✨⚡ A thermodynamic miracle: Compute and energy are key to humanity's continued evolution

New study: "Increases in energy harvesting and information processing better describe the success of our species." | Also: ⤴⤵ Up Wing/Down Wing #13

When you hear about the existential need to a) slow down or stop work on artificial intelligence and/or b) the existential need for humanity to conserve energy, consider the following …

Human society has grown dramatically in scale and complexity since the emergence of Homo sapiens some 300,000 years ago. That seems obvious, I know, but let’s quantify that observation in a few different ways.

• Back then, there weren’t many of us in the world, maybe around 10,000 living in small groups as hunters and gatherers. Scattered and just surviving.

• Each person used about as much energy as their body needed to function, roughly 2000 calories per day.

• Everyone in the group did a bit of everything. There weren't many specific job roles.

• Tools were pretty basic, fire and some stone implements — and yet, the first sparks of our technological journey. The barest flicker of humanity's potential.

Today, there are eight billion of us. We are multitude. We each use 10 to 100 times more energy per day than our ancestors did. And we're all connected in a worldwide web of relationships — both personal and commercial, physical and virtual — where people do incredibly specialized jobs, from calling balls-and-strikes in a professional baseball game to working in a semiconductor fab.

One thing that is the same: brainpower. The same cognitive hardware, rewired for a new age. The same potential, unlocked by the twin keys of energy and information. Our individual cognitive capabilities remain constant — a stark contrast to our ever-evolving social structures and energy use.

Explaining humanity’s societal evolution is the goal of the marvelous new (and preliminary) paper “The computational power of a human society: a new model of social evolution” by David H. Wolpert (Santa Fe Institute,) and Kyle Harper (University of Oklahoma, Santa Fe Institute.) It’s a big-picture, big-think analysis that syncs well with what I write about both in this premium newsletter and in my 2023 book, The Conservative Futurist: How To Create the Sci-Fi World We Were Promised. But more on that later. First the paper’s summary:

Social evolutionary theory seeks to explain increases in the scale and complexity of human societies, from origins to present. Over the course of the twentieth century, social evolutionary theory largely fell out of favor as a way of investigating human history, just as advances in complex systems science and computer science saw the emergence of powerful new conceptions of complex systems, and in particular new methods of measuring complexity. We propose that these advances in our understanding of complex systems and computer science should be brought to bear on our investigations into human history. To that end, we present a new framework for modeling how human societies co-evolve with their biotic environments, recognizing that both a society and its environment are computers. This leads us to model the dynamics of each of those two systems using the same, new kind of computational machine, which we define here

Again, Wolpert and Harper are trying to explain how societies have grown and become more complex in ways that broadly apply to all cultures. They find to be inadequate the many theories that mainly focus on how societies have gotten better at harvesting energy (like through better technology) as the thermodynamic key to human expansion.

Necessary, perhaps, but not sufficient.

Information + Energy = WOW!

To really understand how human societies have grown and changed, we need to look at how we've gotten better at using energy and how we've gotten better at handling information. The authors argue that understanding this thermodynamic relationship is key to explaining how human societies have grown from small hunter-gatherer bands to our current global civilization. That, despite no significant changes in our individual cognitive capacities.

The formula: Better energy harvesting (allowing societies to do more work and support more people) + improved information processing (allowing societies to use that energy more effectively and to coordinate more complex social structures) = greater complexity (which manifests in various ways such as larger populations, more diverse occupations, more advanced technologies, and more intricate social organizations).

The Industrial Revolution? Not just about coal. Not merely the story of steam and iron. It was about blueprints. About knowledge. About turning potential into power, ideas into reality. It was the marriage of energy and information, giving birth to a new world. Coal became available, yes, but it was the creation of "blueprints" for technologies like steam engines that truly revolutionized society. (To that I would add: the freedom to innovate and reap the rewards of entrepreneurship.)

The Second Industrial Revolution? Basic science unleashing a tsunami of innovation. Light bulbs banishing the night. Electricity rewiring our cities. Telecommunications shrinking our world. But it wasn't just about the end products. It was about the fundamental discoveries in chemistry and electromagnetism that led to these innovations. Fertilizer synthesis, polymers, pharmaceuticals, internal combustion engines — each a testament to our growing mastery over energy and information. Each a step towards greater complexity, greater capability, greater humanity.

Adam Smith, Futurist

To measure all of this stuff — social complexity, computational capabilities, and evolutionary development of human societies throughout history — the researchers look to occupational specialization as a proxy that reflects the total stock of knowledge and information processing capabilities in a society. (The paper uses this famous Adam Smith quote: “It is the great multiplication of the productions of all the different arts, in consequence of the division of labor, which occasions, in a well governed society, that universal opulence which extends itself to the lowest ranks of the people.”) They suggest that an occupation can be considered an "ensemble of algorithms" for interacting with other humans, occupations, technologies, and institutions.

These are pretty cool examples:

Hunter:

-If it is spring, hide upwind of narrow defile where prey passes

-Aim arrow toward central chest cavity of larger, older prey

-Clean prey to consume muscle and organs

-Leave muscle tissues over smoke for hours to reserve for later consumption

Farmer:

-If Pleiades are still invisible at dawn, plant wheat

-Manure, weed, harvest, winnow

-Allocate gross harvest between taxes, consumption, seed, and storage

 Chemical engineer:

-If market signals indicate demand for fertilizer, start by creating iron-based

catalyst

-Extract hydrogen from methane

-Circulate nitrogen and hydrogen over catalyst at high pressure and temperature to produce ammonia

The paper then compares, for further illustration, the occupational diversity of ancient Rome to modern cities (which are also described as “social reactors, creating dense networks for the flow of information”). Despite having a population of about 1 million, ancient Rome had far fewer distinct occupations than a modern city of similar size. This illustrates how the "stock of information" (and thus the capacity for information processing) has grown over time, leading to greater social complexity.

From the paper:

Occupational diversity in US cities was distinctly lower in the 1940s (in the aftermath of the Second Industrial Revolution) than at present, and lower still in the 1850s (in the midst of the First Industrial Revolution). In preindustrial times, specialization was even more constrained. It is not simply that urban populations were smaller in the past. For instance, if ancient Rome existed in today’s urban network, with its population of 1M, we would predict it to have ca. 400 unique occupations, whereas in fact it has fewer than half of that. … Ancient Rome did not have chemical engineers, software developers, flight attendants, web designers, etc., and even at infinite resolution, real shifts have occurred because the stock of information has grown. We hypothesize that the relationship between occupational specialization and population size has changed as a function of social evolution - that the computational-metabolic capacities of human societies have grown due to increases in the stock of information.

“The computational power of a human society: a new model of social evolution”

The return of Up Wing economics

It’s amazing how much the Wolpert-Harper approach matches the economic theory at the core of my Up Wing economics, as I describe it in his newsletter and in my book. In my Up Wing view, information isn't just data — it's the very arrangement of atoms. A microchip? A marvel of ordered complexity. A Tesla? Not just metal and plastic, but a physical embodiment of our collective knowledge. (A crashed Tesla? Less complex.)

Our economies? They're not just markets and money. They're vast supercomputers that use energy to reorder matter and creating value. The strength of a nation's economy depends on its network size and density (including workers, universities companies, cities, and governments). The stronger and denser the network, the greater the computational power, the higher we soar. This is the engine of progress, the driver of prosperity.

As I write:

Countries with less computational capacity might only be able to produce basic commodities or simple products such as clothing. They’re less able to create the physical objects that embody and accumulate complex information, what statistical physicist César Hidalgo elegantly calls “crystals of imagination,” adding that some economies “are able to produce packets of information that embody concepts begotten by science fiction. Others are not quite there.”

If you accept the broad theory of the paper — societal evolution and economic growth are driven by the interplay between information processing and energy — then both compute and energy can become significant anti-progress bottlenecks.

As societies grow more complex, the demand for computational capacity increases as we are currently seeing with AI. (For me, this doesn’t just refer to computers but also to the collective ability of a society to process information through institutions, technologies, and social structures.) If a society's computational capacity is limited, it cannot effectively process the information needed to manage its complexity. This can slow down innovation and limit the ability to respond to challenges like economic crises or environmental changes.

Computational processes — both in the literal sense (like running data centers) and in the broader societal sense (like manufacturing complex goods) — require energy. If energy resources are scarce or inefficiently used, this directly limits the computational capacity of a society. 

On the flip side, advances in energy technology (like more efficient solar power, advanced nuclear, or energy storage and transmission solutions) can alleviate these bottlenecks, enabling greater computational capacity. Societies that innovate in energy production and management are better positioned to expand their computational capabilities, which in turn can drive further innovation and economic growth. And, of course, better information processing can, in turn, accelerate advances in energy technology.

It's a virtuous Up Wing cycle, a feedback loop of progress. From AI-optimized power grids to quantum computing unlocking new realms of scientific discovery, the interplay between energy and information is the beating heart of Team Humanity’s advancement.

For policymakers, the path is clear: Unleash AI. Liberate energy. Invest in our computational future. Break down barriers. Build up capabilities. Ignite the engines of progress. Regulations shouldn’t hinder AI development or energy innovation. Public and private investment must flow to these crucial areas.

Our future depends on it. The Wolpert-Harper model suggests societies can reach a tipping point that leads to rapid, self-reinforcing growth in complexity and capabilities. As a society's computational power increases (through more diverse occupations, better communications, more compute, better software, better-trained workers), it might suddenly become much better at predicting and controlling its world, leading to a jump in energy harvesting and resource utilization, which in turn allows for even greater computational power.

This Up Wing feedback loop suggests that once a society crosses certain thresholds, it might experience a cascade of rapid advancements, each breakthrough quickly leading to the next. A thermodynamic miracle.

Faster, please!

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⤴⤵ Up Wing/Down Wing #13

A curated selection of pro-progress and anti-progress news items from the week that was

Up Wing Things

🤖 OpenAI blocked accounts linked to an Iranian misinformation campaign. The company reported that they had discovered Iranian state actors as well as private companies that were using ChatGPT to generate false information regarding the US presidential election and the war in Gaza. They were also using the chatbot “to generate text and images for a covert Iranian campaign that the company called Storm-2035.” OpenAI identified these users before the misinformation produced had spread across the Internet. (NYT)

🤖 The AI craze is making its way into British government. The Tony Blair Institute for Global Change has centered its focus on harnessing technological advancements in order to wring out inefficiencies and redundancies in government and the private sector. The think tank has claimed that AI integration “could save up to £40 billion annually and shed one million civil servants.” Tony Blair himself hopes that Britain will be at the forefront of the AI revolution, and has been promised $375 million from Oracle CTO Larry Ellison. (POLITICO)