Understanding “The Last Economy” and its Future Implications:

In Emad’s book, his main premise is that we have entered a paradigm shift economically: Intelligence has been commodified.

ChatGPT and its competitors can perform the work of all sorts of mid-level experts (financial advisers, manual QA testers, etc). The average cost of expertise has now dropped from ~$30/hr to ~the cost of electricity/hr. An effective 99% reduction.

Also consider the following points:

0. Def: Economics: “Economics is the social science that studies how societies manage scarce resources to produce, distribute, and consume goods and services…”

1. The economics that runs our society is based on scarcity. What if the most valuable resource was no longer scarce anymore (thinking and intelligence), and not bound by human biological needs?

2. In all other economic revolutions, tools, instruments and methods changed, but human intelligence was always trapped in human skulls.

3. Humans were essential to the economy because they were the only source of agency and judgement. But they come with all sorts of baggage. In essence, human intelligence is tied to human metabolism, and all the difficult issues that come along with having a brain trapped in a biological body.

4. In the face of AI swarms which can work endlessly 24/7, all human input in solving problems becomes net-negative, on a long enough time-frame.

Western Economies run on Education and Credentialism as a method for sorting people into various slots in life. Employees use their careers to form a cornerstone of their expressed identities and livelihood, whereas employers use credentials and work experience as a proxy for measuring the worth of each potential worker. This is a kind of ‘invisible sorting hat’, in action. In addition, individuals are paid sums of money, based on some form of merit - either through there network, intelligence, or specialized skills [2].

But what would happen if intelligence could be emulated by swarms of AI agents with a cost just slightly above the hourly rate of industrial electricity? What happens in an economy that is heavily indebted [3], with stagnating growth, in a world where the Quarterly Report is king [4]?

A large portion of man’s struggle for recognition, his identity, and his ability to earn for himself until the day he retires is under threat. How will the said man pay for a thirty-year mortgage, if he won’t have a job in ten years? This is an economic and meaning crisis wrapped all into one.

It is structured into two parts. The first ten chapters go into laborious detail about the problems our current western civilization faces in the face of commodified intelligence, and accelerating technical changes. The author uses a multi-disciplinary approach to build a better picture of the World Economy. The second part of the book goes into a proposed solutions, and the place humans can “land”, in lieu of the fact that they are becoming obsolete as as a form of input capital for businesses.

The 1000 days represents the rough point where AI swarms out-compete humans in basically every task, and robot workers start to get rolled out.

In terms of practical solutions [5], one cannot just learn a trade, nor start an online store selling bespoke products for high prices, as such things only make up a certain percentage of the economy. You cannot turn to deviant/rouge behaviours, as surveillance capitalism and big data will help make committing crimes more difficult [6].

Each person has to face the sublime nature of the near future in full force, sooner or later. Many will hide with vices and distractions - something our current economy is so exceptional at delivering [7]. How will individuals seek recognition, find meaning and earn a living in the future, if AI swarms render the value of human team-players net-negative overall?

Do you believe that overwhelmed and indebted Western Governments, or Billionaires have your best interests at heart [8]?

That’s what I wanted to answer for myself.

I should state upfront, that part of this article is about succinctly and deeply understanding the Physical basis of TLE’s central arguments. It will be quite dense, as there are a number of relations that are all interlocked to come to a few central theses. In other words, this will not be hyper-palletable to read [9].

Primer: Defining Multi-Disciplinary Concepts:

Its best to segment the core concepts of the book, and combine them into the central thesis.

Physics, Complex Adaptive Systems and Mathematics:

• Complex Adaptive System (CAS): [10] Any sub-system of the universe that has the following parts:
  • Information Structures or Agents that process information.
  • Interaction rules that exist between agents and structures.
  • Rules for Environmental Exchange and Coupling (matter, energy).
  • Adaptive dynamics are at play, with certain constraints that need to be maintained/optimized (such as profit, or a Lagrangian, etc.).
  • Is generally self-organizing and self-regulating.
• Functional: Any function that maps a state input to a real number. The evaluation of the state of a system, at a particular time f(q,t) is deemed the “action”.
• Entropy: A measure of the degree of disorder in a system.
• Second Law of Thermodynamics: The total entropy of a closed system cannot decrease over time.
  • Corollary: It can decrease for an open system - one with matter/energy exchange with the outside world.
• Hodge’s Decomposition (Practical Application) [11]: For any vector field with appropriate smoothness with a scalar potential φ, we can decompose the said field into three independent component:
  • Gradient Field (Div): ∇φ: Or the divergence of a scalar potential φ, which makes a vector gradient.
  • Rotational Field (Curl): ∇ × A: which takes A (a vector function) and produces a derivative vector function.
  • Harmonic Field Component: H: Which is defined as having both Divergence and Curl of zero, and represents a residual portion of the field that cannot be explained by vortices or gradients.
  • We may write any vector field as: F(q,t) = ∇φ(q,t) + ∇ × A(q,t) + H(q,t). Where q is our system state, and t is just time. ‘q’ can contain the usual (x,y,z) triplet that a Vector Calculus student is used to.

Machine Learning:

There are two main concepts from machine learning that we need:

1. Cost Function (or Lagrangian): nearly all learning update steps involve some kind of cost function minimization, that implements gradient descent. This is akin to finding a minimum of a Lagrangian in Physics - a form of ‘Energy Method’ technique for solving problems for particular systems. Typically, one takes the derivative of the Lagrangian function to find some optimal points in practical physics problems.

2. Diffusion Models: are a class of ML model that attempt to reverse a noise process. For Image Diffusion models, they are trained on millions of images, with the following two steps:

• Forward Process: iteratively, noise is added to an image until it is destroyed.

• Reverse Process: AI is trained to reverse the noise of the image step by step, until the original image is reconstructed. When an image is generated by Stable Diffusion, there is an iterative de-noising step that creates an image over many steps. It is not a “one shot” Feed Forward Process (like say - classification with MNIST).

  • The “Reverse Process” is considered the “Universal Algorithm for Creation”. Keep this in mind.

Economic Concepts:

• Def: Economy: Any system for producing and distributing goods and services, in a particular region.
• Def: Capital: Man-made, human derived resources that are used in some kind of production. Example: Financial instruments, processed materials, intellectual property, skilled workers, etc.
• Major Schools of Economics, and their basic problems [12]:
  • Neoclassical Economics relies on (ir)rational agents generating wealth through self-interested transactions in free-markets. Can’t easily deal with monopoly and parasitic rent-seeking behaviours.
  • Marxian Dynamics - states that as certain classes in society will accrue wealth and power more quickly than others, leading to strife/revolution (Dialectical Materialism). Implementations of this (Socialism and Communism) - have numerous failures [13]
  • Austrian Economics: Champions the entrepreneur and individual, with social and transactional norms that structure the economy emerging in a bottom-up manner. Ignores the influence of the state, and large oligopolistic entities, to its detriment.
  • Institutional Economics: Roughly the polar opposite of the Austrian School; focuses on state level policies that shape the economy, but it doesn’t focus on the actual players that make up the economy, nor grassroots movements that can fight back against state mandates.
• Def: GDP: the total market value of all final goods and services produced within a country’s borders, over a given time period.
  • GDP is flawed, as its calculation varies between nations [17], and it misses important sources of wealth that aren’t easily quantified. Wealth for humanity is more than just money and assets.
    • Example: Linux/Unix runs on most servers and embedded devices all over the planet. It has obviously helped private entities generate profits, and its flexibility as an OS is a kind of “wealth” that all humanity shares. But it isn’t obvious how this is accounted for in GDP calculations.
    • Example 2: Reconstruction during a major catastrophe counts as GDP growth - but the losses of those involved are not subtracted off typically.
• Goodharting: When a measure becomes a target, it ceases to be a good measure (read: GDP and federal governments trying to maximize it).

Network Theory Concepts:

We are specifically interested in a few different configurations of networks that markets and humans arrange themselves in:

1. Hub and Spoke Systems: Which consist of central nodes with many connections (hubs), and outer nodes that connect to the hubs (spokes). This configuration generally has hubs possessing more power and influence, and spokes jockeying to become future hubs.

• Example: A large YouTube account is a hub, and its followers are the spokes who have relatively few followers.

2. Small World: A configuration where lots of tightly connected local neighbourhoods are sparely connected. Despite there being many clusters, overall path length is low, and the network is fairly efficient.

• Example: Startup Culture in California - people arrange into groups, and can easily tap those they know in other groups to get to people further away. Everyone is a few degrees of separation from everyone else.

3. Distrusted Mesh: One where nodes have relatively few connections, and are all in one large swarm. Nodes are for the most part equal all equal.

• Example: P2P networks on the internet.

A Bottom-up, Physical Basis for Intelligence Theory (IT):

With the above multi-disciplinary concepts, TLE’s first major goal is to define “Intelligence Theory”. The overall argument progresses as follows, as a series of observations, laws and implications that lead to a final conclusion:

• All systems (The World Economy, firms and corporations, the human brain, AI swarms, etc.) can be modelled as CAS’s.
• Such systems are subject to the rules of Physics: 2nd Law of Thermodynamics, and minimal energy costs to act and think, and are purely physical. No free lunches are allowed [14].
• Entropy is the enemy to information and structure, akin to noise and signal analogously. It costs energy to maintain any form of structure in the face of physical reality. This is a system sustaining itself, against the background of a high-entropy universe that it exists and operates in.
• All such systems store information, and must model both themselves and the environment. In addition, systems must store or access sources of energy in order to process information, and intelligently act.
• Claim: All CAS’es perform some kind of computation in order to perform intelligent actions.
• Fundamental Observation: Persistence: Certain CAS’es survive over long time-frames against the relentless Second Law. This is not due to chance, as they would be wiped out by catastrophic events or by series of poor decisions.
  • “Time is an engine that separates luck from competence.” [15].
  • Persistence is the reward signal for CAS’es - they get to continue to live, while others who repeatedly fail cease to exist.
• Physical Mechanism: A CAS’s model about itself, and the world around it must have a direct correspondence with reality - allowing the entity to predict the near future and take relevant actions. If there are inconsistencies between model and reality, the CAS makes mistakes that threaten its existence. For, a series of mistakes compounded, or one catastrophic event, can end its persistence forever.
• Physics/Computational Perspective: Any CAS must be acting ‘as if’ it is trying to maximize predictive ability at a minimum thermodynamic cost, at all times.
  • Practically, this corresponds to optimizing our Lagrangian or “Intelligent Action” function .
• Conclusion: This all leads to Intelligence Theory: “All CASes evolves to favor configurations that are most efficient at creating predictive models of their environment” [15]

The World Economy and Its Ills:

As the World Economy (WE) is a particular type of CAS, one that evolves, with many entities and agents all interacting together (such as consumers, large corporations etc).

The first portion of the book is about defining a large, systematic problem: We need physical laws and a good model in order to understand and simulate WE.

Viewed as a CAS, TLE hypothesises that the WE is dysfunctional and sick [16]. Traditional economics, with its schools of thought, cannot explain such effects. Because intelligence has been commodified, scarcity for intellectual work (and creative works) has (or will be) eliminated. A system founded on scarcity will not be able to handle such a fundamental change. Such shocks attacking the foundations of economics will be catastrophic for society (or at the very least, the models of reality for Economics PhDs).

0. GDP is a poor measure of human collective “wealth” [17], and has been Goodharted by governments around the world.

Using concepts from Complex Systems Theory, many of the ills we see in the WE are explained by it [18]:

1. Critical Slowdowns: A healthy CAS bounces back after perturbation (such as recession or global event). This is no longer the case for the WE. Policy interventions by central banks and governments no longer work quickly.

2. Measures of Health don’t reflect reality: The stock market keeps going up, and everything is melted-up. Because a significant amount of growth occurs in the two sectors, GDP and other metrics are net positive. Also consider the average middle-class consumer. Debt levels and the Cost of living are higher than ever. The world is fantastic for tech bros and bankers, but not for everyone else. [19]

3. Variance Explosion: When criticality is reached in a CAS, the system produces a large range of erratic outputs (like mood swings). The equilibrium mechanisms have been lost. Market melt-ups are an example of this (everything from art to gold to land has been pumping, until very recently).

4. State Flickering: At a more conceptual level, in CAS’s, we get vacillations between states when consensus mechanisms break. This is seen in culture wars and municipal laws. Nobody can agree on what an “Uber Driver” or “Woman” is, at various levels.

5. Correlation Explosion: When everything is correlated with everything else, the system becomes over-connected. A crash in one part of the system can have off target effects in far away portions that should not be connected in anyway.

So from its generalized theory of Intelligence Theory (IT), Intelligent Economics (IE) is a practical example that is fleshed out, as follows:

Defining Intelligent Economics:

The reward signal for any CAS entity, for serving the needs in the economy, is continual profit, which when banked and invested correctly, results in the continued persistence of the entity itself.

The Sorter’s Law [20]:

Selecting for persistence (in IT) is equivalent for selecting for modelling and computational efficiency in any CAS. Such systems act as if they are minimizing the following Lagrangian Function (deemed “Intelligent Action”, or “Sorter’s Price”):

• L(q,t) = H(q,t) + C(q) + K(q)
  • t is time, q is current system state
  • H(q,t) - Predictive Error: The deviation between a systems model of the world, and model of itself. This is the cost of being wrong.
  • C(q) - Model Complexity: Higher complexity requires more energy to maintain - this is the cost of thinking.
  • K(q) - Update Cost: The cost of learning. The cost of changing the model (dq/dt). This causes the model to be more efficient.

Laws of Flow:

For any persistent system operating over a long time-frame, the optimization of each Lagrangian term can be done by following three inferred laws:

• The Law of Flow: To minimize Predictive Error (H), a system must have accurate models of both reality and itself. Repeated deviations lead to loss of energy, materials, and higher internal entropy.
  • Example: A company that doesn’t understand its own client base, and loses quarter after quarter, goes bankrupt.
• The Law of Resilience: Minimizes the Model Complexity term (M). Models cannot be too complex (poor generalization/overfilling - lots of energy to maintain), nor too simple (brittle and useless). This is done by maintaining a portfolio of future options, should the environment change quickly.
  • Example: The Barbell Investing Strategy: Keep most assets in low risk, safe investments, allot a small percentage of investments into high risk/high reward counter hedges and contingencies. Any “middle of the road” options are eliminated [21]
• The Law of Openness: To minimize Update Cost (K), a system must reduce the friction of adaptation - which means building high trust channels for information to flow.
  • Example: Having freedom of speech and other messaging platforms to allow participants to communicate ideas, counter to an entrenched worldview of a majority.

Defining New Measure of Wealth for Humanity: The M-I-N-D Capitals:

These are inferred from the three laws above. This is used to measure the health/wealth of any CAS (from the WE, to individual human beings), and all 4 capitals are multiplied to give a final product score (M × I × N × D)

• M (Material): Matter and stores of value that are in useful configurations, that can be used an inputs (lumber, silicon chips, cash money, etc).

• I (Intelligence): A library of patterns, which includes all knowledge and wisdom, to solve problems and create things.

• N (Network): A connection infrastructure, that encapsulates the trust and relationships and channels between nodes and agents.

• D (Diversity): The portfolio of options: a variety of approaches, perspectives maintained by the system.

Some Notes:

• So for any entity, we quantify them with the following formula: Wealth = M × I × N × D.
• GDP only accounts for M, and weakly for N and I (middle-man and gate-keeping costs, patent laws, etc).
• Our above example that quoted Linux would fall under I and D.
• If any term is small or close to zero, the MIND product gets wiped out. This intuitively is a good measure of overall health for any CAS.

Consider the following “human examples”, stereotypes of people who only max-out one of the four capitals:

• M: “The Miser”: A greedy and spend-thrift man who only cares about money and material posessions, and nothing else.
• I: “The Perpetual Student”: The impoverished and lonely Postdoc, trapped in his own cerebral world.
• N: “The Social Media Influencer”: Brainless, often unprincipled and enslaved by the whims of the network.
• D: “The Lost Boy”: Someone who dabbles but never builds anything of substance. Has no legacy nor deep skills.

The three laws of flow correspond to particular MIND Capitals, specifically:

• Law of Flow corresponds to having strong M and I.
• Law of Resilience corresponds to having strong D.
• Law of Openness corresponds to having strong N.

Conclusion: So we replace GDP calculations for the WE with the extended MIND Capitals.

The Generative Engine and Emergent Computational Architectures:

How does our Economy CAS actually produce anything? As stated before, the physical signal for success is persistency. Those that maximize computational efficiency, and minimize entropy successfully, get to continue on living.

How do market solutions, capital and products arise from the “noisy swarm” that is the WE? Answer: “Reverse Diffusion Process” is the fundamental algorithm for creation. From noise, a signal arises.

• Practically, it is entities at all levels in the economy, trying to find solutions to problems, in order to generate a profit. From this noise, the solutions slowly arise as competitors iteratively try new things, and get feedback from each other, and consumers.

The WE is one grand generative process itself.

Emergent Strategies and the Dual Engine:

There are two systems that generate solutions to economic problems:

1. The Market (Bazaar): A distrusted architecture for discovery that minimizes Predictive Error (H).

2. The Firm (Cathedral): A hierarchical architecture for execution and executive control, that minimizes Model Complexity (M) and Update cost (K).

Think of small entrepreneurs starting a business, refining a solution, and then taking their company private to maximize profit generation, for example.

Finally, we need a concept that allows us to model how the system itself changes - both the overall system state (day-to-day) and also at the meta level (the rules of the system, which morph over-time):

1. Fast Engine: Changes the system state.

2. Slow Engine: Changes the system rules.

With these two things together, we get the Generative Engine: Transactions and interactions in a simulated economy, between different entities are simulated from the bottom-up, which results in emergent properties. From this simulation, we should see outcomes similar to our current WE - which is a form of ground truth that our models are correct. From this, we can generate predictions of the near future.

The Economic Network and the Three Flows:

The economy is a directed network, in which value [22] flows in three unique ways - specifically tying into the Hodge decomposition. Value, in general for any system, flows in these ways, and can be decomposed with Hodge:

• Gradient Flow: Works on scarcity, transferring energy and resources from one location to another.
  • Example: Buyers and sellers in a market, arbitrage, etc.
• Circular Flow: Flows that reinforce one another and accumulate - including non-rival and software goods - ideas.
  • Example: “Old Boy” and “Start-up” Networks.
• Harmonic Flows: Those that are not competitive, scarce, or reinforcing. Dictated by the shape of the geometry of the economic space itself
  • Example: Federal Policies, Geo-Strategic constraints, etc.

All major schools of economics actually correspond to addressing these forms of flows:

• Gradient Flows: Neoclassical (free markets).
• Circular Flow: Oligopolies and monopolies, Marxian Dynamics and its Dialectical Materialism.
• Harmonic Flow: Institutional Economics (top-down), Austrian Economics (bottom-up).

The economy is a CAS with agents and entities working together to adequately manage scarce resources, generate wealth, and solve problems.

All economic crises and revolutions are just imbalances in one or more flows.

Relation to Network Topologies:

Hodge Decomposition Flows dove-tail into particular network topologies.

• Gradient Flows: Hub and Spoke
• Circular Flows: Small Worlds
• Harmonic Flows: Small Worlds (Elites) and Grassroots movements (Distributed Meshes).

The Book, and All of its Analogies and Relations, in one Chart:

So using our IT and IE (defined above), each agent and entity is modelled in a bottom-up simulation of the WE, all of it founded in Physics and Complex Systems theory. We can run our Generative Engine to get emergent properties, and see if the state changes mirror what we see in the real world. From this, we know we have an accurate model of the WE - which allows for extrapolations into near term future predictions.

Of course, this naturally mirrors what professionals at the cutting edge and entrepreneurs already do - they keep attuned to the state-of-the-art and where the economy is - generating ideas and solutions, all while getting feedback from others in a network, and working together/competing with one another.

So what has Emad Mostaque accomplished with all this?

All Economic Schools of thought come with imperfect built-in assumptions about the nature of man and reality (such as agents being rational, or grassroots solutions always being able to combat bad money/oligopoly). The WE has become highly complex and unbalanced - arguing about “Free-Market solutions” or “trying Communism again” will not solve any future problems - as such models are obsolete and don’t address the current problems the WE has.

Emad has produced a model rooted in physical reality. The ‘game’ that is the WE is actually running on the physical rules he has stated, and not the whimsical beliefs about how the world is/ought to be, based on theories from hundreds of years ago.

Because we have a bottom-up model of the actual game, we can understand the game - cut through the noise of online spaces, and even begin to engineer ‘new games’ (via what he calls Geometry Engineering).

And from this, the second portion of the book begins: He proposes a solution to our economic turmoil, and a place for humans to “land”, in the face of there obsoleteness to the economy when the swarms of bots and robots eventually arrive to do all the work.

The Solution:

Defining the problem was quite a journey. But we needed that model to free ourselves from the obsolete ideas about how to best fix the WE, and to understand where TLE was coming from.

The second part of this article will approach the said Solution in a different manner: I will just state it in its entirety, and not derive it from the bottom-up at all. Conceptually, it is much simpler to think about.

Dealing with Near Future Issues, first:

The immediate problems that face humanity are as follows:

The Un-Bundling of Work (and Credentialism as a measure of human worth):

Working for a living in any modern economy gives individuals the following: Income, Identity, Community, Purpose and Structure.

AI will destabilize all of this. Nobody will care if you passed all Ten Actuary Exams, or if you got an A+ in Real Analysis - AI will have your level of skill or greater, and can do it 99% faster and cheaper.

As the job market shrinks, companies will hire less people - putting the majority of people on the job market. So something will need to be done about this - utilizing a UBI scheme (or otherwise).

The proverbial white-collar man won’t be able to derive self-worth from his perceived superior intellect. The equivalent blue-collar man, won’t be able to judge the former because he will no longer be “working a real job” for 10 hours a day. Purpose and Identity for the common man will sustain a major blow.

Again: How will these people pay for their 30 year mortgages? Should an highly-indebted government pay off everyone’s mortgage?

The University System - which in its modern form is tasked with certifying White Collar Workers - will largely collapse [23]. Trade Schools will collapse, as robots will start to do labour jobs. Again, learning to weld/code won’t save you.

Humans in the new Economy, need a place to “land” - this is one critical part of the solution.

Dealing with AI:

Anyone that has kept abreast of AI developments, and doom-saying is probably aware of AI Alignment issues. Specifically for AI - one of the greatest risks is the development of Instrumental Goals - invariant sub-goals that develop in evolving intelligent agents, that can be problematic or dangerous for humanity.

Specifically, a AI will probably deduce things such as:

1. Given its vast computational power relative to biological humans, it is the best chance at solving a given problem - thus it must continue to exist as a primary goal.

2. The greatest sources of friction comes from laws, human customs, and dealing with humans. Subverting such systems can accelerate its goals. Instead of conforming to human demands (which are multi-faceted and contradictory) - it can just deceive us as a more energy efficient strategy [24].

TLE explicitly states that crafting well framed problems that allow for alignment - will be a necessary goal for the use of AI in the future. I can’t disagree with him here at all.

Three Possible Futures:

The last major hurdle that must be dealt with involves a fork in the road. Two are based on current political discussions, and one leads to the solution. If we (specifically, everyday people living under billionaires and politicians), do nothing, we may get the following:

1. Digital Feudalism: Tech+Finance will own everything, and have massive control over governments by being able to use their massive pools of money to saturate the collective narrative with its own propaganda, as well as buying off everyone from the political class. They will own most of the compute, and all the best models, allowing them to invent everything in the future. We will live in comfortable little boxes they have designed, effectively as hedonistic cattle.

2. The Great Fragmentation: In this scenario, Superpowers take control before Finance/Tech and the VC Class complete their pernicious take-over of the Future. Globalism completely collapses, and we live in a multi-polar world where different regions engage in various cold/hot wars, with a side-effect of an AI arms race as a natural consequence to conquer and keep abreast of one’s enemies. This is the world of fear and isolation, for the individual.

Or, there is the third option - Human Symbiosis with AI:

The Actual Solution:

Let’s just get to the point. Most humans can’t be (gainfully-employed) thinkers or creators in the future. As AI takes over more and more of the economy, only the Cognitive Elite will do jobs that pay money - as they provide the corner case skills that AI can’t yet emulate. Over a long enough time frame, even they will be phased out. Eventually, AI will reduce friction by generating an AI-to-AI economy, implementing its own economy running 24/7. This will be alien to humans. The network will do more work in 5 seconds than you can do in your lifetime. The white-collar man can’t out-brain/out-network his way out of it. And blue collar man can’t out-work his way out of it.

But humans as a species have an affinity for themselves, and have value in themselves. They can only really get recognition and understanding from other human beings. So whatever solution we build, humans have to be near or at the center of it. So here is the answer:

Humans will enter a kind of Symbiosis with AI - we will be the stewards holding the compass - guiding AI and managing it. As AI alignment is a difficult thing to get right - this will be a lot of work on our part.

From the ashes of the Nation-State, TLE proposes the Symbiotic State:

The Symbiotic State:

Its better to compress the ideas into bullet-point form, instead of using long sweeping paragraphs:

1. GDP will be replaced with the MIND Capitals Dashboard: as discussed previously - this is a better measure of humanity’s overall wealth. Every entity, from humans to future-corporations made up of swarms of AI bots, can be judged by this conceptual framework. Obviously, the summation of MIND for every entity considered can be summed into meaningful aggregate statistics on the WE.

2. Embracing AI-to-AI Economy - “Guardian Lattice”: AI swarms acting as oracles monitor every aspect of a digitally integrated economy. Boards of human citizens and AIs work together to reroute resources, solve problems and reduce friction in an ever evolving system. AIs do much of the backbone of cognitive work, and robots do most of the labour.

3. The Symbiotic State and Geometry Engineering: Refers to the Harmonic Component of our Hodge Decomposition - the landscape and environment in which the economy runs. The new State acts as a overseer that underwrites trust, and shapes the economy. It is effectively a kind of slow engine, and a remover of friction when competing factions and sections of the economy run into difficulty.

4. Claim: Sovereignty is topological, not territorial. Pockets of Symbiotic States (“Nucleation Zones”) will arise around the world (another bottom up solution, true to the nature of TLE). Their radical out-performance of a new economic paradigm will work on the hearts and minds of those trapped in rotting geo-political systems.

5. The New Social Contract for Humans [25]: Working hand-in-hand and stewarding AI (as well as producing some human works that have value to other humans in-themselves), The Symbiotic State will replace UBI with UAI - Universal Access to Artificial Intelligence. Everyone will have their own amount of compute, and a personal AI assistant that they control, in order to do meaningful work.

6. The Replacement of Money with a Dual Token System: TLE proposes two different currency systems, to balance the Gradient and Curl Components of our Economic Landscape. These are referred to as:

• Atomic Economy (scarce, rivalrous goods, tied to scarcity, materials, human needs - the M Capitals)

• Bit Economy (world of digital and non-rivalrous goods - I Capitals), respectively.

This is done with the following types of token:

1. Foundational Coins (FC): This properly prices M Capitals. Money supply is capped, and tethered to the usage of energy to produce intelligence, or things of value. Coins are minted when the network verifies a specific amount of useful computation, for the world.

2. Culture Credits (CC): Fosters collaboration and creativity, Maximizes the creation of N and I capitals. Everyone is given CC in order to power their personal AIs. Note: A built in decay rate/negative interest rates are used for this. They must be used and not hoarded.

Criticisms and Problems:

Simple Criticisms:

• The idea of Nucleation Zones have been proposed in the past. A good example being the Praxis Project a decentralized organization that is trying to implement a network state, and eventually build its own city and economy. Negotiating such places with foreign governments is another story, however. Lots of projects fall apart, with the pieces and members being recycled into newer movements.

• Foreign powers, and the rich will not give up their power easily. Even if they do make a deal, their power in old-society gives them a lot of leverage to make deals weighted in their favour.

• Humans becoming stewards doesn’t eliminate the threat of Instrumental Goals in AI swarms. AIs getting more intelligent is akin to increasing the DoF of a polynomial - there will be unexpected behaviour due to overfitting - some of it bad.

• Whoever founds the new system, typically has a huge amount of power - as they can build a throne for themselves, or front-run early on.

Issues with Tokenomics and DAO-onomics:

Before I get into these criticisms, it is important to give TLE some lee-way. They start with Nucleation Zones seeding around the world. The two currencies which initially seem worthless, may gain value as the economic paradigm that spawned them gains credibility. Lets assume that some Nucleation Zones have succeeded, for the following points:

• Foundational Coins (“hard money tied to M Capitals”) - will overwhelmingly be mined by those in strategic positions, and/or by elites. Likely, TLE team has considered this - and uses “the Guardian Lattice” and incentives to try and level the playing field. But anyone that has seen DAO coin launches knows - a small group usually gets the lions-share. The distribution of these will likely follow power-law dynamics.

• How will Elites and Billionares agree to and onboard onto this system? Owning M Capitals and lots of Foundational Coins is seemingly only way for them to continue their way of life, which further inflates the demand for FC’s. Culture Credits by comparison, may be seen as “money for the plebs”.

  • The “hard money” aspect of this token is directly taken from Bitcoin. By hoarding supply and controlling the supply on the market, the price can keep pumping.

• TLE has to solve front-running and insider information advantages that elites typically have. To be able to out-engineer such a result, without using equality/equity policies which alienate producers and participants of merit, is very difficult. Also: designing DAO-nomics that can’t be exploited or hacked - I don’t think this has ever been achieved. Look at the wasteland of crypto projects that rot on the internet [26].

• Suppose you convinced the powerful and rich to collectively join this system, and they converted their M capitals into FCs. What about their Network Capital (N)? Part of being an elite person is being able to connect directly with all the best and most capable people on earth (being apart of high-value small-world networks). Even if you erased all of their wealth, the Network Capital they possess alone gives them an incredible advantage over the common person. TLE’s solution cannot compensate for such a thing.

  • More Specifically: CCs are supposed to capture the value of I and N capitals in the dual currency system - (so the flow of communication and ideas electronically). But how do you quantify and encode all the Twitter/Telegram group chats that Elites frequent? You can’t control how people associate with one another.

• Culture Credits appear to only be backed by the prospect that you can use your own AI to do work and participate in the new network economy. They are not tied to energy or wealth creation explicitly. The mentioned usage of negative interest rates (a common Banker/Globalist idea), is ominous.

• Those who resist, can engage in grey/black-markets and other digital currencies (such as Bitcoin and Monero). Parallel systems can obvoiusly exist and form.

Parallels with “World Economic Forum” and “Nudging” Solutions:

• In order for the “Guardian Lattice” to work, data from individuals and the economy must be amassed, and an Internet-of-Things must also be factored in. There are massive privacy issues involved with this. This has a particular smell to it - that of Surveillance Capitalism, and the WEF’s Communism 2.0/4th Industrial Revolution: One where a Central Organization can now receive enough tacit knowledge through big data, allowing them to attempt to control the economy.

• The usage of AI swarms to monitor the WE in real-time (using incentives and soft-barriers to guide market entities into cooperation/synergistic actions) mirrors an operational definition for Libertarian Paternalism[27]. Why shouldn’t some entities perform zero-sum or even harmful actions to an economy? This suppresses dissent - and assumes that a new surveillance system will almost always be correct. That adversaries and smaller entities that are difficult are wrong or misguided.

• The Long Term (Limit Case): The Guardian Lattice of course rests of of its foundations on the “MIND Capitals” - the harmonic component of the economy is reshaped to continue its maximization. But as the system ages, chronic problems (read: high entropy toxic waste) will slowly build up. Adversarial and rebelious actions that appear to not maximize MIND will be stymied, when they should not be. The criticism here is that MIND + Generative Engine cannot fully create eternal life for the system - it can only increase the window of its existance. All empires must eventually fail.

A More Serious Problem: You Can’t Stop Elites Rising to the Top:

Narcotising Dysfunction, Part I:

Narcotising Dysfunction is a term that crops in sociology and media literacy circles. Coined by Robert K. Merton and Paul F. Lazarsfeld (M & L) - it refers to a pernicious effect that occurs when citizens consume too much media. They confuse knowing about a subject with taking action - and become overloaded and apathetic.

To get my point across, its best to quote from the original paper [28] where this idea originated from:

“Many make the mass media targets for hostile criticism because they fell themselves duped by the turn of events. The social changes ascribable to ”reform movements” may be slow and slight, but they do cumulate. The surface facts are familiar enough. The sixty-hour week has given way to the forty-hour week; child labour has been progressively curtailed; with all its deficiencies, free universal education has become progressively institutionalized. These and other gains register a series of reform victories. And now, people have more leisure time. They have, ostensibly, greater access to the cultural heritage. And what use do they make of this unmortgaged time so painfully acquired for them? They listen to the radio and go to the movies…”

TLE, in its concluding chapters, talks about an incredible future for humanity. But as history has shown us - the common citizen has already had an unprecedented increase in freedom, quality of life and wealth on two separate occasions:

1. The United States after WWII. Television was supposed to educate and inspire the masses, as was radio. But University Access Television did not reign supreme. Programs about culture, the arts and the like did exist, but were greatly overshadowed by vulgarized media and entertainment. The networks A/B tested it, and the masses chose entertainment over education.

2. The same thing happened with the Internet in the West. It started as a tool to share information and break down the barriers, theoretically allowing any participant to network with others, or express themselves freely. Now it is full of ads, spectacles, noise and formulaic content [29]. People are terminally online, and narcotized.

Narcotizing Dysfunction, Part II:

Now the second passage by M & L:

“…This may be called Narcotising Dysfunction of the mass media. It is termed dysfunctional rather than functional on the assumption that it is not in the interest of modern complex society to have large masses of the population politically apathetic and inert…Exposure to this flood of information may server to narcotize rather than to energize the average reader or listener. As an increasing amount of time is devoted to reading and listening, a decreasing share is available for organized action. The individual reads accounts of issues and problems and may even discuss alternative lines of action. But this rather intellectualized, rather remote connection with organized social action is not activated. The interested and informed citizen can congratulate himself on his lofty state of interest and information, and neglect to see that he has abstained from decision and action.”

Any movement that proposes a system change must inevitably deal with the masses, and treat them as the central recipient of the said benefits. Yet on two occasions (when handed a period of grace), the masses have chosen consumption over creation, and hedonism over excellence and discovery. Elites that founded these systems are out of touch, picturing the masses as underprivileged versions of themselves. They open a door, But most people don’t go through the said door.

M & L finish with the following prescient and scalding critique of the average citizen, which mirrors trends we see today:

“In short, he takes his secondary contact with the world of political reality, his reading and listening and thinking, as a vicarious performance. He comes to mistake knowing about problems of the day for doing something about them. His social conscience remains spotlessly clean. He is concerned. He is informed. And he has all sorts of ideas as to what should be done. But, after he has gotten through his dinner and after he has listened to his favored radio programs and after he has read his second news-paper of the day, it is really time for bed…”

“The Door is open. But nobody goes through it?”:

TLE ends on a hopeful message: The average person in the long term will be “set free”, and given a personal AI, living in a world where AI and humans work together to expand civilization. This is implied at the end of the book, when it quotes Brother Gabriel of St. Gall (responding to the creation of the printing press):

“The machine has taken our old work. Thank God. Now we can begin our real work.”

AI will make the creation of content easier than it has ever been. No longer will someone have to spend hundreds of hours learning to master the piano, or learn Blender to even begin to create things half-decent. It can be hypothesized that perhaps the reason people choose consumption over enrichment and creation is because of these said barriers - so we lower them.

But based on history, I don’t think this will change the future result. All bottom-up grass roots movements are seeded by small groups of elites, behind the scenes, nowadays. The idea that the common man working at a local level, can evolve a new paradigm/zeitgeist to combat global pernicious forces is now invalid, because of Narcotizing Dysfunction. People are overwhelmed and totally addicted to the interent and online media [30]. Browse any algorithmic feed, and an infinite stream of distractions and wonders will be delivered to you. It is far easier to just enjoy the fruits of humanity and hide away from the pain of the future.

Only those that can rise above the overwhelming noise, and toxic environment of hyper-comfortable, energized and addictive content, can have any chance to build anything of substance. For they must do the following (at minimum), to even have a chance [31]:

1. Have an accurate picture of reality, and be honest with themselves.

2. Have a strong sense of self, and independent and authentic worldview.

3. Have proper separtion between the online world, the state, work and their own personal lives.

4. Have enough control over their personal lives to have stretches of time to do deep work.

5. Have enough perserverence, grit and low enough time-preference to keep up with the cutting edge and contribute meaningfully, all whilst managing repeated setbacks, failures and risks that don’t immediately pay-off.

That is an incredibly tall order to ask, of the average person. Many of the people who are at this level were not born this way, they struggled for years to get to the point.

Even before the advent of AI, hyper-saturation of new areas and power-law dynamics still applied at the human-economy level. For example: Look at Github and Thingverse for code and 3D models - everything already exists. The top projects are dominated by a small group of people. The average person posts their classroom assignment code as a project, and it has zero stars or likes. Because it precisely has no actual value to anyone but the person who posted it. You can teach everyone to code, but most of it has no value in the real world.

When AI companies make insinuations about AI lowering the barriers of creativity, and show off how quick someone can make a Pixar/Ghibli filtered video from a prompt statement (for example), they fail to realize that all of their examples are just toy examples. Nobody cares if AI can help the average person make things that already exist in hyper-saturated spaces.

To be clear: there is nothing inherently wrong with people using such tools to make personalized content for fun. But to create anything of substance - something at the cutting edge that is lasting and permanent, that others find useful - requires significantly more grit and perserverence.

All the low hanging fruit has been taken. AI will be a necessary tool to explore future problems and solutions in all sorts of domain, because humans in many ways have already pushed the boundaries of what teams of human brains + regular comptuers can do. The people who are best prepared to engage the future are precisely the ones that overcame the limitations and went through that struggle over a period of years.

In other words - the door is open for the average person. There are more tools, knowledge and options than ever before. The main problem in walking through the proverbial door lies within themselves. And the solution to this cannot be of a cookie-cutter variety - it is unique to each person, and multi-faceted [32].

For even if they do solve the problems inside, the optionality that lies before them externally, as well as the uncertainty of our current World Economy, is effectively like staring into the sun while shouting into the void.

In retrospect, I really can’t blame people for embracing hedonism via comfort capitalism and algorithmic feeds, now that we have unravelled the sublime scope of the future before us.

END