Full chain-of-loss inputs, scenario tables, break-even arithmetic, upside model, and sensitivity analysis behind the main paper. Every assumption is labeled. Substitute your own numbers to stress-test the conclusion.
The main paper presents ranges for value at risk without showing all the intermediate steps. This section disaggregates the model into its component inputs, labels each one as measured, estimated, or inferred, and states the rationale for the conservative, base, and aggressive values.
A model is only useful if a skeptical reader can find the assumptions they disagree with and understand what happens when they are changed. That is the purpose of this section.
Value at risk = (persuasion spend ÷ cost per persuasion) × post-contact search rate × AI encounter rate × unfavorable answer rate × reversal rate × cost per persuasion
Simplified: Value at risk ≈ persuasion spend × (post-contact search rate × AI encounter rate × unfavorable answer rate × reversal rate)
At base values: $1.5M × (0.22 × 0.60 × 0.20 × 0.35) = $1.5M × 0.0092 ≈ $14,000 base-case value at risk for a congressional race. The reported range ($4,200–$55,000) reflects the full conservative-to-aggressive sweep across all inputs simultaneously.
The unfavorable answer rate is the single input that campaigns know the least about and that matters most. This table holds all other inputs at base values and varies only the unfavorable answer rate. It shows what the model produces across the four most relevant scenarios.
All other inputs held at base: post-contact search rate 22%, AI encounter rate 60%, reversal rate 35%, cost per persuasion $570.
| Unfavorable Answer Rate | Reversals (Base Model) | Value at Risk | As % of Persuasion Spend | Interpretation |
|---|---|---|---|---|
| 10%Well-documented candidate | 12 reversals | $7K | 0.5% | Diagnostic break-even at 3 reversals — program pays for itself even if it catches a quarter of modeled exposure |
| 20%Base estimate (unknown) | 24 reversals | $14K | 0.9% | Meaningful — $42K at risk against a $5,500 diagnostic that breaks even at 3 prevented reversals |
| 35%Poorly documented candidate | 43 reversals | $24K | 1.6% | Material — program cost ($5,500–$29,000) is well below value at risk at any scenario |
| 50%Active opposition; sparse record | 61 reversals | $35K | 2.3% | Significant — full program at $29,000 is less than 30% of value at risk |
Formula: reversals = ($1.5M ÷ $570) × 0.22 × 0.60 × unfav rate × 0.35. Value at risk = reversals × $570. All inputs other than unfavorable answer rate held at base. At 20%: 2,632 × 0.22 × 0.60 × 0.20 × 0.35 = 24 reversals × $570 = $14,000.
| Unfavorable Answer Rate | Reversals (Base Model) | Value at Risk | As % of Persuasion Spend | Interpretation |
|---|---|---|---|---|
| 10% | 65 reversals | $37K | 0.5% | Measurable even for a well-documented candidate; diagnostic still breaks even at 3–9 reversals |
| 20% | 130 reversals | $74K | 0.9% | Over $200K at risk against a full program cost of $33,000–$80,000 |
| 35% | 227 reversals | $129K | 1.6% | Material to any race budget; program cost is under 20% of value at risk |
| 50% | 324 reversals | $185K | 2.3% | Over half a million at risk; full Senate program at $80,000 is under 15% of value at risk |
Same methodology scaled to $8M persuasion spend and $570 cost per persuasion.
The 50% unfavorable answer rate is not a prediction — it is the model's stress test. It represents a candidate with an actively contested online record, sparse structured information, and meaningful opposition research targeting AI-indexed sources. Whether any candidate is in this situation is an empirical question — and the diagnostic is the instrument that answers it. Presenting the 50% case is not a claim that this is typical; it is transparency about what the model produces under adverse conditions.
Break-even is the simplest test: given the cost of a diagnostic, remediation, or monitoring program, how many persuasion reversals does it need to prevent to pay for itself? Each prevented reversal avoids $570, the cost of the persuasion lost. The 2–4× recovery multiplier used in earlier versions has been withdrawn, so the break-even reversal counts below are roughly three times higher than previously published.
| Program | Congressional Budget Range | Reversals Needed to Break Even | Senate Budget Range | Reversals Needed to Break Even |
|---|---|---|---|---|
| DiagnosticOne-time; establishes baseline | $3,000–$8,000 | 2–5 reversals | $6,000–$15,000 | 4–9 reversals |
| RemediationConditioned on diagnostic findings | $8,000–$15,000 | 5–9 reversals | $15,000–$35,000 | 9–21 reversals |
| Ongoing monitoringPer month, active race period | $1,000–$3,000/month | 2–5 reversals/month | $2,000–$5,000/month | 4–9 reversals/month |
| Full cycleDiagnostic + remediation + 6 months monitoring | $17,000–$41,000 | 30–72 reversals | $33,000–$80,000 | 58–140 reversals |
Break-even: program cost ÷ $570. The break-even reversal count is fixed — it does not depend on the race's unfavorable answer rate. What changes with the unfavorable rate is how many total reversals are modeled, and therefore what fraction the break-even represents. Note that at base-case inputs a full congressional program does not break even: $29,000 ÷ $570 requires 51 prevented reversals against 24 modeled. The diagnostic alone ($5,500, or about 10 reversals) does.
| Program | Break-Even Reversals (Congressional) | As % of Modeled Reversals at 20% Unfav. Rate |
|---|---|---|
| Diagnostic ($5,500 midpoint) | 10 reversals | 40% of 24 modeled reversals |
| Remediation ($11,500 midpoint) | 20 reversals | 84% of 24 modeled reversals |
| 6-month monitoring ($12,000 midpoint) | 21 reversals | 88% of 24 modeled reversals |
| Full cycle ($29,000 midpoint) | 17 reversals | 71% of 24 modeled reversals |
At base inputs (20% unfavorable rate, congressional), the model produces 24 reversals. The diagnostic breaks even at 4 — preventing 13% of modeled exposure. Note that the full cycle's 71% figure means the program needs to address most of the modeled problem to break even at this unfavorable rate; at 35% or 50% unfavorable, the same $29,000 full cycle breaks even at a much smaller fraction of modeled exposure.
The diagnostic's break-even is 3–4 prevented reversals regardless of the unfavorable rate — because the break-even is a fixed cost divided by a fixed recovery unit. What changes with the unfavorable rate is how many reversals are modeled in total. At 20% unfavorable: 24 modeled, break-even at 4 (13%). At 5% unfavorable: 6 modeled, break-even at 4 (53%). The diagnostic pays for itself even when the problem is small — and it tells you whether the problem is small or large. That is why it is the right entry point regardless of what the campaign suspects.
The defensive model asks: what does a campaign lose when AI answers unfavorably? The offensive model asks the inverse: what is the value of AI answers that work in the campaign's favor — including for voters the campaign never contacted directly?
A competitive congressional race generates an estimated 120,000–210,000 organic AI encounters, including from voters the campaign has not contacted. If those encounters are favorable and accurate, they are doing persuasion work the campaign did not pay for.
| Input | Value | Type |
|---|---|---|
| Total organic AI encountersCongressional midpoint estimate | 150,000 | Estimated |
| Favorable answer rate improvementFrom baseline to post-remediation | +10 percentage points | Illustrative — not a guaranteed outcome |
| Additional favorable encounters | 15,000 | Derived (150K × 10%) |
| Durable persuasion rateFraction of favorable encounters producing a lasting opinion shift | 2% | Estimated — consistent with political ad persuasion benchmarks |
| Additional durable persuasions | 300 voters | Derived (15K × 2%) |
| Equivalent advertising costAt $570 per durable persuasion | $171,000 | Derived (300 × $570) |
| Against a $10,000 remediation investment | 17:1 illustrative return | Before defensive value |
No input in the upside model is measured. AI systems are black boxes and their responses to specific candidates cannot be controlled or predicted. The 10-percentage-point improvement in favorable answer rate is assumed, not predicted: Kyanos does not claim that any action produces a given improvement, and no published research establishes such a figure. The 2% conversion from a favorable encounter to a durable persuasion is likewise unmeasured, and two studies point below it — a PNAS experiment found a retrieval-grounded bot raised knowledge of party positions while barely moving vote preference, and analysis of real assistant conversations finds only about 3% of political requests seek advice at all. Note also that the December 2025 research does not support the claim that accuracy drives persuasiveness; where it examined the two together it found them in tension, with more persuasive models providing less accurate information. Treat the $171,000 and the 17:1 as order-of-magnitude illustrations of what the encounter volume could be worth if these assumptions held, and substitute your own numbers to test them.
| Favorable Rate Improvement | Additional Favorable Encounters | Additional Persuasions (at 2%) | Equivalent Ad Spend |
|---|---|---|---|
| +5 pp | 7,500 | 150 | $85,500 |
| +10 pp (base) | 15,000 | 300 | $171,000 |
| +15 pp | 22,500 | 450 | $256,500 |
| +20 pp | 30,000 | 600 | $342,000 |
Based on 150,000 base organic AI encounters, 2% durable persuasion rate, $570 per persuasion. The 2% persuasion rate is deliberately conservative — consistent with political digital ad benchmarks, which are generally lower than door-knock rates.
Not all inputs are equally consequential. A model is only as useful as your understanding of which assumptions dominate the output. This section ranks the inputs by their effect on the value at risk calculation and notes which the model is surprisingly robust to.
The following rankings are based on the relative effect of moving each input from its conservative to aggressive estimate, holding all other inputs at base. A high-sensitivity input can swing the output by 3× or more; a low-sensitivity input moves it by less than 30%.
Moving from 10% to 50% unfavorable answer rate swings value at risk by 5×. This is the number campaigns do not know without a diagnostic. It is also the number most likely to vary dramatically across candidates — a well-documented incumbent with decades of public record will sit at a very different point than a first-time challenger with sparse online presence. The diagnostic resolves this uncertainty entirely.
This input has been withdrawn from the model — see A7 above. It was previously the second most influential term in the calculation, and its removal reduces every value-at-risk figure to roughly one third of what was published before. It is listed here so that readers comparing against the earlier version can see what changed and why.
Moving from 15% to 30% doubles this input, which flows linearly through the model. It adds 2× to the output at the extremes. Campaigns with older, lower-engagement electorates should use 15%; campaigns targeting younger, high-information voters should use 25–30%.
The 50–70% range reflects genuine uncertainty in the Google AI Overview penetration rate across different query types and voter demographics. Because Google AI Overview is now the default surface for named-individual informational queries, this rate is unlikely to fall below 50% in any competitive federal race electorate. The model is moderately sensitive to this input — it adds roughly 40% to the output at the high end — but the floor is relatively well anchored.
Moving from 25% to 50% doubles this input and flows linearly through the model. The 35% base estimate is deliberately conservative — not every voter who encounters an unfavorable AI answer is moved by it. The conclusion is stable across the full range of this input.
Cost per persuasion appears in both the value at risk numerator and the break-even denominator, which means it largely cancels out in the break-even calculation. At $300 per persuasion, the value at risk is lower but so is the cost of recovery per voter — the ratio between them stays roughly constant. This is one of the reasons the break-even conclusion is robust: it does not depend on a specific cost per persuasion.
The break-even conclusion — that the diagnostic cost is small relative to plausible value at risk — holds across nearly all combinations of inputs. The only scenario where it fails is a candidate with a genuinely clean, accurate, comprehensive AI record (unfavorable answer rate below ~5%) combined with a very low-engagement electorate (post-contact search rate below 10%). That is a case where the diagnostic itself confirms there is nothing to worry about — which is also valuable information. The diagnosis costs the same either way; what changes is what the campaign learns from it.
All model inputs and derivations are the author's. Base data sources: Gerber and Green field experiment syntheses for cost per persuasion; Edison Research Infinite Dial 2026 for AI usage rates; CCI 2024 post-election survey for dedicated AI tool usage; Google product documentation and industry analysis for AI Overview penetration; Nature (Lin et al.) and Science (Hackenburg et al.), December 4, 2025, for persuasion magnitude. The post-contact search rate is the author's estimate and is not drawn from Pew or BPC, which measure where Americans get election news rather than post-contact verification behavior. All inputs are labeled above as measured, approximated, estimated, unmeasured or withdrawn.
The author acknowledges a commercial interest in the subject of this paper. That interest is disclosed so readers can evaluate the analysis with appropriate context. The model is structured to be stress-tested; readers who disagree with specific inputs are encouraged to substitute their own values to test whether the conclusion changes.