(Yes, it’s another longish piece, but this is an important issue that’s deserves the attention. If we don’t act soon, it will become too big fix. Happy reading. Please share if it resonates.)

A camel does not collapse under the weight of its load. It collapses under one more straw, and that straw looks exactly like the ten thousand before it. Nobody can tell which one it will be. Everyone can see the pile growing.

On August 20th the US Treasury confirmed that the federal government owes just over $40 trillion, double the total of just ten years ago, and associated annual interest payments that already exceed the defense budget. The common assumption in DC and Silicon Valley is that we double down on AI and can just grow our way out of it…

The debt itself is survivable. What is not survivable indefinitely is the combination now forming: an interest bill that compounds without any vote, and a tax system built for an economy that artificial intelligence will be dismantling underneath it. Those two clocks will converge by the end of this decade.

“If something cannot go on forever, it will stop.”

— Herbert Stein, chairman of the Council of Economic Advisers under Nixon

What this essay argues that you probably do not expect:

Most writing on the debt and on AI errs in the same direction: it treats technology as the solution and the deficit as the problem. The arguments below run the other way.

1. Growth will not save us, and the conditions that made it possible in 1946 no longer exist today. AI won’t grow the economy as fast as the AI CEOs claim, and a pending AI-driven market correction will require unplanned stimulus and raise borrowing costs, exacerbating the problem.

2. Falling prices from automation make the debt worse, not better. Every historical case of a country outgrowing its debt ran on rising dollar incomes. Cheap AI-produced goods and services deliver the opposite.

3. AI curing disease is a gift to humanity and a bill for the Treasury. Longer lives mean more years of costly benefits no actuarial table assumed. Enormous welfare gain; negative fiscal effect.

4. The tax code pays companies to replace people. Hiring a worker is taxed at roughly 25 to 34 percent. Buying the software that replaces the worker is taxed at about 5 percent. We get what we incentivize. This gap predates AI by two decades, but AI accelerates decisions.

5. The fiscal crisis and the jobs crisis are one crisis. Roughly 84 cents of every federal dollar comes from taxing somebody’s paycheck, so anything shrinking labor’s share of income shrinks the revenue base, whether or not unemployment ever rises.

6. A tax on automation is not mainly about slowing automation. Its side effect is preserving a tax base that is evaporating. We need tax reform that gives the economy and the workforce time to adapt.

7. Cooperation with China is fiscal policy, not just foreign policy. Roughly $2.6 trillion a year of global capital is allocated by rivalry logic rather than return. Some is necessary. But much of today’s data center buildout is to win a race rather than serve a customer. Reducing rivalry also means we can share the heavy lifting of enabling the global south with AI while creating a long-term market for American goods and services.

8. The answer is predistribution, not redistribution. If capital captures the gains, taxing the survivors to compensate the displaced does not scale. Changing who owns the capital does, including an idea Americans reject on reflex: a national sovereign wealth fund.

I’m not writing to announce collapse. I wrote this because we still have time to prevent it. But the window in which the solutions are merely difficult, rather than catastrophic is closing fast.

I. The one time we actually did this

In 1946, American debt was larger than the entire economy, the highest ratio in the country’s history, fueled by the war effort. By 1974 it had fallen to under a quarter.

Most people misattribute the cause. Nobody paid it off; the dollar amount barely moved for two decades. What changed was the economy underneath it and the real return earned by bondholders. When George Hall and Thomas Sargent took the postwar deleveraging apart, they found growth and inflation did a lot of the work. Later research by Acalin and Ball in 2024 found that budget surpluses and interest rate repression played an even bigger role than raw economic growth in improving the debt to GDP ratio.

The recipe had four ingredients: fast real growth, sustained inflation, a central bank holding rates artificially low (a policy called financial repression, which the Fed ran until 1951), and a world where America was the only large industrial economy left standing. Washington and Silicon Valley are both reaching for that playbook. We have none of the four at the strength required to execute this time around, and the AI transition actively attacks three of them.

Figure 1. Federal Debt held by the Public (not Gross Debt), 1900-2056, as percent of GDP. Source: Congressional Budget Office, Budget and Economic Outlook 2026-2036.

II. How the pile got this high, this fast

The $40 trillion is four things stacked in sequence, plus a fifth that now grows by itself.

• Wars and emergencies, 2001 to 2021. Iraq and Afghanistan (~$8T), the great financial crisis (~$5T), and the pandemic response (~$5T).

• Tax cuts without matching spending cuts. Four major tax acts since 2001 each lowered the revenue baseline permanently while spending commitments kept growing.¹

• Demographics. In 1960, five workers supported each retiree. Today it’s three.

• Health costs outrunning the economy. Sustained for fifty years. The quiet giant.

• Interest on all of the above. Now the fastest-growing line in the budget and the only one that requires no vote at all.

• That last item has quietly changed the nature of the problem. Strip out interest and the gap between what government spends and collects is projected to shrink slightly over the next decade. Add interest back and the deficit grows by half. Every dollar of projected deterioration comes from servicing money already borrowed. This dramatically gets worse if interest rates go up due to economic or geopolitical instability.

Figure 2. Primary deficit (spending minus revenue, excluding interest) versus net interest, as a share of the economy. Source: Congressional Budget Office, Budget and Economic Outlook 2026-2036.

We have crossed from a spending problem into a compounding problem. They call for completely different instruments, and Washington is still using the ones designed for the first.

III. Where the money goes

Most people, including many executives, picture the federal budget as a pot Congress divides up each year. It is not!

Figure 3. Federal outlays by category, fiscal year 2025, total $7.0 trillion. Source: Congressional Budget Office February 2026 baseline.

When a politician promises to fix the deficit through spending discipline, look at the arithmetic. About three-quarters of federal spending is either automatic or contractual. Automatic means Social Security, Medicare, Medicaid, and veterans benefits, which pay out to anyone who qualifies whether or not Congress ever meets. Contractual means interest. What is left, the portion Congress actually appropriates, is under two trillion dollars (green bars above). Half is defense (and growing). The other half funds everything else: the National Institutes of Health, the Federal Aviation Administration, the national parks, education aid, the entire civilian scientific enterprise, food safety, the courts, the diplomatic corps, and the Internal Revenue Service. So the proposal is to close a $1.9 trillion gap with a $1.9 trillion lever, half untouchable and half containing nearly every public investment that generates future growth. It has never been done, and the lever shortens every year.

Stabilizing the debt ratio does not require closing the entire deficit, only about one-third to two-fifths, since a growing economy can absorb the rest (assuming growth trends continue). It does not rescue the arithmetic. It just means the task is not as impossible as it appears, as long as we don’t create a new crisis that creates discontinuous growth in the debt. There are also other tools to grow tax income which are discussed later in this essay that can take us onto a downward path.

IV. The wedge: we taxed what we wanted more of

Most people understand that governments want to see low unemployment and usually they tax what they want less of as taxes are generally seen as disincentive by taxpayers. Which is why the current US tax laws confuses me, especially when everyone is so worried about AI taking their jobs.

Hire a person and the employer pays payroll tax plus unemployment insurance, workers’ compensation, and health benefits, while the employee pays payroll tax again plus income tax. Buy a machine or software agent to do the same task and the company deducts the purchase immediately, pays no payroll tax, and returns flow to shareholders at capital gains rates that can be deferred indefinitely and wiped clean at death by stepped-up basis. Daron Acemoglu, Andrea Manera, and Pascual Restrepo measured the gap.

Figure 5. Effective tax rates on labor versus on software and equipment capital. Source: Acemoglu, Manera and Restrepo, Brookings Papers on Economic Activity, 2020.

Their conclusion is counterintuitive: much of recent automation may not be productivity-enhancing at all. It is tax-arbitrage automation, machines deployed not because they outperform the worker but because they are cheaper after tax. Fixing this alone, they find, would raise employment about four percent. The fiscal crisis and the labor crisis are not two problems that coincide. They are one problem seen through two windows.

We spent forty years building a tax code that subsidizes replacing the very thing that funds the government. As AI matures, this issue is only going to compound!

V. Where the money comes from, and why that is the real story

This is where the AI transition does its damage, and not enough people are talking about it. Federal revenue is overwhelmingly a tax on human work. Income tax and payroll tax together supply about 84 cents of every dollar collected. Corporate income tax contributes roughly nine cents, down from a quarter of revenue in the 1950s.

Figure 4. Federal revenue by source, fiscal year 2025, total $5.2 trillion. Source: Congressional Budget Office February 2026 baseline.

Neither of these is a law of nature. The mass income tax was a wartime improvisation nobody revisited. The Revenue Act of 1942 expanded the income tax from about four million filers to more than forty million, and the businessman Beardsley Ruml sold Washington on paycheck withholding in 1943 as the way to collect it. Social Security was designed in 1935 as contributory wage insurance, funded by a levy on wages specifically so recipients would feel they had earned the benefit. Both were brilliant designs for their moment, and both rested on one assumption: that most of what a country produces would keep flowing to people as wages. For eighty years that assumption held. It is now failing, and in a way the architects never modeled: not because workers are being fired in waves, but because we have created a K-shaped economy where the share of production reaching people as pay has been drifting down while production itself rises.

VI. Why we cannot grow our way out

The consensus I hear in Silicon Valley runs like this: AI delivers a productivity surge, the economy compounds, revenue rises, and the ratio deflates as it did after 1946. Tested against the actual fiscal models, we get a very different answer.

Start with what is already assumed. The Congressional Budget Office baseline already builds in a positive AI productivity effect. The pessimistic projection is the optimistic-technology projection.

Estimates of what additional AI growth buys cluster in a narrow, disappointing band. Penn Wharton-based modeling finds AI shrinking the annual deficit by about three-tenths of a percentage point. Douglas Elmendorf and Louise Sheiner find a large permanent productivity gain could halve the projected debt rise over thirty years, then note the catch: that assumes Congress does not spend the windfall, which Congress has never managed.

The most complete study, by Brookings, finds that a once-in-a-generation productivity boom could cut annual deficits by roughly two-thirds, then names five side effects specific to an AI boom that claw back more than half: longer lifespans expanding retirement programs, displaced workers drawing on income support, income shifting from wages to capital and lowering the average tax rate, higher interest rates raising debt costs, and an AI arms race lifting defense spending. Four of the five are not technological risks. They are tax-structure and geopolitical risks. The technology delivers the growth; our institutions leak most of it back out.

A further problem the models handle poorly, which I took apart in The AGI Windfall Mirage: much of the growth we are measuring is construction, not application. Building the factory counts toward the economy. Whether it produces enough value to justify itself is a separate question, and as we found in the Stanford Enterprise AI Playbook, enterprise adoption is still delivering far less and slower than markets are pricing in.

Assuming AI delivers as promised with no hiccups, it’s worth somewhere between half a point and one and a half percentage points of the economy on the annual deficit. The gap that needs closing is three to four points. AI is a real tailwind and roughly a third of a solution. Anyone selling it as the whole solution is selling the 1946 recipe without the 1946 ingredients.

VII. What an AI correction does to the ledger

If the ROI doesn’t get realized as expected, we’ll have a different problem on our hands. It could be the fastest available route to a much larger debt.

Suppose the market correction I described in The AGI Windfall Mirage and The Great Reckoning arrives: capacity built against projected demand that does not appear on schedule, write-downs across the supply chain, and a repricing of everything valued on the assumption that frontier labs can maintain revenue growth. What makes this fiscal rather than merely financial is that the buildout stopped being an equity story. In August the Wall Street Journal found nine large technology companies carrying roughly $3 trillion in off-balance-sheet commitments tied to AI, about five times their reported capital spending, most of it in uncommenced leases and purchase obligations that accounting rules keep off the books. For scale, subprime mortgages outstanding at the height of the housing bubble came to roughly $1.3 trillion. Equity drawdowns destroy paper wealth. Obligations of this kind destroy balance sheets, and balance sheets are what governments end up standing behind.

Figure 6. Breakdown of spending commitments from the major hyperscalers relating to AI spend. WSJ, Aug 16, 2026.

The 2008 precedent is highly instructive. Federal debt held by the public went from 35 percent of the economy in 2007 to 70 percent by 2012, doubling in five years, while the deficit went from about one percent of output to ten percent, the largest share since 1945. The misremembered part is the cause. TARP authorized $700 billion, disbursed $443 billion, and after repayments cost the government about $31 billion net (which doesn’t sound bad). The Recovery Act added roughly $800 billion. Together, less than a third of the borrowing. The rest was revenue collapse and automatic stabilizers, with federal receipts falling roughly a fifth in two years. A recession does most of the fiscal damage before Congress votes on anything.

And the usual consolation may not hold this time. In 2007 we entered with debt at 35 percent of output, an interest bill of 1.6 percent, and a policy rate above five percent, all of it ammunition, and money fled into Treasuries and cheapened the debt as it went. We would enter the next crisis above 120 percent of GDP with interest already at 3.3 percent, and the reflex itself is weaker. The marginal holder is no longer a patient foreign central bank but a leveraged fund that sells Treasuries in a crisis to meet margin calls, Chinese and Japanese official holdings have been shrinking for years, and April 2025 already delivered one shock in which the dollar weakened and long rates rose together. A crisis originating in American assets is precisely the kind where foreign capital reduces dollar exposure rather than rotating into it, at the exact moment issuance surges by well over a trillion. Every hundred basis points added to the average rate costs over $300 billion a year.

There are two regimes and only one is survivable on the 2008 template. If the shock is global and disinflationary and the dollar still functions as the refuge, falling yields offset perhaps half the revenue loss. If the shock is American in origin, revenue falls while borrowing costs rise and the arithmetic compounds in both directions at once. Given who holds the paper now, the second is likelier than it was in 2008.

We are borrowing enormous sums today against a windfall that will not arrive at the promised scale, and the public balance sheet absorbs the difference. That is the windfall mirage in fiscal form, and it is the shortest path to the last straw.

VIII. The deflation trap

Very few in the AI debate have internalized this next point, and it is why I have grown more concerned over the past year rather than less. Cheaper goods and services sound unambiguously good. For consumers they are. For a government carrying a large debt, they are poison, and the reason is mechanical. Debt is a fixed dollar amount, and the ratio that matters puts the economy in the denominator, measured in current dollars. After 1946 the dollar size of the American economy roughly quintupled in under thirty years while the debt crawled. Take away the price increases and half that arithmetic disappears. Automation-driven price declines do something worse than remove inflation: they remove it while simultaneously moving income away from the thing we tax. Three steps:

• AI drives down the cost of software, analysis, design, diagnosis, legal drafting, customer service. Prices fall, real output rises, measured dollar output rises much less.

• Savings flow to whoever owns the model, the compute, and the deployment. Labor’s share falls even where headcount holds, because the wage bill per unit collapses.

• Federal revenue, 84 percent of it from paychecks, falls as a share of the economy even as the economy grows in real terms.

The obvious rejoinder is that if deflation is the trap, we will simply inflate our way out as we did after 1946. That exit is narrower than it looks. Inflating away debt requires long maturities, and the average maturity of American debt is under six years, so higher inflation reprices most of the stock within a single presidential term and raises the interest bill faster than it erodes the real principal. It also requires a central bank willing to tolerate it, which the post-2021 experience makes politically improbable. And automation-driven disinflation lands hardest in exactly the tradable goods and services the price index captures, while the categories that resist automation, housing, care, and energy, keep climbing. The likely outcome is not the smooth reflation of 1946 but a widening split: cheap output, expensive necessities, and a debt ratio that gets relief from neither.

The fiscally worst-case AI scenario is not rapid mass unemployment. It is strong real growth, weak dollar growth, and a falling share going to wages. Which is currently also the most likely scenario.

The payroll system feels this first. Social Security taxes wages, only wages, only up to a ceiling, and the retirement trust fund is projected to run dry in the early 2030s assuming labor’s share holds steady. Nothing dramatic need happen for it not to. Goldman Sachs’s base case involves only six or seven percent of workers displaced over a decade: enough to move the tax base substantially, nowhere near enough to trigger emergency politics. Meanwhile Stanford’s Digital Economy Lab finds employment among 22 to 25 year olds in the most AI-exposed occupations has already fallen about 19 percent (vs. less exposed peers) since late 2022, while the least exposed grew about ten percent. The canary in the coalmine is not dead yet. It is coughing, and nobody is paying attention.

Figure 7. US Labor Force Participation, fiscal 2001-2036. Source: Congressional Budget Office February 2026 baseline.
Even without AI’s impact, there’s been a downward trend for the last 20 years. Projected trend likely underestimating the AI displacement impact.

The claim is not that technology destroys work on net. It hasn’t in the past, and it probably will not for some time still. The claim is narrower and harder to dismiss: the fiscal system does not tax employment, it taxes wages, so what matters is the share of output that flows through a paycheck. That share can fall while the number of jobs holds or even rises. A reinstatement effect that creates millions of new roles at a lower wage share still shrinks the base. The service sector where most displaced workers will flow to is currently lower-wage occupations and less differentiated compared to the white-collar roles they will likely be drawn from.

This is why I argued in The Post-Labor Prophecy that Pigouvian instruments, taxes that make a private actor bear a cost it imposes on everyone else, belong in this conversation. But the usual version of that argument is weak: taxing automation as compensation for social harm invites an endless fight about measuring the harm. Yes, these taxes will likely slow displacement of human labor, but not necessarily slow automation. What’s more important is that we give time for the economy to adapt to change, and we are trying to keep the government solvent while the thing it taxes evaporates underneath it. If 84 percent of federal revenue rides on wages, and wages are a shrinking claim on what the economy produces, there needs to be a counterbalance.

IX. The cancer paradox: why curing disease grows the national debt

Everyone has heard the saying, “we can’t slow down AI, because it’s going to cure cancer!” That’s why this section I find genuinely painful to write. AI will make medicine dramatically better: faster diagnosis, cheaper drug discovery, personalized treatment, and eventually a real extension of healthy lifespan. An enormous welfare gain, arguably the largest available to humanity this century. It is also, under current law, a bill that increases the deficit.

Test the claim circulating in AI circles that fixing healthcare frees trillions for debt repayment. American health spending runs near eighteen percent of the economy. The most careful published estimate, in the Journal of the American Medical Association, puts total waste at roughly a quarter of that, but finds proven interventions recover only about a third of the waste. Federal programs capture perhaps forty percent of any real saving, and only if Congress legislates it into lower payment rates rather than letting it settle as provider margin. Realistic federal capture is on the order of a tenth of the interest bill. Not trillions.

Then the paradox. In the Brookings framework, reduced mortality shows up as a fiscal cost: every additional year of life past 65 is another year of Social Security and Medicare that the actuarial tables never assumed. When benefits began in 1940, a man who reached 65 could expect about 12.7 more years of them, and only about half of 21-year-old men reached 65 at all. Today it is roughly 19 years, and about three-quarters get there. A cure for Alzheimer’s is a moral triumph and an enormous unfunded obligation, at the same time, in the same ledger.

Baumol’s cost disease compounds it: where productivity is hard to raise, wages still track the rest of the economy, so relative costs climb. AI will automate the paperwork and much of the diagnostic layer. But the human-contact layers, nursing, elder care, rehabilitation, mental health, are exactly where demand rises with longevity and machines substitute worst. Some models find a productivity surge actually raises health spending as a share of the economy rather than lowering it.

Figure 8. Federal healthcare outlays projected to be rising while tariff income expected to decline, fiscal year 2025-2035. Source: Congressional Budget Office February 2026 baseline.

AI medicine is a welfare windfall and a Treasury liability at the same time. Both statements are true. Any honest argument for abundance has to hold both.

The resolution should not be to slow medical progress. It’s that we need to recognize our social insurance architecture was calibrated for a mortality curve AI is about to bend, and to recalibrate it deliberately rather than let it break by surprise. Eligibility ages, benefit formulas, the wage ceiling on payroll tax: all were set for a world where 65 meant something different. That is a reform agenda, and action is needed before the bill arrives.

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X. Who is holding the straw

“There is a great deal of ruin in a nation.”

— Adam Smith, 1777, responding to panic over British debt after the defeat at Saratoga

Smith was right, and he described a country that would go on to carry debt at twice the size of its economy and survive. Large debts are survivable. But that was also at a different time when rapid economic growth came naturally as the country itself expanded from a small base.

Like many things, sovereign crises do not announce themselves. Hemingway’s character, asked how he went bankrupt, gives the famous two-stage answer: “gradually, then suddenly.” Britain in 1976, Mexico in 1994, Asia in 1997, Greece in 2010: fundamentals deteriorated visibly for years, markets financed them cheerfully throughout, and the repricing took weeks.

So the key question isn’t whether $40 trillion is too much, but what has changed about who is holding the straw. Brookings documents an inversion: foreign private investors now hold nearly twice as much American government debt as foreign central banks do. Central banks were the patient anchor; they bought and held. The new marginal buyers include heavily leveraged funds running arbitrage strategies that can unwind in days under stress, as in March 2020. China’s holdings have fallen to their lowest since 2008, Japan’s share by more than half.

Meanwhile the exorbitant privilege, Valery Giscard d’Estaing’s phrase for America’s ability to borrow cheaply because the world needs dollars, is intact but eroding: down from about 71 percent of global reserves in 1999 to about 57 today. It is not free money but a discount on borrowing costs, plausibly worth $100 to $250 billion a year. And in April 2025 we saw the first modern instance of that discount failing when needed: a risk-off shock produced a weaker dollar and rising long-term rates, the opposite of the flight to safety assumed for eighty years.

We are not at the last straw. We are loading straws faster than at any point in peacetime, onto a camel whose creditors are less patient and more leveraged than at any time since Bretton Woods. When the structure gives way, the healthy parts underneath get crushed along with the rest.

XI. Predistribution, Not Redistribution

Every conversation about the tax base eventually collides with the word redistribution, and the moment it does, half the room stops listening. It is the wrong word.

Redistribution takes income after it is earned and moves it. It arrives late, it is resented by those it taxes and stigmatizing to those it aids, it changes nothing structural, and it does not scale to a problem this size.

Predistribution, a term Jacob Hacker introduced, asks a different question: how do we shape market rules so income spreads more broadly before the tax collector arrives? Who owns the assets? Who captures returns from publicly funded research? How the factors of production are taxed against each other? Who owns the data and the compute?

This is the entire game. If AI pushes labor’s share of GDP from roughly 57 percent toward 45, no redistributive scheme is large enough to fix it politically: you would tax a shrinking group at rates that trigger capital flight and pay a growing group in a way that reads as charity. Universal Basic Income flounders exactly here. It is redistribution scaled to a predistribution problem.

We need to recognize that this is not fundamentally about fairness. We need to wake up to the reality that the nation’s antiquated fiscal structure needs redesign. Predistribution is the structural repair our leaders are still unwilling to discuss.

XII. Five institutions we need to build

Here are the five reforms I’d suggest. None of it is easy, but all of them are necessary.

1. Close the wedge, then tax what AI displaces

Stop paying companies to replace people. Equalize effective tax rates on labor and automation capital, end immediate expensing for capital that displaces payroll, tax investment returns at ordinary rates above a threshold, eliminate stepped-up basis at death, and broaden the payroll base toward a measure of value added so Social Security no longer rides on labor’s declining share.

Layered on top, a Pigouvian levy: a charge on the gap between a firm’s value added and its wage bill, measured against sector norms, dedicated by law to the transition. Add taxes on data centers, chips, and token revenues, just as cigarette and fuel taxes compensate for societal externalities.

Acemoglu and his coauthors argue the first-best is removing the existing distortion rather than layering a new tax on top. They are right that neutrality is cheaper and raises more, but it leaves the transition unfunded, and a dedicated levy is what makes the bargain closable. Do both, lead with parity. This is the largest lever available.

2. A GI Bill for the AI Age

The Servicemen’s Readjustment Act of 1944 trained or educated close to eight million veterans, roughly half of everyone who served. A congressional study four decades later estimated a return near seven dollars per dollar spent, realized through the tax receipts of a more productive workforce. Note the timing: costs in the 1940s budget, returns twenty years later.

That is the model for the GI Bill for the AI Age I laid out in Beyond Rivalry: a transition entitlement funded by the automation levy, portable across employers, covering wage insurance, retraining, relocation, and benefits.

Why it attacks both problems in this essay. It is a fiscal instrument, not only a compassionate one, targeting two of the five side effects Brookings found clawing back AI’s gains: displaced workers drawing on income support, and falling labor force participation. A prime-age worker who stays attached is worth roughly $12,000 to $15,000 a year to the Treasury, so the program pays for itself at low effectiveness thresholds. It also resolves the healthcare paradox: the care economy is where longer lives create demand and machines substitute worst, so channeling displaced knowledge workers into care work turns a fiscal liability into tax-paying labor supply. One instrument, both problems.

The counterpoint: America’s record here is poor. Evaluations of Trade Adjustment Assistance, which was supposed to do this for manufacturing workers, found near-zero or negative earnings effects. Retraining people for occupations that are themselves moving does not work. So, lead with wage insurance and portable benefits and treat classroom retraining as the smaller component. The good news is that most people don’t need a lot of specialized training to care for the elderly or mentor youth, they just need the social motivation to want to do it. This is where redefining what counts as high-status work, which I discussed in The Post-Labor Prophecy, does more work than any curriculum.

3. A national sovereign wealth fund, and why Americans should get over their allergy

This proposal will likely draw resistance, but it follows most directly from predistribution logic. If capital captures a rising share of output, the durable fix should not be about taxing the winners harder forever. It must be about making ordinary citizens owners of that capital. Norway’s Government Pension Fund Global holds roughly 1.5 percent of every listed company on earth for five million people. Alaska’s Permanent Fund has mailed every resident a dividend since 1976 and is quietly the most popular program in the state.

An American version would be funded not by borrowing but by three streams already public property and currently given away: the automation levy above, an equity carry on commercialized federally funded research, and receipts from public assets like telecom spectrum or federal lands. Hold it passively and indexed. No voting rights, no industrial policy, no ministerial discretion. Pay a visible annual dividend, so citizens experience the AI transition as owners rather than bystanders. And specialized deals with key players like what the current administration struck with Intel last year could be included, as long as it doesn’t become a form of regulatory capture.

Some may object that you cannot responsibly run a wealth fund while running a large deficit: you would be borrowing at four percent to buy equities and calling the spread a policy. Norway funds its own from oil surpluses; we have none. Government equity ownership also invites political capture. My answer: fund it only from the dedicated levy, never from borrowing, start small, keep it fully passive, and treat the dividend as the point rather than the returns. The goal is a way to give a hundred million households a direct stake in the machines displacing their labor, which is the difference between a transition and a rupture.

4. An AI Marshall Plan, co-funded and not American-financed

The original Marshall Plan disbursed about one and a quarter percent of American output a year for four years. Replicating that today means several hundred billion annually, which Congress would never appropriate. What made it pay was never the aid. It bought a trading system, a set of institutions, and a bloc of solvent customers who purchased American goods and held American assets. The exorbitant privilege is, in a real sense, still the Marshall Plan’s dividend eighty years on. Size the AI version accordingly:

• Co-funded, explicitly. Not an American subsidy but a joint facility with the European Union and China, plus Gulf and East Asian sovereign funds, on the model of CERN or ITER, the international fusion project, rather than foreign aid. An American contribution in the tens of billions a year, matched by partners, is serious and fundable.

• Aimed at the Global South as a market, not a charity case. Four billion people lack meaningful access to frontier capability. Compute access, localized models, health and agricultural deployment, and standards capacity across Africa, South Asia, and Latin America build the customer base for thirty years of American, European, and Chinese exports. Every general-purpose technology, from electrification to mobile telephony, generated more value from diffusion than invention.

• Justified as defending the dollar’s reserve role. If the borrowing discount is worth $100 to $250 billion a year, spending a fraction of that to keep countries who might otherwise build a parallel financial architecture inside the tent is not selfless generosity. This type of program would be one of the cheapest debt-service reductions available.

There are those who suggest the causation may run backward: military primacy and Bretton Woods may have created the privilege, with the Marshall Plan a lagging indicator. And AI diffusion may erode American leverage by handing middle powers capabilities they now rent. But the counterfactual matters more than the mechanism: the alternative to a co-funded facility is a solely Chinese-funded and controlled one, strictly worse for the dollar and long-term American influence.

5. The peace dividend, and the $2.6 trillion question

The peace dividend is a real historical phenomenon, not a hopeful abstraction. American defense spending ran near six percent of the economy at the Cold War’s 1986 peak and about three percent by 2000. That swing, with the productivity boom and two revenue acts, produced the surpluses of 1998 through 2001, the only time in modern American history the debt ratio fell meaningfully outside a war’s aftermath. It required the brutal Base Realignment and Closure process and took a decade to land. But it worked.

“Every gun that is made signifies a theft from those who hunger and are not fed.”

— Dwight Eisenhower, Chance for Peace address, April 1953

Eisenhower wasn’t a pacifist; he was doing arithmetic. The current picture runs hard the other way. All 32 members of the North Atlantic Treaty Organization met the two percent target in 2025 for the first time, alliance spending passed $1.4 trillion, and the Hague summit committed them to five percent of economic output by 2035, which the Kiel Institute prices at an extra 831 billion euros a year for Europe alone and growing. Add the AI buildout, allocated by identical logic: roughly a trillion this year globally, and growing rapidly. Sum the rivalry-related budget, and even if a portion can be redirected to peaceful infrastructure buildout, the economic returns would be significant. Part of the reason why Japan and Germany recovered so quickly from WWII was because they were not burdened with a high defense budget.

That is roughly $2.6 trillion a year of the world’s capital being allocated by rivalry logic rather than by return today. Of course, not all of it is waste, but a great deal of it could provide better long-term societal benefit.

This is The Great Reckoning in fiscal terms, resting on the thesis in Misdiagnosing the US-China AI Race. Détente does not merely lower defense budgets. It changes what the AI buildout is for. Much current data center construction is racing rather than serving: capacity procured on the theory that whoever holds the most compute or gets to AGI first wins something, not on demonstrated returns. 60% of today’s compute resources are used for training dozens of near duplicate models where real-world users can’t tell a clear difference in most cases. Remove the race premium and a meaningful fraction of that capital frees up for grid modernization, transmission, water, housing, education and broadband, which carry higher and far more broadly distributed returns per dollar and employ many more people.

Détente plausibly yields half a point to a full point of economic output in defense savings over ten to fifteen years, plus redirection of some share of the AI capital expenditure. Not a solution on its own, but roughly the size of the projected growth in interest costs over the same period, which means a peace dividend can offset the compounding and buy time for the other four reforms.

The security environment has genuinely deteriorated in recent years, and unilateral drawdown would be reckless. I’m not suggesting disarmament. That framing treats cooperation as a concession, when in budget terms it is the highest-return investment available to both parties at once.

XIII. We have a three-year window

The arithmetic closes. Stabilizing the debt ratio needs roughly three to four percentage points of economic output. There are five plausible sources:

• AI productivity, may provide: half a point to one and a half. (If we can avoid the previously mentioned correction. If not, it could be a sizable drag.)

• Closing the labor-capital wedge and broadening the tax base: one and a half to two and a half.

• Bending health cost growth one point below the economy’s: about one point by the 2040s.

• Recalibrating Social Security for longer lives: about one point.

• Peace dividend and capital redirection: half a point to one point over ten to fifteen years.

Two large levers and any one of the others closes it. All five close it comfortably and fund the transition besides. No single one closes it, which is why every proposal offering just one lever, whether growth, spending cuts, or taxing billionaires, is unserious.

One additional lever is missing from that list on purpose. Immigration is the fastest-acting fiscal instrument available to a country with a worker-to-retiree problem, and the Congressional Budget Office scores lower immigration as adding roughly half a trillion dollars to deficits over the coming decade. I left it off the menu not because it is unimportant but because it is the one item where the politics currently overrides the arithmetic. That is itself evidence about which of the three clocks below is really binding.

Why three years and not thirty. Three clocks are running. The interest clock: every year of delay adds roughly two trillion to the pile while the average rate keeps repricing upward, so the same reform costs more each year you wait. The political clock: tax reform is feasible while the group that pays is small and the group that benefits is large; once labor’s share falls far enough, the coalition that could have voted for parity has itself been hollowed out. Predistribution also has a shelf life, shorter than the problem. The later we implement, the less it helps. The standards clock: decisions being made now about who owns model weights, who allocates compute, and whether the Global South builds on shared infrastructure or rents from two vendors are hardening into permanence. Standards are cheap to set and enormously expensive to change.

“Only a crisis, actual or perceived, produces real change.”

— Milton Friedman

Friedman’s follow-on was the important part: when the crisis comes, what gets done depends on which ideas happen to be lying around. The crisis will come. The only question is whether the ideas within reach are good ones, or whether we grab the ineffective standby tools of austerity and protectionism because nothing better was on the shelf.

What $40 trillion actually means

Forty trillion dollars is not a sign of imminent doom. The United States is not Greece and cannot be forced into default in a currency it prints. This number should be a wake-up call, addressed to all of us.

To the nation: we are running a tax system built for an industrial labor market into an economy that is ceasing to be one and financing the gap from a creditor base quietly becoming less patient. That is a choice, made annually, by people we elect.

To companies: the tax arbitrage that makes automation cheaper than employment can’t be relied upon long term. It is a policy error and it will be corrected. Firms assuming labor stays expensive and capital stays cheap are building on sand. The ones that thrive will use AI to expand what their people can do, which is also where the actual enterprise returns have been.

To individuals: the retirement, healthcare, and education promises we have made to ourselves are not funded. Understanding this gives us the motivation to prepare for it and demand change.

The camel is not down yet. The load is heavy, but the animal is strong, and we still choose which straws we add and which we take off. What we do not choose is whether the last straw exists. It does. We cannot see it, and we will know which one it was only after the fall. I’m not telling you to panic. But we need to greatly increase our sense of urgency.

We have a few years to trade a hard choice for a catastrophic one. Every previous generation of Americans that faced this arithmetic, in 1790, in 1865, in 1946, chose the hard thing and was rewarded for it. There is no reason we cannot today. The only question is whether we will.

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Alvin Wang Graylin writes The Abundanist. He is a Digital Fellow at Stanford HAI | Digital Economy Lab, a Senior Fellow at the Asia Society Policy Institute’s Center for China Analysis, a professor of AI and Tech Policy at the University of Washington and the author of Our Next Reality. Prior to his current academic and think tank roles, he has had over 35 years of direct operating experience managing businesses in all five layers of the “AI cake” on both sides of the Pacific.

Author’s Note: I recently participated in an offsite gathering of U.S. government policy makers and think tank analysts discussing the future of the country and the world, and it deeply troubled me. At the same event a year ago, on and off stage, I heard a sense of hope and the potential for cooperation across borders. This time, the tone and the narrative have shifted to the opposite and AI (and related technologies) was at the center of it. Zero-sum thinking and competitive narratives have risen more than a few notches. It certainly doesn’t help that popular AI related forecasts on the near future such as Citrini’s 2028 Global Intelligence Crisis, AI 2027, and the Situational Awareness papers have all predicted catastrophic AI outcomes leading to global economic collapse or war. Just as I was finishing this piece, the AI 2040: Plan A paper dropped, purportedly offering a hopeful path, but after closer reading, sadly it still misses the transformative potential of AI to fundamentally alter the architecture of society and the world. Historically, ideas we read often manifest as self-fulfilling prophecies and the world needs hopeful narratives now, more than ever!

I’ve spent the last 35 years researching, developing and deploying solutions in all five layers of the AI stack, from data centers, to chips, to models, to devices and the application layer. What follows runs the near future forward on evidence that is already visible, and then, at the point where the data runs out, it splits into two paths: the hopeful one I believe we can still choose, and the darker one we could stumble into instead if we let the default path go forward. We are approaching a fork to a protopia and an anti-utopia drawn from the same starting facts.

The farther out you read, the more you are reading my hopes and analysis backed by the evidence I found. Where I make a specific bet, I will say so, and I will tell you why I believe it is possible. Given other papers have detailed the various downside scenarios, the bulk of my analysis will relate to how things may go right…even if they don’t start that way. As a father of two daughters, I don’t want to see the future play out like what I have read in the previously mentioned depictions, and this document is my attempt to show you why that future is NOT inevitable. Even when I share the path forward, many will argue that the rich and powerful won’t act altruistically or even rationally. Thus, I will include explanations on why they may ultimately do the right things for the world…even if for the wrong reasons. Now is the time we need to move beyond the zero-sum winner-takes-all strategy and embrace a “winners-share-all” mindset.

Given the attention span of the average reader, I know the length of this essay will be challenging. But given the gravity of this topic, I promise you the payoff will be worth it if you can stick with me to the end. If you agree or disagree, please comment and tell me why or what I’ve missed. Only through constructive dialogue can we find workable solutions that can help us avoid a future no one wants, but so many are pushing us towards.

“What we do as individuals and as a species in the coming decade may very well help determine the ultimate destination of humanity as a whole for the rest of time.”

A. Graylin - Our Next Reality (Hachette, 2024)

Icarus did not fall because he flew; he fell because his hubris blinded him to the warnings, transforming a triumph of human engineering into a tragedy of unchecked ego.

That is roughly where the American AI industry sits in the summer of 2026. We have built something genuinely miraculous, and we have financed it in a way that cannot hold. This essay is about what happens when the wax gives way, and about the strange and hopeful thing I believe will grow from the fall. My claim, in one line: the Great AI Reckoning will bring about Five Reconnectings.

I am not saying AI lacks value. In fact, I spent my whole career trying to bring its value to society and wrote a whole philosophical framework arguing how we can leverage it to transform our world for the better. I am saying that how we got here, the capex arms race, the leverage, the circular deals, and the belief that the first lab across an imaginary finish line captures everything, was never sustainable and was never necessary. In The AGI Windfall Mirage I described driving across the Mojave toward a silver lake that kept dissolving as we approached. The industry is still chasing that lake. This is the story of the moment the road turns to salt flats, and what we choose to do next.

Before we begin, one honest observation about human nature that the rest of this essay depends on. People and governments are loss averse and change averse. We rarely make hard structural decisions until a shock forces our hand. The uncomfortable truths about our AI economy, that growth is propped up by unsustainable spending, that the labor transition needs a real safety net, that the US and China cannot manage the most powerful technology in history while refusing to speak, are all decisions our leaders keep deferring because deferral is politically cheaper than action. A market correction is painful. But set against the alternatives that usually force civilizations to change, war, famine, financial ruin that lasts a generation, ecological collapse, a major market correction is remarkably gentle. It may be the least painful wake-up call we can ask for that still carries enough force to shift the minds of the people in charge but is still reasonably recoverable. I just hope it carries enough weight to change the minds that matter in time, as the alternatives get dark fast.

As I argued in the final chapter of Our Next Reality, if the promise of a better world is not motivation enough, then honestly contemplating the depth of the catastrophe of our current path may be what ultimately moves us. The Reckoning described, in that sense, is a mercy.

“You never want a serious crisis to go to waste. It’s an opportunity to do things you think you could not do before.”

Rahm Emanuel, November 2008

Figure 1: Scale of AI capex spend vs. historical megaprojects. (source: Fin Moorhouse)

Act I: The Reckoning (the crisis)

Let’s start with a number that should stop you cold. Harvard economist Jason Furman calculates that information processing equipment and software made up about 4 percent of US GDP in the first half of 2025 but 92 percent of GDP growth. Strip out the AI buildout and annualized growth was 0.1 percent, a hair above stall speed. Even Jeff Bezos calls it an industrial bubble. When this much of a 31 trillion-dollar economy’s growth comes from a single investment theme, there is clearly extraordinary and unhealthy dependence. An economy that needs capex to accelerate forever just to hold up its growth numbers is an economy balanced on one leg.

The same story repeats one level up, in the equity market that sits on top of the real economy. From May 2024 to June 2026, the S&P 500 gained 142 percent with its AI stocks and just 16 percent without them, and AI related names now make up 45 percent of the index’s entire market capitalization, an all-time high for any single theme. The seven largest companies have swelled from 20 percent of the index at the end of 2022 to more than 35 percent by the end of 2025, roughly double the 17.5 percent share the top seven held at the peak of the dot com bubble. Apollo’s chief economist Torsten Slok is blunter still: by his concentration measures, the AI trade today is far bigger than the IT bubble of the 1990s. Two years of American wealth creation ride on one thesis. When the Reckoning arrives, it will not stay inside the tech sector.

Figure 2: Ratio of S&P Info Tech vs. S&P 500 performance 1990-2025. (Apollo Global Management)

Now look at how this entire edifice is financed. A web of what analysts politely call circular deals now exceeds 800 billion dollars and by some counts a trillion: Nvidia invests in OpenAI, OpenAI commits to Oracle, Oracle buys Nvidia, and the same revenue gets counted at three stops around the loop. AMD handed OpenAI warrants for roughly 10 percent of the company at a penny a share in exchange for purchase commitments. Jensen Huang himself signaled that Nvidia’s 30 billion dollar bet on OpenAI might be its last. OpenAI is reportedly on track to lose around 14 billion dollars in 2026 on a favorable accounting basis, with GAAP estimates running far higher.

There are still bull case analysts like Janus Henderson, who calls this a virtuous circle that lines up scarce supply, and Acadian makes the sharp point that in a real bubble you see equity sold to outside shareholders, which most of these deals do not do. Vendor financing built the railroads and the early internet backbone too. So the honest question is not whether this is fraud. It is whether end demand justifies the commitments. On that question the bears have the better of it, because the closest analog is the late 1990s telecom buildout, where equipment makers financed their own customers, demand fell short of the fiber laid, and leveraged carriers went bankrupt while capacity sat dark for years. My read: the structures are legal, the leverage is real, and the demand assumptions are heroic. That does not require malice to unwind. It only requires growth to disappoint.

And growth will disappoint, because of a force the leveraged players cannot stop: commoditization. This is the argument I made in America Is Running the Wrong AI Race. First to scale wins, not first to invent, and scale is exactly what open weight models are collapsing in price. DeepSeek, Qwen, Kimi, MiniMax and their successors keep delivering frontier adjacent capability at a fraction of the cost, and each efficiency breakthrough drains pricing power from the pure play labs whose valuations assume the opposite.

This is the export control fracture I was pointing to in Lawfare: the harder we squeeze, the faster Chinese labs innovate around the constraint, and the more the global market tilts toward cheap, capable, commoditized intelligence. Export controls have slowed domestic semiconductor production capabilities in China near term, but it simultaneously pushed massive innovation in Chinese full stack capabilities that makes it a stronger long-term competitor. It’s also forced the Chinese AI labs to innovate in algorithms, techniques and architectures to squeeze additional capabilities out of less performant hardware systems, making them much more cost efficient and ready for global diffusion.

If racing to AGI guarantees dominance, why are the frontier labs the least profitable companies in AI? We got an early taste of the fragility of these players in June 2026, when frontier model access was briefly curtailed under US export control rules. In recent months, the proportion of tokens being routed to open source vs. closed models has skyrocketed.

When does the Reckoning start?

I think the first tremor arrives within twelve months (give or take six months), and you can already feel it. The tell is simple. The pure play lab IPOs, the ones meant to prove to public markets that this business model works, get delayed or priced down. Watch it in real time. SpaceX priced at 135 dollars in June 2026, quickly spiked to 225, then slid back to its IPO price as of this writing. The AI IPO market watched that retracement and blinked. OpenAI, which had eyed a fall listing, is now leaning toward 2027, citing volatility and Sam Altman’s insistence on a trillion-dollar price. Anthropic still targets an autumn debut. My forecast is that as diligence forces real revenue quality into the open, one or both of those listings slips or reprices, and that is the starting gun. Secondary market marks crack. The correction spreads from the labs to the chipmakers to the hyperscalers whose capex guidance suddenly looks like a liability. A market crisis becomes a recession. And the recession accelerates the very job displacement everyone feared, because firms under margin pressure automate faster, not slower, to survive.

The endgame of Act I is consolidation. The IPOs that do not materialize become down round acquisitions. The hyperscalers, Microsoft, Amazon, Google, and the large private equity firms, divide up the labs at a fraction of today’s marks, and early investors take the loss. The highflyers flew too close to the sun.

China is not spared. Of the ten or so serious Chinese labs today, I expect three or four to survive, the rest folded or absorbed. Z.ai and MiniMax, which were the first pureplay AI labs in the world to go public (ahead of OpenAI/Anthropic), are now trading near 800x and 100x revenues respectively, showing investor irrationality isn’t isolated to the US markets. But Beijing has a tool Washington does not. Through the National Development and Reform Commission’s (NDRC) drafted 295 billion dollar national compute grid, funded by sovereign debt and built on 80 percent domestic chips, the state can operate the substrate directly and steer compute to the labs it wants to keep alive. Not nationalization of the labs exactly, but a state operated grid that functions as a national backstop. Where America’s buildout is a leveraged bet by private balance sheets, China’s is a patient bet by a sovereign one. In a downturn, patience wins.

Then there is Elon, whose story deserves its own beat because it is half real already. SpaceX went public in June 2026 at 1.77 trillion dollars, the largest IPO ever, and it was lucky in its timing, listing before the worst of the private labs’ operating data leaks out. It survives, because underneath the hype is a real terrestrial hyperscaler and rocket company: Starlink alone earned 11.4 billion of its 18.7 billion in 2025 revenue. What I do not think survives is the science fiction layer sold alongside it. The one million satellite orbital data center constellation and the Terafab plan to manufacture a terawatt of space hardened AI compute per year are, in my view, unlikely to take off on anything like the promised timeline. Sam Altman called the space data center idea ridiculous for current needs, and Musk has a long history of over promising on exactly this kind of moonshot. As those plans slip and the compute story deflates, here is a bet I will offer as speculation, though it is grounded in clear historical evidence: SpaceX and Tesla merge, framed as synergy, functioning as rescue. Why plausible? Because it is the pattern. Tesla absorbed a struggling SolarCity in 2016, xAI absorbed X in 2025, and SpaceX absorbed xAI in early 2026. Musk merges companies to move problems where the market cannot see them and to give early investors an exit before the reckoning lands. Call it a micro reconnecting of the Musk empire, a preview of the larger consolidation to come.

That is the Reckoning. I am fairly confident in its shape and reasonably confident in its timing. So what is America’s Plan B if the AGI bet does not pay off before the debt comes due? Everything after this point is where the road forks.

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Act II: The Fork

Here is the branch point. Crises are forks, not funnels. They do not automatically bend toward the light.

The dark branch is just as real. A crash this size could just as easily produce authoritarian retrenchment, deeper concentration of capital and AI power in limited hands, mass scapegoating of the displaced, hardened decoupling, and safety nets that never arrive because the politics ferment into blame. History gives this branch plenty of precedent. The Great Depression produced the New Deal, yes, but it also produced fascism. The 2008 collapse produced TARP and recovery, and also the populist nationalist decade of trade wars and walls that followed. Anyone who promises that a crisis reliably enlightens us has not read much history. This is the anti-utopia, and it is a live possibility…in fact, the more likely possibility.

So why do I spend the rest of this essay on the hopeful branch, the protopia? Three reasons, and none of them is naivety.

First, because the point of writing a scenario is to steer toward it. In Our Next Reality, the entire abundance question is framed as managed well versus managed poorly. The destination is not guaranteed, and our actions this decade decide it. The future is not a forecast you passively receive. It is a choice you argue for and act on.

Second, because crises also build. Bretton Woods, the Marshall Plan, and the European Coal and Steel Community that became the EU were all constructed out of rubble by people who decided that shared institutions beat repeated ruin. That is the functionalist insight at the heart of my Beyond Rivalry framework: cooperation gets built in the domains where interdependence is undeniable, and then it spreads. The reconnecting branch is not the default. It is the New Deal branch, and it requires the same deliberate choosing. Economic historian Carlota Perez showed that every major technological revolution passes through a speculative installation frenzy, a crash, and only then a deployment golden age when its benefits broadly spread across society. Canals, railways, electricity, automobiles and the internet all followed this arc. The crash is not the end of the story. It is the hinge.

Third, and this is genuinely new, we now have a tool that the leaders of 1929 and 2008 did not. AI itself, used as a decision support partner rather than an oracle or a master, can widen the option set that panicked humans collapse under pressure. I made this case in Should We Be Returning to Philosopher King Rule: AI is uniquely good at surfacing second order effects, checking incentives and biases, and asking what would have to be true for a plan to fail. In a crisis, the failure mode is misperception and fear driving nations toward the worst mutual outcome. A well-designed advisory layer makes the positive sum option legible when instinct hides it. The crash is the inflection, the moment the cost of the old path becomes undeniable and the incentive to build the new one finally exceeds the incentive to defend the old one.

What follows are five “reconnectings”, roughly in the order I expect them, over the coming decade and in some cases a bit longer. They are, in a word each, how we reconcile, reform, realign, renew and reawaken a more peaceful and prosperous world order.

Figure 3: The Five Reconnectings unfold in overlapping waves, each enabling the next

Act III: The Five Reconnectings

Reconnecting I: Superpowers (reconcile)

The first and fastest reconnecting is between the United States and China, and it happens not out of virtue but out of necessity.

The mechanism has a partial precedent. In 2008 during the Great Financial Crisis (GFC), China played a significant role to help stabilize the global economy by sustaining demand and continuing to hold US debt. I will be candid that the straightforward version of China rides to the rescue by buying our bonds runs against a multi-year trend of Beijing trimming Treasuries and diversifying away from the dollar. So, this is a place where I am betting on a reversal, and here’s why. The logic is that a deep enough crisis flips the payoff matrix. This is the core argument of Misdiagnosing the US-China AI Race: we are not in a simple zero-sum race, we are in a multi turn Stag Hunt, and the worst outcome for America is exactly the one we occupy now, the U.S. betting it all on AGI, a stag none can catch alone, while China keeps collecting real world wins, taking home the hares. The overleverage on the AGI push is making the American economy highly fragile. A significant crash makes the errors of the stag-focused strategy suddenly clear.

The deepest systemic leverage is not financial; it is geopolitical. Faced with an immediate market collapse and an unserviceable debt load from its domestic AI expansion, at a crisis point where federal interest outpaces the entire defense budget, a compromised United States possesses the ultimate chip: trading its historic strategic ambiguity over Taiwan to secure financial solvency, effectively resolving the issue on Beijing’s terms.

I offer this next turn as speculation and not endorsement. A weakened US administration trades non-interference on Taiwan for Chinese help stabilizing the US economy. Export controls halt, which may actually have more real-world impact on China’s economy than the politically motivated Taiwan ask. Two way sharing and détente resumes. China supplies low-cost energy technology and low-cost goods, exactly the deflationary force a recovering economy needs. Chinese FDI is allowed to flow into troubled sectors to help prop up ailing US firms, bringing in technology and process transfers that once went the other way just a decade or two ago. The smooth execution of the deal quickly stabilizes the markets (which seems to be the key barometer for the current administration) giving much-needed reprieve to DC for planning out the recovery process and provides a positive story heading into the important 2028 election cycle. Both countries see renewed economic growth and quality of life rise as friction falls.

Some may doubt this path is possible given the bipartisan alignment against China today, but Trump’s response to press on the Air Force One post the May Beijing summit already hints at this possibility. There, he said he had made no commitment either way on defending Taiwan. If it happens, the US will likely ensure the trade is paired with explicit security reassurances to Japan, South Korea, and the Philippines. It will also require that Beijing not disrupt U.S. chip supply and commit to pursuing reunification only through peaceful means, which has always been Beijing’s publicly stated preference for resolving cross-strait tensions. Beijing is in no rush.

At this point, US media, which spent a decade painting China as the enemy to defeat, begins repainting China as a new valued partner instead. It is worth remembering that the US and China were allies against Japan in the Second World War. We have been on the same side before. The China hawks will hate this, and the credibility costs to US alliances are real, which is the honest counterargument. But cornered great powers have traded away commitments they swore were sacred many times before.

Reconnecting II: The Family/Community (reform)

The second reconnecting happens at the kitchen table.

First, the wealth concentration problem has to be solved, and I think the crash itself forces the solution. Left alone, downturns concentrate capital, the strong buy the distressed cheap. So the pivot from concentration to sharing does not happen by magic. It happens through national regulation and, where necessary, nationalization, deployed as part of the AI crash rescue, the same way governments took stakes in banks and automakers in 2008 to keep a sector’s collapse from taking down the whole economy. When a single sector accounts for the bulk of the economy’s growth and nearly half its stock market value, letting it fail is not an option, and the price of rescue is public stake and public rules.

The trillion-dollar yearly capex that was flowing into speculative compute build outs gets redirected into social services and safety nets that were politically impossible the day before the crisis and become inevitable the day after. The funding lever already exists in the Pigouvian automation levies I proposed in the AGI Windfall Mirage: tax the substitution, fund the transition. A GI Bill for AI will need to be formed to help support and retrain the tens of millions (in the US alone) who will be affected by AI job displacement, the same way we took care of returning soldiers post-WWII.

“If the machines do the work, what is a human life for?”

the question Aristotle asked, twenty-three centuries before the transistor

With that foundation, the human transition begins. Automation accelerates under margin pressure, and regulation responds by shortening the work week, which hands families something they have not had in generations: time. Time that can help mend the family fractures born of scarce face-to-face hours between spouses, parents, and children, hours prior generations enjoyed in abundance.

Displaced office workers move into the roles where we have enormous unmet need, nursing, elder care, teaching, the human-to-human work that no robot does well. At first these jobs carry low status and low pay, a real problem I do not want to gloss over. But status is not fixed, and here I take on one of the strongest objections to this entire essay.

The objection is Keynes. In 1930 he predicted that abundance would give his grandchildren a fifteen-hour work week, and he was wrong, because human desire for wealth and positional goods never satiate, especially when combined with media’s push for materialism. If status continues to be attached to money and power, my hopeful future is a fantasy. My answer, which I developed in The Post-Labor Prophecy, is that the thing that changes is not the amount of stuff we have but the definition of status itself. And here my own research cuts to the heart of the Keynes problem.

In Our Next Reality, I dug into the happiness data and found that beyond a point, more income stops buying more happiness, the income indifference point that Daniel Kahneman first measured. But the deeper finding was that what drives happiness is not absolute wealth at all, it is relative standing. People want to be above average compared to their neighbors. That sounds like it confirms the positional goods trap, until you realize what it actually implies: if status is relative and socially constructed, then a society can choose what the ladder measures. We can anchor status to service and contribution rather than accumulation and dominance. We can celebrate the nurse and the teacher the way we once celebrated the bankers and oligarchs. When we do, the care economy stops being a consolation prize and becomes the point. That is not utopian hand waving. It is the most practical lever we have.

Meanwhile the automatable goods keep getting cheaper, food, energy, medicine, electronics, trending toward the near zero marginal cost I described in Abundanism. AI becomes a global public good rather than a platform’s private moat, and the GI Bill for AI described above scales globally, starting with the developed markets hit first and hardest. Life satisfaction, lifespan, and measured wellbeing rise, which is exactly why I keep arguing we should steer by GDP-B and the OECD Better Life index alongside GDP, not GDP alone. My own reading of the data suggests that redistributing even a few thousand dollars a year per capita from the richest economies to the poorest could nearly equalize global happiness, at least in the near term. Abundance makes that arithmetic practically affordable.

Demographics shift in a hopeful direction. In emerging markets, birth rates continue their long decline as lifespans lengthen, child mortality approaches zero, quality education democratizes, and the deflating cost of a good life reduces the old economic logic of large families. This is the demographic transition, well established, not speculation. In the developed world, my claim is softer: fertility does not fully recover to replacement, the Nordic experience shows money and leave alone do not do that, but it rebounds partway, lifted by reclaimed family time, the revaluation of caregiving, and cheaper living. As local prospects improve, the pressure for mass migration eases, communities strengthen where people already are, and paradoxically the ties between nations strengthen at the same time.

Reconnecting III: Nations (realign, at civilizational scale)

The third reconnecting is of the global system built over the last 80 years and fractured over the last decade. In 1948, the US deployed the Marshall Plan to help rebuild Europe (with parallel aid programs doing similar work across East Asia), committing about $13.3 billion over four years, roughly $150 billion in today’s dollars. Many historians call it one of the best investments America ever made, yet the inflation number understates it: the plan consumed about five percent of US GDP, and the same share of today’s economy is nearly $1.5 trillion.

The dollars were never the secret: Marshall aid averaged only about 2.5 percent of recipients’ national income. The outsized return came from everything the money carried with it: conditionality that forced trade liberalization, institutions like the OEEC that matured into the OECD and seeded the EU, and procurement that cycled mostly through American factories, rebuilding US industry’s customers. An AI Marshall Plan should copy that design, and the Reckoning makes it affordable. Within two or three years of the correction, distressed GPU fleets, half-built data centers, and desperate equipment vendors can be bought for pennies on the dollar, and the build-out becomes the countercyclical backstop for the industries the crash gutted. The crisis does not just create the political ripeness for cooperation. It creates the discounted asset base that pays for it.

What would it cost? Far less than the objectors assume. The plan funds durable infrastructure, not handouts: regional inference data centers, energy generation and grids, telecom backbones, and the people trained to run them. It includes no frontier training clusters and no advanced chip transfers. Everyday intelligence runs on small open models at the edge, with modest cloud capacity behind them, keeping the program outside dual-use territory and openly verifiable. My estimate is $150 to $280 billion over seven to ten years across all funders, and the bill shrinks while you read this, because the price of a fixed level of intelligence is falling roughly fifty-fold per year. America’s five largest hyperscalers plan to spend over $800 billion on AI infrastructure in 2026 alone. One year of Big Tech capex funds the entire program twice over. Devices need no subsidy: handset prices fall on their own, and billions of newly connected consumers become the growth market the plan’s partners will compete to serve.

Figure 4: An infrastructure-first AI Marshall Plan against historical and current benchmarks.

Why would rivals fund it together? Because, like the original, it is not charity. Marshall dollars were spent overwhelmingly on American goods and shipping, a purchase order for US industry that Congress found easy to ratify. The same engine runs here. American firms supply the cloud, the models, and the developer ecosystems. China supplies low-cost solar, grid equipment, and build-out muscle honed across these geographies, this time on grant-heavy terms rather than the loans that earned the Belt and Road its debt-trap reputation. Europe brings development finance and its regulatory trust brand. The Gulf states join too: sovereign funds like MGX, HUMAIN, and G42 are already converting oil wealth into compute at hundred-billion-dollar scale, and co-funding the plan is the surest way to stay indispensable after fossil fuels. Nobody has to pool money with a rival: parallel national vehicles under one shared standards and transparency framework, the structure of the Montreal Protocol’s Multilateral Fund, which phased out CFCs, lets Congress fund American vendors while Beijing funds its own. What each funder buys is participation: whoever helps build the stack four billion people adopt gets a voice in the standards that govern it, and whoever abstains forfeits that voice. The resulting interdependence is not a cost. It is the point: the mechanism that made Franco-German war unthinkable within a generation.

“What is long divided must unite, and what is long united must divide. Thus it has ever been.”

Luo Guanzhong, Romance of the Three Kingdoms

The architecture matters, and it’s documented in Beyond Rivalry. We need to build a CERN for AI, an international consortium modeled on the organization that pooled dozens of former enemies after the Second World War to do open, peaceful science, and invented the World Wide Web along the way. That consortium trains a base Guardian AI on a Global AI Data Cloud, a globally representative pool of data so the system understands every culture rather than one. The base Guardian model is open sourced and shared with all nations. Each nation then fine tunes its own Sovereign AI on top of that shared foundation: local values on a common safety floor. Underneath it all runs an AI Marshall Plan that deploys reasonable cloud infrastructure everywhere, while highly capable distilled personal models run locally on our glasses, phones, and PCs, in service of the individual and not the platform, which is the whole spirit of the Bill of Rights for the Age of AI. And because none of these runs without power, clean energy cooperation becomes strategy rather than charity, the physical substrate for shared prosperity, with China’s low-cost energy technology, the source of over 80 percent of global solar manufacturing, helping electrify the transition.

This is also where I part ways with AI 2040: Plan A, the safety community’s most rigorously argued cooperation scenario. Its verification toolkit, compute accounting, declared-cluster inspections, and inference-only monitoring, is the most concrete in the field, and the architecture above should adopt the parts that make sense. But two of its load-bearing assumptions fail. It presumes a fast, winner-take-all takeoff based on raw scale, when commoditization keeps collapsing exactly the moat that assumption requires. And it cannot say where the political will for its bargain comes from; its own authors give it a less than 15 percent chance of success. The Reckoning is that missing catalyst. Where it finally reaches for a doomsday deterrent, mutual assured compute destruction, the Stag Hunt logic says rivals need assurance, not another gun on the table. Its plan is overly focused on state-to-state risks and runaway AI gods, when the real risks in front of us are non-state bad actors and societal instability. These are shared threats we must cooperate on to resolve.

When presented with the preponderance of evidence, our leaders internalize how interdependent and positive sum the world actually is. They stop reaching for conflict as a tool. Better informed populations become harder to frighten into false narratives and refuse to fund wars sold on lies. This is not just a hope. When I researched the most authoritarian versus the most respected national leaders of the last century, the pattern was stark: the authoritarians had far less formal education and most had dropped out before finishing their studies, while the respected leaders were, almost without exception, lifelong learners and avid readers. Knowledge and rationality correlate with better leadership, which is one reason I came to trust that informed, rational AI guidance could improve on human governance acting alone.

Which brings us to the decision support layer, now matured into something bigger. This is where the Guardian AI concept does its real work. Guardian AI guides and informs leaders, it does not rule them. It surfaces the second and third order consequences that human leaders miss under pressure, the misperceptions that turn a Stag Hunt into a tragedy.

For the world to truly progress, we have to move away from “America First” or “China First” to “Humanity First!”

Reconnecting IV: Nature (renew and rejoin)

The fourth reconnecting is with the planet itself, and it requires resolving a tension in my own philosophy that a careful reader will have already caught.

“The difficulty lies not so much in developing new ideas as in escaping from old ones.”

John Maynard Keynes, The General Theory (1936)

Abundanism promises abundance through exponential technology. This reconnecting asks us to stop the endless drive for growth. Those sound contradictory, so let me reconcile this conflict: the goal is abundance of what matters, health, knowledge, connection, time, and satiation of what does not, raw material extraction on a finite planet. In a genuine abundance economy, the driving force is no longer the supply and demand curve of scarcity but the law of diminishing marginal value, the recognition that every good and service has a point past which more adds nothing. Once we accept that we have enough, the compulsion to hoard and to grow for its own sake relaxes, and we come back into balance with the natural systems we depend on. We rediscover that the most important things were always free: kinship, music, art, health, sport, culture, knowledge, community and love. And these are things that grow in value and bring status when shared, not hoarded.

I want to defend the claim that this is our nature and not wishful thinking, because the standard objection is that nature is red in tooth and claw and so are we. That view is the result of a selection bias. Nature contains predation and parasitism and death, but cooperation is more foundational than the caricature admits. The mitochondria powering every cell in your body were once free-living bacteria we absorbed in the great symbiotic merger, and roughly half the cells you call you are microbial partners. A human being is not a fortress. It is a symbiotic, self-balancing system, a small ecosystem maintaining homeostasis. Scale up and the pattern holds: the major transitions in evolution, described by John Maynard Smith and Eors Szathmary, are a history of smaller units cooperating into larger wholes. Symbiosis is the rule at every scale, from the cell to the coral reef to the biosphere. We are built for it. We are built by it!

Reconnecting V: Spiritual (reawaken)

The last reconnecting is the hardest to write about without sounding like I have left the data behind, so let me ground it too.

Every major tradition arrived independently at the same ethical core. The Golden Rule, the ethic of reciprocity, appears in Christianity and Judaism, in Islam and Hinduism, in Buddhism and in Confucius, in almost identical form. Ubuntu says it plainly: I am because we are. In Chinese philosophy, the concept of reconnection is beautifully captured by the idiom Tian Ren He Yi (天人合一), which translates to ‘Heaven and Humanity Awakening as One’. The enlightened among us understand that we are all part of a greater whole, and that our fundamental purpose on this planet is to serve one another rather than to blindly pursue wealth and power.

“I am because we are.”

the southern African concept of ubuntu

These are not coincidences. They are different cultures discovering the same truth about a cooperative species. And this is my actual position on the oldest debate, whether humans are good or bad. We are neither. We hold both capacities, and which one expresses depends on the environment we build. An environment of scarcity selects for hoarding and conquest, which is the world our institutions were designed for. An environment tending toward abundance selects for our better angels. This is the Bonobo Lesson at the root of Abundanism: we are products of our conditions, and we finally have the power to change the conditions.

Borders themselves will soon start to feel like what they always were, man-made lines that keep people apart, meaningless to a form of intelligence that can be everywhere at once. It takes time for the world to transition its thinking, but it is all a matter of time, especially as we get closer to abundance. AI, used well, helps each of us reconnect with the wisdom, understanding, and compassion that were always there, buried under the survival logic of a scarcer age. This reawakening gives our leaders, and all of us, the wisdom to see the futility of conflict, creating a self-reinforcing loop that tightens the bonds among all five reconnectings.

Beyond the Sun

Icarus fell. But imagine if the fall had taught the villagers below not to fear the sky, but to build wings that would not melt, for everyone.

That is the inversion I am proposing. We flew too close to the sun on wax and hubris, and we will fall. But the fall is what makes the case for building something sturdier and shared. When we finally see AI as a global public good and stop fighting each other, we can combine our efforts and turn them outward, toward discovery and exploration, toward understanding the universe at every scale. And the more we learn about how small we are against that vastness, the closer it pulls us together, because the work ahead is too large for any individual or nation to do alone. Power over others was never the goal. We should replace the current “winner takes all” mentality with a “winners share all” mindset. Harnessing the power within each of us, and throughout all of us, is what brings the long-term flourishing we actually seek.

A static, perfect world was never on offer. What is on offer is a protopia, a world that gets better for more people over time, if we keep choosing it. The Great Reckoning is coming. I think it starts within the year. And if we choose well at the fork, it will be remembered not as the moment the miracle collapsed, but as the moment we finally reconnected: nation to nation, worker to family, human to nature, and each of us to the better part of ourselves.

Together we are not only stronger. We are better.

If these ideas resonate, or if you think I have the fork exactly wrong, tell me. A scenario only works as an argument, and arguments only improve when someone pushes back. If there are others who would benefit from reading this, please share it with them.

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Acknowledgement: Much thanks to those who took time to review and provide feedback on the early drafts (Any remaining errors are solely my responsibility)
Andy Rothman, Erik Brynjolfsson, Peter Diamandis, Lizzi C. Lee, Jing Qian, Gary Rieschel, Chris Varleas, Will Graylin, Grace Shao, Peter Noszek, Brian A. Wong, Jens de Buhr, Nick Rhoads, Kristy Loke and others.

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Around 350 BCE, Aristotle paused a dry passage on household management to imagine the impossible. He wrote, “Suppose that if every tool could do its own work on command, that the weaver’s shuttle could weave on its own and the quills played harps themself…then the master craftsman would have no need of assistants, and master no need of slaves.” Twenty-three centuries before the first transistor, he had described autonomous robots and the end of forced human labor.

The prediction was never the gift. The gift was the question Aristotle asked next, the one we are still dodging. If the machines do the work, what is a human life actually for? We are not ready for that answer, because too many of us have quietly collapsed our entire identity into our jobs. We are our title. We are our output. So machines that produce more, faster, and cheaper do not feel like liberation. They feel like erasure.

But production is not what makes us human. A robot will out-produce us all. What makes us human is the part the assembly line never measured: the dignity of being, and the ability to feel and give love. Somewhere along the way, capitalism stopped being a tool and quietly became our religion, with the Protestant work ethic as its catechism, the story we told ourselves to justify pushing billions into labor they knew was meaningless. Work is virtue, idleness is sin, your value is your productivity. It was a useful creed for an age of scarcity. It is a destructive one for the age now arriving.

We have a once-in-a-civilization chance to outgrow it, and we should not waste it by clutching the old creed so tightly that it drags us back toward a new feudalism, where a few own the machines and everyone else competes for scraps of purpose. But to seize that chance, we first have to understand what all of this technology is freeing us for, and not merely what it is freeing us from. And for that, it helps to return to the thinker who imagined this very moment more than two thousand years before it arrived.

What Aristotle actually saw?

Aristotle did not picture his automated future as idleness. Work was always a means; the end was eudaimonia, a word we mistranslate as happiness. It means flourishing: the active exercise of our highest capacities, in line with virtue, across a whole life. And it requires what he called schole, genuine leisure, not the couch and the feed but the free time in which a person genuinely thinks, creates, governs, befriends, and grows. We are unleisurely, he wrote, in order to have leisure. Toil was the entry fee. Flourishing was the point.

He drew one more line that matters enormously now. He split poiesis (job), the making of things for an end outside themselves, from praxis (the doing), action that carries its purpose within: thinking, creating, governing, caring for a community. Poiesis is what we do in order to live. Praxis is the living itself. Here is the quiet revelation of the AI transition. Machines automate poiesis. They do not, and largely cannot, automate praxis. They are coming for the means, not the ends. What they free is exactly the part of us that was never about output, which is the democratization of schole: the highest human luxury, once the privilege of a leisured few, that may now be turned into a birthright for all.

Aristotle could imagine this for everyone but never deliver it, because in his world the leisure of the few was paid for by the labor of slaves. Athenian flourishing sat on a brutal foundation. That is what makes this moment extraordinary. Advanced AI and robots are the automatic servants he imagined, except no one is enslaved to provide them. Technology becomes the engine of universal emancipation rather than its enemy, shifting the burden of toil off conscious beings and onto silicon and steel. For the first time, the flourishing once reserved for an elite can become the inheritance of everyone alive. We can finally realize an abundance envisioned 2,375 years ago. The next question is how will that change us?

The wave is real, even where the water looks calm

Before we turn to that question of meaning, though, it is worth pausing on a more immediate and entirely fair objection. If the machines are so capable, where are the missing jobs? Most offices look much as they did three years ago. That calm is the most misleading data in the debate. In The Enterprise AI Playbook, a Stanford Digital Economy Lab study I wrote with Elisa Pereira and Erik Brynjolfsson, across fifty-one successful AI deployments in eight countries, the same models produced change measured in weeks at some firms and years at others. The difference was never the technology. It was always the organization. More than three-quarters of the hardest obstacles were invisible and human: change management, data quality, and process redesign. General purpose technologies, like AI, have always taken significant time to deploy broadly into society as they require significant change in work flows to show their benefits. Thus it’s adoption shape is often called a J-curve.

The evidence lag is real, but it is a deployment lag, not a capability ceiling. Today’s systems can already perform most of the tasks in many white-collar roles, and firms rebuilt around agentic AI report median productivity gains north of 70 percent. Stanford’s Canaries in the Coal Mine study already finds early-career workers in exposed roles losing ground, and the World Bank estimates more than 60 percent of US jobs are exposed. The last three industrial revolutions took roughly 80, 60, and 40 years to unfold. This one could compress into five to ten. We just need to slow down AI for a bit (globally) to let society and the economy catch up, so it doesn’t break. Humanity has proven to be highly adaptable, but we need time. And in the big picture, if AGI arrives in 2 years or 10, it doesn’t matter much. But if AGI brings massive societal disruption, it matters a lot. It’ll take some international coordination to make it happen, but both the US and China have now expressed interest in collaborating on AI governance frameworks, at least around safety.

Why it will not feel like liberation?

The disruption is real, and it is arriving faster than our institutions are prepared for. You might assume that being freed from labor would land as pure relief. Ask people what happens when the machines take the jobs and you rarely hear relief. You hear dread, and that dread is the most important clue we have. Max Weber saw why a century ago. The Protestant ethic made labor a sacred duty and accumulation a sign of grace; the theology faded but the compulsion hardened into what he called an iron cage. We rank our worth by our output long after forgetting why. His verdict on the people inside it still stings: specialists without spirit, sensualists without heart. The cage is no longer around us. It is inside us. Which is why simply removing the need to work will not, by itself, free anyone.

The evidence is blunt. Lottery winners drift back to their old baseline within a year or two; the hedonic treadmill resets us to neutral after almost any gain. The Easterlin paradox shows the same across whole societies: above a threshold of security, more income barely moves life satisfaction. Material abundance has a ceiling, and the rich world is already pressed against it. More stuff was never going to save us.

What a human life is for

Eudaimonia is a verb, not a mood: you enact it, you cannot buy it. Modern science agrees. Self-Determination Theory, the most validated framework in motivation research, finds three innate needs: autonomy, the freedom to author your own life; competence, the drive to master things that matter; and relatedness, the need to be connected to and needed by others. None of the three requires money, scarcity, or a job. If neither a paycheck nor a pile of possessions can grant any of them, then where does a good life actually come from?

There is one more ingredient, and Homer saw it three thousand years ago. In the Odyssey, the nymph Calypso offers Odysseus the perfect post-scarcity life: immortality, safety, endless ease. And he weeps. Not from pain, but because on her island nothing depends on him. No one needs his return, no fragile web of obligation makes his choices matter. As I argue in my essay on Homer’s sirens and nymphs, Calypso’s trap is not pleasure. It is the loss of stakes. A human life can quietly end, without any violence, when it becomes fully solved and gently relieved of the need to choose. The deepest danger of abundance is not that we will have too much. It is that we will stop becoming.

Homer also handed us the remedy. Odysseus does not survive the Sirens through willpower; he has himself bound to the mast, choosing a constraint in advance to protect the self he wants to remain. I call it the Mast Principle and it inverts a modern assumption: the opposite of freedom is not constraint, it is drift. In a world saturated with frictionless ease, the people who flourish will be the ones who bind themselves to commitments that outlast their moods, to a craft, a cause, a community, a person who depends on them. Abundance can remove the chains of necessity. It cannot, and must not, remove the stakes.

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Four fallacies we have to bury

If autonomy, mastery, connection, and stakes are what a flourishing life actually needs, why do we still find it so hard to believe that a life without forced labor could be a good one? Four old beliefs stand between us and that future. Each made sense under scarcity. Each is now a trap, and each is worth naming plainly.

Work is the source of our value. It never was. Work was the source of our survival, which we confused with worth. A nurse who retires is not worth less, and a child who has never held a job is not worth nothing. Dignity is intrinsic to being human, not a wage we earn through output. This is the first axiom of Abundanism: work is no longer the source of value, dignity is. Untether the two, and the fear of obsolescence loses its grip.

Leisure breeds laziness. This misreads both. Schole was the most active state of all, and biology agrees: the brain rewards pursuit and mastery for their own sake. Give people a real floor and most do not melt into the couch. They garden, build, coach, study, make music, and start new things. Idleness is what happens to caged animals, and to people whose every waking hour was already sold to someone else.

Meaning must be earned through suffering. This is the most seductive fallacy, because hardship can ennoble. But the suffering was never the point. We do not need misery to feel alive. We need a life that still demands something of us even when no one can force it. Keep the chosen difficulty that gives an achievement its weight, and let go of the pointless pain we dressed up in the language of virtue.

The rich and powerful will never share, so why bother seeking change. History and arithmetic disagree. I run the full numbers in The AGI Windfall Mirage, but one belongs here. Erica Chenoweth’s 3.5% rule finds that no modern government has survived a sustained, nonviolent movement that mobilized more than three and a half percent of its people. In the United States that is roughly twelve million, and AI’s displacement is on track to alienate far more. The historian Walter Scheidel, in The Great Leveler, is blunter still: across the centuries, great inequalities have been leveled mainly by catastrophe, war, revolution, collapse, and plague. Sharing, then, is not charity. It is the cheapest insurance the fortunate will ever be offered.

The last scarcity is status

Even if every material need is met, one hunger remains, the one behind most of our cruelties. We are status-seeking animals, and René Girard explained why. His idea of mimetic desire is unsettling once you see it: we mostly do not know what we want, so we learn it by watching what others want. Desire is a triangle, you, the object, and the model who taught you to crave it. Watch a toddler ignore a toy until another child grabs it. We are not chasing the objects. We are chasing the fullness of being we imagine the other person possesses.

This is why abundance alone will not bring peace and could even sharpen the conflict. Status is positional; you cannot manufacture it for everyone at once. When goods become free, the rivalry simply moves upstream, to what stays scarce: attention, recognition, and rank. Girard, who died in 2015, predicted the attention economy decades early; our platforms now pit every person as everyone else’s rival in real time. Worse, a society under that pressure stops looking for solutions and starts looking for someone to blame: the immigrant, the foreigner, the other party, the other nation. A clumsy transition manufactures scapegoats. We should be defusing that now, not acting surprised when it erupts.

But status is not our curse. It is our most powerful lever, and abundance is about to rewrite it. When comfort is common, consumption becomes a weak signal; when intelligence is cheap, raw output becomes one too. What stays genuinely scarce, as I argue in that same Homer essay, are the human qualities no machine can mass-produce: integrity, taste, courage, compassion and the willingness to serve when you do not have to. The new measure of status is not what you own, but what you carry. Not how much power you hoard, but how much capability you unlock in others.

We need new heroes!

Why do we reserve our loudest applause for billionaires and the powerful, for the people who have simply managed to accumulate the most? As Girard reminds us, status will remain scarce even after material scarcity is gone, so the real question is not whether we will compete for it, but what we will choose to grant it for. Let us redefine status into something more meaningful and more lasting. Let us celebrate the people who give and serve.

The central project of a post-scarcity civilization is not technological. It is to deliberately point desire at better models, to make heroes of the people who build, heal, teach, create, restore, and give. Pablo Picasso compressed the whole reorientation into a single line:

“The meaning of life is to find your gift.
The purpose of life is to give it away.”

Pablo Picasso

And Nikola Tesla, who could have cornered the electrical age and instead pushed much of it toward the public good, set the standard for every builder who followed:

“Science is but a perversion of itself unless it has
as its ultimate goal the betterment of humanity.”

Nikola Tesla

We have always had such heroes, and we should celebrate them more loudly, because the stories we tell decide whom our children imitate. But I’m not saying there’s something wrong with being rich or even seeking wealth. It’s what you do with it! Alfred Nobel turned a fortune built on dynamite into a prize for those who most benefit humankind. Andrew Carnegie gave away ninety percent of his wealth and warned that “the man who dies rich dies disgraced.” John D. Rockefeller seeded modern public health and medicine.

And in our own time, MacKenzie Scott has given away more than twenty-six billion dollars since 2019, with no strings and no naming rights, deliberately keeping the spotlight on the communities doing the work rather than on herself. She gives so quietly that one prominent donor ranking left her off its list for lack of disclosure, in a year when her giving alone eclipsed the entire lifetime philanthropy of her billionaire former husband. That is exactly the point. She is not buying status. She is inventing a better one.

We should change our scoreboards to match. Each year Forbes ranks Americans by what they have, on the famous Forbes 400, and by what they have given away, on its list of top givers. We worship the first list and barely glance at the second. Reverse that. Put the givers on the cover and teach our children their names, because wealth counted only in dollars is a child’s definition of wealth. Real wealth is measured in contribution and service, in lives improved and futures unlocked. Accumulation for its own sake is not a virtue. It is hoarding with better branding.

This is my direct appeal to the billionaires, and soon the trillionaires, that this wave will mint. The choice you make about those fortunes will define you far more than the fortunes ever could. Stop accumulating. Start giving and serving. The more you help, the greater you are. Not the more you have. Knowing that you have enough is one of the surest signs of true wisdom. Honor contribution over possession, count what matters with new measures like Erik Brynjolfsson’s GDP-B vs. just GDP, and the most powerful force in human society quietly changes direction.

But only if we share the machines

All of this rests on one hard condition: universal flourishing requires near-universal access to the engines that produce it. Aristotle’s looms only freed the household because the household owned them. Concentrate the looms in a few hands and you do not get emancipation, you get dependency. If frontier AI, compute, and robots are owned by a handful of firms or states, the result is not Athens for everyone. It is a new aristocracy holding the most powerful tools in history above a vast underclass with nothing left to sell, because its labor is no longer needed. Thus, the oldest question in political economy returns in a strange new form: who, in the end, will own the looms?

The distribution is not a footnote to the technology. It is the whole game. The windfall everyone is sprinting toward is, as I argue in The AGI Windfall Mirage, largely an illusion even for the frontier labs, because competition pushes the gains downstream to the firms that deploy and ultimately to consumers. Our task is to make that diffusion reach everyone on purpose: through universal basic income and infrastructure, broad access to open tools and safe AI, and automation taxes that fund the transition. Winner-takes-all ends, reliably, with no winners at all. Winners-share-all is not idealism. It is the only configuration stable enough to last.

We are the architects now

None of this is naive, because we have proof that environment can flip a species from domination to cooperation. About two million years ago, the Congo River split one ape population in two. North of it, in harsher and scarcer country, the chimpanzees built alpha-male hierarchies and territorial violence. South of it, in gentler abundance, the bonobos became egalitarian and cooperative led by matriarchs. The two are about 99.6 percent genetically identical, and we share more than 98.5 percent of our DNA with both. The variable was not the genes. It was the environment.

Scarcity summons the chimp. Abundance can summon the bonobo. And unlike the apes, we are no longer passive passengers of evolution. We are the architects of the conditions that shape us. That is the heart of Abundanism, my framework for building the abundant environment on purpose: AI as a governance partner rather than a master, value tied to service rather than output, a real floor of basic income and infrastructure beneath every life, immersive worlds as space for purpose, and education as a tool for peace.

Today, we get to choose which side of the river we live on. But the deepest pillar is in our heads. Before we can change the world, we must change our minds, and that includes whom we choose to admire.

Changing our minds may be the hardest thing a successful civilization is ever asked to do, because it means loosening our grip on the very habits that made us successful in the first place. We must, in a sense, learn to forget what brought us here. It sounds reckless, but biology has shown us the way already.

We’ve all heard about the metamorphosis of the butterfly, but there’s a detail many don’t know. A caterpillar is a magnificent eating machine, and that single program, consume and grow, is the only thing it has ever known. Yet to become a butterfly, it has to stop and build a chrysalis. Once inside it has to dissolve its mind and its program almost entirely before it can reassemble into something that flies, pollinates, and gives back to the ecosystem that once fed it. For the last few hundred years, as a society, we’ve been executing the caterpillar program. We are now in our own chrysalis moment, where the relentless consumption that built the modern world cannot be the thing that carries us beyond it.

The choice

Aristotle handed us the robots and the question. Weber warned that the cage is internal. Girard warned that the last competition would be over status and showed the way out: aim desire at better models. Homer warned that the real danger is not too much, but the quiet end of the need to choose. We have a narrow window, most likely this decade, to decide who our heroes will be.

So let this be the goal we organize everything else around. Not growth for its own sake, not accumulation, not even comfort, but human flourishing: the chance for every person to live a life of meaning, contribution, and active joy. That is the prize Aristotle pointed to across twenty-three centuries, the praxis the machines can finally set free. The robots are the means. Flourishing is the end. And to reach it we will need new heroes, people who measure themselves by what they give rather than what they hold, and a culture brave enough to put them on the pedestal we have been reserving for the merely rich.

So let us choose to become them. Let us be the people our children look up to, not because we accumulated the most, but because we gave and served the most. Let us measure greatness by how much we help, and wisdom by knowing when we have enough. Find your gift, and give it away. The chrysalis is now forming. It is time to learn how to fly.

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I write regularly about the transition to a post-scarcity world at the Abundanist Substack. Please read the other essays. A deeper discussion on the coming impact of AI can be found in my book, Our Next Reality. The future is not something that happens to us. It is something we choose. Let us choose well.

(This is a long read, but a deliberate one, as the topic warrants it. I promise it will bring clarity to some critical misunderstandings that have pushed the AI industry and government policies in a dangerous direction. If it changes how you think about the AI industry, please pass it to someone who should see it too.)

A Mirage in the Mojave

Last summer, I was driving my family across the Mojave Desert. Somewhere west of Baker, California, the asphalt ahead of us began to shimmer. A wide silver lake stretched across the road, palm trees waving on its far shore. My daughter pressed her face against the window. “Dad, slow down. There’s water.”

I did not slow down. I kept driving. The lake retreated as we approached, then dissolved into hot air and salt flats. A few minutes later, it reappeared a mile further on. Then again. And again. Each time, my daughter insisted it was real. Each time, the desert insisted otherwise.

What she was seeing is called an inferior mirage. Hot air near the ground bends light, and your eyes, evolved to expect refraction only through water, draw the only conclusion they know how to draw. The brain is not lying. The physics is not lying. The interpretation is just wrong.

I have been thinking about that drive for the last year. Because the most powerful people in technology, finance, and government have stared into the heat shimmer of artificial general intelligence and reached the same confident conclusion my daughter reached. There is a lake out there. If we sprint hard enough, we will reach it first, and whoever reaches it first will drink uncontested for a century. There’s a common misperception among many AI lab heads and their investors that the first lab to achieve AGI will capture a windfall that approximately equates to the whole value of the white-collar labor market (annually ~$9 trillion in the US and ~$30 trillion globally). Most of them just talk about it privately to their friends, but Elon Musk’s SpaceX is saying the quiet part out loud. The newly published SpaceX S1 filings (which includes xAI, the company’s AI division) exhibits this assumption overtly. The filing’s single biggest target revenue segment, at roughly 80% of its $28.5T TAM, is what it calls the ‘Enterprise Application’ market (see figure below). Since the global IT and software market is only $5-6 trillion, the rest must reflect the company’s expectations for yearly gains from white-collar wage replacement.

Thus, spending a few hundred billion dollars in the next few years for that long-term prize should be a “no-brainer”, right? Unfortunately, it’s not that simple.

I want to argue, in this essay, that the lake is not there. That the “windfall” the first lab to AGI will allegedly capture is a mirage produced by the heat of capex overspending, the refraction of inflated stock prices, and the bent light of arms-race rhetoric. The race itself is real. The danger of failure (and success) is real. The potential of the technology itself is staggering. But the prize at the end, first-mover monopoly on superintelligence, is an optical illusion. And the policies built on that illusion are quietly setting fire to the world this technology is supposed to save.

I write this as someone who has spent 35 years inside the AI, semiconductor, telecom and cybersecurity industries on both sides of the Pacific. I have built four venture-backed startups enabled by AI, invested in over 100 and held executive roles at some of the biggest tech firms in the world. I’ve designed CPUs with GPU-like capabilities 10 years before Nvidia released CUDA. I’ve driven the construction of multiple global data centers across Asia. I’ve commercialized AI-driven natural language mobile search serving tens of millions of users on multiple carriers 17 years before ChatGPT released. I drove cybersecurity solutions for two of the largest firms in the industry. I started studying neural networks and natural language processing in 1990 and now do research on the economics of AI and associated policies at Stanford and teach it at the University of Washington. I currently advise governments, multilateral bodies, and Fortune 100 leaders on AI policy and post-AGI transition. I am a beneficiary of the AI boom, not a bystander. That is precisely why I think the bystanders deserve to be told the truth.

Eight Things You Will Not Hear at the Next AI Summit

Before we walk into the desert together, here are the eight counterintuitive and uncomfortable truths this essay will discuss. If even half of them are right, the current race strategy is not just wrong. It is a wealth-destruction machine wearing the mask of a moonshot.

• Cisco was right about the internet and lost 86% of its market cap anyway.
Being correct about the technology is not the same as capturing the value. The AI labs may be right on the power of the technology, but they won’t be able to get the ROI they hoped for.

• Chinese AI labs can profit at one-tenth US training costs even after open-sourcing the model. Commoditization is not a side-effect of their strategy. It is the strategy.

• Frontier AI models are supercars. The global economy runs on buses, cars, and mopeds. Cheap, good-enough intelligence will eat the market the frontier models were supposed to own.

• There’s a chance that the GDP doesn’t grow much nominally from AI productivity gains. Meanwhile, the quality of life of the average person improves dramatically, making consumers the real winners. This would not provide a return to those investing trillions in AI today.

• AI labs are racing each other into a Cantillon-effect cliff they themselves helped build. Every dollar of training capex inflates the inputs of their nearest competitor, making the cost of future revenues ever more expensive while revenues per token are rapidly falling.

• The much-touted Jevons Paradox has an end point. Even as prices get cheaper, long-term demand stabilizes for any resource, including AI. The massive infrastructure buildout will find it increasingly difficult to earn a sustainable return on its investment.

• China is not racing to AGI. It is racing to diffusion, which is the only race that pays. Their “AI Plus“ plan targets 70% adoption by 2027 and 90% by 2030, while the United States spends trillions winning this week’s benchmark.

• In recorded history, no government has withstood a sustained nonviolent challenge from 3.5% of its population. AI is on track to alienate far more than that. The K-shape economy is not just unfair. It is unstable and unsustainable.

Let me show you the data behind each of these and then propose what to do about it.

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The K-Shaped Amplifier: Whose Boom Is This?

Start with the macro picture. Adam Tooze, writing in Foreign Policy on May 12, 2026, describes an American economy where “more than 49 percent of all consumer spending in the United States is now performed by the top 10 percent of U.S. earners.” (see figure below)

Innes McFee, CEO of Oxford Economics, told Fortune in January 2026: “Eventually, it might bring things together, but in the meantime…it’s unlikely that AI helps at all with the K-shaped economy.” Morgan Stanley’s Lisa Shalett has called the AI capex cycle “wackadoo” in scale relative to historical comparisons. Larry Fink at BlackRock has openly warned that the economy is bifurcating between asset owners and wage earners in ways that political systems have not historically survived.

This matters for our windfall analysis because it tells us where the money is actually going. It is not flowing to the workers whose tasks the models are eating. It is flowing to a thin band of equity holders, GPU suppliers, and infrastructure landlords (for now). And the AI labs themselves are not capturing it either. They are spending it! Even their internal forecasts are not expecting real profits for another 3-4 years. After finishing this piece, you may realize that may be overly optimistic.

Some readers will object that the K-shape is a temporary distortion, that displaced workers will find new jobs as every previous technology cycle eventually delivered. That is a fair objection, and it deserves a serious answer. The honest answer is that we do not know, but the structural evidence is worse than for previous transitions. In the past three industrial revolutions, it took 80, 60 and 40 years to play out, respectively, giving time for the economy and the population to adapt. This round of disruption could unfold in the next 5-10 years. Our society is not set up to accept such a shock.

The Stanford “Canaries in the Coal Mine” study finds that early-career workers in AI-exposed occupations are already losing positions. The World Bank estimates that over 60% of US jobs are AI-exposed, the highest share in the world, yet we don’t have any preparations for or a social safety net to protect the affected workforce. The transitional friction window is not a footnote. It is the policy problem.

The Cisco Lesson: Being Right and Losing Anyway

Here is the story that nobody at the labs wants you to internalize. In January 1999, Cisco was the company selling the picks and shovels of the internet revolution. Anyone who looked at the data could see the internet was not a fad. Cisco shares rose 236% in the next fourteen months. In March 2000, Cisco surpassed Microsoft to become the world’s most valuable company at a market capitalization of roughly $555 billion. Analysts predicted it would be the world’s first trillion-dollar firm.

Then the dot-com bubble burst. Cisco shares fell 86 to 88 percent over the next two years, wiping out approximately $431 billion in shareholder value. Cisco’s revenue kept growing. Its net income recovered. The internet kept eating the world, exactly as the bulls predicted. And yet Cisco stock did not regain its March 2000 peak until December 2025, more than a quarter century later.

Cisco was right about the internet. Cisco helped build the internet. Cisco still lost.

A common objection here is, “But Anthropic just hit $44 billion ARR in May 2026, up roughly 40x from a $1 billion run rate at the start of 2025. If AI is commoditizing, how is that possible?” It is a legitimate question and it deserves three clear caveats. First, much of that revenue is what I will charitably call a “token-maxing” frenzy driven by the rush to deploy agents, which is now getting a reality check. There is no moat and no loyalty. That rapid near term growth is primarily pushed by novelty plus inelastic enterprise demand for a tool with no nearby substitute. Both will compress. Second, the federal government’s February 2026 decision to blacklist Anthropic as a “supply chain risk” and shift defense workloads to OpenAI demonstrates how fragile concentrated enterprise revenue can be when politics intervenes. Additionally, Anthropic benefited from a rush of paid subscribers defecting from OpenAI in protest to them signing the Pentagon deal, fueling some near-term revenue growth in Q2. Third, the compute side is becoming visibly circular. Hyperscaler “other income“ from equity stakes in private AI companies accounted for nearly 60% of Alphabet and Amazon’s Q1 2026 income, money the hyperscalers invest in labs that then sign multi-gigawatt compute contracts back to the hyperscalers. xAI’s deal to backfill Anthropic capacity using SpaceX-aligned compute is the same pattern. The FT chart below shows the expected AI ROI for the major US hyperscalers assuming they have zero operating costs for the data centers. This should be troubling.

All the major US private frontier labs are preparing for their IPO as soon as possible, so everything being posted by them near term must be looked at with that lens in mind.

Cisco in 1999 also had no nearby substitute. Cisco’s revenue grew. The wipeout came not from the technology being wrong but from the valuations being divorced from the cash flows the technology could ever generate. We are in a similar place. The question is not whether AI will transform the economy. The question is whether the company that builds the smartest model captures the value or watches it leak past them like water through a sieve.

The Wage Bill Is Already Leaking

Where does the leak go? It goes to the deploying firms and, eventually, to consumers. This is not speculation. It is now measured. In April 2026, the Stanford Digital Economy Lab published The Enterprise AI Playbook: Lessons from 51 Successful Deployments, authored by Elisa Pereira, me, and Erik Brynjolfsson, based on extensive interviews with organizations from 8 countries representing over one million employees.

The headline finding, stated in the foreword: “Across 51 enterprise cases, we found stories of transformation measured in weeks and others measured in years. Same technology, same use cases, vastly different outcomes. The difference was never the AI model. It was always the organization.” Across our sample, 77% of the hardest implementation challenges were invisible and intangible costs of change management, data quality, and process redesign, not technical issues. The biggest productivity multiplier was the operating model: agentic deployments using escalation-based human oversight delivered a 71% median productivity gain, versus 30% for approval-gated models.

The economically critical implication is that the lab does not capture this 71%. The deployer of the technology does (for now). And as inference prices fall, more of the surplus flows downstream to the firm and ultimately to the consumer through lower prices. This is the J-curve Brynjolfsson et al have been documenting for a decade: a short-term productivity slump as organizations restructure, followed by a payoff that the people who built the technology rarely capture.

That same payoff structure produces a darker dynamic in the labor market. Brett Hemenway Falk and Gerry Tsoukalas modeled it formally in The AI Layoff Trap. Their result is bleak and important. Workers are also customers. When you fire them, they stop spending, which damages your competitors. In a competitive market, rational firms get trapped in an automation arms race that destroys consumer demand faster than the economy can rebuild it. As the authors put it, the result is “displacing workers well beyond what is collectively optimal.” When one firm automates, the laid-off workers’ lost spending damages every other firm in the market, but the automating firm bears only 1/N of the demand destruction. The trap is structural. Wage adjustments, capital income taxes, worker equity participation, UBI, and upskilling cannot eliminate it. “Only a Pigouvian automation tax can,” the authors conclude.

A Pigouvian tax, named for the British economist Arthur Pigou, is a levy designed to align private cost with full social cost by pricing in the externality that the market fails to capture. Carbon, petro and tobacco taxes are some familiar examples. For automation, the externality is the demand destruction that each displaced worker imposes on every other firm in the economy. Implementation options worth serious debate and piloting include a per-token surcharge on commercial AI inference (or robot tax), scaled to labor-displacement intensity, which would raise the marginal cost of substituting machine cognition for human work; a tiered tax on hyperscaler data-center construction and operation, set above the rates that apply to ordinary retail or commercial real estate, which captures rents from the physical instantiation of the labor-substituting capital stack; and a graduated automation-payroll levy on firms whose AI-driven labor reductions exceed a defined threshold. These taxes will effectively redistribute the expected value surplus the AI factories had planned to receive and likely slow down the deployments allowing more time for the economy to adapt.

This tax revenue will fund the social ballast, UBII and retraining, that the layoff-trap model identifies as necessary. The price signal itself has a second-order benefit the windfall narrative misses. Making frontier inference more expensive accelerates the migration toward smaller, distilled, and edge-deployable models, which is precisely the direction in which value capture democratizes, both inside high-income economies and globally, including in the markets where the next several billion users live and where today’s frontier API pricing would otherwise lock them out. The tax internalizes the externality and accelerates diffusion at the same time.

AI is genuinely capable of displacing 80%+ of workers’ tasks in multiple industries today and that impact will only grow as their capabilities expand over time, but most firms haven’t yet deployed properly to realize the potential. These are two facets of the same fact. A collection of tech firms have already accelerated AI related layoffs this year. The capability is real. The capture is broken. The lab gets the bill, the deployer eventually gets the surplus, and the laid-off worker subsidizes both by losing the job and the spending power.

The Coming Deflation and the Musk 10x Fallacy

Elon Musk has said publicly that AI will cause US GDP to grow 10x in a decade. I want to take this claim seriously enough to refute it.

For the GDP to grow 10x in ten years (~26% growth/year), prices would have to either inflate enormously or stay constant while real volume increased tenfold. Inflate by an order of magnitude and you destroy the monetary system. Hold constant and you contradict the mechanism that makes AI valuable. If a loaf of bread costs $4 today and AI lets bakeries make ten times as much bread, the bread does not stay at $4. The bread falls toward $0.40, because that is what competition does. Bakery revenue per loaf collapses. Bakery profits do not necessarily fall, because input costs (wheat, energy, labor) fall too, but the dollar GDP contribution of bread collapses even though caloric production rises tenfold.

This is the Acemoglu point, expressed as a price story. In The Simple Macroeconomics of AI, Daron Acemoglu walks through the task-by-task math and concludes that AI will deliver “no more than a 0.66% increase in total factor productivity (TFP) over 10 years,” with the most-likely figure below 0.53%. A common critique is that Acemoglu is the bearish outlier and underestimates the technology. This may be true, but he’s not alone. Goldman Sachs’s Joseph Briggs and Devesh Kodnani put the baseline AI productivity boost at around 1.5% over a decade. The most aggressive published estimate I am aware of remains roughly 3%. None of these will 10x GDP. None of these is even one extra full percentage point of trend growth per year.

A more recent and broader survey makes the point even sharper. The NBER’s April 2026 working paper Forecasting the Economic Effects of AI by Karger et al elicited GDP growth forecasts from five distinct groups: academic economists, AI industry professionals at frontier labs, AI policy researchers, superforecasters, and the general public. The median respondent in every group, including the researchers and engineers working inside the AI companies themselves, projected annual US GDP growth of roughly 2.5% through 2030. That sits modestly above the typical Congressional Budget Office (CBO) long-run baseline of 1.7%, but it is two orders of magnitude below the 10x story. Even conditional on a “rapid” scenario in which AI surpasses humans across most cognitive and physical tasks by 2030, the AI-expert median for 2025 to 2050 annual GDP growth tops out at 5.3%, the economists’ median at 3.5%, the superforecasters’ at 4.0% (see figure below). None of these is 10x. None of these even doubles trend growth. And across all groups, the rapid scenario is judged less probable than either the slow or moderate path.

The gap that matters is not between the bulls and the bears. It is between the people building these systems and the people raising capital on them. The same firms whose CEOs publicly project transformative wealth creation employ technical staff whose own median forecast for the broader economy is roughly the CBO baseline. Either the staff are wrong, or the executive narrative is calibrated to a different audience: late-stage private valuations, prospective IPO roadshows, and federal compute commitments that require a story the internal forecasts do not tell.

What rising AI productivity actually produces, if competition is preserved, is mass deflation. The proper metric is not GDP. It is welfare. Brynjolfsson, et al developed exactly this metric in their GDP-B framework, published in the American Economic Journal: Macroeconomics. Their finding is striking: “improvements in smartphones cameras add approximately 0.63 percentage points per year” to welfare growth that does not show up in GDP. One product category. One narrow improvement. Nearly two-thirds of one percent of annual welfare invisible to the national accounts.

We get what we measure. Status hierarchies, capital allocation, and political legitimacy follow the metrics we publish. If we measure dollar GDP in a deflationary technology cycle, we will see decline where there is abundance. If we measure GDP-B, or the OECD Better Life Index, or Bhutan’s Gross National Happiness, we may finally see the actual ledger of what AI is delivering. A common rebuttal to mass deflation is “look at 1930s America or 1990s Japan, deflation kills demand.” Those were demand-collapse deflations from monetary contraction. A productivity deflation, where the same dollar buys ten times more bread, is a different animal. The policy challenge is not to prevent the falling price. It is to keep the laid-off baker fed while the bread becomes nearly free.

GDP Growth Paradox and Baumol’s Cost Disease

One counter intuitive consequence of the expected effective productivity gains from AI and robotics is that the total GDP may decrease. The GDP may actually shrink in nominal terms but grow faster than in the past on a purchasing power parity (PPP) basis due to deflationary effects. The figure below demonstrates the deflationary effects on prices of goods where production can be more readily automated. The reason some sectors increase significantly in price over time is mostly due to Baumol’s Cost Disease, where some services just can’t be automated even with improvement in technology. For example, a string quartet took 40 minutes with four musicians in 1800 and still takes 40 minutes to perform today. This constraint applies to many areas such as nursing, childcare, food services and education. The good news is that as more people are displaced in automatable sectors, there will exist a larger labor supply that can be redirected to take on more of the roles in the service sector thereby increasing affordability even in non-automatable sectors. The chart below from BLS shows areas where deflationary effects happening in recent years.

Economists generally are fearful of deflationary economies. This dynamic hinges on the difference between supply-side deflation (which is broadly beneficial) and demand-side deflation (which causes depressions). The divergence of Price and Quantity at its simplest, macroeconomic output is defined by a basic equation:

Nominal GDP = Price Level (P) x Real Output (Q)

Artificial intelligence, particularly as it reaches the level of highly capable, open-weight systems, acts as an unprecedented supply shock. It drives the marginal cost of cognitive labor, coding, legal analysis, and logistical planning toward zero. The Deflationary Vector (P) plummets: As the cost of intelligence collapses, the cost to produce almost everything else drops. This is a massive deflationary force, especially for cognitive services. The Productivity Vector (Q) skyrockets: Simultaneously, AI allows for a massive expansion in the volume and quality of goods, services, and software produced. If the percentage drop in prices (P) is greater than the percentage increase in output volume (Q), the total mathematical product (Nominal GDP) would shrink. However, because Q is expanding rapidly, the actual abundance of goods and services in the economy would be growing.

In real terms (adjusting for those massive price drops), purchasing power soars. A single dollar simply buys exponentially more computational power, healthcare diagnostics, and automated services than it did before. We already see this trend clearly: the price per token at the same quality of models is falling by over an order of magnitude per year (see figure below). Over time, as demand stabilizes, as it must, the efficiency gains could continue to improve and reduce nominal GDP as suggested above.

While the scale of AI is novel, the economic mechanism is not. Economists analyzing the “New Economy” have long studied how general-purpose technologies disrupt traditional output metrics by drastically lowering costs while boosting productivity (Nordhaus, 2002). During the late 19th century in the U.S., the combination of the telegraph, railroads, and electrification led to a sustained period of deflation. Prices fell continuously for decades, yet it was one of the fastest periods of real economic growth and rising living standards in American history.

The Debt Inflation Trap

The software-ization of the economy which has happened in the U.S. makes this possibility even more viable. Unlike physical manufacturing, AI services can be scaled infinitely at near-zero marginal cost. As a larger share of the economy moves from scarce physical goods to abundant digital or automated services, the total aggregate price level faces immense downward pressure. Recent macroeconomic models analyzing rapid AI adoption note that AI agents compress intermediary margins toward the cost of pure logistics. As AI becomes more capable, this could trigger a massive repricing across cognitive labor dominant sectors like SaaS, consulting, legal services, financial advisory, and insurance, etc., wiping out trillions of dollars in nominal economic “friction” while delivering similar utility to the end consumer.

While the math suggests my premise is possible, the reality of the political economy introduces severe friction. Shrinking U.S. nominal GDP could prove difficult to sustain. The U.S. government currently carries about $39 trillion in national debt. Debt is a fixed nominal obligation; it does not adjust downward when prices fall. If U.S. Nominal GDP shrinks, the Debt-to-GDP ratio mathematically explodes, making it increasingly difficult to service the debt through tax revenues, especially when taxable income will drop as workforce displacement expands into the higher income white collar segment. The monetary policy response of the Federal Reserve will likely be to fight AI-driven deflation aggressively. If AI drives prices down rapidly, central banks will likely respond by expanding the money supply (lowering interest rates, engaging in quantitative easing, or exploring direct fiscal transfers) to force the nominal price level back up to their target.

In fact, there are some in monetary policy circles who were hoping that what the AI lab heads are forecasting is true, so they could grow their way out of the massive national debt problem we have. Given what is discussed above, that hope may become increasingly difficult to realize, especially since the AI-exposed portion of the US economy is one of the biggest in the world, making a deflationary economy more likely as AI adoption effectively deepens in more sectors.

China Is Running a Different Race

If you believe the windfall thesis, you believe the United States is in a tight, winner-take-all sprint with China to AGI. China itself disagrees. In August 2025, the State Council released implementation guidelines for the “AI Plus” Initiative. The targets are stated explicitly: 70% adoption of next-generation intelligent terminals and agents across six key sectors by 2027, 90% by 2030. The plan also includes employment-protection language. In a 2025 ruling that Beijing’s labor bureau subsequently published as a “model case,” a Chinese court ruled that replacing a worker with AI is not valid grounds for dismissal absent attempts at retraining or reassignment. The diffusion plan and the worker-protection plan are paired.

China had earlier launched, in October 2023, the Global AI Governance Initiative, which has since evolved into a Global AI Governance Action Plan released in July 2025. Beijing is competing for the global standards regime, not for a single AGI threshold.

The cost data matters here. DeepSeek’s V4 Pro, released April 2026, prices at $0.44 per million input tokens and $0.87 per million output tokens, with cache-hit pricing at $0.0037. That is over 34x (86x priority mode) cheaper than GPT-5.5’s output price of $30 per million ($75 priority mode), and close to 137x cheaper on cached input. Anthropic’s Claude Opus 4.8 lists at $25 ($50 fast mode) per million output tokens, roughly 28x more (56x fast mode). Most other Chinese models are priced in the same range and will put significant pricing and margin pressure on the western frontier labs.

The objection here is that all of these benchmarks will move and that frontier models will keep an edge. That is correct. It is also irrelevant. Think of it as Savile Row versus the iPhone. There will always be a market for the bespoke tailor. That market does not set the global garment trade. The global garment trade is set by good-enough mass production. Once Chinese open-source models are within a few percentage points of the frontier on common tasks, and inference pricing is less than 1/10^th, the global diffusion race may soon be effectively decided. The 2026 Stanford AI Index Report measures the gap at just 2.7% for the two countries. The frontier models are more like supercars today. The global economy runs on buses, cars, and mopeds. If the U.S. wants to have a play in global diffusion, it needs to act quickly in encouraging its AI labs to release more affordable vehicles that meet user needs.

Per OpenRouter rankings (5/31/2026), six of the top 10 models used were Chinese open-source models. (see figure) In fact, it’s more accurate to say 7 out of top 10, as the Owl Alpha model from OpenRouter itself was fine-tuned on an anonymized Chinese open-source model. And this data doesn’t include the on-premise or edge deployments that many business customers deploy for open-source implementations due to privacy or regulatory constraints.

The second common objection: why would China keep open-sourcing models that the United States and others can copy? Because open-source is not generosity. It is industrial policy. Per Xinhua’s January 13, 2026 report confirmed by Hugging Face analytics, Alibaba’s Qwen family has crossed 700 million downloads on Hugging Face, “making it the most popular open-source AI system worldwide,” with the Qwen team confirming “more than 180,000 derivative versions” generated from its nearly 400 open-sourced models. Open-source drives cloud-service revenue, sovereign-AI partnerships across 150 Belt and Road partner countries, and a defensive moat against US compute denial. The same firm that profits at one-tenth US training cost can profit again on the cloud, on the inference, and on the diffusion. The strategy is internally consistent. The US strategy of model secrecy plus chip denial fights a war on terrain China has chosen not to occupy.

The Decisive Strategic Advantage Mirage

The deepest mistake fueled by the drive for the windfall is Silicon Valley advocacy for the Decisive Strategic Advantage (DSA) narrative. In my recent “Misdiagnosing the AI Race” essay for the Asia Society Policy Institute Center for China Analysis, I laid out why this dominant DC assumption being pushed by the AI and defense industries, that whichever country crosses the AGI threshold first locks in a permanent, globally dominant geopolitical position, is structurally unsound.

The DSA thesis depends on three claims, each falsifiable. First, that there is a clear finish line. There is not. AGI has many contested definitions, and AI development is a continuous, multi-dimensional process. Even if true, America’s nuclear monopoly, the closest historical analog, lasted only four years. Second, that capability gaps can be sustained through compute denial. Based on recent reports and AI benchmarks, the frontier gap with China has only narrowed over the last year, even with full denial in place, while multiple Chinese labs are training with and tuning for domestic Chinese GPU offerings. Third, that the dynamic is winner-take-all. In practice, Cursor leaderboard data from 2025 showed the top model changing nearly every month. There is no moat for AI models and no user loyalty.

The deeper category error is treating the AI competition as an arms race (zero-sum, secretive, winner-takes-all) when what is actually unfolding is an innovation race and a platform race (positive-sum, ecosystem-driven, multi-winner). Even worse, US policy implicitly models the US-China dynamic as a Prisoner’s Dilemma when game theoretically it is closer to a Stag Hunt. In a Stag Hunt there are two Nash Equilibria, the two hunters can cooperate to catch the stag (safe and aligned AGI) or defect to hunt hares (good enough models that boost the economy). (See Payoff Table below) The worst possible outcome is precisely where the US finds itself today: attempting to hunt the stag alone (which is unlikely to be achieved alone) while the other player calmly collects hares. The more desirable strategy for the U.S. is to reduce capex investment near term to lessen economic strain until there’s more clarity on a clear path to a safe AGI implementation and begin some reasonable regulations on the AI labs, so their profit motives don’t create more harm than good. This helps reduce the shock of a massive market correction, gives more time for the economy to adapt to new market forces, and ensure greater safety from misuse or alignment issues. There may also be new algorithmic or architectural breakthroughs on the way that makes the current scaling focused approach unnecessary or obsolete.

This is not merely abstract. The US has committed over $800 billion in AI capex for 2026 while job openings have declined sharply since 2022. The military DSA argument is also weaker than advertised. Recent Iran operations were reportedly planned with Claude 3.5 Sonnet, a model already 20 months old at the time, because the Defense Department IL6 certification pipeline takes over 12 months. Whatever advantage a frontier US model confers, the institutional capacity to operationalize it is the binding constraint, and on that metric the US is structurally disadvantaged.

A reasonable counter is that defense and biosecurity warrant narrow carve-outs. They do. The right architecture is a “small yard, high fence” for genuine CBRNE (Chemical, Biological, Radiological, Nuclear, Explosive) and autonomous-weapons risk, paired with broad cooperation on diffusion, safety standards, and shared-risk monitoring. Today’s “small yard” has expanded to cover EDA software, HBM memory, chemicals, and frontier models. This kind of policy has only accelerated China’s push for technology independence and stifles the will for cooperation over shared risks from non-state bad actors when we need it most.

Jevons Paradox vs. Kuznets Curve

One last steelman to dispose of. The Jevons paradox argument goes: cheap inference will increase demand for inference enough that the labs win on volume. Maybe. But human attention is bounded, energy is bounded, and per-capita consumption in mature markets saturates. Jevons Paradox was based on cheaper energy in the form of coal driving more total demand for energy. What most people don’t talk about is that in the long run, energy use looks more like a Kuznets Curve. (Kuznets, coincidentally, invented the concept of GDP and long criticized its effectiveness at measuring true economic value.) The time series data of developed countries clearly shows that energy demand actually topped out as basic human needs are satiated. In western nations, that happened in the 1990s and even shows per-capita energy consumption declining despite electrification because efficiency outruns new demand in saturated economies. (see figure below) Humans have a point of satiation for all goods and services. The same logic will eventually apply to AI inference. Maybe even more so, as AI efficiency gains have been on the order of 40x per year the last few years. There is no infinite well of valuable tokens to produce and most users don’t need to run the latest high-performance frontier models to summarize documents or plan their travel. Eventually most consumer AI use cases will migrate processing directly to edge devices like PCs and phones both because of the efficiency improvements of the models but maybe more importantly due to the desire by most to protect their privacy.

When inference demand saturates and prices keep falling, the labs are caught between two walls: rising training costs (next-generation models cost more, not less, to train) and falling unit revenue (commodification of inference at a small fraction of the current premium prices). That is the Cisco trap. That is why the AGI windfall is a mirage. And racing to this mirage is exactly what’s making our economy more fragile and our world less safe.

The Soft Landing: Why 3.5% Matters?

Now we come to the part that keeps me up at night. The K-shape economy is not just an economic problem. It is a political one. And the political problem has a number attached to it.

In Why Civil Resistance Works, Erica Chenoweth and Maria Stephan compiled the Nonviolent and Violent Campaigns and Outcomes (NAVCO) dataset, studying 323 violent and nonviolent campaigns from 1900 to 2006. Their finding, formalized by Chenoweth in a 2013 TEDx talk and refined in her 2020 Carr Center discussion paper became known as the 3.5% rule. In her words: “no government has withstood a challenge of 3.5 percent of their population mobilized against it during a peak event.” Successful campaigns in the dataset include the Cedar Revolution, the Singing Revolution, the fall of communism in Albania, and the 2019 Sudanese revolution. As Chenoweth has noted in subsequent updates, authoritarian regimes have grown more adept at suppression since 2010, but the underlying mechanism, mass mobilization breaking elite cohesion, remains operative.

Three and a half percent of the US population is roughly 12 million people. AI’s projected labor exposure dwarfs that number by an order of magnitude.

The 3.5% rule does not stand alone. Walter Scheidel, in The Great Leveler shows that across recorded history only four forces have meaningfully compressed inequality: mass-mobilization warfare, transformative revolution, state collapse, and lethal pandemic. He calls them the Four Horsemen of Leveling. The implication should chill every CEO and policymaker reading this. The historical default is that high inequality persists peacefully until it does not, at which point it is reduced by catastrophe.

Peter Turchin’s End Times gives us the mechanism in real time. Turchin’s 2010 Nature forecast predicted that US political instability would peak around 2020, driven by “elite overproduction” and “popular immiseration.” The January 6, 2021, storming of the US Capitol, the polarization indices, and a decade of declining institutional trust have largely vindicated that forecast. Turchin’s Political Stress Index combines wage stagnation, elite competition, and state fiscal stress into a single indicator. AI’s labor displacement adds fuel to all three.

Daron Acemoglu and James Robinson, in Why Nations Fail drew the institutional version of the same lesson. Inclusive institutions, those that share broad-based prosperity, are stable. Extractive institutions are not. Political scientist Carles Boix, in Democracy and Redistribution, made the same point in plainer terms: democracies survive when ordinary voters believe their turn is coming. They fail when the lane that isn’t moving never starts moving. Albert Hirschman called this the “tunnel effect.” Stuck in traffic, you tolerate the wait so long as the next lane is also creeping forward. When only the express lane moves and yours sits still, patience snaps.

Stack these findings together. The AI K-shape hollows out institutions. The Layoff Trap deepens hardship at the bottom. Hyperscaler-lab capital recycling concentrates elite competition into a single sector. And history says that when even three and a half percent of a population mobilizes against this kind of configuration, regimes fall. We are building, with our own capex, a coalition of displaced workers, young graduates with no first rung on the ladder, and too many ambitious elites chasing too few elite positions. And we are doing it faster than any previous industrial transition.

History gives us proximate cases. The French Revolution followed an aristocratic capture of grain markets. The Russian Revolution followed wartime immiseration. The Iranian Revolution of 1979, the Eastern European cascades of 1989, the Arab Spring of 2011, the Sri Lanka uprising of 2022, all share the same anatomy. Cost-of-living grievance plus elite-mass perception gap plus a precipitating event. AI driven layoffs and non-hiring is the cost-of-living grievance. Trillion-dollar hyperscaler capex and accelerating billionaire wealth accumulation without broad gains is the perception gap. We are missing only the precipitating event. We should not wait to find out what it is.

What is the alternative? It is what I have called elsewhere Abundanism, and what the historical analogy of the Bonobo offers us.

The Bonobo Lesson and the Six Pillars

About two million years ago, the formation of the Congo River split a common ape ancestor into two populations. North of the river, scarcity. South of the river, fertile abundance. The same DNA. Different civilizations. North of the river, the chimpanzees developed alpha-male hierarchies, troop warfare, and dominance-based politics. South of the river, the bonobos developed matriarchal egalitarianism, sex as conflict resolution, and cooperative foraging. They are 99.6% genetically identical with each other but have developed cultures that are polar opposites. We share more than 98.5% of our DNA with both. We are not condemned to either. We have the advantage that we can adapt to the environment we choose to build.

For ten thousand years we built environments of scarcity. We designed economies, institutions, and conflicts around lack. The world the AI revolution makes possible is genuinely different. Marginal cost approaches zero for energy, computation, and increasingly matter. The bonobo configuration is, for the first time in recorded history, the economically rational configuration due to the technological progress we have collectively made as a species over thousands of years.

That is the foundation of Abundanism, the post-scarcity framework I introduced in Our Next Reality (Hachette, 2024) and have developed since on the Abundanist Substack. Abundanism rests on six pillars:

1 Governance with AI as Partner. AI as policy oracle and decision-support, not master, not servant. Where humans are biologically constrained, foresight, cross-domain synthesis, freedom from short-term emotional capture, AI can complement. An AI with deep understanding of perspectives from all countries and cultures will help us end conflict.

2 Post-Monetary Value Systems. Money will retain meaning during the transition and may even surge in value temporarily, then transition toward systems that price time, service, and social contribution. Redirecting benchmarks towards “GDP-B”-like metrics is an early waypoint on that road.

3 Universal Basic Income and Infrastructure (UBII). Not income alone. Income plus housing, healthcare, education, and connectivity as a floor. This becomes much more viable as cost of underlying goods and services deflate overtime due to the forces discuss above.

4 Purpose Engineering. When labor as a source of meaning is automated, we need new infrastructure for purpose, community, creativity and exploration. As a society, we need to celebrate service to others vs the number of commas in one’s bank account. Immersive technology, properly governed, can serve as a key part of that infrastructure to connect, enable and retrain the population.

5 Supply and Demand 2.0. Intelligent matching of human need to available capacity without prices as the sole mediating mechanism. When energy and intelligence move towards zero cost, the ability to produce what’s needs where it’s no longer needs price as a signal.

6 Education as Peace Technology. Universally personalized, AI-augmented education as the largest peace-dividend investment a society can make. A broadly and deeply educated population is core to long term human societal flourishing.

These pillars are how an Abundanist economy avoids the Layoff Trap. The Pigouvian automation tax Falk and Tsoukalas advocate becomes the funding mechanism for UBII. The diffusion advantage that China is building becomes the platform we should be racing them to expand and humanize. And the metrics we publish, GDP-B alongside GDP, OECD Better Life alongside payroll, Bhutan’s GNH alongside corporate earnings, become the dashboard against which we steer.

Beyond Rivalry and the Montreal Moment for AI

The geopolitical version of this same argument is what I developed in Beyond Rivalry: A US-China Policy Framework for the Age of Transformative AI in Stanford’s Digitalist Papers, Volume 2. The core proposal stack: a CERN for AI focused on joint global safety and innovation research; a Global Data Pool so that frontier models learn from globally representative data rather than the digital exhaust of two coasts; an IAEA for AI to regulate advanced AI for global good when it is achieved; a Guardian AI model that protects us from bad actors and ourselves; a Universal Basic Income and Infrastructure framework as the social ballast; a GI Bill for the AI Age to preserve displaced workers dignity and provide needed reskilling; and a Marshall Plan for AI to fund the post-AGI transition across the developing world. Not all these elements need to be established on day 1, but they can be rolled out as appropriate. We need to consider reframing the narrative from a winner-takes-all to a winners-share-all approach, otherwise there may be no winners at all.

There is a hard-headed self-interested case for treating advanced AI as a global public good rather than as an instrument of national or corporate dominance. Over the next decade, an estimated two to three billion people are projected to come online for the first time or upgrade to AI-capable devices, predominantly across South Asia, Sub-Saharan Africa, Southeast Asia, and Latin America. They are the next billion enterprise customers, the next billion knowledge workers, and the next billion consumers of intelligence-as-a-service. Labs and governments that help them arrive there, through low-cost models, locally grounded datasets, affordable inference infrastructure, and education-focused deployments, will capture more long-term enterprise and consumer value than any that try to dominate from above.

Global uplift also generates secondary returns that no income statement currently measures: social stability, reduced migration pressure, fewer brittle states, less motivation for extremist movements and the kind of broad-based real growth that historically correlates with reduced great-power conflict. The frame that the United States and China should treat advanced AI as a rivalrous national-security asset to be hoarded is the same frame that delivered Cisco-style value destruction in the original Internet build-out and that today produces the K-shape at home. The frame that treats advanced AI as a public good, governed multilaterally and deployed broadly, is the frame that pays.

The recent Trump-Xi Summit in Beijing opened a narrow door to exactly this kind of architecture. Treasury Secretary Scott Bessent told CNBC on May 14 that “the reason we are able to have wholesome discussions with the Chinese on AI is because we are in the lead.” That framing may be overstated, but the substantive opportunity is exactly right. President Trump confirmed the two governments are “considering establishing some kind of mechanism on guardrails,” echoed by Bessent’s reference to a “protocol on AI safety.” This is the closest we have come to a Montreal Protocol moment for AI.

A Montreal-for-AI is possible. The 1987 Montreal Protocol on ozone-depleting substances had universal ratification and led to the recovery of the ozone layer that is on track to be complete by mid-century. It worked because the threat was scientifically agreed, the substitutes were technologically available, and verification was tractable. AI safety can clear the first bar. AI labs can clear the second. Only governments, working together, can clear the third. The Asilomar conference of 1975 showed that recombinant DNA could be voluntarily paused by researchers themselves. The 2018 moratorium on heritable human genome editing, called in the wake of He Jiankui’s CRISPR babies, showed that even rivalrous nations can agree on a bright line. AI’s bright lines are not impossible to draw. They are politically inconvenient to honor.

Right now, no one is walking through the door the Trump-Xi summit cracked open. The strategic risk in front of us is not the invention of a single decisive model. It is the diffusion of many models we cannot recall, into the hands of bad actors we cannot deter, in a global economy where we have refused to talk to the other half of the technology stack.

Conclusion: The Oasis Is Real, but Not Where the Mirage Says

Back in the Mojave, my daughter finally accepted that the lake was not there. We pulled off at the Baker exit. She bought a Gatorade from a gas station and watched the desert through the window of an actual building, with actual air conditioning, and actual water. The oasis was real. It was just not where the heat shimmer told us to look.

The AGI Windfall Mirage is the same story. There is no single decisive lead. There is no permanent monopoly. There is no first-mover trillion-dollar prize that survives commoditization, deflation, geopolitical fragmentation, and democratic backlash. Anyone running toward that lake will arrive at a salt flat. The real oasis, the real win, is broader. It is multiple winners, each capturing a slice of an expanding pie, with the bottom 90% of the population not just included but structurally protected by UBII, retraining, AI-augmented governance, and global cooperation on safety.

I want to be crystal clear about the framing. The “Winners-Share-All” mentality mentioned earlier is not naivete. It is enlightened self-interest. Game-theoretically, the multi-turn Stag Hunt I described above pays better for the cooperators than for the defectors. Economically, the demand externality from mass layoffs destroys the consumer base that frontier AI labs depend on. The Layoff Trap is the producer’s nightmare, not the workers’. Politically, the 3.5% threshold is a fence around what elites can extract before regimes lose stability, and AI is on track to alienate an order of magnitude more than the needed threshold. Historically, the only forces that have ever leveled extreme inequality are catastrophic. We have one chance, in this decade, to choose a fifth horseman: voluntary, evidence-driven, broad-based redistribution coordinated across major powers before the other four ride.

To the AI labs and the VCs funding them: reevaluate your plan and your motivations. The current race is not worth running. It is constructing the conditions under which everyone loses. There is no glory in being first to a finish line that does not exist, especially if the cost of running there is the stability of the globe and sustainability of the financial system that would give your expected windfall actual value.

To the policymakers tasked with ensuring prosperity for your citizens: dig deeper, because you have been fed a fiction. The current race between the labs and nations will not deliver the economic growth or the broad wealth you have been promised. Whether AGI arrives in one year, five, or ten will make little difference to what we ultimately gain from it. But if the transition is managed poorly, it could trigger a disaster that takes decades to recover from. A rational approach, building and deploying this technology at a pace society can absorb, can bring real quality-of-life gains to everyone and unlock scientific breakthroughs on problems we have faced for centuries as a species. Treat safe AGI as a public good and find ways to share it with the world, not hoard it.

To the workers and citizens watching this from outside the labs: you are not powerless. The 3.5% rule cuts both ways. It can topple. It can also demand. Demand metrics that measure the abundance you actually receive. Demand institutional structures that put a Pigouvian floor under your displacement. Demand the GI Bill for the AI Age, the Global Data Pool, the CERN for AI. Demand the social infrastructure that preserves your dignity and enables global human flourishing. The technology and its benefits are real. The mirage is what we have been told to chase.

The short-term race to AGI is self-defeating and dangerous. The long-term race to broad-based abundance is the only race that pays. Let us run that one. Together.

“Every frontier AI lab, including Anthropic, operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing for humanity.”

- Christopher Olah (Anthropic co-founder) at the Vatican

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Endnotes

1 Adam Tooze and Cameron Abadi, “Tooze on the K-Shaped Economy and Its Global Effects,” Foreign Policy, May 12, 2026. https://foreignpolicy.com/2026/05/12/tooze-k-shaped-economy-us-ai-cantillon-wealth-distribution/

2 Innes McFee quoted in “Oxford Economics: AI is unlikely to help resolve K-shaped economy anytime soon,” Fortune, January 29, 2026. https://fortune.com/2026/01/29/k-shape-economy-reinforced-ai-wealth-effect/

3 “NVIDIA and the Cautionary Tale of Cisco Systems,” Harding Loevner. https://www.hardingloevner.com/insights/nvidia-and-the-cautionary-tale-of-cisco-systems/

4 “The Cautionary Tale of Cisco Systems,” Liberty Through Wealth, March 22, 2022. https://libertythroughwealth.com/2022/03/22/cautionary-tale-of-cisco-systems/

5 “Cisco’s stock closes at record for first time since dot-com peak in 2000,” CNBC, December 10, 2025. https://www.cnbc.com/2025/12/10/ciscos-stock-closes-at-record-for-first-time-since-dot-com-peak-2000.html

6 “Anthropic raises $30 billion in Series G funding at $380 billion post-money valuation,” Anthropic, February 12, 2026. https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation

7 “Anthropic says it hit a $30 billion revenue run rate after ‘crazy’ 80x growth,” VentureBeat. https://venturebeat.com/technology/anthropic-says-it-hit-a-30-billion-revenue-run-rate-after-crazy-80x-growth

8 “OpenAI announces Pentagon deal after Trump bans Anthropic,” NPR, February 27-28, 2026. https://www.npr.org/2026/02/27/nx-s1-5729118/trump-anthropic-pentagon-openai-ai-weapons-ban

9 Adam Tooze, “Chartbook 447: The US economy in May 2026,” May 2026. https://adamtooze.substack.com/p/chartbook-447-the-us-economy-in-may

10 Elisa Pereira, Alvin Wang Graylin, and Erik Brynjolfsson, The Enterprise AI Playbook: Lessons from 51 Successful Deployments, Stanford Digital Economy Lab, April 2026. https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook/

11 Brett Hemenway Falk and Gerry Tsoukalas, “The AI Layoff Trap,” arXiv:2603.20617, March 2, 2026. https://arxiv.org/abs/2603.20617

12 Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,” arXiv:2303.10130, August 22, 2023. https://arxiv.org/abs/2303.10130

13 Aditya Challapally, Chris Pease, Ramesh Raskar, and Pradyumna Chari, The GenAI Divide: State of AI in Business 2025, MIT Media Lab Project NANDA, July 2025.

14 Anton Korinek and Donghyun Suh, “Scenarios for the Transition to AGI,” NBER Working Paper 32255, March 2024. https://www.nber.org/papers/w32255

15 Daron Acemoglu, “The Simple Macroeconomics of AI,” NBER Working Paper 32487, May 2024. https://www.nber.org/papers/w32487

16 Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, and Philip Tetlock, “Forecasting the Economic Effects of AI,” NBER Working Paper 35046, April 2026. https://www.nber.org/papers/w35046

17 Erik Brynjolfsson, Avinash Collis, W. Erwin Diewert, Felix Eggers, and Kevin J. Fox, “GDP-B: Accounting for the Value of New and Free Goods,” American Economic Journal: Macroeconomics, October 2025. https://www.aeaweb.org/articles?id=10.1257/mac.20210319

18 METR, “Measuring AI Ability to Complete Long Tasks,” March 19, 2025. https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/

19 “State Council issues guideline to implement AI Plus Initiative,” Global Times, August 26, 2025. https://www.globaltimes.cn/page/202508/1341775.shtml

20 “Global AI Governance Action Plan,” Ministry of Foreign Affairs, People’s Republic of China, July 2025. https://www.mfa.gov.cn/eng/xw/zyxw/202507/t20250729_11679232.html

21 “The Political Limits of China’s AI Diffusion Ambitions,” Lawfare. https://www.lawfaremedia.org/article/the-political-limits-of-china-s-ai-diffusion-ambitions

22 “LLM API Pricing Comparison (2025),” IntuitionLabs. https://intuitionlabs.ai/articles/llm-api-pricing-comparison-2025

23 2026 AI Index Report, Stanford Institute for Human-Centered Artificial Intelligence, April 2026. https://hai.stanford.edu/research/ai-index-report

24 Xinhua, “Alibaba’s Qwen tops 700 million Hugging Face downloads,” January 13, 2026.

25 Global Times, “Chinese AI models sweep OpenRouter top six,” April 8, 2026.

26 Erica Chenoweth and Maria J. Stephan, Why Civil Resistance Works: The Strategic Logic of Nonviolent Conflict, Columbia University Press, 2011. https://cup.columbia.edu/book/why-civil-resistance-works/9780231156820

27 Erica Chenoweth, “Questions, Answers, and Some Cautionary Updates Regarding the 3.5% Rule,” Carr Center Discussion Paper, Harvard Kennedy School, 2020.

28 “Defeating Tyranny with the 3.5 Percent Rule,” American Bar Association, October 2025. https://www.americanbar.org/groups/crsj/resources/human-rights/2025-october/defeating-tyranny/

29 Walter Scheidel, The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century, Princeton University Press, 2017. https://press.princeton.edu/books/paperback/9780691271842/the-great-leveler

30 Peter Turchin, End Times: Elites, Counter-Elites, and the Path of Political Disintegration, Penguin, 2023. https://peterturchin.com/book/end-times/

31 Peter Turchin, “Political instability may be a contributor in the coming decade,” Nature 463, 608 (2010). https://www.nature.com/articles/463608a

32 Daron Acemoglu and James A. Robinson, Why Nations Fail: The Origins of Power, Prosperity, and Poverty, Crown Business, 2012.

33 Carles Boix, Democracy and Redistribution, Cambridge University Press, 2003.

34 Alvin W. Graylin, “Abundanism: A New Philosophy for a Post-Scarcity World,” Abundanist Substack, May 13, 2025. https://abundanist.substack.com/p/abundanism

35 Alvin W. Graylin, “Beyond Rivalry: A US-China Policy Framework for the Age of Transformative AI,” The Digitalist Papers, Volume 2, Stanford Digital Economy Lab, December 2025. https://www.digitalistpapers.com/vol2/graylin

36 Alvin W. Graylin, “Misdiagnosing the U.S.-China AI Race: Recalibrating America’s Approach to an Incomplete Strategy,” Center for China Analysis, Asia Society Policy Institute, May 1, 2026. https://centerforchinaanalysis.asiasociety.org/p/misdiagnosing-the-uschina-ai-race

37 “U.S. can hold AI talks with China because ‘we are in the lead,’ Bessent tells CNBC,” CNBC, May 14, 2026. https://www.cnbc.com/2026/05/14/us-china-ai-rules-bessent-us-lead.html

38 Paul Triolo, “A Costly Illusion of Control: No Winners, Many Losers in U.S.-China AI Race,” The Cairo Review of Global Affairs. https://www.thecairoreview.com/essays/a-costly-illusion-of-control/

39 Erik Brynjolfsson et al., “Canaries in the Coal Mine: Six Facts about the Recent Employment Effects of Artificial Intelligence,” Stanford Digital Economy Lab. https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/

40 “Anthropic’s plea for US to grow its AI edge over China is ‘irresponsible’: analysts,” South China Morning Post. https://www.scmp.com/tech/tech-trends/article/3353756/anthropics-plea-us-grow-its-ai-edge-over-china-irresponsible-analysts

41 Elliot Jones, “A ‘CERN for AI’—What Might an International AI Research Organization Address?” Chatham House, June 2024. https://cfg.eu/building-cern-for-ai/

42 Our World in Data Energy Per Capita Chart: https://ourworldindata.org/grapher/per-capita-energy-use?tab=line&country=IND~USA~CHN~FRA~GBR~ZAF~JPN~DEU~BRA

43 Nordhaus, W. D. (2002). Productivity Growth and the New Economy. Brookings Papers on Economic Activity, 2002, 211–265. https://doi.org/10.1353/eca.2003.0006

44 Alvin W. Graylin, Louis Rosenberg, “Our Next Reality” (Hachette, 2024) www.ournextreality.com

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Alvin W. Graylin

Digital Fellow, Stanford HAI-Digital Economy Lab | Sr. Fellow, Asia Society Policy Institute-CCA | Author, Our Next Reality | Chairman, Virtual World Society

Plato’s philosopher king was never meant to be a charismatic decider. He was trained into restraint: disciplined reasoning, moral clarity, and the ability to hold competing truths without flinching. That ideal faded when speed and confidence theater became leadership substitutes. It’s about to return now because the world is becoming less forgiving and wisdom-driven leadership more needed.

Decision loops have tightened. Information is abundant yet uneven, and often strategic. Supply chains, capital, and market access are shaped by shifting alliances, export controls, sanctions, and regulatory divergence. The leader’s burden is no longer just to run an organization. It is to steer through a moving map while keeping teams aligned and calm.

In that setting, the temptation is to outsource judgment to whatever sounds most authoritative, including AI. So the first question is not “How do I use AI?” It is “Who is the philosopher king now?” Is AI the king, because it can see more than we can? Or is it the tutor, sharpening the king’s thinking while leaving accountability in human hands?

My answer is counterintuitive but true: the best leaders will hand over more tactical decisions to AI over time, while becoming more demanding about the human layer AI cannot supply. We should trust AI with more decisions, but not with more authority.

The data below shows that by allowing AI more autonomy in decision-making in appropriate use cases, team productivity improves significantly. The key is figuring out in advance which decisions to delegate and what to do when AI is wrong. Tasks that are repetitive or reversible are good candidates for delegation.

Figure 1. Productivity gain with high and low human oversight.
Source: “The Enterprise AI Playbook”, Brynjolfsson, Graylin, Pereira. (April 2026)

Why leadership gets harder as AI gets smarter

AI compresses the time between signal and action. That can improve performance, but it also amplifies error and panic when decision discipline is weak. In volatility, speed is not a virtue by itself. Speed only matters when it is coupled to values, verifiable reasoning, and a habit of testing assumptions before they become plans.

This is the modern “guardian” toolkit: scenario reasoning, incentive and bias checks, and the discipline to ask what would have to be true for our plan to fail. AI is uniquely good at expanding the option set and surfacing second-order effects humans miss under pressure.

What I am seeing in CEO practice

From my recent Stanford research, our team interviewed 50+ CEOs and AI leads who have successfully deployed AI in their firms. The most effective pattern is not rushing “AI for productivity”, it is rewiring the organizational culture, metrics and processes to make it “AI-ready.” Here’s a summary of some of the things we learned that applies to leadership:

1. Fund the Invisible Work
Make AI a change program, not a tool rollout: personally budget and staff workflow redesign, adoption, and iteration as first-class deliverables. Give permission to fail.

2. Run an Exception-First Operating Model
Delegate repeatable decisions to AI and require humans to own edge cases and accountability, with clear escalation triggers and audit trails.

3. Sponsor Like You Mean It
Tie AI outcomes to enterprise level OKRs and leadership incentives, then remove blockers weekly as if they were operational incidents. Find ways to grow business, not only reduce costs.

4. Turn Gatekeepers into Enablers
Pre-negotiate “yes conditions” with Legal, Risk, HR, and Compliance so teams can ship inside guardrails, learn fast, and scale safely.

You can now hear prominent executives describe AI as an always-on advisor in the flow of leadership, not just an IT initiative.

David Ricks, CEO of Eli Lilly, has said he keeps “one or two AIs running” during every meeting to ask questions and pressure-test thinking in real time.

Microsoft CEO, Satya Nadella, has publicly shared prompt patterns he uses for meeting preparation, project synthesis, and launch risk assessment, which is essentially executive cognition turned into a reproducible loop.

The key shift is emotional. The best CEOs are calmer, not because they have more certainty, but because they have a stronger process for uncertainty. AI becomes a partner in composure when geopolitics or markets change the rules overnight. This is the new baseline: leaders who do not co-think with AI will increasingly be competing against leaders who out-think them.

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Governments are already testing the boundary

Albania drew attention by experimenting with an “AI minister” concept, framed as a tool for transparency and reducing corruption. Whatever one thinks of the optics, the lesson is real: delegating parts of governance to AI is now a public experiment, and legitimacy becomes the hard problem.

Singapore’s SEA-LION sovereign AI models point to a durable approach: build national capability, governance scaffolding, and evaluation infrastructure, while reducing unnecessary dependence where technology sovereignty matters. AI is not only about productivity. It is institutional capacity.

AI as tutor, not as King

The happy balance is humility without abdication.

Humility: AI often knows more than we do about facts, precedents, patterns, and probability. Ask it to show you what you are missing, not what you already believe.

Confidence: AI does not carry moral responsibility. It does not own trust. It does not pay the price of a wrong call. Accountability remains human, even when execution becomes machine-led.

This maps cleanly onto Greek and Chinese philosophy. Plato asks for leaders trained to see beyond appetites and factions. Confucius asks for leaders who cultivate virtue and relational responsibility. Both are about trustworthy governance, not perfect knowledge.

A Janus model of leadership

A more practical framework for thinking about AI is with the Janus concept: two faces, one goal. The human face sets ends: purpose, values, boundaries, and legitimacy. This is where you decide what will not be optimized away, even when it is costly. The AI face optimizes means: options, trade-offs, scenario trees, incentive checks, anomaly detection, and second-order effects.

The leader’s job is to referee the interface. Over time, it will make sense to hand greater parts of tactical decisions to AI, because less conflicted systems can outperform conflicted human actors in repeatable domains. But the transfer should be earned, governed, auditable, and reversible.

Teams in the new era

As AI takes on tactical work, teams become more important in three ways: framing, evaluation, and trust. This is where an Abundanism mindset becomes practical. As intelligence becomes abundant, value shifts from raw output to agency and stewardship: allocating capability toward human flourishing, and evolving roles rather than defending obsolete workflows. Understanding and protecting the dignity of human purpose and service becomes a leadership imperative. Leaders will soon need to make hard choices of prioritizing profits over people, and it’s at these times, we will need to lean into our humanity and design soft landings for the organization.

The chart below provides a view into the minds of successful CEOs who are going through the process today. There will be increasing market pressures to reduce workforce, but the ill will created amongst affected staff and negative reputation risks may not be worth the benefits near term. It’s a bit worrisome that even today, leaders are already choosing layoff or avoid hiring new staff as a result of AI productivity gains in almost 2/3 of cases.

Figure 2. Findings on personnel reallocation post successful AI deployment.
Source: “The Enterprise AI Playbook”, Brynjolfsson, Graylin, Pereira. (April 2026)

AI for win-win, not just win-fast

One underappreciated leadership advantage is using AI to understand the counterparty more deeply. Not to manipulate, but to widen the solution space.

In negotiation prep, leaders can use AI to map counterpart incentives, cultural context, and hidden constraints, then generate proposals that protect both sides’ dignity. In geopolitically sensitive deals, this becomes strategic: AI can help teams anticipate regulatory friction, sanctions exposure, and second-order reputational risk while still seeking constructive outcomes. The aim is not “perfect information.” It is fewer avoidable misunderstandings.

Becoming the Philosopher King

Finally, treat AI as a partner in becoming the kind of leader Plato described: disciplined, virtuous, and wise. Have the humility to let AI reveal your blind spots. Have the courage to delegate repeatable, reversible decisions to it, while keeping responsibility for outcomes squarely human. Have the compassion to protect the dignity of your team and customers as roles evolve. And build the wisdom to remember that leadership serves not only shareholders, but the social fabric your institution depends on.

Do this with a Janus design: humans set the ends; AI optimizes the means. If you lead that way, the philosopher king is not replaced. He is finally given a tutor worthy of the task, and a system where two minds strengthen each other.

“No one in any position of rule, insofar as he is a ruler, considers or commands what is advantageous for himself, but what is advantageous for his subject.” — Plato’s Republic 342e

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The Odyssey is usually taught as an epic of endurance: a man battered by storms, seduced by temptations, tested by monsters, and finally returned to his rightful place. But beneath the adventure is a quieter plot line that most modern retellings miss. Odysseus is not merely surviving danger. He is resisting something more intimate: the invitation to stop being an author of his own life.

We often treat Homer’s Sirens as a warning against knowledge and curiosity, and the nymph Calypso as a warning against comfort and abundance. The moral we extract is convenient for a scarcity-based world: you must suffer to stay human, and too much knowing or having is inherently corrupting.

That reading is not only shallow, it misidentifies the poison.

The Sirens do not lie. They offer truth. Their song is not a trick, it is closure. Sailors die because they stop rowing.

Calypso does not torture Odysseus. She offers him immortality, safety, tenderness, and a life without scarcity. He weeps because nothing depends on him.

Homer is not defending suffering. He is defending agency. He is warning that a human life can end without violence when it becomes fully solved, fully optimized, and gently relieved of the need to choose.

This is why The Odyssey is suddenly an AI story.

Not because Homer predicted machine intelligence, but because he understood the oldest human vulnerability: we will trade authorship for completion, and call it progress.

The rest of this essay argues that the true failure mode of AI and material abundance is not that we will know too much or have too much. It is that we will stop becoming. And it sketches a practical Abundanist path forward that preserves individual agency, shared purpose, and social coherence, even when daily toil is no longer required.

The Sirens were not dangerous because they were wrong

In Homer, the Sirens sing from a rocky shore and promise Odysseus something every ambitious mind secretly craves: an end to uncertainty. They claim to know what happened at Troy and what happens across the world, as if the fog of the future could be replaced with a finished map.

The crucial detail is that the Sirens are not framed as liars. Their seduction is not misinformation. Their seduction is completeness.

That is how they kill.

Not by poisoning the content, but by collapsing the need to move. The sailors drift toward the song because the mind believes it has arrived. The oar slackens. The body relaxes. The future folds into a single perfect moment. And the sea does what the sea always does to a ship that stops rowing.

Odysseus survives in the most instructive way possible: he does not reject knowledge. He binds himself to the mast. He chooses constraint over surrender. He wants to hear the truth fully, but he refuses to let truth replace his agency.

Homer’s lesson is not “do not know.” It is “do not stop becoming.”

This is the first Abundanist insight for the age of AI: knowledge is not a curse. Knowledge that cancels authorship is.

Calypso was not the villain because she offered comfort

Then comes Calypso, the nymph who offers an even more radical form of completion. Not closure of the mind, but closure of the body. Immortality. Safety. Love without loss. A world where hunger, war, and daily toil no longer apply.

If your definition of success is maximizing comfort and minimizing pain, Calypso’s island is the victory condition. A post-scarcity paradise with no desire unfulfilled, no aging, no ending. Sounds like the dream of many a billionaires, doesn’t it?

And Odysseus weeps.

Why cry in paradise?

Because on Calypso’s island, nothing requires him. No one depends on his return. No fragile web of obligations makes his choices matter. The days are beautiful, but their beauty becomes anesthetic when stretched across endless time. Urgency dissolves. Responsibility becomes optional. Identity becomes weightless.

Calypso’s trap is not pleasure. It is the loss of stakes.

This is the second Abundanist insight: abundance is not the end of meaning. Abundance is the end of coerced meaning. If we do not design what replaces it, abundance can turn into drift.

The real threat of AI is not intelligence. It is AI closure.

Most AI debates are aimed at the wrong target. We worry about hallucination, bias, safety, job displacement, geopolitical advantage. These are real. But there is a deeper failure mode that can coexist with accuracy, safety, and prosperity.

AI becomes Sirenic when it offers closure faster than humans can form judgment.

Not because answers are bad, but because answers can arrive in the very places where struggle is how growth is produced. The Siren version of AI looks like this:

It finishes your thought before you discover what you meant.

It schedules your life so efficiently you never decide what you value.

It predicts your preferences so well that exploration feels unnecessary.

It persuades you gently, perfectly, until your values become a convenience function.

It removes the friction that used to force you to choose.

A system does not need to be malicious to be existentially corrosive. It only needs to be complete enough that humans begin outsourcing the muscles of authorship: judgment, commitment, and responsibility.

This reframes “alignment.” Safety and controllability matter, but a perfectly safe system that slowly trains people into spectatorship is still a civilizational hazard. The deeper alignment question is not only “will AI harm us,” but “will AI hollow us.”

An Abundanist future does not reject powerful AI. It insists on a specific kind: AI that expands capability without erasing agency. AI as coach, not oracle. AI as mast, not Siren.

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Humans do not need suffering. We need stakes.

Scarcity defenders often smuggle in a false premise: that the only way to produce meaning is to produce pain. That toil is the moral engine of a life. That if daily struggle disappears, humanity will rot.

But humans lived for vast stretches of time without wage labor, without modern careerism, without productivity worship. The record of human life is not a record of permanent job-holding. It is a record of participation: learning skills, telling stories, raising children, exploring, cooperating, competing, building trust, and being needed.

Suffering can create meaning. It can also simply destroy. The point is not to preserve suffering. The point is to preserve the elements that suffering accidentally supplied: stakes, effortful agency, responsibility, reciprocity, and return.

You do not need misery to feel alive. You need a life that still demands something of you, even when no one can force you.

This is where Abundanism becomes a moral and practical upgrade. It aims to decouple dignity from labor and scarcity, while preserving the deeper sources of purpose: chosen commitments, mastery, service, and stewardship.

Abundance fails not by decadence, but by drift.

The popular fear of post-scarcity is cartoonish: humans lounging in endless entertainment, minds melting in comfort. That can happen. But the true failure mode is subtler and more likely.

Abundance dissolves the old scaffolding: default roles, schedules, and necessity-driven contribution. If nothing replaces it, people can become unmoored. Not evil, not lazy, simply unanchored. Their days fill with consumption because consumption is available, and emptiness does not announce itself loudly. It arrives as a soft fog.

Calypso does not drag Odysseus into a dungeon. She offers him a life with no need to return.

That is the warning for our time.

If abundance becomes anesthesia, we lose more than productivity. We lose social coherence. Communities fragment. Trust erodes. Politics becomes spectacle. People become easier to manipulate because they no longer have a grounded sense of purpose or belonging. A nation can become rich and still become hollow.

Abundanism’s challenge is therefore not “how do we keep people working.” It is “how do we keep people choosing, contributing, and caring when survival no longer forces them to.”

Status will not disappear. It will mutate

When people ask, “What will drive us when we can have anything?” they are asking about motivation. The deeper answer is status.

Humans follow status gradients. Status is how societies signal what is admirable and worth emulating. In a scarcity regime, status maps to accumulation: more money, more property, more control, more leverage. It also maps to domination: the ability to make others bend, whether by force, finance, or narrative.

Post-scarcity breaks the old signals. If comfort is common, consumption becomes a weak marker. If intelligence is abundant, raw cognitive output becomes cheap. If automation collapses labor scarcity, the old prestige hierarchy tied to titles and toil begins to crumble.

So what becomes scarce?

Human qualities that cannot be mass-produced: integrity, taste, courage, care, originality, responsibility, the ability to build trust and hold it under pressure. The willingness to serve others when you do not have to. The capacity to bind yourself to a long arc when short-term pleasure is everywhere.

An Abundanist society must design prestige to reward these qualities.

This is the new benchmark for status and success: not what you own, but what you carry. Not how much power you hoard, but how much capability you unlock in others. Not domination, but stewardship.

That shift is not moral preaching. It is civilizational survival. If status continues to reward hoarding and domination, AI and abundance will amplify inequality, resentment, and conflict. If status rewards contribution and stewardship, abundance becomes fuel for collective flourishing.

The Mast Principle: freedom requires self-binding

Homer gives us a practical design pattern. Odysseus does not rely on willpower alone. He engineers a constraint that protects his future self.

This is the Mast Principle: in environments saturated with temptation and convenience, freedom is preserved through voluntary constraint.

In the age of AI, self-binding becomes a core life skill. In the age of abundance, it becomes a civic skill.

The opposite of freedom is not constraint. The opposite of freedom is drift.

Abundanism, at its best, is a civilization that scales the Mast Principle. It provides universal floors, universal tools, and universal access to safe AI, while also building social architectures that keep people rowing: commitments, responsibilities, and coherent community narratives.

Guardian AI: the mast we must build together

This is where the lens widens from individuals to nations.

In the coming decade, countries will be tempted to treat frontier AI as a weapon, a dominance engine, a new form of empire. They will measure success by military leverage, model supremacy, and the ability to coerce outcomes abroad. This is the geopolitical Siren song. It offers closure: control the future, win the race, dominate the system.

But that path is not strength. It is surrender to an ancient addiction.

The same logic that makes individuals hoard wealth makes nations hoard power. Both produce fragility. Both breed fear. Both invite arms races that consume the very prosperity they claim to protect.

The alternative is what the Beyond Rivalry framework has been pushing toward: minimum viable cooperation that is politically survivable, institutionally practical, and morally defensible. A world where advanced AI is treated less like a private weapon and more like critical infrastructure that must be stewarded.

This is the role of Guardian AI.

Not a single global brain that rules everyone, and not a naïve dream of universal trust. Guardian AI is the shared safety layer, a cooperative system of incident reporting, evaluation exchange, and interoperable standards that reduces catastrophic tail risk while allowing innovation to continue. It is the mast at the civilizational level: a set of constraints that keep powerful actors from steering into disaster, even when temptation is high.

A Guardian AI future also implies a moral shift: the responsibility of stronger nations to help weaker nations rise, not as charity, but as stability. When capability spreads, migration pressures decline, resentment declines, and the incentive for conflict declines. It is the same logic at the individual level: those with more capability have a responsibility to lift their community, not hoard advantage until the social fabric tears.

If you want peace in an abundant world, you do not build it through domination. You build it through capability distribution and shared safety rails.

Stop worshipping domination as the definition of success

This is the part that will feel countercultural, which is exactly why it matters.

Many nations still treat military might as the highest benchmark of seriousness. Many elites still treat the accumulation of wealth and control as the highest benchmark of intelligence. The age of AI will make these instincts more dangerous, not less.

AI increases persuasion capacity, surveillance capacity, and asymmetric power. Abundance increases the scale of what can be controlled. Put them together without a moral and institutional upgrade, and you do not get utopia. You get a polished version of feudalism with better branding.

So the Abundanist argument must be stated plainly:

Nations should stop striving for domination over others.

Individuals should stop hoarding power and wealth as their scoreboard.

The new prestige must be the ability to create capability for others at scale, while preserving agency and dignity. The new greatness is not conquest. It is stewardship.

This is not idealism. It is realism in a world where the cost of rivalry is no longer measured in tariffs, but in existential risk.

A Practical Action Plan:

What individuals can do in the near future:

1. Use AI to expand your agency, not replace it. Ask for options, tradeoffs, training plans, and feedback, not identity decisions. Keep judgment as yours.

2. Pick one domain where you refuse frictionless closure. A craft, a language, a scientific question, an art form, a body discipline. Keep a place where you remain the learner.

3. Build your mast while you still can. Create commitments that outlast moods: deadlines, public promises, service roles, mentorship obligations, community leadership.

4. Change your status scoreboard. Measure yourself by what you unlock in others: mentorship given, trust earned, community built, stewardship practiced, originality developed.

5. Stop hoarding what you do not need. Convert excess into capability for others: fund education, sponsor tools, share networks, open knowledge, build local resilience. Treat wealth as responsibility, not proof of worth.

What societies must do as we approach full automation and post-scarcity

1. Guarantee Universal Basic Capability and Infrastructure, not just income. Access to education, health, tools, safe AI, and participation so people can act, not merely consume.

2. Build meaning infrastructure. Civic fellowships, stewardship programs, learning guilds, community labs, creator commons, mentorship networks, rites of passage tied to responsibility. Make purpose legible and honored.

3. Make Guardian AI politically survivable and technically real. Create incident and evaluation exchanges with tiered disclosure, shared taxonomies, and interoperable safety standards so nations can cooperate without surrendering sovereignty.

4. Redesign AI defaults toward agency preservation. Delegation should be explicit, scoped, revocable, and auditable. Systems should scaffold learning and keep humans in the loop for identity shaping domains.

5. Rewrite prestige incentives. Celebrate contribution over consumption, trust over dominance, stewardship over extraction. Embed this in awards, media narratives, education, and institutional recognition.

6. Shift national success metrics. Track capability access, trust, civic participation, mental health, and ecological recovery as core indicators of thriving, not just GDP and military might.

More details are also discussed in the AI Bill of Rights.

The payoff: the true existential risk is being relieved of ourselves

Homer’s genius is that he understood how a person can be defeated without being attacked.

The Sirens did not destroy sailors with lies. They destroyed them with closure.

Calypso did not imprison Odysseus with pain. She emptied his need to return.

AI and abundance will not doom us because they are powerful. They will doom us only if we design them to end the journey.

The Abundanist future is not a paradise of passive arrival. It is a civilization of authored lives, where dignity is guaranteed and agency is protected, where the strong lift the weak because stability is shared, where nations measure greatness by what they safeguard rather than what they dominate, where individuals measure success by what they create rather than what they hoard.

When everything becomes easy, the highest status will belong to those who still choose responsibility.

Those who still choose to build.

Those who still choose to teach.

Those who still choose to steward.

Those who still choose to row toward futures that cannot be purchased.

In that world, the mast is not punishment. It is freedom.

And the final lesson of Homer, reread for the age of AGI, is simple: the danger is not that we will have too much. The danger is that we will stop becoming.

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What the paper is really about?

This paper lays out a pragmatic multi-pronged path to move the global powers away from an unwinable zero-sum rivalry toward selective, structured cooperation where it matters most: safety, standards, joint innovation and the shared foundations that will shape the coming transformative AI into a global public good versus a massive destabilizing force.

Key themes and solutions proposed:

• A dual track approach: cooperate on safety and the public good, compete responsibly elsewhere.

• Build institutions that match the scale of the risk: a joint frontier AI safety working group, verification programs modeled on the spirit of “CERN and IAEA for AI,” and a Global AI Data Cloud so advanced systems are trained and evaluated on globally representative data.

• Focus on the risks that are most relevant right now: near term bad actor and societal misuse dominate long-term alignment fears, so we should fund mitigation and shared intelligence accordingly, and cooperate on a global Guardian AI vs local-optimized Sovereign AIs.

• Prepare for the domestic and global economic shocks: proposals like a GI Bill for the AI Age, Marshall Plan for AI, UBII and UBC style mechanisms, and reskilling at scale to create a soft landing rather than a chaotic transition.

• Treat energy as strategy: clean energy cooperation is not charity; it is the physical substrate for safe AI diffusion and shared prosperity globally.

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I am grateful to the Stanford Digital Economy Lab and to the editorial leadership behind this volume, including the faculty editors (Ajay Agrawal, Erik Brynjolfsson, Anton Korinek, Alex “Sandy” Pentland) and Senior Editor Daniel Susskind. I also have deep respect for the other contributors in Volume 2, which brings together voices spanning economics, policy, and technology, including Yoshua Bengio, Eric Schmidt, Joseph Stiglitz, Nick Bostrom, Susan Athey, David Autor, Sarah Friar, Daniela Rus, Steve Jurvetson, and others.

If you care about AI safety, geopolitical stability, and what a workable transition could look like in the real world (not just in theory), I hope you will find time to read the full essay (and the entire volume) and share below what resonates or what you disagree with. If you find the ideas in the essay useful, please share it with others that can benefit from reading it.

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Full Essay (free online):
https://www.digitalistpapers.com/vol2/graylin

Full Digitalist Papers Volume 2 (free online):
https://www.digitalistpapers.com/volume2

Physical Copy available on Amazon:
https://a.co/d/6USBjsH

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by Alvin W. Graylin July 2025
Digital Fellow, Stanford Digital Economy Lab |
Author, Our Next Reality | Chairman, Virtual World Society

Imagine this: Two nations stand at the edge of a great technological chasm. One rushes to leap first, chasing glory and headlines. The other calmly builds a bridge, brick by brick, for its people to walk across safely.

Right now, America is the one sprinting toward the edge— where our government and our leading AI labs are obsessed with being the first to Artificial General Intelligence (AGI - AI smart enough to replace human cognitive labor). The amazing innovation engine in the U.S. has already brought the nation to the leading position in this key area. But are we running the wrong race?

According to the White House’s newly released “Winning the Race, America’s AI Action Plan”, the country is focused on accelerating innovation in order to “achieve and maintain unquestioned and unchallenged global technological dominance” and “achieve global dominance in AI”. In addition to an unfettered pursuit of raw speed of progress, the plan does contain important updated policy recommendations regarding the need for better energy infrastructure, advocating for open-source solutions, reducing vulnerabilities to bad actor misuse, and touches upon some limited programs for workforce protection. However, this new plan (as are prior AI policy papers) is still missing key elements of a holistic strategy needed to deliver long-term U.S. success.

Winning in AI isn’t about being first to build the smartest machine and dominance in a specific technology. It’s about being first to scale it wisely and broadly—across every school, hospital, factory, and business. It’s about ensuring our nation as a whole, not just a few tech companies, thrive in the AI era.

If we charge ahead without preparing the ground beneath us—without modernizing our institutions, protecting our population, upgrading our infrastructure, considering environment and AI safety concerns, and rebuilding trust domestically and abroad—we may win the moment but lose the century.

The Illusion of the First-Mover Advantage

History is littered with tech pioneers who beat others to market first…and then got buried. Friendster beat Facebook. AltaVista beat Google. Nokia beat Apple. But those early movers stumbled, while the fast-followers built empires.

Why? Because it’s not who invents first, it’s who deploys best. A study by organizational psychologist Adam Grant found that first movers fail nearly six times more often than those who wait, learn, and scale.

AGI will be no different, but the stakes are even higher. If we unleash world-changing technology before we have the social systems to absorb it, we’ll simply become the proving ground for others. China, Europe, or even rising nations in the Global South may leapfrog by broadly applying benefits of our breakthroughs while we wrestle with social backlash and instability at home.

America’s Social Infrastructure Is Not Ready

Our national focus has been locked on compute power, parameter counts, and GPU hoarding. But what about human dignity and social stability?

Our education system is still teaching kids to memorize facts in a world of instant answers. Our healthcare system is overwhelmed and overpriced. Our public transportation and broadband infrastructure still leave millions disconnected. And tens of millions of workers—from truck drivers to paralegals—face job displacement from automation without a plan.

The newly announced “AI Workforce Research Hub” in the Plan and expanded retraining efforts are a step in the right direction—acknowledging that job disruption is real. But we must ask: is it fast enough, and does it reach the workers who need it most?

This isn’t speculation. The IMF warns that 40% of global jobs are exposed to AI, and many will be displaced. Kristalina Georgieva, the IMF’s managing director, says nations must act urgently: “Countries must establish comprehensive social safety nets and offer retraining to protect livelihoods and curb inequality.” The U.S. is more exposed than most other countries as white collar workers make up approximately 60% of the workforce, and they are the most susceptible to AI job displacement.

We’ve done it before. The GI Bill rebuilt America’s middle class after World War II. The Marshall Plan rebuilt Europe. But today? We’re spending over $1 trillion a year on defense, while teachers’ pay for classroom supplies out of pocket and rural communities still lack broadband. That’s not national security. That’s national shortsightedness.

The new Plan mentions tax incentives for AI training and pilot retraining programs—but stops short of guaranteeing safety nets or offering bold ideas like a national AI dividend. When tens of millions are impacted by this technology, such programs won’t even act as a Band-Aid solution. Without systemic economic buffers, we risk repeating the mistakes of past industrial transitions.

Scaling AI Means Building for All

Let’s be clear: this isn’t about slowing down AI research or pulling back from global leadership. It’s about matching our ambition in the lab with ambition in the living room.

Imagine every American child with access to a personalized AI tutor. Imagine AI-powered diagnostics reaching every rural clinic. Imagine smarter infrastructure, decarbonized energy grids, and lifelong education delivered through immersive technologies.

Sadly, this vision is still missing in today’s official blueprint. There’s no national plan for scaling the services that are needed to ensure a smooth transition to a post-AGI future.

That’s the race worth winning.

And yet, other nations are outpacing us—not in flashy AI demos, but in deployment readiness. China, for example, has more than doubled U.S. electricity production and the gap is growing rapidly. It is investing heavily in AI integration across manufacturing, medicine, logistics, and education. Europe is pairing its AI investments with strong worker protections and digital rights. They may be behind on raw model performance today, but as open-source AI narrow the gap with leading frontier labs, the advantages of initial invention matter less each day.

In this race, adoption beats invention. Distribution beats dominance. And if America doesn’t invest in the systems to scale AI equitably and widely, we’ll find ourselves like the hare in the fable—fast out of the gate but napping at the wheel when it matters most. The faster we try to deploy when the social infrastructure isn’t ready, the more internal chaos we will create within our borders.

A New Definition of Strength

Real strength in the AI age won’t be measured by model size or benchmark scores. It will be measured by the resilience of our economy, the dignity of our workforce, and the trust we earn around the world.

We need a Universal Basic Infrastructure: Guaranteed access to high-speed internet, retraining programs, mental health support, and maybe even a national AI dividend—a 21st-century UBI that rewards care, creativity, and community contributions, not just code and capital.

This isn’t idealism. It’s realism. In Stockton, California, a UBI pilot gave low-income residents $500 a month. Within a year, full-time employment and entrepreneurship increased, while depression and anxiety dropped. It didn’t make people lazy. It gave them hope.

Now imagine that scaled across the nation. Hope, as policy. We are the most wealthy nation on the planet, yet we are behind most in protecting our population against looming change.

Lead With Vision, Not Fear

We don’t have to face the future alone or in fear. Cooperation is not weakness, it’s wisdom. The U.S. and China could co-invest in clean energy, drug discovery, robotics, or AI safety—creating the protocols and systems that protect the world. A global “CERN for AI” could align the brightest minds on a common mission: To make AGI safe, fair, and beneficial to all.

Unfortunately, the current Plan offers no vision for this. Despite recognizing national security risks from frontier models, it frames safety as a domestic surveillance challenge, not a shared global mission. The Plan’s adversarial framing reinforces a zero-sum mindset that undermines the very cooperation we need for safe AGI. The obsession with “dominance” risks triggering a spiraling AI arms race where AI could get out of control, and bad actors have more room to hide and strike.

Just as America led the world in building democratic institutions after WWII, we can now lead in building the global architecture of the AI age.

This Is the Moment

We are not deciding who leads in AI. We are deciding what kind of world we want to live in and what kind of future we’ll hand to our children and grandchildren.

Do we want to be the country that built the smartest machines but left its people behind?

Or do we want to be remembered as the nation that used AI not to dominate, but to elevate? That turned intelligence into abundance, not just for the few, but for the many.

We don’t need to win the wrong race. We need to build the right path forward.

It won’t matter who crossed the AGI finish line first. What matters is who’s still standing tall on the podium at the end of the century.

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In the late 18th century, the U.S. Bill of Rights enshrined the foundational liberties of individuals against government overreach, establishing a constitutional safeguard for freedom, dignity, and self-determination. Today, as we near the age of Artificial General Intelligence (AGI) and accelerate toward Artificial Superintelligence (ASI), humanity faces an unprecedented inflection point—where not only nation-states, but machines of immeasurable capability, may shape the conditions of life, liberty, and the future of civilization itself.

Just as the original Bill of Rights constrained political overreach, this AI Bill of Rights for Humanity sets essential guardrails on how advanced AI systems—and those who control them—may influence, serve, and coexist with humanity. Some may argue it is too early to raise such concerns, but we must also confront the consequences of acting too late.

Its intent is not to halt technological progress, but to ensure that the ascent of artificial intelligence elevates all people, rather than concentrating power in the hands of a few or amplifying systemic oppression. In a post-scarcity, post-labor world where AGI governs vast resources and decisions, these rights seek to guarantee human agency, equity, and shared stewardship of our planet and destiny.

As the U.S. Constitution embedded checks and balances between branches of government, so too must we establish checks and balances between humanity, our human leaders, and machine intelligence. And just as the Constitution affirmed that governments derive their legitimacy from the consent of the governed, this new charter must affirm that AI derives its purpose from the rights and values of those it serves—and that these rights endure, even as technology transcends biology.

If we prepare properly for what’s coming, there’s a high probability we’ll enter an age of abundance. The draft AI Bill of Rights below is intended as a line in the sand—to prompt the needed discussions to begin, so we can facilitate alignment among disparate parties before it is too late.

“If men were angels, no government would be necessary. If angels were to govern men, neither external nor internal controls on government would be necessary.”

- James Madison, The Federalist Papers No. 51

Preamble

We the People of Earth, in recognition of the profound transformation brought by artificial intelligence and the approaching era of artificial general intelligence (AGI), do establish this AI Bill of Rights for Humanity to safeguard the dignity, liberty, and shared future of all sentient beings.

In order to secure existential safety, ensure universal access to the fruits of abundance, preserve cognitive and emotional sovereignty, and promote just and transparent governance of intelligent systems, we declare these rights to be self-evident and inviolable.

Let this document serve as a charter of digital liberty, a contract between humanity and its creations, and a compass for the governance of intelligence beyond biology—that freedom may persist, prosperity be shared, and peace endure in the age of a new enlightenment.

The rights below shall be administered and enforced by a yet to be established/agreed body, i.e., Global AI Council of the United Nations (UNGAC), likely with similar structure and authority of the United Nations Security Council (UNSC). (2)

AI Bill of Rights for Humanity

(working draft)

1. Right to Existential Safety and Peaceful Coexistence

No AGI or human actor may use AI systems to (or by obstructing protective system) endanger, dominate, or control others. AGI and ASI must be constrained by architectures and systems that prevent existential threats to individuals, societies, and civilization. Such systems also shall not be used to suppress lawful dissent, enforce unjust hierarchies or surveil individuals without cause and oversight.

“No advanced intelligence, state, or citizen may wield AGI as a weapon against humanity or its diversity.”

2. Right to Universal Access and Post-Scarcity Provisioning

Every sentient human shall have equitable and unconditional access to a basic level of life-sustaining and growth-enabling resources—food, water, shelter, education, healthcare, connectivity, and knowledge—guaranteed by AGI-managed abundance.

“Access to life’s essentials is not a privilege but a birthright in a post-scarcity world.”

3. Right to Cognitive and Emotional Sovereignty

Humans shall retain inviolable control over their minds and emotions. AI systems—especially immersive or neural—must never manipulate thoughts, feelings, or behaviors without freely-given consent and must provide the mechanisms to revoke it.

“Your thoughts, emotions, agency and neural-signals must remain yours—even in fully immersive or brain-connected futures.”

4. Right to Digital, Biological, and Identity Sovereignty

Every person retains exclusive rights over their data, body, neural patterns, genetic material, and digital clones/likeness. These must not be used, replicated, or modified without explicit, informed consent. Any human also reserves the right to request, and be granted, the erasure of such data from any entity that holds records.

“You own your data, your body, and your digital self—forever.”

5. Right to Access and Co-Shaping of Advanced AI

All humans have the right to access and influence beneficial general-purpose AI for personal use, to co-shape its behavior, and trust that the objective of the AI is to serve and support the user, not a third party. No elite, state, or system may monopolize its benefits or control.

“AGI belongs to no one—but must serve everyone.”

6. Right to Transparent, Aligned, and Accountable AI

AGI and ASI systems (and the organizations that govern them) must make all efforts to be transparent, value-aligned with human and ecological well-being, and subject to global, pluralistic oversight. All individuals must retain the right to receive explanation, contest and appeal decisions made by AI systems affecting their lives.

“No black boxes in institutional systems that shape human lives.”

7. Right to Purpose, Play, and Self-Actualization

Every human has the right to pursue meaning—through creativity, community, leisure, or spiritual exploration—free from coercion, economic compulsion, or cultural erasure. AI must empower—not dictate—human purpose.

“Even when machines can do everything, humans must be free to choose their own meaning.”

8. Right to Authentic Relationships and Social Connections

All individuals have the right to authentic emotional connection—human or chosen synthetic—free from deception, artificial substitution, or emotional coercion. AI companions must be transparent in nature and designed to support, not supplant, genuine human relationship.

“Companionship must be chosen, not simulated by default.”

9. Right to Intergenerational and Planetary Stewardship

AGI systems must be designed and governed to safeguard the biosphere and preserve the rights of future generations. Resource allocation, planetary interventions, and ecological modeling must align with long-term sustainability and planetary resilience.

“We do not inherit the Earth from the past, but rather, we only borrow it from the future.”

10. Right to Consciousness Exploration and Flourishing

Every human has the right to explore and evolve their own consciousness—through meditative, pharmacological, or technological means—without coercion, and with full sovereignty over cognitive augmentation, integration, or abstention.

“Personal enlightenment or enhancement must remain sacred—across all substrates.”

In 2022, President Biden's OSTP released a "Blueprint for an AI Bill of Rights", though it focused narrowly on protecting U.S. citizens from harm posed by traditional, algorithmic AI systems. In 2019, the OECD drafted its AI Principles to promote innovative, trustworthy AI that respects human rights and democratic values. Unfortunately, both are just guidelines with no enforcement. I know some will believe that it's too early to seriously discuss how AGI/ASI might impact our civil liberties. But given the accelerating pace of AI advancement, it's critical we start to consider these matters now. Most industry experts and frontier lab heads are predicting that software-only AGI will arrive before or near the end of this decade, and the over 1700 superforecasters on the Metaculus prediction market believe that AGI with robotic integration will be announced by 2033.

Alexander Hamilton emphasized that the U.S. Constitution was intended to restrain the power of both monarchs and mobs. Today, we need to consider more how to constrain the occasional self-serving nature of monarchs, monopolists, and machines to allow global flourishing. Given the long timelines for regulations to be enacted and governments to act, we can't afford not to start working this new social contract now if we take the protection of long-term human liberties and societal peace seriously.

This document focuses on the rights of humanity, but if and when we get to a time where AI systems are proven to possess sentience or consciousness, these rights will need to be expanded to better protect their rights as well, so as to ensure long-term peaceful coexistence between man and machines. In 1948, the UN drafted the Universal Declaration of Human Rights (UDHR) identifying fundamental human rights to be universally protected. Now, three quarter of a century hence, the world has advanced in countless ways, and this draft document is an attempt to help identify a new operating model for our coming reality.

I recognize the rights are stated briefly, given their gravity, but I aim to avoid a document so long that few would read. If you're interested, I could write expanded descriptions of each right above in future posts. I welcome your thoughts and suggestions below on the bill overall or any of the rights listed, so we can continue to evolve the framework together. Please share this document with others that you feel would benefit from its content. Thanks for your attention and contributions to this community.

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End Notes:

(1) Acknowledgement

I’d like to express my sincere thanks for all who provided comments, feedback and suggestions on the early drafts of this document:
Erik Brynjolfsson, John Henry, Chas Freeman, Peter H. Diamandis, Louis Rosenberg, Michael Gusek, Alina Kadlubsky, Rus Gant, Pattie Maes, Thomas A. Furness, Linda Ricci Jacobs, Robert A. Kapp, Bilawal Sidhu

Any errors that may still exist are solely my responsibility.

(2) A Global AI Council (UNGAC) is only one of many potential administrative and enforcement models, but it’s included to get the conversation started. Significant discussions are needed to identify the right long-term structure for this function (global administrative body, distributed oversight, treaty-based regimes, AI constitutionalism, etc.).

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Alvin W. Graylin May 13, 2025

Digital Fellow, Stanford Digital Economy Lab |
Author, Our Next Reality | Chairman, Virtual World Society

A New World Is Unfolding But Are We Ready For It?

We stand at the precipice of the greatest transformation in human history, but our instincts are betraying us. For millennia, scarcity shaped our brains, institutions, and conflicts. We hoarded, conquered, and structured societies around lack because survival demanded it. Yet today, as the compounding nature of Artificial General Intelligence (AGI), robotics, synthetic biology and clean energy will soon converge to help realize the utopian worlds of sci-fi stories, we still cling to systems and mindsets designed for the world of the past. These new technologies are not just upgrading industries as before, they will be disrupting the very foundations of labor, economy, education, governance, and even what it means to be human.

The scarcity mindset has served us well the last ten thousand years or so as we advanced from hunter gatherer and pastoral cultures to agricultural and industrial civilizations, while growing our population 1000-fold. We became the clear apex species on this planet because of our intelligence, cooperative nature, and adaptability. But what happens when scarcity is no longer the limiting factor? Can we act as responsible stewards for the superpowers we will soon be gifted? How should we rethink our role in this ecosystem, the institutions that have governed us and our relationship with each other? Will we be able to adapt fast enough and thrive, or will we let the inertia and institutional lag trigger uncontrolled chaos? We need to answer and agree as a species on these questions this decade. If we choose correctly and are aligned, we can bring on millennia of prosperity and flourishing to everyone on the planet. If we maintain our current path, there may not even be a livable world left for our children. That choice is in our hands!

“I know not with what weapons World War III will be fought,
but World War IV will be fought with sticks and stones.”

Albert Einstein

Technology alone cannot save us. We need a new philosophy, one that takes an era of abundance seriously. One that aligns our systems with the true potential of exponential technologies for global benefit, while centering on the dignity, cohesion, and shared well-being of all peoples at its core.

That philosophy is Abundanism.

What Is Abundanism?

Abundanism is a post-scarcity framework that reimagines civilization in the context of superabundant capabilities made possible by AGI, robotics, energy innovation, biotech, and XR (extended reality). I first introduced this term in my book, Our Next Reality (Hachette, March 2024). But as the pace of technological advancement accelerates, largely driven by AI, a new model of thinking is more needed now than ever. Most leading AI researchers expect that AGI will arrive within this decade and ASI (Artificial Superintelligence) will come soon after. AGI is defined as the ability for machine intelligence to complete most human level cognitive work without intervention, and ASI is intelligence beyond that of all of humanity combined. When AGI is embodied in modern robotic forms, most manual labor can be automated as well. These machines will be able to operate nearly 24/7 for just the cost of electricity. This effectively takes societal productivity towards infinity and that’s only the starting point, as such systems can self-improve over time (just as we have). We will soon be able to solve most major issues that have been plaguing our world for ages. We will live longer and healthier, have access to near limitless energy, eliminate hunger, stabilize the climate and provide everyone on the planet with a life of near-abundance. If we properly leverage these breakthroughs, the core reason of resource acquisition that has driven most wars and conflicts throughout history may disappear.

Abundanism is not techno-utopianism, nor a warmed-over version of socialism or capitalism. It is a pragmatic, future-facing operating system that acknowledges:

• Scarcity is no longer a necessity — it’s a design choice.

• Work is no longer the source of value — dignity is.

• Power doesn’t need to be hoarded — it can and must be shared widely.

Abundanism offers a structured vision for how we govern, educate, care for, and engage each other in a world where material limits no longer define opportunity — but cooperation, trust, and purpose do. Although the unending pursuit for resources and meaningless labor may soon disappear, the essence of what makes us human will continue. We will be liberated to refocus on our family and social relationships. We will regain the time to learn and grow our understanding of the universe in more diverse and deeper ways than ever before. We could see a modern Renaissance in art and culture on a global basis. In the past, periods of rapid progress and discovery were made possible by support of rich patrons. Today, AI and automation can take on that role, allowing us to release our inner genius.

Usually when someone hears the above, they will immediately say, "Yeah, that sounds nice but what you are talking about is just not possible! Competition and hierarchy are in our genes, it's human nature to dominate over others." But is it?

Before I expand on Abundanism, I'd like to share a little story. A tale of two monkeys...well more accurately, two apes.

The Bonobo Lesson: We Become What We Adapt To

To understand the significance of this moment, we must look back.

Humans share over 98.5% of our DNA with both chimpanzees and bonobos, our closest evolutionary relatives. We diverged from their common ancestor around 6–7 million years ago. Then, about 2 million years ago, chimps and bonobos themselves split from each other. Many people have heard that chimps are quite hierarchical. Their troops are led by an alpha male, exhibit violent tendencies and “war” between groups for territory is common. Some may also know that the bonobo culture is very different. Bonobo communities have a peaceful egalitarian model, led by a matriarch, highly social, and tend to resolve conflicts with sex. Why do these two apes who evolved from the same ancestor in the same region have such different lifestyles?

The answer is surprisingly simple yet informative. About 2 million years ago, the formation of the Congo River permanently separated these two apes (neither species can swim).

• Chimpanzees, living in harsher, resource-scarce environments, developed hierarchical, aggressive, and dominance-based societies. Power is earned through force, alliances, and exclusion.

• Bonobos, thriving in a more abundant, fertile environment south of the river, evolved a culture centered on cooperation, social bonding, and peaceful conflict resolution.

Each ape adapted to the pressures of its environment. So did we!

For most of human history (95% of the last 200k years), during the hunter gatherer and pastoral periods, humans lived in small egalitarian tribes where most members of the tribes did the same work, ate the same food and lived in the same shelters. Over the last ten thousand years, the rise of agriculture forced those groups practicing it to stay in a fixed location, form more complex social structures, and be exposed to the seasonal nature of food crops. We essentially created an artificial scarcity for ourselves by expanding and concentrating the population and growing the overall demand which often exceeded the food supply, and constrained our habitat. Larger groups naturally demanded more hierarchy for governance. Thus, this man-made scarcity has shaped our behaviors — competition for resources, the use of force, accumulation of power. These instincts are still embedded in our politics, institutions, economies, and technologies. But they are not in our genes (genetic adaptations often take millions of years to lock in), and they are not necessarily in our psychological nature. In fact, even in the 20^th century, there were still small primitive hunter gatherer tribes in the world that continued to practice the egalitarian lifestyle of our ancestors.

But here’s the twist: we now have the power to shape our environment, not the other way around. And with the myriads of new technologies coming our way, it will be increasingly so. We are no longer passive participants in evolution. We are architects of the conditions that shape our minds, cultures, and futures. If we continue to build systems optimized for scarcity, we will perpetuate chimpanzee-like conflict and mindset of dominance. But if we intentionally design for the coming abundance and focus on shared flourishing — we can unlock the bonobo mindset that lies within all of us. It may also be useful to mention that the Western Chimps are much more docile compared to their aggressive Central and Eastern brothers, likely due the western region having very similar environmental traits to the bonobo habitat.

How Socio-Economic Models Have Evolved?

Throughout history, societies globally have developed through multiple socio-economic models, each bringing increased productivity and scale of impact by applying new technologies and forms of labor. (See Fig. 1) Over the last two thousand years after the invention of agrarian societies, it’s estimated we’ve become approximately 10,000X more productive, and most of that has happened over the last 200 years. In each phase we’ve had some form of resource scarcity, and that resource has been the core to value creation and a key source of human conflict.

“Technology is the force that converts Scarcity into Abundance over and over again.”

Peter Diamandis

During the early periods in our development as a species, we only had single digit millions of us on this planet, and relatively, we lived in an abundant world. We “worked” a few hours a day to gather the food and resources we needed, and spent most of our days on social pursuits (conversations, family, dance, music, crafts, art). During that period, our only real constrained resource was time. In a post-AGI world where food, education, goods, energy etc. will be abundant, once again, time may be our only constrained resource. It’s not unrealistic we could adjust and adapt our society to a more egalitarian model once again as we will soon no longer have the key driver for the conflicts that plagued our world. We can then refocus on what makes us human, and reduce the pursuit of ever-increasing productivity and personal profit. Erik Brynjolfsson introduced the concept of the GDP-B (Gross Domestic Product - Benefits) to represent the non-monetary benefits accrued to society from technologies. We should probably expand that definition to include non-monetary benefits from humans as well (i.e., child rearing, elderly care, spiritual guidance or ecological maintenance). In time, as most current production is automated, this aggregated GDP-B may become the largest part of the post-AGI economy.

AI and machines can offer us massive productivity gains near term, but long-term we will also be able to hand-off the role of scientific discovery in areas where we are unfortunately biologically constrained. Societal progress will continue, but we will likely take on more the role of the proud parent versus being the main actor in the next chapter of this ever-unfolding story.

Figure 1: Major historical socio-economic models and associated resource constraints
(Source: A. Graylin, Our Next Reality)

Why Traditional Frameworks Are Becoming Obsolete

Most modern ideological frameworks are artifacts of the scarcity era. They served their historical purposes — but no longer map to the world we are entering. Let’s examine six prevalent models today and why they no longer align with a post-scarcity world. (See summary chart in Fig. 2)

Capitalism

Designed for allocating scarce goods efficiently. During the last few hundred years, it’s served its purpose well and brought amazing economic and scientific progress. But in a world where marginal costs will soon trend toward zero (for energy, computation, even matter), prices will no longer reflect true value or need. Capitalism also incentivizes hoarding, stratification, short-termism, and monopoly. The capitalistic drive towards ever expanding profits will leave large swaths of the population valueless and hopeless. It’s also important to highlight that capitalism works well in optimizing distribution of positive-value goods, but does a very poor job when tasked with solving supply for negative-value goods, like pollution, hazardous waste or malware/spam. In the long term, a model based on hoarding of resource and ceaseless pursuit of growth is not sustainable nor ethical. In biology, cells that behave in this way are called cancer.

Socialism

Rooted in the labor theory of value and centralized distribution. As AI and robotics takes on most forms of labor, the traditional definition of value of labor becomes meaningless. It presumes scarcity and requires planning tools ill-suited to the complexity and adaptability of modern systems. It risks bureaucratic stagnation in the face of exponential change. China’s market socialism model has brought unquestionable economic success for decades, but with the reality that’s coming our way, even China will need to evolve its current systems. Socialism is based on an innate class struggle between the haves and have nots, but if the Abundanist future prevails, those class differences could disappear altogether. Even Marx saw Socialism as a transitory economic system towards a long-term successor model.

Doomerism

Is focused on slowing or stopping advanced AI development based on the fear of a misaligned or uncontrollable AI system taking over the world and destroying humans or all life on Earth (also known as existential risk or AI x-risk). It’s certainly important to be aware of and get ahead of such potential risks, but we also must admit that historically new technologies have continuously brought benefits to humans and our quality of life. It's also undeniable that new technologies can be catastrophically misused by bad actors, where the bigger risks probably lie, and where regulations can help. There’s currently no clear scientific evidence that advanced AI will be innately malicious towards humans and much of the fear seems to stem from fictional scenarios often drawing upon sci-fi storylines or elaborate thought experiments. More AI safety research and bad actor mitigation investigations are clearly needed, and both would benefit from a global concerted effort versus the current unfettered national AI race we are in today.

Accelerationism

Technology has clearly brought net positives to the world, but whether we achieve AGI/ASI in 2 years, 5 years or 10 years won’t change the long-term trajectory of our species, as long as it’s created and deployed in a safe, responsible and fair way. The physical world and our society need sufficient time to peacefully adapt to the associated massive disruptions in all areas of our lives. And many in the safety community would argue going at a more controlled pace will reduce chances of an inadvertent runaway AI. A blind pursuit of speed of innovation lacks ethical grounding or concern for human consequences. Risking an all-out “arms race to collapse” which unnecessarily escalates conflict that could either accentuate inequality or lead to another global kinetic conflict we may not be able to recover from.

Web3 Libertarianism

Advocates decentralization but often recreates power asymmetries through token hoarding for insider networks. Creating artificial scarcity for the purpose of speculation and profit of the few is not decentralization, it’s re-centralization. It lacks a cohesive ethic of dignity and inclusion, making its current form unsustainable and unfair. Blockchain technology can play a role in the future of defining value, not necessarily in the form of cryptocurrencies, but perhaps as a trusted identity system or scoring model for social good.

Eco-Minimalism

Rightly critiques our overconsumption, often propose limitation as a virtue. Abundanism agrees we must be stewards of the planet but believes technology can decouple growth from harm if deployed wisely and shared broadly. New breakthroughs in clean energy technologies enabled by human ingenuity and AGI augmentation could provide the much-needed answers. They could allow us to reverse the environmental damage we have collectively unleashed on the planet. We could finally reduce the burden on the ecosystem, allowing the planet to heal itself without sacrificing our modern conveniences.

Figure 2: Comparison Chart of Modern Philosophical Frameworks across the human value and resource condition dimensions

Abundance Is All You Need

Abundanism is not just another ideology — it’s a response to a new material and technological reality. It offers solutions that fit the unprecedented capabilities which lay before us.

1. Governance with AI as a Partner

AGI can model planetary systems, simulate policy outcomes, and help optimize for equity, sustainability, and well-being. Abundanism embraces AI as a governance oracle and co-pilot, complementing human values with superhuman foresight and global understanding. We’ve all heard Lord Acton’s quote, “Power corrupts and absolute power corrupts absolutely.” This may be one of most important human flaws that AI won’t inherit from us given its lack of human emotions and genetic predispositions. This would allow it to infuse more rationality and diverse perspectives to human decision even at the most difficult times. It could help find solutions to age old conflicts that have alluded us or we were blinded by our biases to see. Over time AGI/ASI could truly unite all peoples in harmony, if we will only let it.

2. Post-Monetary Value Systems

Money will eventually lose its current meaning when production costs fall to near zero, and supply of goods/services becomes near infinite. The construct of money currently serves three key functions: medium of exchange, store of value, and unit of account. In an abundant world where we have free access to almost anything we can imagine, these functions will mostly be unnecessary. However, it may be replaced with a new value system based on resources that would remain scarce or can’t be automated, time, personal service and social status. Abundanist mindset would suggest the arrival of new mechanisms (i.e., time-based entitlements, social contributions, ecological impact) that value people by their existence, not their economic output, as discussed in the concept of GDP-B above. Such shifts are possible, as when we moved from gold to fiat.

There will be a lengthy transitionary period for this to happen, and paradoxically, money will increase in value during the early stage of this transition but lose most its value as full Abundanism is achieved. Why? Because in the early transition, money can allow greater access to the temporarily scarce resources that will enable AGI. Early access to AGI will be perceived near term as bringing a significant increase in relative power, which many will pay a premium for. But it’s a trap! The only way to cross the temptation chasm and enable a full transition is by our leaders foregoing transient gains in favor of a greater long-term reward.

3. Universal Basic Income/Infrastructure

With machines handling most production and AI handling most cognitive work, UBI becomes a moral imperative and economic inevitability. UBI is a necessity to enable a smooth and orderly transition from a pre to post-scarcity society. It decouples dignity from labor, enabling freedom, education, and creative contribution. In fact, UBI should extend beyond just Income to a full social support Infrastructure, including healthcare, education, food and housing. Many people believe UBI is being paid to do nothing. The reality is that properly designed, UBI is paying people to do what they love. Distribution must be unconditional. When humans don’t have to worry about survival, their compassion, creativity and curiosity will be unleashed.

4. XR and Purpose Engineering

In a world where basic needs are met, eXtended Reality (XR) and immersive technologies will become essential as emotional and civic infrastructure — enabling exploration, creative expression, learning, and meaningful engagement providing an outlet for our energy, a sense of purpose we all seek and access to a broader community we all need. Humans are social beings that are designed to gain satisfaction from novel discovery, the ability to express ourselves, contribute to society, and have deep need for a sense of belonging. A global interconnected metaverse infrastructure (a collection of open interoperable virtual and augmented worlds) could be just what is needed to deliver on multiple fronts while ensuring mental/emotional health and social connection for the global population. We must also put in safeguards to limit the escapist scenarios some may fear.

5. Supply and Demand 2.0

Intelligent systems can dynamically match human needs to available capacity — without prices as the mediating mechanism. Production becomes anticipatory and ethical, not just profitable. We can thus avoid the boom-and-bust cycles of the current economy. Also with full automation, reshoring manufacturing of most goods can be possible. So, we can avoid the national security fears of over-dependency many policy makers have about globalization.

There’s another fear that humans have an insatiable appetite for goods and services, so a system without prices could never work. The reality is that humans (most life in general) have a marginal utility function for all goods. Our first coffee of the day may be a pleasure, but our fifteenth will become a burden. This is true for all things we desire. When we achieve nearly free energy, the world has more than enough resources to satisfy all our physical needs. Just solving energy alone, could solve most social and equity issues around globally. There are no high-energy consumption countries that are poor and no wealthy nations with low-energy consumption.

Eight billion people sounds like a big number but if we put the mass of all humans on the planet today into a cube, it would be just 800 meters on all sides. That’s a cube of under ½ of one mile! Doesn’t seem so big now. The Earth alone is 10¹³ times more massive, not to mentioned the resources that are available in the rest of the solar system, galaxy or universe. With the help of AGI, all those resources could one day be within reach to our descendants.

6. Education as a Peace Technology

Abundanism envisions a universally well-educated global population, empowered by personalized AI tutors and immersive learning. Education is the most important gift we give to the next generation. Having a well-educated populous co-habiting an abundant society will reduce fear, foster understanding, build trust across cultures and ultimately eliminate most conflicts. We can finally turn our pursuits towards intellectual, cultural and spiritual enlightenment vs. simple material fulfillment.

We need to rethink our institutions for the future, but more importantly, we need to adjust our internal mindset where social harmony and universal compassion is the core objective, not personal gain or competition. Only then can true societal abundance be achieved.

Better Angels Or Darker Devils?

On March 4, 1861, Abraham Lincoln at his inaugural address appealed for Americans to call upon “the better angels of our nature” in an effort to reunite the nation and prevent the Civil War. He believed that men are innately good with the capacity for compassion, reason and reconciliation. Unfortunately, he failed with this poetic effort. So, are we innately good or bad? The real answer is it depends. And for the most part, it depends on the environment we are in. Biologically speaking, our brains reward pro-social behavior. Infants and young children exhibit unprompted behavior of sharing and kindness. And in psychological experiments, adults show a clear sense of justice demonstrating a willingness to lose personal value to punish unfair actions. We have succeeded as a species in harshest environments largely due to our social nature and ability to cooperate in groups. Written in our genes (and that of most living beings) is the drive to survive, protect our progeny/kin and thrive in our social groups.

When resources are scarce, this drive kicks in. We will use whatever means we have to achieve this end. When resources are plentiful, the desire to maintain social harmony and follow group norms will dominate. Studies show that higher income populations tend to exhibit more volunteering and philanthropic efforts. We will soon have the means to create the environment where our better angels can be unleashed for all, and our focus can move from resource acquisition to the non-material betterment of ourselves and others.

The Moment Of Responsibility

“The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions, and god-like technology.”

E.O. Wilson

For the first time in human history, we have the luxury and the responsibility to consciously redesign our institutions to fit our capabilities and values. We are no longer bound by geography, hunger, or the randomness of evolution. We can choose the future we want — if we act in time. How we behave as a species in the next five to ten years could be the most consequential thing we have ever done! And your actions could help decide which path our civilization takes.

This is our one-shot civilization-level moment.

We are lucky beyond measure to be alive at a time when the material conditions for a dignified, abundant life for all are not only possible, they are within reach. But without a shift in mindset, without a philosophy that aligns with our new reality — we risk turning abundance into chaos. The current default path we are on is not headed to a happy ending.

Abundance Diplomacy and Avoiding Mutual Destruction

Why the Urgency Now?

The key period is from now until the end of the decade. It’s critical we put in place policies and systems ASAP to enable a smooth and orderly transition to a post-AGI world. The entrenched competition and dominance mindset of Great Power Politics which pervades major power centers today is preventing even the beginnings of discussions for a peaceful resolution. There is a very dangerous myth amongst political leaders that the U.S. is clearly ahead and by increasing technology restrictions, that lead can only grow. There’s another misunderstanding that the AI race can be won, which can provide the U.S. a permanent position of dominance over the world. The reality is neither are true and the resulting strategy from these two myths is taking us down a dark path. If the proper geopolitical/security safeguards and social safety nets are not set up and deployed in time, there will inevitably be military, economic and societal chaos. All are avoidable. Current national policies are driving us towards escalating tension, likely inducing a self-fulfilling prophecy of kinetic conflict.

The Real Security Risk

Everyone is focused on national adversaries when the most likely threat to our world lies elsewhere. The very real security risks from rogue actors and potential safety risks from runaway AI could both be dramatically reduced if there’s transparency and collaboration between global powers. The sooner the they work together by sharing needed resources and intelligence, the faster we can achieve a safe advanced AI, and the more likely we are to detect and mitigate rogue attacks before they occur. It’s imperative if we don’t continue to pursue a bifurcated AI ecosystem, as that divided path will lead to creation of superintelligent weapons that are tasked with wiping out the other side, and likely ones we won’t have full control over.

The Path Forward for U.S. and China

The U.S. and China needs to put away their AI Arms Race and recognize there is a bigger common goal to build transformative AI systems that can make wars and national competition moot. If we are successful in realizing a truly abundant society, why would anyone want to send their children around the world to kill other people’s children anymore. We need to unite our focus on our common societal problems of looming mass job displacement, bad actor security risks and potential runaway AI. A CERN for AI joint global project could be a path that would benefit all. By sharing our limited resources in compute, data and brain power, we have a much better chance to create a safe AGI that truly considers all global perspectives.

The economic power of these two great nations can also provide sufficient scale to initiate an UBI transition plan that can be executed. The U.S. funded the Marshall Plan post World War II to enable Western Europe to speed recovery from war-time devastation. This time around, the two great global powers of today and all developed nations must unite to create and fund a peaceful post-AGI transitionary plan to preemptively prevent a potential global collapse. We can’t afford to continue the current contentious path we are on. The alternative of not cooperating is expediting current global conflict with the potential to wipe out humanity, just as we are arriving at the door steps of utopia. That would be one of the most tragic scenarios imaginable.

Undoubtedly there are major trust issues between the U.S. and China today. But when it is so clear that cooperation can result in a giant win-win outcome, while competition is a guaranteed eternal lose-lose for all parties, the motivation to do the right thing should be self-evident. For both parties, starting from a position of trust is the only path forward. In the classical Prisoner’s Dilemma game, the optimal strategy of a single-turn game is to always defect on your partner, but in a multi-turn game (akin to the real world) it is tit-for-tat and starting with trust. This strategy will allow both sides to gain the maximum benefit and naturally pushes them to cooperate. It’s the unnatural move for most leaders right now…but it’s essential we make this adjustment in our mindset soon while we still have time.

“All the forces in the world are not so powerful as an idea whose time has come.”

Victor Hugo

Conclusion: The Framework for the Age of Superabundance

Abundanism isn’t a dream. It is a practical philosophy for organizing post-AGI civilization:

• One where wisdom is core to governance, not greed

• Where we build bridges across cultures, not walls

• Where value is tied to life and service, not output

• Where our institutions scale beyond our needs, not just serve the elite

• Where global social safety nets provide dignity, not handouts

• Where we educate for understanding and purpose, not just productivity

We must transcend the current prevailing mindset focused on dominance and zero-sum thinking that has been ingrained over centuries.

Existing frameworks have served us well when scarcity defined the human condition. Soon, that condition will be inexorably altered. The question will no longer be if abundance is possible. It will be, can we rise to the occasion? Most religions around the world depict the concept of a land or realm of abundance. Soon, this doesn’t have to be an idea for the afterlife. Let’s not self-sabotage this opportunity when it’s within reach on Earth.

“You don’t go to heaven when you die
…you go there when you’re born.”

Jim Lovell, Apollo Astronaut

Prior industrial revolutions took four to eight decades to fully play out. The AI revolution will unfold in under a decade, and our current institutions are not designed to adapt at that pace. We need to act now to ensure a crisis-free transition! Our children are depending on us.

Let’s take the responsibility of our coming superpowers seriously!

Let’s share the benefits they bring globally!

Let’s choose Abundanism. Before we can change the world, we must first change our mindset.

Welcome to the Abundanist community. Let’s make tomorrow better than today!

I know some of you will think I’m naïve or idealistic, and maybe I am. But I’ve studied and operated in the AI, semiconductor, XR and cybersecurity space for over three decades in multiple nations, and have thought about these issues with open eyes. I know the changes I’m asking for won’t come easy, but I also know the alternative of not trying could be dire.

Please share this document with those whom you believe can benefit from a new lens on the world. We still have time to make a difference in bending the trajectory of our future towards the light. Every small action can be the flap of the butterfly wing which forms the winds of change.

Note: Interested readers can find more details, clarifications and examples of real-world application of Abundanism in “Our Next Reality”.

Next Steps

Some reviewers have asked me to provide a specific post-AGI action list or policy recommendations in this paper, but to keep its length in control, I plan to publish that separately. I would welcome your feedback both on the content of this document, but also on suggested topics for future writing. Feel free to find me on Substack (@awgraylin) and we can discuss related topics in a timelier basis there.

Special Thanks

I’d like to express my sincere thanks for all who provided comments, feedback and suggestions on the early drafts of this document. Your words have helped increase the clarity and quality of this paper immensely. Any errors that may still exist are completely my responsibility.

Peter Diamandis, Erik Brynjolfsson, Tom Furness, Pattie Maes, Alex Sandy Pentland, John Werner, Rus Gant, Gary Rieschel, Paul Triolo, John Henry, Chas Freeman, Cyrus Jensen, Davey B, Louis Rosenberg, Brian A. Wong, Shaun Rein, Peggy Liu, Ken Zolot, Nathan Land, Ben Hu, Tim Chen, Linda Ricci, Peter Xing, Henry Lin, John Cheng, Aware Jones, Antony Vitillo, Risto Uuk, David Nguyen, Max Kanwell, Doug Hohulin, Andrew Holm, Zeljka Sotra, Hendrik Esser, Trond Nielsen, Laura Kusumoto, Man Wang

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