This paper is not a trading system.

It is not a factor model.

It is not a promise of prediction.

It is a proposal about what kind of object a market is, and therefore what kind of reasoning it allows.

The central claim is simple, structural, and uncomfortable:

Markets are non equilibrium, partially observable, state dependent systems whose most important properties survive changes in coordinate descriptions. The following proposal suggests such systems demand a geometric, specifically gauge theoretic, framework.

Everything that follows is an attempt to take the claim seriously and make it investable.

1. The Error Investors Keep Repeating

Most investment frameworks quietly inherit their intuition from equilibrium physics and static optimization. Prices are treated as objects. Risk is treated as intrinsic. Diversification is treated as structural. History is treated as stationary.

Then markets break. Not cosmetically, structurally.

Correlations spike. Volatility clusters. Liquidity evaporates. Strategies that were “diversified” fail together. Models calibrated on levels collapse when derivatives dominate motion.

These are not anomalies. They are the system revealing its true nature.

Markets do not revert to equilibrium; they move through distinct phase states, analogous to physical transitions between solid, liquid, and gas.

2. Six Empirical Market Axioms

Beware any model that ignores the following six structural market constraints:

2.1 Non-equilibrium

Markets transition; they do not settle.

2.2 Partial Observability

Prices and data are projections of a hidden state.

2.3 Dynamics Dominate Levels

Velocity and acceleration matter more than position.

Price levels are shadows of a higher dimensional object.

2.4 Finite Propagation

Effects travel with delay, distortion, and phase shift.

2.5 Redundant Description

Multiple models using different gauge selections can describe the same reality without contradiction. The reality is investors are the three blind men describing different parts of a higher dimensional elephant.

2.6 State Dependent Risk

Risk belongs to environments, not assets. The same way a cabin in the woods is more likely to burn down in a drought.

Any framework that violates these constraints may work briefly.

It will not survive stress.

3. Why Geometry and Why Gauge Theory

Geometry is the study of structure that survives coordinate change.

Gauge theory is the study of systems where:

• Observables depend on description

• Multiple representations coexist

• Invariants persist beneath narrative disagreement

Gauge theory did not arise in physics because it was elegant. It arose because nothing else worked once equilibrium and absolute quantities failed.

Markets satisfy the same structural conditions and so force us into gauge theoretic mathematics. That gauge theory is both elegant and broadly useful across disciplines strongly suggests it captures scale invariant structure, adding additional support for this proposal.

This framework proposes treating markets as a geometric system with three irreducible elements:

• A base manifold describing macroeconomic state

• Fibers describing conditional market configurations

• Curvature describing instability and risk

This is not analogy but existing mathematical scaffolding.

4. The Proposed Framework

This is the conceptual architecture.

4.1 The Base Manifold: Growth & Inflation Dynamics

The global market state is defined by growth and inflation, not by their levels but by their first and second derivatives.

• First derivatives → direction of motion

• Second derivatives → pressure, transition, and instability

This defines a two dimensional dynamic, continuous macroeconomic manifold.

“Regimes” are not labels.

They are regions of this manifold plus momentum.

Two markets at the same growth and inflation levels but with different accelerations are not in the same state.

Most macro errors originate here.

4.2 The Fiber: Conditional Market Expression

Above each point on the macro manifold exists a fiber:

the space of possible prices, correlations, volatilities, and relative performance consistent with that macro state.

Assets do not carry fixed meaning.

They are conditional expressions of state.

Building on the Nobel Prize winning work of Daniel Kahneman and Amos Tversky, portfolio construction begins by defining a reference class for the investable opportunity set within a given regime and asking:

Which assets have historically exhibited coherent, stable behavior in this region of the manifold?

This is not belief in history. It is conditional inference.

4.3 Curvature Detection

Volatility is not noise.

It is not sentiment.

It is geometry signaling stress

Rising volatility indicates increasing curvature:

• Local linear assumptions fail

• Correlations reorganize

• Historical relationships lose transportability

The volatility overlay exists for one reason: To detect when the geometry that made the opportunity set reliable is deforming.

When this signal triggers, the model does not panic. It withdraws confidence.

4.4 Tactical Layer: Price Stability Envelopes

Markets are global objects while trades are local actions.

For tactical entries and exits, the framework constructs price stability envelopes, upper and lower bounds within which price action remains statistically coherent given the current state and curvature.

These envelopes:

• Are volatility and volume adjusted

• Are state conditional

• Are not forecasts

They define the local, structurally stable price path across the market’s base manifold.

Price near a boundary implies asymmetry only if curvature remains low. A boundary breach implies local coordinate failure, not opportunity.

This distinction prevents most tactical errors.

4.5 Managing Exposure

The governing principle is simple:

Exposure must scale inversely with curvature.

As curvature rises:

• Position sizes shrink

• Confidence degrades

• Optionality replaces conviction

As curvature falls:

• Structure stabilizes

• Transport becomes reliable

• Exposure is earned

When an asset’s price exits the envelope’s channel, its historical conditional return function begins to deform and collapse.

This is not discretion. It’s systematic, geometrically imposed discipline.

5. A Brief History of Convergence on the Financial Primitives

What makes this framework credible is not novelty. It is that directionally, this structure is what modern investment strategies have all been pointing at.

5.1 Bottom Up Empiricism: Jim Simons and Renaissance Technologies learned through data that levels fail, dynamics dominate, ensembles outperform explanations, and risk must scale with instability.

5.2 Top Down Macro Structure: Bridgewater Associates, under Ray Dalio, elevated regimes, propagation, timing, and state-dependent risk through mechanistic reasoning.

5.3 Reflexivity And Feedback: George Soros rejected equilibrium outright, identifying nonlinear feedback and path dependence as dominant, though without a unifying geometric formalism.

5.4 Explicit Gauge Intuition: Eric Weinstein and Pia Malaney recognized that economic quantities are often gauge-dependent and that invariants, not values, carry meaning.

These approaches did not converge by imitation. They converged because markets eliminated alternatives.

Different language with the same constraints.

6. Sanity Check: Why This Geometry Appears Everywhere

This framework is built by adapting gauge theoretic principles from fundamental physics that have only recently been made explicit.

To avoid abstraction drifting free of reality, the framework must ground itself in systems we already understand.

6.1 Music: Dissonance as Volatility Across Time Scales

Harmony is not a property of notes.

It is a property of relationships across frequencies and time.

When frequencies align, music resolves.

When they interfere, dissonance emerges.

Markets behave the same way.

Capital flows operate across multiple horizons:

• Short-term trading

• Intermediate positioning

• Long-term allocation

When these flows align, markets trend. When they clash, volatility appears. Volatility is financial dissonance and structured interference, not randomness.

Low curvature is harmony. High curvature is unresolved sound.

6.2 Oceans: Cross Seas and Structural Danger

Sailors have a name for one of the most dangerous ocean states: cross seas.

Cross seas arise when multiple orderly wave systems intersect. Each wave is normal. Their superposition is not.

Markets behave identically during regime transition:

• Growth signals propagate one way

• Inflation another

• Policy and liquidity introduce phase shifts

The result is volatility, correlation breakdown, and tail risk. The danger does not come from any single wave. It comes from interference. The model’s volatility layer captures additional edge differentiating cross seas from chop.

In geometry, this is curvature. In finance, it is crisis. In music, it is dissonance. In ocean faring, it is cross seas.

6.3 Spinors: Why Markets Flip Without Turning Around

Spinors are objects in fundamental physics that require a 720 degree rotation to return to their original state.

This is not a trick, it is topology. Markets behave like this.

A market can return to the same growth and inflation levels and yet behave entirely differently because the path taken matters.

Leverage was built or unwound.

Volatility was compressed or released.

Positioning was crowded or flushed.

Markets are not vectors.They are spinor like objects, sensitive to traversal, orientation, and history.

This is why:

• “Same levels” do not imply same risk

• Mean reversion fails after crises

• History rhymes without repeating

7. What This Framework Does and Does Not Do

It does not:

• Predict prices

• Eliminate drawdowns

• Replace judgment

It does:

• Prevent false confidence

• Align exposure with structural stability

• Degrade gracefully during transition

• Replace storytelling with measurement

It shifts the problem from forecasting outcomes to respecting shape.

8. Why This is Hard to Formalize

Markets are faster, more reflexive, and more path dependent than the theories we still use to justify them.

Narratives multiply.

Signals overfit.

Certainty becomes dangerous.

What survives is structure. Geometry describes what remains structurally when description changes. Rather than fear or chaos, volatility is unresolved geometry.

This paper proposes an investing methodology that combines seasonal asset selection with aggressive position management, pressing winners while ruthlessly cutting under performers and reallocating the capital.

This approach builds a portfolio that captures the benefits of concentration by betting only on winners. When failed capital is redeployed into historically strong performers uncorrelated with current positions, the portfolio simultaneously gains the benefits of diversification.

In short, the strategy paradoxically delivers both concentration and diversification while exploiting the power law distribution of outcomes through repeated, high probability bets on capital growth each season.

First Identify the Season

A season is a recurring pattern in how energy distributes through a system. In nature, Earth’s axial tilt creates predictable cycles of solar energy concentration that life evolved to exploit. Plants store energy in roots and seeds before winter, deploying them for growth when warmth returns. This constraint isn’t about effort or desire, it’s thermodynamics. Winter soil lacks the thermal energy required for germination and photosynthesis. No amount of will can make a seed take root in frozen dirt.

Markets move in similar cycles and seasons. Capital flows through asset classes in recurring patterns, like solar energy gradients. Yet modern investors routinely expect returns from assets mismatched to the current macro regime, believing price will rise on narrative and conviction rather than seasonal constraints.

Market seasons are defined by structural constraints; the interplay of inflation, growth, and interest rates that shape which assets can thrive. Growth stocks historically outperform in low rate, low inflation, stable growth regimes. Commodities flourish in inflationary, strong growth environments. Bonds excel in disinflationary or recessionary conditions.

The fundamental lesson: growth depends on planting the right seed at the right time in the right season. Identify your market season first, then select assets accordingly.

Plant Incrementally as the Season Confirms

Timing determines returns more than selection. This follows directly from how compounding works in bounded time windows. A seed planted at the season’s start accumulates growth for the entire season. One planted halfway through loses half the growth window. If a tomato plant has 120 days to grow and you plant on day 60, half the potential harvest is surrendered regardless of perfect seed selection or soil composition.

This creates an asymmetric payoff structure: plant too early and a surprise freeze kills the whole crop (left tail risk). Plant at optimal time for maximum harvest potential. Plant too late and you’ve reduced risk but sacrificed total growth.

The rational strategy is to plant when conditions are good enough, not perfect. By the time consensus forms that conditions are perfect, you’ve traded certainty for harvest as competition for resources intensifies. A better approach is to act near 80% certainty. When tree sap flows, flower bulbs emerge, and buds swell, it’s time to get the first seeds in the ground.

As emerging transition signals near 80% confidence, plant incrementally. Begin with a few seeds to hedge against an early freeze. Once the season transition is confirmed, these seedlings can be nourished into large positions. This maximizes time in the ground, optimizing for the maximum growth allowed in specific circumstances.

The investor should enter positions concurrently as the regime transition is being confirmed, not after it’s priced in by consensus. Those who only buy growth stocks at the consensus peak of a growth regime will always perform average to poorly. Those who buy too much too early, anticipating a regime that fails to arrive, suffer catastrophic losses.

Observe Every Response Then Nurture Vigor, Starve Weakness

With seeds in the ground comes the most difficult step: observe, be patient, and see what grows.

Investment outcomes follow a power law distribution. Most positions produce underwhelming or negative returns, a small percentage generate good returns, and a tiny percentage produce exceptional returns that dominate total portfolio gains. This isn’t a methodological flaw, it’s the structural reality of probabilistic, dynamic systems.

When assets that historically outperform in the current season begin rising with momentum and breadth, showing biological vigor, the market is confirming the statistical patterns of the past. This is your signal to increase position size. Press winners aggressively. The market’s complex computational system is validating your thesis.

This is the Matthew effect in action: “For to everyone who has, more will be given, and he will have an abundance.” Accumulated advantage accrues to those who have, rather than those who have not. Let your winners run, allowing compounding to work its mathematical magic.’

When assets that historically outperformed fail to grow or behave sickly, the market is providing information. Something about this specific instance contradicts history. When this happens, don’t argue with the market.

The gardener must accept when the right seed in the right season, for unknowable reasons, isn’t going to take root. Similarly, the investor must aggressively sell an underperforming asset and reallocate to a different historical outperformer. Cut losers quickly, always protecting capital.

The mathematics here is unforgiving. A 50% loss requires a 100% gain just to break even. A 90% loss demands a 900% gain to recover. This asymmetry means protecting against the left tail matters far more than capturing every upside opportunity. As Charlie Munger said: “The only thing I want to know is where I’m going to die so I never go there.”

Recycle capital from losers into different assets with signal confirmation. Kill ruthlessly that which is sick. The goal isn’t to produce prize winning pumpkins, it’s to avoid catastrophic financial losses.

Diversify Within Seasons

Even with perfect execution and best practices, some assets that usually thrive will inexplicably fail. Gardens don’t behave like the non-interacting models of classical economics. In real gardens and real markets, probability matters more than prediction.

Like the hidden hydrodynamics of garden soil, markets are networks of agents whose collective behavior produces emergent, nonlinear outputs. These agents observe each other’s actions, follow trends, trigger stop-losses, face margin calls, and create cascading feedback loops. Assets move in fat tailed distributions where statistically impossible events occur once a decade.

This means some seeds that usually fail will thrive. The practitioner cannot know which specific ones will succeed, only play the most favorable odds across many attempts. Plant a variety of seeds in each season to nourish what grows and prune what fails.

For investors, examine historical financial data across decades over many market cycles. Identify which assets statistically outperformed in similar environments. This provides an empirical topology map that helps select assets most likely to outperform. These base rates aren’t guarantees, but planting what worked in the past while avoiding what didn’t provides a durable, repeatable edge. Critically, this methodology helps investors rigorously avoid regions where they might get killed.

If you redeploy failed capital into another historical outperformer uncorrelated with current portfolio positioning, you orient toward both concentration (betting only on winners) and diversification (uncorrelated positions). The strategy paradoxically delivers both benefits while taking advantage of the power law distribution of outcomes through repeated, high probability bets each season.

Prune Winners

The gardener cannot allow one plant to dominate the entire garden. What begins as vigorous growth eventually becomes overgrowth consuming resources, blocking light from neighbors, and creating systemic fragility. The healthiest gardens require pruning winners, not just removing failures.

Similarly, when a position grows to dominate portfolio allocation, it creates dangerous concentration risk. A single asset’s reversal can destroy accumulated gains across the entire portfolio. Trim what grows disproportionately. This isn’t abandoning conviction, it’s maintaining the conditions for sustainable, long-term growth.

Systematic trimming serves two purposes: it locks in gains from successful positions and provides capital to deploy into emerging opportunities within the current season. The capital freed from pruning winners can be reallocated to other historically favorable assets showing early signs of vigor, maintaining diversification while remaining aligned with the prevailing regime.

Compound Across Seasons

Seasons don’t last forever. Just as winter inevitably follows autumn, market regimes transition. The investor who remains rigid, continuing to plant summer crops as autumn approaches, faces systematic destruction. Capital preservation across seasonal transitions is how wealth compounds over decades.

When signals emerge that a season is ending, when historically reliable performers begin to falter broadly, when correlation structures shift, when volatility patterns change, the disciplined investor begins rotation. This doesn’t mean exiting all positions immediately. Like the gradual approach to planting, the transition between seasons requires incremental adjustment.

Reduce exposure to assets that thrived in the ending season. Begin exploratory positions in assets that historically perform in the emerging regime. As confidence in the new season grows, complete the rotation. This methodical approach allows you to capture late season gains while positioning for early-season growth in the next cycle.

The power of compounding emerges not from one exceptional season, but from preserving capital through multiple seasons. A modest return sustained across five regimes dramatically outperforms a spectacular return in one regime followed by catastrophic loss in the next. Size positions according to signal strength and volatility. Time entry to capture regime transitions early. Exit what fails to grow as conditions change.

The Art of Cultivating Returns

Both gardening and investing demand accepting a profound constraint: you cannot control outcomes, only tilt probabilities.

The gardener cannot command a seed to grow. They can only plant what historically thrives in current conditions, time planting to maximize growth windows, provide water and nutrients, prune overgrowth, and remove what dies. The seed’s growth emerges from interaction between its genetics, soil nutrients, water availability, solar energy, temperature, and countless microorganisms in the rhizosphere. The system is complex, adaptive, and only partially observable.

Similarly, the investor cannot command an asset to grow. They can only buy what historically performs in current regimes, time entry to capture regime transitions, size positions according to signal strength, trim disproportionate growth, and exit failures.

Some seeds will fail despite perfect conditions. Some investments will falter despite rigorous analysis. This isn’t failure, it’s the nature of complex systems. Accept the failed seedling. Accept the underperforming position. What matters is the systematic process of protecting capital, capturing favorable odds, and maintaining the capacity to plant again next season.

The gardener who cannot accept destruction becomes paralyzed, unable to prune the dead or plant anew. The investor who cannot accept losses compounds them, throwing good money after bad in denial of market reality. Wisdom lies in acknowledging what you cannot control while diligently managing what you can.

In the end, both practices teach the same lesson: work with the system’s constraints, respect its complexity, and cultivate patience. Returns, like harvests, emerge not from force of will but from alignment with forces greater than ourselves.

The initial post on this systematic investing approach was grounded in a simple idea: I needed enough understanding and conviction in the model to make it public.

If I’m managing my family’s hard-earned capital, then beyond standard risk-management safeguards, public transparency serves as an additional check. Win, lose, or blow up, radical accountability helps strip away bias and self-deception, the mind killer of investors.

After publishing that first post, I hesitated on a Q3 update.

Sharing investment returns felt cringe and I wasn’t sure I wanted to keep doing it. But that discomfort is just ego resistance. It doesn’t undermine the core objective: protecting my family’s capital by continually refining the system while aiming for market-beating performance.

So even though we’re already two months into Q4, I’ve chosen to continue updates for both my own disciplined improvement and anyone following along in their own accounts.

Third Quarter Strategy Returns & Market Debrief

Strong performance continued through Q3. Systematic Behavioral Arbitrage (SBA) delivered more than twice that of a defensive 60/40 portfolio while also outpacing even a fully S&P 500-invested allocation by a similar margin.

Like a three body problem, financial markets in Q3 were governed by the interaction of three fundamental centers of gravity:

1. The Cost of Capital Reset the Entire Market Regime

When risk free rate falls, the present value of all future cash flows rises. High duration assets, like technology or AI, are most sensitive to this. When the Federal Reserve initiated a dovish pivot in September, it marked the beginning of a rate cutting cycle.

This lowered the discount rates across the curve, producing three immediate effects: Demand for duration increase so large cap tech and AI surged. The cost of capital for small cap stocks finally eased, driving the Russell 2000 Small Cap Index to outperform. Bonds rallied as yields fell, improving asset allocation liquidity.

It’s our stance that this shift in the cost of capital was the dominant tailwind of the quarter. It reset the regime from a tightening and defensive posture to loosening and risk seeking after the tariff induced stock market crash during Q2.

2. The Real Economy Delivered Enough Cash Flow to Validate Risk Appetite

Asset prices can rise sustainably only when underlying cash flows are stable or improving.

Despite pockets of labor market softening during Q3, consumer spending remained resilient, particularly amongst upper income households. Remember that consumer spending accounts for roughly 70% of GDP in the United States, making it the largest component of the real economy.

Corporate earnings across many sectors outperformed expectations, confirming aggregate demand had not rolled over as previously feared. This provided the real earnings support required for stock valuations to expand and reinforced the credibility of the Fed’s dovish posture.

Thus, real economic output acted as a second tailwind, validating that the rate cut rally was not purely speculative, a rally that has been called one of the most hated equity rallies. However since investing is not a game of analysis but meta analysis, the logic requires an inversion; if everyone is bearish and has moved into a defensive posture, the market can’t go down without an additional catalyst, a catalyst that never materialized.

3. The AI Investment Flywheel Concentrated Market Leadership

When a new technology paradigm emerges, capital concentrates in firms with the highest expected marginal return on that technology.

During Q3, AI emerged as the dominant marginal return frontier. Markets priced AI infrastructure, chips, and hyperscale compute as having exceptionally high expected productivity, and megacap firms were the primary entities positioned to capitalize given their balance sheet strength.

Although it remains unclear where value will ultimately concentrate within this new technological regime, assuming that a disproportionate share of AI-driven cash flows will accrue to a small set of firms with already robust revenue is a defensible hypothesis.

George Soro’s famously remarked “When I see a bubble forming, I rush in to buy, adding fuel to the fire.”

Still, vigilance is strongly warranted. Concerns about an AI-driven bubble are not unfounded. Historically, major technological shifts have frequently coincided with large financial bubbles, and both the magnitude and duration of such episodes can only be recognized in hindsight. Practically, it often feels better to be inside the bubble making money than outside the theater shouting fire.

Whether this level of concentrated outperformance persists is unknowable. Just remember that fortunes are both made and lost inside the bubble.

End of Year Q4 Portfolio Positioning

Per the current economic signals, the strategy remains positioned for an overheating economy—characterized by decelerating growth and accelerating inflation.

On the following radar chart, the white solid line is the geometry of our current economic posture.

The allocation stays gold-heavy, providing protection against geopolitical or inflation-driven shocks while continuing to benefit from the monetary-debasement narrative amid persistent fiscal deficits.

Our simple, trend-following risk-management overlay, the portfolio seatbelt, continues to signal “party on.” If and/or when the AI bubble bursts, it will protect our capital from any killer long left tail events lurking unseen in the deep.

Expect a Q4 2025 update sometime January 2026.

Until then, stay safe out there. Investing is a professional bloodsport.

• The Capital Market Flywheel — Ethereum reduces trust friction, accelerating the compounding of global capital.

• Blockchain as a Trust Machine — By automating trust through code, Ethereum lowers costs and expands participation.

• Foundations of Value — Ethereum unites utility, scarcity, and network effects, positioning itself as programmable infrastructure.

• Utility Premium — Programmability transforms Ethereum into a global computer layer.

• Innovation Premium — Composability compounds innovation, making Ethereum a financial Lego system.

• Scarcity Premium — ETH becomes scarcer as usage rises, directly linking network demand to value.

• Network Flywheel Premium — Adoption compounds value in a self-reinforcing feedback loop.

• Institutional Applications — Stablecoins, tokenization, and collateral markets now run on Ethereum, embedding it into global finance.

• Regulatory Clarity — Ethereum has been classified as a commodity, shifting from speculation to investable infrastructure.

• AI Convergence — AI produces intelligence; Ethereum provides the trust layer that authenticates and monetizes it.

• Risks — Complexity, L2 siphoning, L1 competition, regulation, and adoption risks remain.

• Balanced View — Ethereum is a high-upside call option on programmable finance: asymmetric reward, bounded risk.

Introduction

This paper presents the investment case for Ether (ETH), the native asset of the Ethereum blockchain. It evaluates Ethereum from first principles: programmability and utility, scarcity, and network effects, and their implications for long-term value.

This is not a primer on blockchain technology. Rather, it examines Ethereum’s impact on capital markets and its ability to boost the velocity of money, enhancing productivity.

The paper proceeds in three parts: the thesis (the bull case), the antithesis (the risks), and the synthesis (a balanced perspective).

Background

The Capital Market Flywheel

Capital markets are a social technology that mobilizes savings into productive investment. This creates a flywheel effect: investment drives innovation, innovation drives growth, growth generates incomes and taxes, and new savings feed back into more capital formation.

The genius of this design is compounding momentum. Once spinning, the flywheel resists slowing down and each rotation reinforces the next.

Well-functioning markets also improve governance. Investors demand transparency, rewarding well-governed firms with lower capital costs and penalizing opaque firms with higher risk premiums. States benefit too, as stronger property rights and contract enforcement attract more capital, reduce systemic risk, and increase tax revenues.

Trust is the clutch that engages this machine. Without trust, capital seizes up and the machine stalls. Russia’s exclusion from Western financial markets after its invasion of Ukraine shows how quickly trust can vanish.

Historically, financial intermediaries like banks, clearinghouses, ACH, and SWIFT provided this trust, but at a cost: fees, delays, and operational friction.

Blockchain as a Trust Machine

Blockchain technology minimizes reliance on intermediaries by shifting trust to math and protocol rules. Transactions are public, verifiable in real time, tamper-resistant, and economically secured by validators who risk their own capital if dishonest.

Blockchain does not eliminate trust but automates its enforcement, reducing costs and accelerating the velocity of money. Even a modest efficiency gain compounds into enormous downstream value over time.

Ethereum is the leading proof for this new trust infrastructure.

Thesis: Ethereum is Digital Infrastructure

The Foundations of Value

All assets derive value from two vectors: utility and scarcity. Gold has retained value for millennia because it is both useful and hard to extract. Ethereum shares these vectors and adds a third: network effects.

To concretize Ethereum, think of the network as a digital highway system:

• Ethereum is the highway — the base infrastructure.

• Ether (ETH) is the gasoline — fuel that powers movement.

• Smart contracts and dApps are the vehicles — programmable units carrying transactions, goods, and services.

• Gas fees are the tolls — payments to move vehicles, priced dynamically to prioritize traffic.

• Validators are the highway patrol — enforcing rules, ensuring honest traffic, and penalizing misconduct.

• Network effects are traffic density — the more vehicles moving freight, the more indispensable the highway becomes.

• Composability is convoys and Lego blocks — vehicles link together to carry more value, or financial building blocks snap together into more powerful systems.

These foundations of value — utility, scarcity, and network effects — positions Ethereum as uniquely capable of compounding value at scale.

Utility Premium: Programmable Vehicles

Ethereum’s core utility is programmability. Smart contracts are automated vehicles: once fueled with ETH, they execute routes without intermediaries like bankers, brokers, or lawyers.

This turns Ethereum into a global computer layer. Processes once dependent on human intermediation can now be automated, secured, and executed at scale. A vending machine is the simplest analogy: insert money, get a soda. Sending ETH to a smart contract triggers self executing code that outputs a predefined software product.

Innovation Premium: Composability

Composability is Ethereum’s innovation engine. On this highway, vehicles don’t just move independently — they link into convoys. A stablecoin truck can connect to a lending trailer, then hitch to an exchange module, all operating seamlessly.

It works like Lego blocks: simple financial primitives — lending, trading, stablecoins — snap together into complex systems. A lending protocol can use stablecoins for deposits, route liquidity through an exchange, and wrap staking rewards into collateral — all without requiring permission from intermediaries.

In traditional finance, products are siloed across custodians and brokers. Fidelity ETFs cannot simply plug into Schwab ETFs to build hybrid products. On Ethereum, everything is interoperable by default.

This creates a compounding innovation flywheel: every new contract enhances the utility of all others. Composability transforms Ethereum from a single platform into an open financial operating system.

Scarcity Premium: Limited Fuel Supply

Scarcity is hard-coded into Ethereum. After the 2022 Merge, issuance fell by ~90%, making ETH’s annual supply growth lower than Bitcoin or gold. Since EIP-1559, every transaction burns a portion of ETH, permanently removing it from circulation. In high-use periods, burns exceed issuance, making ETH net-deflationary.

Validators reinforce this scarcity by staking ETH as collateral to secure the network. Dishonesty leads to slashing — a direct financial penalty. This ensures incentives align with the security of the network.

Scarcity is directly tied to usage: the more traffic, the more tolls burned, and the scarcer ETH becomes. Said another way, as the network increases in utility as indicated usage, ETH also rises in value. This deflationary economic protocol makes ETH not just fuel to use the network but a store of value that increases as adoption grows.

Network Flywheel Premium: Compounding Adoption

Networks grow non-linearly. By Metcalfe’s Law, value scales with the square of participants (n²). Ethereum exemplifies this: more users attract more developers, more developers build more applications, more applications increase liquidity, and greater liquidity attracts more users.

Network value ~ n², where n = number of participants.

This compounding adoption powers a closed-loop flywheel:

Utility → Transactions → Fees → ETH Burns → Scarcity → Higher Value → Staking → Security → Adoption → back to Utility.

Unlike most assets, which can claim utility, scarcity, or network effects, Ethereum unites all three in one interlocking protocol. This is the foundation of its self-reinforcing growth dynamic.

Institutional Applications

Tokenization: Collapsing Space and Time

Tokenization converts real-world assets into digital tokens that can be traded and settled with the same efficiency as native crypto.

Legacy brokers like Fidelity give the illusion of instant trading, but settlement still runs through clearinghouses like DTCC, historically T+2 and now T+1. That means capital remains tied up, introducing counterparty and reconciliation risk.

Transformative technologies always collapse friction along an axis of space and time; tokenization does both. It detaches ownership from geography, enabling borderless transfer of value, and it collapses time by settling instantly rather than over days. Capital becomes more fluid, reducing time spent as dead collateral.

Stablecoins: Tokenized Cash

Stablecoins bridge the legacy financial system with blockchain, bringing dollar stability to Ethereum’s programmable rails. They exchange fiat stability for blockchain speed and composability.

Today, stablecoins are Ethereum’s largest institutional use case. USDC and Tether — representing hundreds of billions in supply and tens of billions in daily settlement — primarily circulate on Ethereum. They move across protocols, serve as collateral, and settle trades in tokenized assets.

Stablecoins have effectively turned Ethereum into the backbone of digital dollars, available for anyone with an internet connection to transact in.

ETH as Institutional Collateral

ETH is emerging as deep, liquid, yield-bearing collateral. With $400B+ market cap, high volumes, and native staking yield, it rivals Treasuries and gold while offering productivity.

MicroStrategy pioneered the use of Bitcoin as corporate collateral, borrowing against it to fund operations. Ethereum can go further: ETH is not just scarce and liquid, but also productive, generating yield while securing obligations. Imagine if a unattended barrel of oil or bar of gold could produce more of itself while idle.

This creates a new institutional-grade asset: reserve collateral that compounds value.

Regulatory Clarity

In 2024, regulators approved spot Bitcoin and Ethereum ETFs, attracting billions in inflows. Europe’s MiCA framework and the U.S. GENIUS Act went further, classifying Ethereum and Bitcoin as commodities rather than securities — the same category as oil or gold.

This recognition lowers risk, reduces capital costs, and accelerates institutional adoption. It reinforces the view of ETH as digital energy: fuel that powers economic activity at scale.

AI Convergence: Automated Intelligence Meets Automated Trust

AI data centers are becoming factories of intelligence. Blockchains are factories of trust. Ethereum’s reliability makes it the natural anchor for their convergence.

As AI permeates the economy, authenticating human intent will become critical. Tokenized “proof-of-human” signatures could gate trillions in activity — from robotics to financial transactions. They could also watermark human expression amid a flood of synthetic AI generated content.

If AI is an engine of intelligence, Ethereum is the trust layer that ensures outputs remain safe, verifiable, and monetizable. This positions ETH not only as digital oil, but also as programmable collateral for the AI economy.

Antithesis: Risks

Ethereum’s design gives it extraordinary potential, but investors must recognize that the very qualities that make it powerful also expose it to vulnerabilities. Its programmability is a double-edged sword: flexibility breeds complexity, and complexity breeds fragility. Each additional layer of smart contracts introduces new attack surfaces. Governance disputes, often messy and drawn-out, lack clear resolution mechanisms — a contrast to Bitcoin’s deliberately narrow scope and simpler social contract.

Scalability presents another paradox. Ethereum’s growth depends heavily on Layer-2s, but these extensions threaten to siphon value away from the base chain. If activity consolidates on L2s while minimizing interaction with L1, Ethereum risks declining fee revenue and weaker ETH burns — the mechanics that underpin its scarcity thesis. Worse, many L2s rely on centralized sequencers. Over time, trust could shift from Ethereum itself to the L2 brand. If those ecosystems mature further, they may evolve validator sets, detach, and compete directly as independent L1 blockchains. In that scenario, Ethereum could be relegated to a commoditized back-office settlement layer with limited upside capture.

Competition from rival L1s compounds the risk. Chains like Solana and Avalanche already offer faster throughput and lower costs. Ethereum’s network effects are formidable, but history shows that no technology maintains dominance forever. Users, especially institutions, can be mercenary: if another chain proves faster, cheaper, or easier to build on, liquidity and applications could migrate quickly.

Ethereum’s scalability “solution” remains incomplete. L2s improve throughput but fragment liquidity and reintroduce centralization risks, leaving scalability only partially resolved.

Regulation is another wild card. While recent clarity has reduced existential risk, the landscape is far from settled. Political shifts, new tax regimes, or compliance frameworks could slow adoption. Ironically, ETFs and custodial products may centralize ownership, putting a supposedly decentralized asset into the hands of a few large financial institutions.

Finally, adoption may simply disappoint. Tokenization forecasts assume trillions will flow on-chain, but the status quo in legacy finance may remain “good enough” for many institutions. Incumbent systems, while slower and less efficient, are entrenched, familiar, and deeply integrated.

In short, while Ethereum’s bull case is compelling, its risks are structural, competitive, regulatory, and behavioral. Any one of them could stall, dilute, or even derail the investment thesis.

Synthesis: Balanced View

Ethereum demonstrates that utility, scarcity, and network effects can be programmatically linked into a single system. That is genuinely new — and potentially transformative. The result is an asset that is at once a currency, a commodity, and financial infrastructure. If adoption compounds, the payoff could be measured in the trillions.

But Ethereum is still a live experiment. The risks are not trivial: scalability is only partly solved, Layer-2s may divert value capture, rival L1s compete aggressively, and institutional adoption could fall short of forecasts. The outcome remains path-dependent.

The rational investor stance is not maximalism, but measured positioning. Ethereum should be sized like a high-upside call option on programmable finance — an asymmetric bet where upside is vast, but downside is bounded if confined to a risk sleeve. In portfolio terms, it is less a core allocation than a convex exposure: small in weight, but meaningful in impact if the thesis plays out.

Conclusion

Bitcoin has won the race for digital gold — a simple, scarce, non-sovereign store of value. Ethereum is something different. It is a scarce, yield-bearing, programmable asset that doubles as infrastructure for a tokenized global economy. Utility sparks its engine, scarcity sustains it, network effects accelerate it, and global adoption trends supercharge it.

This combination elevates Ethereum beyond a mere cryptocurrency. It is best understood as a programmable energy substrate like oil: a dynamic digital fuel that powers the base settlement layer of innovative financial rails that are faster and more secure than incumbent financial intermediaries.

For investors, the takeaway is clear: Ethereum is not risk-free, but it is one of the few assets in markets today with credible potential to move from niche to systemic. Treated prudently, it offers asymmetric exposure to the next phase of financial technology.

Ethereum Investor Takeaways

Thesis in One Line
Ethereum is a scarce, yield-bearing, programmable asset that doubles as financial infrastructure — a high-upside call option on the future of programmable finance.

Why Ethereum Matters

• Utility — Smart contracts make Ethereum a programmable global computer, automating processes once dependent on lawyers, brokers, and bankers.

• Innovation — Composability turns Ethereum into a Lego system of finance, where applications link together to create increasingly complex products.

• Scarcity — ETH supply is deflationary under high usage: more activity burns more ETH, directly tying scarcity to demand.

• Network Effects — Adoption compounds by Metcalfe’s Law (n²): more users bring more apps, liquidity, and value, reinforcing growth.

• Institutional Integration — Stablecoins, tokenization, and collateral markets now run on Ethereum, embedding it into global finance.

• Regulatory Clarity — ETH has been classified as a commodity in the U.S. and EU, lowering risk and reducing the cost of capital.

• AI Convergence — As AI produces intelligence, Ethereum provides the trust layer to authenticate and monetize it.

Key Risks

• Complexity — More programmability creates more fragility and attack surfaces.

• Layer-2s — Extend scalability but risk siphoning value away from L1 Ethereum.

• Competition — Rival L1s (Solana, Avalanche) offer faster throughput and lower costs.

• Regulation — Political shifts, tax regimes, or custody centralization could undermine decentralization.

• Adoption — Tokenization and institutional forecasts may prove over-optimistic; legacy finance may remain “good enough.”

Investor Framing
ETH is not Bitcoin 2.0. Bitcoin is digital gold; ETH is programmable digital fuel to power an innovative financial technology.
The bull case is multi-trillion in scale if adoption accelerates.
The bear case is not zero, but stalled growth or dilution of value capture.
The prudent stance: size ETH as convex exposure — a high-upside call option with bounded downside if kept in a risk sleeve.

Final Takeaway
ETH is one of the few assets today with a credible path from niche to systemic. For investors, it represents scarcity, yield, and programmability combined in a single instrument — the settlement layer of a tokenized, global economy.

Ancient sailors navigated oceans using stars as fixed reference points while adjusting their sails to capture whatever winds and currents nature provided. The Ouroboros Strategy mirrors this proven navigational approach.

Rather than relying on discretionary judgment to predict an unknowable future, this strategy establishes fixed reference points by mapping current economic data to historical base rates—a methodology called reference class forecasting. By identifying our approximate position within economic cycles (driven by persistent behavioral patterns), we adjust asset allocation to capture momentum from prevailing economic "trade winds" of growth and inflation.

This strategy applies a well known macroeconomic framework to orient our portfolio’s within the four dimensions of motion available to the economy.

Investment Philosophy

• The future is unknowable and unpredictable

• Human brains are hardwired with cognitive biases, making discretionary judgment systematically unreliable

• Financial markets amplify these behavioral errors, which persistently recur across time and drive market cycles

Q2 Update, July 1st 2025

As of July 1, 2025, the Ouroboros Strategy has delivered a +12.31% YTD return, outperforming both the S&P 500 (+6.40%) and the 60/40 benchmark (+6.05%).

The strategy’s performance in Q2 was notably resilient amid broader market volatility in April. The diversified asset mix helped capture upside in May and June, outpacing traditional portfolios

State of the Economic Trade Winds

Our quantitative model analyzes 9 well validated economic indicators (standardized as Z-scores) to determine our position within the economic cycle.

The model places us in an overheating regime, where growth is expected to decline while inflationary pressures persist to the upside.

The model outputs a Shannon Entropy (H) of 0.79, indicating low to moderate uncertainty in its regime forecast.

The model outputs a Confidence Score is 0.61, suggesting moderate confidence in the regime forecast.

Q3 Asset Allocation

Based on our location within the economic cycle, we maintain our gold and equity-heavy allocation—a portfolio positioned for late-cycle overheating dynamics. Equity markets often, but not always, produce their strongest gains during the late stages of this regime as investors rush in from the sidelines fearing the start of a new cyclical bull market.

Risk Management: Trend Following Overlay

The strategy employs trend-following rules to limit drawdowns by exploiting empirically validated volatility clustering patterns. This approach captures the majority of upside while limiting downside risk, enabling consistent compounding from a higher base.

Trend-following applies only to assets where research supports its effectiveness: S&P 500, Gold, and Oil.

Since both S&P 500 and Gold remain above their 120-day moving averages, we maintain current allocations to these assets.

René Girard's mimetic theory illuminates how localized human communication networks evolved into today's algorithm-driven social media landscape, shifting the mediation of mimetic desire from religious institutions to mass media conglomerates and now to personalized digital ecosystems. Modern algorithmic media platforms amplify mimetic rivalries without the traditional restraining mechanisms, generating novel social divisions that will require innovative solutions to mediate our innate human tendency to violence.

The Mimetic Engine of Desire, Mediation & Violence

René Girard's mimetic theory proposes that human desire is fundamentally imitative rather than autonomous. We desire objects and experiences not for their inherent value but because others—our models—desire them. This mimetic nature forms the bonds of friendship and love that connect us while, simultaneously, containing the seeds of violence if desires converge on objects that cannot be shared. 1

When humans mirror models of desire with others in close proximity, there is a tendency towards competition for the same objects, leading to what Girard called "mimetic rivalry." As these rivalries intensify, they can spread contagiously throughout a community, creating a mimetic crises that threatens social stability. Girard proposes that this triangular relationship between subject, model, and object is the fundamental driver both human culture and human violence.

To prevent self-destruction from escalating rivalry, human societies developed the scapegoat mechanism—identifying and sacrificing victims whose elimination temporarily restores social harmony. The scapegoat mechanism appears with remarkable consistency across differing human cultures, where scapegoating became institutionalized in religious rituals, cultural prohibitions, and social norms.

Sacrificial rituals thus serve as societal safety mechanisms, channeling destructive mimetic competition away from the community while simultaneously strengthening group cohesion through collective participation in sanctioned victimization.

The universally acknowledged human tendencies toward envy and acquisitiveness suggest that Mimetic conflict may actually be an essential regulatory mechanism rather than a defect in human nature.

Human societies across time and space independently discovered the scapegoating mechanism, typically encoded in religious frameworks, to mediate mimetic desires and constrain violence. This suggests that (a) mimesis and violence is built into our biological hardware (b) is a regulatory feature rather not a bug (c) human beings create religion to shelter society from itself in the same way birds build nests.

The standard account of conflict is violence arises when people fight about differences. However, the key insight of mimetic theory is violence arises within society when people fight about the same thing.

The Transmission Rails of Mimesis

Mimesis requires informational transmission rails to communicate desires. Individuals use the same biological perceptual sensory apparatus wired directly into our neurocognitive social hardware, such as the mirror neuron network. At the scale of community, knowledge historically existed primarily through oral traditions, with essential cultural wisdom and understanding passed between generations through narrative and storytelling.

Communication networks function as evolutionary operating platforms that solve large-scale coordination problems in nature. This design recurs throughout biology because individuals cannot perceive all relevant environmental changes through observation alone. 2

A network of interconnected organisms exploring their environment possesses a significant survival advantage over solitary individuals. When 100 organisms function as a coordinated system, they collectively perceive and understand reality beyond any single member's observations, capturing a crucial survival premium through shared intelligence.

Many plant systems demonstrate this type of collective unconscious, as seen in the interconnected root system of individual aspen trees that practically function as a single organism.

As our species harvested an increasing survival premium via communication networks, the newly available energetic surplus was directed towards developing more sophisticated information-sharing technologies, the advancement of which has recently gone parabolic.

The printing press enabled mass reproduction of printed media, though access initially remained limited to institutions and higher social classes.

Steam-powered presses during the Industrial Revolution democratized printed media by making it both affordable and accessible to the broader public.

The telegraph and telephone later created channels to transmit information instantly across vast distances, while radio and television transformed the very forms of content available for audience consumption.

Each breakthrough in communication technology fundamentally reshaped the infrastructure of information transmission, as systems naturally evolve to integrate the most advanced available methods while simultaneously cannibalizing the old technology.

From this communication revolution emerged mass media, a sophisticated ecosystem for collective observation-sharing that enables millions to simultaneously witness events and process developments in real time, transcending previous constraints of time and space. The fundamental value proposition of mass media is simple: quality information creates clearer perception of reality, enhancing decision-making abilities across both individual and collective levels.

With each improvement in bandwidth, latency, network topologies, and propagation speed the velocity of data moving throughout human network began to accelerate. In the era of spoken stories, network information flow was constrained by the physics of sound waves.

After the scientific revolution, information began to flow at the speed of light. 3

Sacred Mediators: Mass Media's Rise as Society's Mimetic Arbiters

As modern society secularized and traditional religious constraints on mimetic desire weakened, new institutions emerged to channel and mediate society-wide human desires. Mass media networks became foremost among these new mediators, simultaneously offering models of desire while regulating social tensions through controlled narratives. The Boomer nostalgia for post-World War II mid-century America is nostalgia for an entire country collectively orienting along the same few TV channels and news outlets.

Girard would recognize mass media institutions as serving a quasi-religious function in managing societal desire. Just as the Catholic Church historically monopolized mediation between individuals and God through systematic power consolidation, mass media companies came to consolidate the flow of information to society into a entire vertical infrastructure, paving the way for their monopoly of economic and mimetic mediation. This was accomplished through three primary mechanisms:

First, mass media companies controlled physical distribution channels—printing presses, broadcast networks, cable infrastructure—creating high barriers to entry that gave them monopolistic control over which models of desire received widespread attention. 4

Second, they established institutional credibility through consistent practices, effectively forming a cartel of "mimetic arbitration"—determining which desires were legitimate and which were taboo. 5

Third, they captured advertising markets through bundling, systematically channeling mimetic desire toward commercial ends while maintaining the appearance of editorial independence.

Today, just six companies control 90% of traditional media outlets.

These monopolistic dynamics allowed media companies to extract extraordinary value via advertising while portraying themselves as a shining city on a hill; trusted public services, despite the glaring conflicts of interest.

Critical theorists who assail these institutions for such conflict of interest, for example Noam Chomsky, generally fail to recognize mass media as not just economic monopolies but also as necessary mediators of mimetic desire—serving as society wide violence dampeners in the same way religion once did. By building an economic monopoly, traditional media came to function as what Girard called an "external mediator"— constructing models of desire for the entire herd to create societal cohesion without triggering destructive rivalries and preventing runaway mimetic contagion.

Today, the monopolistic grip held by traditional mass media has begun to loosen under pressure from highly personalized content delivery algorithms known as social media.

This dramatic shift is vividly illustrated in the revenue streams of traditional media companies like The New York Times, which now derives over half its revenue from digital subscriptions to online games and puzzles rather than from its traditional news content. Even the most established gatekeepers of information must adapt to a media ecosystem where personalized engagement increasingly displaces centralized editorial authority as the primary economic and mimetic mechanism in society. 6

The Mimetic Crisis of the 21st Century: The Fragmentation of Shared Reality by Algorithmic Amplification

Algorithmic social media represents a fundamental paradigm shift: while traditional media competed through curating scarcity, algorithmic systems compete by personalizing abundance. This inversion creates a Faustian bargain—offering unprecedented access to tailored content while fragmenting shared reality and intensifying desires through hyper-targeted models operating below conscious awareness.

Goethe’s “Faust and Mephistopheles”, 1827-1828, oil on canvas

These systems iteratively improve to detect emerging desires and amplify them through engagement-optimized feedback loops. While "engagement" sounds neutral, algorithms systematically orient users toward like-minded communities, forming content clusters that reinforce existing beliefs. The result is divergent information diets where two users of the same platform experience fundamentally different realities.

A social network graph with content clusters based on user engagement

These digital spaces aren't merely echo chambers but mimetic incubators where desires and fears compound without traditional constraints. Algorithms function as what Girard would call "model-mediators," simultaneously suggesting what to desire, identifying scapegoats, and intensifying these impulses through social proof. Consequently, citizens downstream of their media diets lose control over their own beliefs and desires.

The scapegoat mechanism manifests vividly in internet cancel culture: the decentralized collective targeting of individuals who cannot effectively defend against anonymous swarms. The shared moral righteousness of a digital lynch mob reinforces the unity of their group in a shared belief in the scapegoat's guilt. It’s not surprising that a digital lynch mob constructed in binary bits, rather than atoms, does not speak nuance.

This creates a mimetic crisis where ideological echo chambers align and entrench users against different scapegoats and desire amplification outpaces society's ability to channel it productively.

The Sacrifice of Isaac, 1602 by Caravaggio, the canonical scapegoat examples in Western Judeo-Christian civilization include the binding of Isaac by Abraham or the crucifixion of Jesus Christ.

What is perceived as a cultural or political crisis is actually mimetic acceleration now operating without traditional constraints. Political polarization, culture wars, and weekly viral outrage cycles are all symptoms of unregulated mimetic contagion.

Where traditional media monopolized physical distribution, algorithmic systems monopolize attention distribution—creating an economy operating largely below conscious awareness. Users engage in continuous transactions with every scroll and click, paying not in dollars but in time.

The New Media Monopoly

What we're witnessing is not informational democratization, as was the hope of the early internet, but its reconfiguration into a never before witnessed monopolistic architecture. Within the algorithmic ecosystem, new monopolistic nodes are forming—individuals and organizations accumulating disproportionate influence through algorithmic distribution's compounding advantages.

While traditional media competed within geographic boundaries, algorithmic platforms operate globally with virtually zero marginal distribution costs, creating unprecedented winner-take-all dynamics.

In attention markets, differentiation creates monopolistic niches, making the emerging horizontal monopoly of social media algorithms simultaneously more powerful and less visible. Joe Rogan exemplifies this shift—his distinction stems not merely from institutional independence and a very high IQ but from mastering algorithmic distribution mechanics while self portraying as a meathead.

From a Girardian perspective, we need better desire mediators—not better content filters—systems that acknowledge humanity's mimetic nature while channeling it toward non-rivalrous ends. The fundamental question becomes whether society can develop new mechanisms to manage accelerating mimetic desire in the algorithmic age.

In the 1891 book “The Decay of Lying,” Oscar Wilde articulates an anti-mimetic vision of the artist:

“Art doesn’t imitate life, as the saying goes—life imitates art,” according to Wilde. The artist is a creator par excellence. The artist brings new realities into being and changes how we see and experience the world. The artist enters unexplored wilderness and returns with a map, if he returns at all.

Most proposed solutions misidentify the problem by focusing on content moderation or platform regulation. The core issue isn't the content itself but the intensification of mimetic desire without corresponding mechanisms to manage mimetic rivalry. Mimetic aggression is the easiest of all mimesis; a free and loving anti-mimetic response has the power to change hearts and minds. In short, the problem lies not with technology but within the hearts of men.

The task at hand is to beat contagious mimetic rivalry without the traditional pressure release mechanism used by society historically. Girard succinctly lays out the challenge: “Everyone leaves the beaten path only to fall into the same ditch.”

Don’t get lost in the hall of mirrors, for there are many here amongst us who have yet to return.

In the mid-19th century, a French economist named Leon Walras embarked on an ambitious mission: to transform economics from philosophical musings into a rigorous science. Inspired by the mathematical breakthroughs of Isaac Newton and the growing field of physics, Walras sought to create a "science of economic forces, analogous to the science of astronomical forces."

Before Walras, economics was more closely aligned with philosophy than science. Economic discussions were embedded within philosophical, theological, and ethical frameworks, focusing on moral principles, justice, and practical advice for trade and governance.

Living in an era of tremendous scientific progress, Walras witnessed how Newton's discoveries were being expanded upon to develop calculus—a mathematical language that could describe complex natural phenomena through differential equations. Walras became convinced that if calculus could precisely calculate the motion of planets, it could also capture the movements of human agents trading within an economy.

Walras's breakthrough came when he recognized parallels between physical equilibrium systems and economic markets. To understand this connection, imagine a large bowl with a rubber ball. If you release the ball from the rim, it will roll back and forth until settling at the bottom—a state of equilibrium where all forces acting on the ball cancel each other out. Gravity pulls the ball down, the bowl pushes it up, and eventually, it rests where forces cancel out.

Similarly, Walras reasoned that market forces would drive prices toward a natural equilibrium point. Just as physical forces guide the ball to rest, supply and demand would guide markets toward stable prices where transactions between buyers and sellers converge.

Walras's mathematical approach to economics gained momentum as subsequent thinkers expanded on his work.

William Jevons developed the concept of marginal utility, describing how a consumer's satisfaction changes with additional units of consumption. Since every consumer has different personal utility for a given good, market participants would inevitably trade until reaching equilibrium.

Adam Smith introduced the invisible hand, describing how individuals pursuing self-interest unintentionally benefit society as a whole. Through supply, demand, and competition, markets efficiently allocate resources without central planning.

Vilfredo Pareto introduced the concept of Pareto Efficiency, a state where it's impossible to make anyone better off without making someone else worse off. Pareto argued markets would continue trading until all beneficial trades were exhausted—the point of market equilibrium. He proudly declared that "the theory of economic science has thus acquired the rigor of rational mechanics."

By the 20th century, economists synthesized these ideas into neoclassical economic theory. Starting with foundational assumptions about rational consumers and producers optimizing utility among finite resources, they could mathematically model economic forces through the lens of equilibrium, from individual decisions to international trade.

Economic Balls Never Settle in Their Bowls When Markets Refuse to Stay Still

Walras’s adoption of 19th-century physics brought mathematical precision but also a critical limitation: it locked economics into a static, reductionist framework. Physics of that era focused on closed systems trending toward equilibrium, like the ball in the bowl. Walras assumed economies behaved similarly, with rational actors and predictable outcomes.

This ideological lock-in of an entire field - favoring simplicity over realism—persists in traditional economics, despite evidence of its shortcomings.

As economists gained access to better data, computing power, and research methods there was an uneasiness taking hold as they began questioning Walras's equilibrium model and its underlying assumptions.

For example, the economic “law of one price” asserts that identical goods should sell at the same price across markets absent market distorting economic barriers. Yet, post-1999 Eurozone data showed prices diverging—43% above the EU average in Denmark, more than 40% below in Bulgaria and Romania—defying equilibrium predictions.

Cracks emerged in traditional economics consideration of time scales, which traditional economic models barely consider. When they do, it's often simplified into short, intermediate, or long-term horizons.

Reality is messier: it takes time to extract crude oil, refine it into gasoline, transport it to stations, and for consumers to make purchasing decisions.

Yale economist Herbert Scarf calculated that equilibrium time grows exponentially with an economy’s complexity. The more unique items available to trade, the longer it takes for a economic system to settle. The roughly 10^10 unique economic products available today, Scarf calculated that reaching equilibrium after a single shock would take longer than the universe’s age, rendering the entire concept impractical.

The behavioral research of psychologists Daniel Kahneman and Amos Tversky undermines the "rational actor" axiom essential to traditional economic theory. Real people are poor at complex logical decision-making and subject to biases. American polymath and scholar Herbert Simon observed that people often "satisfice"—settling for "good enough" rather than optimal outcomes.

When filling up gas, people don't drive to every station in town for the cheapest price; they choose among convenient options. Time matters and is considerably more challenging to untangle then the idealized physics of billiard balls colliding.

While traditional economics models supply and demand through negative feedback mechanisms (like the ball returning to the bottom of the bowl), real economies experience positive feedback—seen in market bubbles and crashes (negative bubbles). Human behavior creates herd dynamics, and tail events can completely alter a country's economic trajectory with cascading effects.

As T.S. Eliot observed, "We can't help but disturb the universe"—our actions create ripples that amplify through complex systems in ways that simple equilibrium models cannot capture.

Without the axioms of traditional economics, it's as if the bowl's shape continuously changes over time. Economic shocks, a trade war for example, cause the bottom to develop bumps and the walls to distort, preventing the ball from ever settling. The economy may instead move from one temporary equilibrium to another. To import the language of calculus, rather then settle at an idealized global maximum equilibrium, the economy moves through a series of shifting local maximum & minimum.

Economic Metabolism: How Systems Consume Energy to Create Structure

Finally, Walras missed a key physics development: the second law of thermodynamics, which was basically unknown during his time so it’s hard to fault him.

While he grasped energy conservation (the first law), the second law—entropy increases in closed systems or said another way, “nature hates a gradient”—reveals that order decays without external energy.

The economy is not a closed equilibrium system but an open, disequilibrium system—specifically, a complex adaptive system. Closed systems trend toward maximum entropic equilibrium and cannot spontaneously generate order. Yet economic growth throughout human civilization has been a story of fighting entropy, something only possible in an open system.

Since social and economic systems are physical systems made of matter, energy, and information they are subject to physical laws. Energy enters economies to fight entropy and create order, while economies export waste, pollution, and heat. Cut off the energy flow to an economy, and disorder increases, trending toward societal heat death. If there was no water diversion to desert cities like Phoenix or Las Vegas it’s easy to imagine the entropic slide towards structural disorder.

Countries isolated by war, policy or sanctions, like North Korea, move toward a local minimum of starvation and misery as the entire structure of the system decays. The Soviet Union collapsed from economic stagnation and declining productivity in the face of excessive defense spending during the Cold War arms race. Unable to import energy and unable to produce enough economic growth domestically to fight the entropic slide of a closed system, the Soviet Union was eaten from the inside by corruption, which unintentionally accelerated social demands for political freedom and exposed the fragility of the system.

Traditional economics, locked into a 19th-century physics mindset, overlooks how such physical laws are a structural, load bearing feature of real economies.

According to a 2023 UN report, almost half of North Korea’s population is undernourished

QWERTY Markets: Path Dependency and the Stickiness of Suboptimal Systems

A significant limitations of equilibrium-based economics is its failure to account for "lock-in" phenomena, where inferior government policy, technologies or cultural norms persist despite the theoretical availability of better alternatives. Traditional economics would suggest markets naturally move toward optimal solutions, but history shows this isn't always the case.

Lock-in occurs when a technology becomes dominant and persists due to high switching costs, network effects, or ecosystem dependencies.

The QWERTY keyboard layout, designed in the 19th century to prevent typewriter jams, remains dominant today despite more efficient alternatives like Dvorak. ‘

The VHS format prevailed over the superior Betamax due to business decisions around movie libraries and pricing.’

Gasoline-powered vehicles have dominated transportation for over a century, despite their environmental drawbacks, because of massive infrastructure investments (gas stations), manufacturing supply chains, and consumer habits. The science fiction subgenre of steampunk imagines an alternative history where humanity continued to iterate and refine on steam powered technology rather than gas combustion.

The United States continues to use imperial units (feet, pounds, fahrenheit) despite the simplicity and global adoption of the metric system due to cultural inertia.

The Frankenstein monster that is the U.S. healthcare system is highly fragmented, expensive and inefficient compared to universal healthcare systems in other developed countries. Despite this being self evident to all participants, it suffers from lock in due to the entrenchment of special interest groups, political polarization and high switching costs.

As economic complexity scientist Brian Arthur notes, while linear systems add up to the sum of their parts (like sound and light), economic systems are non-linear, with interdependencies and feedback loops that create persistent patterns resistant to change.

Economies can thus become trapped in suboptimal temporary equilibria due to increasing returns, network effects, and path dependency—concepts that don't exist in traditional economic models based on 19th-century physics.

The Heraclitus Economy: Why You Never Step in the Same Market Twice

Just as early physics models failed to account for emergent properties in nature, traditional economics fails to capture the emergent properties of markets and economies.

To understand emergence, consider water molecules: individually, none possess the property of "liquidity," yet put a few zillion together, and liquidity emerges as a property of the collection. Similarly, several billion neurons following biological laws give rise to consciousness—an emergent property not predictable from studying a single neuron.

Economic systems, like other living systems, demonstrate spontaneous self-organization that depends on self-reinforcement—positive feedback that amplifies during favorable conditions. While negative feedback (like diminishing returns) prevents small negative changes from growing out of control, positive feedback (increasing returns) can drive growth, innovation, and change. This rich but complex mixture of positive and negative feedback magnifies differences into patterns.

Human organizations reflect this complexity. Social dynamics expand to fill all space (Parkinson’s Law), often prioritizing internal politics over goals. Resilience, not just efficiency, becomes key—efficiency locks in structure, but resilience also requires adaptability which requires flexibility. High-trust teams, diverse perspectives, and minimal coordination maximize innovation, mirroring economic self-organization. High trust networks with minimal coordination is the entire thesis of the Bitcoin network and cryptographic smart contracts.

The universe forms a hierarchy; at each level of complexity, entirely new properties appear with new laws governing them. Psychology is not merely applied biology, biology is not just applied chemistry, and economics cannot be reduced to simple cause and effect physics equations.

The concept of systems organizing themselves without central direction isn't new to human understanding. Ancient Chinese philosophy captured this phenomenon centuries ago through principles like Yin and Yang and Wu Wei ("non-action" or "effortless action"), which describe how natural order emerges without forced intervention.

“The Tao does nothing, yet nothing is left undone.” - Tao Te Ching

Greek philosopher Heraclitus noted, you can "never step in the same river twice because it is fluid, ever-changing, and alive." Economies, too, are constantly evolving systems rather than static equilibrium states. Said another way, you never step in the same market twice.

Fire Resistant Economics: Resilient Systems in an Age of Turbulence

While the ghost of Newton and Walras endures, complexity economics offers a path forward. Unlike physics’s predictable orbits, economies resemble something closer to slime molds—adaptive, distributed systems where intelligence emerges bottom-up. Strategy shifts from predicting equilibria to fostering conditions for growth: planting diverse “acorns” and nurturing those that sprout.

Slime molds solve complex network problems using non-neural systems

Walras’s physics-inspired economics brought rigor but locked the field into a 19th-century mindset, ill-equipped for the non-linear, emergent nature of real economies.

The invasive, ideological lock in suffered by neoclassical economists is like the Eucalyptus tree that takes advantage of favorable conditions to grows as fast and tall as possible until it towers over its surroundings. However, eucalyptus trees grow fast but are brittle, easily toppled by wind and quick to combust during forest fires

Eucalyptus became an invasive California tree species when they were transplanted to increase the lumber supply.

To move beyond the equilibrium trap, consider the deliberate, slow and strong growth strategy of an oak tree: survive, keep costs low, explore iteratively, and embrace compounding opportunities.

The breadth and deep roots of a live oak tree means it can handle severe winds while its hard wood and thick bark make it fire resistant.

Complexity economics—rooted in open systems, iterative experimentation and adaptive behavior—better reflects reality. The next economic revolution won’t mimic physics but will embrace the messy, vibrant complexity that defines all open, living systems.

At its heart, complexity economics is more like gardening than engineering.

How to Decide by Annie Duke

Seven Brief Lessons on Physics by Carlo Rovelli

Zero to One by Peter Thiel

Poor Charlie’s Almanack by Charles T. Munger

This is Marketing by Seth Godin

Scaling People by. Claire Hughes Johnson

Mastery by Robert Greene

Breathe by Rickson Gracie

WAR by Sebastian Junger

Conscious by Annaka Harris

The Case Against Reality by Donald Hoffman

Expectations Investing by Michael J. Mauboussin

How States Think by John J. Mearsheimer

The Missing Billionaires by Victor Haghani, James White

Hell Yeah or No by Derek Sivers

Good to Great by Jim Collins

On Tyranny by Timothy Snyder

Liars Poker by Michael Lewis

In My Time of Dying by Sebastian Junger

Global Brain by Howard Bloom

Scale Geoffrey West

Complexity by M. Mitchell Waldrop

Why Nations Fail by Daron Acemoglu

Slow Productivity by Cal Newport

While perturbations in one system (e.g., deforestation or Milankovitch orbital variations) ripple through the others and create cascading effects, the lack of simple, linear, cause and effect dynamics make forecasting near impossible. There’s no local forecast for the weather in your town on this day a year from now.

Fundamentally, this means it is not knowable whether a climate 2 degrees warmer is substantially worse than one 1 degree warmer or considerably better than one 4 degrees warmer. Being in the bucket of the unknowable means no amount of research can move it into the knowable. The only certainty about our future climate is that it will be different.

At a moment in history where people earnestly debate the morality of bringing children into the world due to climate change, I intend to lay out the following case:

1. The climate is not and has never been on the side of humans

2. Policy won’t save us but technology will

3. Cognitive biases have grossly distorted the climate conversation

4. The path forward into an uncertain future is to build and not panic

The Myth of the Benevolent Biosphere

The Spaceship Earth theory imagines our planet as a perfectly engineered vessel speeding through the cosmos at 67,000 miles per hour. On board, generations of passengers live and die, sustained by a self-contained biosphere—clean, recycling, life-giving, powered by a nuclear sun reactor. It’s a nice story, but it’s just that: a story.

The Spaceship Earth myth contains echoes the oldest human narratives—the Garden of Eden of Judaism, the Hesiod’s Greek Golden Age, the Hindu’s Satya Yuga, the Nordic Asgard, the Tibetan Shangri-La, Rousseau’s Noble Savage—visions of harmony, balance, and simplicity. These stories share a fatal flaw: they assume nature is on our side, a virtuous system built top down for our benefit and we are its caretakers.

George Catlin, Bird’s-eye View of the Mandan Village, 1800 Miles Above St. Louis, 1837-1839, oil on canvas

This is anthropocentrism, the most human of errors. The belief that Earth is here for us. That its systems are designed to sustain us. The anthropocentric view of our place in the natural world is intuitive, comforting, and utterly wrong. Nature is not benevolent. It is not even neutral. Rather, nature is actively hostile.

Anthropocentrism, like the geocentrism of the Middle Ages, has historically begot harmful oversights, delayed progress and self inflicted crisis by being off mark in describing the material world.

The actual biosphere is not a nurturing garden; it is a brutal, self-regulating complex system that filters life through the merciless mechanisms of natural selection—competition, extinction, and inequality. In the Great Rift Valley, the “Cradle of Humankind,” proto-humans didn’t thrive because of the biosphere—they survived in spite of it. No clean water. No shelter. Predators, parasites, and plagues everywhere. Most living things died horrible deaths—starved, frozen, baked, eaten, or infected.

And this isn’t some historical anomaly. Natural living systems exist in perpetual crisis, just at the edge of survival. It is the default state of nature.

Populations outgrow food supplies. Resources dwindle. Mass death restores the balance of biological systems via the four levers of war, conquest, famine and pestilence. Forward progress in nature comes not from organic life support systems, but through the ruthless extinction of what does not work. The climate existed long before organic life scratched its way into emergence.

Albercht Dürer, The Apocalypse, the Four Horseman, 1498, Woodcut

The settlers of Manifest Destiny weren’t welcomed and supported by an open frontier—they encountered starvation, disease, hostile landscapes and people. The most common cause of pediatric death on the Oregon Trail was falling between and being crushed by the wagon wheels. Native American history reminds us that one group’s opportunity is another group’s ruin; in the natural world, one group’s unknown horizon is another group’s ancestral home.

Nature doesn’t care about equality, freedom, or fairness. It hasn’t read the American Declaration of Independence nor the The French Declaration of the Rights of Man. In nature, pursuing your own interests freely comes at the expense of perfect equality with others. All mammals have to eat their energy. The tension between freedom and equality isn’t a social construct; it’s a natural law.

The entire mechanism is mostly a practice in subtraction.

The pre-Enlightenment and post-Enlightenment views of humanity share the same anthropocentric arrogance. We either put ourselves at the center of the universe or pretend we have dominion over it. Neither is true. Nature doesn’t serve humanity. It kills indiscriminately, lubricating its innovation engine through perpetual, systemic competitive destruction.

The biosphere is not your friend. The climate is not your life support system. Humans are not and have never been the benevolent caretakers the climate, which existed a long time before it ever had a human name.

In order to propagate the causal structure of the human species, we have always had to build the life support system ourselves.

Humans Have Always Built Our Own Life Support System

A recent report from Citizens Climate International reveals a stark truth: despite trillions of dollars spent globally, only 4.6% of climate action policies have succeeded over the last 20 years. This suggests a misalignment of capital with effective outcomes, inefficiency in policy and capital deployment and need for better leverage points. The levers of policy, even when backed by trillions in capital, are weak and slow-moving tools.

Fortunately policy is only one tool in humanity’s toolkit—and it has never been the primary one that solves our biggest challenges.

The biosphere has no intrinsic priority to support human life, no moral posture, no care for human survival. It is a hostile, indifferent system. We survive not because Earth is designed for us but because we have learned to build technology—to construct artificial life support systems where none exist.

A pile of raw minerals forged in a supernovae strewn about the Earth and a stream of energy from the sun is not enough. An overemphasis of climate policy spending without accountability is not enough. Evolution gave us opposable thumbs, not instruction manuals. Nature provides the ingredients but we must still grasp for a solution through experimentation. It was knowledge—hard-earned, painstakingly discovered—that turned raw minerals into steel, fire into energy, and chaos into civilization.

Consider this: I’m writing this from the mountains of Colorado in December. If I walked outside without clothing or shelter, I would die of exposure in hours. The natural world doesn’t care about my survival. It’s not a nurturing home; it’s a death sentence. We don’t live on Spaceship Earth. We are not passengers. We are the architects, the engineers, and the captains of our own technological life support system.

Mount Sneffels, 14,153’, Colorado

Common Biases That Corrupt the Climate Change Discussion

The morality of a civilization evolves alongside its technology. Hunter-gatherers lived by different rules than agricultural societies. The industrial age shifted those rules again. And today, in the digital era, technology has fundamentally altered what we can achieve, what we consider moral, and how we perceive risk.

But here’s the problem: we don’t see the world as it actually is. We see it through a dramatic attention filter—a filter built into us over millions of years of evolution. Humans are wired to overestimate threats because, for our ancestors, underestimating a threat usually meant death. This bias was survival-enhancing in the wild, but it wreaks havoc on our understanding of modern, complex, probabilistic systems.

In his book Factfulness, Hans Roslings outlines strategies to recognize and control our dramatic filter instincts.

Catastrophe sells. Nuance does not. Headlines highlight outliers and worst-case scenarios, creating a distorted sense of reality. As traditional media has atomized into social media which atomized into individual influencers, the trappings of personalized distortion generating content feeds continues to increase. In this new media landscape, isolated scientific disagreement becomes a false “gap” in consensus when meanwhile, 97%+ of climate scientists agree on human-caused climate change.

Humans tend to extrapolate trends linearly. Pessimists see leading data trends and conclude: apocalypse is inevitable. Optimists cling to magical thinking: the climate will fix itself. Neither is true. The future is neither binary nor linear. It’s a continuum of probabilities shaped by human action—particularly technological innovation.

So why wouldn’t humanity bend the trend line? When has human ingenuity failed to rise to the challenge of existential threats? The Green Revolution solved global hunger. Vaccines eradicated diseases. Renewable energy, particularly solar, is scaling faster than anyone predicted 20 years ago.

Complex dynamic systems exhibit “fat tailed” outcome distributions rather than normal distributions, meaning there is a higher probability of extreme events occurring to the upside and downside. Combine this structural feature with our dramatic attention filter and sociological groupthink, via authority bias and social proof, tends to simultaneously over estimate the risks and also underestimate the benefits.

However, progress is real. To avoid our tendency to underestimate the benefits, let’s look in the rearview mirror and ask: what has humanity gained in exchange for its industrial carbon emissions?

• Global poverty is collapsing and on track to hit zero

• Famines have virtually disappeared outside of war zones

• Child labor and child mortality are at all time lows while child cancer survival is at all time highs

• The marginal cost of energy production is dropping towards zero

• Natural disaster deaths are down 99% in the past 100 years

• War between countries has become unprecedentedly rare (but does still occur) as food, energy and resources have become abundant

• Democracies are expanding while autocracies are declining

It’s essential to realize that progress does not mean everything is immediately better for everyone, everywhere at the same time. Progress is not the same as a miracle.

Only you can answer for yourself if all that relief of human suffering was worth the cost of industrial emissions.

Climate Change Doesn’t Have to Mean the Apocalypse

So here’s the good news: nowhere in the recent Intergovernmental Panel on Climate Change (IPCC) report are climate scientists warning us to prepare for the apocalypse. There will be no dramatic, Hollywood-style climate change event. No singular apocalypse. Climate change is already here.

“If I had to rate odds, I would say the chances of climate change driving us to the point of human extinction are very low, if not zero,” says Adam Schlosser, the Deputy Director of the MIT Joint Program on the Science and Policy of Global Change

The impacts of climate change will vary significantly by region. Low-lying coastal areas and deserts are likely to become less habitable, but we shouldn’t overlook the potential upsides: warming could make currently inhospitable regions—like parts of Canada, Russia, and Scandinavia—more arable and livable. Meanwhile, the melting polar ice is already fueling competition for control of newly accessible Arctic shipping routes.

Technologies will evolve to mitigate the worst effects. The rise of AI is likely to drive a renaissance in materials science, enabling the synthesis of new elements and the advanced chemical doping of existing ones to unlock groundbreaking applications. Solutions will emerge at the speed of progress—incrementally, unevenly, and quietly. And humans will adapt and migrate, just as we always have.

Here’s the irony: if humanity succeeds in blunting climate change, the doomsayers will never admit they were wrong. If disaster strikes, they will claim victory for predicting it. Either way, the path-dependent process of solutions will be erased by hindsight bias and everyone will agree it was inevitable all along.

The real world doesn’t unfold that way. The real world is messy. It’s ambiguous. It evolves in real time through experimentation, iteration, and innovation. Progress doesn’t come from certainty. It comes from human action—building in the face of uncertainty.

People always have and always will crave the certainty of the apocalypse. But to sit in the discomfort of ambiguity is to sit within the actual geometry of the universe.

American historian Will Durant writes: “Every day the sea encroaches somewhere upon the land or the land upon the sea; cities disappear under the water, and sunken cathedrals ring their melancholy bells. Mountains rise and fall in the rhythm of emergence and erosion; rivers swell and flood, or dry up, or change their course; valleys become deserts, and isthmuses become straits. To the geologic eye all the surface of the earth is a fluid form, and man moves upon it as insecurely as Peter walking on the waves to Christ.”

In my view, the real immediate existential threat isn’t carbon emissions—it’s nuclear war. If you wanted to be a single issue voter, that would be it. Climate change isn’t the end of the story but the latest in a long series of crisis that humanity has conquered.

No one is coming to save us. There is no magical policy, no benevolent planet, no divine intervention. There’s only us—humans, doing what we’ve always done: adapt, migrate and solve problems by building solutions.

Zoom out if you really want to get existentially stressed. Eventually the Earth and its climate will be swallowed by the sun, which will explode and wipe everything clean. If humans fail to colonize space extinction is already, right now, a certainty.

But today’s climate crisis? An existential threat?

Climate change is not the end. It’s just the next chapter in humanity’s endless cycle of migration, adaptation and building. It’s really important to internalize that history is just one thing after another. Fortunately, the history of humanity has been on balance additive rather than mostly a practice in subtraction.

Its easy to forget how one piece of new technology can transform the way we build and solve problems. So don’t panic. Instead, build, and the opportunities will multiply. Because if we don’t, the sun is coming for us anyway.

Nuclear war is the only immediate existential threat, so stop scaring your kids about climate change.

The majority of deep intergalactic space, which is the majority of the known universe, lies far from any galaxy. Without a gravitational well of matter to transmute into heat or energy, the typical location in the universe is cold, dark and empty.

In this void, everything is pitch black. Even if you were placed there and stared directly at a distant exploding supernova, the light wouldn’t even appear as a faint glimmer on the horizon. Even if you cranked the power dial on the supernova, there would be no visible matter for the light to interact anyways.

Matter is so scarce in deep space that atomic density falls below one atom per cubic meter. That is a million times sparser than the space between stars and far sparser than the best vacuums humans can create.

With no matter to convert into energy deep space is unimaginably cold: just 2.7 degrees above absolute zero, the coldest possible temperature. This is enough to freeze every known substance, apart from helium. And the sparse atoms that are present, mostly hydrogen and helium, allow no chance for chemistry to spontaneously occur.

And so a typical place in the universe is a cold, inert sea of dead chemistry. Without energy, nothing changes, and nothing happens.

Now compare that to our solar system, where the fire of life seems to have spontaneously kindled. (As of this writing, it’s not at all clear whether or not this is the first time life has this has ever happened.)

The basic equation or structure of organic biological systems is the import of matter (food) to transmute into energy used for self organizing followed by the export of entropy as heat.

The energy flow creates structure and patterns, constantly countering the natural tendency toward disorder and decay, a process governed by the second law of thermodynamics. If Earth did not happen to lie on a river of energy streaming out from the sun, no life on Earth could exist.

Understanding that the basic equation for life is the import of energy and matter and the export of entropy does not answer how structure and order arise in the universe, nor does it explain where this structure and order come from.

With the aim of answering such deep, cosmic questions, Nobel laureate Ilya Prigogine explored self-organizing systems and demonstrated that, under far-from-equilibrium conditions, energy flow through a system can spontaneously generate order. His work became Prigogine’s theorem of non-equilibrium thermodynamics, known as the Prigogine Principle.

Maintaining any structure is challenging because atoms, left to themselves, mix and randomize over time. The second law of thermodynamics ensures that entropy—disorder—inevitably increases. A simple example is rust: iron atoms on the surface of a solid object react with atmspheric oxygen to form iron oxide, an irreversible process often summarized as "rust never sleeps." This is entropy in action, driving all matter toward equilibrium. To put it more intuitively, you can’t unscramble an egg.

Fortunately, Earth sits within a hot river of electromagnetic radiation emanating from the Sun. This energy flow temporarily reverses entropy, allowing systems to self-organize into complex patterns and structures. Over limited periods and in localized spaces, far-from-equilibrium conditions allow complexity to emerge.

For example, when water is heated in a pot on a stove, energy flow destabilizes the system. At low heat, little happens, but at high heat, random molecular motion transitions into organized patterns like convection cells, visible as a hexagonal pattern on the surface of boiling water. The system exports entropy in the form of steaming water vapor. In this way, the water "self-organizes" into a dynamic, structured state.

Due to convection, the temperatures in the top and bottom layers gradually equalize, and the amount of cells changes: they can join together to form larger structures

Such self-organization is ubiquitous in nature and is consistent with open, complex, dynamical systems. Hurricanes, driven by solar energy, are self-organizing systems as are the convection cells in the atmosphere and deep in the molten interior of the Earth. These systems arise from local interactions among simpler components, creating emergent properties without external direction.

Living cells exemplify Prigogine's work on self-organizing systems, whereby they use energy from the ingestion of food to sustain complex organization. The modeling of biological systems however is vastly more complex and less mechanistic then convection cells which can be modeled by the comparatively easy mathematics of physics and chemistry. For example, we cannot write an equation for an amoeba, let alone an oak tree. Yet, at the molecular level, biology arises from the same fundamental carbon based chemistry and thus shares common levers that give rise to complex life.

All biological life shares astonishing molecular similarities. DNA, the blueprint of life, is remarkably conserved across species. Humans share 60% of their DNA with bananas, reflecting a distant common ancestor before natural selection diverged into the genetic evolutionary pathways of fruit and primates.

DNA is the universal mechanism for transmitting genetic information, and every cell in your body contains the entire blueprint. But while the genetic blueprint of DNA is a little different all living organisms on Earth, the organism is constructed using the same molecular code breaking machinery of ribosomes, tRNA and various enzymes.

DNA imaged with electron microscopy

Different organisms exist because there are a small fraction of genes arrayed along the DNA molecule that function as tiny switches called your epigenome. The epigenome, the system stack above DNA, regulates which genes are switched on or off in different cell types. Toggling a local set of genes on or off via chemical reactions propagates a cascade of additional downstream genetic toggling until the cellular DNA eventually settles down into a new and stable genetic blueprint pattern. This new, stable blueprint is customized to construct the molecules needed for a specific type of cell.

This is the difference between a chimpanzee and a human or a primate and a banana - series of local chemical interactions along the DNA molecule that self propagate and self organize into all the variety of living organisms on Earth. This is how a fertilized egg, with two strands of DNA each from a set of reproducing organisms, starts to divide and differentiate from the same cell into pancreas cells that produce insulin or the photoreceptor cells in your eyes.

In a way, the on or off toggling of genetic switches in the epigenome is to a molecular scale computer that orchestrates cellular development and interaction with the environment. This cascading process propagates over time, creating an intricate web of cause and effect that builds complex organisms.

Natural selection amplifies advantageous traits, eliminates neutral or harmful traits, and creates the rich diversity of life. However this interplay of chance and selection makes life inherently unpredictable. Small genetic changes to the start conditions may have no effect—or transform an organism profoundly. The evolutionary "experiment" unfolds only as life is lived. Said another way, you live in the sample not the sample size.

The epigenetic molecular mechanism indicate biology exists as a world of power laws and tail events rather than the simple normal distribution of a bell curve. Biological systems are path dependent, meaning past events impact future events and the history of the system matters. Chance events can transform the final form and you can’t ever predict the final structure from the initial conditions.

Path dependency diagram of organizational structures

This is why biology is a complex adaptive system. It is why we cannot write a mathematical equation for a complex living object. This spontaneity is what makes biological life feel alive, with all its comedy, tragedy and absurdity.

Living systems import matter and energy and export entropy to create a causal structure that is self organizing and self propagating across time. From the same biological molecular workshop comes a handful of pieces and an infinity of possible patterns.

This sprawling set of self consistent patterns evolves and changes in response to the outside world. Like a kaleidoscope, living systems lock into one pattern and hold it until natural selection causes a slow turn of the barrel, causing it to suddenly cascade into a new configuration.

All living systems, you, me and all of us, are holding onto this causal structure for dear life in an endless struggle against a universe trending towards the equilibrium of deep space.

A place that is cold, dark and empty, where nothing changes and nothing happens.

So reflect for a moment on how lucky you are to have ever been alive and how strange it is to be anything at all.

Strategic Thinking

Concept: Michael Watkins' Six Key Skills for Strategic Thinking

• Pattern recognition

• System analysis

• Mental agility (cloud to ground thinking)

• Structured problem solving

• Visionary thinking

• Political savvy (Sequencing Strategy)

Technology and Progress

Principle: Technology as a Driver of Value and Progress

• The purpose of technology is to reduce scarcity

• Technology theory of value (TTOV) is superior to labor theory of value

• Progress is abstraction, reducing the number of things to think about

• Technology changes microscopic leverage, expanding individual options

Insight: Early Perceptions of Technology

• People often mistake the value of new technologies due to initial frivolous appearances

Power Dynamics and Trust

Concept: Powerful Agent Use of Hard Power Doomloop Flywheel

• Loss of Trust → Decline in Soft Power → Less Voluntary Compliance → Increased Use of Hard Power → Further Reduction in Soft Power → More Reliance on Hard Power → Continued Loss of Trust (cycle repeats)

Yoga and Physical Techniques

Technique: Ninja Pop-up Alternative

• Start with cobra pose, then move to scorpion

• Use back leg momentum to create space for foot placement between hands

• Sequence: Cobra → Pick line with eyes → Move feet

Philosophical Reflections

Insight: Multifaceted Nature of Self

• Walt Whitman: "We contain multitudes"

Metaphor: Salt Crystallization

• Howard Bloom's metaphor: Boil salt to dissolve, add a single grain, and all salt reappears

• Represents finding one's community and the non-linear nature of progress

Principle: Wittgenstein's Approach to Meaning

• "Don't look for meaning, look for use"

Insight: AI Limitations

• AI provides who, what, when, maybe how, but not why

Financial and Economic Insights

Concept: End of Easy Money Period (Howard Marks)

• Potential consequences: slower economic growth, eroding profit margins, less positive investor psychology, less reliable appreciation of ownership interests, inconsistent downward trend in borrowing costs, reduced leverage benefits, harder financing for businesses, higher default rates

Principle: Adaptability to Changing Circumstances

• "When events change, I change my mind"

Concept: Power Laws in Various Domains

• Human history: a handful of people carry most of the weight

• Philosophy: "All of philosophy is a series of footnotes to Plato"

• Personal relationships: 4-5 people have extreme power law effects on one's life

• Languages: Most popular languages dominate usage

• Attention: A few pieces of content carry most of the viewership

• Thoughts: Breakthrough ideas are rare but impactful

• Investing: Top 4% of publicly traded firms account for the entire US stock market gains

Time and Decision Making

Insight: Time as a Unique Asset

• Time is the only asset where we don't know how much we have until we're dead

Principle: Value-Based vs. Options-Based Thinking

• Orient around time, not money or resources

• Engage the world based on values, not options

Technique: Decision-Making Across Time Scales

• Good decisions make sense across different time scales (e.g., optimize for both a day and ten years)

Portfolio Strategies

Concept: Growth Portfolio

• 50% SP500 (Trend Followed), 40% Small Value Stocks, 10% Gold (Trend Followed)

• Overweight equities, especially small cap value, during crisis environments

Concept: Inflation Portfolio

• 50% SP500 (trend followed), 10% Gold (Trend followed), 10% small cap value

Principle: Countercyclical Investing

• Act countercyclically during market panics, loading up on riskier assets

Patterns and Connections

1. Cyclical Nature of Systems: The "Powerful Agent Use of Hard Power Doomloop Flywheel" and economic cycles both demonstrate self-reinforcing loops in different domains.

2. Importance of Adaptability: Howard Marks' quote about changing one's mind and the emphasis on adapting to demographic changes in healthcare both highlight the value of flexibility in thinking and strategy.

3. Power Law Distributions: This concept appears in multiple contexts, from human history and philosophy to investing and attention economics, suggesting it's a fundamental principle across various domains.

4. Time as a Central Concept: The importance of time management, decision-making across time scales, and the unique nature of time as an asset form a recurring theme.

5. Technology as a Transformative Force: The notes emphasize technology's role in value creation, progress, and potential future developments like AI and nuclear energy.

Dissonance

1. While there's emphasis on long-term thinking and legacy (e.g., "100-year vision for family wealth"), there's also discussion of rapid technological changes and the importance of adaptability, creating tension between stability and flexibility.

2. The notes discuss both the benefits of technology and its limitations (e.g., AI's inability to provide the "why"), highlighting a nuanced view of technological progress.

"Geographical Portfolio,” Levi Walter Yaggy, 1887

Humans have been co-evolving with technology for a very, very long time, even on evolutionary timescales. The building and application of technology is arguable the defining feature of our species.

Our oldest technologies involved the recombination of fire, stone and metals which humans dug out of dirt bare handed. Much like the self-assembling and self-replicating complex carbon molecules in the prebiotic soup, early human technologies were replicated, encoded, and transmitted across early human networks, such as the Silk Road.

It’s not enough to discover scientific laws. Science is only theory until it is applied as technology.

The Birth of Technology (Hands + Stone)

Ancient human scavengers, indistinguishable from other animals, once dug through the dirt with their bare hands in search of food.

For a long time, they overlooked the rocks scattered on the ground, deeming them useless. After all, any creature that tried to consume such inorganic material would quickly suffer fatal consequences, like a bowel obstruction.

Early humans risked serious injury when striking objects too forcefully, as broken hand bones could drastically reduce their ability to grasp and manipulate objects—a potentially life-threatening injury.

But around 2.6 million years ago, humans discovered that they could reduce the risk of injury by using rocks to deliver stronger impacts. This realization allowed them to wield stones as tools, throwing and sharpening them with increasing accuracy and precision.

Technological leverage had been just laying on the ground waiting to be discovered.

And so, without ceremony, stone tools emerged.

The oldest known stone tools, Oldowan stone tools, discovered in modern day Ethiopia 2.6 million years ago

The discovery of stone tools introduced a revolutionary force-multiplying technology. Compared to the fragile human hand alone, the combination of an ape grip and the hardness of stone gave early humans a dramatic increase in striking power.

Since metallurgy had not yet been invented, tools made from pure elements (a single type of atom) were rare. Humans instead relied on rocks constructed from highly compressed elements called minerals.

The earliest stone tools were made from minerals harder than bone, capable of holding a sharp edge, and readily available within the top layer of the Earth’s crust. This was quartz, quartzite, basalt, or obsidian, and later flint. Obsidian, a naturally occurring volcanic glass that’s made up of silicon, oxygen, aluminum, sodium and potassium that could be picked up in chunks right off the ground, was crafted and sharped into the most advanced tools and weapons of the Paleolithic era.

By combining the cooperative power of early social networks with the force-multiplying effect of stone tools, humans began to outcompete every other animal on the planet. It was as if, overnight, humans had suddenly evolved lethal hands of stone.

With natural selection unable to keep pace, animals had no time to adjust to this rapid shift in the ecosystem’s power dynamics. Early stone technology made humans so powerful they hunted the colossal wooly mammoths to extinction. Soon humans dominated any ecosystem they entered, surpassing their stronger, faster, but less technologically advanced rivals.

Rouffignac Cave, France, Upper Paleolithic era, 13,000 BC

Fire and the Transmutation of Metal (Hands + Metal + Fire)

Fire has been integral to human existence for nearly a million years. Early humans captured and propagated fire from naturally occurring land wildfires.

The strategy of animal carnivores is to outsource food scavenging and food metabolism to smaller prey animals. By pre-digesting scavenged food with fire instead, humans became the first species to outsource part of their metabolic process to technology. The energy saved from digestion was redirected to fuel the brain, which today accounts for 20% of our total metabolic energy consumption. Unlocking these scarce calories removed evolutionary constraints, making our brains larger and making us smarter.

Humans soon discovered fire could soften not just food but rock, metal and other materials.

It hardened clay into the pottery necessary for early agricultural societies to cook and store food.

Before fire, humans shaped stone tools by striking one rock against another. With fire, they discovered that heated minerals became more malleable, allowing humans to shape them into better technology.

Outsourcing to fire the energy and labor needed to manually shape minerals into better technological devices marked the beginning of the Metal Ages.

In the Copper Age (6200 BC), humans smelted copper ore to craft stronger tools and weapons.

In the Bronze Age (3800 BC), copper was smelted and alloyed with tin to produce bronze, which was much stronger than copper alone.

Although older civilizations had used meteoric iron for thousands of years, the Iron Age (1380 BC) saw the mass production of iron tools and weapons scaled up and refined. Carbon from charcoal was soon alloyed with iron to create steel — harder and lighter than bronze.

Mass produced nine inch iron Roman nails affixed Jesus of Nazareth to the cross. Numerous iron nails are venerated as those of Jesus’s crucifixion at museums throughout the world today.

Fire revolutionized not just the metabolism of food and the shaping of minerals but the very way humans stored economic value. Fire facilitated the extraction of precious metals like lead, silver, and gold from raw metal ore. Better extraction techniques lead to the mass production of metal currencies, a better store of value technology.

Metal currency revolutionized trade, as metal coins were more practical to transport than sea shells, livestock, or slaves. Traveling with a pouch of silver along the Silk Road was far easier than traveling with a herd of oxen. For a modern day analogy, consider that moving wealth across borders is far easier using a memorized Bitcoin seed phrase than carrying a suitcase full of cash or navigating multi national bank account transactions.

Geographia, Greek Historian Ptolemy, 150 AD. This atlas depicts the Silk Road, which economically connected “Sinae” (China) to the West

Fire was used by ancient humans to explore the combinatorial space of how rocks and minerals could be transformed into tools and weapons. The experimental recombination of environmental objects using fire during the three Metal Ages laid the foundation for modern materials science with the rise of Alchemy.

Alchemy, though often dismissed as pseudoscience, was foundational to the development of modern chemistry. In the search for the philosophers stone, a mythical substance said to grant immortality and transmute base metals into gold, early alchemists developed many of the tools and methods still used in laboratory science today, such as distillation, crystallization, sublimation and filtration. By experimenting with various chemical mixtures, alchemists discovered new elements such as mercury, sulfur, and arsenic.

In the 9th century, Chinese alchemists accidentally discovered the explosive power of gunpowder by applying fire to a novel mixture of sulfur, charcoal, and potassium nitrate. Gunpowder sparked a revolution in weaponry and demolition, and laid the groundwork for the chemical rocket fuel that eventually took humans to the moon one thousand years later.

The alchemical cipher for the Philosophers Stone, the Elixir of Eternal Life

Technology Is A Defining Trait of Our Species

The innovation arc of early human technologies—using stone, fire, and metal—suggests that technological progress occurs when mature, existing components are recombined to create new technologies. These innovations reduce scarcity by improving efficiency and unlocking hidden value.

The technological flywheel gains momentum as new technologies are combined with existing ones to create more innovation. Each additional technological part expands the combinatorial space of future innovation, paving the way for the next breakthrough.

Technology lies upstream of human culture and even morality. A clear example of a technology-driven moral shift is happening in the 2020s. As the population-level effects of weight loss medications like Ozempic begin to emerge in the data, notice how the moral narrative surrounding obesity shifts. What was once seen as a failure of self-discipline or moral character is increasingly reframed as a medicalized issue, akin to an addiction to food.

Technology also lies upstream of politics and history. Only what is technologically feasible can be promised on the campaign trail or implemented in the arena of geopolitics and war.

Humans are inseparable from the technologies we create. Our unique ability to creatively recombine resources into innovative technologies that unlock hidden value is arguably one of the defining traits of our species.

Technology is what makes us human.

Promethesus Bound, Peter Paul Rubens, 1611 AD

The Greek Titan Prometheus was eternally chained to a rock where an eagle devoured his liver every day, only for it to regenerate over night, as punishment for stealing fire from the Gods and gifting it to humanity.

Let's explore how these powers can create either virtuous cycles (flywheels) or vicious cycles (doom loops).

Soft Power: The Carrot Approach

Soft power, on the other hand, relies on attraction and persuasion. It's about building relationships, fostering cooperation, and shaping perceptions.

Soft Power Flywheel:

• Building Trust: Positive actions and diplomacy can build trust and goodwill.

• Increased Influence: This leads to increased influence and cooperation.

• Strengthened Relationships: Stronger relationships further enhance soft power.

Hard Power: The Stick Approach

Hard power relies on coercion and force to achieve desired outcomes. While it can be effective in the short term, it often leads to unintended consequences and long-term instability.

Hard Power Doom Loop:

• Erosion of Trust: Excessive use of force or coercion can erode trust and respect.

• Resistance and Backlash: This leads to resistance, protests, or even violent conflict.

• Escalation of Force: To maintain control, the power-holder may resort to even harsher measures.

Real-World Examples

Let's explore how these dynamics play out in various contexts:

Governmental Relations:

• Hard Power Doom Loop: Authoritarian regimes often rely on surveillance and repression, leading to civil unrest and further crackdowns.

• Soft Power Flywheel: Democratic governments prioritize transparency, accountability, and civic engagement, fostering trust and stability.

International Relations:

• Hard Power Doom Loop: Countries that resort to sanctions or military intervention may face diplomatic isolation and further conflict.

• Soft Power Flywheel: Nations that promote diplomacy, aid, and cultural exchange can build global influence and partnerships.

Organizational Culture:

• Hard Power Doom Loop: Micromanagement and strict rules can stifle creativity, reduce morale, and harm productivity.

• Soft Power Flywheel: Empowering employees, fostering collaboration, and recognizing achievements can boost morale, innovation, and overall performance.

• Knowledge Capital Flywheel: Consume diverse content → Atomize into idea building blocks → Recombine blocks via thematic interlinking → Reinvest as frameworks → Easier retention and association

• Health Capital Flywheel: Knowledge acquisition → Reliable habits and experimentation → Biofeedback → Iteration → Continuous learning

• Financial Capital Flywheel: Earn → Save → Invest → Growth → Dividends → Repeat

• Current Events Flywheel: Consume diverse perspectives → Develop point of view → Share → Feedback → Model adjustment → Consume diverse perspectives. Example: Read the main English news outlets from America, India, China and Russia to get a holistic view on a current event.

2. Insight: Capital as a Multifaceted Concept

• Capital types: social, health, time, financial

• Time capital is permanently destroyed at a fixed rate

• Energy is renewable, a component of health capital

3. Principle: Simplicity in Design and Ambition

• Simplicity allows for easier framing and filtering of product decisions

4. Concept: Internet Trends

• Pendulum swings between highly aggregated and highly decentralized services

• Users tire of "Swiss Army knife" products, preferring specialized ones

• Future trend: disaggregated services in smaller companies

5. Insight: Capital Allocation and Innovation

• Institutionalization of capital allocation moves towards fees rather than thoughtful risks

• Most innovation over the next 50 years will be technological in nature

• Criteria for investing: critical future resource, monopolistic potential, "secret hiding in plain sight"

6. Concept: Business Evaluation

• Four key variables: Product market fit, Management integrity, Industry trends, internal politics

• Importance of judgment in weighing these variables

7. Insight: Technology and Disruption

• Technological growth is about compounding rate of technological capability

• Potential for residential solar and storage to disrupt utilities

8. Concept: Network Architecture in Social Contexts

• Family can be framed as a network architecture in various configurations

9. Insight: AI and User Interaction

• Potential for AI to understand emotional states and deliver tailored content or experiences

• AI could be used as an external tool for self-optimization or transhumanism

Connections and Patterns:

• Multiple flywheel models suggest a systems thinking approach to personal and professional growth

• Recurring themes of capital allocation, technological innovation, and disruption across various domains

• Emphasis on simplicity and specialization in both product design and investment strategies

Broader Patterns:

• The notes reflect a holistic view of personal and business development, integrating concepts from finance, technology, health, and social dynamics

• There's a consistent focus on optimization and efficiency across various aspects of life and business

Dissonance:

• While there's emphasis on simplicity and specialization, there's also discussion of complex, interconnected systems and flywheels, which might seem contradictory at first glance

• The notes discuss both centralized (e.g., monopolistic companies) and decentralized (e.g., disaggregated services) concepts, reflecting the tension in current technological and economic trends

In other words, if all cells come from pre-existing cells, how did the first cells originate?

The 1952 Miller–Urey experiment was a groundbreaking experiment that set out to study this very question.

Conducted by Stanley Miller and Harold Urey at the University of Chicago, the experiment used a closed glass system to test whether complex organic molecules—the building blocks of life—could spontaneously form from simpler inorganic compounds under the conditions of the early Earth.

By introducing methane (CH₄), ammonia (NH₃), hydrogen (H₂), and water (H₂O) into the system, they hoped to simulate Earth’s prebiotic atmosphere. After constructing a primitive atmosphere they next shocked the gas with electrical sparks to simulate lightning strikes.

Remarkably, it took just seven days for the electrochemical mixture to spontaneously produce seven of twenty essential amino acids required for complex organic life.

The Random Walk of Carbon Through Recombinatorial Space

Methane (CH₄), was the only ingredient in the experiment to contain carbon atoms. This was crucial, since carbon's unique structure is necessary for organic life.

Valence electrons are the farthest from the atomic nucleus and the least tightly bound by the powerful forces holding the atom together. Carbon’s high reactivity stems from its four valence electrons, which were ready to interact in early primordial biochemical reactions.

After billions of years of random wandering through the prebiotic chemical space, carbon formed the stable, complex rings and chains that serve as the backbone of all organic life.

Carbon's unique reactivity made it disproportionately important in the structure of organic life. It can bond with 99% of the atoms in organic chemistry, including hydrogen, nitrogen, oxygen, phosphorus, sulfur, and other carbon atoms. As a result, carbon now makes up 20% of the dry weight of humans, despite comprising only 0.2% of the Earth’s crust."

Networks of Matter and The Singular Chemistry of Water

Without the perfect network to traverse, carbon would have failed to form organic life.

Gaseous environments, like the prebiotic atmosphere, lacked the density and stability to encode and propagate spontaneous biochemical structures. Solid surfaces, like Earth's crust, were too rigid for the random interactions needed for complex life to emerge.

However, Earth's saltwater oceans—an ideal 'Goldilocks' network with just the right balance of density, stability, and randomness—enabled the combinatorial exploration, biochemical stabilization, and propagation of carbon’s structures.

Without this perfect water-based environment, carbon would not have been able to form organic life.

Earth's oceans didn't boil away into space because water’s broad liquid temperature range, enabled by its strong hydrogen bonds, kept them intact.

If water weren’t a universal solvent, random elements and molecules would have simply floated on the ocean surface instead of dissolving, preventing carbon from interacting with them. Ocean water is so soluble it can even dissolve gold given enough time.

It’s no surprise that the human body is mostly water—without water’s solvency, biology wouldn’t exist.

Had Earth been burdened with Titan’s methane oceans, it would have remained a realm of dead chemistry forever.

The First Cellular Structures

After billions of years of exploration, carbon found stable biochemical structures with oxygen and phosphate.

Ironically, half of these new molecules—the hydrophobic tail—wanted nothing to do with the surrounding ocean water.

In an effort to escape the water, these molecules spontaneously assembled into tiny molecular bubbles, trapping random samples of the prebiotic soup. But the prebiotic mix was becoming biotic, as carbon-based molecules like amino acids, carbohydrates, and nucleotides began to propagate throughout the oceans.

These bubbles, made of a carbon husk, became the first protocells—the foundation upon which natural selection could build complex organic life.

Encapsulating the biotic soup into cells also compressed the random chemical solutes into a smaller physical space, enhancing their reactivity. The ideal gas law (PV = nRT) tells us that this reduced volume from ocean scale to cell scale increased the pressure and heat, sparking organic life.

After countless random iterations of protocells, simple metabolic pathways spontaneously emerged. The cells developed the ability to replicate, consume other cells for energy, and eject metabolic waste to keep from poisoning themselves to death.

Soon, one prokaryote ingested another, marking the birth of the first predator-prey relationship and the rise of eukaryotes. By consuming the energy of weaker cells, eukaryotes grew larger, more complex, and predatory.

By using a calcium-containing metabolic waste product, eukaryotes built a scaffolding of bones, which were used to hoist themselves out of the oceans for the first time onto dry land.

Prokaryotes Under Electron Microscopy

This is the story of a highly reactive atom called carbon, who sifts through a large prebiotic recombinatorial space over billions of years, traversing a goldilocks network of liquid water to self assemble all organic life on Earth.

It’s the story of the first hero to undertake the first hero’s journey.

It’s the shared organic journey we’re all still on, one that continues to unfold with a continuous horizon of uncertainty.

It’s the narrative arc of how atomic physics built dead chemistry which built organic chemistry which built biology which built our human civilization.

“The Creation of Adam” by Michelangelo, 1508-1512, Sistine Chapel, Vatican City

In mathematics, combinatorial growth represents exponential growth layered on top of exponential growth.

In systems theory, it is complexity building upon complexity.

I aim to show how combinatorics is a key driver of global growth and innovation, as each new idea or technology disproportionately expands the combinatorial possibility space leading to a self reinforcing cycle of growth and innovation.

Comparing Different Types of Growth

As a simple illustration, consider the yellow circle one element (N).

A single element yields one possible combination.

Adding a second elements yields two combinations.

With three elements, there are six combinations.

Four elements results in twenty four combinations.

Five elements yields one hundred and twenty combinations.

The following graph illustrates how any system that follows combinatorics has a comparatively explosive growth acceleration as you add more elements. By only five elements, the linear growth function looks like a flat linen compared to the combinatorial growth function, which is already going parabolic.

Linear growth occurs at a constant rate, increasing by a fixed amount with each step or time unit. For example, if a business adds 10 new customers every month, after one month there are 10 new customers, after two months 20, and so forth. Graphically, this appears as a straight line.

Exponential growth increases at an accelerating rate, where the growth rate is proportional to the current value. For instance, if each customer tells two more customers, who in turn tell two more, the number doubles consistently. Graphically, exponential growth starts slowly but rapidly increases, resembling a J-shaped curve.

Combinatorial growth involves multiplying the number of combinations as new elements are added, resulting in a rapid escalation of possibilities. Due to the sheer multitude of combinations it propagates faster than standard exponential or linear functions.

Combinatorial growth is evident in businesses like social media platforms that leverage network effects. The value of such platforms grows not just by adding users linearly or exponentially, but combinatorially through the connections between users. As the number of users increases, the network’s value increases disproportionately from the potential user interactions.

The Paradox of Global Wealth

Combinatorial growth is the answer to the paradox of global wealth, which asks, how does global wealth continue to grow despite an increasing population? As new ideas and technologies emerge, they combine with existing ones, expanding the combinatorial possibilities disproportionately.

For example, Uber combined the existing technologies of mobile internet connectivity, GPS tracking and digital payment processing with the almost 300 million privately owned vehicles in the United States to create an entirely new product, unlocking $150 billion in value and growing the global economic pie.

As a more concrete illustration, if we frame the combinatorial growth space of innovation as physical land, it’s as if each new land discovery brings forth even more new land rising from the oceans.

So despite the omnipresent doomsayers, global wealth may actually be infinite.

Combinatorial Growth Both Expands and Compresses Time

Due to the limitations of physics, all available elements in a system cannot be experimentally combined simultaneously. Said another way, it takes time to rearrange physical objects or elements into different configurations. Time and distance and inextricably linked.

In classical mechanics, the relationship between distance and time is related through motion.

Distance = Speed x Time

In special relativity, space and time are interconnected in a four dimensional continuum known as spacetime. As objects approach the speed of light, time dilation and length contraction occurs, linking time and distance in a way that doesn’t exist in classical mechanics.

In general relativity, time and distance are influenced by gravitational fields. For near massive objects, time slows down (gravitational time dilation), and distances start to warp. For example, in cosmology light years (distance) are often used to describe astronomical scales of time since the speed of light is constant at that scale.

Since sifting through a vast recombinatorial space also takes a proportionately immense amount of time, it can stretch seconds into billions of years.

Consider the origins of complex life and the origins of human civilization.

Combinatorics was the Earth’s Early Prebiotic Innovation Engine, which took 4.5 billion years to recombine carbon with other simple molecules, evolving a primordial soup into the vast diversity of modern organic life.

Once complex life had evolved, the Combinatorial Innovation Engine of Early Civilization took only 2.6 million years for humans to dig up minerals and elements from the ground, apply fire and iterate from stone tools into a digital, spacefaring civilization.

Notice the time compression. Once an evolutionary search function hits on valuable recombination of elements, all the time it took finding the recombination gets rolled up into the new element, making it easier to replicate. Growth begets growth.

This is the Matthew effect of accumulated advantage, a natural phenomena so important and widespread it was even written down in the Bible.

“For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.” Matthew 25:29, RSV.

“The Calling of Saint Matthew” by Caravaggio, 1599-1600

“Zero to One” by Peter Thiel is about creating groundbreaking businesses that move from “zero to one” by building something entirely new, rather than incrementally improving on what already exists. The book explores how to find unique opportunities, cultivate innovative thinking and create monopolies through technological breakthroughs, rather than competing in crowded markets.

Said another way, once a novel company or object is assembled, the time and work that was first required for construction gets compressed, making it easier to replicate. It’s harder to go from Zero to One then to One + N because of this compression phenomena.

Take modern humans for example. The spatial volume of the average human is about 62,000 cubic centimeters. However, when considering the required 4.5 billion years of time packed into the creation of those 62,000 cubic centimeters, unrolled we are enormous spatiotemporal beings. It’s no surprise that in terms of network power and depth of function, the human is considered the most complex thing in the known universe.

Remember that you are more ancient, towering and powerful than you think. After all, you have been traveling for 4.5 billion years in a wake of reassembled ancestral parts to be reading this text right now.

Carbon is structurally essential for all organic life and its molecular constituents.

Carbon's four valence electrons enable it to form stable, versatile, and complex molecules, including the long chains and rings that are the structural backbone for all the molecules of life.

These include nucleic acids, amino acids, proteins, carbohydrates and lipids.

The nucleic acids (DNA and RNA) are molecules that, when assembled in the double helix of the human genome for example, contain the genetic information controlling the growth, development and reproduction of all living organisms. They are built on a platform of stable carbon rings.

The amino acids are molecules that assemble into long chains called proteins. They are built on interlinked chains of carbon atoms.

Proteins are long chains of amino acids that fold into complex structures, serving diverse functions in the body, including acting as digestive enzymes, forming antibodies for immune defense, providing the structural framework of muscle tissue, and creating hormones for chemical signaling throughout the body. When you consume meat in your diet, the proteins that construct the muscle tissue are broken back down into amino acids. Depending on the complexity of the protein, they are built on either a chain of carbon, a ring of carbon or both.

Carbohydrates, known as colloquially sugar, are “hydrated carbon” i.e. carbon and water restructured as one molecule.

Glycogen a type of carbohydrate that is the storage form of glucose. When you consume sugar in your diet, it’s absorbed from the GI tract into the bloodstream where the glucose can be utilized for immediate energy needs or stored as glycogen in liver and muscle tissue. Glycogen’s highly branched structure allows for a rapid release of glucose, making it an efficient form of energy storage and retrieval in biological systems.

Lipids, also called fat, play a broad role in biological systems.

Phospholipids and cholesterol form the structure of cells.

Fat under the skin, known as adipose tissue, acts as an insulator to maintain body temperature and cushions vital organs against blunt trauma.

Fats and oils like triglycerides, store and provide more than twice the energy (ATP) per gram compared to carbohydrates or proteins.

Human steroids, such as testosterone, estrogen, cortisol, progesterone or vitamin D, are structurally lipids. They function as hormones or signaling molecules that influence a wide range of physiological processes such as metabolism, inflammation and reproduction.

Steroids are also constructed on a, you guessed it, carbon platform.

Thinking in Systems by Donella H. Meadows

Playing to Win by Michael Lewis

12 Rules for Life by Jordan B. Peterson

The Paradox of Choice by Barry Schwartz

Richer, Wiser, Happier by William Green

The Laws of Human Nature by Robert Greene

Breath by James Nestor

The Creative Act by Rick Rubin

The Beginning of Infinity by David Deutsch

The 80/20 Principle by Richard Koch

The Art of Learning by Josh Waitzkin

The Price of Time by Edward Chancellor

The Lessons of History by Will & Ariel Durant

Influence, the Psychology of Persuasion by Robert Cialdini

The Pleasure of Finding Things Out by Richard Feynman

The Joys of Compounding by Gautam Baid

Mastering the Market cycle by Howard Marks

Mushashi by Eiji Yoshikawa

Maps of Meaning by Jordan Peterson

Six Easy Pieces by Richard Feynman

Why Buddhism is True by Robert Wright

The Order of Time by Carlo Rovelli

Buffet by Roger Lowenstein

The Uncertainty Solution by John M. Jennings

The Gulag Archipelago 1918-1956 by Aleksandr Solzhenitsyn

Fallen Leaves by Will Durant

Man’s Search for Meaning by Viktor Frankl

The Most Important Thing by Howard Marks

The Almanack of Naval Ravikant

The Coddling of the American Mind by Greg Lukianoff and Jonathan Haidt

Leadership: Six Studies in World Strategy by Henry Kissinger

Quit. by Annie Duke

Higher Love by Kit Deslauriers

In Order to Live by Yeonmi Park

Die with Zero by Bill Perkins

Principles by Ray Dalio

However, they soon realized that some systems defy this approach, as understanding local cause-and-effect interactions alone was insufficient to fully explain system-wide behavior.

Small changes in initial conditions could lead to vastly different outcomes, revealing that the relationships were not linear but super-linear.

Moreover, instead of independent variables driving dependent ones, it became apparent that some systems were interconnected with only dependent variables.

Enter chaos theory and complex adaptive systems.

A complex adaptive system (CAS) is a dynamic network of diverse, autonomous agents that interact, self-organize, and adapt their behavior in response to a constantly changing environment. Over time, the repeated interactions among agents lead to system-wide behaviors that cannot be fully understood by examining individual interactions alone. Due to the dynamic and nonlinear nature of these interactions, the system produces emergent outcomes that are greater than the sum of its parts.

The agents within a CAS lack awareness of the broader system they are part of, making it impossible to fully explain emergent phenomena by studying local interactions alone. The complex, super-linear relationships between agents, initial conditions, outputs, and feedback loops obscure cause and effect, rendering CAS effectively a "black box."

Biology offers many great examples of complex adaptivity because nature uses this organizational structure to solve highly complex coordination problems.

Consider the emergent behavior of a beehive.

Without any single bee in control, a beehive effectively adapts to the changing needs of the brood, fluctuating food availability, and internal temperature. The hive coordinates foraging patterns, swarming behavior, wing fanning for air circulation, and adjusts worker and drone reproduction rates to respond to a constantly shifting environment.

Or consider the beautifully synchronized and emergent murmuration behavior of a flock of birds.

Ant colonies solve complex coordination problems without any leadership, relying only on simple rules and chemical signals.

Complex adaptive systems are all around us. They are the structure of the economy and financial markets, of societies, of families, of the climate, of supply chains, of the immune system and even the neuroplastic network of the human brain. They all share the same features of heterogenous agents who interact leading to emergent system wide phenomena.

Complex adaptivity is the reason why legendary investor George Soros thought the idea of market equilibrium was bullshit. In his book the “Alchemy of Finance,” he instead calls the phenomenon “reflexivity. “

Reflexivity occurs because the views and expectations of market participants influence market events, and in turn, these events shape participants' perceptions. The participants are thinking agents with imperfect information who interpret facts through their personal biases, interacting and self-organizing often with flawed models of the world.

This led Soros to reject the traditional economic model of market equilibrium.

Instead of having independent and dependent variables, markets are entirely composed of dependent variables, preventing markets from ever settling into an equilibrium.

Soros argues emergent phenomena like bubbles and crashes arise because actions like bidding up or selling down a stock continually alter perceptions of that stock’s value. As a result, markets are always on a pendulum swing away from equilibrium rather then towards it. If market equilibrium exists it is a pushing force rather than pulling force.

The lens of complex adaptivity doesn’t fit neatly into cause-and-effect mathematical models, but it is often the most accurate perspective to use. Soros believed that using a faulty model is more dangerous than using no model at all.

A related concept is the observer effect, which is prevalent across various fields. In social sciences, it’s known that people change their behavior when they know they are being observed. This effect is also embedded in the fundamental architecture of the universe, where a photon changes its behavior from a wave to a particle when observed.

The Egyptians provided one of the earliest symbolic representations of complex adaptive systems with the ancient symbol of Ouroboros, a snake endlessly eating its own tail.

Hieroglyphs of Ouroboros near the sarcophagus of the Egyptian pharaoh Tutankhamun, 1300s BC.

Once you understand the structure of complex adaptivity, you quickly realize that we are deeply embedded within and are born into these systems. Like nested Russian dolls, these systems often exist within one another.

“Systems gardener” Alex Komoroske says “everything in life is a game within a game within a game, endlessly layered.”

To play well, the first step is recognizing you’re embedded inside a game.

The second step is discerning the rules and structure. In my experience, people confuse rules with the pressure of social structures. There are less rules than you think.

After awakening and discernment, the last and most freeing step is discovering that what feels like a death after a botched play in one game merely transitions you into another game because life is iterative.

Instead of turtles, it's complex adaptivity all the way down.

Drawing from an ancient Hindu ceramic depicting the tortoise Akupara supporting the elephants that hold the Earth, all surrounded by the self-devouring serpent, Asootee.