Solve Everything

00

Prologue

Three Futures

Before we hand you the blueprints for the future, we must first show you the building. The following three scenarios are extrapolations based on the "Industrial Intelligence Stack" and the economic physics described in this essay. They use the specific vocabulary we coin to describe the coming era, such as terms like The Muddle, RoCS, and Targeting Authorities. We will define these in detail in the chapters ahead. For now, do not worry about the definitions. Worry about the speed. We are dropping you directly into the deep end of the timeline to let you feel the texture of the acceleration. This is what it feels like when the exponential progress curve turns vertical.

01

Chapter 1

The War on Scarcity

Every civilizational step-change has been defined by the reallocation of a single, critical scarce variable. When we identify the bottleneck that is holding humanity back and invent a technology to break it, the world transforms.

The Scientific Revolution was a war on Ignorance. Before it, we didn't know why things happened. Its weapon was The Method, a systematic way to find truth.

The Industrial Revolution was a war on Muscle. Before it, all work was limited by the strength of a human or a beast of burden. Its weapon was The Engine, which turned heat into near infinite power.

The Digital Revolution was a war on Distance. Before it, information moved at the speed of a horse, a truck and eventually a plane. Its weapon was The Bit, which allowed ideas to travel instantly across the planet.

The Intelligence Revolution, the one we are entering today, is a war on Attention. Before it, complex problem-solving was limited by the number of experts we could train. Its weapon is The Token, artificial cognition that turns intelligence into a cheap, abundant utility.

The Pattern of Victory

Revolutions are not random events driven by slogans or charismatic leaders. They obey a specific "physics," a predictable structural progression that occurs whenever a civilization learns to break a bottleneck. Whether we are looking at the invention of science in the 1600s or the rise of AI today, the pattern always moves through four distinct stages: Legibility, Harnessing, Institutionalization, and Abundance.

Stage 1: From Scarcity to Legibility. A revolution begins when we invent a new instrument that allows us to see a hidden signal. In the Scientific Revolution, the telescope and the calorimeter made the invisible visible. Today, our "telescope" is the Benchmark Harness, a tool that allows us to precisely measure intelligence and performance. Once a thing can be seen and measured, it can be controlled.

Stage 2: The Harness. Once the problem is visible, we build a "Harness" to control it. This is a set of procedures that translates our intent into a predictable outcome.

The Scientific Method was a harness for truth.

Factory Discipline was a harness for labor.

The Operating System was a harness for computation.

Today, the Industrial Intelligence Stack is the harness for AI. It ensures that "smart" models are also reliable and safe.

Stage 3: The Institutions. A harness allows new institutions to form. Markets and governments build structures to convert this new power into trust and capital. In the past, these were scientific journals, corporations, and internet protocols. Today we are building Abundance Targets and Outcome-Based Contracts. These are the new rules of the road that determine how AI gets funded and deployed.

Stage 4: Abundance. Finally, the unit cost of the new capability collapses. Light, travel, information, and now intelligence, become demonetized and democratized. The primary social question shifts from "Can we do this?" to "Who aims this weapon, and under what guardrails?"

The Moral: The locus of prestige shifts. It moves from the "Hero" (the lone artisan genius) to the "Harness Builder" (the industrialist who builds the system).

The Scientific Revolution: Making Knowledge Legible

Before the 17th century, the primary bottleneck to progress was the lack of a common language for truth. Discoveries were "artisanal," a local alchemist might find something amazing, but because there was no way to verify or share it, the knowledge died with them. Claims were non-comparable and easily lost.

The revolution's harness was the Scientific Method itself. It created a standard protocol: make a hypothesis, run an experiment, and see if others can replicate it. Instruments like lenses, clocks, and balances raised the resolution at which we could query reality.

The institution that emerged was Reproducibility. The concept of "citation" became a way to allocate capital and prestige. If your work could be reproduced by others, you got funding. The result was an abundance of methods and predictions.

The moral of this revolution is simple: make truth cheap to verify. In our era, that means we must build public, adversarial benchmark authorities. We need "scoreboards" for AI that are stress-tested by red teams to ensure that a claim of intelligence is actually true.

The Industrial Revolution: Mechanizing Energy

The next bottleneck was the physical limit of muscle. For thousands of years, the total output of civilization was capped by the number of people and oxen we could feed. Time and space were rooted in physical effort.

The harness that broke this limit was the Heat Engine, paired with the discipline of the factory. The engine transformed dead fuel (coal) into live work. Critically, the invention of interchangeable parts and standardized gauges made production "composable." You could take a gear from one factory and put it in a machine in another city, and it would work. This allowed for systems of unprecedented scale.

The institutions that scaled this were the connective tissues of a new economy: standards bodies, rail tariffs, and the limited liability corporation. The abundance that followed was tectonic. The cost of light, textiles, and transit collapsed.

The moral is to treat energy as working capital. In our era, the direct equivalent is to treat computing power (compute) the same way. We must co-locate massive data centers with clean power sources and schedule their workloads like a critical grid resource.

The Digital Revolution: Dematerializing Information

The bottleneck of the 20th century was the distance and delay of information. Paper moved at the speed of boats; expertise lived in individual heads and was difficult to scale.

The harness was a stack of abstractions: transistors, protocols (like TCP/IP), and operating systems turned computation into a programmable substrate. Tools like search engines and version control made cognitive complexity tractable for the first time.

The institutional genius of this revolution was Permissionless Composability. The Internet’s design meant that anyone could ship a new service without asking for a license. The resulting abundance was staggering. The cost to send a message or process a transaction nosedived. Coordination across continents became real-time.

The moral is that abstractions liberate scale. However, the corresponding risk is "monoculture": if everyone relies on the same few systems, a single bug can crash the world. We must guard against this by insisting on diversity, through multi-vendor clouds and multi-compiler safety checks.

The Intelligence Revolution: Industrializing Discovery

This brings us to the present day. We have already solved the problems of physical strength and information transfer; the bottleneck holding back civilization now is scarce expert attention. The most crucial tasks in our society, such as designing a novel drug, diagnosing a complex patient, or proving a mathematical theorem, have always been limited by the small, expensive pool of humans with the necessary training.

To break this bottleneck, we rely on the harness described in Chapter 3: the Industrial Intelligence Stack. This stack acts as a translator, taking a messy, real-world domain and making it "legible" to artificial intelligence. From a clear definition of tasks to a rigorous testing harness, this system fully describes a domain in code. When a field is mapped this clearly, it enters a predictable "countdown to being solved."

To support this harness, we are building new institutions that change how we pay for progress. The first is Outcome Procurement, which fundamentally changes contracts. Instead of paying a hospital for the effort of treating a patient, the contract pays for the verified result of curing them. This is paired with Compute Escrow, a financial mechanism where training budgets are held in a locked account and released only when the AI team meets specific performance milestones. We enable these systems with Data Trusts, which are legal wrappers that turn messy, private institutional data into lawful, reusable capital that can train models without violating privacy.

The resulting abundance will be cognition that behaves like a utility (e.g. electricity). We will be able to "pour" thought onto a problem, running millions of simulations or design iterations, until the difficulty disappears. Scientific discovery itself ceases to be a series of lucky breaks and becomes a reliable, pipelined process.

The moral of this revolution is to automate the evaluation before you automate the work. If your scoring system is cheap, credible, and resistant to cheating, improvement will compound naturally. If you cannot accurately grade the test, you are merely rehearsing, not industrializing.

What Revolutions Break, and What They Preserve

A study of history reveals a consistent, somewhat uncomfortable truth: technological revolutions ruthlessly break the model of "artisanal heroics." In every prior era, value was centered on the lone genius or the master craftsman: the individual with the "golden touch." The revolution shifts the locus of prestige away from that individual. It moves from the "Hero" who solves a single problem to the "Harness Builder" who creates the industrial system that allows anyone to solve that problem.

To understand this shift, consider the transition from the master weaver to the textile engineer. In the 1700s, a master weaver was a local celebrity, revered for their unique skill. By the 1800s, the prestige had shifted to the engineer who designed the power loom, a machine that allowed thousands of people to weave perfectly. The same shift is happening today. The "hero" coder who stays up all night fixing a bug is being replaced by the "harness builder" who designs the automated testing system that prevents bugs from ever occurring.

However, while revolutions commoditize the means of doing work, they elevate the human purpose. When energy, information, and now cognition become cheap utilities, the scarce variable shifts. It is no longer about "how" we do something, but "what" we choose to aim these powerful machines at. Ethics, law, and culture stop being abstract debates and become the front lines of engineering. We must decide exactly where to point these abundance machines. This is why we argue that new social contracts are not afterthoughts; they are an integral part of the harness itself. Mechanisms like re-training programs, guaranteed floors for opportunity (such as a minimum learning gain per hour for every student), and new rights regimes for data are the "rails" that keep the high-speed engine of revolution from derailing the society it is meant to serve.

How to Read Today’s Hinge with Historical Eyes

We can use these historical patterns as a diagnostic toolkit to understand our current moment. To determine if the "Intelligence Revolution" is real in any specific domain, whether it is healthcare, law, or education, you only need to ask three questions.

First, do we have "Instrumented Legibility"? Are there public, transparent targeting systems for the problems we claim to be solving? If a company claims to be "solving education" but cannot show you a verified, public scoreboard of learning gains, they are still in the "pamphlet phase." They are marketing, not engineering. A revolution is only real when the problem is visible and measured.

Second, do we have "Harness Integrity"? Do our data pipelines and evaluation systems survive attack? We must subject our systems to "red-teaming": paying adversaries to try and break them. If the harness collapses under stress, we have not earned the right to automate the task.

Third, do we have "Institutional Buy-in"? Are the buyers actually paying for outcomes? Are government budgets gated on hitting specific targets? If the money is still flowing toward "hours worked" or "reports filed," the cost curves will not collapse. The revolution will die in a pile of paperwork because the economic incentives have not shifted to support the new technology.

Correcting Common Misreadings

We must also correct the common "misreadings" of this historical moment.

When you hear critics say, "This time is just hype," the historical correction is that every prior revolution looked like hype until the measurement and payment layers snapped into place. The "snap" that makes it real is institutional, not rhetorical. It becomes real when a bank agrees to lend money based on the new metric.

When you hear, "Automation erases human value," the correction is that automation commoditizes means, but it does not select ends. An AI can generate a thousand different architectural blueprints (the means) in seconds, but it cannot (yet) decide which building will best serve the community's needs (the end). That remains, for now, a strictly human value judgment.

And when you hear, "We must slow everything down to be safe," the correction is that we must shape and condition speed, not stop it. We do not want to stop the car; we want to install better brakes and steering so we can drive faster safely.

The Bridge Forward

The bridge from 2026 to the Era of Abundance is not a mystery. It is a construction project. The "playbook" for this bridge is simple, and it consists of four specific actions that mirror the successful revolutions of the past.

First, we must Publish the Targets, Then the Budgets. We need to define exactly what success looks like, in numbers, before we spend a dime. All funding should be tied to "blinded clears" on public targets, where teams are rewarded only when they solve a problem they haven't seen before.

Second, we must Stand Up Action Networks. Intelligence needs a body. We must fund shared robotic labs, micro-factories, and clinical device networks that allow AI code to affect the physical world. These should operate with "Outcome Service Level Agreements," guaranteeing that the physical actions meet rigorous quality standards.

Third, we must Escrow Compute. Instead of handing out cash grants that might be wasted, we should pre-commit credits for training and inference power. These credits sit in a locked account (escrow) and are unlocked only when a team proves they have made a benchmark gain. This ensures that the fuel of the revolution is spent only on progress.

Fourth, we must Pay for Results. We must systematically retire every procurement contract that buys "hours," "data silos," or "press releases." If a contract doesn't pay for a verified outcome, it belongs to the old world.

These are not metaphors. They are the contemporary equivalents of the scientific journals, pressure gauges, and standards bodies that tamed previous centuries. They are the mechanisms that convert a bright idea into a reliable machine.

The Arc of Progress Towards Abundance

The arc of progress is not a smooth, inevitable curve that happens while we watch. It is a set of rails that we must lay. Each "rail" is a specific decision we make: the decision to make a domain legible; the decision to build a rigorous harness; the decision to pay for outcomes rather than effort; and the decision to stop the system automatically when it drifts into danger.

That is how prior revolutions crossed from promise to policy to prosperity. How the Scientific Revolution moved from alchemy to chemistry, how the Industrial Revolution moved from blacksmithing to manufacturing, and how the Intelligence Revolution will move from "chatbots" to abundance.

Choose your rail, and help lay it.

From History to the Present

The lesson of these past revolutions is clear: abundance is not an accident. It is an engineering result. We have successfully broken the bottlenecks of ignorance, muscle, and distance. Now, we stand before the final bottleneck: the scarcity of intelligence itself.

But history also offers a warning. The “snap” from scarcity to abundance is never smooth. It requires a deliberate architectural choice to build the harness before the engine tears the machine apart. We possess the raw power: the “fire” of intelligence is already here. What we lack is the engine block to contain it and the transmission to direct it.

The rest of this blueprint is not about history. It is about the specific, operational thesis for how we build that engine in the next decade. The revolution has already begun. This is how we steer it.

02

Chapter 2

The Thesis

The era of Artificial Superintelligence (ASI) has effectively begun. The most critical question facing us is no longer if superintelligence can be created, but rather who aims this powerful weapon, and at what.

When we deliberately route large-scale intelligence toward "Positive-Sum Moonshots," ambitious projects that benefit everyone rather than just creating winners and losers, and measure our progress against rigorous, tested standards, we achieve two specific outcomes:

Entire domains are solved in bulk. We stop trying to solve one problem at a time (like discovering a single new drug) and instead build industrial systems that solve the entire field (like building a platform that can cure any pathogen on demand).

Problems become "Compute-Bound." A domain is considered "solved" when we no longer need a stroke of human genius to fix a problem. Instead, we can solve it simply by applying enough computing power and data.

Defining Our Terms: The Scale of the Force

To understand where we are going, we must clarify what we are building. We are not talking about significantly better chatbots.

AGI (Artificial General Intelligence): This is the milestone where an AI system is as capable as a median human expert across all economically valuable tasks. If you can hire a human to do it, an AGI can do it, too. We anticipate this will be common and accessible in 2026.

ASI (Artificial Superintelligence): This is the moment AI exceeds human capability not just by a small margin, but by orders of magnitude. By 2035, we are speaking about systems trained on 10²⁹ FLOPs of compute. To put that number in perspective, such a system has ingested and synthesized more information than all human beings who have ever lived, combined. This is no longer a tool; it is a force of nature.

Three Foundational Claims

Our thesis rests on three claims. These explain how we move from today's world to a future of abundance.

Claim 1: Cognition is a Commodity.

The old "Input-Based" economy, where you get paid for the hours you work, is dead.

For the last twenty years, intelligence was like an artisanal craft: it required scarce researchers and custom-built data systems. However, between 2026 and 2030, three trends are converging to destroy that old model.

Model Quality is rocketing past human limits. We are moving from "smart software" to a surplus of super-human intelligence that can handle messy, real-world tasks.

The Unit Cost of Cognition is collapsing. The cost to make a decision, or verify a mathematical proof, is dropping toward its physical limit: the price of the electricity required to flip the switches in a computer chip.

The Friction of Integration is falling to zero. This means AI is moving out of the browser and into the real world. It is shifting from a chatbot writing a poem to an autonomous agent executing a binding legal contract or controlling the temperature in a fusion reactor. Beyond this, AI is moving into the physical world with autonomous drones, robotaxis, and humanoid robots.

Once super-human thought becomes cheap and accessible, like a utility bill you pay as you go, we can "pour" computing power onto a problem the same way we pour concrete to build a foundation. The bottleneck is no longer human brainpower. The new bottleneck is routing: deciding where to point this firehose of intelligence.

Claim 2: Targeting Systems Industrialize Progress.

Artisans rely on personal taste. Industries rely on Targeting Systems (formerly known as "benchmarks"). A field allows for industrial-scale progress only when we can state, with mathematical precision, "This number is what success looks like."

In Biomedicine: Success isn't "we tried hard." It is measured in Time-to-Drug-Approval (hours, not years) and minimized side effects.

In Education: It is measured in Learning Gain per Hour (LG/H). As an example: If a student uses an AI tutor for one hour, do they retain the skill 180 days later? That is a measurable metric.

Targeting systems don't just record history; they create the future. When you measure something and offer a reward for it, capital and research flood into that area. The moment a government says, "We have placed $1 billion in an escrow account. It will be released to the first system that beats Target X," a flywheel of progress ignites. Prices fall, capabilities rise, and the public sector starts buying actual outcomes rather than just reading proposals.

Claim 3: The "Shaped-Charge" Model.

Much of today's AI research focuses on constraining what systems are allowed to do. Our thesis adds a focus on where those systems should be aimed.

Think of ASI as raw explosive energy. A shaped charge is an explosive designed to focus all its energy into a tiny point to penetrate armor. We must focus ASI by routing it through Moonshots. These are massive missions validated by strict, adversarially-tested targets. A project qualifies as a Moonshot if it is:

Positive-Sum: It increases the size of the pie for everyone (e.g., creating unlimited organs for transplant) rather than fighting over a fixed pie (e.g., deciding who gets the one available kidney).

Auditable: Performance is verified by independent, automated tests.

Composable: The pieces used to build the solution are open and verified, allowing others to build on top of them like LEGO bricks.

When intelligence flows through these specific channels, safety improves organically because every step is measured against guardrails.

The Enemy: "The Muddle"

The obstacle to this future is not technology. It is "The Muddle."

The Muddle is the entrenched layer of bureaucracy, pricing based on "inputs" (like hours worked), and scarcity-minded institutions that currently run the world. The Muddle thrives on friction and inefficiency. We are in a race: The Rails (our new efficient systems) vs. The Muddle. If we build the targeting systems fast enough, we win. If The Muddle throttles the ASI with red tape, we lose the century.

What We Mean by "Solved"

We use the word "solved" in a very specific, game-theoretic way.

The Theoretical Definition: A domain is solved when the main bottleneck shifts from human genius to available computing power. It means we know how to solve the problem; we just need to spend the energy to do it.

The Operational Definition: A domain is solved when tasks can be automated to beat human experts reliably, with transparent failure modes.

Solved Math: You input a conjecture, and a swarm of AI agents generates a formal proof within hours.

Solved Physics: An AI identifies a gap in experimental data, proposes a new theory, and designs the collider experiment to test it.

Solved Biology: A patient has a new virus. The system sequences it, designs a protein to bind to it, and outputs a recipe for the cure in 24 hours.

An Operational Playbook for the War on Scarcity

To win, we must mobilize:

Fire the "Press Release" Department. Stop announcing intentions. Publish the Targeting System. Convene experts to define 3-7 core metrics that capture public value.

Build the Test Harness First. Turn your goals into code. Build data pipelines that include "adversarial cases" (difficult scenarios designed to trick the AI) from day one.

Treat Compute as Ammo. Forecast your "compute cash flow." Know exactly how much processing power you need to achieve your targets.

Route for Impact. Aim at the Moonshots. Insist on open datasets and protocols so that we create a wider defensive perimeter against misuse.

Trust but Verify . Publish Decision Records. These are auditable logs showing exactly who changed what, when, and why. Pre-commit to "kill switches": if safety scores drop by X%, the system automatically slows down.

This is not a map of a future that will happen automatically. It is a call to build it. Abundance is not about a world of luxury; it is about a world of possibilities. It is about having more ways to learn, heal, and build per unit of energy.

Choose your Moonshot. The rest of this essay shows you how to aim the charge.

03

Chapter 3

The Mechanics

Thesis: When we say a problem is "solved," we mean it has become compute-bound.

This is a critical shift. We are moving from the realm of genius (waiting for a brilliant insight) to the realm of logistics (organizing resources). A domain is "solved" when we can get a predictable result just by pouring more computing power into the system. What was once a craft accessible only to a world-class expert becomes a system accessible to anyone.

To map the future, we must define the destination. "Solved" is not a vague hope; it is an engineering reality with a specific anatomy, a predictable maturation curve, and a set of unmistakable signatures that signal its arrival. The concept of a “solved” domain is the fundamental unit of progress in the age of AGI.

The Industrial Intelligence Stack

A domain, whether it is accounting, dermatology, or structural engineering. cannot be industrialized until its foundations are solid. We call these layers the Industrial Intelligence Stack. If a layer is missing, the system will fail.

Think of early aviation. We couldn't "industrialize" air travel until we first understood the physics of lift and drag. We had to make the physics legible before we could make the flight safe.

Here are the layers required to turn a craft into an industry:

Purpose and Payoff (The Goal): The bottom layer. We must move beyond vague mission statements like "improve health" to quantifiable metrics, such as "reduce sepsis rates by 50%."

Task Taxonomy (The Map): We break the complex job down into tiny, measurable actions. It is the assembly line instruction manual for cognitive work.

Observability (The Eyes): You cannot fix what you cannot see. This layer consists of sensors, logs, and data streams that act as the system's nervous system.

The Targeting System (The Harness): This is the engine of the stack. It is a collection of difficult tests that an AI model must pass. It acts as quality control, actively trying to break new models to ensure they work.

The Model Layer (The Brain): This is the AI agent itself, the software making the decisions, trained against the Harness.

Actuation (The Hands): Decisions are useless unless they affect the world. This layer allows the AI to act: via a robotic arm, an API that sends an email, or a smart contract that releases funds.

Verification and Red Teaming (The Immune System): Continuous, independent attacks on the system to find flaws before they cause damage.

Governance and Incentives (The Rules): We realign the money. Instead of paying for "hours worked," we switch to "pay-for-performance" contracts.

Distribution and Maintenance (The Scale): Ensuring the system works as a reliable utility, like the power grid, rather than a one-off science project.

The Maturation Curve: From L0 to L5

As we pour intelligence, data, and capital into any specific field, whether it is customer service, radiology, or software coding, it does not just get "better" randomly. It evolves through five predictable stages of maturity.

This progression is not a matter of opinion; it is a ladder that every industry climbs.

L0: The Ill-Posed Domain ("The Muddle")

The State of Ambiguity: At this stage, we don't even agree on the rules of the game. The objectives are contested, the data is messy or non-existent, and decisions are driven by anecdote, charisma, or "gut feeling" rather than evidence. There is no standard way to do the work, so every success feels like a lucky accident.

The Human Role: Humans do everything, relying on intuition and politics.

The AI Role: Non-existent. AI cannot learn because there is no clear target to aim at.

Real-World Example: Corporate Strategy. In most boardrooms, decisions are made based on who makes the most persuasive PowerPoint presentation, not on rigorous data. One executive thinks "success" is market share; another thinks it is profit margin. It is a Muddle.

L1: The Measurable Domain

The Era of Scoreboards: This is the first step toward sanity. We may not know how to solve the problem perfectly yet, but we have agreed on what we are measuring. We start instrumenting the process and creating basic leaderboards. We can finally see who is winning, even if we don't fully understand why they are winning.

The Human Role: Humans still do the work, but they are now being graded.

The AI Role: The AI acts as a referee or scorekeeper, collecting data and showing us the baseline performance.

Real-World Example: Sales Call Tracking. We start recording calls and measuring "conversion rates." We don't have an AI that can sell the product yet, but we know exactly which human salespeople are closing deals and which aren't.

L2: The Repeatable Domain

The Era of Playbooks: Because we can measure success (L1), the best human performers start to identify patterns. They write down what works. We develop "Standard Operating Procedures" (SOPs) and checklists. The process is still manual, but it is now consistent. We have reduced the variance; the outcome is no longer a roll of the dice.

The Human Role: Humans follow a strict script or checklist.

The AI Role: AI begins to assist by offering templates or auto-completing simple steps, acting like a smart spell-checker for the domain.

Real-World Example: Commercial Flight. Pilots follow rigorous pre-flight checklists. They don't just "wing it." The process is highly standardized, making safety predictable.

L3: The Automated Domain

The Tipping Point: This is the critical inflection point. The checklists from L2 become code. AI agents begin executing the majority of the primitive tasks. The "grunt work" is handed over to the machine. Humans move "up the stack," shifting their focus from doing the work to supervising the agents and handling weird, edge-case exceptions that the AI hasn't seen before.

The Human Role: The human becomes a manager and exception-handler, stepping in only when the AI gets confused.

The AI Role: The AI is the primary worker for 80% of the volume.

Real-World Example: Modern Call Centers. You talk to a chatbot or voice-AI first. It solves the easy problems (resetting a password, checking a balance). If your problem is complex, it routes you to a human.

L4: The Industrialized Domain

The Economic Flip: The market fundamentally changes here. Buyers stop hiring humans to do the task entirely. Instead, they buy "verified outcomes" from a system. The unit economics of the AI system permanently beat human-only methods. It is no longer cheaper or sensible to hire a person to do this job from scratch. The industry has been standardized.

The Human Role: Humans act as auditors and system architects, ensuring the machine is running safely.

The AI Role: AI runs the show, delivering high-quality work at a speed and cost humans cannot match.

Real-World Example: Factory Assembly Lines. We don't hire artisans to hand-build cars anymore. We have industrialized the process with robotics. We buy the outcome (a working car), not the hours of hammering metal.

L5: The Commoditized Domain ("Solved")

The Compute-Bound State: The problem is solved. It has moved from the realm of genius to the realm of simple logistics. If you want more output, you just plug in more servers (compute). Multiple providers can solve the problem perfectly, so they compete purely on price. The service becomes as boring and reliable as tap water.

The Human Role: The consumer. We simply use the output without thinking about how it was made.

The AI Role: The invisible utility running in the background.

Real-World Example: Gene Sequencing. Twenty years ago, sequencing a human genome was a billion-dollar "Moonshot" requiring the world's best scientists. Today, it is an L5 service. You can mail a saliva kit to a lab, and machines do it for $100. It is fast, cheap, standardized, and requires zero human genius to execute.

The Great Conversions: The Signatures of Victory

How do you know when a domain has tipped from a craft (L2) to a solved industry (L5)? Look for these seven specific changes in how the world works.

From Effort to Outcomes: The economy changes. We stop paying for labor hours or software licenses. We pay for results. Example: Instead of paying a pest control company for a monthly visit (effort), you pay a subscription that is free if you see a single bug (outcome).

From Documents to Data: We stop relying on PDF reports and audits. We switch to "machine-verifiable proofs": live data streams that prove safety guardrails are holding in real-time.

From Projects to Pipelines: Product development ceases to be a discrete event (e.g., "releasing a drug in 2030"). It becomes a continuous pipeline where AI is constantly simulating and submitting new candidates to the FDA harness.

From Heroics to Harnesses: Prestige shifts. We stop celebrating the brilliant individual who fixes a crisis at 2 AM. We celebrate the team that built the test harness so robust that the crisis never happened.

From Secrecy to Shaped Openness: Companies open-source their safety components to build ecosystem resilience, while competing on user experience.

From Averages to Tails: An average reliability of 99.9% is not good enough if the 0.1% failure is a plane crash. We stop optimizing for the average day and start optimizing for the worst-case scenario (the "tail risk").

From Talent to Compute Liquidity: In a solved domain, the strategy is no longer hoarding experts. The strategy is deciding where to allocate your computing power. Capital is measured in FLOPs (Floating Point Operations).

From Applied Problems to Grand Domains

The framework we have described, turning a craft into an industrial process, is fractalline. This means the exact same rules apply regardless of the size of the problem. The steps you take to industrialize a small process, like diagnosing a skin rash, are the same steps you take to industrialize a massive challenge, like curing cancer or solving energy.

Solving a narrow, foundational problem is the prerequisite for solving a broad, complex one. This creates a Domino Effect of discovery. When you solve the bottom layer, the layer above it suddenly becomes easier.

Consider this specific progression of how we get to infinite clean energy (Fusion):

1. The Foundation: Solved Math Everything starts here. Mathematics is the language of the universe, but verifying complex proofs is currently slow and prone to human error.

The Shift: We build AI systems that can instantly verify formal mathematical proofs with zero error.

The Result: Math becomes a reliable, automated utility. We no longer need to trust a human mathematician's intuition; we have a "spell-checker" for the fundamental logic of reality.

2. Unlocks: Solved Physics Because we have "Solved Math," we can now model the physical world with perfect precision.

The Shift: We don't have to wait for an Einstein to have a sudden epiphany. AI agents can use the tools of Solved Math to propose unified theories and run billions of simulations to see which ones match reality.

The Result: We understand the laws of the universe, from how atoms bond to how gravity waves move, at a resolution humans could never achieve alone.

3. Unlocks: Solved Materials Science Because we have "Solved Physics," we know exactly how atoms behave under any condition. This allows us to become "digital alchemists."

The Shift: We stop discovering new materials by trial-and-error in a wet lab (mixing chemicals and hoping they don't explode). Instead, we simulate new materials at the atomic level on a computer.

The Result: We can design a material with the exact properties we need, like a metal that doesn't melt at 3,000 degrees or a superconductor that works at room temperature, before we ever mine the ore. Crucially, this allows us to unlock the 'Forever Battery'—energy storage densities that utilize common earth elements like sodium or sulfur, eliminating supply chain bottlenecks and allowing us to bank solar energy for weeks at a time.

4. Unlocks: Solved Fusion This is the ultimate prize. We have known the theory of fusion energy for decades, but we couldn't build the machines because our materials weren't good enough (the magnets would melt or the containment would fail).

The Shift: With "Solved Materials" (specifically, those better superconductors we just designed), we can finally build the hardware capable of containing a fusion reaction.

The Result: Commercially viable, infinite clean energy. We didn't solve fusion by attacking fusion directly; we solved it by solving the three layers beneath it.

The Quiet Hum of a Solved World

When this chain reaction takes hold, the macroeconomy transforms. We will see a structural decline in volatility, with fewer energy crises and fewer supply chain shocks, and a dramatic acceleration in discovery.

Crucially, the public will experience this not as a series of shocking, disjointed miracles, but as the quiet, reliable hum of a solved world. Just as you don't marvel at the "miracle" of electricity every time you flip a light switch, you will not marvel at the miracle of AI curing a disease or balancing the power grid. It will simply work, reliably and cheaply, in the background.

04

Chapter 4

The Lock-In

Claim: The timeline of 2026 starts with "The Lock-In." Intelligence has shifted from a bespoke craft to a programmable utility.

The primary constraint is no longer whether models can think, but how fast our institutions can route that cognition into targeted, real-world outcomes. The next 24 to 36 months will determine whether we systematically industrialize abundance, achieving a wholesale solving of humanity's grand challenges by 2035, or drift into a future of concentrated, brittle systems.

The operating system of the next century is being written right now. If we hard-code "bureaucracy" into the ASI, we get a super-intelligent DMV. If we hard-code "outcomes," we get the Star Trek economy.

The debate about if AGI is coming is over. The only relevant question is kinetics: how fast it moves and where it lands.

The Physics of the Inflection

Four interdependent trends have reached a critical velocity. Separately, they represent progress; together, they represent a phase change, like water turning into steam.

1. The Quality-Breadth Curve: Frontier models, when properly scaffolded with tools and retrieval mechanisms, now meet or exceed expert human baselines across a massive range of tasks. The long tail of complex cognitive work is rapidly coming into scope.

This is not merely about better chatbots; it is about systems that can digest the entirety of the world’s scientific literature, reason over it, and propose new experiments. Error rates are no longer just shrinking with scale; they are narrowing under competent orchestration. We are turning raw, fallible intelligence into reliable science using techniques like "voting" (where multiple agents debate an answer until they agree) and "verification" (where a separate agent critiques the work of the first). As an example, consider an AI that reads 10,000 papers on Alzheimer's, proposes a new drug target, at the same time that three other AI agents critique the proposal to find flaws before a human ever sees it.

2. The Cost-per-Cognition Curve: The price of a "unit of thought," whether it’s writing a paragraph or verifying a math proof, is collapsing. It is dropping toward its physical floor: the cost of the electricity required to flip the transistors in the chip plus the depreciation of the compute hardware and the scarcity rents on high-bandwidth memory.

When the cost of exploring a billion-dollar research question drops to the price of the electricity required to run the simulation, the very definition of an "intractable" problem changes. As an example consider that today, designing a new car engine costs millions in R&D hours. In the future, running the simulation to design it might cost $50 in electricity.

3. The Friction-of-Integration Curve: The difficulty of connecting AI to the real world (i.e., the difficulty of embedding model capabilities into existing workflows) is plummeting. "Agentic" systems can now operate other software (APIs), write code (IDEs), control robots, and negotiate commercial contracts all with minimal help.

This enables an Accelerated Closed-Loop Scientific Method. An AI can come up with a hypothesis, write the Python script to direct a robot in a lab to mix the chemicals, and then gather the data to update its hypothesis and run the scientific method loop over and over again, all in minutes or hours, not months, while the human researchers are asleep.

4. The Capital-Liquidity Curve ensures the core inputs to AI systems (compute, data feeds, and model access) are becoming increasingly liquid. They can be leased, reserved, containerized, and paid for out of operating budgets. This allows for a "Manhattan Project" level of focus to be brought to bear on any problem, funded not just by nation-states but by agile consortia or individual philanthropists. Access is no longer the primary constraint; the bottleneck has shifted to the logic of portfolio allocation. This liquidity also means individuals can now launch their own Moonshots. A small group, or even a single person, can rent the cognitive equivalent of a large research institute to solve a problem meaningful to them. Imagine a parent whose child has a rare disease could rent enough computing power to simulate cures for that specific genetic mutation, without needing to build a lab or hire a staff of top-tier scientists.

When the product of cost and friction drops below a certain threshold, projects that were previously economically irrational, such as universal personalized tutoring, demand-shaping smart grids, or closed-loop materials discovery, suddenly become frictionless and the default path of progress.

The AlphaFold 3 Precedent: The Universal Template for Domain Collapse

To truly grasp the magnitude of the technological shift we are currently experiencing, we must look closely at a recent historical precedent that serves as a blueprint for the future. The release of AlphaFold 3, along with the broader work of Isomorphic Labs, represents far more than just an isolated breakthrough in the field of biology. It stands as the universal template for what we call "domain collapse": the rapid transition of an entire field of science from a slow, manual struggle to an automated, high-speed industrial process.

For over fifty years, the "protein folding problem" was one of the central bottlenecks in biology and medicine. The challenge lay in determining the three-dimensional structure of a protein based solely on its genetic sequence. This is critical because a protein's shape determines its function: how it interacts with other molecules, how it fights disease, or how it builds tissue. Historically, figuring out this shape was an artisanal, expensive, and painstakingly time-consuming process. A PhD student might spend an entire year using X-ray crystallography to map the structure of a single protein. It was a craft that required deep expertise, specialized equipment, and vast amounts of patience.

AlphaFold did not merely get slightly better at this task; it effectively collapsed the entire problem space. In the language of our maturity curve, it moved the domain of structural biology from Level 2, where results were repeatable but required world-class human experts, to Level 5, where the process is commoditized. Today, determining a protein's structure is no longer a doctoral thesis project; it is a computational query that takes minutes and costs pennies. This is what we mean by "domain collapse": the moment a problem shifts from being bound by human labor to being bound only by computing power.

This transformation was not accidental. It occurred because the domain possessed four critical layers of the "Industrial Intelligence Stack" we described earlier:

First, there was a clear Purpose: the goal was unambiguously to predict how molecules interact.

Second, there was a well-defined Task Taxonomy. The messy biological reality was translated into a precise mathematical problem: mapping a sequence of amino acids to specific X, Y, and Z geometric coordinates.

Third, the domain had vast Observability in the form of the Protein Data Bank, a massive, public digital library of previously solved structures that served as training data.

Finally, and perhaps most importantly, there was a rigorous Targeting System called CASP (Critical Assessment of Structure Prediction). This acted as a biennial "Olympics" for protein folding, providing a blind, adversarial test that prevented researchers from grading their own homework.

Because these layers were in place, DeepMind was able to pour scaled computing power and algorithmic innovation into the system, achieving a result that permanently altered the landscape of biological science. The "miracle" was actually a predictable engineering outcome.

This is the exact template we will use to industrialize every other field of human endeavor. The logic that solved protein folding is transferable. A similar scaled effort, utilizing the same stack of clear metrics, vast data, and adversarial testing, will be applied to discover novel battery chemistries that hold twice the charge of today's cells. It will be used to identify room-temperature superconductors, which would revolutionize energy transmission. It will be applied to formally prove mathematical conjectures that have stumped humans for centuries, and to design the magnetic containment fields necessary for commercially viable fusion reactors. We must stop viewing these potential breakthroughs as a series of independent, unconnected miracles. Instead, we should see them as the expected, reliable outputs of a new industrial process for discovery that is finally coming online.

The Convergences Driving the Shift

This massive inflection point in history is not occurring spontaneously, nor is it merely a lucky breakthrough in a single laboratory. It is the result of seven distinct technical and economic "engines" reaching maturity at the exact same moment. These seven forces are converging to push our technological capabilities past their tipping points.

The first major shift has transformed the physical substrate of intelligence itself through Hardware Packaging. We have moved beyond the era of flat, two-dimensional computer chips. Engineers have mastered 3D stacking, effectively building skyscrapers of logic and memory on a single wafer. This proximity allows for high-bandwidth memory, which makes "large-context reasoning" economically viable for the first time. To understand the impact, imagine a lawyer trying to solve a complex case while only being able to remember one page of evidence at a time. That was the old constraint. With this new hardware, the AI can effectively hold the entire library of case law and evidence in its "working memory" simultaneously, allowing it to draw connections across vast amounts of information instantly and cheaply.

This powerful hardware is made useful by a second convergence called Algorithmic Scaffolding. A raw AI model, on its own, is essentially a brilliant but unreliable improviser. Scaffolding is the "management layer" that surrounds that model to make it robust. It turns a probabilistic system into a reliable, agentic problem-solver. Think of this like the difference between a lone genius shouting random answers and a disciplined engineering team. The team has a workflow: they propose a solution, critique it, test it, and refine it. Algorithmic scaffolding automates this workflow, orchestrating multiple AI agents to convert raw cognition into trustworthy, open-ended problem solving.

These agents require fuel, which leads us to the third convergence: Data Interconnects. Historically, the world's most valuable data has been locked away in silos, such as hospital records, proprietary chemical databases, or financial logs, because of privacy concerns. We are now seeing the maturity of privacy-preserving technologies and domain "ontologies" (standardized ways of labeling data) that turn these messy, isolated silos into reusable capital. This acts like a secure diplomatic channel, allowing an AI to learn from a hospital's cancer data without ever actually seeing a specific patient's name or private file.

However, intelligence is useless if it is trapped inside a computer screen. This necessitates the fourth convergence: the proliferation of Action Surfaces. These are the "hands and feet" of the digital mind. They include Application Programming Interfaces (APIs) and robotic fleets that allow digital decisions to flow outward into the physical world. This is the mechanism that allows an AI to move from merely designing a new molecule on a screen to actually controlling the robotic pipette that mixes the chemicals in the lab.

The quality of these actions is disciplined by the fifth convergence: a maturing Evaluation Infrastructure. We are moving away from measuring AI progress based on rhetorical claims or cherry-picked demos. We are adopting rigorous targeting systems, namely public and private test harnesses, that turn progress into measured, falsifiable results. This is similar to a flight simulator that a pilot must pass before flying a real jet; the evaluation infrastructure relentlessly tests the AI against hard, adversarial scenarios to prove it is ready for the real world.

Powering this entire stack is the sixth convergence: increasingly efficient Energy-to-Compute Pipelines. As the demand for intelligence grows, we are changing where and how we build the physical infrastructure. We are moving toward "grid-interactive" data centers located directly next to "stranded" power sources, such as a solar farm in a remote desert or a natural gas generator that isn't connected to a city grid. By converting this excess, unreachable energy directly into useful computation, we drastically reduce the cost variance of a floating-point operation (FLOP), creating a stable economic floor for intelligence.

Finally, the entire system is unlocked by Procurement Innovation. This is an economic shift in how organizations buy technology. We are moving toward outcome-based contracting, where organizations finally buy results instead of effort. Instead of paying a software vendor for a license or a consulting firm for hours worked, a city might pay a company only for the specific number of potholes fixed or the measurable reduction in traffic congestion.

Any single one of these convergences would be a significant historical event in isolation. Occurring together, they are flipping the entire system from a heroic, artisanal model defined by scarcity to an industrial one defined by abundance.

The Strategic Window: 18 Months to Sovereignty

We are currently living through a brief, critical period we call the "Regulatory Foundry Window." Think of a foundry where molten metal is poured into a mold. Right now, the metal is hot and liquid; we can shape it into anything we want. However, within the next 18 months, that metal will cool and harden. The decisions we make today regarding technical standards, data rights, and supply chains will set "path dependencies": permanent tracks that will guide (or constrain) the economy for decades to come.

This period is defined by four distinct pressures:

1. The Lock-In Effect: History teaches us that once technical standards are set, they are nearly impossible to change. Consider the QWERTY keyboard: it was designed in the 1800s to prevent mechanical typewriters from jamming, yet we still use it on digital touchscreens today simply because it became the standard. Similarly, the first credible Targeting Systems (benchmarks) that achieve widespread adoption today will define the "physics" of the new economy. If the first major benchmark optimizes for "ad clicks," the economy will bend toward advertising. If it optimizes for "scientific discovery," the economy will bend toward abundance.

2. Supply Chain "Seats": We are witnessing a rush for physical infrastructure that looks less like a market and more like a game of musical chairs. Early commitments for critical resources, specifically baseload energy, water for cooling, and advanced semiconductor packaging, are creating "privileged lanes." Companies and nations that secure these contracts now are effectively buying a seat at the table. Latecomers will find that these resources are not just expensive; they are simply unavailable at any price.

3. Data Economies & The Synthetic Shift: We are currently navigating a fundamental shift in the economics of intelligence. While early models relied heavily on historical human archives, the engineering frontier is rapidly moving toward synthetic data, where systems learn from high-fidelity simulations and self-generated reasoning. The critical economic challenge is now defining the interface between biological creativity and digital scale. The standards we establish today will determine how we value the unique human insights that serve as the ultimate ground truth, ensuring a sustainable ecosystem for both data creators and system architects.

4. Cultural Expectations: Finally, the first mass-market experiences with AI are setting the default "social contract." If the public learns to see AI primarily as a tool for cheating on homework or generating spam, that stigma will harden. If they experience it as a tool for personalized education and better healthcare, a different culture will emerge. These initial interactions are establishing the trust, or lack thereof, that will govern the next generation of technology.

The Scenarios: The Muddle vs. The Machine

The confluence of all these factors places us at a definitive fork in the road. We see three possible futures emerging from this moment.

Scenario 1: The Bright Path (The Abundance Machine): In this scenario, we successfully build the Industrial Intelligence Stack. We see a rapid proliferation of "Moonshots" targeted at the things that matter: climate, health, education, and energy. Governments and companies shift to "outcome-based procurement," meaning they stop funding vague research proposals and start paying for verified results. By 2035, this process compounds to the point where entire fields of engineering, medicine, and the formal sciences are considered largely "solved." We achieve abundance.

Scenario 2: The Muddle Path (Stagnation): This is the path of least resistance. We end up with fragmented standards and concentrated gains. We use super-intelligence for trivial things, like optimizing ad clicks, automating spam, and writing better grant proposals for broken, bureaucratic systems. We get efficiency, but we do not get abundance. The economy grows, but progress shows up primarily as corporate margin expansion (higher profits for companies) rather than public good (better lives for citizens). The bureaucracy absorbs the technology without changing.

Scenario 3: The Dark Path (The Freeze): In this scenario, a catastrophic safety incident, perhaps a cyber-attack or a biosecurity failure, leads to a global panic. Policies freeze, and capital flees the sector. The industrial engine for discovery stalls. The "Muddle" wins by using safety as an excuse to strangle progress with red tape. Simultaneously, physical constraints in energy and chip packaging bite hard, limiting growth. Meanwhile, "shadow AI markets" grow underground, unregulated and dangerous. The great Moonshots of our time go unaddressed.

"Why now?" Why is this the deciding moment? Because the physics finally allows it. The technology has matured to the point where solving these problems is possible. The window to shape the fundamental architecture of the next economy is open, but like the molten metal in the foundry, it is cooling fast.

05

Chapter 5

The Mobilization

This chapter is not a forecast of what might happen. It is a mobilization schedule for what must happen.

We are mapping out the "Solution Wavefront": the specific sequence in which different fields of science and industry will be solved. Understanding this order is critical because it dictates investment and strategy. You cannot build the roof before you pour the foundation. We map what gets solved when, why the order matters, and the critical infrastructure we must build first to make it possible.

The "Messy Middle": Infrastructure is Destiny

Before we can "solve the world," we must build the Industrial Base. Just as you cannot run high-speed trains on dirt roads, the wavefront of AI progress will stall without the right foundational infrastructure. This "Messy Middle" period consists of building three specific pillars.

1. The Scoring Systems (The Targeting Authorities)

First, we must build the scoreboards. We call these Targeting Authorities. These are standing public leaderboards for critical domains like health, climate, and safety.

The Mechanism: To work, these systems must use "blinded clears." This is similar to a standardized test where the student (the AI) has never seen the questions before. This prevents "cheating" (memorizing the answers) and ensures the model actually understands the problem.

The Audit: These scores must be paired with Decision Records for AI Systems (DR-AIS): permanent, unchangeable logs that show exactly how the AI made its decisions.

2. The Plumbing (Data and Action)

Second, we must build the pipes that move information and decisions.

Data Trusts: Most of the world's useful data is messy and locked inside institutions. A Data Trust is a legal and technical pipeline that converts this messy data into "reusable capital": clean, organized information that can be legally used to train models.

Action Surfaces: Intelligence is useless if it cannot act. We must build the APIs (software connections), robotic controllers, and contract protocols that allow AI agents to safely affect the real world. This is the "handshake" between the digital brain and the physical hand.

3. The Energy (Energy-to-Compute Capacity)

Finally, we must secure the fuel. We need a massive build-out of Energy-to-Compute (E2C) capacity.

The Strategy: In this new era, we stop treating data centers like office buildings and start treating them like aluminum smelters: heavy industrial facilities that must be co-located with clean power. We will place massive computing clusters directly next to solar farms or nuclear plants to run "schedulable" training jobs (work that can wait for the sun to shine). The strategic allocation of computing power becomes the primary driver of human progress.

In parallel we will begin to build and deploy massive orbital data centers constellations, the beginning of a Dyson Swarm, absorbing energy from our Sun 24x7, converting it into compute, and encircling our planet and our star in an ever-growing cloud of intelligence.

The Wavefront at a Glance

The mobilization of these technologies will not happen all at once. It follows a logical sequence, a "Solution Wavefront" that moves from the digital realm to the physical world, and finally to the massive systems that sustain humanity and our planet.

Phase 1 (2026-2027): The Era of Pure Information. The first wave conquers the digital realm. Mathematics is effectively solved; metrics on the hardest benchmarks, such as FrontierMath Tier 4, have shown a vertical rate of improvement, jumping from 13% to 19% in mere months. This progress implies we are approaching a saturation point where AI can verify formal logic better than any human. Following closely is Computer Science. We are entering a period where models routinely write, debug, and verify complex code with superhuman speed and accuracy. Finally, much of Physics will likely be solved in the next couple of years, as the tools of math and code allow us to mature a simulation stack that spans everything from sub-atomic quantum particles to astrophysical galaxies.

Phase 2 (2028-2031): The Era of the Physical World. Once we can perfectly simulate the laws of physics, we can master matter itself. This phase is defined by the solution of Chemistry and Materials Science. We will see "Closed-Loop Labs" running 24/7, where the only metric that matters is Time-to-Property: how fast we can go from a digital idea to a physical sample. This paves the way for the solution of Biology in the later half of this phase, right on schedule as predicted by Ray Kurzweil. We are currently waiting for the "Virtual Cell" to trigger the final collapse of this domain. Once we have a high-fidelity simulation of a cell, an organ, and eventually an entire organism, biology effectively transitions into a software problem. The ultimate result is our full understanding, and the subsequent curing, of all disease. Beyond curing disease we will also learn why humans age, how to slow, stop and eventually reverse the process.

Phase 3 (2032-2035): The Era of Planetary Systems. With mastery over materials and biology, we can finally tackle the massive, chaotic systems that support human civilization. This is the era of Energy and Infrastructure. We will deploy Fusion, Fission, and Geothermal energy at scale, integrated with a massive, robotically deployed solar fabric spanning hundreds of kilometers. Buffered by grid-scale storage, this infrastructure makes intermittent renewables indistinguishable from baseload power. Simultaneously, the electrical grid will be transformed from a dumb copper network into a software problem, orchestrated by "schedulable compute" that balances supply and demand in real time. Finally, we will expand our systems beyond the surface of Earth, into orbit, the Moon, Mars and asteroid belt.

Domain Deep Dives

We now examine exactly what "solved" looks like in the seven critical domains that define our future.

1) Mathematics & Software: The Foundation

The first domain to fall is the one that builds all others. As noted, Mathematics and Computer Science are effectively solved. However, "solved" in this context means something specific: the transition from probabilistic guessing to formal verification.

In the past, we had "copilots" that might write code with bugs. In the solved state, specifications become executable contracts. This means a human engineer writes down exactly what the software must do (the contract), and the AI agent writes the code and mathematically proves that the code satisfies the contract. We move to "Agentic Toolchains" that produce formally verified software by default.

The benchmarks for this industrialization will be uncompromising. We will track the Spec-to-Artifact Score, which measures the percentage of software modules that provably meet their specification on the first try. We will measure Proof Robustness by subjecting code to "adversarial perturbations": deliberate attacks designed to break the logic without changing the input. In a solved world, the Defect Rate in safety-critical software repositories must asymptote near zero.

2) Physics & Cosmology: The Laws of Reality

With the tools of formal proof in hand, we turn to the laws of the universe. A "solved" physics domain is defined by an industrial simulation stack that spans all scales, from the quantum to the astrophysical, all with traceable error bars.

The way science is done will fundamentally change. Competing theories, such as String Theory versus Loop Quantum Gravity, will no longer be debated in academic papers. Instead, they will be compared by their Predictive Loss on shared data corpora. We will feed the theories raw data from telescopes and particle accelerators and see which one predicts the next observation with the least error. Instrument pipelines will stream directly into these models, with AI agents automatically generating new experiment designs optimized against budget and time constraints.

We will measure success by Predictive Cross-Validation, which is the model's accuracy on data it has never seen before (like a withheld portion of the sky). We will also track a Unification Score, rewarding models that can explain multiple phenomena (like gravity and electromagnetism) with a single, simple theory.

Guardrail: We must publish Decision Records for AI Systems (DR-AIS) for all analysis pipelines. Furthermore, we require independent "Replication Packs": downloadable files that allow any third party to re-run the analysis and verify the result, ensuring science remains open and reproducible.

3) Chemistry & Materials Science: The Digital Alchemy

From the abstract laws of physics, we move to physical matter. Solving chemistry means achieving "Inverse Design." In the old world, we mixed chemicals and measured their properties. In the solved world, we tell the computer the property we want (e.g., "a battery electrolyte that does not catch fire"), and the AI calculates the molecular structure that achieves it.

Dark laboratories will close the "design-make-test" loop, turning discovery from a years-long academic quest into a daily industrial operation. We will see commodity materials libraries for specific properties, like ion conductivity or fracture toughness, baked directly into the robotic rigs. The defining benchmark is Time-to-Property ( TtP ): the number of hours it takes to go from a target vector (the wish list) to a validated physical sample.

The obvious risk here is dual-use chemistries: tools that can design cures can also design toxins. This necessitates strict gating of export-controlled pathways, using on-device safety models and continuous compliance streams to flag dangerous requests.

4) Biology & Medicine: From Sick Care to Health Maintenance

The "solved" state for biology is a world where care shifts from episodic (fixing you when you break) to continuous maintenance (keeping you from breaking). Biology is waiting for the Virtual Cell to trigger the final collapse. Once we have a high-fidelity simulation of a cell, an organ, and an organism, biology effectively becomes software. We can "debug" a disease in a computer before we ever touch a patient.

The vision is a radical departure from traditional "sick care." Traditional hospitals respond to patients after disease onset. The new system uses fully predictive models to detect disease at inception, or better yet, predict oncoming disease and enable prevention. Capital and data will shift toward Healthspan Extension, supporting discoveries of why humans age, how to slow, stop and eventually reverse the process allowing us to reach "longevity escape velocity," where science adds more than one year of life for every year that passes.

The benchmarks will be life-changing. We will track Time-to-Therapy (TTT), measured in days from diagnosis to personalized intervention. We will measure Outcome Uplift, the reduction in mortality and morbidity, tracked with strict fairness bands to ensure all demographics benefit equally. And we will track Biofab SLAs: guarantees on the delivery times and rejection rates for manufactured organs and tissues.

5) Engineering & Manufacturing: The Speed of Information

Solving manufacturing means compressing the physical supply chain to the speed of information. The "solved" state is Design-to-Part-to-Verification in under 24 hours (D2P24).

Imagine sending a digital file to a factory. A "lights-out" microfactory (a facility with no humans inside) receives the file, produces the part, verifies its quality, and ships it. Digital twins that are perfect virtual copies of the factory and product will be the non-negotiable source of truth. Compliance becomes a live data stream proving quality in real-time, not a PDF report filed weeks later.

Key Benchmarks:

D2P24: The percentage of products delivered within 24 hours of specification with first-pass yield.

Zero-Defect Corridor: A guarantee that defects will remain below a certain parts-per-million (ppm) count, bounded with real-time root-cause proofs.

Sustainability Ledger: Attested kilowatt-hours per kilogram and embodied carbon per unit.

6) Planetary-Scale Challenges: Industrializing Stability

In the domains of Energy, Climate, Food, and Water, "solved" means the industrialization of stability.

The electrical grid will cease to be a dumb copper network and will become a software problem. "Schedulable Compute" (data centers) will act as a primary demand-response asset. This means data centers can ramp their power usage up when the sun is shining and wind is blowing, and ramp it down instantly when supply is low. They act like a giant virtual battery, stabilizing the grid.

Clean baseload power will grow via next-generation fusion, fission, and flexible renewables, accelerated by solar transitioning into “infrastructure-as-code.” Autonomous swarms will deploy gigawatts of capacity per month at a marginal cost approaching the price of raw glass and silicon. When paired with high-density chemical storage, this creates ubiquitous terrestrial 24/7 electricity. Direct Air Capture (DAC) will reach price transparency, becoming a true commodity where we pay for carbon removal by the ton. Food production and water purification systems will close their loops, integrating synthetic proteins, precision fermentation, and vertical farms to create a system where waste becomes nutrients for the next cycle.

Key Benchmarks:

The Energy-to-Compute (E2C) Index: Useful cognitive work per kilowatt-hour by region.

Reliability SLAs: Minutes of power outages avoided per year.

The CO₂e Ledger: Dollars per ton of carbon (equivalent) durably removed, verified by third parties.

7) Humanities & Social Domains: Augmentation and Justice

In pluralistic domains like Education, Law, and Governance, "solved" does not mean we have found a single final answer. It means we have industrialized the tools for augmentation and justice.

In Education, this means the digitization, demonetization, and democratization of learning. Every child on Earth will have access to the best education delivered by personalized agents over 5G/6G networks on their smartphones and glasses. In Law and Governance, it looks like "Policy Sandboxes": simulation environments that run automatic impact tests before a law is passed, much like testing software before release. We will have "Continuous Compliance" systems that check if regulations are being followed in real-time, and citizens will have the power to audit these systems via open Decision Records.

The Playbook to Make the Wavefront Real

This future is not inevitable; it must be built. The operational playbook below is straightforward, imperative, and actionable.

1. Publish Targeting Systems, Then Budgets: We must stop writing vague checks. Tie all funding to "Blinded Clears" on public targets. This means the model is tested on data it has never seen before, preventing cheating. Attach "Red-Team Bounties" to reward those who find safety flaws.

2. Stand Up Action Networks: Intelligence needs hands. Fund shared robotic labs, microfactories, and clinical devices with open scheduling and outcome-based Service Level Agreements (SLAs). This gives AI agents the ability to affect the physical world safely.

3. Compute Escrow: Money talks, but compute acts. Pre-commit training and inference credits that are unlocked only when a team hits a specific benchmark gain. This aligns incentives perfectly with progress.

4. Outcome Procurement That Only Funds Results: Stop funding slide decks. Write contracts that pay only for verified results. If the pothole isn't fixed, or the patient isn't cured, the payment doesn't release.

5. Instrument for Trust: To ensure these systems remain safe, trust must be engineered rather than assumed. We accomplish this by mandating Decision Records for AI Systems (DR-AIS), which act like a "black box" flight recorder for algorithms, creating a permanent, unchangeable forensic log of exactly why an AI made a specific high-stakes choice. Crucially, these logs must be wired directly to Programmatic Down-Shifting, an automatic safety brake similar to the mechanical governor on an elevator. If the live data shows that an AI's safety, bias, or reliability scores have dropped below a set threshold, the system doesn't wait for human permission to act; the code automatically throttles the AI back to a safer operating mode or halts it entirely, preventing harm the instant a regression is detected.

06

Chapter 6

The Engine

In the previous chapters, we defined the thesis, the stack, and the timeline. Now, we must discuss the engine. The primary mechanism for driving this revolution are Benchmarks, they are our Targeting System.

It is crucial to understand that benchmarks are not merely scoreboards. In the old model of AI, we used benchmarks to look backward and record who was winning. We treated them like a scoreboard.

In the new model, we build Targeting Systems to predict, direct, and implement a future. A Targeting System is not passive; it is an active guidance mechanism. When we make a measurement legible (clearly defined), adversarial (hard to cheat), and above all, payable (tied to a financial reward), human and machine cognition automatically routes itself to solve that challenge. Where the target is set, capital follows. And where capital follows, capabilities compound.

This dynamic creates the Abundance Flywheel, the central mechanism for industrializing discovery. It operates as a virtuous 5-step cycle:

Commitment: Pre-committed compute is aimed at a specific, hard problem.

Focus: Research and Development (R&D) efforts are intensely focused until that target "Clears" (meaning a team finally passes the rigorous threshold).

Collapse: This ‘clear’ triggers a "Domain Collapse," shifting the field from an artisanal craft to an industrial process.

Surplus: This industrialization generates massive economic "Surplus," value created because the cost of doing the task has plummeted.

Reinvestment: Finally, that surplus is reinvested into even more compute, which is then aimed at the next, harder target.

This chapter makes that flywheel concrete. It specifies exactly how to build the targeting systems that will industrialize progress rather than merely report on it.

From Leaderboards to Targeting Systems

The first era of AI "leaderboards" was a necessary prelude, but that era is over. Those early benchmarks were narrow, academic, and easily "saturated," with AIs eventually achieving a perfect score.

The next era belongs to Targeting Systems. These are not static tests; they are engines that are prospective, blinded, and anti-gaming by design. A true Targeting System acts like a living weapons system. Following are five attributes of such a system:

1. Outcome-Grounded Optimization: A Targeting System does not care about abstract statistics like "accuracy on a dataset." It optimizes for real-world value. For example, an education-focused Targeting System doesn't measure how well an AI answers a multiple-choice question. It measures "Learning Gain per Hour": did the human student actually learn faster? In healthcare, it measures "Risk-Adjusted Clinical Outcomes": did the patient actually get better without side effects?

2. Prospective and Blinded Testing: A Targeting System is prospective, meaning it tests models on events and population cohorts that did not exist when the model was trained. This prevents the AI from simply memorizing the past. A weather prediction model, for example, is not tested on last year's hurricanes (which it could memorize). It is tested on next week's weather, where the answer is currently unknown to everyone.

3. Adversarial and Anti-Gaming: A Targeting System is adversarial, funded with a "red-team budget": money set aside specifically to hire experts (or other AIs) to try to trick, break, or "game" the system. The system constantly injects hard cases and shifts the data distribution to ensure the model isn't just getting lucky.

4. Auditable and Equity-Constrained: It is auditable, requiring public Decision Records for AI Systems (DR-AIS) and "replication packs" so that any claim of success can be verified by a third party. Critically, it is equity-constrained. Fairness bands and subgroup floors are built directly into the win condition. For example, in the case of a medical AI that cures cancer in 99% of patients but fails for 100% of a specific minority group, the Targeting System would mark it as a failure. Fairness is not an afterthought; it is a prerequisite for victory.

5. Continuous Operation Finally, it is continuous. It allows for rolling submissions and automated scoring 24/7, rather than being an annual pageant or competition.

The Abundance Flywheel in Motion

The flywheel is the engine that converts "compute as a utility" into "abundance as a reality." Here is how the cycle works in practice.

Step 1: Compute as Working Capital. It begins by treating compute as a form of financial capital. An organization pre-commits a budget for training and inference into an escrow account. This isn't just a vague promise of funding; it is a line item that is locked and ready to be deployed against a specific target.

Step 2: Focusing Effort. This money creates a gravity well and the target becomes the public "API surface" between ambition and capital. Because the reward is clear and the test is fair, the entire world is invited to compete. This focuses all Target-Aligned R&D in one direction, preventing wasted effort on problems that don't matter.

Step 3: Domain Collapse. This intense focus leads to Domain Collapse. The moment the right target is "cleared" (solved), the problem tips. Latent capacity floods into the field. What was once a heroic, artisanal feat, like discovering a novel drug, writing a formal mathematical proof, or designing a new material, suddenly becomes a routine, automated service.

Step 4: Surplus Capture This industrialization unlocks enormous Surplus Capture. The unit cost for the service plummets, while the quality of the outcome improves. This opens up entirely new business models. In this model, we move from paying lawyers by the hour to using "Pay-Per-Outcome" contracts. We move from hoping for electricity reliability to enforcing "Reliability SLAs" (Service Level Agreements) where the provider pays you if the lights flicker. We move to "Guaranteed Learning-Gain Floors" where schools are paid only if students learn.

Step 5: Reinvestment and Safety. Finally, this new value is Reinvested. The money saved is poured back into funding more compute, collecting richer datasets, and building broader "action surfaces" (robots and APIs). This new capital is aimed at the next, harder target, and the flywheel spins faster.

Take note that this entire process only compounds if the guardrails are programmatic. Safety is not a separate step; it is a mechanical component of the engine. We must install automatic "downshift" mechanisms and kill-switches. If the system detects a regression in safety or equity (i.e., if the AI starts making biased decisions or dangerous errors), the flywheel's governor kicks in and slows the system down instantly. This ensures we don't just go fast; we go fast safely.

How to Build an Engine, Not a Scoreboard

There is a fundamental difference between measuring progress and creating it. A poorly designed target creates nothing but paperwork and bureaucracy. A well-designed one creates an engine of progress. To ensure we build the latter, the following three design principles are strict and non-negotiable.

Principle 1: Bind it to a Real-World Outcome. We must stop measuring "proxies," which are metrics that look like success but aren't. We must measure the actual result we want in the physical world.

In Healthcare: Do not measure "number of patients seen." Measure Time-to-Therapy (TTT) (how fast did they get treated?) and Risk-Adjusted Readmissions Avoided (did they stay healthy, or did they bounce back to the hospital?).

In Education: Do not measure "hours of class time." Measure Learning Gain per Hour (LG/H). Furthermore, verify it with retention floors that check if the student still knows the material 30, 60, and 180 days later.

In Infrastructure: Do not measure "uptime." Measure Reliability Minutes Avoided and Avoided-Loss Dollars per Megawatt. How much money did you save the economy by not crashing the grid?

Principle 2: Make it Prospective, Rolling, and Resistant to Gaming. If you test a student on last year's exam, they will memorize the answers. The same is true for AI. We must "freeze" a scoring harness that only admits future data.

The Method: Run the test weekly, not yearly. This prevents "teaching to the test."

Goodhart-Resistant Design: We must prevent the AI from optimizing one metric at the expense of others (e.g., being fast but dangerous). To do this, we use Multi-Objective Scoring that optimizes a "Pareto Frontier." This means the AI only wins if it improves accuracy without sacrificing safety, latency, equity, or cost.

Calibrated Abstention: We must reward the model's "right to remain silent." If an AI's uncertainty is high, it should get points for saying, "I don't know," rather than guessing.

Hidden Test Sets: The test data must be managed by independent third-party stewards and constantly shifted so no one can game the system.

Principle 3: Attach Automatic Payment and Publish the Artifacts. A target clear must be a contractual event, not a suggestion.

The Payment: When the target is hit, the system must automatically unlock the reward, whether that is compute credits, cash rebates, or outcome-based fees. There should be no human gatekeepers and no "RFP theater" (Request for Proposal delays).

The Price of Winning: In return for the money, the winner must pay with transparency. They must ship a "Replication Pack": the code, the evaluation scripts, the "ablations" (breakdowns of what parts worked), and the Decision Records for AI Systems (DR-AIS) that state exactly what changed and why.

Case Patterns: How Targets Tip Domains

This is not a theoretical model or a wish list. We have already seen this pattern work in the real world. Here are four specific examples where a Targeting System tipped a domain from an art into a science.

1. AlphaFold (Structural Biology): This is the archetype example. The ingredients were simple:

A Clear Target: Predict the 3D structure of a protein from its amino acid sequence.

A Shared Corpus: The Protein Data Bank (massive training data).

A Public Competition: CASP (Critical Assessment of Structure Prediction). These conditions created a discontinuity. Once the target became credible, DeepMind poured scaled compute into the stack, and the domain collapsed. The release of the open artifact allowed the entire world to compound the win, accelerating biology globally.

2. Tutoring (Education): This will be one of the next domains to fall. Currently, schools buy software licenses. Soon, they will buy "Learning Gain per Hour." The market will re-align overnight. AI Copilots that provably beat the learning-gains per hour (LG/H) floor will be procured automatically. Those that drift or fail to teach will be automatically "downshifted" (removed). Public dashboards will create a tournament of ideas that pays the students first, not the vendors.

3. The Reliability Pattern (Power Grids): This pattern will solve our energy infrastructure. "Dispatcher Agents" (AI systems that manage power flow) will compete to reduce outage minutes.

The Test: Models will have to prove their resilience under adversarial weather (simulated hurricanes) and massive demand spikes (simulated heatwaves) inside the harness.

The Reward: Clearing the reliability target will earn the AI agent a "Capacity Contract" to manage a portion of the grid. Failure will trigger automatic throttles to prevent real-world blackouts.

4. The Time-to-Property ( TtP ) Pattern (Materials Science): This pattern will industrialize the laboratory. With the amount of time and compute (Time-to-Property) required to make the discovery becoming the North Star metric.

The Shift: Instead of measuring "how many papers you published," we measure "how many hours it took you to find a material with Property X" (e.g., a specific conductivity or strength).

The Mechanism: Robotic rigs with built-in MRV (Measurement, Reporting, Verification) will automatically upload results to the scoreboard. Agents that clear TtP targets on new chemistries will automatically unlock batches of lab time and compute credits to keep going.

The Institutions That Make the Engine Run

The Abundance Flywheel does not spin in a vacuum. It requires a new set of "primitives": the fundamental building blocks of the institutional infrastructure that supports the new economy. Just as the industrial economy needed banks, limited liability corporations, and property rights to function, the abundance economy relies on five specific structures we’ve described throughout this essay thus far.

1. Targeting Authorities: These are the public-private bodies responsible for defining the rules of the game. They define, host, and govern the Abundance Targets. Think of them as the modern equivalent of a standards body like NIST or ISO, ensuring the targets remain relevant and fair.

2. Data Trusts: We need legal and technical wrappers that turn raw data into safe fuel. A Data Trust is a system that holds high-quality data with consent. It uses code to enforce "privacy budgets" (limiting how much information can be extracted) and "revocation rights" (allowing a user to pull their data back). It transforms messy, risky data into a lawful asset with a clear lineage.

3. Action Networks: Intelligence needs a physical body to affect the world. Action Networks are shared facilities, such as robotic laboratories, microfactories, and clinical device hubs, accessible via API. They allow a "winner" of a Targeting System to instantly translate their digital solution into a physical reality, effectively giving a software agent hands and feet without requiring it to own a factory.

4. Compute Escrow: This is a new financial primitive. Instead of writing a check, funders place training credits or cash into a "Compute Escrow" account. This is a smart contract that programmatically releases funds only when a specific threshold is cleared. Crucially, it creates accountability; the system can "claw back" funds if the performance subsequently regresses, aligning financial incentives perfectly with sustained performance.

5. Outcome Procurement: Finally, we must overhaul government and corporate contracting. We are retiring the old model of paying for "deliverables" (reports, meetings, effort). In its place, we establish contracts that pay strictly for verified outcomes. If the pothole is fixed, the payment clears. If the student learns, the school is paid.

The New Economic Dashboard: We will know this system is working not by looking at traditional GDP, but by tracking a new set of economic indicators. The new "jobs report" will focus on metrics like RoCS (Return on Cognitive Spend), which measures the value created per dollar of compute. We will track the D2P24 (the percentage of designs that turn into verified parts in 24 hours) and the E2C Index (useful cognitive work per kilowatt-hour). We will monitor the CO₂e Ledger (the cost per ton of carbon durably removed), and the LG/H (Learning Gain per Hour). These numbers tell us if we are actually building abundance or just spinning our wheels.

Failure Modes and Programmatic Safety

This engine is powerful, but it has specific "failure modes" regarding ways it can break or be gamed. We must build programmatic safety measures to counter them effectively.

1. Countering "Spec Capture": One major risk is "Spec Capture," where the metric stops reflecting the true mission, similar to "teaching to the test" in schools. This happens when an AI optimizes for the score but ignores the real-world goal. We fix this by publishing explicit maps that link Purpose, Task, and Metric, and by rotating independent stewards. By constantly refreshing the people and the tests, we prevent the system from becoming stagnant or gamed.

2. Countering Data Leakage: Another risk is cheating via "Data Leakage," where a model performs well because it has memorized the answers from the test set. We fix this using "rolling, cryptographically-committed holdouts." This means the test questions are kept in a digital vault that the AI cannot see until the moment of the exam, ensuring that every pass is a genuine display of capability.

3. Countering Monoculture: A subtle but dangerous risk is "Monoculture," where everyone relies on the same single AI model. If that model has a bug, the entire system crashes. We fix this by mandating "multi-compiler and multi-toolchain rules" for safety-critical domains.

4. Countering Reliability Variance: Finally, we must prevent "Performance Drift," where a system optimizes for the "easy" majority while failing on edge cases or specific cohorts. In a solved world, high variance is a defect. We fix this by enforcing Universal Quality Floors. If a model improves the aggregate score but allows reliability to degrade for any specific segment of the population, it is rejected as unstable. We use "automatic throttling" to arrest the system the moment such variance appears, ensuring that abundance is delivered as a reliable standard, not a lottery.

07

Chapter 7

The Moonshots

This chapter is the battle plan. We have identified fifteen specific "Moonshots": ambitious, targeted missions designed to act as shaped charges aimed at a grand challenge, at a “mega-XPRIZE”. Ultimately, these projects are designed to force the industrialization of entire scientific or engineering domains.

The goal is not just to win the prize; the goal is the "spillover." When you solve the hardest problem in a field (the prize), you inadvertently build the tools to solve every other problem in that field (the spillover).

How We Bulk-Solve a Domain: The process of solving these domains follows a precise, four-step industrial logic:

Legibility: First, an AI system ingests the entire domain. It reads every paper, patent, dataset, and simulation ever produced, turning human knowledge into machine-readable data.

Tractability: The system then maps the Task Taxonomy (as described in Chapter 2). It breaks an impossible, vague problem like "cure aging" into a million solvable, benchmarked sub-problems, like "repair this specific protein" or "clear this specific waste product."

Bulk-Solution: AI agents explore the solution space for each of these sub-problems simultaneously. Because the cost of trying a new idea collapses toward zero, the AI can test millions of hypotheses in the time it takes a human to test one.

Automation: Finally, this digital pipeline connects to Action Networks (robots and labs) to turn digital solutions into physical reality.

The prize is just the first, hardest target. Achieving it means the AI-driven engine is complete. Once built, that engine can be pointed at every other problem in the field, effectively making the rest of the domain a matter of simple computing power.

We group our fifteen Moonshot projects into four categories: Human Needs, the Frontier of Mind, the Planetary Substrate, and the Frontier of Physics.

Part 1: Human Needs (The Basics)

We begin with the fundamentals of human life: the body, the means of survival, and the capacity to learn. These projects industrialize biology, agriculture, and pedagogy.

Part 2: The Frontier of Mind

We transition now from the hardware of the body to the software of the mind. These projects industrialize neuroscience, utilizing AI to bridge the gap between biological intelligence and digital substrates.

Part 3: The Planetary Substrate

We now scale our ambition from the individual to the planetary systems that support civilization. These projects industrialize geoscience, ecology, and energy, transforming Earth management from a reactive struggle into a proactive engineering discipline.

Part 4: The Frontier of Physics & Expansion

Finally, we aim the charge at the very laws of reality and our place within the cosmos. These projects industrialize materials science, manufacturing, and fundamental discovery, using AI to manipulate the building blocks of the universe.

08

Chapter 8

The Muddle vs. The Machine

Thesis: When thinking power becomes as cheap and ubiquitous as electricity, the core challenge of civilization shifts. We stop worrying about how to get things done and start worrying about what we should do and why. Scarcity is no longer about muscle, raw data, or even expert hours; it is about purpose, agreement, and safety. The old world, built on GDP, 9-to-5 jobs, and bureaucratic friction, is incompatible with the new world of Abundance, Purpose, and Automation. We must architect new rules to keep this transition safe, fair, and growth-oriented.

We have established a world where AI-driven platforms are solving entire domains, from medicine to energy. The "how" is the Industrial Intelligence Stack. The "engine" is the Abundance Flywheel. The "targets" are the Moonshots. Now, we must answer the most difficult question of all: What happens after we win?

What Abundance Does to an Economy

The Problem: Traditional metrics like Gross Domestic Product (GDP) are becoming obsolete. GDP measures the cost of activity. In an economy where the cost of thinking, creating, and solving problems plummets toward zero, GDP may actually shrink even as human capability explodes.

Example: If an AI cures heart disease for pennies, the trillion-dollar pharmaceutical industry shrinks. GDP goes down, but human welfare goes up. We need a metric that captures this reality.

The Solution: We must shift from measuring Transaction Volume (money changing hands) to measuring Potential (capacity to solve problems). Nations and cities must begin publishing Capability Accounts: real-time balance sheets of their productive power. These reports will replace GDP as the signal that attracts capital, talent, and industry.

The Abundance Capability Index (ACI)

Instead of quarterly earnings, nations will compete on the ACI, which tracks:

Energy-to-Compute Advantage: How efficiently does a nation convert raw energy into useful intelligence? In an economy built on thought, energy is the new oil, and compute is the new steel. High ACI requires massive, clean, cheap energy grids feeding local data centers.

Targeting Advantage: The velocity of improvement. How fast are a nation’s robots, healthcare systems, and educational tools improving? This is measured via transparent, real-world performance benchmarks (e.g., "Our cancer survival rates improved 4% this month").

Data Advantage: The presence of trusted "Data Trusts." Does the nation have the legal framework to allow citizens to safely pool their genomic or financial data to train AI models without losing privacy?

Outcome Procurement: A measure of government efficiency. What percentage of the budget pays for results (cleaner air, cured patients) versus paying for effort (hours worked, reports written)?

Work, Status, and the Division of Labor

The Shift: The "job" defined as a bundle of repetitive tasks is dead. The expert human is no longer a "prompt engineer" (someone who just asks the AI questions); they are a Conductor of Intelligence.

From this transition, three new high-status archetypes emerge:

The first is the Explorer of Purpose. The AI is a powerful optimization engine, which can figure out how to get anywhere, but it cannot (yet) decide where we should go. The Explorer sets the "North Star." They translate human values into the mathematical objective functions that drive the machine.

The second is the Ethical Anchor. This professional holds the "Kill Switch." They are the compliance officers of the cognitive age. They design the safety constraints and maintain the immutable "black box" logs of every decision the AI makes. They have the absolute authority to hit the "slow-down" button when a system begins to hallucinate or drift from its safety guardrails.

The third is the Creator of Meaning. As material scarcity recedes, the value of human connection skyrockets. When "perfect" content is free, we crave the imperfect, messy, human story behind it. These are the architects of culture, art, community, and care. An AI can generate a pop song, but a Creator builds the live experience and the tribal connection between fans that an algorithm cannot simulate.

This transition creates new roles: target designer, professional "ethical hacker" paid to find flaws, data-rights auditor, outcome-based contract manager, and human judge for AI disputes. Career ladders will be based on the importance of the problems you've solved, not the number of people who report to you.

New Career Ladders: Corporate hierarchy is being inverted. Status will no longer be defined by "Headcount" (how many people report to you). In an AI world, a large headcount implies inefficiency and high cost. Instead, status will be defined by "Compute-Count" (how much processing power you direct). The new CEO might run a billion-dollar company with only three human employees but millions of GPU hours. New Roles include: Target Designers who translate fuzzy business goals into precise, machine-solvable math; Fairness Auditors who "Stress-test" AI models to find hidden biases before they go live; Data-Rights Brokers who manage data assets for individuals, ensuring they get paid when their data is used; Outcome Contract Managers who negotiate deals based on results (e.g., "potholes fixed") rather than hours billed; and AI Dispute Mediators who investigate liability when two autonomous agents crash or fail.

Distribution: Floors, Freedom, and Feedback

The Problem: Abundance is not abundance if it is sequestered. However, the old model of redistribution (taxing income to send cash checks) is too slow and imprecise for an automated economy.

The Solution: The "New Abundance Contract" is built on three pillars:

Floors (Universal Basic Capability): Not just UBI (cash), but UBC. This guarantees every citizen access to the results of the solved domains: free, high-quality AI education, AI healthcare, and clean energy. You don't just get money to buy a doctor's visit; you get the doctor (the AI) directly.

Freedom (Compute Allowances): Give every citizen a "Compute Wallet" with a monthly allowance of processing power and access to open-source foundation models. This gives everyone the agency to build their own business, art, or tools, preventing a divide between the "AI Haves" and "AI Have-Nots."

Feedback (Fairness Dashboards): A system of public, real-time dashboards that monitor the outcomes of society. If a specific neighborhood is suffering from worse air quality or lower educational attainment, the system automatically flags it and redirects resources.

Power, Monopolies, and the Politics of Cognition

The Risk: When "thinking" becomes the new utility, the easiest way to centralize power is to own the "Electric Company." The danger is not a single “Terminator” robot; it is a single corporation owning the measurement systems and the "rails" that all society runs on.

The Counter-Design: To preserve a free society, we must enforce three structural rules:

Mandate "Open Rails": Just as different email providers can talk to each other, AI assistants must be interoperable. We cannot allow a "walled garden" where a medical AI cannot speak to an insurance AI because they are owned by rivals.

The "Two-Source" Rule: For critical domains (medicine, energy, justice), any high-stakes decision must be confirmed by at least two independent AI models trained on different datasets. This "Second Opinion" protocol prevents a single algorithmic flaw from causing a catastrophe.

Data Fiduciaries: We must establish neutral "Data Trusts" that hold public data (traffic, health trends) in escrow. These trusts grant access to any team that wants to solve a public problem, preventing data monopolies where only the incumbents have enough data to innovate.

Antitrust 2.0: Regulators must stop trying to pick "winners" or break up companies based on size alone. They must regulate the interfaces, ensuring that the rails remain open and that no one can turn off a competitor's access to the grid.

Safety, Alignment, and Misuse

Our Core Safety Thesis: We believe the most effective defense against AI risk is Attraction. By routing the vast majority of our computing power and top talent into public, clearly defined Moonshots (like curing cancer or solving fusion), we effectively "starve" malicious actors of the resources and brainpower needed to create harmful systems.

However, beneficial intent is not enough. The systems themselves must be robust. The guardrails cannot be voluntary checklists; they must be automatic, universal, and code-based.

1. Public Decision Logs (Transparency): Every major AI system (in healthcare, finance, or justice) must generate an immutable, read-only log of why it made a decision. It’s not a black box; it’s a glass box. If an AI denies a loan or recommends a surgery, we need to be able to audit the logic. This prevents "algorithmic drift" where a system slowly becomes biased over time without anyone noticing.

2. Epistemic Humility ("I Don't Know"): We must engineer systems that recognize the edge of their own competence. Instead of hallucinating a confident (but wrong) answer, the AI must be programmed to say, "My confidence is low; I am escalating this to a human expert." This solves the "silent failure" problem. An AI that knows what it doesn't know is safer than a genius that never asks for help.

3. Red-Team Endowments (Incentives): Safety cannot be an afterthought. We must establish large, permanent financial endowments that pay "white hat" hackers and researchers to attack our systems. If the only people paid to find bugs are criminals, the criminals will win. We must create a market where finding a flaw in the electric grid's AI pays more than exploiting it.

4. Kill/Slow Switches (The Safety Brake): These are not physical plugs in a wall. They are software-based "Circuit Breakers" embedded in the infrastructure. If a system's behavior deviates from its safety parameters (e.g., trading velocity becomes too high, or chemical mixtures become unstable), the switch triggers automatically. It instantly downgrades the AI's permissions from "Action Mode" (doing things) to "Draft Mode" (suggesting things), requiring human override to proceed. Importantly, independent watchdogs, not just the company owners, must hold a key to this switch.

The Geopolitics of Abundance

The Shift: On the global stage, the fundamental "factors of production" have been rewritten. Industrial strategy is no longer about securing access to coal or deep-water ports. Compute is the new Steel, the raw material from which all economic value and military defense is built. Energy is the new Land, the finite, strategic resource that limits how much intelligence you can generate.

The nation that solves the "Biology-Energy-Compute" equation first wins the century. This is a hard security issue, and here’s the strategy for the United States or any nation who dares to claim it:

Energy-to-Compute Alliances: We must co-locate massive data centers with sovereign clean power plants (fission/fusion/solar) and create "Compute-Lend-Lease" agreements. Just as we shipped tanks to allies in the 20th century, we will stream high-fidelity medical and industrial intelligence to allies in the 21st, binding their economies to our "stack."

Standards Diplomacy: The most powerful empire is the one that writes the rulebook. We must export trusted "rails," including the safety protocols, data standards, and API definitions, that the rest of the world builds upon. If the world adopts our safety standards, they play by our rules.

Secure Openness: The old model of national security was Secrecy ("hide the technology"). The new model is Resilience ("distribute the technology"). We move to "Open Procedures, Private Keys." We encourage the use of AI from multiple, verified sources to avoid reliance on a single provider (like a single weak dam). Secrecy is fragile; true security comes from a multi-source, redundant network that cannot be decapitated.

This future is not guaranteed, it is engineered. And like any complex engineered system, it has specific, known failure modes that we must design against.

The Four Failure Modes:

Spec Capture (The "Teaching to the Test" Trap): This occurs when the measurement stops reflecting the real-world mission. For example, if we pay schools solely for high test scores, they stop teaching critical thinking and only teach test-taking. The AI maximizes the metric but destroys the intent.

Monoculture (The "Potato Famine" Risk): This emerges when one single AI model dominates an entire sector. If every hospital uses the exact same diagnostic AI, a single hidden bug or bias affects every patient simultaneously. We need "biodiversity" in our algorithms.

Coverage Drift: The tendency for gains to pool at the top. The rich get bespoke, longevity-focused AI doctors, while the poor get generic, hallucinating chatbots. Bias becomes hard-coded into the infrastructure.

Outcome Gaming (The "Cobra Effect"): When an actor manufactures a problem just to get paid for "solving" it. Imagine for example, if you pay people to catch cobras, they will start breeding cobras to collect the bounty. The system must verify the source of the problem, not just the solution.

The Playbook: To build this future while mitigating the above risks, we run the Social-Scale Operating System described throughout this essay:

Publish the Rails: Stand up public "Targeting Authorities" that define the goals (e.g., "Cure Alzheimer's") without dictating the method.

Pay for Outcomes: Replace bureaucratic grant proposals with guaranteed payments for verified results. Don't pay for the research paper; pay for the cure.

Stand Up Action Networks: Build the shared physical infrastructure, including robotic labs, testing grounds, and factories, so innovators can move bits into atoms.

Escrow Compute: Create "Compute Trusts" that automatically release processing power to anyone who solves a public problem, democratizing access to the means of production.

Guarantee Floors & Freedom: Enact Universal Basic Capability (access to solved domains) and Compute Allowances (agency to build).

Teach the Rails: Make "Civic Intelligence Literacy" a national priority. Citizens must understand how to query the system, how to audit the logs, and how to challenge the machine.

09

Chapter 9

Build the Rails

Thesis: Abundance will not arrive by inspiration alone. It arrives when we build the "rails": the public scorecards, the data-sharing agreements, the robotic "action networks," the pre-funded compute, the outcome-based contracts, and the transparent decision logs. We must build these rails and then aim them at the problems that matter. This is the ignition sequence.

The Gears of the Abundance Engine

The entire argument of this essay can be distilled into ten core decisions. These are not suggestions; they are the ten interlocking gears of the new industrial-scale engine for solving problems. When they mesh, a domain flips from being a field of "artisanal craft" to a solved industrial process.

Build the Targeting System (The Foundation): A public, honest, and continuous target makes a complex problem legible and forces it to become solvable.

Pay for Results (The Fuel): Replace old-fashioned contracts with payments for verified outcomes.

Pre-commit Compute (The Ammo): Set aside compute resources in "escrow," to be automatically unlocked by any team that clears the target.

Build Action Networks (The Hardware): Pool robotic labs and advanced micro-factories to give AI systems "hands and feet."

Pool Data (The Lubricant): Turn data into a shared, safe resource via new "data trusts."

Demand Logs (The Truth): Require a public, unchangeable record of why an AI made a choice.

Two-Source Rule (The Safety Brake): Require critical decisions to be confirmed by two independent AI systems.

Co-locate Power & Intelligence (The Base): Build data centers next to clean energy sources.

Fairness by Design (The Steering): Build fairness goals directly into the targets we pay for.

Teach the Rules (The Manual): Literacy in how to read a target and check a decision log is the new essential civic skill.

Your New Job: Steward of the Industrial Base

This transformation is not monolithic; it happens at every level of society simultaneously. This is not a list of new tasks. This is your new job description as a steward of the Abundance Economy.

1. If you are a National Leader, you are not the Architect. Your mission is to aim the entire cognitive output of your nation. You stop managing bureaucracy and start designing the "Grand Strategy" of intelligence. During the first 90 days, aim to achieve consensus with civic and corporate leaders on the top Moonshots faced by your nation (e.g., "Energy Independence," "Curing Dementia"). Prioritize and activate. You will make your nation's potential visible by chartering a National Capability Account, measuring productive power, not just GDP. By Month 6, endeavor to launch "Compute-for-Outcomes" Auctions. Instead of giving cash grants, you auction off vast blocks of state-secured computing power to whichever consortium guarantees the best solution to a specific public problem. By the end of the first year, you will begin to shift your R&D budget from "proposals" to "prizes."

2. If you are a Mayor or Governor, your mission is to solve physical reality. You are no longer just an administrator; you are the CEO of a city-state competing for talent. During your first 90 days, create a City Outcome Ledger. This is a weekly, public dashboard showing your city's real-time performance on the basics: water reliability, power outages, student learning velocity, and hospital wait times. Within six months, you will launch the first pilot programs for industrializing your city: a "24-hour" micro-factory for municipal parts, a district-wide AI tutor program to bulk-solve educational gaps. Within a year, you will convert a quarter of your city's procurement to these new outcome-based contracts. You don't pay for road work hours; you pay for "pothole-free days."

3. If you’re a Health System Operator, your mission is to stop managing sickness and begin industrializing wellness. In your first 90 days, you will publish your baseline numbers and install the secure "action" pathways for AI tools. By six months, you will start procuring results, like "a 10% reduction in readmissions," and tie every AI's authority directly to its live safety score.

4. If you’re an Infrastructure Operator, your mission is to industrialize reliability and stability in the grid. In your first 90 days, you will define your Reliability Target: the "minutes of outages avoided." By six months, you will sign new contracts that treat data centers not just as customers, but as grid stabilizers. Sign contracts where they agree to throttle their power usage instantly during peak load times in exchange for cheaper rates, acting as a "virtual battery" for the grid.

5. If you’re an Industrial Leader, your mission is to solve the problem of physical creation and supply chain resilience. In your first 90 days, you will set a target for "24-hour delivery" on your key products forcing the adoption of local, automated manufacturing. By six months, you will adopt the "two-source" rule, migrating your most safety-critical systems to stacks that are verified by two independent AIs running on different codebases.

6. If you’re an Investor, your mission is to fund the infrastructure of the new world. Your model must shift from "Venture Capital" (betting on apps) to "Infrastructure Capital" (betting on rails). You are the Armorer. In 90 days, you will launch a Rails Fund that invests only in the primitives: the targeting platforms, the auditing tools, the data trusts, and the action networks. You will fund the industrialization itself. By the end of year 1, stop funding "AI for X" applications. Fund the industrialization of the sector itself.

7. If you are a Citizen, this is not a spectator sport. You are a Governor of the new system. Today, you can join a "data trust" that pays you for the use of your data. In 90 days, you can host a "neighborhood outcome ledger" night, asking your local leaders to show you the targets behind their claims. You are the ultimate customer of the solved world.

What We Must Stop Doing

To build this new world, we must have the courage to dismantle the old. We must stop our "innovation theater": the meetings, proposals, and press releases that feel like progress but solve nothing. This theater is the enemy of industrial-scale solving.

We must stop running bureaucratic proposal processes that score "demos" but have no follow-through. We must stop celebrating "vanity scorecards" that don't test against real-world, future problems or check for fairness. We must stop paying for PDFs, hours, and pilots that have no outcome-based clauses. We must stop allowing our most critical systems to depend on a single AI provider. We must stop accepting secret AI model changes that don't come with a public decision log. And we must stop building data systems that can't automatically trigger a safety "slow-down."

How We Pay for It and Know It's Working

The most common objection to a project of this scale is cost, but this transition does not require printing new money. It requires a shift in how we spend the money we already have. It is about efficiency, not extravagance. We can fund the industrialization of abundance simply by reallocating a fraction of our waste.

Nations can reallocate just 1% of their existing budgets to "outcome procurement" and "rail building." Cities can tie 10% of their capital budgets to proven results. Philanthropy can convert half of its program spending to "payment-for-clear" prizes. This is how we fund the industrialization of abundance. This capital already exists; it is currently trapped in low-efficiency systems. By freeing it, we fund the future.

We will know this strategy is working because we will stop measuring effort and start measuring impact. Success will be visible in the hard data. We will track the raw efficiency of our energy-to-thought conversion and the real-world value we extract from our massive computing investments. We will measure human outcomes with precision, tracking how much students actually retain rather than just how many hours they sat in class, and measuring the "healthspan" of patients (the time between sicknesses) rather than just the volume of treatments.

On the infrastructure side, we will count the minutes of power outages avoided and the reduction in cost per kilowatt-hour. We will audit the all-in cost per ton of carbon permanently removed from the atmosphere. And perhaps most importantly, we will track the safety of the system itself by counting the number of times our automated safety brakes were triggered and how quickly the issues were resolved.

The Last Doubts

As we stand at this starting line, the final doubts will inevitably surface. It is natural to question a shift of this magnitude, but each fear has a clear answer rooted in the engineering principles we have discussed.

"But the targeting systems will be gamed!" some will say. And they will be, unless they are built correctly: tested against future data, relentlessly "red-teamed" by paid adversaries, and built with fairness floors that are non-negotiable.

"But paying for outcomes is too risky!" others will claim. It is not half as risky as our current system of paying for hours and hope. In the new model, you cap your downside with automatic safety throttles and you only pay for what has been proven to work.

"But we will lose control!" This is the most backward-looking fear of all. We have no control now. The rails are what give us control. They make a system's behavior clear, measurable, and, for the first time, truly stoppable.

"But this will sideline workers!" No, it upgrades their roles. It moves people from being cogs in the machine to being the conductors of intelligence, the stewards of safety, and the adjudicators of purpose.

What to Do Before Monday Noon

This is not a 20-year plan that requires a committee to approve. The work begins now. Before Monday noon, you can take the first, concrete steps to industrialize your corner of the world.

Start by picking one single outcome metric that you are responsible for, whether in your job, your community, or your home, and publish it, even if only to yourself. Once you have chosen your target, draft a one-page "Target Charter" that defines exactly what success looks like.

Next, identify one partner. This could be a colleague, a clinic, a school, or a vendor in your factory. Propose a tiny, low-stakes, outcome-based contract with them to test the waters. To support this, open a new line item in your budget called "Compute Escrow." Even if it is just a concept for now, define clearly what conditions would force you to release those funds to a problem-solver.

Finally, write down the template for your first "Decision Log." Decide today how you will record the "why" behind your decisions, so you can audit them later.

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Epilogue

The Quiet Hum

When the rails are finally in place, progress undergoes a phase shift. It "snaps" from rhetoric to routine. We stop seeing AI and abundance as miracles, and we start treating them like utilities: boring, reliable, and omnipresent, just like electricity or running water.

This is the "snap" that signals a domain has been solved. But our work does not end there; it compounds. In this new world, the scarce resource is no longer intelligence, energy, or capital. The scarce resource becomes Aiming. Our collective challenge shifts to choosing purposes that are worthy of this new power and holding ourselves accountable to the safety floors that keep that abundance humane.

This essay was not a forecast of what might happen. It was a field manual for industrializing discovery and execution. The rails are the factory. The AI is the power. The targets are the product.

We will measure our era not by the wonders we promised, but by the solutions we delivered safely, fairly, and for everyone.

Build the rails. Aim the charge.

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Reference

Glossary