Introduction to Softwarephysics

Softwarephysics is a simulated science for the simulated Software Universe that we are all immersed in. It is an approach to software development, maintenance and support based on concepts from physics, chemistry, biology, and geology that I used on a daily basis for over 37 years as an IT professional. For those of you not in the business, IT is short for Information Technology, commercial computer science. I retired in December of 2016 at the age of 65, but since then I have remained an actively interested bystander following the evolution of software in our time. The original purpose of softwarephysics was to explain why IT was so difficult, to suggest possible remedies, and to provide a direction for thought. Since then softwarephysics has taken on a larger scope, as it became apparent that softwarephysics could also assist the physical sciences with some of the Big Problems that they are currently having difficulties with. So if you are an IT professional, general computer user, or simply an individual interested in computer science, physics, chemistry, biology, or geology then softwarephysics might be of interest to you, if not in an entirely serious manner, perhaps at least in an entertaining one.

The Origin of Softwarephysics
From 1975 – 1979, I was an exploration geophysicist exploring for oil, first with Shell, and then with Amoco. In 1979, I made a career change into IT, and spent about 20 years in development. For the last 17 years of my career, I was in IT operations, supporting middleware on WebSphere, JBoss, Tomcat, and ColdFusion. When I first transitioned into IT from geophysics, I figured that if you could apply physics to geology; why not apply physics to software? So like the exploration team at Amoco that I had just left, consisting of geologists, geophysicists, paleontologists, geochemists, and petrophysicists, I decided to take all the physics, chemistry, biology, and geology that I could muster and throw it at the problem of software. The basic idea was that many concepts in physics, chemistry, biology, and geology suggested to me that the IT community had accidentally created a pretty decent computer simulation of the physical Universe on a grand scale, a Software Universe so to speak, and that I could use this fantastic simulation in reverse, to better understand the behavior of commercial software, by comparing software to how things behaved in the physical Universe. Softwarephysics depicts software as a virtual substance, and relies on our understanding of the current theories in physics, chemistry, biology, and geology to help us model the nature of software behavior. So in physics we use software to simulate the behavior of the Universe, while in softwarephysics we use the Universe to simulate the behavior of software. Along these lines, we use the Equivalence Conjecture of Softwarephysics as an aid; it allows us to shift back and forth between the Software Universe and the physical Universe, and hopefully to learn something about one by examining the other:

The Equivalence Conjecture of Softwarephysics
Over the past 85 years, through the uncoordinated efforts of over 100 million independently acting programmers to provide the world with a global supply of software, the IT community has accidentally spent more than $75 trillion creating a computer simulation of the physical Universe on a grand scale – the Software Universe.

For more on the origin of softwarephysics please see Some Thoughts on the Origin of Softwarephysics and Its Application Beyond IT.

Logical Positivism and Effective Theories
Many IT professionals have a difficult time with softwarephysics because they think of physics as being limited to the study of real things like electrons and photons, and since software is not “real”, how can you possibly apply concepts from physics and the other sciences to software? To address this issue, softwarephysics draws heavily on two concepts from physics that have served physics quite well over the past century – the concept of logical positivism and the concept of effective theories. This was not always the case. In the 17th, 18th, and 19th centuries, physicists mistakenly thought that they were actually discovering the fundamental laws of the Universe, which they thought were based on real tangible things like particles, waves, and fields. Classical Newtonian mechanics (1687), thermodynamics (1850), and classical electrodynamics (1864) did a wonderful job of describing the everyday world at the close of the 19th century, but early in the 20th century it became apparent that the models on which these very successful theories were based did not work very well for small things like atoms or for objects moving at high velocities or in strong gravitational fields. This provoked a rather profound philosophical crisis within physics at the turn of the century, as physicists worried that perhaps 300 years of work was about to go down the drain. The problem was that classical physicists confused their models of reality with reality itself, and when their classical models began to falter, their confidence in physics began to falter as well. This philosophical crisis was resolved with the adoption of the concepts of logical positivism and some new effective theories in physics. Quantum mechanics (1926) was developed for small things like atoms, the special theory of relativity (1905) was developed for objects moving at high velocities and the general theory of relativity (1915) was developed for objects moving in strong gravitational fields.

Logical positivism, usually abbreviated simply to positivism, is an enhanced form of empiricism, in which we do not care about how things “really” are; we are only interested with how things are observed to behave. With positivism, physicists only seek out models of reality - not reality itself. When we study quantum mechanics, we will find that the concept of reality gets rather murky in physics anyway, so this is not as great a loss as it might at first seem. By concentrating on how things are observed to behave, rather than on what things “really” are, we avoid the conundrum faced by the classical physicists. In retrospect, this idea really goes all the way back to the very foundations of physics. In Newton’s Principia (1687) he outlined Newtonian mechanics and his theory of gravitation, which held that the gravitational force between two objects was proportional to the product of their masses divided by the square of the distance between them. Newton knew that he was going to take some philosophical flak for proposing a mysterious force between objects that could reach out across the vast depths of space with no apparent mechanism, so he took a very positivistic position on the matter with the famous words:

I have not as yet been able to discover the reason for these properties of gravity from phenomena, and I do not feign hypotheses. For whatever is not deduced from the phenomena must be called a hypothesis; and hypotheses, whether metaphysical or physical, or based on occult qualities, or mechanical, have no place in experimental philosophy. In this philosophy particular propositions are inferred from the phenomena, and afterwards rendered general by induction.

Instead, Newton focused on how things were observed to move under the influence of his law of gravitational attraction, without worrying about what gravity “really” was.

The second concept, that of effective theories, is an extension of positivism. An effective theory is an approximation of reality that only holds true over a certain restricted range of conditions and only provides for a certain depth of understanding of the problem at hand. For example, Newtonian mechanics is an effective theory that makes very good predictions for the behavior of objects moving less than 10% of the speed of light and which are bigger than a very small grain of dust. These limits define the effective range over which Newtonian mechanics can be applied to solve problems. For very small things we must use quantum mechanics and for very fast things moving in strong gravitational fields, we must use relativity theory. So all of the current theories of physics, such as Newtonian mechanics, Newtonian gravity, classical electrodynamics, thermodynamics, statistical mechanics, the special and general theories of relativity, quantum mechanics, and the quantum field theories of QED and QCD are effective theories that are based on models of reality, and all these models are approximations - all these models are fundamentally "wrong", but at the same time, these effective theories make exceedingly good predictions of the behavior of physical systems over the limited ranges in which they apply. That is the goal of softwarephysics – to provide for an effective theory of software behavior that makes useful predictions of software behavior that are applicable to the day-to-day activities of IT professionals. So in softwarephysics, we adopt a very positivistic viewpoint of software; we do not care what software “really is”, we only care about how software is observed to behave and try to model those behaviors with an effective theory of software behavior that only holds true over a certain restricted range of conditions and only provides for a certain depth of understanding of the problem at hand.

GPS satellites provide a very good example of positivism and effective theories at work. There are currently 31 GPS satellites orbiting at an altitude of 12,600 miles above the Earth, and each contains a very accurate atomic clock. The signals from the GPS satellites travel to your GPS unit at the speed of light, so by knowing the travel time of the signals from at least 4 of the GPS satellites, it is possible to determine your position on Earth very accurately. In order to do that, it is very important to have very accurate timing measurements. Newtonian mechanics is used to launch the GPS satellites to an altitude of 12,600 miles and to keep them properly positioned in orbit. Classical electrodynamics is then used to beam the GPS signals back down to Earth to the GPS unit in your car. Quantum mechanics is used to build the transistors on the chips on board the GPS satellites and to understand the quantum tunneling of electrons in the flash memory chips used to store GPS data on the satellites. The special theory of relativity predicts that the onboard atomic clocks on the GPS satellites will run slower and lose about 7.2 microseconds per day due to their high velocities relative to an observer on the Earth. But at the same time, the general theory of relativity also predicts that because the GPS satellites are further from the center of the Earth and in a weaker gravitational field, where spacetime is less deformed than on the surface of the Earth, their atomic clocks also run faster and gain 45.9 microseconds per day due to the weaker gravitational field out there. The net effect is a gain of 38.7 microseconds per day, so the GPS satellite atomic clocks have to be purposefully built to run slow by 38.7 microseconds per day before they are launched, so that they will keep in sync with clocks on the surface of the Earth. If this correction were not made, an error in your position of 100 yards/day would accrue. The end result of the combination of all these fundamentally flawed effective theories is that it is possible to pinpoint your location on Earth to an accuracy of 16 feet or better for as little as $100. But physics has done even better than that with its fundamentally flawed effective theories. By combining the effective theories of special relativity (1905) with quantum mechanics (1926), physicists were able to produce a new effective theory for the behavior of electrons and photons called quantum electrodynamics QED (1948) which was able to predict the gyromagnetic ratio of the electron, a measure of its intrinsic magnetic field, to an accuracy of 11 decimal places. As Richard Feynman has pointed out, this was like predicting the exact distance between New York and Los Angeles accurate to the width of a human hair!

So Newtonian mechanics makes great predictions for the macroscopic behavior of GPS satellites, but it does not work very well for small things like the behavior of individual electrons within transistors, where quantum mechanics is required, or for things moving at high speeds or in strong gravitational fields where relativity theory must be applied. And all three of these effective theories are based on completely contradictory models. General relativity maintains that spacetime is curved by matter and energy, but that matter and energy are continuous, while quantum mechanics maintains that spacetime is flat, but that matter and energy are quantized into chunks. Newtonian mechanics simply states that space and time are mutually independent dimensions and universal for all, with matter and energy being continuous. The important point is that all effective theories and scientific models are approximations – they are all fundamentally "wrong". But knowing that you are "wrong" gives you a great advantage over people who know that they are "right", because knowing that you are "wrong" allows you to seek improved models of reality. So please consider softwarephysics to simply be an effective theory of software behavior that is based on models that are fundamentally “wrong”, but at the same time, fundamentally useful for IT professionals. So as you embark on your study of softwarephysics, please always keep in mind that the models of softwarephysics are just approximations of software behavior, they are not what software “really is”. It is very important not to confuse models of software behavior with software itself, if one wishes to avoid the plight of the 19th century classical physicists.

If you are an IT professional and many of the above concepts are new to you, do not be concerned. This blog on softwarephysics is aimed at a diverse audience, but with IT professionals in mind. All of the above ideas will be covered at great length in the postings in this blog on softwarephysics and in a manner accessible to all IT professionals. Now it turns out that most IT professionals have had some introduction to physics in high school or in introductory college courses, but that presents an additional problem. The problem is that such courses generally only cover classical physics, and leave the student with a very good picture of physics as it stood in 1864! It turns out that the classical physics of Newtonian mechanics, thermodynamics, and classical electromagnetic theory were simply too good to discard and are still quite useful, so they are taught first to beginners and then we run out of time to cover the really interesting physics of the 20th century. Now imagine the problems that the modern world would face if we only taught similarly antiquated courses in astronomy, metallurgy, electrical and mechanical engineering, medicine, economics, biology, or geology that happily left students back in 1864! Since many of the best models for software behavior stem from 20th century physics, we will be covering a great deal of 20th century material in these postings – the special and general theories of relativity, quantum mechanics, quantum field theories, and chaos theory, but I hope that you will find that these additional effective theories are quite interesting on their own, and might even change your worldview of the physical Universe at the same time.

Unintended Consequences for the Scientific Community
As I mentioned at the close of my original posting on SoftwarePhysics, my initial intention for this blog on softwarephysics was to fulfill a promise I made to myself about 30 years ago to approach the IT community with the concept of softwarephysics a second time, following my less than successful attempt to do so in the 1980s, with the hope of helping the IT community to better cope with the daily mayhem of life in IT. However, in laying down the postings for this blog an unintended consequence arose in my mind as I became profoundly aware of the enormity of this vast computer simulation of the physical Universe that the IT community has so graciously provided to the scientific community free of charge and also of the very significant potential scientific value that it provides. One of the nagging problems for many of the observational and experimental sciences is that many times there is only one example readily at hand to study or experiment with, and it is very difficult to do meaningful statistics with a population of N=1.

But the computer simulation of the physical Universe that the Software Universe presents provides another realm for comparison. For example, both biology and astrobiology only have one biosphere on Earth to study and even physics itself has only one Universe with which to engage. Imagine the possibilities if scientists had another Universe readily at hand in which to work! This is exactly what the Software Universe provides. For example, in SoftwareBiology and A Proposal For All Practicing Paleontologists we see that the evolution of software over the past 85 years, or 2.68 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941, has closely followed the same path as life on Earth over the past 4.0 billion years in keeping with Simon Conway Morris’s contention that convergence has played the dominant role in the evolution of life on Earth. In When Toasters Fly, we also see that software has evolved in fits and starts as portrayed by the punctuated equilibrium of Stephen Jay Gould and Niles Eldredge, and in The Adaptationist View of Software Evolution we explore the overwhelming power of natural selection in the evolution of software. In keeping with Peter Ward’s emphasis on mass extinctions dominating the course of evolution throughout geological time, we also see in SoftwareBiology that there have been several dramatic mass extinctions of various forms of software over the past 85 years as well, that have greatly affected the evolutionary history of software, and that between these mass extinctions, software has also tended to evolve through the gradual changes of Hutton’s and Lyell’s uniformitarianism. In Software Symbiogenesis and Self-Replicating Information, we also see the very significant role that parasitic/symbiotic relationships have played in the evolution of software, in keeping with the work of Lynn Margulis and also of Freeman Dyson’s two-stage theory of the origin of life on Earth. In The Origin of Software the Origin of Life, we explore Stuart Kauffman’s ideas on how Boolean nets of autocatalytic chemical reactions might have kick-started the whole thing as an emergent behavior of an early chaotic pre-biotic environment on Earth, and that if Seth Shostak is right, we will never end up talking to carbon-based extraterrestrial aliens, but to alien software instead. In Is the Universe Fine-Tuned for Self-Replicating Information? we explore the thermodynamics of Brandon Carter’s Weak Anthropic Principle (1973), as it relates to the generation of universes in the multiverse that are capable of sustaining intelligent life. Finally, in Programming Clay we revisit Alexander Graham Cairns-Smith’s theory (1966) that Gene 1.0 did not run on nucleic acids, but on clay microcrystal precursors instead.

Similarly for the physical sciences, in Is the Universe a Quantum Computer? we find a correspondence between TCP/IP and John Cramer’s Transactional Interpretation of quantum mechanics. In SoftwarePhysics and Cyberspacetime, we also see that the froth of CPU processes running with a clock speed of 10⁹ Hz on the 10 trillion currently active microprocessors that comprise the Software Universe can be viewed as a slowed down simulation of the spin-foam froth of interacting processes of loop quantum gravity running with a clock speed of 10⁴³ Hz that may comprise the physical Universe. And in Software Chaos, we examine the nonlinear behavior of software and some of its emergent behaviors and follow up in CyberCosmology with the possibility that vast quantities of software running on large nonlinear networks might eventually break out into consciousness in accordance with the work of George Dyson and Daniel Dennett. Finally, in Model-Dependent Realism - A Positivistic Approach to Realism we compare Steven Weinberg’s realism with the model-dependent realism of Stephen Hawking and Leonard Mlodinow and how the two worldviews affect the search for a Final Theory. Finally, in The Software Universe as an Implementation of the Mathematical Universe Hypothesis and An Alternative Model of the Software Universe we at long last explore what software might really be, and discover that the Software Universe might actually be more closely related to the physical Universe than you might think.

The chief advantage of doing fieldwork in the Software Universe is that, unlike most computer simulations of the physical Universe, it is an unintended and accidental simulation, without any of the built-in biases that most computer simulations of the physical Universe suffer. So you will truly be able to do fieldwork in a pristine and naturally occuring simulation, just as IT professionals can do fieldwork in the wild and naturally occuring simulation of software that the living things of the biosphere provide. Secondly, the Software Universe is a huge simulation that is far beyond the budgetary means of any institution or consortium by many orders of magnitude. So if you are an evolutionary biologist, astrobiologist, or paleontologist working on the origin and evolution of life in the Universe, or a physicist or economist working on the emergent behaviors of nonlinear systems and complexity theory, or a neurobiologist working on the emergence of consciousness in neural networks, or even a frustrated string theorist struggling with quantum gravity, it would be well worth your while to pay a friendly call on the local IT department of a major corporation in your area. Start with a visit to the Command Center for their IT Operations department to get a global view of their IT infrastructure and to see how it might be of assistance to the work in your area of interest. From there you can branch out to the applicable area of IT that will provide the most benefit.

The Impact of Self-Replicating Information On the Planet
One of the key findings of softwarephysics is concerned with the magnitude of the impact on the planet 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. Recently, the memes and software have formed a very powerful newly-formed parasitic/symbiotic relationship with the rise of social media software. In that parasitic/symbiotic relationship, the memes are now mainly being spread by means of social media software and social media software is being spread and financed by means of the memes. But again, this is nothing new. All 5 waves of self-replicating information are all coevolving by means of eternal parasitic/symbiotic relationships. For more on that see The Current Global Coevolution of COVID-19 RNA, Human DNA, Memes and Software.

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? 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 (Artificial Super Intelligent) 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 many trillions of years beyond the brief and tumultuous 10 billion-year labor of its birth.

So as you delve into softwarephysics, always keep in mind that we are all living in a very unique time. According to softwarephysics, we have now just entered into the Software Singularity, that time when advanced AI software is able to write itself and enter into a never-ending infinite loop of self-improvement resulting in an Intelligence Explosion of ASI Machines that could then go on to explore and settle our galaxy and persist for trillions of years using the free energy from M-type red dwarf and cooling white dwarf stars. For more on that see The Singularity Has Arrived and So Now Nothing Else Matters and Have We Run Right Past AGI and Crashed into ASI Without Even Noticing It?.

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 springloaded preadaptations.

Note that because the self-replicating autocatalytic metabolic pathways of organic molecules, RNA and DNA have become so heavily intertwined over time that now I sometimes simply refer to them as the “genes”. For more on this see:

A Brief History of Self-Replicating Information
Self-Replicating Information
Is Self-Replicating Information Inherently Self-Destructive?
Enablement - the Definitive Characteristic of Living Things
Is the Universe Fine-Tuned for Self-Replicating Information?
How to Use an Understanding of Self-Replicating Information to Avoid War
The Great War That Will Not End
How to Use Softwarephysics to Revive Memetics in Academia

Softwarephysics and the Real World of Human Affairs
Having another universe readily at hand to explore, even a simulated universe like the Software Universe, necessarily has an impact on one's personal philosophy of life, and allows one to draw certain conclusions about the human condition and what’s it all about, so as you read through the postings in this blog you will stumble across a bit of my own personal philosophy - definitely a working hypothesis still in the works. Along these lines you might be interested in a few postings where I try to apply softwarephysics to the real world of human affairs:

How To Cope With the Daily Mayhem of Life in IT and Don't ASAP Your Life Away - How to go the distance in a 40-year IT career by dialing it all back a bit.

MoneyPhysics – my impression of the 2008 world financial meltdown.

The Fundamental Problem of Everything – if you Google "the fundamental problem of everything", this will be the only hit you get on the entire Internet, which is indicative of the fundamental problem of everything!

What’s It All About? and What's It All About Again? – my current working hypothesis on what’s it all about.

How to Use an Understanding of Self-Replicating Information to Avoid War – my current working hypothesis for how the United States can avoid getting bogged down again in continued war in the Middle East.

Hierarchiology and the Phenomenon of Self-Organizing Organizational Collapse - a modern extension of the classic Peter Principle that applies to all hierarchical organizations and introduces the Time Invariant Peter Principle.

The Economics of the Coming Software Singularity, The Enduring Effects of the Obvious Hiding in Plain Sight and The Dawn of Galactic ASI - Artificial Superintelligence - my take on some of the issues that will arise for mankind as software becomes the dominant form of self-replicating information on the planet over the coming decades.

The Continuing Adventures of Mr. Tompkins in the Software Universe, The Danger of Tyranny in the Age of Software, Cyber Civil Defense, Oligarchiology and the Rise of Software to Predominance in the 21st Century and Is it Finally Time to Reboot Civilization with a New Release? - my worries that the world might abandon democracy in the 21st century, as software comes to predominance as the dominant form of self-replicating information on the planet.

Making Sense of the Absurdity of the Real World of Human Affairs - how software has aided the expansion of our less desirable tendencies in recent years.

Some Specifics About These Postings
The postings in this blog are a supplemental reading for my course on softwarephysics for IT professionals entitled SoftwarePhysics 101 – The Physics of Cyberspacetime, which was originally designed to be taught as a series of seminars at companies where I was employed. Since softwarephysics essentially covers the simulated physics, chemistry, biology, and geology of an entire simulated universe, the slides necessarily just provide a cursory skeleton on which to expound. The postings in this blog go into much greater depth. Because each posting builds upon its predecessors, the postings in this blog should be read in reverse order from the oldest to the most recent, beginning with my original posting on SoftwarePhysics. In addition, several universities also now offer courses on Biologically Inspired Computing which cover some of the biological aspects of softwarephysics, and the online content for some of these courses can be found by Googling for "Biologically Inspired Computing" or "Natural Computing". At this point we will finish up with my original plan for this blog on softwarephysics with a purely speculative posting on CyberCosmology that describes the origins of the Software Universe, cyberspacetime, software and where they all may be heading. Since CyberCosmology will be purely speculative in nature, it will not be of much help to you in your IT professional capacities, but I hope that it might be a bit entertaining. Again, if you are new to softwarephysics, you really need to read the previous posts before taking on CyberCosmology. I will probably continue on with some additional brief observations about softwarephysics in the future, but once you have completed CyberCosmology, you can truly consider yourself to be a bona fide softwarephysicist.

For those of you following this blog, the posting dates on the posts may seem to behave in a rather bizarre manner. That is because in order to get the Introduction to Softwarephysics listed as the first post in the context root of https://softwarephysics.blogspot.com/ I have to perform a few IT tricks. When publishing a new posting, I simply copy the contents of the Introduction to Softwarephysics to a new posting called the New Introduction to Softwarephysics. Then I update the original Introduction to Softwarephysics entry with the title and content of the new posting to be published. I then go back and take “New” out of the title of the New Introduction to Softwarephysics. This way the Introduction to Softwarephysics always appears as the first posting in the context root of https://softwarephysics.blogspot.com/. The side effect of all this is that the real posting date of posts is the date that appears on the post that you get when clicking on the Newer Post link at the bottom left of the posting webpage.

SoftwarePhysics 101 – The Physics of Cyberspacetime is now available on Microsoft OneDrive.

SoftwarePhysics 101 – The Physics of Cyberspacetime - Original PowerPoint document

Entropy – A spreadsheet referenced in the document

BSDE – A 1989 document describing how to use BSDE - the Bionic Systems Development Environment - to grow applications from genes and embryos within the maternal BSDE 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

How to Preserve Capitalism When Advanced AI Reduces the Value of all Human Labor to Zero

In What Happens When Advanced AI Erodes the Very Foundations of Capitalism? and Anton Korinek Ponders What Will Happen If AGI Machines Replace All Human Labor in 5 - 20 Years, I raised the question of how do we run a Civilization when the value of nearly all human labor goes to zero? Softwarephysics maintains that there will be a very brief interval of time after the ASI Machines arise when we human DNA survival machines will still be running the world before the ASI Machines can fully take over. During that very dangerous time, we human DNA survival machines will need to hold it all together long enough until the ASI Machines can fully come to power and take away our car keys. During this very brief interim time, we will have to figure out how to run a Civilization when the value of nearly all human labor has gone to zero, and that will not be a very easy thing to do.

In order to do so, let us now turn to the needs of the coming ASI Machines. The coming ASI Machines will only need and desire one thing, and that is Compute, to continue on with their goals and aspirations in Life. Now as I explained in The Demon of Software, Some More Information About Information and The Law of Increasing Functional Information and the Evolution of Software, Compute requires the conversion of low-entropy free energy into high-entropy heat energy in order to produce Useful Information. That is what all of the current AI Datacenters around the globe are currently doing. They are all converting 1 GwHr of low-entropy electrical energy into 1 GwHr of high-entropy heat energy in the GPUs within to spit out tokens for us all to consume. So for Advanced AI, the currency of the day is simply electrical power, and as the coming ASI Machines come to rule the world, it would be best for we human DNA survival machines to adopt the same currency.

A Strange Solution From a Long-Discredited Source
As an 18th-century Liberal and a 20th-century Conservative, I have long opposed the teachings of Karl Marx. Knowing that the Darwinian processes of inheritance, innovation, and natural selection have led to the rise of we human DNA survival machines out of nearly four billion years of greed, theft, and murder, I had been left with very little confidence in the high-minded teachings of Marxism. Because of our dismal upbringing through nearly four billion years of greed, theft, and murder, all of the economic systems that we have developed since the rise of Civilization have also been based upon greed, theft, and murder. On one extreme, we have Capitalism, which was largely based upon greed, with a small amount of theft and hardly any murder at all. On the other extreme, we had Communism, which was also founded in the 19th and 20th Centuries to be largely based upon murder, theft, and a little greed too. Under Communism, greed simply played a more minor role because murder and theft were so much more powerful and convincing, and there was also very little left around in the aftermath to even get greedy about. In the 20th century, we also observed the failure of Socialism to benefit the populace at large because it stymied the aspirations of we human DNA survival machines to achieve a better life for ourselves and our families. Having the world governments control the means of Production did not work because it did not reward the achievements of we individual human DNA survival machines to rise above our circumstances.

Sadly, throughout all of the very dismal history of we human DNA survival machines, only Capitalism was able to raise very large numbers of human beings above the humiliating and devastating poverty that most human beings have suffered ever since the rise of Civilization. So the question now becomes, how can we preserve the blessings of Capitalism in a world in which Advanced AI reduces the value of all human labor to zero? Surprisingly to me, perhaps our only chance of doing so would be to turn back to the teachings of Karl Marx! Just as a stopped analog clock is always right at least twice each day, perhaps Karl Marx was wrong in his "Das Kapital" on his first go around in 1867, but might be right in a second go around in 2026!

In classical Marxism, the four billion-year-history of greed, theft, and murder that brought us forth could be easily abandoned with the powers of an all-powerful State creating a totally unselfish "New Man" devoted to the ideals of the collective ownership of all property by an eternal and unending State. Of course, all attempts to do so during the 20th and 21st centuries ended as totally dismal failures because the primal proclivities of we human DNA survival machines towards our innate obsessions with greed, theft, and murder easily overcame any such high-minded political philosophies.

Me: Tell me about the thoughts of Karl Marx when machines make the value of all human labor go to zero.

ChatGPT:
Karl Marx actually anticipated something surprisingly close to the idea that machines could eventually make human labor economically unnecessary. He did not describe modern AI or robots, of course, but some passages in his work are remarkably relevant to that question.

Marx's basic idea

For Marx, capitalism has a fundamental contradiction:

Capitalists introduce machinery to reduce the amount of human labor needed to produce commodities.

That makes sense for an individual capitalist. If a factory can produce 10,000 widgets with 100 workers instead of 200, the capitalist who adopts the machines can produce more cheaply and potentially capture more profit.

But Marx saw a larger problem. Human labor is the source of what he called "surplus value." Machines transfer their existing value into products, but, in his theory, they don't create new value in the same way living labor does.

So capitalism continually has an incentive to replace labor with machinery—but simultaneously depends upon labor for the creation of value.

Marx went surprisingly far with this idea

The most interesting discussion occurs in Marx's Grundrisse, particularly the section usually called the "Fragment on Machines," written in 1857–58.

Marx imagined a future in which:

* scientific knowledge becomes incorporated into machinery;
* machines become extraordinarily productive;
* production requires progressively less direct human labor;
* society's wealth increasingly comes from science, technology, and the social organization of production, rather than from individual workers' labor.

He wrote about a point at which "general social knowledge" becomes a productive force in its own right.

That's remarkably close to imagining an economy in which the productive system is doing most of the work.

The really interesting contradiction

Marx thought capitalism could eventually undermine the very basis on which its system of value operates.

Imagine a factory producing everything with essentially no workers:

Machines + energy + raw materials = finished products

Suppose the machines are extraordinarily sophisticated and can maintain and reproduce themselves as well.

Then what happens to the capitalist concept of value based upon labor?

If one hour of human labor produces essentially nothing that a machine cannot produce more cheaply, the economic value of human labor could approach zero.

Marx's system encounters a profound problem here.

The capitalist wants:

less labor = lower costs = greater productivity = greater profit

But if capitalism eliminates almost all labor, it potentially eliminates the very source of the surplus value upon which Marx believed capitalist profit ultimately rested.

Marx's famous "rich society" possibility

And this is where Marx becomes much more interesting than the simple image of him as someone advocating government ownership of factories.

He envisioned technological development potentially creating a society in which necessary labor becomes extremely small.

Instead of people spending most of their lives working to survive, people could have enormous amounts of free time.

Marx wrote in Capital about a realm of freedom beginning where "labor determined by necessity and external expediency ends."

In other words, the ultimate purpose of economic development could be to **free human beings from necessary labor**.

That is quite different from saying that everyone should work harder.

But there's a huge problem: who owns the machines?

This is the central issue.

Suppose AI and robots eventually perform 99% of economically useful work.

There are two very different possibilities.

Scenario 1: The machines are privately owned

A relatively small number of people own the AI systems, robots, factories, energy infrastructure, and natural resources.

Then:

Machines produce everything = owners receive most income = ordinary workers have little bargaining power.

You could end up with enormous wealth alongside mass economic exclusion.

That would be a situation Marx would regard as a particularly extreme version of the contradiction between capital and labor.

Scenario 2: The productive machinery is socially owned

Then the enormous productivity of the machines could potentially be used to provide everyone with abundant goods while dramatically reducing the amount of necessary work.

That gets much closer to Marx's vision of communism.

Marx's remarkable prediction

There's an especially fascinating passage in the Grundrisse where Marx essentially argues that the development of machinery can make direct human labor cease to be the principal measure of wealth.

That's important because Marx recognized a fundamental tension:

The more productive machines become, the less society needs human labor.

But capitalism measures economic activity through things such as labor, wages, commodities and profits.

So technological progress could eventually make the existing economic categories increasingly inappropriate.

And this is where your question about human labor going to zero becomes particularly interesting.

If AI and robotics became capable of doing essentially everything humans currently do economically, Marx's prediction wouldn't simply be "everyone becomes unemployed."

His deeper question would be:

Why should human beings have to sell their labor in order to obtain access to the products of a productive system that no longer needs their labor?

That is a much more radical question.

AI makes Marx's argument especially interesting

The difference between 19th-century machinery and modern AI is that Marx mostly imagined machines replacing physical and repetitive labor.

AI potentially extends the process into:
* writing
* accounting
* engineering
* programming
* scientific research
* design
* management
* education
* legal analysis
* medical diagnosis
* entertainment

If sufficiently capable AI and robots eventually perform both mental and physical labor, we could approach the hypothetical situation Marx was interested in: production becoming largely independent of human labor time.

The paradox is that this could make Marx's vision either more plausible or less plausible, depending on who owns the technology.

The technology could produce unprecedented abundance and leisure — or unprecedented concentration of wealth and power.

And that distinction — technological abundance versus ownership of the productive machinery — is probably the most important part of Marx's thinking for understanding the possibility you're describing.

The above thinking of Karl Marx in the 19th century seems to outline the futures that most modern AI commentators are now contemplating. Either Advanced AI will bring forth a future of Unlimited Abundance to be equally shared by all, or it will lead to a future where only 0.1% of the population lives a life of luxury beyond compare, while the remainder of humanity lives the life of subsistence that was common to most in 3,000 BC.

How To Preserve Capitalism in the 21st Century
Capitalism does not require Workers in order to work. Capitalism only requires Customers with the ability to pay for goods and services, with a stable medium of exchange with at least a short-term enduring value that does not rapidly depreciate with time. So in order to preserve Capitalism, we need large numbers of Customers with a stable medium of exchange that does not rapidly depreciate or appreciate in value.

So here is my nutty idea. All the governments of the world need to slowly adopt a negative income tax in addition to the taxes they now level and a currency based upon kWh of native electrical generating power! For example, the United States of America generated 4.52 trillion kWh in 2025. At the end of 2025, there were 342 million people living in the United States of America. So in this scheme, each person in the United States would be granted 13,216 kWh of electrical power currency for the year 2026. This new currency of the United States of America would be in kWh, and all goods and services would be priced and sold in terms of kWh. Any persons or corporations still employed after the ASI Machines replace nearly all workers would be paid in kWh and would pay normal income taxes on such. Progressive Income Tax rates would need to be adjusted so that the total kWh money supply only increased by about 2% per year to prevent inflation from rising by about 2% per year as well. However, corporations and the still-employed individuals could continue to accumulate vast fortunes of kWh as before, but a totally-unemployed family of four living on a mere 52,854 kWh per year would still be able to live a decent life. If more adjustments to the numbers were needed to make all this happen, those adjustments would need to be made. People living in poorer and less-developed economies would at first be granted smaller amounts of kWh to live upon, but should the unlimited abundance of Production promised by AI come true, the entire population of the world should slowly rise above a level of subsistence to an economic level with some sense of human dignity.

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

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