Free sample · Chapter 1

Why I'm Writing This

"The dogmas of the quiet past are inadequate to the stormy present. … As our case is new, so we must think anew, and act anew."

— Abraham Lincoln, 1862

Opening

I track how AI changes money. Every day, across markets worldwide, I watch capital flow toward intelligence.

Not the headlines. Not the predictions. The actual flows: earnings reports, SEC filings, capital allocation decisions, the quiet shifts that happen before anyone writes about them. Which companies generate actual revenue from AI. Which are still burning cash on roadmaps. Where venture capital concentrates. What happens to labor markets when automation reaches a threshold.

What I've seen has made me write this book.

The Observer's Position

I founded Via News Network because AI had learned to write beautifully while blurring the trail back to the source. The markets were filling with fluent, confident content you couldn't trace. Claims without origin. Analysis without data. Predictions without accountability. I built the response AI-first.

The newsroom is AI-native end to end, sourcing, classification, drafting. What separates Via News from the slop it competes with is architectural: every claim carries a transparent pointer to its source document, and nothing publishes without that pointer. The verification layer is not a brake on AI. It is what makes AI-native journalism legitimate.

A source document is anything independently verifiable: an SEC filing, an earnings call transcript, a regulatory document, a court filing, a video interview, a peer-reviewed paper, and yes, an article from a credible news outlet. Via News doesn't compete with those documents. It points to them. We are a via, a conduit. The reader, or the AI agent, can always trace any claim back to the document it came from and decide what to trust. That is what transparency means here: not a judgment on other reporting, but a structural commitment to show our work.

This structure taught me something beyond finance.

When information has no traceable source, it concentrates power in those who generate it. The audience becomes passive. They consume what reaches them without owning any of it. They can't verify. They can't challenge. They receive what they're given.

The same structure applies to capital.

When economic value has no traceable distribution mechanism, it concentrates in those who control it. Citizens become passive. They receive without owning. They can't verify how gains are distributed. They can't challenge the allocation. They receive what they're given. Or they don't.

Via News exists because information should be owned, not just consumed. The same principle applies to economic participation. It should be owned, not just permitted. The commitment is the same in both: trace to source, make transparent, turn recipients into owners.

Verification showed me something I didn't expect to see so clearly, so soon.

The system is breaking.

Not in the dramatic way people imagine. No sudden collapse, no revolution in the streets. It's breaking slowly, in the data I read every day. Capital is concentrating at a rate that current economic structures cannot redistribute. Where AI raises productivity, the gains are flowing to those who own the infrastructure, not to the workers it displaces. And the gap between those who can leverage AI and those who cannot is widening fast.

What I Believe

I believe in capitalism. I believe in meritocracy.

Markets allocate resources better than committees. Competition drives innovation. People who create more value should capture more reward. The alternatives that have been tried, Soviet-style communism, Maoist central planning, and their descendants, have failed, often catastrophically.

This book doesn't argue against these principles. It argues that the AI paradigm requires their evolution.

Capitalism without broad ownership becomes oligarchy. Meritocracy without accessible pathways becomes aristocracy. The system I propose preserves what works, markets, merit, free enterprise, and adds the one thing capitalism was never built to supply: broad ownership of the growth it generates. This isn't a criticism of capitalism. It's a way of finishing what it started. (Chapter 3 makes the case that Stakism completes capitalism rather than replacing it.)

I believe in AI.

I am an optimist about artificial intelligence: I see far more promise in it than peril. The chapters that follow diagnose real harm, but none of it is an argument for halting the technology. I believe in the reach of intelligence that only AI can help us achieve, and in the symbiosis between human and machine, each bringing something the other cannot. I believe this with the risks fully in view, and the risks are real.

I also believe this transformation is going to happen. People call for a pause, even for a halt, and I understand the impulse, but we are past the point of turning back. What remains open is not whether AI reshapes the economy, but who shares in what it produces. The technology is set; the distribution is not. That distinction is the reason this book exists.

From enough distance, I suspect we will look back on this moment as a confirmation of human ingenuity, the human spark doing what it has always done.

And if we are going to be afraid, we should be precise about what we fear. The danger I take most seriously is human: powerful tools in the hands of people who misuse them. I take the question of autonomous, misaligned systems seriously too, but I believe the nearer and larger threat is us, not the machine. If AI ever becomes a genuine danger, it will be a human failure, of judgment, of governance, of care, before it is a failure of the technology.

What Qualifies Me

I track how AI reshapes finance globally, reading the primary flows — earnings, filings, capital allocation — rather than the commentary about them. I'm not an economist or a policymaker; I'm an observer, and that vantage is what this book is built on.

The current system was designed for an era when individual labor was the primary input to economic value. That era is ending. The transition is happening now, and the data shows it clearly.

That's what this book proposes: a way to extend capitalism, step by step, rather than tear it down.

The Epistemological Commitment

The method matters as much as the conclusion.

The same standard that governs Via News governs this book: no claim without source. Every claim in this book that can be verified, will be verified. Sources cited. Data shown. Where I speculate, I'll say so. Where I'm uncertain, I'll acknowledge it.

This matters because the problem I'm diagnosing, and the solution I'm proposing, are both about clarity.

Current capitalism has a clarity problem. Value is created, but the rules governing its distribution are opaque. Productivity gains appear in aggregate statistics, but the mechanisms that allocate them are invisible to most participants. You can't challenge rules you can't see.

What I propose is system transparency, not individual exposure. The rules are public. The mechanisms are verifiable. Your personal position remains private, just as tax codes are public while tax returns are not. Privacy-preserving technology makes this possible. Clear methodology and verifiable rules make information trustworthy; the same commitment can make economic participation trustworthy.

This is a proposal, not a manifesto, and it is built to be checked. Trace my claims. Verify my sources. Improve what I've started.

The data doesn't support complacency. But it does support iteration.

What the Data Shows

Capital concentration is accelerating. The top 1% of US households now hold about 32% of the nation's wealth, the highest share since the Federal Reserve began tracking it in 1989, with much of the latest gain coming from rising equity prices, concentrated in AI-exposed stocks. This isn't new. What's new is the rate of acceleration, and the mechanism driving it.

I track earnings calls. I read the language companies use when they discuss AI investments. Five years ago, AI was a line item in R&D budgets. Today, it's the entire strategic narrative. And buried in those calls is a consistent pattern: AI investment correlates with headcount reduction. Not always announced publicly. Often described as "efficiency gains" or "operational optimization." The pattern I read is consistent, even if it is an observation from the filings rather than a controlled study: revenue per employee climbing while total headcount stays flat or falls, with AI in the gap.

I don't only read this from the outside. I own several small local businesses, and I have made these calls myself. One of them ran its back office on three administrative staff; since late 2025 it runs on one. Another had a three-person software team, a senior developer, a junior, and a project manager, and today it has one. These are ordinary local firms, not tech companies, and two examples prove nothing on their own. But that is the point: this isn't something being done to distant people by faceless corporations. I am on the capital side of it, and I signed the paperwork. It is also why I don't trust the reassuring version, that the displaced work quietly reappears as new jobs, or that the gains find their way back to the people who used to do the work. In my own businesses I watched the first half happen. I am still waiting for the second.

That gap flows somewhere. It flows to margins. To stock buybacks. To executive compensation tied to efficiency metrics. To shareholders. It does not flow to the workers whose tasks were automated. This is rational behavior under current incentive structures. But trace the flows forward ten years, and the arithmetic stops working: you can't keep stripping wage income out of an economy that still runs on consumer spending.

Labor's share of income is declining. For decades, workers captured roughly 60-65% of economic output. That share is now falling, a decline that predates AI and tracks automation, globalization, and market concentration, and that AI now accelerates. The Bureau of Labor Statistics, the OECD, and academic economists have documented this extensively.

AI productivity gains flow to capital, not labor. When a company deploys AI that raises output per worker, that gain doesn't show up in wages. It shows up in margins, in stock prices, in returns to shareholders.

The pattern is consistent across sectors, and the destination is the same: those who own capital. Economists still debate how large AI's overall productivity boost will be, and the honest answer is that it's early. But the direction is already visible in the firm-level data, and it points to capital.

The intelligence gap is real. Previous automation waves displaced physical labor but created new cognitive work: factory workers became service workers, service workers became knowledge workers. This wave is different. AI performs cognitive tasks once thought to require human intelligence, legal research, medical diagnosis, financial analysis, code generation, and the new jobs it creates require capabilities that don't distribute evenly across populations.

The Problem with Current Responses

The standard responses to this data fall into three categories. I've watched each one play out in policy debates, earnings calls, and economic commentary. All three are inadequate.

"Technology always creates more jobs than it destroys."

Historically true. The Luddites, early 19th-century English textile workers who destroyed machinery fearing it would eliminate their livelihoods, were wrong about industrialization's long-term effects. Each wave of automation displaced workers in one sector while creating opportunities in another.

This argument assumes the pattern will continue. It likely won't.

Previous automation displaced physical tasks while leaving human judgment irreplaceable. AI is different: it performs the cognitive work that used to be the safe ground, the analysis, the writing, the diagnosis.

As every investment disclaimer warns, past performance is not indicative of future results. I detail why this wave differs in Chapter 2.

"Universal Basic Income will solve it."

UBI proposes a baseline income for everyone, enough to survive, regardless of employment. As a floor under material dignity, this is sound. The retired person, the disabled person, the caregiver, the student, the unemployed-between-roles person, they need a floor.

What UBI is not, on its own, is a ladder.

When you receive an allowance, you have no claim on how it's determined. No vote on its size. No stake in its growth. The productivity gains from AI still accrue to capital owners. Workers receive a floor, but no ceiling, and the gap between those who own and those who receive risks hardening into a permanent divide. A society in which people only receive, and never own a share of what they help build, is a managed decline.

Stakism is the ladder, and it stands on its own: every citizen holds an unconditional ownership share that grows with the economy, and accrues more above it through contribution. Where a society also runs UBI, the two work together rather than compete, funded from different bases. (I work out how they fit together in Chapter 5.)

"Regulate the tech companies."

Necessary but not sufficient.

I'm not against regulation. Monopoly power should be checked. Privacy should be protected. Safety standards should be enforced. But regulation is a constraint on behavior, not a restructuring of flows.

You can regulate how companies use AI. You can't regulate who owns the productivity gains. You can prevent specific harms. You can't redirect value at the scale required.

Consider: even if you break up every tech monopoly, even if you enforce the strictest privacy laws, even if you require impact assessments for every AI deployment, the fundamental dynamic remains. AI substitutes for labor. Labor's share declines. Capital's share increases. The regulatory apparatus addresses the how, not the what.

There's also a speed problem. AI development moves faster than regulatory processes. By the time a framework is debated, passed, and implemented, the technology has already transformed. We've watched this play out repeatedly. GDPR took effect in 2018, years after the data practices it governed had become standard. Social media harms were recognized years before meaningful regulation appeared. AI regulation will likely follow the same pattern, reacting rather than anticipating.

Regulation remains a necessary tool. On its own, it can't do the job.

What's Missing

What's missing is a mechanism. Not a policy tweak or a program expansion or a new regulatory framework, but something structural: a change in how value is distributed. It makes every citizen an owner rather than a recipient. It ties individual wellbeing to collective productivity. It preserves what works about capitalism, merit, markets, innovation, property rights, while addressing what's breaking: the concentration of AI-driven gains in fewer and fewer hands.

That mechanism is what this book proposes.

What I'm Proposing

I call it Stakism.

The core idea: every citizen should own a share of the economy they participate in. Your claim on it wouldn't come from being a worker or from being a beneficiary; it would come from being a stakeholder, someone who holds a share by membership and accrues more through what they contribute.

Stakism has two parts, like a franchise in the old civic sense of the vote: an equal ownership floor that every citizen holds by membership, and surplus stake that accrues on top of it in proportion to what each person contributes. The floor is universal; the surplus tracks what each person actually does. (Chapter 5 builds out the franchise in full.)

The stake pool is funded from a base distinct from the taxes on wages: productivity levies on AI-driven value-added, taxed where it lands, plus sovereign wealth fund returns and corporate stake allocations. Because the base is broad output rather than payroll, it holds up as labor's share shrinks. (Chapter 6 details the funding.)

In an economy where AI generates increasing productivity, that productivity should benefit everyone who participates in society, and reach them through ownership, not charity.

This is not easy. A proposal like this has to land with two audiences at once — those who want a floor under every life, and those who defend what merit and markets build — and its architecture, a universal floor with an earned ladder above it, is built to make that possible.

Why Now

I could have written this book five years ago. I didn't, because the data wasn't clear enough. The trends were visible, but the acceleration wasn't. Now it is.

Three factors make this the right moment:

The AI transition is no longer hypothetical.

Not long ago, AI in business was mostly PowerPoint. Now it's infrastructure. The shift happened between 2023 and 2025. Companies started reporting actual efficiency gains, actual headcount impacts, actual margin improvements. None of this is hype. It is specific, measurable, auditable.

We now have enough data to project trajectories. Waiting for more data means waiting until the transformation is irreversible. The window for shaping the transition exists now.

The political center is unstable.

Look at election results across developed economies. Look at polling on trust in institutions. Look at the rise of movements, left and right, that reject the established consensus.

These aren't random fluctuations. They're symptoms of a system that's failing to deliver for large portions of the population.

When people feel the economy isn't working for them, they become receptive to alternatives. Some lead to zero-sum politics, scapegoating, withdrawal. Some lead to constructive reform that addresses legitimate grievances.

Stakism is a constructive alternative. It offers something across the political spectrum: to the left, genuine redistribution through ownership; to the right, the preservation of markets, merit, and property rights; to the center, the stability of a society of owners rather than dependents. That last offer reaches beyond voters: business, governments, and international institutions all gain more from broad ownership and the order it sustains than from the widening unrest that concentration breeds.

This matters because the window for moderate reform closes when extremism gains ground. If the center cannot deliver answers, the edges will. Better to propose a workable framework now, while the political space for it exists, than to wait until the only options are reaction or revolution.

The administrative capacity exists.

Stakism requires tracking stake, distributing dividends, verifying citizenship, and measuring contribution. Five years ago, doing this at scale would have meant a large bureaucratic build-out: new agencies, new databases, new overhead.

Today, most of that capacity is already in place. Digital identity systems, real-time payment rails, and transparent, auditable record-keeping now operate at national scale in many countries. No new monetary system is required; existing banking infrastructure is enough. Where a society wants stronger public verifiability, distributed-ledger (blockchain) record-keeping can add it, but it is an option, not a requirement. Whatever the rails, one rule governs their design: the system is federated, never centralized — many funds and no single registry or console — so that no one authority ever holds the switch to an entire population's stake.

Every stake position, every dividend, every contribution measurement can be recorded in ways citizens can audit, the same traceability I built for information, applied to economic participation.

This doesn't make implementation easy. It makes it possible. A decade ago, universal stake distribution would have been a logistical fantasy. Today the logistics are solvable. The open question is no longer whether we can do it, but whether we will.

A Note on Tone

This book is neither an alarm nor a utopia. The world is not ending, capitalism is not evil, and no framework delivers paradise.

What it does is show you what the data shows, explain why current structures cannot absorb it, and propose one that can. Evaluate it on its merits, and improve on it where you can.

Let's begin.