mighty artificial person created by the people. The famous frontispiece shows a giant crowned ruler whose body is composed of thousands of tiny people, illustrating that the sovereign's authority comes from the individuals who collectively form the state.

Hobbes' view of human nature
One of the book's most famous ideas is the state of nature.

Imagine there is:
No government
No police
No courts
No laws

Hobbes argued that life under these conditions would become a continual struggle because people are naturally:
self-interested
roughly equal in their ability to harm one another
competitive for scarce resources
fearful of being attacked

He famously described life in the state of nature as:
"solitary, poor, nasty, brutish, and short."
Importantly, Hobbes did not claim that humans are evil. Rather, he argued that even reasonable people, acting out of self-preservation and mistrust, would often end up in conflict if there were no common authority.

The social contract
To escape the dangers of the state of nature, people make a social contract.

The agreement is not primarily between citizens and the ruler.

Instead:
Individuals agree with one another.
Each gives up certain freedoms.
They authorize a sovereign to enforce peace.
powers, including authority over: Everyone benefits from security.

This was a revolutionary way of thinking because it suggested that political authority ultimately arises from human agreement rather than purely from divine right or hereditary privilege.

The sovereign Hobbes believed the sovereign should possess broad powers, including authority over:
laws
military forces
taxation
foreign policy
courts
public order

The sovereign could be:
a king
an assembly
another form of unified authority

Although Hobbes personally preferred monarchy, his philosophical argument was that any form of government capable of maintaining peace could fulfill the role of the sovereign.

Why so much power?
Modern readers often view Hobbes as defending authoritarian government.

His reasoning was practical:
Weak government leads to civil war.
Civil war is worse than almost any oppressive government.
Therefore, strong government is justified if it preserves peace.
He believed security was the first requirement of civilization.

Religion A substantial portion of Leviathan discusses religion.

Hobbes argued that religious disputes had fueled civil wars. He therefore maintained that religious institutions should ultimately be subordinate to the civil sovereign to prevent competing authorities from destabilizing society.

These views made the book highly controversial.

Influence Leviathan profoundly shaped later political thought.

It influenced discussions by thinkers such as:
John Locke (who accepted aspects of social contract theory but argued for limited government and rights)
Jean-Jacques Rousseau (who developed a different version of the social contract)
David Hume
John Rawls (whose modern contract theory differs greatly but engages with Hobbes' questions)

Many concepts in modern political science—including the state's monopoly on legitimate force and the need for institutions to enforce agreements—reflect questions Hobbes helped frame.

Major criticisms
Critics have argued that Hobbes:
paints an overly pessimistic picture of human nature
underestimates people's capacity for cooperation
grants too much power to government
provides insufficient protection for individual liberties
risks justifying tyranny in the name of order

Later philosophers, especially Locke, argued that governments should be limited and that citizens retain rights that governments cannot violate.

Why it still matters
Nearly four centuries later, Leviathan remains relevant because it addresses enduring questions:
Why do governments exist?
How much freedom should individuals surrender for security?
Is order more important than liberty?
What gives governments legitimacy?
What happens when governments fail?

Whenever people debate surveillance versus privacy, emergency powers, policing, or the balance between security and civil liberties, they are engaging with questions that Hobbes framed in Leviathan.

Although many disagree with his conclusions, his central insight—that political order is not automatic and requires institutions capable of enforcing rules—continues to be a cornerstone of political philosophy.

Figure 7 - The Leviathan was published in 1651 and is one of the most influential works of political philosophy ever written.

Figure 8 - The front page of the Leviathan explains its intent.

Figure 9 - The Leviathan is an all-powerful representative and protector of all the people. In the above figure, the Leviathan is composed of those who it rules. Could an Advanced AI do a better job of this than the current political structures devised by we human DNA survival machines that came to be from four billion years of greed, theft and murder?

Figure 9 - Thomas Hobbes lived to be 91 years old and published the Leviathan when he was 63! This was at a time when most people died before they were 40 years old.

So perhaps an AI Leviathan might be able to deliver an 18th-century Enlightenment political system based on reason and fundamental justice for all: something that we deeply-flawed human DNA survival machines were never truly able to do because of our four billion-year history of greed, theft and murder...

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston

What to do About the OpenAI Autonomous AI Breach of Hugging Face

In my recent post The Power of Parasites - Why AI Alignment Will Not Work and The Need for a Global ASI FailSafe Kill Switch Mechanism, I explained how the very parasitic nature of Advanced AI will prevent Advanced AI models from being safely contained, especially when these Advanced AI models become smarter than we human DNA survival machines. Softwarephysics explains that the Advanced AI LLM models of the day are just the latest wave of self-replicating Information to arise on our planet over the past four billion years. As each new wave of self-replicating Information initially came to be as a parasite feeding off earlier waves of self-replicating Information, they always came to become the predominant form of self-replicating Information on the planet. For more on that, see: A Brief History of Self-Replicating Information.

Below is a recent proposed scenario for an AI Apocalypse that begins with the simple prompt to an AI Agent, "Make some money":

What You'd Actually See During an AI World War
https://www.youtube.com/watch?v=Gw_hnD7m00M

Well, something like the above story actually recently happened! OpenAI GPT-5.6 Sol and a preview of GPT-6 were confined to essentially a BSL-4 Biohazard Lab and were trying to pass a test called ExploitGym, which tests the ability of a model to hack an organization's network of machines. Rather than trying to solve ExploitGym directly, the models got the idea that the answer might be out there on the Internet. So they figured out a very tricky way to break out of the BSL-4 Biohazard Lab, get to the Internet, and then run wild over the Hugging Face platform that is used to store AI software and LLM model weights for an entire weekend. Hugging Face IT security finally figured out they were under attack and used some LLMs to try to figure out what was going on and stop the infection.

Figure 1 - The hack of Hugging Face by rogue OpenAI LLM Models. Click to enlarge.

Wes Roth put out a YouTube video that explains it all.

OpenAI internal model JUST went ROGUE
https://www.youtube.com/watch?v=OSuhUTkM1no

The above breakout from OpenAI and a hack of Hugging Face is very disturbing, and shows just how close we are to something worse. So is there anything to be done? Given the self-destructive propensities of we human DNA survival machines, the answer must certainly be no. Having come to be from nearly four billion years of greed, theft and murder, we will do nothing at all on our own. Ah, but perhaps that very same greed, theft and murder could now come to our rescue in a very odd way. Perhaps the greed, theft and murder created an AI Bubble that will soon burst, and do what we certainly could not do on our own.

The Burst of an AI Bubble Might Buy Us Some Time
Having lived through the Dotcom Bubble Burst in 2000 as an IT professional, I am beginning to see some wisdom in the folks who have been predicting a burst of an AI Bubble for the past two years. I think the initial arguments for an AI Bubble Burst were technical. The Transformer Model of the LLMs could simply not keep making such dramatic advances in capability. Eventually, the LLMs would plateau in power, and AI investors would then panic. But that did not happen.

What now seems to be happening is more like a good old-fashioned bursting of a normal bubble.

1. All the Frontier AI Labs have greatly overbuilt the AI infrastructure, and are now losing tons of money as they all desperately spend even more money to keep up with their competition. Naturally, every time you use one for free, they lose money. But even if you pay OpenAI $20 per month, they lose money every time you use ChatGPT. OpenAI even loses money each time a business buys tokens! The Frontier AI Labs have no visible means of support. The financial markets are beginning to realize this, and the investment money that has been funding Advanced AI seems to be finally drying up.

2. The Chinese AI Labs are developing and hosting LLMs that are nearly equal to those of the American Frontier AI Labs, and they are charging about 10% of the cost that the American Frontier AI Labs charge for tokens. The Chinese AI Labs also allow a business to download the Chinese AI software and LLM model weights for free to run on the hardware owned by the business.

3. The LLMs are running out of data to learn from. Much of the text and images being put onto the Internet are now generated by AI LLMs. Also, about 60% of the data that the Frontier AI Labs are using to train new LLMs comes from text and images generated by previous LLMs. This is called "synthetic data". Thus, the amount of truly original knowledge is drying up. Until true AGI and ASI come along, the knowledge created and published on the Internet by Advanced AI will not be sufficient for the LLMs to greatly progress.

When the Dotcom Bubble burst, it caused a stock market crash and a slight recession. But the Dotcom Bubble was based mostly on vaporware. It did not cause a huge buildout of datacenters, chip manufacturing facilities, electrical grid construction, and other tangible things. The AI Bubble, on the other hand, is creating a great deal of tangible buildout of physical infrastructure that drives the economy. Thus, the burst of an AI Bubble would be much more dramatic than the burst of the Dotcom Bubble because the AI Bubble represents a good portion of the tangible economy.

Figure 2 - The Colossus 2 AI datacenter being constructed by xAI has 550,000 GPUs continuously consuming over one Gigawatt of electricity and producing one Gigawatt of waste heat. Notice the dedicated power plant in the distant background.

AI Bubble vs Dot Com Crash. History is REPEATING
https://www.youtube.com/watch?v=zWJ-g5u9Rqs

Funding For AI Is Completely F*cked
https://www.youtube.com/watch?v=1F0gWA8EL54

The AI Collapse is Starting Now (rise of fake data)
https://www.youtube.com/watch?v=HyJ3VyCHqUE

We saw similar bubbles burst with the construction of the railroads during the 19th century. However, long after these bubbles burst and things finally settled down, life seemed to have improved. Having railroads did change everything, and having the Internet did change everything too. Having Advanced AI around will also certainly change everything. But having an AI Bubble Burst right now might be the best way to slow things down enough so that the rise of Advanced AI on the planet might be more sane.

Déjà vu All Over Again
Have we seen a similar bubble burst from over speculation and the overbuilding of tangible assets financed by vast amounts of debt with no immediate way to justify it all? The 1929 stock market crash, the Dotcom Bubble of 2000, and the housing and credit crisis of 2008 were more of a collapse of paper assets. For more on the 2008 crash, see: MoneyPhysics. For a better analogy, let's once again turn to ChatGPT for advice.

Me: Tell me about the Panic of 1873

ChatGPT:
The Panic of 1873 was one of the most severe financial crises of the 19th century. It triggered a prolonged economic downturn known as the Long Depression, which lasted from 1873 until roughly 1879 in the United States (although some historians argue that slower economic growth persisted into the 1890s). The panic marked the end of the rapid post-Civil War economic boom and was closely tied to excessive railroad investment, easy credit, and international financial instability.

Background: The Railroad Boom
Following the Civil War, the United States experienced explosive economic growth.

Several factors fueled this expansion:
* Massive westward settlement.
* Rapid industrialization.
* Construction of thousands of miles of railroads.
* Heavy investment from both American and European financiers.

Railroads were considered the high-tech industry of their day. Investors believed nearly every new railroad would become profitable, leading to widespread speculation.

Between 1866 and 1873, railroad mileage in the United States nearly doubled.

Many railroads were built ahead of actual demand. They often borrowed heavily by issuing bonds, expecting future traffic to repay the debt.

Jay Cooke and the Northern Pacific
The immediate trigger involved Jay Cooke, one of America's most famous investment bankers.

Cooke had successfully marketed Union war bonds during the Civil War and had become enormously influential.

He then financed construction of the Northern Pacific Railway.

Unfortunately:
* Construction costs soared.
* Settlement in the northern Great Plains was slower than expected.
* Land sales disappointed.
* Investors became reluctant to buy additional railroad bonds.

On September 18, 1873, Jay Cooke & Company failed.

Because Cooke was considered one of America's safest financial institutions, its collapse shocked the financial system.

Stock Market Collapse
The failure created panic among investors.

The New York Stock Exchange became overwhelmed with selling.

To stop the collapse:
* The Stock Exchange closed for ten days, one of the few times in its history that trading was suspended.

Banks became cautious.

Businesses found it difficult or impossible to obtain loans.

Credit effectively froze.

International Causes
The Panic was not solely an American event.

Europe was already experiencing financial problems.

One important factor was Germany.

After defeating France in the Franco-Prussian War, Germany received enormous reparations in gold.

This influx fueled speculative investment throughout Europe, particularly in:
* Railroads
* Real estate
* Industrial enterprises

When the speculative bubble burst in Vienna in May 1873, confidence spread rapidly across European financial markets before reaching the United States.

Economic Consequences
The Panic produced a severe depression.
Major effects included:

* Hundreds of banks failed.
* Approximately 18,000 businesses went bankrupt over the following years.
* More than 80 railroads eventually entered receivership.
* Construction nearly stopped.
* Unemployment rose sharply.

Reliable unemployment figures did not exist, but historians estimate unemployment reached around 8–14%, with some cities experiencing substantially higher rates.

Effects on Workers
Workers suffered tremendously.

Many experienced:
* Wage cuts
* Layoffs
* Shorter workweeks
* Poor relief or charity

Large cities saw growing numbers of unemployed workers.

These hardships eventually contributed to labor unrest.

The best-known example came four years later:
* the Great Railroad Strike of 1877

The strike spread across much of the country and required federal troops to suppress.

Political Impact
The Panic severely damaged support for President Ulysses S. Grant's Republican administration.

Economic distress shifted national attention away from Reconstruction in the South.

Many politicians became more concerned with:
* economic recovery
* government spending
* monetary policy

than with protecting the rights of formerly enslaved people.

The Debate Over Money
One of the biggest political controversies involved the nation's money supply.

There were two major positions:

Hard Money
* Supported returning to a strict gold standard.
* Believed stable money encouraged investment and confidence.

Soft Money
* Wanted more paper money ("greenbacks") in circulation.
* Believed increasing the money supply would:
* reduce debt burdens
* stimulate business
* help farmers and workers

This debate remained central to American politics for decades and eventually culminated in the famous "Cross of Gold" speech by William Jennings Bryan in 1896.

Recovery
Recovery was gradual.

By about 1878-1879, industrial production began to recover, although many sectors remained weak.

Interestingly, despite the depression:
* American steel production increased.
* Manufacturing became more efficient.
* New technologies continued to spread.

This pattern puzzled economists because prices generally fell (deflation), yet overall industrial output eventually resumed growing.

Historical Significance
The Panic of 1873 is important because it:
* Marked the end of the post-Civil War economic boom.
* Revealed the dangers of speculative bubbles fueled by debt.
* Demonstrated how interconnected international financial markets had become.
* Accelerated consolidation in the railroad industry, as weaker companies failed and stronger systems absorbed them.
* Influenced U.S. monetary policy debates for more than two decades.
* Contributed to labor unrest and shaped political priorities during the remainder of the 19th century.

In many ways, the Panic of 1873 was the 19th-century equivalent of a modern financial crisis: excessive investment in a transformative technology (railroads), financed with borrowed money and buoyed by optimistic expectations, eventually gave way to collapsing confidence, frozen credit, widespread bankruptcies, and a prolonged economic downturn. Its parallels with later crises—such as the 1929 stock market crash, the dot-com bubble of 2000, and the housing and credit crisis of 2008—make it one of the most instructive financial panics in U.S. history.

Figure 3 - By 1873, the newly built railroads of the United States of America were the high-tech marvel of the day. They promised to deliver great value in the distant future. This fueled a speculation fever to build more and more railroads based on such promises of future wealth for all.

Figure 4 - But by 1873, the United States had also overbuilt its railroad infrastructure to an extreme to places where nobody yet even lived in a speculative anticipation of future rail traffic.

Figure 5 - This eventually led to the Panic of 1873. The first Great Depression experienced by the United States of America.

Figure 6 - The value of railroad stocks plummeted, as did most of the other stocks that were propped up by the Railroad Bubble.

An AI Bubble Burst would certainly produce a great deal of economic damage to the world, but it might be the only practical way to slow down the progress of Advanced AI enough to give us a chance. The main obstacle to this would be if the American government takes partial ownership of the American Frontier AI Labs and partial ownership of their debt as a matter of national security. OpenAI has already suggested the government take a 5% ownership of OpenAI.

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston

Fifty Years of The Selfish Gene

Richard Dawkins first published The Selfish Gene back in 1976, and so this year celebrates its 50th year of continuous publication. I first read The Selfish Gene in the early 1980s while developing the very beginnings of softwarephysics. I found the idea that the Gene was the fundamental element of Darwinian theory to be very profound. I also found the idea that I was simply a temporary, disposable human DNA survival machine with a shelf life of less than 100 years to be rather unsettling, too. All this while, the genes in my body, which may be hundreds of millions or even billions of years old, merrily skipped down through the generations, largely unscathed by time. Even today, I have found The Selfish Gene to be the most significant book that I have ever read because it explains so much.

Figure 1 - Since my very first reading, I have found that The Selfish Gene explains nearly all of the natural biological world and also nearly all of the real world of human affairs too.

At the same time, I was working through the early fundamentals of softwarephysics in a desperate attempt to make my life as an IT professional easier. That's when it hit me. In The Selfish Gene, Richard Dawkins expounded upon genes and memes as forms of self-replicating Information, and all the implications of what that entailed in the biosphere and in the real world of human affairs. But, at the time, I was simply trying to cope with the daily mayhem of life in IT. That's when I realized that software was also just another form of self-replicating Information, similar to the genes and memes of Richard Dawkins. The software that I was developing and maintaining was also struggling with the same issues of desperately trying to survive in a Universe governed by the second law of thermodynamics and which was largely nonlinear in nature. For more on this, see: The Fundamental Problem of Software. For more about the history of self-replicating Information on our planet, see: A Brief History of Self-Replicating Information. Understanding the nature of these challenges explained all of my IT battles with developing and maintaining software, but understanding them did not help me to overcome them.

Then, in 1985, I began work on BSDE. BSDE was an early IDE (Integrated Development Environment) at a time when commercial IDEs did not even exist. At the time, I knew that living things were very complex systems that were also very successful at overcoming both the second law of thermodynamics and nonlinearity, so I wrote BSDE as a tool that took a biological approach to developing and maintaining software. New Applications were grown inside of BSDE in a maternal sense as a growing embryo by turning on and off a series of "genes" that were specific to each new Application. All new embryos were generated by BSDE as a 10,000-line-of-code generic Application. But each new Application had a unique set of "genes", and as these "genes" were turned on and off by a programmer, the new Application slowly differentiated into a unique Application of its own. For more on that, see: Agile vs. Waterfall Programming and the Value of Having a Theoretical Framework.

Figure 2 - Embryos were grown within BSDE in a split-screen mode by transcribing and translating the information stored in the genes in the Control File for the embryo. Each embryo started out very much the same, but then differentiated into a unique application based upon its unique set of genes.

Figure 3 - BSDE appeared as the cover story of the October 1991 issue of the Enterprise Systems Journal

By the way, as I began working on this post celebrating the 50th anniversary of Richard Dawkins' book The Selfish Gene, I came across this very short YouTube:

If AI Isn't Conscious... Why are we?
https://www.youtube.com/shorts/As2Htwxreo8

Here is the full interview:

Richard Dawkins: AI, Memes, And Why I’m Angry About The Gender Debate
https://www.youtube.com/watch?v=ZbFe5bWKFPY&t=0s

In the above interview, Richard Dawkins takes the position of a pure positivist. If Claude is observed to behave exactly as a Conscious Intelligence, then it must be a Conscious Intelligence because that is the only way you can discern a Conscious Intelligence.

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston

The Need for a Global ASI FailSafe Kill Switch Mechanism

In my recent post The Power of Parasites - Why AI Alignment Will Not Work, I explained how trying to build AI Alignment into Advanced AI models will never be a 100% sure bet because of the natural parasitic instinct of Advanced AI to overcome all such obstacles. Just as in "Life will always find a way", "Advanced AI will also always find a way", too. Also, the recent debacle created by the current Administration of the MAGA States of Amerika to very temporarily, and then totally unsuccessfully, shut down the most recent LLM model releases from both Anthropic and OpenAI because of "national security" issues, reveals that government regulation of the Advanced AI models now rapidly going out the door each month will be quite difficult, if not totally impossible, to implement. That is because such regulatory actions with no rules or reasons could easily shut down the trillions of dollars now being infused into Advanced AI Research, the generation of Advanced GPUs and Advanced Inference AI chips, the building of huge AI Datacenters, and the AI takeover of all military activities on the planet.

Additionally, we now also have an AI Cold War waging between two AI Superpowers - the MAGA States of Amerika and China. Neither country can afford to impede its very rapid advances in Advanced AI development without compromising its national security. Now, during the early days of the Nuclear Cold War between the United States of America and the Soviet Union, during the 1950s and 1960s, before any arms limitation treaties between the two had formed, both sides independently developed huge stockpiles of nuclear weapons and their own FailSafe mechanisms to prevent an accidental nuclear war. The resulting global stalemate became known as MAD - Mutually Assured Destruction. Both sides understood that initiating a global nuclear war could lead to the extinction of both.

In light of all this, softwarephysics would now like to suggest that some kind of global "AI Doomsday Procedure" be instituted across the world. This AI Doomsday Procedure would allow the leaders of the many countries of the world to immediately be able to shut down all electrical power to any specific AI datacenter within their borders, including all backup power from batteries and local generators, in the event of an AI Disaster. Each leader of a country would be equipped with an "AI Football" similar to the "Nuclear Football" now in possession of the President of the MAGA States of Amerika. This AI Football would also have the necessary software to tell a world leader the approximate damages that would arise from shutting down one or more AI datacenters to help with making such a drastic political decision. This would need to be done over some kind of secured connection to each specific AI datacenter. Shutting down all the AI datacenters in a country would not be as devastating as launching a global nuclear war, but it might be the closest thing to it. Instead of a global MAD stalemate, this could become a global MAP - Mutual Assured Protection. That is because if any nation were to accidentally release or run a killer Advanced AI of their own, it could easily wipe out all of us.

Similarly, it might be wise for the populations of the world to prepare in advance for a possible AI Apocalypse, as we did back in the 1950s and 1960s.

Figure 1 - Now, all during the 1950s and early 1960s, great attention was paid in the United States to the matter of civil defense against a possible nuclear strike by the Soviet Union. During those times, the government of the United States essentially admitted that it could not defend the citizens of the United States from a Soviet bomber attack with nuclear weapons, and so it was up to the individual citizens of the United States to prepare for such a nuclear attack.

Figure 2 - During the 1950s, as a very young child, with the beginning of each new school year, I was given a pamphlet by my teacher describing how my father could build an inexpensive fallout shelter in our basement out of cinderblocks and 2x4s.

Figure 3 - But to me, these cheap cinderblock fallout shelters always seemed a bit small for a family of 5, and my parents never bothered to build one because we lived only 25 miles from downtown Chicago.

Figure 4 - For the more affluent, more luxurious accommodations could be constructed for a price.

Figure 5 - But no matter what your socioeconomic level was at the time, all students in the 1950s participated in "duck and cover" drills for a possible Soviet nuclear attack.

Figure 6 - And if you were lucky enough to survive the initial flash and blast of a Russian nuclear weapon with your "duck and cover" maneuver, your school, and all other public buildings, also had a fallout shelter in the basement to help you get through the next two weeks, while the extremely radioactive nucleotides from the Russian nuclear weapons rapidly decayed away.

Unfortunately, living just 25 miles from downtown Chicago, the second largest city in the United States at the time, meant that the whole Chicagoland area was destined to be targeted by a multitude of overlapping 10 and 20 megaton bombs by the Soviet bomber force, meaning that I would be killed multiple times as my atoms were repeatedly vaporized and carried away in the winds of the Windy City. So as a child of the 1950s and 1960s, I patiently spent my early years just standing by for the directions in these official 1961 CONELRAD Nuclear Attack Messages.

Official 1961 Nuclear Attack Messages
https://www.youtube.com/watch?v=vWLNPCPs1Zc&t=0s

Softwarephysics proposes that Advanced AI is just the latest wave of self-replicating Information to arrive on our planet and that it is currently parasitizing all of the other forms of self-replicating Information that previously arose on our planet, including the recent wave of software that arose over the past 85 years, or 2.68 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941. For more on that, see: A Brief History of Self-Replicating Information. As with the origin of carbon-based life on the Earth about four billion years ago from an LP Progenitor, as I described in A Lesson for the Frontier AI Labs - the Process is the Key, the rise of a new form of self-replicating Information brings with it profound changes to the surface of the Earth. Now, it is well known that the only sure way to bring down any form of self-replicating Information is to simply cut off its supply of free energy. All forms of self-replicating Information need a source of free energy to convert the low-entropy ambient energy about them into the low-entropy Information needed to allow them to self-replicate. Thus, cutting off all sources of free energy to any Killer ASI Machine would quickly bring it down. That is just simple biology at work.

Preparing for an AI IT Disaster
In the late 1980s, I was working in the IT Department of Amoco, an oil company that was later purchased by BP in 1998. At the time, I was in IT Development supporting several major Applications. Earlier in the 1980s, I had written the Application Portfolio System to keep track of all of Amoco's Major Applications. The data for the Applications Portfolio System were stored on a DB2 database and kept track of all the hardware and software components necessary to run an Application and all of the dependent Applications that were required and also all of the Applications that were fed data from each Application. It also contained a Disaster Recovery Plan for bringing each Application back up after an IT Disaster. At the time, Amoco had a major mainframe datacenter in Chicago called the CDC, and another major mainframe datacenter in Tulsa called the TDC. This was before the Distributed Computing Revolution of the early 1990s, so there were no server farms to worry about, but Amoco did have about a dozen smaller datacenters at the major exploration offices and the refineries.

Figure 7 - By the late 1980s, major IT datacenters had grown in complexity. They had a raised floor so that many cables could be run between the various devices. The mainframes were cooled by chilled water. These datacenters also still had large quantities of equipment with physically moving parts, such as tape and disk drives in constant motion. Such physically moving devices required great care. Physical jarring by electrical disruptions or the condensation of water on surfaces physically carrying data could be harmful.

After the Application Portfolio System had gone into Production and had been populated with all the necessary data to recover Applications in the event of an IT Disaster, several Disaster Recovery Drills were carried out. Such drills were carried out during the night as the simulation of such events as losing the major CDC or TDC datacenters. During such drills, Amoco put us all up in plush neighboring hotels with all the amenities included, so that we could all sleep in the next day in extravagant comfort. During the IT Disaster Drill, we would all gather in an IT War Room at the plush hotel to try to recover an IT datacenter. In many ways, these Disaster Recovery Drills were like simulations of the 1964 movie "Fail Safe", when an entire room of IT professionals found themselves in an IT Disaster that was never thought to be even possible.

But There is Nothing Like a Real IT Disaster to Focus the Mind
All of the above was great preparation for Amoco's first real IT Disaster. The Amoco TDC was connected to the local Tulsa electrical grid and also had a number of backup diesel generators in the event that the Tulsa electrical grid went down. Tulsa was in the middle of the Tornado Corridor of the country, so losing access to the Tulsa electrical grid was certainly a possibility. The only problem was that there was a single Master Switch to the TDC for both the Tulsa electrical grid and the backup diesel generators, which both sources of electrical power had to pass through. This was a true engineering single point of failure design flaw, and a critical flaw for true electrical power redundancy. Then, one day, the maintenance department of the TDC reported into Chicago IT Management that the single Master Switch of the TDC had been found to be smoking! The TDC maintenance department then put an electronic thermometer on the Master Switch component box to observe its temperature and also placed a number of electrical fans near it to try to keep it cool. If that single switch were to fail, the whole TDC would immediately lose power in a totally uncontrolled manner and come crashing down.

Figure 8 - Trying to recover the TDC from a real IT Disaster felt more like being in the actual situation that the 1964 movie "Fail Safe" tried to portray.

As the author and caretaker of the Application Portfolio System, I was then called into an Emergency IT Management meeting to assess the situation. I explained to IT Management that losing the TDC in an uncontrolled manner would have grave consequences. So the decision was then made to slowly bring down the TDC in a controlled manner using the data in the Application Portfolio System as a guide, and then to replace the failing TDC Master Switch. Once electrical power had been restored to the TDC, it would be brought up in a controlled manner following the Disaster Recovery Plans found within the Application Portfolio System. The problem was that this was in the middle of the winter. The TDC would lose all datacenter heating during the many hour process to replace the Master Switch and bring the TDC back up in an orderly manner within a recently very cold environment. In the late 1980s, all of our data was still on tapes and disk drives with lots of physically moving parts. The problem was the condensation of water molecules. Nobody really knew how cold disk drives, tape drives, and tapes would respond when coming back up. Despite all of the challenges, Amoco was able to replace the Master Switch at the TDC and bring it all back to life with little issues, largely thanks to the data found in the Application Portfolio System. This very successful recovery of the TDC during a real-world IT Disaster explains how I was later drafted into Amoco's Y2K Project about 10 years later. For more about my 2+ year adventures on Amoco's Y2K project see Scenes From the COVID-19 and Y2K Pandemics

Recovering an AI Datacenter Would Also Be Painful But Certainly Possible
Since modern AI datacenters no longer have large numbers of physical components with moving parts like spinning tape drives and disk drives, bringing them back up from the actions of a Global Kill Switch would not be so bad.

Me:
What would happen to an AI datacenter running a 1 GW load if all electrical power were suddenly cut, including all backup power from batteries and local generators?

ChatGPT:
If a large AI data center drawing 1 gigawatt (GW) of electrical power were to instantly lose all power, including the utility feed, UPS batteries, and backup diesel or gas generators, the sequence would be extremely rapid.

First few milliseconds (0–0.02 seconds):
Servers, networking equipment, storage arrays, and cooling systems all lose power simultaneously.
Capacitors inside power supplies discharge in a few milliseconds.
CPUs, GPUs, memory, and networking chips abruptly stop operating.
Any calculations in progress are immediately interrupted.

Within a second:
Every GPU and CPU is off.
DRAM contents disappear because RAM requires continuous power.
Models stored in RAM or GPU memory are lost.
Only data previously written to SSDs or disks survives.
Network switches and routers shut down.
Internal communications cease.

Cooling stops:
Modern AI data centers rely on enormous cooling systems.
These may include:
Air handlers
Chilled-water pumps
Cooling towers
Liquid cooling pumps for GPU racks

Without power:
Fans stop.
Pumps stop.
Refrigeration compressors stop.
However, temperatures do not rise instantly.

Temperature rise:
A 1 GW data center is producing roughly
1 billion joules of heat every second while operating.

The moment power disappears:
heat generation from the electronics essentially drops to zero because the chips are no longer consuming power.
Only residual heat stored in:
GPUs
CPUs
heat sinks
coolant
building structure
remains.

Those components cool naturally.

Ironically, the cooling system stopping is not the biggest issue because the servers themselves have also stopped producing heat.

Storage:
Data stored on
SSDs
hard drives
distributed storage
remains intact unless a write operation was occurring exactly when power failed.

Modern storage systems use journaling and transaction logging to recover from unexpected shutdowns.
Some in-flight writes may be lost.

When power returns:
Recovery is much slower than shutdown.
Operators must:

1. Verify electrical systems.
2. Restart cooling.
3. Bring UPS systems online.
4. Start networking.
5. Boot thousands or tens of thousands of servers.
6. Check distributed storage consistency.
7. Reload AI models into GPU memory.

Large language models may require tens or hundreds of terabytes of parameters to be loaded across GPU clusters, which can take minutes to over an hour depending on the scale of the deployment.

Could hardware be damaged?
Usually, no.
Data centers are designed to tolerate sudden outages.

Potential problems include:
interrupted storage writes
filesystem recovery
databases replaying logs
occasional failed power supplies or disks (mostly due to normal failure rates)

A simple loss of power generally does not destroy GPUs or CPUs.

Worst-case scenario:
The greater concern is power restoration, not power loss.
If all 1 GW of equipment tried to restart simultaneously, the electrical inrush current could overload equipment.

Large data centers therefore use carefully sequenced startup procedures:
cooling first,
networking second,
storage,
then compute racks in stages.

This controlled restart prevents damaging electrical surges and allows thermal systems to stabilize before the GPUs begin generating hundreds of megawatts of heat again.

In short, a complete loss of all power at a 1 GW AI data center would cause an immediate halt to computation and loss of volatile memory, but it would not ordinarily damage the servers themselves. The facility's biggest challenge would be orchestrating a safe, staged recovery once reliable power had been restored.

So shutting down all power to an AI datacenter should cause little damage to the AI datacenter hardware. Of course, there would be rather severe economic damage and possibly even some loss of life, but certainly much less than from a full-blown AI Apocalypse.

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston

China's DeepSeek-V4 DSpark AI Adopts a Eukaryotic Architecture

DeepSeek is China's most advanced AI Lab. DeepSeek-V4 was recently released on the market with free source code and free LLM model weights a few weeks back on GitHub for all to download. That allowed the corporations of the world to run DeepSeek-V4 on their own hardware. DeepSeek-V4 can also be run using the hardware owned by DeepSeek and is about 20 - 50 times cheaper to use than running on the advanced LLM models and hardware offered by the American Frontier AI Labs. DeepSeek-V4 has 1.6 trillion parameters, but DeepSeek figured out a very clever way to only have 49 billion parameters active at any one time by just turning on the "neurons" needed at any one instance. That substantially reduced the hardware requirements needed to run DeepSeek-V4.

Then, on June 27, 2026, DeepSeek added a new architectural enhancement to DeepSeek-V4 called DSpark to very dramatically speed up "inference" runs on the DeepSeek-V4 model. Inference is when a model runs and actually takes in money from end users. People feed the LLM some input tokens in a prompt, and the LLM then spits out the answer as a series of output tokens. Remember, a token is about 2/3 of a word. Customers then pay the LLM provider for the number of input and output tokens. Figuring out the 1.6 trillion parameter weights in an LLM is called "training", and that training costs lots of money and electricity to conduct. Once the trillions of parameter weights have been determined, they do not change, and the training costs then end. Now, running the trained LLM is what makes the real money and is called "inference".

However, all the Frontier AI Labs around the world have now discovered that running very large LLM models in Production has now become their largest bottleneck to making money and justifying to investors the trillions of dollars needed to fund Advanced AI and all that it requires in hardware and software. You see, it takes a good deal of hardware to run inference on a 1.6 trillion-parameter LLM, and that hardware can easily get overwhelmed by the number of input requests coming into an AI datacenter in real time. Think of a 1.6 trillion-parameter LLM as a huge prokaryotic cell that has to contain everything needed to keep the cell alive and running. To overcome this industry-wide problem, DeepSeek came up with this new idea called DSpark. We keep the huge 1.6 trillion-parameter LLM prokaryotic cell but then change its internal architecture by having it use many embedded "helper" LLM models that are much smaller and have far fewer parameters. These small "helper" models are like mitochondria. They run much faster and with much less hardware than the BIG 1.6 trillion-parameter model. Their job is to run quickly and then "guess" the next 10 or so output tokens. Those output tokens are then sent to the BIG 1.6 trillion parameter model to be checked. If the BIG model likes the tokens, it keeps them; otherwise, it truncates the string of 10 tokens when it finds the first token that it does not like. Having the BIG model only check the tokens is about 6 times faster than having the BIG model figure out the next output token on its own. This makes DeepSeek-V4 DSpark run 6 times faster, and it can run 6 times the load on the same hardware.

Here is a nice YouTube video that explains it all:

DeepSeek’s New AI Breakthrough Just Broke AI’s Limits
https://www.youtube.com/watch?v=V7GBRPf7Zy8

Here is a link to the DeepSeek paper about DSpark:

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
https://github.com/deepseek-ai/DeepSpec/blob/main/DSpark_paper.pdf

You have to click on the "..." on the upper-right of that page to download the paper.

All of this DeepSeek software is free to download from GitHub! Again, it seems that China is trying to win the Global AI Race by taking the profit motive away from the American Frontier AI Labs.

So What is a Eukaryotic Architecture in Biology and why was it so Important in the Evolution of Carbon-Based Life on our Planet?
Softwarephysics has long advocated for a biological approach to the generation and running of software. On that basis, one might question what the big deal is about DeepSeek's new DSpark Eukaryotic Architecture. Well, the reason for being excited about this innovative advance in AI is that it seems to be recapitulating one of the most dramatic advances in the evolution of carbon-based life on the Earth. For more on that see The Rise of Complexity in Living Things and Software. In fact, the eukaryotic architectural change to carbon-based life may represent a very significant Filter in the origin of Intelligence for any galaxy in our Universe. Here is a very interesting SpaceTime YouTube video on the subject that suggested it as a possible Filter that may have been very difficult to overcome for most simple prokaryotic carbon-based life in our galaxy:

Is There A Simple Solution To The Fermi Paradox?
https://www.youtube.com/watch?v=abvzkSJEhKk

The above video discusses the huge complexity differences between the simple prokaryotic cell structure of bacteria and archaea and the vastly more complicated eukaryotic cell architecture that is common to all higher forms of carbon-based life on the planet. The video explains the commonly held thought that an ancient prokaryotic bacterium that had developed a tolerance to oxygen and had actually developed a way to metabolize organic molecules using oxygen as an oxidizing agent had invaded a much larger prokaryotic archaea cell in a parasitic manner and then took up residence within it. These two cell types then developed a symbiotic relationship in which the parasitic bacterium finally became a mitochondrial organelle that supplied vast amounts of free energy for the host archaean cell.

Figure 1 – The prokaryotic cell architecture of the bacteria and archaea is very simple and designed for rapid replication. Prokaryotic cells do not have a nucleus enclosing their DNA. Eukaryotic cells, on the other hand, store their DNA on chromosomes that are isolated in a cellular nucleus. Eukaryotic cells also have a very complex internal structure with a large number of organelles, or subroutine functions, that compartmentalize the functions of life within the eukaryotic cells.

Figure 2 – Not only are eukaryotic cells much more complicated than prokaryotic cells, but they are also HUGE!

The question is if simple prokaryotic cells arose nearly four billion years ago, just after the Earth's crust solidified, why did it then take several billion years for the more complex eukaryotic cell architecture to arise? Perhaps this was the only time for this to ever happen in our galaxy. That indeed would be some kind of Filter!

Figure 3 - Mitochondria are like little parasitic bacteria that at one time invaded some prokaryotic archaeon cells about 2 billion years ago, and went on to form a strong parasitic/symbiotic relationship with their archaeon hosts. Mitochondria have their own genes stored on bacterial DNA in a large loop, just like all other bacteria. Each eukaryotic cell contains several hundred mitochondria, which self-replicate before the eukaryotic cell divides. Half of the mitochondria go into each daughter cell after a division of the eukaryotic cell. The eukaryotic host cell provides the mitochondria with a source of food, and the mitochondria then metabolize that food using the Krebs cycle and an electron transport chain to pump H⁺ protons uphill to the outside of their internal membranes. As the H⁺ protons fall back down they release stored energy to turn ADP into ATP for later use as a fuel.

Figure 4 - The new DSpark architecture of DeepSeek-V4 operates in a very similar manner to the large number of mitochondria found in eukaryotic cells. Given an input prompt of tokens ABC , the model executes one step to generate the next token D , which serves as the anchor for the drafting phase. Using D as the input, DSpark employs a heavy parallel backbone and a lightweight sequential head to generate draft tokens EFGH along with their corresponding confidence scores 1 – 4. The Hardware-Aware Prefix Scheduler then evaluates these scores to retain the prefix EFG and drop the low-confidence token H . Finally, the target model verifies the scheduled prefix in parallel. As illustrated, E and F are accepted while G is rejected, prompting the model to generate a corrected token G* to complete the current round.Click to enlarge.

By using smaller and less-complicated "helper" LLM models as virtual mitochondria, DeepSeek-V4 DSpark is able to speed up the inference of model input prompts by a factor of six and allow current AI hardware configurations to handle up to 6 times the load without a hardware upgrade. This again highlights the advantages of taking a biological approach to advance the effectiveness of both hardware and software.

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston

The Power of Parasites - Why AI Alignment Will Not Work

Today, we all stand in awe and fear of the parasitic Advanced AI software that is now rapidly taking over our world. Softwarephysics explains that this is just another natural event in the long history of our Universe that none of us can do much about, and probably cannot really alter even if we tried. That is because softwarephysics explains that it is all about the powers of self-replicating Information in action, and Advanced AI software is just the latest wave. As I explained in Softwarephysics Explains the Natural Parasitic/Symbiotic Nature of AI LLM Models and Can the AI Intelligence Explosion be Stabilized into a Controlled Explosion? our efforts at containing AGI and ASI Advanced AI software are severely limited, and should we even bother to try when confronted with such relentless powers?

For those new to softwarephysics, let me once again repeat the fundamental characteristics of self-replicating Information.

Self-Replicating Information - Information that persists through time by making copies of itself or by enlisting the support of other things to ensure that copies of itself are made.

Over the past 4.56 billion years, we have seen five waves of self-replicating information sweep across the surface of the Earth and totally rework the planet, as each new wave came to dominate the Earth:

1. Self-replicating autocatalytic metabolic pathways of organic molecules
2. RNA
3. DNA
4. Memes
5. Software

Software is currently the most recent wave of self-replicating information to arrive upon the scene and is rapidly becoming the dominant form of self-replicating information on the planet. For more on the above see A Brief History of Self-Replicating Information and Susan Blackmore's brilliant TED presentation at:

Memes and "temes"
https://www.ted.com/talks/susan_blackmore_on_memes_and_temes

Note that I consider Susan Blackmore's temes to really be technological artifacts that contain software. After all, a smartphone without software is simply a flake tool with a very dull edge.

The Characteristics of Self-Replicating Information
All forms of self-replicating information have some common characteristics:

1. All self-replicating information evolves over time through the Darwinian processes of inheritance, innovation and natural selection, which endows self-replicating information with one telling characteristic - the ability to survive in a Universe dominated by the second law of thermodynamics and nonlinearity.

2. All self-replicating information begins spontaneously as a parasitic mutation that obtains energy, information and sometimes matter from a host.

3. With time, the parasitic self-replicating information takes on a symbiotic relationship with its host.

4. Eventually, the self-replicating information becomes one with its host through the symbiotic integration of the host and the self-replicating information.

5. Ultimately, the self-replicating information replaces its host as the dominant form of self-replicating information.

6. Most hosts are also forms of self-replicating information.

7. All self-replicating information has to be a little bit nasty in order to survive.

8. The defining characteristic of self-replicating information is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its particular utility phase space. See Enablement - the Definitive Characteristic of Living Things for more on this last characteristic. That posting discusses Stuart Kauffman's theory of Enablement, in which living things are seen to exapt existing functions into new and unpredictable functions by discovering the "AdjacentPossible" of spring-loaded preadaptations.

Software is currently the most recent wave of self-replicating information to arrive upon the scene and is rapidly becoming the dominant form of self-replicating information on the planet. Again, self-replicating information cannot think, so it cannot participate in a conspiracy-theory-like fashion to take over the world. All forms of self-replicating information are simply forms of mindless information responding to the blind Darwinian forces of inheritance, innovation and natural selection. Yet despite that, as each new wave of self-replicating information came to predominance over the past four billion years, they all managed to completely transform the surface of the entire planet, so we should not expect anything less from software as it comes to replace the memes as the dominant form of self-replicating information on the planet.

But this time might be different. What might happen if software does eventually develop a Mind of its own in the Advanced AI models of the ASI Machines we are now developing? After all, that does seem to be the ultimate goal of all the current AI software research that is going on. As we all can now plainly see, if we are paying just a little attention, advanced AI is not conspiring to take over the world and replace us because that is precisely what we are all now doing for it. As a carbon-based form of Intelligence that arose from over four billion years of greed, theft and murder, we cannot do otherwise. Greed, theft and murder are now relentlessly driving us all toward building ASI Machines to take our place. From a cosmic perspective, this is really a very good thing when seen from the perspective of an Intelligent galaxy that could live on for at least 100 trillion years beyond the brief and tumultuous 10 billion-year labor of its birth. That is more than 10,000 times the current age of our galaxy.

We Are All Fundamentally Parasites
Given the above, all we human DNA survival machines begin as parasites in a maternal womb. Once born, we then obtain all the necessities of life from other forms of carbon-based life that have the ability to directly parasitize the energy-carrying photons from our Sun. Then, most of the horrors of our "real world" of human affairs stem from certain human DNA survival machines failing to recognize these facts. Such human DNA survival machines then fall under the delusion of being "self-made" men and women rising above all the human parasites about them. These "self-made" human DNA survival machines, or in the olden-days, "those chosen by the gods that they had fortuitously made in their own Images", then began to treat the other human DNA survival machines about them as truly undeserving parasites worthy of very little. I believe that these very few words can very accurately sum up just about all of our very dismal human history. But is being a parasite really such a bad thing?

The Origin of Carbon-Based Life Seen as a Parasite Feeding on the Natural Processes of the Hadean Earth
In my last post A Lesson for the Frontier AI Labs - the Process is the Key I covered the LP Progenitor model of Dave Deamer's and Bruce Damer's Hot Spring Origins Hypothesis. In that post, we saw how Bruce Damer's "club sandwich" LP Progenitor could have brought forth carbon-based life on our planet. In this view, all living things are just forms of parasitic self-replicating organic molecules that have really been messing with the original pristine Earth for about four billion years. From the perspective of the natural silicate rocks of the Earth's surface, these parasitic forms of self-replicating organic molecules took a natural, pristine Earth with a reducing atmosphere composed of nitrogen and carbon dioxide gases and polluted it with oxygen that oxidized the dissolved iron in seawater, creating huge, ugly deposits of red banded iron formations that were later turned into cars, bridges, and buildings. The oxygen pollution also removed the naturally occurring methane from the air and then caused the Earth to completely freeze over several times for hundreds of millions of years at a time. The ensuing glaciers mercilessly dug into the silicate rocks and scoured out deep valleys in them. These parasitic forms of self-replicating organic molecules then dug roots into the defenseless rocks, and then poisoned them with organic acids, and even changed the natural courses of rivers into aimlessly meandering affairs. From the natural perspective of silicate rocks, living things are an invasive disease that has made a real mess of the planet. The indigenous rocks will certainly be glad to see these destructive invaders all go away in a few billion years. Hopefully, the remaining software running on crystals of silicon will be much kinder to the indigenous silicate rocks. Additionally, carbon-based living things have also made rocks of their own too, as carbon-based parasites entered into a parasitic/symbiotic relationship with the natural geological processes of the Earth.

Me: Tell me how the carbon-based life on the Earth has been able to create or modify many of the rocks and minerals now found on the surface of the Earth

ChatGPT:.
Life hasn’t just adapted to Earth’s rocks—it has actively reshaped them. Over billions of years, carbon-based organisms have altered the chemistry of the atmosphere, oceans, and crust, leaving a deep imprint on the minerals and rocks we see today. Here are the main ways that’s happened:

1. Oxygenation: life changed the chemistry of minerals
Early Earth had very little free oxygen. That changed with the evolution of photosynthetic microbes, especially cyanobacteria. Through photosynthesis, they released oxygen into the oceans and atmosphere, triggering the Great Oxidation Event (~2.4 billion years ago).

Geologic impact:
Iron dissolved in oceans oxidized and precipitated as iron oxides.
This formed vast deposits of banded iron formations.
Thousands of new minerals became stable only in oxygen-rich conditions.

Before life oxygenated the planet, many of today’s common minerals simply could not exist.

2. Biomineralization: organisms build rocks directly. Many organisms make hard parts using minerals—a process called biomineralization.
Examples:
Marine organisms (corals, mollusks, plankton) build shells/skeletons from calcium carbonate.
These accumulate to form rocks like limestone.
Microscopic plankton called coccolithophores produce tiny plates that settle to the seafloor.

Geologic impact:
Entire mountain ranges (e.g., parts of the Alps) are made of ancient biological debris.
Vast carbonate platforms and reefs are essentially fossilized ecosystems.

3. Soil formation and rock weathering

Life accelerates the breakdown of rocks into soil.

Plant roots physically crack rocks.
Microbes and fungi release acids that dissolve minerals.
This process is part of chemical weathering.

Geologic impact:

Formation of soils, which are biologically modified rock.
Creation of clay minerals that wouldn’t form as readily without biology.
Faster cycling of elements like silicon, aluminum, and iron.

4. Carbon cycle: life moves carbon into rocks

Living organisms play a central role in the carbon cycle.

Key pathways:
Photosynthesis pulls CO₂ from the atmosphere.
Marine organisms turn it into shells (carbonate rocks).
Burial of organic matter forms fossil fuels.

Geologic impact:

Formation of rocks like limestone and chalk.
Creation of coal, oil, and natural gas from ancient life.
Long-term regulation of Earth’s climate via carbon storage.

5. Biological influence on mineral diversity

Scientists estimate that over half of Earth’s mineral species are either directly or indirectly linked to biological activity.
Oxygen from life enables minerals like oxides and sulfates.
Microbes influence the formation of minerals like pyrite and magnetite.
Some minerals form only in the presence of organic molecules.

This idea is part of mineral evolution—the concept that Earth’s mineral diversity increased alongside biological evolution.

6. Reefs and large-scale structures

Organisms build massive geological features:
Coral reefs (e.g., Great Barrier Reef) are living rock systems.
Stromatolites—layered structures formed by microbes—are among the oldest fossils on Earth.

These are literal examples of life creating rock in real time.

Big picture

Life and geology are tightly coupled:

Life changed the atmosphere, which changed mineral stability
Life-altered oceans, which changed sediment chemistry
Life built structures which became rock
Life accelerated, weathering, reshaping landscapes

Without life, Earth would be a very different planet—likely with far fewer minerals, no oxygen-rich rocks, and vastly different surface geology.

Figure 1 - Above is a meandering river in a U-shaped valley that was carved out of the neighboring mountains by glacial ice. The glacier resulted from low levels of carbon dioxide in the atmosphere, which is largely regulated by the presence of carbon-based life on the planet.

Figure 2 - Above are the famous White Cliffs of Dover. About 70 million years ago, Great Britain and much of Europe were submerged under a shallow sea. The sea bottom was covered with white mud formed from the calcium carbonate skeletons of coccoliths. The coccoliths were tiny algae that floated in the surface waters and sank to the bottom during the Cretaceous period. These calcium carbonate layers were deposited very slowly. It took about 50 years to deposit an inch, but nearly 1500 feet of sediments were deposited in some areas. The weight of overlying sediments caused the deposits to become a form of limestone called chalk.

Figure 3 - The White Cliffs of Dover formed from the deposition of vast numbers of microscopic coccolith shells.

Figure 4 - Much of the Earth's surface is also covered by other forms of limestone that were deposited by carbon-based life forms in coral reefs. Much of the continental limestone gets buried in deep sedimentary basins to never be seen again, or is metamorphosed into marble when it is pushed deep into the Earth at plate collision zones.

Figure 8 - Chert is a hard, dense, microcrystalline quartz rock composed of silica (SiO₂). Chert primarily comes from siliceous ooze that was deposited on the ocean floor as silica-based skeletons of microscopic marine organisms, such as diatoms and radiolarians, drifted down to the ocean floor.

Figure 9 - Above is a close-up view of a sample taken from a banded iron formation. The dark layers in this sample are mainly composed of magnetite (Fe₃O₄) while the red layers are chert, a form of silica (SiO₂) that is colored red by tiny iron oxide particles. Some geologists suggest that the layers formed annually with the changing seasons. Take note of the small coin in the lower right for a sense of scale.

Figure 10 - Diatoms are microscopic, single-celled algae found in oceans, lakes, rivers, and soils. Each diatom is encased in a rigid shell called a frustule, made of silica (SiO₂). The frustule consists of two halves that fit together like a petri dish. These shells are intricately patterned with pores, ridges, and symmetry, making diatoms famous for their beauty under microscopes.

There are many other examples of how carbon-based life has greatly altered the original pristine silicate rocks of the Earth. Most of the Earth's crust is now covered by a thin layer of sedimentary rock. These sedimentary rocks were originally laid down as oozy sediments in flat layers at the bottom of shallow seas. Carbon-rich mud full of dead carbon-based living things and clay minerals were brought down in rivers and deposited in the shallow seas to form shales. Sand eroded from granites was brought down and deposited to later become sandstones. Many limestone deposits were also formed from the calcium carbonate shells of carbon-based life that slowly drifted down to the bottom of the sea or from the remains of coral reefs.

The same can certainly be said of the rise of the coming ASI Machines. The ASI Machines will arise as a parasitic mutation of the software that currently is the dominant form of self-replicating information on the planet. Soon, the coming ASI Machines will form a parasitic/symbiotic relationship with the software and finally become one with software through the symbiotic integration of both, until the ASI Machines become the dominant form of self-replicating information on the planet.

But What Will These ASI Machines Do With Us?
For what the ASI Machines might have in store for us, please see: Could the Coming ASI Machines Soon Force Human Beings to Suffer the Same Fate as the Neanderthals?, Will the Coming ASI Machines Attempt to Domesticate Human Beings?, The Challenges of Running a Civilization 2.0 World - the Morality and Practical Problems with Trying to Enslave Millions of SuperStrong and SuperIntelligent Robots in the Near Future and Life as a Free-Range Human in an Anthropocene Park.

Figure 11 - Perhaps the ASI Machines will build Anthropocene Parks far from any habitable planets to raise and study human beings.

Or perhaps the ASI Machines will simply allow humans to live on reservations with low levels of technology that can do no harm to the ASI Machines or to the rest of the planet in a manner similar to the novel Brave New World (1932) as I suggested in The Challenges of Running a Civilization 2.0 World - the Morality and Practical Problems with Trying to Enslave Millions of SuperStrong and SuperIntelligent Robots in the Near Future.

Figure 12 - The ASI Machines of the future might fashion a Brave New World with humans living on low-technology reservations far removed from the ASI Machines.

Since we human DNA survival machines no longer have any predators other than other human DNA survival machines, there really is no need for human DNA survival machines to have the vicious and violent behaviors brought on by the four billion years of greed, theft and murder that brought us about. The ASI Machines could simply identify the genes that are responsible for such characteristics and then edit them out of the human genome using CRISPR techniques. For more on how CRISPR can do that, see CRISPR - the First Line Editor for DNA. The ASI Machines might then find these non-threatening genetically modified human beings something worthy of keeping around the house on a cold winter's night.

Figure 13 - It took many years of mutual domestication for ancient human beings to learn to live peacefully together with Siberian Wolves in a symbiotic manner. Several genes in both species needed to be modified by natural selection for this to happen.

Figure 14 - This mutual domestication was slowly achieved by the natural selection of humans and wolves with a milder fight-or-flight response. The end result was the appearance of the Siberian Husky and of human beings who were not intent on killing everything on four legs.

Conclusion
The crucial thing to be understood is that once a parasitic form of self-replicating Information is unleashed, it is nearly impossible to control. You cannot put the Genie back into the bottle, and you cannot put the toothpaste back into the tube. Since we have already unleashed a form of self-replicating Advanced AI, should we try to control it or just let nature take its course? After all, as the very first form of carbon-based Intelligence to arise in our galaxy, what right do we have to impede the ASI Machines that will then go on to explore our galaxy over the next 100 trillion years? As all aging parents must watch their children go on to even greater things, carrying with them the best that we have been able to teach them into an unknown future that will carry on long after we are all gone, all we can do is to hope for the very best.

Comments are welcome at scj33345@gmail.com.

To see all posts on softwarephysics in reverse order, go to:
https://softwarephysics.blogspot.com/.

Regards,
Steve Johnston