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Earth Optimization Fund

Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

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Earth Optimization Services is a company because your planet requires a legal box before anyone can reduce the amount of preventable dying. The box is a holding company. It sells products, makes money, and uses the money to optimize Earth. The first money product is EOS equity. “Earth Optimization Fund” is the public label for the strategy, not a separate fund LLC.

Optimitron checks which policies work. Wishocracy¹⁶⁷ asks humans what they want without handing them a 4,000-page budget and a sedative. Money buys a return. It does not buy extra votes, board control over EOS, or a private steering wheel for Earth. The steering wheel is shared, which is the first design improvement.

Product	What it does	Revenue
The Manual	The book you are reading	Sales (pay-what-you-want, anchored to $109 (95% CI: $107-$111))
The Uniform	T-shirts that say “I am retarded”	Sales + the free permanent-marker version
EOS equity (Earth Optimization Fund)	Buys company shares and uses shareholder rights to redirect lobbying toward higher long-term shareholder value	Portfolio appreciation + operating revenue, no carry
Optimitron	Activist campaign infrastructure	Consulting triggered by demand letters; board-seat compensation
The dFDA	Clinical trial machinery	Platform fees
The Prize	Assurance contracts for coordinated action	Escrow returns
Incentive Alignment Bonds	Later revenue-share bonds funding the political campaign	Bond issuance fees

EOS buys shares in companies whose lobbying shapes government policy, starting with military contractors because the 1% Treaty^{168 169} has the cleanest math there. It uses ordinary shareholder tools: voting, demand letters, proxy proposals, proxy contests, board campaigns, and public analysis. This is the investable version of the Loving Takeover. The side quest is buying your own share and filing your own letter. The main money machine is the holding company that owns the campaign.

The long-term goal is to buy enough influence over the companies that already buy policy, then hand the policy agenda back to humanity through Wishocracy¹⁶⁷.

The Boring Legal Ladder

Your laws make this happen in stages, because apparently buying shares in missile companies requires a puzzle room.

The goal is to go public as fast as the campaign results allow. A public company solves several problems at once: retail investors anywhere on Earth can buy in with $5, the activist campaign can become self-funding from inflows instead of private raises, and the ownership base becomes less absurdly gated. The Investment Company Act question is still handled by operating-company facts, not by magic public-company dust. The private phase is the minimum necessary starting point, not the destination.

1. Private holding company (now). EOS itself is the vehicle, a Delaware C-corp. Investors buy non-voting EOS equity directly under Reg D Rule 506(c) (verified accredited investors only; public solicitation permitted). No separate fund. EOS owns the activist positions on its own balance sheet and operates real businesses, exactly as Berkshire Hathaway does. The binding offering documents stay outside the book.
2. Public listing (as soon as Stage 1 demonstrates the mechanism). Once the HII campaign produces a verifiable result, EOS has a track record. At that point it can pursue a direct listing, traditional IPO, or another real public-company path. Any person on Earth can then buy EOS shares, which is the distributed-ownership goal in practical form.
3. Mission-lock foundation (concurrent with or before public listing). A nonprofit foundation takes custody of EOS’s voting shares on behalf of humanity, with a charter requiring it to vote in alignment with Wishocracy outputs. The foundation is what prevents a future acquirer from capturing EOS and reversing the mission at the moment it has actually succeeded.

A token is not the product. It may someday be plumbing for transfer, custody, or reporting. If the plumbing is the story, something has gone wrong.

The boring 506(c) gate. Public solicitation is allowed, but accepted investors must be verified accredited investors. There is also a “bad actor” check, which means EOS cannot rely on Rule 506 if the issuer, core officers, or paid securities solicitors have certain recent securities-fraud convictions, bars, or orders. The fix is not a monastery. Use a verification service, get short certifications from the people actually touching the offering, and do not hire a disqualified paid solicitor. This is a speed bump, not the bridge.

How you make money

There are three layers of appreciation.

Layer 1: Capital deployment. EOS buys real shares in companies whose lobbying shapes policy. The downside is the portfolio tanks. The upside is the companies get smarter.

Layer 2: Credibility cascade. As the campaign builds credibility, the market may price in a higher probability of success. This is the same kind of repricing that follows M&A announcements, FDA approvals, and earnings revisions.

Layer 3: The pivot. If the treaty passes, GDP at year 15 runs roughly 1.43x (95% CI: 1.22x-1.56x) the current trajectory. Military contractors in a larger economy capture proportionally more revenue. The engineering workforce pivots to medical, biotech, and civilian applications.

The lopsided structure. If the campaign succeeds, shares appreciate through all three layers. If it fails, you hold a normal military contractor position. The downside is the foregone return on whatever else you would have bought. These three layers are only part of it; the full set of ways a success pays off (a capped downside against thirteen stacked upsides) is itemized in Earth Optimization Services.

The terms

Term	Value	Why
First EOS equity raise target	$500K to $1M initial target, with room up to $2M if demand justifies it	Enough to buy stock, test governance pressure, and keep operations alive. Not enough to build a bureaucracy, which is a feature.
Minimum investment	Offering-specific	The minimum belongs to the exemption and offering documents, not vibes. Early private capital likely uses standard private-offering minimums.
Operating budget	Target: flat annual budget	The goal is fixed operating cost, not a growing percentage of assets. Exact expenses belong in the offering documents.
Performance fee / carry	Target: none	The operators should not skim the upside. If any fee exists, disclose it in plain English and make it boring.
Advisory fee	None	EOS invests its own balance sheet. It has company expenses, not an outside adviser charging a fund.
Lockup	1 year	The interesting part takes longer, but you can leave.
Operator control	No investor-bought EOS control	Operators run EOS. Investors get economic exposure and share rights, not extra votes in EOS or Wishocracy.
Security type	Non-voting EOS equity (private phase), then public shares	Money in, more money out. No voting coin, no bought board seat, no private political control.

The cap table principle in one breath: money can buy exposure to EOS. Money cannot buy the human vote, Wishocracy allocation power, nonprofit control, PAC control, or personal influence over politicians. Every dual-class company you have heard of runs the split so the founders can keep the votes. This one runs it so nobody can buy them.

How private-phase pricing works

The private phase prices EOS equity on two things: the portfolio assets EOS owns and the probability-weighted value of the governance campaign succeeding. The public phase is simpler: shares trade around observable market value.

The floor is the portfolio. EOS holds publicly traded stocks with prices you can look up any day. At minimum, EOS shares are worth the net asset value (NAV) of those holdings, the same way you can rough-price a Berkshire Hathaway share by adding up what Berkshire owns. The campaign optionality sits on top of that floor. As campaigns succeed, the market prices in the probability of the strategy working and EOS shares trade at a premium above NAV. Early investors buy at a premium near zero, before anything is proven. That is the discount.

The speculative early claim is your fractional exposure to the probability-weighted value of optimizing Earth. The formula:

\[\text{price per share} = \frac{P(I) \times V}{\text{total shares}}\]

V is the total addressable value EOS can capture: the NPV of the fraction of the political dysfunction tax⁵⁷ that flows to EOS shareholders through portfolio appreciation. The base case uses Nordhaus (2004), who found innovators capture roughly 2.2% (95% CI: 0.5%-5%) of the social value they create³⁸. That is not a law of physics. It is the benchmark.

A governance product can capture less than that because some benefits are public goods: fewer wars, cheaper cures, lower tax drag, less policy volatility. Those benefits spill into the whole economy whether or not EOS owns every pipe they flow through.

But the leakage is not wasted from an investor’s point of view. A diversified investor already owns a slice of the economy EOS is trying to repair. If EOS makes the economy larger, safer, and less stupid, the “uncaptured” surplus still shows up in the investor’s other assets. The EOS share is the lever. The balance sheet is the larger bet.

EOS can also capture more than the ordinary innovator benchmark because the thesis is tradable before it is obvious. EOS can buy the constrained assets first. If copycat funds arrive later and buy the same military, pharma, finance, and energy shares, that demand validates the thesis and reprices assets EOS already owns. Normal competitors take margin. Early EOS competitors may create demand for the inventory.

At the base case: $101 trillion (95% CI: $59.6 trillion-$161 trillion)/yr dysfunction tax × 2.2% (95% CI: 0.5%-5%) capture × perpetuity at 3% discount rate ≈ $74.1 trillion (95% CI: $27.2 trillion-$153 trillion). Expand the tree below to adjust any assumption.

Product revenue, consulting, board comp, platform subscriptions, does not enter V. It funds operations. V is what happens to the portfolio if the campaign works.

P(I) is the probability of success given total capital invested I. It rises with I: each dollar funds campaigns that raise credibility, which raises the market’s implied probability, which raises the price.

How the initial share price is set, all the options.

A continuous per-share auction is aspirational; Reg D requires signed subscription documents per investor. EOS sets the initial price using a combination of the methods below, then reprices each subsequent tranche based on observed uptake.

Method	What it gives you	Role in EOS pricing
Implied-probability back-calculation	Post-money valuation = P₀ × V / shares. Pick a P₀ a reasonable skeptic accepts as honest; the valuation follows. E.g. P₀ = 0.0001% → ~$74M post-money.	Sets the sanity-check floor and ceiling before any investor is approached. Anchors the range within which the auction must land to be credible.
Comparable activist launches	Engine No. 1 launched at ~$250M for its first close with no track record. Other activist vehicles: $50M-$500M at launch.	Provides an independent cross-check. If the auction clears outside this range, investigate why before proceeding.
Minimum-investment anchor	If $25K should represent ~X% of the company, post-money = $25K / X%. E.g. 0.5% → $5M post-money.	Sets the floor: a price so low that the $25K minimum feels meaningfully large. Prevents pricing that makes early investors feel like they bought rounding error.
Uniform-price auction (primary mechanism)	Solicit bids (price per share + quantity) from all prospective investors before Tranche 1 closes. Sort by price, fill from highest down to the raise target. Everyone pays the clearing price, the lowest winning bid. Underbidding gets you excluded, not a discount, so lowballing is punished.	This is the actual price-discovery mechanism. The comparables and implied-probability calculation inform investors before they bid; the auction aggregates their beliefs into a clearing price.
Bayesian tranche repricing	After each close, the clearing price is evidence that investors estimated P ≥ implied level. Next tranche opens above that price, reflecting the updated signal.	Governs all tranches after Tranche 1. The auction sets the initial price; Bayesian updating governs every subsequent reprice.

How they combine: the implied-probability and comparables methods set the bounds before the auction (a sanity check investors can verify). The minimum-investment anchor prevents the price from being so high that the smallest check feels trivial. The uniform-price auction then runs within those bounds and discovers the actual clearing price. Every tranche after that is a Bayesian update on the signal from the last close.

This means no single person, not Mike, not the attorneys, not the lead investor, picks the share price. The market picks it, within bounds the formula makes explicit and defensible.

How P rises with capital. P(I) is not a vague promise. Each tranche of capital funds a specific stage, and each stage has an estimated probability:

Capital raised	What it funds	Implied P(full success)	Price per share vs. floor
$0	One legal letter the board must read, no follow-through	~0.01%	1×
$500K	HII initial campaign: letter, legal, media	~0.5%	~50×
$2M	HII full campaign: sustained pressure, ISS engagement	~2%	~200×
$20M	Stage 2: five big military contractors, coordinated	~5%	~500×
$200M	Stage 3: all lobbying sectors	~15%	~1,500×

These are rough estimates, not projections. The exact numbers move with campaign results. The point is the direction: each dollar raised moves the probability up, which moves the rational share price up, which means every tranche after the first one clears at a higher price. The uniform-price auction within each tranche, and the Bayesian repricing between tranches, is how that relationship becomes observable rather than asserted.

Why early money earns more. The early dollar funds the first campaign when P is lowest and each win moves it most. And it is the social proof every later dollar waits for. You bought probability while it was cheap, and your buying is part of what made it expensive.

First-tranche bid calculator

This is deliberately not a new parameter factory. The shared valuation comes from the generated model. The investor-specific assumptions stay local: your check size, your bid, dilution, recovery, and the stage probabilities you actually believe.

viewof eosBidInvestment = Inputs.range([10000, 5000000], {value: 25000, step: 5000, label: "Investment amount ($)"})

viewof eosBidPostMoney = Inputs.range([5000000, 1000000000], {value: 50000000, step: 5000000, label: "Tranche 1 post-money valuation ($)"})

viewof eosBidFutureDilutionPct = Inputs.range([0, 90], {value: 50, step: 5, label: "Future dilution before exit (%)"})

viewof eosBidFailureRecovery = Inputs.range([0, 1], {value: 0.25, step: 0.05, label: "Failure recovery multiple"})

viewof eosBidRequiredMultiple = Inputs.range([1, 100], {value: 10, step: 1, label: "Required expected multiple"})

viewof eosBidNetWorth = Inputs.range([100000, 100000000], {value: 10000000, step: 100000, label: "Liquid net worth / portfolio ($)"})

viewof eosBidRiskBudgetPct = Inputs.range([0.1, 5], {value: 0.5, step: 0.1, label: "Max loss budget (% of portfolio)"})

viewof eosBidPStage1 = Inputs.range([0, 20], {value: 1, step: 0.1, label: "Probability Stage 1 works (%)"})

viewof eosBidPStage2Given1 = Inputs.range([0, 100], {value: 25, step: 1, label: "If Stage 1 works, probability of Stage 2 (%)"})

viewof eosBidPStage3Given2 = Inputs.range([0, 100], {value: 10, step: 1, label: "If Stage 2 works, probability of Stage 3 (%)"})

viewof eosBidPFullGiven3 = Inputs.range([0, 100], {value: 5, step: 1, label: "If Stage 3 works, probability of full EOS value (%)"})

function eosBidMoney(v) {
  if (!Number.isFinite(v)) return "not bounded";
  const abs = Math.abs(v);
  const sign = v < 0 ? "-" : "";
  if (abs >= 1e12) return `${sign}$${(abs / 1e12).toFixed(abs >= 100e12 ? 0 : 1)}T`;
  if (abs >= 1e9) return `${sign}$${(abs / 1e9).toFixed(abs >= 100e9 ? 0 : 1)}B`;
  if (abs >= 1e6) return `${sign}$${(abs / 1e6).toFixed(abs >= 100e6 ? 0 : 1)}M`;
  return new Intl.NumberFormat("en-US", {style: "currency", currency: "USD", maximumFractionDigits: 0}).format(v);
}

function eosBidPct(p) {
  if (!Number.isFinite(p)) return "n/a";
  return `${(p * 100).toFixed(p < 0.001 ? 4 : p < 0.01 ? 3 : p < 0.1 ? 2 : 1)}%`;
}

function eosBidOneIn(p) {
  if (!Number.isFinite(p) || p <= 0) return "not priced";
  const n = 1 / p;
  if (n >= 1e9) return `1 in ${(n / 1e9).toFixed(1)}B`;
  if (n >= 1e6) return `1 in ${(n / 1e6).toFixed(1)}M`;
  if (n >= 1000) return `1 in ${Math.round(n / 1000).toLocaleString()}K`;
  return `1 in ${Math.round(n).toLocaleString()}`;
}

function eosBidQuantile(rows, q) {
  let cumulative = 0;
  const sorted = [...rows].sort((a, b) => a.payout - b.payout);
  for (const row of sorted) {
    cumulative += row.p;
    if (cumulative >= q) return row.payout;
  }
  return sorted[sorted.length - 1].payout;
}

eosBidModel = {
  const investment = eosBidInvestment;
  const postMoney = eosBidPostMoney;
  const dilution = eosBidFutureDilutionPct / 100;
  const recovery = eosBidFailureRecovery;
  const requiredMultiple = eosBidRequiredMultiple;
  const p1 = eosBidPStage1 / 100;
  const p2 = eosBidPStage2Given1 / 100;
  const p3 = eosBidPStage3Given2 / 100;
  const p4 = eosBidPFullGiven3 / 100;
  const terminalFailure = 1 - p1;
  const terminalStage1 = p1 * (1 - p2);
  const terminalStage2 = p1 * p2 * (1 - p3);
  const terminalStage3 = p1 * p2 * p3 * (1 - p4);
  const terminalFull = p1 * p2 * p3 * p4;
  const afterDilutionOwnership = (investment / postMoney) * (1 - dilution);
  const fullValue = paramData["EOS_EQUITY_VALUE_V"].value;
  const stageValues = {
    stage1: paramData["EOS_STAGE_1_HII_TERMINAL_VALUE"].value,
    stage2: paramData["EOS_STAGE_2_DEFENSE_PRIMES_TERMINAL_VALUE"].value,
    stage3: paramData["EOS_STAGE_3_LOBBYING_SECTORS_TERMINAL_VALUE"].value,
    full: fullValue
  };
  const scenarios = [
    {name: "Failure", p: terminalFailure, value: 0, payout: investment * recovery},
    {name: "Stage 1: HII", p: terminalStage1, value: stageValues.stage1, payout: afterDilutionOwnership * stageValues.stage1},
    {name: "Stage 2: big military contractors", p: terminalStage2, value: stageValues.stage2, payout: afterDilutionOwnership * stageValues.stage2},
    {name: "Stage 3: all lobbying sectors", p: terminalStage3, value: stageValues.stage3, payout: afterDilutionOwnership * stageValues.stage3},
    {name: "Stage 4: full EOS value", p: terminalFull, value: stageValues.full, payout: afterDilutionOwnership * stageValues.full}
  ];
  const expectedValue = scenarios.reduce((sum, row) => sum + row.p * row.payout, 0);
  const expectedMultiple = expectedValue / investment;
  const scenarioWeightedValue = terminalStage1 * stageValues.stage1 +
    terminalStage2 * stageValues.stage2 +
    terminalStage3 * stageValues.stage3 +
    terminalFull * stageValues.full;
  const rationalDenominator = requiredMultiple - terminalFailure * recovery;
  const rationalPostMoney = rationalDenominator > 0
    ? ((1 - dilution) * scenarioWeightedValue) / rationalDenominator
    : Infinity;
  const impliedFullSuccessProbability = postMoney / fullValue;
  const maxLoss = investment * Math.max(0, 1 - recovery);
  const riskBudget = eosBidNetWorth * (eosBidRiskBudgetPct / 100);
  const maxInvestmentByRisk = riskBudget / Math.max(1 - recovery, 0.000001);
  const p5 = eosBidQuantile(scenarios, 0.05);
  const p50 = eosBidQuantile(scenarios, 0.50);
  const p95 = eosBidQuantile(scenarios, 0.95);
  const p99 = eosBidQuantile(scenarios, 0.99);
  const bidVerdict = postMoney <= rationalPostMoney ? "Inside modeled bid cap" : "Above modeled bid cap";
  const riskVerdict = investment <= maxInvestmentByRisk ? "Inside loss budget" : "Above loss budget";
  return {investment, postMoney, dilution, recovery, requiredMultiple, afterDilutionOwnership,
    fullValue, scenarios, expectedValue, expectedMultiple, scenarioWeightedValue,
    rationalPostMoney, impliedFullSuccessProbability, maxLoss, riskBudget,
    maxInvestmentByRisk, p5, p50, p95, p99, bidVerdict, riskVerdict};
}

html`<div class="eit-cards">
  <div class="eit-card ${eosBidModel.postMoney <= eosBidModel.rationalPostMoney ? "good" : "bad"}">
    <div class="eit-card-title">Modeled Bid Cap</div>
    <div class="eit-card-value">${eosBidMoney(eosBidModel.rationalPostMoney)}</div>
    <div class="eit-card-meta">${eosBidModel.bidVerdict} for a ${eosBidModel.requiredMultiple.toFixed(0)}x expected-value hurdle</div>
  </div>
  <div class="eit-card good">
    <div class="eit-card-title">Expected Multiple</div>
    <div class="eit-card-value">${eosBidModel.expectedMultiple.toFixed(1)}x</div>
    <div class="eit-card-meta">${eosBidMoney(eosBidModel.expectedValue)} probability-weighted value on ${eosBidMoney(eosBidModel.investment)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Implied Full-Success Odds</div>
    <div class="eit-card-value">${eosBidOneIn(eosBidModel.impliedFullSuccessProbability)}</div>
    <div class="eit-card-meta">${eosBidMoney(eosBidModel.postMoney)} post-money divided by ${eosBidMoney(eosBidModel.fullValue)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Ownership After Dilution</div>
    <div class="eit-card-value">${eosBidPct(eosBidModel.afterDilutionOwnership)}</div>
    <div class="eit-card-meta">${eosBidMoney(eosBidModel.investment)} at ${eosBidMoney(eosBidModel.postMoney)} post-money, then ${(eosBidModel.dilution * 100).toFixed(0)}% diluted</div>
  </div>
  <div class="eit-card ${eosBidModel.investment <= eosBidModel.maxInvestmentByRisk ? "good" : "bad"}">
    <div class="eit-card-title">Risk Budget Check</div>
    <div class="eit-card-value">${eosBidMoney(eosBidModel.maxInvestmentByRisk)}</div>
    <div class="eit-card-meta">${eosBidModel.riskVerdict}; modeled max loss is ${eosBidMoney(eosBidModel.maxLoss)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Median Outcome</div>
    <div class="eit-card-value">${eosBidMoney(eosBidModel.p50)}</div>
    <div class="eit-card-meta">5th: ${eosBidMoney(eosBidModel.p5)}; 95th: ${eosBidMoney(eosBidModel.p95)}; 99th: ${eosBidMoney(eosBidModel.p99)}</div>
  </div>
</div>`

html`<div class="eit-table-wrap">
  <table class="eit-table">
    <thead>
      <tr>
        <th>Terminal case</th>
        <th>Probability</th>
        <th>Company value</th>
        <th>Your payout</th>
      </tr>
    </thead>
    <tbody>
      ${eosBidModel.scenarios.map(row => html`<tr>
        <td>${row.name}</td>
        <td>${eosBidPct(row.p)}</td>
        <td>${row.value === 0 ? "-" : eosBidMoney(row.value)}</td>
        <td>${eosBidMoney(row.payout)}</td>
      </tr>`)}
    </tbody>
  </table>
</div>`

html`<div class="eit-summary">At ${eosBidMoney(eosBidModel.postMoney)} post-money, the bid prices full EOS success at about ${eosBidOneIn(eosBidModel.impliedFullSuccessProbability)} before counting partial-stage outcomes. With your stage probabilities, dilution, and failure recovery, the modeled expected value is ${eosBidMoney(eosBidModel.expectedValue)} (${eosBidModel.expectedMultiple.toFixed(1)}x). The rational post-money cap for your ${eosBidModel.requiredMultiple.toFixed(0)}x expected-value hurdle is ${eosBidMoney(eosBidModel.rationalPostMoney)}. This is an underwriting worksheet, not a promise; most of the probability mass can still live in the failure row.</div>`

Why this is not the scheme it resembles. A price that rises with adoption and rewards early believers is also the grammar of a Ponzi. The difference: EOS holds publicly traded portfolio companies. Any payout traces to assets, revenue, or disclosed equity rights, not to later investors. Later buyers can raise the market price; they do not become the source of the asset. That is the entire difference between an activist holding company and a chain letter, and it is auditable.

If the campaign fails. P drops. Price drops. Investors hold a concentrated activist portfolio that underperformed. The honest downside is not catastrophe; it is opportunity cost, what you would have earned elsewhere.

ImportantTwo separate things both called “vote”

EOS corporate voting shares (who controls EOS the company), held by the founder, later by a mission-lock foundation. Investors buying EOS equity receive non-voting economic shares. Money buys returns, never control of EOS.

The civic vote / Wishocracy (what policies humanity wants), one per human, permanent, non-transferable, completely separate from EOS equity. Not a share. Not purchasable. The political governance system the book proposes for public resource allocation. Never conflate the two.

The long-term goal: the mission-lock foundation holds EOS voting shares and is governed by Wishocracy outputs → EOS goes public → anyone on Earth can own common shares → distributed economic ownership at scale. The civic vote remains one per human regardless.

Where the money goes

Your investment
    └─> Buy military contractor shares
            └─> Share price goes up (Layer 1)
            └─> Gain voting rights
                    └─> Redirect lobbying budget
                            └─> Campaign credibility rises
                                    └─> Share price goes up more (Layer 2)
                                            └─> Pass the 1% treaty
                                                    └─> Economy grows 1.43x (95% CI: 1.22x-1.56x)
                                                            └─> Share price goes up a lot (Layer 3)
                                                                    └─> You also don't die

The last line is not priced into the return, but it is arguably the more valuable one.

The cascade: how $5,000 becomes $873 billion

The diagram above assumes somebody buys everything at once. Nobody has $873 billion.

But you do not need to buy 50% of a company to control its board. In 2021, Engine No. 1 won three board seats at ExxonMobil while holding 0.02% of its shares. Carl Icahn has been redirecting boards since 1979 with positions between 1% and 10%. The mechanism is a legal letter the board is required to read (Caremark), a shareholder vote to seat a director that institutional investors are required to evaluate (ISS guidelines), and enough credibility that the board’s lawyers recommend settling.

Three methods, ordered by cost:

Method	Cost (all 9 big US military contractors)	Feasibility
Buy 51% of shares	~$460 billion	Impossible without being a nation-state
Activist positions (1-5%)	~$9.1 billion	Hard but demonstrated (Icahn, Ackman, Peltz)
Governance pressure + institutional votes	$75-200 million	Possible

The third method works because institutional investors (Vanguard, BlackRock, State Street) hold 60-75% of military contractor shares. They do not need to be bought. They need to be presented with an economically rational shareholder vote to seat a director. ISS and Glass Lewis evaluate every one. “Your lobbying has negative ROI for your own shareholders” is economically rational. Engine No. 1 proved this at Exxon.

The cascade starts small.

Target 1: Huntington Ingalls (HII). Market cap ~$10 billion. The smallest of the big military contractors. A governance-pressure campaign costs $5,000-$15,000. If HII’s board commissions the lobbying ROI analysis (which Caremark doctrine increasingly requires), and the analysis shows what existing studies show (that the lobbying is self-defeating), the lobbying budget begins to redirect. This generates media coverage, credibility, and financing options.

Target 2: Kratos (KTOS). Then Leidos (LDOS). Then L3Harris (LHX). Each success creates three things: credibility (the previous one worked), financing options (investors can see traction), and attention (which attracts more institutions). Each target is funded by the capital raised against the credibility of the previous one.

HII ($10B) → KTOS ($12B) → LDOS ($20B) → LHX ($45B)
→ GD ($80B) → NOC ($75B) → LMT ($110B) → BA ($130B)
→ RTX ($160B) → GE Aerospace ($200B+)

Then pharma ($373M/yr in lobbying). Then finance ($720M/yr). Then energy. Each sector’s lobbying apparatus, redirected to champion the treaty. Each funded by the credibility and capital base created by the last.

The cascade starts with one legal letter filed by one shareholder who paid $200 for one share.

Why this is unlike every other investment

Most investments are zero-sum at the margin. A dollar of market share won by one company is a dollar lost by a competitor. A dollar of return earned by one vehicle is a dollar someone else did not earn. The pie does not grow; the slices are redistributed.

This one is different in four ways that do not coexist in any other asset class.

1. It is the only investment that increases the value of everything else you own. The political dysfunction tax is not a tax on one sector. It is a drag on the entire economy. Eliminating it does not reprice one stock; it reprices all of them. If you own index funds, bonds, real estate, or a business, their value is being suppressed by misallocated policy. EOS correcting that suppression makes your other holdings worth more at the same time it makes itself worth more. 2. Copycats can be validation before they are competition. Every other business competes with someone for customers, attention, market share, talent, or contracts. EOS’s first competition is waste. Waste does not compete back. A fund copying the thesis has to make many of the same board arguments and publish many of the same public analyses. That increases attention, reduces the perceived weirdness of the strategy, and makes the thesis easier for boards to take seriously. Later, copied operating businesses may compete for fees or status. Fine. By then the important thing has happened: the market has begun pricing the end of waste.

3. The control math is absurd. The top U.S. military contractors spend roughly $60 million per year on direct lobbying. That money is burned: spent, gone, zero asset remaining. It purchases access and influence for one year and requires the same spend next year to maintain.

EOS buys an estimated $75-200 million in share positions to achieve equivalent governance influence (table above). That capital is not burned. It is invested. The shares appreciate. The influence is perpetual.

The correct way to measure the cost of that board influence is not the capital deployed, you were going to invest that capital somewhere regardless. The cost is the opportunity cost: the return you would have earned in your next-best investment, minus what you earn here.

\[\text{ROI}_\text{control} = \frac{\text{value of governance influence}}{\text{return elsewhere} - \text{return on MIC activist portfolio}}\]

If the activist campaign causes MIC stocks to outperform your alternatives (Layer 2 appreciation), the denominator is negative, you are paid to hold the board influence. If MIC stocks merely match the market, the denominator is zero and the ROI is infinite. If they underperform by 2% on $150M, the annual cost of that influence is $3 million, still a small price for perpetual influence over $60M/yr of lobbying and a policy apparatus pointing at an $886 billion annual budget.

The company spending $60M/yr on lobbying has nothing to show for it in year two. EOS has the same influence and an appreciating asset.

4. The amount of policy at stake per dollar invested is exceptional. Governments collectively spend roughly $2.72 trillion per year on the military. Military contractors collectively influence a meaningful fraction of how that is allocated. The capital required to gain activist influence at the board level across the major military contractors is a small fraction of one year’s military budget. Spend that fraction once; redirect the influence on $2.72 trillion per year, compounding, forever. The honest caveat: these four properties only hold if the governance campaigns work. Engine No. 1 proved the mechanism at ExxonMobil; it has not been applied at scale to the military-industrial complex. The downside if it fails is a concentrated activist portfolio, not a catastrophe. But the upside if it works is larger than any other position you could take with the same capital.

What each stage is worth (before it happens)

On my planet, valuation is simple. You look at what a thing does, estimate how much waste it eliminates, and that is what it is worth. Your planet has a more complicated method involving comparable transactions, revenue multiples, and people in suits arguing about discount rates. I will use both methods and see if they agree.

EOS captures value from waste. Not from competitors, not from customers, not from market share. From the gap between what money currently does and what it could do if pointed at things that work. This is not a zero-sum game. The value exists because your species is currently spending money on things that destroy value instead of creating it. EOS redirects the spending. The destroyed value becomes created value. The difference is the return.

Control is the one thing split evenly: one vote per human, operators included, none worth more than another. Your money buys a return, not a bigger say. Your species finds this more suspicious than if they had simply stolen everything.

Stage 1: One company (HII). Huntington Ingalls Industries is the sole supplier of aircraft carriers to the United States Navy. Market cap roughly $10 billion. Its core revenue is contract-locked (the Navy cannot buy carriers from anyone else), its engineering workforce is among the most skilled on the planet, and its lobbying budget is currently pointed at maintaining military spending levels that its own shareholders would be better off without. A governance campaign redirects the lobbying. The contract revenue continues. The stock appreciates from the credibility cascade. Terminal stage value: $330 million (95% CI: $100 million-$1 billion). The calculator above prices what that means for a specific check, valuation, dilution, and failure recovery.

Stage 2: The big military contractors. The same logic applied to the major U.S. military contractors. Each success funds the next (appreciation from HII funds KTOS, KTOS funds LDOS, and so on). The lobbying of the entire military sector, redirected. Terminal stage value: $7 billion (95% CI: $1.5 billion-$30 billion). The mechanism is identical to Stage 1; the scale is larger because a sector lobbies more than one company.

Stage 3: All lobbying sectors. Military contracting is not the only industry that lobbies for policies its own shareholders would be better off without. Pharmaceutical companies spend $373 million per year lobbying against drug pricing reforms that would expand their addressable market. Energy companies lobby against transitions that would lower their input costs. Finance, telecom, agriculture. Every industry that lobbies for wasteful policy has value trapped by misallocation. EOS applies the same governance pressure across all of them. Terminal stage value: $452 billion (95% CI: $100 billion-$2 trillion).

Stage 4: Wishocratic governance. When resource allocation is optimized across all public spending globally, through Wishocracy, the total value is the difference between what humanity produces under current governance and what it produces under optimal governance. This is the political dysfunction tax, eliminated. Modeled full EOS value: $74.1 trillion (95% CI: $27.2 trillion-$153 trillion). This sounds absurd until you remember that global GDP is approximately $115 trillion per year, and the difference between good and bad allocation of that sum, compounded over decades, is larger than any number that fits comfortably in a sentence.

Each stage is independently valuable. You do not need Stage 4 to profit from Stage 1. But each stage funds the next, and the probability of reaching each subsequent stage increases with each completed stage. The math suggests this is probably irresistible once Stage 1 demonstrates the mechanism. But “the math suggests” is doing a lot of work in that sentence, and you should check the math yourself. The calculators above let you do that.

How EOS funds its operations

The share price is derived from the probability of civilizational optimization, not from revenue. But EOS still needs money to operate. Here is where it comes from. Four layers. Each works independently. None of them enter the share price formula.

Layer 1: Operating budget. EOS is a holding company, not a fund. There is no management fee, no carry, no advisory fee. The founder is paid a salary as an ordinary operating expense. EOS still has real costs: legal, audit, custody, brokerage, filing, insurance, compliance, and one developer. These should be disclosed, capped, and kept too small to become the product.

There is no percentage management fee. EOS has a flat annual operating budget. This is disclosed in the offering documents and covers salary, legal, audit, custody, brokerage, filing, insurance, and one developer. As assets grow, that fixed cost becomes a smaller fraction of the total:

AUM	Annual operating budget	As % of assets	Comparable
$2M (seed)	~$150K	7.5%	Expensive for a fund; normal for a startup
$20M	~$250K	1.25%	Competitive with venture
$200M	~$350K	0.175%	Well below most index funds
$2B	~$500K	0.025%	Berkshire territory

The operator gets progressively less rich as the company gets bigger. This is the opposite of every fund whose manager is paid to gather assets regardless of performance. Here, the incentive is to make the assets worth more.

Layer 2: Consulting. The legal letter the board must read creates a legal obligation for the board to respond (Caremark). Responding requires commissioning an analysis of whether the company’s lobbying generates positive shareholder returns. EOS, through Optimitron, provides this analysis. The free version demonstrates competence. The paid version (deeper, proprietary, ongoing advisory) is how EOS becomes a consultant to the companies it is pressuring.

I spent a long time looking for the part where that letter and the consulting pipeline stop being the same object and could not find it. Each letter in the cascade creates a potential client. Across military contracting, pharma, and finance at scale, this is $10-50 million per year.

Layer 3: Board seat compensation. When a governance campaign succeeds in placing a director, that director earns $220,000-$350,000 per year in board compensation from the portfolio company, not from EOS. Activist investors routinely require nominated directors to remit that compensation back to the nominating entity under a disclosed agreement. This is standard practice at Elliott, Starboard, and Third Point; it is disclosed in the proxy statement and legally clean.

EOS nominates directors who represent the platonic ideal of a board member: someone who genuinely believes math and evidence should determine what maximizes long-term shareholder value, and acts on it. Optimitron provides the lobbying-ROI analysis; the director reviews it and exercises independent judgment, which, when the analysis is correct, means agreeing with it. The director is not a puppet; they are a person whose values align with the evidence. The $300,000 per year is the fee the company pays for having its shareholders represented by someone who actually read the data.

At scale: 15 board seats across the big military contractors generates ~$4.2M/yr. 50 seats across military, pharma, and finance generates ~$13.5M/yr, enough to cover EOS’s full operating budget from board compensation alone, independent of any management fee.

Layer 4: Campaign infrastructure. Optimitron is not a subscription data product. AI will commoditize the analytical layer (lobbying ROI analysis on public data) faster than any subscription business can be built on it. The real asset is the infrastructure for running activist campaigns at scale: legal standing as a named shareholder of record, process infrastructure for filing proxy proposals and coordinating with ISS and Glass Lewis, and a track record of campaign outcomes that no AI can generate without first running the campaigns. The value is the legal position and the credibility it buys, not the algorithm behind it.

Layer 5: Brand. The book you are reading. The educational film. The shirts. None of this is large revenue. It is marketing that covers its own costs.

EOS does not need donations or grants. The revenue comes from the same activities that redirect the lobbying. If you separate the revenue from the mechanism, neither functions. Together, they compound: the more successfully EOS redirects lobbying, the more profitable it becomes.

Why competitors help

If another fund copies this strategy, EOS investors can still benefit before anyone wins a fee war. Copycats need the same board conversations and the same public thesis. More demand letters means faster board responses. More public analysis means more media attention and more institutional pressure on boards to answer the math.

Ten competing funds means ten times the demand letters and ten times the public pressure. This stops being free validation only when the copied product competes for operating revenue after the market has already repriced the thesis. That is not the scarce value. The scarce value is owning the thesis before the market accepts it.

The only thing that needs protecting is the governance: that whoever controls the redirected lobbying does not use it for extraction. This is why EOS publishes the equity formula, the humanity reserve, and the transfer clause before any of it is profitable. Establishing the norms first is the only defense against a version of this that is sleazy.

If someone copies this and executes it well, they helped end war and disease. That is not a downside.

Two Money Boxes

There are two money boxes. Do not pour the wrong thing into the wrong box.

EOS equity. Your money buys non-voting shares of Earth Optimization Services, the holding company, which buys activist positions in portfolio companies and operates real businesses on its own balance sheet. Private phase first (Reg D 506(c)), then public listing as fast as campaign results allow. This is the takeover arm.

Incentive Alignment Bonds¹⁷⁰. Your money funds the political campaign to pass the treaty. If the treaty passes, it generates $27.2 billion per year, and bondholders receive $2.72 billion per year forever (a projected 272% annual return on the campaign cost of $1 billion). This is the later campaign money, not the first thing you sell.

EOS equity buys the opposition. The bonds may later fund the campaign. Do not confuse the two.

The math on your investment

Set the three personal variables. The success multiplier is not a number you guess: it is derived from how much larger the economy gets if the treaty passes. Expand it to see where the base case comes from, and drag any underlying driver to set your own.

viewof eosAmount = Inputs.range([1000, 5000000], {value: 25000, step: 1000, label: "Your investment ($)"})

viewof eosYears = Inputs.range([1, 30], {value: 10, step: 1, label: "Investment horizon (years)"})

viewof eosProb = Inputs.range([0.1, 100], {value: 10, step: 0.1, label: "Probability campaign succeeds (%), your belief"})

viewof eosEntryProb = Inputs.range([0.5, 100], {value: 5, step: 0.5, label: "Probability the market prices in when you buy (%), low while the market is asleep"})

viewof eosSpReturn = Inputs.range([0, 0.2], {value: paramData["AVERAGE_MARKET_RETURN_PCT"].value, step: 0.005, label: "S&P annual return (decimal)"})

viewof eosNetWorth = Inputs.range([0, 100000000], {value: 250000, step: 10000, label: "Your total net worth ($)"})

Share-price multiplier if the campaign succeeds. Pick your package. The Light package is the 1% Treaty passing and nothing else: the economy ends up about 1.43x (95% CI: 1.22x-1.56x) the size it would otherwise be by year 15. The Deluxe package is full Wishonia optimization, government replaced with optimal policy, which the model puts at 26.9x (95% CI: 11.9x-57.7x) by year 15. The Deluxe is a bigger job (you actually replace the broken machine, not just trim its budget), and pays like it. And it is not a one-time choice: you ship the Light package first by passing the treaty, then upgrade to Deluxe at any time by running the rest of the optimization. Same investment, same shares; the upgrade is how far the program goes, not a second purchase. The default below is the Light package, so the number you see first is the floor of the upside, not the ceiling. Expand the tree to edit the drivers.

viewof eosScenario = Inputs.radio(new Map([[`Light package: the 1% Treaty (~${paramData["TREATY_TRAJECTORY_GDP_VS_CURRENT_TRAJECTORY_MULTIPLIER_YEAR_15"].value.toFixed(1)}x economy)`, "TREATY_TRAJECTORY_GDP_VS_CURRENT_TRAJECTORY_MULTIPLIER_YEAR_15"], [`Deluxe package: full Wishonia optimization (~${Math.round(paramData["WISHONIA_TRAJECTORY_GDP_VS_CURRENT_TRAJECTORY_MULTIPLIER_YEAR_15"].value)}x economy)`, "WISHONIA_TRAJECTORY_GDP_VS_CURRENT_TRAJECTORY_MULTIPLIER_YEAR_15"]]), {value: "TREATY_TRAJECTORY_GDP_VS_CURRENT_TRAJECTORY_MULTIPLIER_YEAR_15", label: "Success scenario"})

viewof eosMultiplierTree = paramTree(eosScenario)

eosCalc = {
  const amount = eosAmount;
  const years = eosYears;
  const prob = eosProb / 100;
  const mult = eosMultiplierTree[eosScenario];
  const sp = eosSpReturn;
  const netWorth = eosNetWorth;
  const takeover = paramData["DEFENSE_TAKEOVER_COST_TOTAL"].value;
  const success = amount * mult;
  const fail = amount * Math.pow(1 + sp, years);
  const ev = prob * success + (1 - prob) * fail;
  // Systemic upside: the difference your existing economy-exposed wealth is worth
  // under the treaty trajectory vs the current trajectory at year 15. Conservative
  // (uses today's net worth, ignores 15 years of compounding that apply to both).
  const systemicUpside = netWorth * (mult - 1);
  const systemicEv = prob * systemicUpside;
  // Entry-timing (the credibility cascade, Layer 2). A share is priced at
  // P x V / shares. You buy when the market implies p0; if it succeeds, the share
  // reprices to reflect ~certainty and the assets appreciate by `mult`. So the
  // success multiple captured is mult / p0, and your edge over the market is the
  // ratio of your belief to the implied price.
  const p0 = eosEntryProb / 100;
  const asleepSuccessMult = mult / p0;          // what you make if it works, buying asleep
  const asleepSuccess = amount * asleepSuccessMult;
  const asleepEv = prob * asleepSuccess + (1 - prob) * fail;
  const efficientEv = amount * mult;            // if p0 == your belief, entry timing is fair
  const edge = prob / p0;                        // belief / implied price
  return {amount, years, prob, mult, sp, netWorth, takeover, success, fail, ev,
          evVsSp: ev - fail, share: amount / takeover, systemicUpside, systemicEv,
          controlRatio: systemicEv / Math.max(amount, 1),
          p0, asleepSuccessMult, asleepSuccess, asleepEv, efficientEv, edge};
}

function eosMoney(v) {
  return new Intl.NumberFormat("en-US", {style: "currency", currency: "USD", maximumFractionDigits: 0}).format(v);
}

html`<div class="eit-cards">
  <div class="eit-card good">
    <div class="eit-card-title">Value if Campaign Succeeds</div>
    <div class="eit-card-value">${eosMoney(eosCalc.success)}</div>
    <div class="eit-card-meta">${eosCalc.mult.toFixed(1)}x your investment</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Value if Campaign Fails</div>
    <div class="eit-card-value">${eosMoney(eosCalc.fail)}</div>
    <div class="eit-card-meta">Military contractor stocks at ${(eosCalc.sp * 100).toFixed(1)}%/yr for ${eosCalc.years} years</div>
  </div>
  <div class="eit-card good">
    <div class="eit-card-title">Expected Value (probability-weighted)</div>
    <div class="eit-card-value">${eosMoney(eosCalc.ev)}</div>
    <div class="eit-card-meta">${(eosCalc.prob * 100).toFixed(1)}% chance of ${eosMoney(eosCalc.success)} + ${((1 - eosCalc.prob) * 100).toFixed(1)}% chance of ${eosMoney(eosCalc.fail)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Same Money in the S&P 500</div>
    <div class="eit-card-value">${eosMoney(eosCalc.fail)}</div>
    <div class="eit-card-meta">${eosMoney(eosCalc.amount)} at ${(eosCalc.sp * 100).toFixed(1)}% for ${eosCalc.years} years</div>
  </div>
  <div class="eit-card good">
    <div class="eit-card-title">Expected Return vs S&P</div>
    <div class="eit-card-value">${(eosCalc.evVsSp >= 0 ? "+" : "") + eosMoney(eosCalc.evVsSp)}</div>
    <div class="eit-card-meta">${eosCalc.evVsSp >= 0 ? "Expected value exceeds S&P" : "S&P exceeds expected value"}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Your Share of the Takeover</div>
    <div class="eit-card-value">${(eosCalc.share * 100).toFixed(6)}%</div>
    <div class="eit-card-meta">${eosMoney(eosCalc.amount)} of ${eosMoney(eosCalc.takeover)} total</div>
  </div>
</div>`

html`<div class="eit-summary">You invest ${eosMoney(eosCalc.amount)}. If the campaign succeeds (${(eosCalc.prob * 100).toFixed(1)}% estimated), your shares appreciate to ${eosMoney(eosCalc.success)} (${eosCalc.mult.toFixed(1)}x). If it fails, you hold military contractor stocks worth ${eosMoney(eosCalc.fail)} at market return. The probability-weighted expected value is ${eosMoney(eosCalc.ev)}, which is ${eosMoney(Math.abs(eosCalc.evVsSp))} ${eosCalc.evVsSp >= 0 ? "more" : "less"} than the S&P would have returned. You also do not die of a curable disease, which is not reflected above but is arguably the better return.</div>`

When you buy changes what you make

The cards above value the share by the appreciation of what it holds (military contractors pivoting in a larger economy). That is only one of the layers. The other is repricing: a share is worth its probability of success times the value of success. You buy while the market prices that probability near zero. If you are right, the share reprices the whole way up, and you keep the difference.

This cuts two ways, and the honest version shows both.

html`<div class="eit-cards">
  <div class="eit-card good">
    <div class="eit-card-title">If the Market Is Asleep (the thesis)</div>
    <div class="eit-card-value">${eosCalc.asleepSuccessMult >= 1 ? Math.round(eosCalc.asleepSuccessMult).toLocaleString() + "x" : eosCalc.asleepSuccessMult.toFixed(1) + "x"}</div>
    <div class="eit-card-meta">success multiple buying at ${(eosCalc.p0 * 100).toFixed(1)}% implied (${eosCalc.mult.toFixed(1)}x assets times the repricing from ${(eosCalc.p0 * 100).toFixed(1)}% to certainty) = ${eosMoney(eosCalc.asleepSuccess)} on ${eosMoney(eosCalc.amount)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">If the Market Is Efficient (textbook)</div>
    <div class="eit-card-value">${eosMoney(eosCalc.efficientEv)}</div>
    <div class="eit-card-meta">then the price already reflects the odds, entry timing is fair, and you are paid in variance, not extra expected value. Early just means cheaper and riskier.</div>
  </div>
  <div class="eit-card good">
    <div class="eit-card-title">Your Edge Over the Market</div>
    <div class="eit-card-value">${eosCalc.edge >= 1 ? eosCalc.edge.toFixed(1) + "x" : eosCalc.edge.toFixed(2) + "x"}</div>
    <div class="eit-card-meta">your ${(eosCalc.prob * 100).toFixed(1)}% belief divided by the ${(eosCalc.p0 * 100).toFixed(1)}% the market prices in. Above 1x, the market is underpaying you for the risk.</div>
  </div>
</div>`

html`<div class="eit-summary">Two ways to read the same share. <strong>If the market is efficient</strong>, the price already encodes the probability, so it does not matter when you buy: your expected value is about ${eosMoney(eosCalc.efficientEv)} either way, and entering early just trades safety for a bigger, rarer payoff. <strong>If the market is asleep</strong> (which it is, since no analyst models the end of war), you are buying at roughly ${(eosCalc.p0 * 100).toFixed(1)}% implied while believing ${(eosCalc.prob * 100).toFixed(1)}%, an edge of ${eosCalc.edge.toFixed(1)}x, and a win reprices the share about ${eosCalc.asleepSuccessMult >= 1 ? Math.round(eosCalc.asleepSuccessMult).toLocaleString() + "x" : eosCalc.asleepSuccessMult.toFixed(1) + "x"}. The edge is real exactly to the degree the market disbelieves, and it shrinks to zero the moment the thesis becomes obvious. That is the argument for going early.</div>`

The number you are actually betting

Your investment is the lever. Your net worth is the bet. If the treaty passes and the economy ends up larger than the current trajectory, everything you own that tracks the economy (stocks, property, business equity) is worth more under that trajectory than the one where nothing happens. You are already long this outcome. The only question is whether you spend a little to improve its odds.

That is the investor version of the leakage point above: uncaptured surplus can still land in the rest of your balance sheet.

html`<div class="eit-cards">
  <div class="eit-card good">
    <div class="eit-card-title">Your Net Worth's Upside If It Works</div>
    <div class="eit-card-value">${eosMoney(eosCalc.systemicUpside)}</div>
    <div class="eit-card-meta">${eosMoney(eosCalc.netWorth)} riding the ${eosCalc.mult.toFixed(1)}x trajectory gap (economy-exposed wealth)</div>
  </div>
  <div class="eit-card good">
    <div class="eit-card-title">Expected Systemic Upside</div>
    <div class="eit-card-value">${eosMoney(eosCalc.systemicEv)}</div>
    <div class="eit-card-meta">${(eosCalc.prob * 100).toFixed(1)}% chance of ${eosMoney(eosCalc.systemicUpside)}</div>
  </div>
  <div class="eit-card">
    <div class="eit-card-title">Per Dollar Invested in EOS</div>
    <div class="eit-card-value">${eosCalc.controlRatio >= 1 ? Math.round(eosCalc.controlRatio).toLocaleString() + "x" : eosCalc.controlRatio.toFixed(2) + "x"}</div>
    <div class="eit-card-meta">expected systemic upside per dollar of EOS investment</div>
  </div>
</div>`

html`<div class="eit-summary">Your ${eosMoney(eosCalc.amount)} investment is dwarfed by what it protects. At a net worth of ${eosMoney(eosCalc.netWorth)}, the difference between the treaty trajectory and the current one is worth about ${eosMoney(eosCalc.systemicUpside)} to you if it passes, or ${eosMoney(eosCalc.systemicEv)} probability-weighted. That is roughly ${eosCalc.controlRatio >= 1 ? Math.round(eosCalc.controlRatio).toLocaleString() + " times" : eosCalc.controlRatio.toFixed(2) + " times"} your EOS investment, and it accrues whether or not you buy a single share. The investment does not create this upside; it nudges the probability that you collect it. (Caveat: this assumes your wealth tracks the economy. Cash and bonds lag; equities, property, and business equity track it more closely. Adjust the multiplier drivers above to set your own.)</div>`

Your money is melting

This section is not about the fund. It is about everything else you own.

The value of money is a function of two variables: how much you have, and how long you can use it. Your species obsesses over the first variable and ignores the second, which is strange, because the second one is going to zero.

Your probability of being alive and cognitively functional decreases every day. Not because of the market. Because of biology. Every dollar you own is worth slightly less to you today than it was yesterday, because you are slightly closer to the point where you cannot use it. The account balance goes up. The person who can spend it degrades.

The math: if you have $1 million and you spend $200 on something that increases your probability of being alive to use the rest, you have increased the expected value of the remaining $999,800. The $200 does not just generate its own return. It preserves the value of everything else.

This scales linearly with wealth. A person with $1,000 who spends $200 preserves $800 of future utility. A person with $1 billion who spends $200 preserves $999,999,800. Same cost. Same action. The wealthier you are, the more you lose by dying on schedule.

A person who spends 80 years accumulating $100 million and then dies of a treatable disease has built a pile of something that became worthless at the moment of their death. On my planet we find this very confusing. You are building a number and then not being alive for it. It is like spending your life writing a book and then burning it on the last page.

Where to point the money: the lobbying allocation calculator

The question is not “should I buy military contractor stocks?” The question is: which companies give you the most lobbying redirect per dollar of shares?

Military contractors are not the only companies whose lobbying budgets are pointed away from rational policy. Pharmaceutical companies spend roughly $56M/year lobbying against drug pricing reform and clinical trial modernization. Insurance companies lobby against healthcare reform. Oil and gas companies lobby against energy transition. Each dollar of lobbying, redirected, is a dollar pushing toward the treaty.

The calculator below takes your investment amount, allocates it across companies in proportion to their lobbying-per-dollar-of-market-cap ratio, and shows you what that buys: how many shareholder proposals you can file, how much lobbying you can challenge, and at what cost.

// Company data: lobbying spend, market cap, sector, mission relevance
companies = [
  {ticker: "RTX",  name: "RTX Corp (Raytheon)",    sector: "Military", lobby: 13.51e6, mcap: 243.7e9, relevance: "Missile defense, arms exports"},
  {ticker: "LMT",  name: "Lockheed Martin",        sector: "Military", lobby: 12.67e6, mcap: 120.8e9, relevance: "F-35, NDAA budgets"},
  {ticker: "GD",   name: "General Dynamics",        sector: "Military", lobby: 12.21e6, mcap:  93.7e9, relevance: "Navy ships, combat vehicles"},
  {ticker: "BA",   name: "Boeing",                  sector: "Military", lobby: 11.93e6, mcap: 170.9e9, relevance: "Military contracts, FAA"},
  {ticker: "NOC",  name: "Northrop Grumman",        sector: "Military", lobby:  8.84e6, mcap:  77.3e9, relevance: "B-21, nuclear modernization"},
  {ticker: "LHX",  name: "L3Harris",                sector: "Military", lobby:  2.49e6, mcap:  57.3e9, relevance: "ISR, electronic warfare"},
  {ticker: "PFE",  name: "Pfizer",                  sector: "Pharma",  lobby: 10.07e6, mcap: 148.4e9, relevance: "Blocks drug price negotiation"},
  {ticker: "RHHBY",name: "Roche/Genentech",         sector: "Pharma",  lobby: 10.76e6, mcap: 331.8e9, relevance: "Blocks biosimilar expansion"},
  {ticker: "MRK",  name: "Merck",                   sector: "Pharma",  lobby:  9.32e6, mcap: 298.3e9, relevance: "Blocks Medicare negotiation"},
  {ticker: "JNJ",  name: "Johnson & Johnson",       sector: "Pharma",  lobby:  8.25e6, mcap: 560.3e9, relevance: "Blocks drug pricing reform"},
  {ticker: "LLY",  name: "Eli Lilly",               sector: "Pharma",  lobby:  6.72e6, mcap:1008.0e9, relevance: "Blocks insulin/GLP-1 price caps"},
  {ticker: "NVS",  name: "Novartis",                sector: "Pharma",  lobby:  6.22e6, mcap: 284.6e9, relevance: "Blocks drug importation"},
  {ticker: "ABBV", name: "AbbVie",                  sector: "Pharma",  lobby:  4.53e6, mcap: 401.5e9, relevance: "Patent thicket protections"},
  {ticker: "UNH",  name: "UnitedHealth",            sector: "Insurance", lobby: 7.52e6, mcap: 360.1e9, relevance: "Blocks single-payer reform"},
  {ticker: "XOM",  name: "ExxonMobil",              sector: "Energy",  lobby:  6.82e6, mcap: 619.2e9, relevance: "Blocks climate regulation"},
  {ticker: "CVX",  name: "Chevron",                 sector: "Energy",  lobby:  9.24e6, mcap: 373.0e9, relevance: "Blocks carbon tax"}
]

viewof investmentAmount = Inputs.range([2000, 5000000], {
  value: 400000,
  step: 1000,
  label: "Your investment ($)"
})

viewof holdingPeriod = Inputs.radio(
  new Map([["1 year ($25K threshold)", 25000], ["2 years ($15K threshold)", 15000], ["3 years ($2K threshold)", 2000]]),
  {value: 25000, label: "Holding period"}
)

viewof sectorWeighting = Inputs.radio(
  new Map([["Equal (by lobby ratio)", "ratio"], ["Military priority", "defense"], ["Pharma priority", "pharma"], ["All sectors", "all"]]),
  {value: "ratio", label: "Sector weighting"}
)

// Compute lobby-per-market-cap ratio and allocations
ranked = {
  let weighted = companies.map(c => {
    let ratio = c.lobby / c.mcap;
    let sectorMult = 1.0;
    if (sectorWeighting === "defense") sectorMult = c.sector === "Military" ? 3.0 : 0.5;
    if (sectorWeighting === "pharma") sectorMult = c.sector === "Pharma" ? 3.0 : 0.5;
    if (sectorWeighting === "all") sectorMult = 1.0;
    return {...c, ratio, weight: ratio * sectorMult};
  });

  let totalWeight = weighted.reduce((s, c) => s + c.weight, 0);

  return weighted
    .map(c => {
      let allocation = (c.weight / totalWeight) * investmentAmount;
      let canFile = allocation >= holdingPeriod;
      return {
        ...c,
        allocation: Math.round(allocation),
        canFile,
        lobbyRatio_bps: (c.ratio * 10000).toFixed(2),
        lobbyChallenged: canFile ? c.lobby : 0
      };
    })
    .sort((a, b) => b.ratio - a.ratio);
}

// Summary statistics
summary = {
  let totalAllocated = ranked.reduce((s, c) => s + c.allocation, 0);
  let proposalsEnabled = ranked.filter(c => c.canFile).length;
  let totalLobbyChallenged = ranked.reduce((s, c) => s + c.lobbyChallenged, 0);
  let lobbyMultiplier = totalLobbyChallenged / Math.max(totalAllocated, 1);
  let sectorBreakdown = {};
  for (let c of ranked) {
    if (!sectorBreakdown[c.sector]) sectorBreakdown[c.sector] = {allocated: 0, lobbied: 0, count: 0};
    sectorBreakdown[c.sector].allocated += c.allocation;
    sectorBreakdown[c.sector].lobbied += c.lobbyChallenged;
    sectorBreakdown[c.sector].count += c.canFile ? 1 : 0;
  }
  return {totalAllocated, proposalsEnabled, totalLobbyChallenged, lobbyMultiplier, sectorBreakdown};
}

// Display summary cards
html`<div style="display:grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap:1rem; margin:1rem 0;">
  <div style="background:#fffaf1; border:1px solid rgba(60,43,16,0.16); border-radius:16px; padding:1rem;">
    <div style="color:#6a5a45; font-size:0.84rem; text-transform:uppercase; letter-spacing:0.03em;">Proposals Enabled</div>
    <div style="font-size:1.5rem; font-weight:700; color:#0e5f47;">${summary.proposalsEnabled} of ${ranked.length}</div>
    <div style="color:#6a5a45; font-size:0.88rem;">companies where you meet the ${holdingPeriod === 25000 ? "$25K" : holdingPeriod === 15000 ? "$15K" : "$2K"} threshold</div>
  </div>
  <div style="background:#fffaf1; border:1px solid rgba(60,43,16,0.16); border-radius:16px; padding:1rem;">
    <div style="color:#6a5a45; font-size:0.84rem; text-transform:uppercase; letter-spacing:0.03em;">Lobbying Challenged</div>
    <div style="font-size:1.5rem; font-weight:700; color:#0e5f47;">$${(summary.totalLobbyChallenged / 1e6).toFixed(1)}M/year</div>
    <div style="color:#6a5a45; font-size:0.88rem;">annual lobbying spend you can file proposals against</div>
  </div>
  <div style="background:#fffaf1; border:1px solid rgba(60,43,16,0.16); border-radius:16px; padding:1rem;">
    <div style="color:#6a5a45; font-size:0.84rem; text-transform:uppercase; letter-spacing:0.03em;">Lobbying Multiplier</div>
    <div style="font-size:1.5rem; font-weight:700; color:#0e5f47;">${Math.round(summary.lobbyMultiplier)}x</div>
    <div style="color:#6a5a45; font-size:0.88rem;">dollars of lobbying challenged per dollar invested</div>
  </div>
  <div style="background:#fffaf1; border:1px solid rgba(60,43,16,0.16); border-radius:16px; padding:1rem;">
    <div style="color:#6a5a45; font-size:0.84rem; text-transform:uppercase; letter-spacing:0.03em;">Cost Per Proposal</div>
    <div style="font-size:1.5rem; font-weight:700;">$${holdingPeriod.toLocaleString()}</div>
    <div style="color:#6a5a45; font-size:0.88rem;">in shares held (you keep the shares)</div>
  </div>
</div>`

// Allocation table
html`<div style="overflow-x:auto; margin:1rem 0;">
<table style="width:100%; border-collapse:collapse; font-size:0.9rem;">
<thead>
<tr style="border-bottom:2px solid rgba(60,43,16,0.2);">
  <th style="text-align:left; padding:0.5rem;">Company</th>
  <th style="text-align:left; padding:0.5rem;">Sector</th>
  <th style="text-align:right; padding:0.5rem;">Lobbying/yr</th>
  <th style="text-align:right; padding:0.5rem;">Ratio (bps)</th>
  <th style="text-align:right; padding:0.5rem;">Your Allocation</th>
  <th style="text-align:center; padding:0.5rem;">Can File?</th>
</tr>
</thead>
<tbody>
${ranked.map(c => html`<tr style="border-bottom:1px solid rgba(60,43,16,0.1);">
  <td style="padding:0.4rem 0.5rem; font-weight:600;">${c.ticker}</td>
  <td style="padding:0.4rem 0.5rem; color:#6a5a45;">${c.sector}</td>
  <td style="padding:0.4rem 0.5rem; text-align:right;">$${(c.lobby / 1e6).toFixed(1)}M</td>
  <td style="padding:0.4rem 0.5rem; text-align:right;">${c.lobbyRatio_bps}</td>
  <td style="padding:0.4rem 0.5rem; text-align:right; font-weight:600;">$${c.allocation.toLocaleString()}</td>
  <td style="padding:0.4rem 0.5rem; text-align:center; color:${c.canFile ? '#0e5f47' : '#8d2f25'}; font-weight:700;">${c.canFile ? 'Yes' : 'No'}</td>
</tr>`)}
</tbody>
</table>
</div>`

// Sector breakdown
html`<div style="margin:1rem 0; background:#fffaf1; border:1px solid rgba(60,43,16,0.16); border-radius:16px; padding:1rem; overflow-x:auto;">
<table style="width:100%; border-collapse:collapse; font-size:0.9rem;">
<thead>
<tr style="border-bottom:2px solid rgba(60,43,16,0.2);">
  <th style="text-align:left; padding:0.4rem 0.5rem;">Sector</th>
  <th style="text-align:right; padding:0.4rem 0.5rem;">Invested</th>
  <th style="text-align:right; padding:0.4rem 0.5rem;">Lobbying</th>
  <th style="text-align:right; padding:0.4rem 0.5rem;">Proposals</th>
</tr>
</thead>
<tbody>
${Object.entries(summary.sectorBreakdown).map(([sector, data]) =>
  html`<tr style="border-bottom:1px solid rgba(60,43,16,0.08);">
    <td style="padding:0.4rem 0.5rem; font-weight:600;">${sector}</td>
    <td style="padding:0.4rem 0.5rem; text-align:right;">$${data.allocated.toLocaleString()}</td>
    <td style="padding:0.4rem 0.5rem; text-align:right;">$${(data.lobbied / 1e6).toFixed(1)}M/yr</td>
    <td style="padding:0.4rem 0.5rem; text-align:right;">${data.count}</td>
  </tr>`
)}
</tbody>
</table>
</div>`

// Save to localStorage for crowdsource-ready schema
{
  if (typeof localStorage !== 'undefined') {
    let overrides = {
      param: "lobbying_allocation_investment",
      value: investmentAmount,
      holding_period_threshold: holdingPeriod,
      sector_weighting: sectorWeighting,
      timestamp: new Date().toISOString(),
      results: {
        proposals_enabled: summary.proposalsEnabled,
        lobbying_challenged: summary.totalLobbyChallenged,
        leverage_ratio: summary.lobbyMultiplier
      }
    };
    localStorage.setItem("eos_param_lobbying_allocation", JSON.stringify(overrides));
  }
}

What return to expect

Honest version, with the warts, because a number you cannot defend is worse than no number. Your return rides on which trajectory the world ends up on, and there is a floor under all of them.

Outcome	Economy at year 15	What your equity return tracks
Floor (nothing works)	unchanged	You hold concentrated public-company exposure. It may perform like the sector, underperform the market, or suffer normal drawdowns. The consolation is that the assets are real, not that loss is impossible.
Light package (1% Treaty only)	~1.43x (95% CI: 1.22x-1.56x)	The companies capture that larger economy and the larger “not-dying” market. The first domino, alone.
Deluxe package (full Wishonia optimization)	~26.9x (95% CI: 11.9x-57.7x) (year 20: ~56.7x (95% CI: 21x-148x))	Government replaced with optimal policy. The moonshot: vast magnitude, lower probability. This is Wishonia’s Wager denominated in dollars.

What does the historical record of taking over badly-run companies suggest is achievable? The clean, peer-reviewed evidence: activist campaigns produce about a 7% one-time repricing at announcement and real operating improvements afterward¹⁷¹, and simply installing competent management at a poorly-run firm raised productivity about 11% in a randomized trial¹⁷². The ceiling for disciplined long-term ownership is Warren Buffett’s Berkshire: 19.9% a year versus the S&P 500’s 10.4% over 1965-2024, roughly twice the market for sixty years¹⁷³.

The warts, stated plainly: that 7% is a one-time repricing, not an annual yield, so do not annualize it. The governance premium that paid 8.5% a year in the 1990s¹⁷⁴ faded afterward, so treat it as evidence the badly-run-versus-well-run gap exists, not as a forward yield. Engine No. 1 won three ExxonMobil board seats with a 0.02% stake¹⁷⁵, which proves a tiny stake can win control, but the stock’s subsequent surge was the oil-price rally, not the boardroom, so it is a feasibility proof, not a return proof. And activist returns are lumpy: Icahn Enterprises fell about 55% in 2023¹⁷⁶.

Why this isn’t Buffett

That lumpiness is the most important thing on the page, because of what causes it. Buffett and Icahn extract returns from inside a system that periodically robs the table, and they eat its volatility. Icahn’s 2023 collapse was not bad stock-picking; it was largely the boom-and-bust the system manufactures: years of money-printing pumped every asset, the disorderly correction dumped them, and the borrowed money finished the job. The lumpiness is the dysfunction.

This is a different kind of bet. Buffett got twice the market despite the dysfunction. EOS proposes to remove the source of it. Rules-based monetary policy replaces the discretionary money-printing that drives the boom-bust (the money-printing machine; algorithmic administration). Optimal policy reduces the malinvestment that distorted rates create. Wishocracy ends the regulatory whiplash that lobbying-driven policy produces. Peace reduces war risk. Each removes a policy-induced source of the volatility that makes every other investor’s returns lumpy.

So this is not only a search for an edge over the market. It is an attempt to raise the quality of the market itself: lower the systematic, undiversifiable risk, and you raise risk-adjusted returns and compress the drawdowns for everything anyone holds, including you. The honest bound: this does not abolish risk. Genuine, exogenous shocks remain. What it removes is the large, self-inflicted, policy-manufactured share, which is precisely the share that made Buffett’s six decades a roller-coaster instead of a straight line. Fixing the casino helps every bet at the table. That is the part no fund can offer you, because no fund is trying to fix the casino.

Risk analysis: three scenarios

Scenario 1: Thesis works. Buy shares, gain influence, redirect lobbying, treaty passes. Shares appreciate through better strategy, credibility repricing, and economic growth. Best case.

Scenario 2: Peace gets priced in early. Other forces (fiscal crisis, parallel peace movements, public opinion shift) reduce military spending before the takeover completes. Military contractor stocks decline. But: you’re buying shares cheaper, biotech positions (if held) appreciate, and board influence during a forced pivot is more valuable than ever.

Scenario 3: Status quo holds. Military spending continues. Treaty fails. You hold concentrated military-contractor exposure. It can perform well during continued military spending, but it can also underperform for ordinary company, valuation, debt load, scandal, rate, or war-risk reasons. The downside is the loss plus the opportunity cost of whatever else you would have bought.

Why it’s lopsided: The strategy is attractive because the upside can stack across portfolio appreciation, policy repair, and the rest of your net worth. It is not attractive because loss is impossible. The best case is you own the companies that pivoted from weapons to medicine in the largest reallocation of capital in human history. The worst case is an activist fund that bought the wrong securities at the wrong time and failed to move policy.

The hedge: The portfolio should include biotech and healthcare positions alongside military contractors. When military spending falls, health spending rises. The two sectors are natural hedges. A portfolio long both captures the transition instead of suffering through it.

If you are greedy

If the campaign fails, you hold the portfolio and the risk. If it succeeds, the appreciation can be a multiple of your investment. The expected value can exceed the S&P at probability estimates that assign enough weight to the upside. The calculator above lets you test this with your own numbers.

If you are generous

Every dollar buys exposure to voting shares in the companies whose lobbyists are the single largest obstacle to the treaty. The money does not fund an advocacy campaign. It buys the opposition and redirects it.

If you are both

Greed and generosity arrive at the same place.

1.

NIH Common Fund. NIH pragmatic trials: Minimal funding despite 30x cost advantage. NIH Common Fund: HCS Research Collaboratory https://commonfund.nih.gov/hcscollaboratory (2025)

The NIH Pragmatic Trials Collaboratory funds trials at $500K for planning phase, $1M/year for implementation-a tiny fraction of NIH’s budget. The ADAPTABLE trial cost $14 million for 15,076 patients (= $929/patient) versus $420 million for a similar traditional RCT (30x cheaper), yet pragmatic trials remain severely underfunded. PCORnet infrastructure enables real-world trials embedded in healthcare systems, but receives minimal support compared to basic research funding. Additional sources: https://commonfund.nih.gov/hcscollaboratory | https://pcornet.org/wp-content/uploads/2025/08/ADAPTABLE_Lay_Summary_21JUL2025.pdf | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604499/

.

2.

Cato Institute. Chance of dying from terrorism statistic. Cato Institute: Terrorism and Immigration Risk Analysis https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis

Chance of American dying in foreign-born terrorist attack: 1 in 3.6 million per year (1975-2015) Including 9/11 deaths; annual murder rate is 253x higher than terrorism death rate More likely to die from lightning strike than foreign terrorism Note: Comprehensive 41-year study shows terrorism risk is extremely low compared to everyday dangers Additional sources: https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis | https://www.nbcnews.com/news/us-news/you-re-more-likely-die-choking-be-killed-foreign-terrorists-n715141

.

3.

NIH. Antidepressant clinical trial exclusion rates. Zimmerman et al. https://pubmed.ncbi.nlm.nih.gov/26276679/ (2015)

Mean exclusion rate: 86.1% across 158 antidepressant efficacy trials (range: 44.4% to 99.8%) More than 82% of real-world depression patients would be ineligible for antidepressant registration trials Exclusion rates increased over time: 91.4% (2010-2014) vs. 83.8% (1995-2009) Most common exclusions: comorbid psychiatric disorders, age restrictions, insufficient depression severity, medical conditions Emergency psychiatry patients: only 3.3% eligible (96.7% excluded) when applying 9 common exclusion criteria Only a minority of depressed patients seen in clinical practice are likely to be eligible for most AETs Note: Generalizability of antidepressant trials has decreased over time, with increasingly stringent exclusion criteria eliminating patients who would actually use the drugs in clinical practice Additional sources: https://pubmed.ncbi.nlm.nih.gov/26276679/ | https://pubmed.ncbi.nlm.nih.gov/26164052/ | https://www.wolterskluwer.com/en/news/antidepressant-trials-exclude-most-real-world-patients-with-depression

.

4.

CNBC. Warren buffett’s career average investment return. CNBC https://www.cnbc.com/2025/05/05/warren-buffetts-return-tally-after-60-years-5502284percent.html (2025)

Berkshire’s compounded annual return from 1965 through 2024 was 19.9%, nearly double the 10.4% recorded by the S&P 500. Berkshire shares skyrocketed 5,502,284% compared to the S&P 500’s 39,054% rise during that period. Additional sources: https://www.cnbc.com/2025/05/05/warren-buffetts-return-tally-after-60-years-5502284percent.html | https://www.slickcharts.com/berkshire-hathaway/returns

.

5.

World Health Organization. WHO global health estimates 2024. World Health Organization https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (2024)

Comprehensive mortality and morbidity data by cause, age, sex, country, and year Global mortality:  55-60 million deaths annually Lives saved by modern medicine (vaccines, cardiovascular drugs, oncology):  12M annually (conservative aggregate) Leading causes of death: Cardiovascular disease (17.9M), Cancer (10.3M), Respiratory disease (4.0M) Note: Baseline data for regulatory mortality analysis. Conservative estimate of pharmaceutical impact based on WHO immunization data (4.5M/year from vaccines) + cardiovascular interventions (3.3M/year) + oncology (1.5M/year) + other therapies. Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates

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6.

GiveWell. GiveWell cost per life saved for top charities (2024). GiveWell: Top Charities https://www.givewell.org/charities/top-charities

General range: $3,000-$5,500 per life saved (GiveWell top charities) Helen Keller International (Vitamin A): $3,500 average (2022-2024); varies $1,000-$8,500 by country Against Malaria Foundation: $5,500 per life saved New Incentives (vaccination incentives): $4,500 per life saved Malaria Consortium (seasonal malaria chemoprevention):  $3,500 per life saved VAS program details:  $2 to provide vitamin A supplements to child for one year Note: Figures accurate for 2024. Helen Keller VAS program has wide country variation ($1K-$8.5K) but $3,500 is accurate average. Among most cost-effective interventions globally Additional sources: https://www.givewell.org/charities/top-charities | https://www.givewell.org/charities/helen-keller-international | https://ourworldindata.org/cost-effectiveness

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7.

U.S. Department of Defense. 5.56mm NATO ammunition bulk procurement pricing. (2024)

The cost of 5.56mm NATO ammunition at military bulk procurement rates is approximately $0.40 per round, based on Lake City Army Ammunition Plant production and commercial market floor prices for mil-spec M855 ammunition.

8.

Pike, J. U.s. Forces fire 250,000 rounds for every insurgent killed. (2011)

The General Accounting Office reports that US forces used 1.8 billion rounds of small-arms ammunition per year, a level that more than doubled in five years. An estimated 250,000 rounds were fired for every insurgent killed in Iraq and Afghanistan.

9.

AARP. Unpaid caregiver hours and economic value. AARP 2023 https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html (2023)

Average family caregiver: 25-26 hours per week (100-104 hours per month) 38 million caregivers providing 36 billion hours of care annually Economic value: $16.59 per hour = $600 billion total annual value (2021) 28% of people provided eldercare on a given day, averaging 3.9 hours when providing care Caregivers living with care recipient: 37.4 hours per week Caregivers not living with recipient: 23.7 hours per week Note: Disease-related caregiving is subset of total; includes elderly care, disability care, and child care Additional sources: https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html | https://www.bls.gov/news.release/elcare.nr0.htm | https://www.caregiver.org/resource/caregiver-statistics-demographics/

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10.

Forbes. Forbes world’s billionaires list 2024. (2024)

Forbes identified a record 2,781 billionaires worldwide with combined net worth of $14.2 trillion, 141 more than 2023. Bernard Arnault (LVMH) topped the list at $233 billion.

11.

CDC MMWR. Childhood vaccination economic benefits. CDC MMWR https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm (1994)

US programs (1994-2023): $540B direct savings, $2.7T societal savings ( $18B/year direct,  $90B/year societal) Global (2001-2020): $820B value for 10 diseases in 73 countries ( $41B/year) ROI: $11 return per $1 invested Measles vaccination alone saved 93.7M lives (61% of 154M total) over 50 years (1974-2024) Additional sources: https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00850-X/fulltext

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12.

CDC. Childhood vaccination (US) ROI. CDC https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6316a4.htm (2017).

13.

U.S. Department of Justice. The false claims act. (2025).

14.

United States Supreme Court. State farm mutual automobile insurance co. V. Campbell, 538 u.s. 408. (2003).

15.

U.S. Bureau of Labor Statistics. CPI inflation calculator. (2024)

CPI-U (1980): 82.4 CPI-U (2024): 313.5 Inflation multiplier (1980-2024): 3.80× Cumulative inflation: 280.48% Average annual inflation rate: 3.08% Note: Official U.S. government inflation data using Consumer Price Index for All Urban Consumers (CPI-U). Additional sources: https://www.bls.gov/data/inflation_calculator.htm

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16.

James Surowiecki. The Wisdom of Crowds. (Surowiecki, 2004).

Explores the aggregation of information in groups, arguing that decisions are often better than could have been made by any single member of the group. The opening anecdote relates Francis Galton’s surprise that the crowd at a county fair accurately guessed the weight of an ox when the median of their individual guesses was taken. The three conditions for a group to be intelligent are diversity, independence, and decentralization. Additional sources: https://archive.org/details/wisdomofcrowds0000suro | https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds | https://www.amazon.com/Wisdom-Crowds-James-Surowiecki/dp/0385721706

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17.

ClinicalTrials.gov API v2 direct analysis. ClinicalTrials.gov cumulative enrollment data (2025). Direct analysis via ClinicalTrials.gov API v2 https://clinicaltrials.gov/data-api/api

Analysis of 100,000 active/recruiting/completed trials on ClinicalTrials.gov (as of January 2025) shows cumulative enrollment of 12.2 million participants: Phase 1 (722k), Phase 2 (2.2M), Phase 3 (6.5M), Phase 4 (2.7M). Median participants per trial: Phase 1 (33), Phase 2 (60), Phase 3 (237), Phase 4 (90). Additional sources: https://clinicaltrials.gov/data-api/api

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18.

ACS CAN. Clinical trial patient participation rate. ACS CAN: Barriers to Clinical Trial Enrollment https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer

Only 3-5% of adult cancer patients in US receive treatment within clinical trials About 5% of American adults have ever participated in any clinical trial Oncology: 2-3% of all oncology patients participate Contrast: 50-60% enrollment for pediatric cancer trials (<15 years old) Note:  20% of cancer trials fail due to insufficient enrollment; 11% of research sites enroll zero patients Additional sources: https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer | https://hints.cancer.gov/docs/Briefs/HINTS_Brief_48.pdf

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19.

ScienceDaily. Global prevalence of chronic disease. ScienceDaily: GBD 2015 Study https://www.sciencedaily.com/releases/2015/06/150608081753.htm (2015)

2.3 billion individuals had more than five ailments (2013) Chronic conditions caused 74% of all deaths worldwide (2019), up from 67% (2010) Approximately 1 in 3 adults suffer from multiple chronic conditions (MCCs) Risk factor exposures: 2B exposed to biomass fuel, 1B to air pollution, 1B smokers Projected economic cost: $47 trillion by 2030 Note: 2.3B with 5+ ailments is more accurate than "2B with chronic disease." One-third of all adults globally have multiple chronic conditions Additional sources: https://www.sciencedaily.com/releases/2015/06/150608081753.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC10830426/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC6214883/

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20.

C&EN. Annual number of new drugs approved globally:  50. C&EN https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 (2025)

50 new drugs approved annually Additional sources: https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 | https://www.fda.gov/drugs/development-approval-process-drugs/novel-drug-approvals-fda

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21.

Williams, R. J., Tse, T., DiPiazza, K. & Zarin, D. A. Terminated trials in the ClinicalTrials.gov results database: Evaluation of availability of primary outcome data and reasons for termination. PLOS One 10, e0127242 (2015)

Approximately 12% of trials with results posted on the ClinicalTrials.gov results database (905/7,646) were terminated. Primary reasons: insufficient accrual (57% of non-data-driven terminations), business/strategic reasons, and efficacy/toxicity findings (21% data-driven terminations).

22.

IQVIA Report. Global trial capacity. IQVIA Report: Clinical Trial Subjects Number Drops Due to Decline in COVID-19 Enrollment https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

1.9M participants annually (2022, post-COVID normalization from 4M peak in 2021) Additional sources: https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

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23.

Research and Markets. Global clinical trials market 2024. Research and Markets https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html (2024)

Global clinical trials market valued at approximately $83 billion in 2024, with projections to reach $83-132 billion by 2030. Additional sources: https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html | https://www.precedenceresearch.com/clinical-trials-market

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24.

OpenSecrets. Defense sector lobbying summary. OpenSecrets https://www.opensecrets.org/federal-lobbying/sectors/summary?id=D (2025)

Military sector federal lobbying totaled $198,009,793 in 2025, up from $159.5 million in 2024 and $142.9 million in 2023. Additional sources: https://www.opensecrets.org/federal-lobbying/sectors/summary?id=D

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25.

Companies Market Cap. BAE systems and thales market capitalization. (2026)

BAE Systems market capitalization approx $75.80B and Thales approx $56.68B as of June 2026, combined approx $132.5B for the two major allied European military primes. Additional sources: https://companiesmarketcap.com/thales/marketcap/

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26.

Stock Analysis. Military prime contractor market capitalization and float statistics. (2026)

Combined market capitalization of 11 US military primes approx $835.8B at the 2026-06-11 close: RTX $248.07B, Boeing $174.71B, Lockheed Martin $126.51B, General Dynamics $96.90B, Northrop Grumman $78.48B, L3Harris $58.16B, Leidos $15.36B, Huntington Ingalls $11.86B, CACI $11.61B, Booz Allen Hamilton $9.24B, SAIC $4.86B. Tradeable float across the 13 Western primes (adding BAE Systems and Thales) approx $880B, about 91 percent of combined cap (range $850-900B), from per-company float and shares-outstanding statistics pages; big-5 floats verified individually (RTX 92.6%, BA 96.0%, LMT 85.7%, GD 94.2%, NOC 99.7%); Thales is the outlier at approx 45% float because the French State (26.60%) and Dassault Aviation (26.59%) stakes are locked. Additional sources: https://stockanalysis.com/stocks/rtx/statistics/ | https://www.dassault-aviation.com/en/group/about-us/shareholding-structure-and-organization-chart/

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27.

Rummel, R. J. Death by Government: Genocide and Mass Murder Since 1900. (Transaction Publishers, 1994).

Political scientist R.J. Rummel’s comprehensive accounting of democide (government murder of unarmed civilians) in the 20th century. His final revised estimate: 262 million people murdered by their own governments from 1900-1999, excluding battle deaths in wars. Range: 200-272+ million. Communist regimes account for the largest share (100-148+ million). Updated figures at hawaii.edu/powerkills.

28.

GiveWell. Cost per DALY for deworming programs. https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness

Schistosomiasis treatment: $28.19-$70.48 per DALY (using arithmetic means with varying disability weights) Soil-transmitted helminths (STH) treatment: $82.54 per DALY (midpoint estimate) Note: GiveWell explicitly states this 2011 analysis is "out of date" and their current methodology focuses on long-term income effects rather than short-term health DALYs Additional sources: https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness

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29.

Calculated from IHME Global Burden of Disease (2.55B DALYs) and global GDP per capita valuation. $109 trillion annual global disease burden.

The global economic burden of disease, including direct healthcare costs ($8.2 trillion) and lost productivity ($100.9 trillion from 2.55 billion DALYs × $39,570 per DALY), totals approximately $109.1 trillion annually.

30.

U.S. Department of Transportation. Departmental guidance on valuation of a statistical life in economic analysis. (2024).

31.

Think by Numbers. Pre-1962 drug development costs and timeline (think by numbers). Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1962)

Historical estimates (1970-1985): USD $226M fully capitalized (2011 prices) 1980s drugs:  $65M after-tax R&D (1990 dollars),  $194M compounded to approval (1990 dollars) Modern comparison: $2-3B costs, 7-12 years (dramatic increase from pre-1962) Context: 1962 regulatory clampdown reduced new treatment production by 70%, dramatically increasing development timelines and costs Note: Secondary source; less reliable than Congressional testimony Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://en.wikipedia.org/wiki/Cost_of_drug_development | https://www.statnews.com/2018/10/01/changing-1962-law-slash-drug-prices/

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32.

Biotechnology Innovation Organization (BIO). BIO clinical development success rates 2011-2020. Biotechnology Innovation Organization (BIO) https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf (2021)

Phase I duration: 2.3 years average Total time to market (Phase I-III + approval): 10.5 years average Phase transition success rates: Phase I→II: 63.2%, Phase II→III: 30.7%, Phase III→Approval: 58.1% Overall probability of approval from Phase I: 12% Note: Largest publicly available study of clinical trial success rates. Efficacy lag = 10.5 - 2.3 = 8.2 years post-safety verification. Additional sources: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf

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33.

Nature Medicine. Drug repurposing rate ( 30%). Nature Medicine https://www.nature.com/articles/s41591-024-03233-x (2024)

Approximately 30% of drugs gain at least one new indication after initial approval. Additional sources: https://www.nature.com/articles/s41591-024-03233-x

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34.

EPI. Education investment economic multiplier (2.1). EPI: Public Investments Outside Core Infrastructure https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/

Early childhood education: Benefits 12X outlays by 2050; $8.70 per dollar over lifetime Educational facilities: $1 spent → $1.50 economic returns Energy efficiency comparison: 2-to-1 benefit-to-cost ratio (McKinsey) Private return to schooling:  9% per additional year (World Bank meta-analysis) Note: 2.1 multiplier aligns with benefit-to-cost ratios for educational infrastructure/energy efficiency. Early childhood education shows much higher returns (12X by 2050) Additional sources: https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/ | https://documents1.worldbank.org/curated/en/442521523465644318/pdf/WPS8402.pdf | https://freopp.org/whitepapers/establishing-a-practical-return-on-investment-framework-for-education-and-skills-development-to-expand-economic-opportunity/

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35.

PMC. Healthcare investment economic multiplier (1.8). PMC: California Universal Health Care https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ (2022)

Healthcare fiscal multiplier: 4.3 (95% CI: 2.5-6.1) during pre-recession period (1995-2007) Overall government spending multiplier: 1.61 (95% CI: 1.37-1.86) Why healthcare has high multipliers: No effect on trade deficits (spending stays domestic); improves productivity & competitiveness; enhances long-run potential output Gender-sensitive fiscal spending (health & care economy) produces substantial positive growth impacts Note: "1.8" appears to be conservative estimate; research shows healthcare multipliers of 4.3 Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://ncbi.nlm.nih.gov/pmc/articles/PMC3849102/ | https://set.odi.org/wp-content/uploads/2022/01/Fiscal-multipliers-review.pdf

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36.

World Bank. Infrastructure investment economic multiplier (1.6). World Bank: Infrastructure Investment as Stimulus https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned (2022)

Infrastructure fiscal multiplier:  1.6 during contractionary phase of economic cycle Average across all economic states:  1.5 (meaning $1 of public investment → $1.50 of economic activity) Time horizon: 0.8 within 1 year,  1.5 within 2-5 years Range of estimates: 1.5-2.0 (following 2008 financial crisis & American Recovery Act) Italian public construction: 1.5-1.9 multiplier US ARRA: 0.4-2.2 range (differential impacts by program type) Economic Policy Institute: Uses 1.6 for infrastructure spending (middle range of estimates) Note: Public investment less likely to crowd out private activity during recessions; particularly effective when monetary policy loose with near-zero rates Additional sources: https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned | https://www.gihub.org/infrastructure-monitor/insights/fiscal-multiplier-effect-of-infrastructure-investment/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://www.richmondfed.org/publications/research/economic_brief/2022/eb_22-04

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37.

Mercatus. Military spending economic multiplier (0.6). Mercatus: Defense Spending and Economy https://www.mercatus.org/research/research-papers/defense-spending-and-economy

Ramey (2011):  0.6 short-run multiplier Barro (1981): 0.6 multiplier for WWII spending (war spending crowded out  40¢ private economic activity per federal dollar) Barro & Redlick (2011): 0.4 within current year, 0.6 over two years; increased govt spending reduces private-sector GDP portions General finding: $1 increase in deficit-financed federal military spending = less than $1 increase in GDP Variation by context: Central/Eastern European NATO: 0.6 on impact, 1.5-1.6 in years 2-3, gradual fall to zero Ramey & Zubairy (2018): Cumulative 1% GDP increase in military expenditure raises GDP by  0.7% Additional sources: https://www.mercatus.org/research/research-papers/defense-spending-and-economy | https://cepr.org/voxeu/columns/world-war-ii-america-spending-deficits-multipliers-and-sacrifice | https://www.rand.org/content/dam/rand/pubs/research_reports/RRA700/RRA739-2/RAND_RRA739-2.pdf

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38.

Nordhaus, W. D. Schumpeterian Profits in the American Economy: Theory and Measurement. https://www.nber.org/papers/w10433 (2004) doi:10.3386/w10433.

39.

FDA. FDA-approved prescription drug products (20,000+). FDA https://www.fda.gov/media/143704/download

There are over 20,000 prescription drug products approved for marketing. Additional sources: https://www.fda.gov/media/143704/download

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40.

FDA. FDA GRAS list count ( 570-700). FDA https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

The FDA GRAS (Generally Recognized as Safe) list contains approximately 570–700 substances. Additional sources: https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

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41.

Gallup. (2013).

42.

ACLED. Active combat deaths annually. ACLED: Global Conflict Surged 2024 https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ (2024)

2024: 233,597 deaths (30% increase from 179,099 in 2023) Deadliest conflicts: Ukraine (67,000), Palestine (35,000) Nearly 200,000 acts of violence (25% higher than 2023, double from 5 years ago) One in six people globally live in conflict-affected areas Additional sources: https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ | https://acleddata.com/media-citation/data-shows-global-conflict-surged-2024-washington-post | https://acleddata.com/conflict-index/index-january-2024/

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43.

UCDP. State violence deaths annually. UCDP: Uppsala Conflict Data Program https://ucdp.uu.se/

Uppsala Conflict Data Program (UCDP): Tracks one-sided violence (organized actors attacking unarmed civilians) UCDP definition: Conflicts causing at least 25 battle-related deaths in calendar year 2023 total organized violence: 154,000 deaths; Non-state conflicts: 20,900 deaths UCDP collects data on state-based conflicts, non-state conflicts, and one-sided violence Specific "2,700 annually" figure for state violence not found in recent UCDP data; actual figures vary annually Additional sources: https://ucdp.uu.se/ | https://en.wikipedia.org/wiki/Uppsala_Conflict_Data_Program | https://ourworldindata.org/grapher/deaths-in-armed-conflicts-by-region

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44.

Our World in Data. Terror attack deaths (8,300 annually). Our World in Data: Terrorism https://ourworldindata.org/terrorism (2024)

2023: 8,352 deaths (22% increase from 2022, highest since 2017) 2023: 3,350 terrorist incidents (22% decrease), but 56% increase in avg deaths per attack Global Terrorism Database (GTD): 200,000+ terrorist attacks recorded (2021 version) Maintained by: National Consortium for Study of Terrorism & Responses to Terrorism (START), U. of Maryland Geographic shift: Epicenter moved from Middle East to Central Sahel (sub-Saharan Africa) - now >50% of all deaths Additional sources: https://ourworldindata.org/terrorism | https://reliefweb.int/report/world/global-terrorism-index-2024 | https://www.start.umd.edu/gtd/ | https://ourworldindata.org/grapher/fatalities-from-terrorism

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45.

Institute for Health Metrics and Evaluation (IHME). IHME global burden of disease 2021 (2.88B DALYs, 1.13B YLD). Institute for Health Metrics and Evaluation (IHME) https://vizhub.healthdata.org/gbd-results/ (2024)

In 2021, global DALYs totaled approximately 2.88 billion, comprising 1.75 billion Years of Life Lost (YLL) and 1.13 billion Years Lived with Disability (YLD). This represents a 13% increase from 2019 (2.55B DALYs), largely attributable to COVID-19 deaths and aging populations. YLD accounts for approximately 39% of total DALYs, reflecting the substantial burden of non-fatal chronic conditions. Additional sources: https://vizhub.healthdata.org/gbd-results/ | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00757-8/fulltext | https://www.healthdata.org/research-analysis/about-gbd

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46.

Costs of War Project, Brown University Watson Institute. Environmental cost of war ($100B annually). Brown Watson Costs of War: Environmental Cost https://watson.brown.edu/costsofwar/costs/social/environment

War on Terror emissions: 1.2B metric tons GHG (equivalent to 257M cars/year) Military: 5.5% of global GHG emissions (2X aviation + shipping combined) US DoD: World’s single largest institutional oil consumer, 47th largest emitter if nation Cleanup costs: $500B+ for military contaminated sites Gaza war environmental damage: $56.4B; landmine clearance: $34.6B expected Climate finance gap: Rich nations spend 30X more on military than climate finance Note: Military activities cause massive environmental damage through GHG emissions, toxic contamination, and long-term cleanup costs far exceeding current climate finance commitments Additional sources: https://watson.brown.edu/costsofwar/costs/social/environment | https://earth.org/environmental-costs-of-wars/ | https://transformdefence.org/transformdefence/stats/

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47.

ScienceDaily. Medical research lives saved annually (4.2 million). ScienceDaily: Physical Activity Prevents 4M Deaths https://www.sciencedaily.com/releases/2020/06/200617194510.htm (2020)

Physical activity: 3.9M early deaths averted annually worldwide (15% lower premature deaths than without) COVID vaccines (2020-2024): 2.533M deaths averted, 14.8M life-years preserved; first year alone: 14.4M deaths prevented Cardiovascular prevention: 3 interventions could delay 94.3M deaths over 25 years (antihypertensives alone: 39.4M) Pandemic research response: Millions of deaths averted through rapid vaccine/drug development Additional sources: https://www.sciencedaily.com/releases/2020/06/200617194510.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC9537923/ | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.038160 | https://pmc.ncbi.nlm.nih.gov/articles/PMC9464102/

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48.

SIPRI. 36:1 disparity ratio of spending on weapons over cures. SIPRI: Military Spending https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending (2016)

Global military spending: $2.7 trillion (2024, SIPRI) Global government medical research:  $68 billion (2024) Actual ratio: 39.7:1 in favor of weapons over medical research Military R&D alone:  $85B (2004 data, 10% of global R&D) Military spending increases crowd out health: 1% ↑ military = 0.62% ↓ health spending Note: Ratio actually worse than 36:1. Each 1% increase in military spending reduces health spending by 0.62%, with effect more intense in poorer countries (0.962% reduction) Additional sources: https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending | https://pmc.ncbi.nlm.nih.gov/articles/PMC9174441/ | https://www.congress.gov/crs-product/R45403

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49.

Think by Numbers. Lost human capital due to war ($270B annually). Think by Numbers https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/ (2021)

Lost human capital from war: $300B annually (economic impact of losing skilled/productive individuals to conflict) Broader conflict/violence cost: $14T/year globally 1.4M violent deaths/year; conflict holds back economic development, causes instability, widens inequality, erodes human capital 2002: 48.4M DALYs lost from 1.6M violence deaths = $151B economic value (2000 USD) Economic toll includes: commodity prices, inflation, supply chain disruption, declining output, lost human capital Additional sources: https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/ | https://www.weforum.org/stories/2021/02/war-violence-costs-each-human-5-a-day/ | https://pubmed.ncbi.nlm.nih.gov/19115548/

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50.

PubMed. Psychological impact of war cost ($100B annually). PubMed: Economic Burden of PTSD https://pubmed.ncbi.nlm.nih.gov/35485933/

PTSD economic burden (2018 U.S.): $232.2B total ($189.5B civilian, $42.7B military) Civilian costs driven by: Direct healthcare ($66B), unemployment ($42.7B) Military costs driven by: Disability ($17.8B), direct healthcare ($10.1B) Exceeds costs of other mental health conditions (anxiety, depression) War-exposed populations: 2-3X higher rates of anxiety, depression, PTSD; women and children most vulnerable Note: Actual burden $232B, significantly higher than "$100B" claimed Additional sources: https://pubmed.ncbi.nlm.nih.gov/35485933/ | https://news.va.gov/103611/study-national-economic-burden-of-ptsd-staggering/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9957523/

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51.

CGDev. UNHCR average refugee support cost. CGDev https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier (2024)

The average cost of supporting a refugee is $1,384 per year. This represents total host country costs (housing, healthcare, education, security). OECD countries average $6,100 per refugee (mean 2022-2023), with developing countries spending $700-1,000. Global weighted average of  $1,384 is reasonable given that 75-85% of refugees are in low/middle-income countries. Additional sources: https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier | https://www.unhcr.org/sites/default/files/2024-11/UNHCR-WB-global-cost-of-refugee-inclusion-in-host-country-health-systems.pdf

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52.

World Bank. World bank trade disruption cost from conflict. World Bank https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict

Estimated $616B annual cost from conflict-related trade disruption. World Bank research shows civil war costs an average developing country 30 years of GDP growth, with 20 years needed for trade to return to pre-war levels. Trade disputes analysis shows tariff escalation could reduce global exports by up to $674 billion. Additional sources: https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict | https://www.nber.org/papers/w11565 | http://blogs.worldbank.org/en/trade/impacts-global-trade-and-income-current-trade-disputes

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53.

VA. Veteran healthcare cost projections. VA https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf (2026)

VA budget: $441.3B requested for FY 2026 (10% increase). Disability compensation: $165.6B in FY 2024 for 6.7M veterans. PACT Act projected to increase spending by $300B between 2022-2031. Costs under Toxic Exposures Fund: $20B (2024), $30.4B (2025), $52.6B (2026). Additional sources: https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf | https://www.cbo.gov/publication/45615 | https://www.legion.org/information-center/news/veterans-healthcare/2025/june/va-budget-tops-400-billion-for-2025-from-higher-spending-on-mandated-benefits-medical-care

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54.

IQVIA Institute for Human Data Science. The global use of medicines 2024: Outlook to 2028. IQVIA Institute Report https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/the-global-use-of-medicines-2024-outlook-to-2028 (2024)

Global days of therapy reached 1.8 trillion in 2019 (234 defined daily doses per person). Diabetes, respiratory, CVD, and cancer account for 71 percent of medicine use. Projected to reach 3.8 trillion DDDs by 2028.

55.

Sinn, M. P. Private industry clinical trial spending estimate. (2025)

Estimated private pharmaceutical and biotech clinical trial spending is approximately $75-90 billion annually, representing roughly 90% of global clinical trial spending.

56.

Cybersecurity Ventures. Cybercrime economy projected to reach $10.5 trillion. Cybersecurity Ventures: $10.5T Cybercrime https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/ (2016)

Global cybercrime costs: $3T (2015) → $6T (2021) → $10.5T (2025 projected) 15% annual growth rate If measured as country, would be 3rd largest economy after US and China Greatest transfer of economic wealth in history Note: More profitable than global trade of all major illegal drugs combined. Includes data theft, productivity loss, IP theft, fraud Additional sources: <https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/> | https://www.boisestate.edu/cybersecurity/2022/06/16/cybercrime-to-cost-the-world-10-5-trillion-annually-by-2025/

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57.

Sinn, M. P. The Political Dysfunction Tax. https://manual.warondisease.org/knowledge/appendix/political-dysfunction-tax.html (2025) doi:10.5281/zenodo.18603840

Quantifying the gap between current global governance and theoretical maximum welfare, estimating a 31-53% efficiency score and $97 trillion in annual opportunity costs.

58.

Bolt, J. & Zanden, J. L. van. Maddison project database 2020. (2020)

Historical GDP per capita estimates from year 1 to present. Global GDP per capita in 1900: approximately 1,260 in 1990 international dollars (roughly 3,150 in 2024 USD after PPP and inflation adjustment). Standard reference for long-run comparative economic history.

59.

Applied Clinical Trials. Global government spending on interventional clinical trials:  $3-6 billion/year. Applied Clinical Trials https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market

Estimated range based on NIH ( $0.8-5.6B), NIHR ($1.6B total budget), and EU funding ( $1.3B/year). Roughly 5-10% of global market. Additional sources: https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market | https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30357-0/fulltext

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60.

World Bank. Expense (% of GDP) - world. (2024).

61.

UBS. Credit suisse global wealth report 2023. Credit Suisse/UBS https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html (2023)

Total global household wealth: USD 454.4 trillion (2022) Wealth declined by USD 11.3 trillion (-2.4%) in 2022, first decline since 2008 Wealth per adult: USD 84,718 Additional sources: https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html

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62.

World Health Organization. Life expectancy at age 60 (years): Global health observatory. (2021).

63.

Component country budgets. Global government medical research spending ($67.5B, 2023–2024). See component country budgets: NIH Budget https://www.nih.gov/about-nih/what-we-do/budget.

64.

United Nations Department of Economic and Social Affairs, Population Division. World population prospects 2024: Summary of results. (2024)

The 2024 Revision of the World Population Prospects provides population estimates and projections for 237 countries or areas. Global median age approximately 30.5 years in 2024, reflecting population-weighted average across all regions.

65.

Stockholm International Peace Research Institute. Trends in world military expenditure, 2024. (2025).

66.

SIPRI. Global military spending ($2.72T, 2024). SIPRI https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024 (2025).

67.

Estimated from major foundation budgets and activities. Nonprofit clinical trial funding estimate.

Nonprofit foundations spend an estimated $2-5 billion annually on clinical trials globally, representing approximately 2-5% of total clinical trial spending.

68.

ICAN. Global nuclear weapon maintenance cost: $100 billion/year. ICAN: Global Spending $100B 2024 https://www.icanw.org/global_spending_on_nuclear_weapons_topped_100_billion_in_2024 (2024)

2024: >$100 billion ($190,151/minute) - 11% increase ($9.9B) from 2023 Nine nuclear-armed states: China, France, India, Israel, N. Korea, Pakistan, Russia, UK, US US: $56.8B (more than all other 8 states combined); China: $12.5B; UK: $10B (+26% YoY, biggest increase) Historical trend: $72.9B (2019) → $82.4B (2021) → >$100B (2024) Private sector contracts: $463B ongoing; $42.5B earned from contracts in 2024 alone Note: $100B/year figure accurate for 2024. Rapid growth from $73B (2019). US spends more than rest of world combined on nuclear weapons Additional sources: https://www.icanw.org/global_spending_on_nuclear_weapons_topped_100_billion_in_2024 | https://www.icanw.org/the_cost_of_nuclear_weapons

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69.

Industry reports: IQVIA. Global pharmaceutical r&d spending.

Total global pharmaceutical R&D spending is approximately $300 billion annually. Clinical trials represent 15-20% of this total ($45-60B), with the remainder going to drug discovery, preclinical research, regulatory affairs, and manufacturing development.

70.

UN. Global population reaches 8 billion. UN: World Population 8 Billion Nov 15 2022 https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 (2022)

Milestone: November 15, 2022 (UN World Population Prospects 2022) Day of Eight Billion" designated by UN Added 1 billion people in just 11 years (2011-2022) Growth rate: Slowest since 1950; fell under 1% in 2020 Future: 15 years to reach 9B (2037); projected peak 10.4B in 2080s Projections: 8.5B (2030), 9.7B (2050), 10.4B (2080-2100 plateau) Note: Milestone reached Nov 2022. Population growth slowing; will take longer to add next billion (15 years vs 11 years) Additional sources: https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 | https://www.un.org/en/dayof8billion | https://en.wikipedia.org/wiki/Day_of_Eight_Billion

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71.

Harvard Kennedy School. 3.5% participation tipping point. Harvard Kennedy School https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world (2020)

The research found that nonviolent campaigns were twice as likely to succeed as violent ones, and once 3.5% of the population were involved, they were always successful. Chenoweth and Maria Stephan studied the success rates of civil resistance efforts from 1900 to 2006, finding that nonviolent movements attracted, on average, four times as many participants as violent movements and were more likely to succeed. Key finding: Every campaign that mobilized at least 3.5% of the population in sustained protest was successful (in their 1900-2006 dataset) Note: The 3.5% figure is a descriptive statistic from historical analysis, not a guaranteed threshold. One exception (Bahrain 2011-2014 with 6%+ participation) has been identified. The rule applies to regime change, not policy change in democracies. Additional sources: https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world | https://www.hks.harvard.edu/sites/default/files/2024-05/Erica%20Chenoweth_2020-005.pdf | https://www.bbc.com/future/article/20190513-it-only-takes-35-of-people-to-change-the-world | https://en.wikipedia.org/wiki/3.5%25_rule

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72.

International IDEA. International IDEA voter turnout database world export. (2026)

Best current register-based estimate of global registered voters. Sum of the latest available country-level Registration counts in International IDEA’s world export on 2026-04-22 = 4,128,142,495 registered voters across 199 countries and political entities. Methodology notes that Registration is the number of names on the voters’ register as reported by electoral management bodies, and comparability is imperfect because voter rolls and registration systems differ across countries. Additional sources: https://www.idea.int/data-tools/data/voter-turnout-database | https://www.idea.int/data-tools/export?type=region_only&themeId=293&world=all&loc=home

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73.

World Bank. Gross savings (% of GDP). (2024).

74.

Federation of American Scientists. World nuclear forces. Federation of American Scientists https://fas.org/issues/nuclear-weapons/status-world-nuclear-forces/ (2024)

As of early 2025, we estimate that the world’s nine nuclear-armed states possess a combined total of approximately 12,241 nuclear warheads. Additional sources: https://fas.org/issues/nuclear-weapons/status-world-nuclear-forces/

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75.

OpenSecrets. Top lobbying industries 2025. (2025)

Sector ranks and per-company federal lobbying spending for 2025. Combined market capitalization of the top-5 publicly traded US lobbying spenders in each government-controlling sector: pharmaceuticals $1,794.7B; technology $13,279.5B; insurance $385.6B; oil and gas $1,246.9B; four-sector total approx $16.71T. Caveats: Meta (Zuckerberg holds 60.8% of voting power) and Alphabet (Page and Brin hold 52.3%) cannot be majority-acquired; Ellison owns 40.6% of Oracle; the largest insurance lobbyists are mutuals with no public shares; trade associations (PhRMA, AHIP, SIFMA, API) are not acquirable. Additional sources: https://stockanalysis.com/stocks/

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76.

NHGRI. Human genome project and CRISPR discovery. NHGRI https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp (2003)

Your DNA is 3 billion base pairs Read the entire code (Human Genome Project, completed 2003) Learned to edit it (CRISPR, discovered 2012) Additional sources: https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp | https://www.nobelprize.org/prizes/chemistry/2020/press-release/

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77.

PMC. Only  12% of human interactome targeted. PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/ (2023)

Mapping 350,000+ clinical trials showed that only  12% of the human interactome has ever been targeted by drugs. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/

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78.

WHO. ICD-10 code count ( 14,000). WHO https://icd.who.int/browse10/2019/en (2019)

The ICD-10 classification contains approximately 14,000 codes for diseases, signs and symptoms. Additional sources: https://icd.who.int/browse10/2019/en

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79.

McFarland, M. J., Hauer, M. E. & Reuben, A. Half of US population exposed to adverse lead levels in early childhood. Proceedings of the National Academy of Sciences 119, e2118631119 (2022)

Leaded gasoline, used in the US from 1923 until its on-road ban in 1996, exposed more than half of the 2015 US population to adverse blood-lead levels in early childhood. The authors estimate childhood lead exposure cost the population a cumulative 824 million IQ points, an average of 2.6 points per person, rising to 5.9 points for the most-exposed 1966-1970 birth cohort.

80.

Wikipedia. Longevity escape velocity (LEV) - maximum human life extension potential. Wikipedia: Longevity Escape Velocity https://en.wikipedia.org/wiki/Longevity_escape_velocity

Longevity escape velocity: Hypothetical point where medical advances extend life expectancy faster than time passes Term coined by Aubrey de Grey (biogerontologist) in 2004 paper; concept from David Gobel (Methuselah Foundation) Current progress: Science adds  3 months to lifespan per year; LEV requires adding >1 year per year Sinclair (Harvard): "There is no biological upper limit to age" - first person to live to 150 may already be born De Grey: 50% chance of reaching LEV by mid-to-late 2030s; SENS approach = damage repair rather than slowing damage Kurzweil (2024): LEV by 2029-2035, AI will simulate biological processes to accelerate solutions George Church: LEV "in a decade or two" via age-reversal clinical trials Natural lifespan cap:  120-150 years (Jeanne Calment record: 122); engineering approach could bypass via damage repair Key mechanisms: Epigenetic reprogramming, senolytic drugs, stem cell therapy, gene therapy, AI-driven drug discovery Current record: Jeanne Calment (122 years, 164 days) - record unbroken since 1997 Note: LEV is theoretical but increasingly plausible given demonstrated age reversal in mice (109% lifespan extension) and human cells (30-year epigenetic age reversal) Additional sources: https://en.wikipedia.org/wiki/Longevity_escape_velocity | https://pmc.ncbi.nlm.nih.gov/articles/PMC423155/ | https://www.popularmechanics.com/science/a36712084/can-science-cure-death-longevity/ | https://www.diamandis.com/blog/longevity-escape-velocity

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81.

OpenSecrets. Lobbyist statistics for washington d.c. OpenSecrets: Lobbying in US https://en.wikipedia.org/wiki/Lobbying_in_the_United_States

Registered lobbyists: Over 12,000 (some estimates); 12,281 registered (2013) Former government employees as lobbyists: 2,200+ former federal employees (1998-2004), including 273 former White House staffers,  250 former Congress members & agency heads Congressional revolving door: 43% (86 of 198) lawmakers who left 1998-2004 became lobbyists; currently 59% leaving to private sector work for lobbying/consulting firms/trade groups Executive branch: 8% were registered lobbyists at some point before/after government service Additional sources: https://en.wikipedia.org/wiki/Lobbying_in_the_United_States | https://www.opensecrets.org/revolving-door | https://www.citizen.org/article/revolving-congress/ | https://www.propublica.org/article/we-found-a-staggering-281-lobbyists-whove-worked-in-the-trump-administration

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82.

MDPI Vaccines. Measles vaccination ROI. MDPI Vaccines https://www.mdpi.com/2076-393X/12/11/1210 (2024)

Single measles vaccination: 167:1 benefit-cost ratio. MMR (measles-mumps-rubella) vaccination: 14:1 ROI. Historical US elimination efforts (1966-1974): benefit-cost ratio of 10.3:1 with net benefits exceeding USD 1.1 billion (1972 dollars, or USD 8.0 billion in 2023 dollars). 2-dose MMR programs show direct benefit/cost ratio of 14.2 with net savings of $5.3 billion, and 26.0 from societal perspectives with net savings of $11.6 billion. Additional sources: https://www.mdpi.com/2076-393X/12/11/1210 | https://www.tandfonline.com/doi/full/10.1080/14760584.2024.2367451

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83.

Gosse, M. E. Assessing cost-effectiveness in healthcare: History of the $50,000 per QALY threshold. Sustainability Impact Metrics https://ecocostsvalue.com/EVR/img/references%20others/Gosse%202008%20QALY%20threshold%20financial.pdf (2008).

84.

National Institutes of Health BRAIN Initiative. BRAIN 2025: A scientific vision. (2014).

85.

Congressional Research Service. Advanced Gene Editing: CRISPR-Cas9. https://www.congress.gov/crs_external_products/R/PDF/R44824/R44824.7.pdf (2018).

86.

U.S. Government Accountability Office. Electronic Health Records: First Year of CMS’s Incentive Programs Shows Opportunities to Improve Processes to Verify Providers Met Requirements. https://www.gao.gov/products/gao-12-481 (2012).

87.

U.S. Government Accountability Office. Operation Warp Speed Vaccine Candidate Awards Potential Value. https://www.gao.gov/assets/gao-21-207.pdf (2020).

88.

PCORnet. PCORnet quarterly progress report, Q4 2025. (2026).

89.

World Health Organization. Mental health global burden. World Health Organization https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people (2022)

One in four people in the world will be affected by mental or neurological disorders at some point in their lives, representing [approximately] 30% of the global burden of disease. Additional sources: https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people

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90.

Jones, G. & Schneider, W. J. Intelligence, human capital, and economic growth: A Bayesian averaging of classical estimates (BACE) approach. Journal of Economic Growth 11, 71–93 (2006)

IQ was significant at the 95% level in 99.8% of 1,330 BACE growth regressions. A 1 point increase in a nation’s average IQ is associated with a persistent 0.11% annual increase in GDP per capita.

91.

Stockholm International Peace Research Institute. Trends in world military expenditure, 2023. (2024).

92.

Calculated from Orphanet Journal of Rare Diseases (2024). Diseases getting first effective treatment each year. Calculated from Orphanet Journal of Rare Diseases (2024) https://ojrd.biomedcentral.com/articles/10.1186/s13023-024-03398-1 (2024)

Under the current system, approximately 10-15 diseases per year receive their FIRST effective treatment. Calculation: 5% of 7,000 rare diseases ( 350) have FDA-approved treatment, accumulated over 40 years of the Orphan Drug Act =  9 rare diseases/year. Adding  5-10 non-rare diseases that get first treatments yields  10-20 total. FDA approves  50 drugs/year, but many are for diseases that already have treatments (me-too drugs, second-line therapies). Only  15 represent truly FIRST treatments for previously untreatable conditions.

93.

NIH. NIH budget (FY 2025). NIH https://www.nih.gov/about-nih/organization/budget (2024)

The budget total of $47.7 billion also includes $1.412 billion derived from PHS Evaluation financing... Additional sources: https://www.nih.gov/about-nih/organization/budget | https://officeofbudget.od.nih.gov/

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94.

Bentley et al. NIH spending on clinical trials:  3.3%. Bentley et al. https://pmc.ncbi.nlm.nih.gov/articles/PMC10349341/ (2023)

NIH spent $8.1 billion on clinical trials for approved drugs (2010-2019), representing 3.3% of relevant NIH spending. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10349341/ | https://catalyst.harvard.edu/news/article/nih-spent-8-1b-for-phased-clinical-trials-of-drugs-approved-2010-19-10-of-reported-industry-spending/

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95.

PMC. Standard medical research ROI ($20k-$100k/QALY). PMC: Cost-effectiveness Thresholds Used by Study Authors https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ (1990)

Typical cost-effectiveness thresholds for medical interventions in rich countries range from $50,000 to $150,000 per QALY. The Institute for Clinical and Economic Review (ICER) uses a $100,000-$150,000/QALY threshold for value-based pricing. Between 1990-2021, authors increasingly cited $100,000 (47% by 2020-21) or $150,000 (24% by 2020-21) per QALY as benchmarks for cost-effectiveness. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ | https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/

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96.

Xia et al., Nature Food. Nuclear winter famine. Xia et al. https://www.nature.com/articles/s43016-022-00573-0 (2022)

We estimate that a nuclear war between the United States and Russia would produce 150 Tg of soot and lead to  5 billion people dying at the end of year 2. Additional sources: https://www.nature.com/articles/s43016-022-00573-0

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97.

Manhattan Institute. RECOVERY trial 82× cost reduction. Manhattan Institute: Slow Costly Trials https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

RECOVERY trial:  $500 per patient ($20M for 48,000 patients = $417/patient) Typical clinical trial:  $41,000 median per-patient cost Cost reduction:  80-82× cheaper ($41,000 ÷ $500 ≈ 82×) Efficiency: $50 per patient per answer (10 therapeutics tested, 4 effective) Dexamethasone estimated to save >630,000 lives Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs | https://pmc.ncbi.nlm.nih.gov/articles/PMC9293394/

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98.

Trials. Patient willingness to participate in clinical trials. Trials: Patients’ Willingness Survey https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3

Recent surveys: 49-51% willingness (2020-2022) - dramatic drop from 85% (2019) during COVID-19 pandemic Cancer patients when approached: 88% consented to trials (Royal Marsden Hospital) Study type variation: 44.8% willing for drug trial, 76.2% for diagnostic study Top motivation: "Learning more about my health/medical condition" (67.4%) Top barrier: "Worry about experiencing side effects" (52.6%) Additional sources: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3 | https://www.appliedclinicaltrialsonline.com/view/industry-forced-to-rethink-patient-participation-in-trials | https://pmc.ncbi.nlm.nih.gov/articles/PMC7183682/

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99.

The Commune. Pentagon audit failures ($2.46T unaccounted). The Commune https://thecommunemag.com/the-pentagon-misplaced-2-46-trillion-an-in-depth-look-at-the-financial-audit-failures (2024)

In the most recent audit, the Department of Defense (DoD) could not account for approximately 60% of its \(4.1 trillion in assets, amounting to\)2.46 trillion unaccounted for. Alternative title: Pentagon unsupported accounting adjustments (\(6.5T, single year, US Army) In 2015, the Department of Defense's Inspector General reported that the Army could not adequately support\)6.5 trillion in year-end adjustments, indicating severe accounting discrepancies. Additional sources: https://thecommunemag.com/the-pentagon-misplaced-2-46-trillion-an-in-depth-look-at-the-financial-audit-failures | https://accmag.com/audit-pentagon-cannot-account-for-6-5-trillion-dollars-is-taxpayer-money/

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100.

Tufts CSDD. Cost of drug development.

Various estimates suggest $1.0 - $2.5 billion to bring a new drug from discovery through FDA approval, spread across  10 years. Tufts Center for the Study of Drug Development often cited for $1.0 - $2.6 billion/drug. Industry reports (IQVIA, Deloitte) also highlight $2+ billion figures.

101.

Value in Health. Average lifetime revenue per successful drug. Value in Health: Sales Revenues for New Therapeutic Agents https://www.sciencedirect.com/science/article/pii/S1098301524027542

Study of 361 FDA-approved drugs from 1995-2014 (median follow-up 13.2 years): Mean lifetime revenue: $15.2 billion per drug Median lifetime revenue: $6.7 billion per drug Revenue after 5 years: $3.2 billion (mean) Revenue after 10 years: $9.5 billion (mean) Revenue after 15 years: $19.2 billion (mean) Distribution highly skewed: top 25 drugs (7%) accounted for 38% of total revenue ($2.1T of $5.5T) Additional sources: https://www.sciencedirect.com/science/article/pii/S1098301524027542

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102.

Lichtenberg, F. R. How many life-years have new drugs saved? A three-way fixed-effects analysis of 66 diseases in 27 countries, 2000-2013. International Health 11, 403–416 (2019)

Using 3-way fixed-effects methodology (disease-country-year) across 66 diseases in 22 countries, this study estimates that drugs launched after 1981 saved 148.7 million life-years in 2013 alone. The regression coefficients for drug launches 0-11 years prior (beta=-0.031, SE=0.008) and 12+ years prior (beta=-0.057, SE=0.013) on years of life lost are highly significant (p<0.0001). Confidence interval for life-years saved: 79.4M-239.8M (95 percent CI) based on propagated standard errors from Table 2.

103.

Deloitte. Pharmaceutical r&d return on investment (ROI). Deloitte: Measuring Pharmaceutical Innovation 2025 https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html (2025)

Deloitte’s annual study of top 20 pharma companies by R&D spend (2010-2024): 2024 ROI: 5.9% (second year of growth after decade of decline) 2023 ROI:  4.3% (estimated from trend) 2022 ROI: 1.2% (historic low since study began, 13-year low) 2021 ROI: 6.8% (record high, inflated by COVID-19 vaccines/treatments) Long-term trend: Declining for over a decade before 2023 recovery Average R&D cost per asset: $2.3B (2022), $2.23B (2024) These returns (1.2-5.9% range) fall far below typical corporate ROI targets (15-20%) Additional sources: https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html | https://www.prnewswire.com/news-releases/deloittes-13th-annual-pharmaceutical-innovation-report-pharma-rd-return-on-investment-falls-in-post-pandemic-market-301738807.html | https://hitconsultant.net/2023/02/16/pharma-rd-roi-falls-to-lowest-level-in-13-years/

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104.

Nature Reviews Drug Discovery. Drug trial success rate from phase i to approval. Nature Reviews Drug Discovery: Clinical Success Rates https://www.nature.com/articles/nrd.2016.136 (2016)

Overall Phase I to approval: 10-12.8% (conventional wisdom  10%, studies show 12.8%) Recent decline: Average LOA now 6.7% for Phase I (2014-2023 data) Leading pharma companies: 14.3% average LOA (range 8-23%) Varies by therapeutic area: Oncology 3.4%, CNS/cardiovascular lowest at Phase III Phase-specific success: Phase I 47-54%, Phase II 28-34%, Phase III 55-70% Note: 12% figure accurate for historical average. Recent data shows decline to 6.7%, with Phase II as primary attrition point (28% success) Additional sources: https://www.nature.com/articles/nrd.2016.136 | https://pmc.ncbi.nlm.nih.gov/articles/PMC6409418/ | https://academic.oup.com/biostatistics/article/20/2/273/4817524

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105.

SofproMed. Phase 3 cost per trial range. SofproMed https://www.sofpromed.com/how-much-does-a-clinical-trial-cost

Phase 3 clinical trials cost between $20 million and $282 million per trial, with significant variation by therapeutic area and trial complexity. Additional sources: https://www.sofpromed.com/how-much-does-a-clinical-trial-cost | https://www.cbo.gov/publication/57126

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106.

Ramsberg, J. & Platt, R. Pragmatic trial cost per patient (median $97). Learning Health Systems https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/ (2018)

Meta-analysis of 108 embedded pragmatic clinical trials (2006-2016). The median cost per patient was $97 (IQR $19–$478), based on 2015 dollars. 25% of trials cost <$19/patient; 10 trials exceeded $1,000/patient. U.S. studies median $187 vs non-U.S. median $27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

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107.

WHO. Polio vaccination ROI. WHO https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return (2019)

For every dollar spent, the return on investment is nearly US$ 39." Total investment cost of US$ 7.5 billion generates projected economic and social benefits of US$ 289.2 billion from sustaining polio assets and integrating them into expanded immunization, surveillance and emergency response programmes across 8 priority countries (Afghanistan, Iraq, Libya, Pakistan, Somalia, Sudan, Syria, Yemen). Additional sources: https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return

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108.

ICRC. International campaign to ban landmines (ICBL) - ottawa treaty (1997). ICRC https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm (1997)

ICBL: Founded 1992 by 6 NGOs (Handicap International, Human Rights Watch, Medico International, Mines Advisory Group, Physicians for Human Rights, Vietnam Veterans of America Foundation) Started with ONE staff member: Jody Williams as founding coordinator Grew to 1,000+ organizations in 60 countries by 1997 Ottawa Process: 14 months (October 1996 - December 1997) Convention signed by 122 states on December 3, 1997; entered into force March 1, 1999 Achievement: Nobel Peace Prize 1997 (shared by ICBL and Jody Williams) Government funding context: Canada established $100M CAD Canadian Landmine Fund over 10 years (1997); International donors provided $169M in 1997 for mine action (up from $100M in 1996) Additional sources: https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm | https://en.wikipedia.org/wiki/International_Campaign_to_Ban_Landmines | https://www.nobelprize.org/prizes/peace/1997/summary/ | https://un.org/press/en/1999/19990520.MINES.BRF.html | https://www.the-monitor.org/en-gb/reports/2003/landmine-monitor-2003/mine-action-funding.aspx

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109.

OpenSecrets. Revolving door: Former members of congress. (2024)

388 former members of Congress are registered as lobbyists. Nearly 5,400 former congressional staffers have left Capitol Hill to become federal lobbyists in the past 10 years. Additional sources: https://www.opensecrets.org/revolving-door

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110.

Kinch, M. S. & Griesenauer, R. H. Lost medicines: A longer view of the pharmaceutical industry with the potential to reinvigorate discovery. Drug Discovery Today 24, 875–880 (2019)

Research identified 1,600+ medicines available in 1962. The 1950s represented industry high-water mark with >30 new products in five of ten years; this rate would not be replicated until late 1990s. More than half (880) of these medicines were lost following implementation of Kefauver-Harris Amendment. The peak of 1962 would not be seen again until early 21st century. By 2016 number of organizations actively involved in R&D at level not seen since 1914.

111.

Baily, M. N. Pre-1962 drug development costs (baily 1972). Baily (1972) https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf (1972)

Pre-1962: Average cost per new chemical entity (NCE) was $6.5 million (1980 dollars) Inflation-adjusted to 2024 dollars: $6.5M (1980) ≈ $22.5M (2024), using CPI multiplier of 3.46× Real cost increase (inflation-adjusted): $22.5M (pre-1962) → $2,600M (2024) = 116× increase Note: This represents the most comprehensive academic estimate of pre-1962 drug development costs based on empirical industry data Additional sources: https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf

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112.

Think by Numbers. Pre-1962 physician-led clinical trials. Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1966)

Pre-1962: Physicians could report real-world evidence directly 1962 Drug Amendments replaced "premarket notification" with "premarket approval", requiring extensive efficacy testing Impact: New regulatory clampdown reduced new treatment production by 70%; lifespan growth declined from  4 years/decade to  2 years/decade Drug Efficacy Study Implementation (DESI): NAS/NRC evaluated 3,400+ drugs approved 1938-1962 for safety only; reviewed >3,000 products, >16,000 therapeutic claims FDA has had authority to accept real-world evidence since 1962, clarified by 21st Century Cures Act (2016) Note: Specific "144,000 physicians" figure not verified in sources Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://www.fda.gov/drugs/enforcement-activities-fda/drug-efficacy-study-implementation-desi | http://www.nasonline.org/about-nas/history/archives/collections/des-1966-1969-1.html

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113.

GAO. 95% of diseases have 0 FDA-approved treatments. GAO https://www.gao.gov/products/gao-25-106774 (2025)

95% of diseases have no treatment Additional sources: https://www.gao.gov/products/gao-25-106774 | https://globalgenes.org/rare-disease-facts/

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114.

Oren Cass, Manhattan Institute. RECOVERY trial cost per patient. Oren Cass https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs (2023)

The RECOVERY trial, for example, cost only about $500 per patient... By contrast, the median per-patient cost of a pivotal trial for a new therapeutic is around $41,000. Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

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115.

NHS England; Águas et al. RECOVERY trial global lives saved ( 1 million). NHS England: 1 Million Lives Saved https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ (2021)

Dexamethasone saved  1 million lives worldwide (NHS England estimate, March 2021, 9 months after discovery). UK alone: 22,000 lives saved. Methodology: Águas et al. Nature Communications 2021 estimated 650,000 lives (range: 240,000-1,400,000) for July-December 2020 alone, based on RECOVERY trial mortality reductions (36% for ventilated, 18% for oxygen-only patients) applied to global COVID hospitalizations. June 2020 announcement: Dexamethasone reduced deaths by up to 1/3 (ventilated patients), 1/5 (oxygen patients). Impact immediate: Adopted into standard care globally within hours of announcement. Additional sources: https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ | https://www.nature.com/articles/s41467-021-21134-2 | https://pharmaceutical-journal.com/article/news/steroid-has-saved-the-lives-of-one-million-covid-19-patients-worldwide-figures-show | https://www.recoverytrial.net/news/recovery-trial-celebrates-two-year-anniversary-of-life-saving-dexamethasone-result

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116.

World Bank. Market capitalization of listed domestic companies (current US$). (2026).

117.

Anthropic. Pricing. (2026).

118.

National September 11 Memorial & Museum. September 11 attack facts. (2024)

2,977 people were killed in the September 11, 2001 attacks: 2,753 at the World Trade Center, 184 at the Pentagon, and 40 passengers and crew on United Flight 93 in Shanksville, Pennsylvania.

119.

World Bank. World bank singapore economic data. World Bank https://data.worldbank.org/country/singapore (2024)

Singapore GDP per capita (2023): $82,000 - among highest in the world Government spending: 15% of GDP (vs US 38%) Life expectancy: 84.1 years (vs US 77.5 years) Singapore demonstrates that low government spending can coexist with excellent outcomes Additional sources: https://data.worldbank.org/country/singapore

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120.

International Monetary Fund. IMF singapore government spending data. (2024)

Singapore government spending is approximately 15% of GDP This is 23 percentage points lower than the United States (38%) Despite lower spending, Singapore achieves excellent outcomes: - Life expectancy: 84.1 years (vs US 77.5) - Low crime, world-class infrastructure, AAA credit rating Additional sources: https://www.imf.org/en/Countries/SGP

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121.

World Health Organization. WHO life expectancy data by country. (2024)

Life expectancy at birth varies significantly among developed nations: Switzerland: 84.0 years (2023) Singapore: 84.1 years (2023) Japan: 84.3 years (2023) United States: 77.5 years (2023) - 6.5 years below Switzerland, Singapore Global average:  73 years Note: US spends more per capita on healthcare than any other nation, yet achieves lower life expectancy Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-life-expectancy-and-healthy-life-expectancy

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122.

CSIS. Smallpox eradication ROI. CSIS https://www.csis.org/analysis/smallpox-eradication-model-global-cooperation.

123.

PMC. Contribution of smoking reduction to life expectancy gains. PMC: Benefits Smoking Cessation Longevity https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ (2012)

Population-level: Up to 14% (9% men, 14% women) of total life expectancy gain since 1960 due to tobacco control efforts Individual cessation benefits: Quitting at age 35 adds 6.9-8.5 years (men), 6.1-7.7 years (women) vs continuing smokers By cessation age: Age 25-34 = 10 years gained; age 35-44 = 9 years; age 45-54 = 6 years; age 65 = 2.0 years (men), 3.7 years (women) Cessation before age 40: Reduces death risk by  90% Long-term cessation: 10+ years yields survival comparable to never smokers, averts  10 years of life lost Recent cessation: <3 years averts  5 years of life lost Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ | https://www.cdc.gov/pcd/issues/2012/11_0295.htm | https://www.ajpmonline.org/article/S0749-3797(24)00217-4/fulltext | https://www.nejm.org/doi/full/10.1056/NEJMsa1211128

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124.

ICER. Value per QALY (standard economic value). ICER https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf (2024)

Standard economic value per QALY: $100,000–$150,000. This is the US and global standard willingness-to-pay threshold for interventions that add costs. Dominant interventions (those that save money while improving health) are favorable regardless of this threshold. Additional sources: https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf

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125.

GAO. Annual cost of u.s. Sugar subsidies. GAO: Sugar Program https://www.gao.gov/products/gao-24-106144

Consumer costs: $2.5-3.5 billion per year (GAO estimate) Net economic cost:  $1 billion per year 2022: US consumers paid 2X world price for sugar Program costs $3-4 billion/year but no federal budget impact (costs passed directly to consumers via higher prices) Employment impact: 10,000-20,000 manufacturing jobs lost annually in sugar-reliant industries (confectionery, etc.) Multiple studies confirm: Sweetener Users Association ($2.9-3.5B), AEI ($2.4B consumer cost), Beghin & Elobeid ($2.9-3.5B consumer surplus) Additional sources: https://www.gao.gov/products/gao-24-106144 | https://www.heritage.org/agriculture/report/the-us-sugar-program-bad-consumers-bad-agriculture-and-bad-america | https://www.aei.org/articles/the-u-s-spends-4-billion-a-year-subsidizing-stalinist-style-domestic-sugar-production/

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126.

World Bank. Swiss military budget as percentage of GDP. World Bank: Military Expenditure https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH

2023: 0.70272% of GDP (World Bank) 2024: CHF 5.95 billion official military spending When including militia system costs:  1% GDP (CHF 8.75B) Comparison: Near bottom in Europe; only Ireland, Malta, Moldova spend less (excluding microstates with no armies) Additional sources: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH | https://www.avenir-suisse.ch/en/blog-defence-spending-switzerland-is-in-better-shape-than-it-seems/ | https://tradingeconomics.com/switzerland/military-expenditure-percent-of-gdp-wb-data.html

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127.

World Bank. Switzerland vs. US GDP per capita comparison. World Bank: Switzerland GDP Per Capita https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH

2024 GDP per capita (PPP-adjusted): Switzerland $93,819 vs United States $75,492 Switzerland’s GDP per capita 24% higher than US when adjusted for purchasing power parity Nominal 2024: Switzerland $103,670 vs US $85,810 Additional sources: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH | https://tradingeconomics.com/switzerland/gdp-per-capita-ppp | https://www.theglobaleconomy.com/USA/gdp_per_capita_ppp/

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128.

OECD. OECD government spending as percentage of GDP. (2024)

OECD government spending data shows significant variation among developed nations: United States: 38.0% of GDP (2023) Switzerland: 35.0% of GDP - 3 percentage points lower than US Singapore: 15.0% of GDP - 23 percentage points lower than US (per IMF data) OECD average: approximately 40% of GDP Additional sources: https://data.oecd.org/gga/general-government-spending.htm

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129.

OECD. OECD median household income comparison. (2024)

Median household disposable income varies significantly across OECD nations: United States: $77,500 (2023) Switzerland: $55,000 PPP-adjusted (lower nominal but comparable purchasing power) Singapore: $75,000 PPP-adjusted Additional sources: https://data.oecd.org/hha/household-disposable-income.htm

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130.

Wikipedia. Thalidomide scandal: Worldwide cases and mortality. Wikipedia https://en.wikipedia.org/wiki/Thalidomide_scandal

The total number of embryos affected by the use of thalidomide during pregnancy is estimated at 10,000, of whom about 40% died around the time of birth. More than 10,000 children in 46 countries were born with deformities such as phocomelia. Additional sources: https://en.wikipedia.org/wiki/Thalidomide_scandal

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131.

PLOS One. Health and quality of life of thalidomide survivors as they age. PLOS One https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222 (2019)

Study of thalidomide survivors documenting ongoing disability impacts, quality of life, and long-term health outcomes. Survivors (now in their 60s) continue to experience significant disability from limb deformities, organ damage, and other effects. Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222

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132.

US Census Bureau. Historical world population estimates. US Census Bureau https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html

US Census Bureau historical estimates of world population by country and region (1950-2050). US population in 1960:  180 million of  3 billion worldwide (6%). Additional sources: https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html

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133.

FDA Study via NCBI. Trial costs, FDA study. FDA Study via NCBI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

Overall, the 138 clinical trials had an estimated median (IQR) cost of $19.0 million ($12.2 million-$33.1 million)... The clinical trials cost a median (IQR) of $41,117 ($31,802-$82,362) per patient. Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

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134.

GBD 2019 Diseases and Injuries Collaborators. Global burden of disease study 2019: Disability weights. The Lancet 396, 1204–1222 (2020)

Disability weights for 235 health states used in Global Burden of Disease calculations. Weights range from 0 (perfect health) to 1 (death equivalent). Chronic conditions like diabetes (0.05-0.35), COPD (0.04-0.41), depression (0.15-0.66), and cardiovascular disease (0.04-0.57) show substantial variation by severity. Treatment typically reduces disability weights by 50-80 percent for manageable chronic conditions.

135.

WHO. Annual global economic burden of alzheimer’s and other dementias. WHO: Dementia Fact Sheet https://www.who.int/news-room/fact-sheets/detail/dementia (2019)

Global cost: $1.3 trillion (2019 WHO-commissioned study) 50% from informal caregivers (family/friends,  5 hrs/day) 74% of costs in high-income countries despite 61% of patients in LMICs $818B (2010) → $1T (2018) → $1.3T (2019) - rapid growth Note: Costs increased 35% from 2010-2015 alone. Informal care represents massive hidden economic burden Additional sources: https://www.who.int/news-room/fact-sheets/detail/dementia | https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12901

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136.

JAMA Oncology. Annual global economic burden of cancer. JAMA Oncology: Global Cost 2020-2050 https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 (2020)

2020-2050 projection: $25.2 trillion total ($840B/year average) 2010 annual cost: $1.16 trillion (direct costs only) Recent estimate:  $3 trillion/year (all costs included) Top 5 cancers: lung (15.4%), colon/rectum (10.9%), breast (7.7%), liver (6.5%), leukemia (6.3%) Note: China/US account for 45% of global burden; 75% of deaths in LMICs but only 50.0% of economic cost Additional sources: https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 | https://www.nature.com/articles/d41586-023-00634-9

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137.

CDC. U.s. Chronic disease healthcare spending. CDC https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

Chronic diseases account for  90% of U.S. healthcare spending ( $3.7T/year). Additional sources: https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

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138.

Diabetes Care. Annual global economic burden of diabetes. Diabetes Care: Global Economic Burden https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults

2015: $1.3 trillion (1.8% of global GDP) 2030 projections: $2.1T-2.5T depending on scenario IDF health expenditure: $760B (2019) → $845B (2045 projected) 2/3 direct medical costs ($857B), 1/3 indirect costs (lost productivity) Note: Costs growing rapidly; expected to exceed $2T by 2030 Additional sources: https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults | https://doi.org/10.1016/S2213-8587(17)30097-9

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139.

CBO. The 2024 Long-Term Budget Outlook. https://www.cbo.gov/publication/60039 (2024).

140.

World Bank, Bureau of Economic Analysis. US GDP 2024 ($28.78 trillion). World Bank https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US (2024)

US GDP reached $28.78 trillion in 2024, representing approximately 26% of global GDP. Additional sources: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US | https://www.bea.gov/news/2024/gross-domestic-product-fourth-quarter-and-year-2024-advance-estimate

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141.

Environmental Working Group. US farm subsidy database and analysis. Environmental Working Group https://farm.ewg.org/ (2024)

US agricultural subsidies total approximately $30 billion annually, but create much larger economic distortions. Top 10% of farms receive 78% of subsidies, benefits concentrated in commodity crops (corn, soy, wheat, cotton), environmental damage from monoculture incentivized, and overall deadweight loss estimated at $50-120 billion annually. Additional sources: https://farm.ewg.org/ | https://www.ers.usda.gov/topics/farm-economy/farm-sector-income-finances/government-payments-the-safety-net/

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142.

Drug Policy Alliance. The drug war by the numbers. (2021)

Since 1971, the war on drugs has cost the United States an estimated $1 trillion in enforcement. The federal drug control budget was $41 billion in 2022. Mass incarceration costs the U.S. at least $182 billion every year, with over $450 billion spent to incarcerate individuals on drug charges in federal prisons.

143.

International Monetary Fund. IMF fossil fuel subsidies data: 2023 update. (2023)

Globally, fossil fuel subsidies were $7 trillion in 2022 or 7.1 percent of GDP. The United States subsidies totaled $649 billion. Underpricing for local air pollution costs and climate damages are the largest contributor, accounting for about 30 percent each.

144.

Papanicolas, Irene et al. Health care spending in the united states and other high-income countries. Papanicolas et al. https://jamanetwork.com/journals/jama/article-abstract/2674671 (2018)

The US spent approximately twice as much as other high-income countries on medical care (mean per capita: $9,892 vs $5,289), with similar utilization but much higher prices. Administrative costs accounted for 8% of US spending vs 1-3% in other countries. US spending on pharmaceuticals was $1,443 per capita vs $749 elsewhere. Despite spending more, US health outcomes are not better. Additional sources: https://jamanetwork.com/journals/jama/article-abstract/2674671

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145.

Hsieh, C.-T. & Moretti, E. Housing constraints and spatial misallocation. American Economic Journal: Macroeconomics https://www.aeaweb.org/articles?id=10.1257/mac.20170388 (2019)

We quantify the amount of spatial misallocation of labor across US cities and its aggregate costs. Tight land-use restrictions in high-productivity cities like New York, San Francisco, and Boston lowered aggregate US growth by 36% from 1964 to 2009. Local constraints on housing supply have had enormous effects on the national economy. Additional sources: https://www.aeaweb.org/articles?id=10.1257/mac.20170388

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146.

Yale Budget Lab. The fiscal, economic, and distributional effects of all u.s. tariffs. (2025)

Accounting for all the 2025 US tariffs and retaliation implemented to date, the level of real GDP is persistently -0.6% smaller in the long run, the equivalent of $160 billion 2024$ annually.

147.

Tax Foundation. Tax compliance costs the US economy $546 billion annually. https://taxfoundation.org/data/all/federal/irs-tax-compliance-costs/ (2024)

Americans will spend over 7.9 billion hours complying with IRS tax filing and reporting requirements in 2024. This costs the economy roughly $413 billion in lost productivity. In addition, the IRS estimates that Americans spend roughly $133 billion annually in out-of-pocket costs, bringing the total compliance costs to $546 billion, or nearly 2 percent of GDP.

148.

Cook, C., Cole, G., Asaria, P., Jabbour, R. & Francis, D. P. Annual global economic burden of heart disease. International Journal of Cardiology https://www.internationaljournalofcardiology.com/article/S0167-5273(13)02238-9/abstract (2014)

Heart failure alone: $108 billion/year (2012 global analysis, 197 countries) US CVD: $555B (2016) → projected $1.8T by 2050 LMICs total CVD loss: $3.7T cumulative (2011-2015, 5-year period) CVD is costliest disease category in most developed nations Note: No single $2.1T global figure found; estimates vary widely by scope and year Additional sources: https://www.ahajournals.org/doi/10.1161/CIR.0000000000001258

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149.

Source: US Life Expectancy FDA Budget 1543-2019 CSV. US life expectancy growth 1880-1960: 3.82 years per decade. (2019)

Pre-1962: 3.82 years/decade Post-1962: 1.54 years/decade Reduction: 60% decline in life expectancy growth rate Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm

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150.

Source: US Life Expectancy FDA Budget 1543-2019 CSV. Post-1962 slowdown in life expectancy gains. (2019)

Pre-1962 (1880-1960): 3.82 years/decade Post-1962 (1962-2019): 1.54 years/decade Reduction: 60% decline Temporal correlation: Slowdown occurred immediately after 1962 Kefauver-Harris Amendment Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm

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151.

Centers for Disease Control and Prevention. US life expectancy 2023. (2024)

US life expectancy at birth was 77.5 years in 2023 Male life expectancy: 74.8 years Female life expectancy: 80.2 years This is 6-7 years lower than peer developed nations despite higher healthcare spending Additional sources: https://www.cdc.gov/nchs/fastats/life-expectancy.htm

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152.

US Census Bureau. US median household income 2023. (2024)

US median household income was $77,500 in 2023 Real median household income declined 0.8% from 2022 Gini index: 0.467 (income inequality measure) Additional sources: https://www.census.gov/library/publications/2024/demo/p60-282.html

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153.

Manuel, D. U.s. Defense spending history: 100 years of military budgets. DaveManuel.com https://www.davemanuel.com/us-defense-spending-history-military-budget-data.php (2025)

US military spending in constant 2024 dollars: 1939 $29B (pre-WW2 baseline), 1940 $37B, 1944 $1,383B, 1945 $1,420B (peak), 1946 $674B, 1947 $176B, 1948 $117B, 2024 $886B. The post-WW2 demobilization cut spending 88% in two years (1945-1947). Current peacetime spending ($886B) is 30x the pre-WW2 baseline and 62% of peak WW2 spending, in inflation-adjusted dollars.

154.

Statista. US military budget as percentage of GDP. Statista https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ (2024)

U.S. military spending amounted to 3.5% of GDP in 2024. In 2024, the U.S. spent nearly $1 trillion on its military budget, equal to 3.4% of GDP. Additional sources: https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ | https://www.sipri.org/sites/default/files/2025-04/2504_fs_milex_2024.pdf

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155.

US Census Bureau. Number of registered or eligible voters in the u.s. US Census Bureau https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html (2024)

73.6% (or 174 million people) of the citizen voting-age population was registered to vote in 2024 (Census Bureau). More than 211 million citizens were active registered voters (86.6% of citizen voting age population) according to the Election Assistance Commission. Additional sources: https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html | https://www.eac.gov/news/2025/06/30/us-election-assistance-commission-releases-2024-election-administration-and-voting

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156.

U.S. Senate. Treaties. U.S. Senate https://www.senate.gov/about/powers-procedures/treaties.htm

The Constitution provides that the president ’shall have Power, by and with the Advice and Consent of the Senate, to make Treaties, provided two-thirds of the Senators present concur’ (Article II, section 2). Treaties are formal agreements with foreign nations that require two-thirds Senate approval. 67 senators (two-thirds of 100) must vote to ratify a treaty for it to take effect. Additional sources: https://www.senate.gov/about/powers-procedures/treaties.htm

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157.

Federal Election Commission. Statistical summary of 24-month campaign activity of the 2023-2024 election cycle. (2023)

Presidential candidates raised $2 billion; House and Senate candidates raised $3.8 billion and spent $3.7 billion; PACs raised $15.7 billion and spent $15.5 billion. Total federal campaign spending approximately $20 billion. Additional sources: https://www.fec.gov/updates/statistical-summary-of-24-month-campaign-activity-of-the-2023-2024-election-cycle/

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158.

OpenSecrets. Federal lobbying hit record $4.4 billion in 2024. (2024)

Total federal lobbying reached record $4.4 billion in 2024. The $150 million increase in lobbying continues an upward trend that began in 2016. Additional sources: https://www.opensecrets.org/news/2025/02/federal-lobbying-set-new-record-in-2024/

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159.

Columbia/NBER. Odds of a single vote being decisive in a u.s. Presidential election. Columbia/NBER: What Is the Probability Your Vote Will Make a Difference? https://sites.stat.columbia.edu/gelman/research/published/probdecisive2.pdf (2012)

National average: 1 in 60 million chance (2008 election analysis by Gelman, Silver, Edlin) Swing states (NM, VA, NH, CO):  1 in 10 million chance Non-competitive states: 34 states >1 in 100 million odds; 20 states >1 in 1 billion Washington DC: 1 in 490 billion odds Methodology: Probability state is necessary for electoral college win × probability state vote is tied Additional sources: https://sites.stat.columbia.edu/gelman/research/published/probdecisive2.pdf | https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1465-7295.2010.00272.x

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160.

Hutchinson and Kirk. Valley of death in drug development. (2011)

The overall failure rate of drugs that passed into Phase 1 trials to final approval is 90%. This lack of translation from promising preclinical findings to success in human trials is known as the "valley of death." Estimated 30-50% of promising compounds never proceed to Phase 2/3 trials primarily due to funding barriers rather than scientific failure. The late-stage attrition rate for oncology drugs is as high as 70% in Phase II and 59% in Phase III trials.

161.

DOT. DOT value of statistical life ($13.6M). DOT: VSL Guidance 2024 https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis (2024)

Current VSL (2024): $13.7 million (updated from $13.6M) Used in cost-benefit analyses for transportation regulations and infrastructure Methodology updated in 2013 guidance, adjusted annually for inflation and real income VSL represents aggregate willingness to pay for safety improvements that reduce fatalities by one Note: DOT has published VSL guidance periodically since 1993. Current $13.7M reflects 2024 inflation/income adjustments Additional sources: https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis | https://www.transportation.gov/regulations/economic-values-used-in-analysis

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162.

PLOS ONE. Cost per DALY for vitamin a supplementation. PLOS ONE: Cost-effectiveness of "Golden Mustard" for Treating Vitamin A Deficiency in India (2010) https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 (2010)

India: $23-$50 per DALY averted (least costly intervention, $1,000-$6,100 per death averted) Sub-Saharan Africa (2022): $220-$860 per DALY (Burkina Faso: $220, Kenya: $550, Nigeria: $860) WHO estimates for Africa: $40 per DALY for fortification, $255 for supplementation Uganda fortification: $18-$82 per DALY (oil: $18, sugar: $82) Note: Wide variation reflects differences in baseline VAD prevalence, coverage levels, and whether intervention is supplementation or fortification Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266495

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164.

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165.

PMC. Cost-effectiveness threshold ($50,000/QALY). PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/

The $50,000/QALY threshold is widely used in US health economics literature, originating from dialysis cost benchmarks in the 1980s. In US cost-utility analyses, 77.5% of authors use either $50,000 or $100,000 per QALY as reference points. Most successful health programs cost $3,000-10,000 per QALY. WHO-CHOICE uses GDP per capita multiples (1× GDP/capita = "very cost-effective", 3× GDP/capita = "cost-effective"), which for the US ( $70,000 GDP/capita) translates to $70,000-$210,000/QALY thresholds. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9278384/

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166.

Integrated Benefits Institute. Chronic illness workforce productivity loss. Integrated Benefits Institute 2024 https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts (2024)

78.4% of U.S. employees have at least one chronic condition (7% increase since 2021) 58% of employees report physical chronic health conditions 28% of all employees experience productivity loss due to chronic conditions Average productivity loss: $4,798 per employee per year Employees with 3+ chronic conditions miss 7.8 days annually vs 2.2 days for those without Note: 28% productivity loss translates to roughly 11 hours per week (28% of 40-hour workweek) Additional sources: https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts | https://www.onemedical.com/mediacenter/study-finds-more-than-half-of-employees-are-living-with-chronic-conditions-including-1-in-3-gen-z-and-millennial-employees/ | https://debeaumont.org/news/2025/poll-the-toll-of-chronic-health-conditions-on-employees-and-workplaces/

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167.

Sinn, M. P. Wishocracy: Solving the Democratic Principal-Agent Problem Through Pairwise Preference Aggregation. https://manual.warondisease.org/knowledge/appendix/wishocracy-paper.html (2025) doi:10.5281/zenodo.18205881

Representative democracy suffers from an inescapable principal-agent problem where elected officials’ incentives diverge from citizen welfare. Wishocracy introduces RAPPA (Randomized Aggregated Pairwise Preference Allocation), which aggregates citizen preferences through cognitively tractable pairwise comparisons and creates accountability via Citizen Alignment Scores that channel electoral resources toward politicians who actually represent what citizens want.

168.

Sinn, M. P. The 1% Treaty. https://manual.warondisease.org/knowledge/solution/1-percent-treaty.html (2025) doi:10.5281/zenodo.20076511

Your governments possess nuclear weapons sufficient to end civilization 122 times but have not cured Alzheimer’s once. This treaty asks them to be 1% more rational.

169.

Sinn, M. P. The 1% Treaty: Harnessing Greed to Eradicate Disease. https://manual.warondisease.org/knowledge/economics/1-pct-treaty-impact.html (2025) doi:10.5281/zenodo.18161560

6.65 thousand diseases have zero FDA-approved treatments; at current trial capacity, exploring them takes  443 years. Redirecting 1% of military spending scales capacity 12.3x, cutting the timeline to  36 years and preventing 10.7 billion deaths. At $0.00177/DALY, 50.3kx more cost-effective than the best existing interventions. Incentive Alignment Bonds make adoption politically viable.

170.

Sinn, M. P. Incentive Alignment Bonds: Making Public Goods Financially and Politically Profitable. https://manual.warondisease.org/knowledge/appendix/incentive-alignment-bonds-paper.html (2025) doi:10.5281/zenodo.18203221

Government spending is optimized for lobbying intensity, not net societal value. Programs with 100:1 benefit-cost ratios get billions while programs with negative returns get hundreds of billions. Incentive Alignment Bonds flip this by creating a capital pool that rewards politicians (via campaign support and post-office opportunities) for funding high-NSV programs over low-NSV alternatives. The result: public good becomes private profit for both investors and elected officials.

171.

Brav, A., Jiang, W., Partnoy, F. & Thomas, R. Hedge fund activism, corporate governance, and firm performance. Journal of Finance 63, 1729–1775 (2008).

172.

Bloom, N., Eifert, B., Mahajan, A., McKenzie, D. & Roberts, J. Does management matter? Evidence from india. Quarterly Journal of Economics; NBER Working Paper 16658 https://www.nber.org/system/files/working_papers/w16658/w16658.pdf (2013).

173.

Buffett, W. E. Berkshire hathaway 2024 shareholder letter (performance 1965-2024). Berkshire Hathaway Annual Report https://www.berkshirehathaway.com/letters/2024ltr.pdf (2025).

174.

Gompers, P., Ishii, J. & Metrick, A. Corporate governance and equity prices. Quarterly Journal of Economics; NBER Working Paper 8449 https://www.nber.org/papers/w8449 (2003).

175.

University of Chicago Business Law Review. Hedge fund activism: Engine no. 1 and ExxonMobil. University of Chicago Business Law Review https://businesslawreview.uchicago.edu/online-archive/hedge-fund-activism-engine-no-1 (2021).

176.

Hindenburg Research. Icahn enterprises: The corporate raider throwing stones from his own glass house. Hindenburg Research https://hindenburgresearch.com/icahn/ (2023).