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

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.

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