Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack

Adam Kleinman; Adrien Ecoffet; A. Feder Cooper; Alex Taylor; Anita Srinivasan; Ben Bucknall; Bri Treece; Cristian Trout; Derek Blum; Desiree Spain

REVIEW 4 major objections 5 minor 300 references

Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack

T0 review · 4 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read Affirmative insurance for AI agents with billion-dollar limits is achievable by 2030 if insurers build shared infrastructure.

desk verdict A serious, well-structured blueprint for AI-agent insurance with a load-bearing but unvalidated pricing premise; deserves a real referee. read the letter →

arxiv 2607.11999 v2 pith:U7KULTZJ submitted 2026-07-13 cs.CY

Cristian Trout , Sanmi Koyejo , Sasha Romanosky , Giorgio Ripamonti , Lynn Thompson , Desiree Spain , Alex Taylor , Kevin Casey

show 29 more authors

Stephen Casper Matthew Botvinick Sean McGregor Miles Brundage A. Feder Cooper Patricia Paskov Adrien Ecoffet Ben Bucknall Kevin Wei Markus Anderljung Lukasz Szpruch Bri Treece Tom Zick Gabriel Weil Ugur Ozer Kevin Kalinich Jesus Gonzalez Vitaly Baranov Moran Koren Guy Laban Gil Arazi Henri Winand Derek Blum Toby Clowes Adam Kleinman Anita Srinivasan Tom Fehring Rune Kvist Rajiv Dattani

This is my paper

classification cs.CY

keywords AIinsuranceagentssilentcoverageaffirmativeaccumulationriskcatastrophemodelingredteaminginsurability

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The report argues that the emerging AI agent economy—projected to move trillions of dollars by 2030—is currently insured through unpriced 'silent coverage' inside cyber, professional, and general liability policies, leaving insurers exposed and businesses unprotected. It claims insurability is trending the wrong way: agent capabilities are outpacing reliability, incident severity is climbing, and dependence on a few foundation-model providers creates correlated-loss risk. Against that backdrop, the report's central claim is that affirmative, explicitly priced AI coverage with enterprise limits in the billions is achievable by 2030, but only if the industry coordinates on shared infrastructure. The blueprint is an eight-component 'AI insurance stack'—incident data pooling, catastrophe modeling, standards, contract design, risk selection, pricing, monitoring, and claims management. A sympathetic reader would care because, if true, insurance becomes a lever for safe adoption of a transformative technology rather than a brake on it.

What carries the argument

The central object is the 'AI insurance stack,' eight interlocking components spanning the whole policy lifecycle, with the claims-to-underwriting feedback loop as the connective tissue. The identity that makes the argument run is the pricing formula expected loss = severity × probability × usage, where severity is bounded by red teaming, probability is estimated from performance-evaluation scores used as 'quasi-actuarial data,' and usage is measured through telemetry partnerships. Standards and certifications act as the underwriting shortcut that ties the components together, much as fire-rating schedules let property insurers price communities at a glance.

What would settle it

Collect a cohort of several hundred insured AI-agent deployments, record pre-deployment performance-evaluation scores and red-team findings, then track claims frequency and severity for two years. If scores show no correlation with claims after controlling for usage and sector—or if scores are easily gamable—the stack's pricing engine is falsified and the 2030 achievability claim collapses.

Watch

Extended reading notes

Core claim

The paper's central discovery is a design: a complete insurance infrastructure that lets carriers underwrite AI agents before actuarial data matures. The load-bearing move is to treat performance evaluations and red teaming as 'quasi-actuarial data'—structured, forward-looking signals about failure rates and tail severity that can be substituted for historical loss tables. On this basis, the paper argues that a coordinated eight-component stack can turn today's silent, unpriced AI exposure into affirmative coverage: explicit definitions and exclusions, system-specific risk selection, usage-based pricing, ongoing monitoring, and a claims-forensics feedback loop. The paper also argues that soc

Load-bearing premise

Insurers can obtain reliable, non-gamable signals—from performance evaluations, red teaming, and telemetry—that predict expected AI-agent losses well enough to price and select risk, despite the absence of mature actuarial data.

Editorial extensions

If this is right

Silent AI exposure inside legacy cyber, D&O, E&O, and general liability policies would be replaced by affirmative endorsements or standalone policies, giving portfolio managers and regulators visibility into concentration.
Premiums would differentiate on the basis of system specifications, safeguards, and evaluation scores, so heavy users stop subsidizing light users and responsible deployers pay less.
Accumulation risk—especially dependence on a handful of upstream model providers—would be carved out and managed with exclusions, sub-limits, and catastrophe models rather than blanket denials.
Standards and third-party audits would become de facto insurability signals, creating a market race-to-the-top in agent safety similar to crashworthiness ratings in auto insurance.
Societal-scale frontier AI risk would move to alternative risk-transfer vehicles—mutuals, CAT bonds, and government backstops—because private markets alone cannot carry that tail.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Testable extension: if evaluation scores and red-team results do not predict subsequent claims, the pricing engine fails quietly; a cohort study linking pre-deployment test scores to claims over several hundred policies would settle this within a few years.
The report's declining incident-per-usage claim implies premiums should track AI usage volume more than revenue or headcount; usage-telemetry pilots can test whether usage is indeed the better exposure basis.
The cold-start logic implies consolidation around shared standards and databases will need to happen before limits can reach the billions, and that antitrust constraints on joint policy language may be the quiet bottleneck.
Government disclosure mandates, which the report treats as a complement to industry action, may end up being the binding condition; voluntary pooling alone has repeatedly failed in cyber.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Read this if you care about how insurance could shape AI risk governance. It's not an academic paper; it's an industry blueprint with a policy argument, but it's a serious one. What's new is the synthesis: eight components—incident data, CAT modeling, standards, contract design, risk selection, pricing, monitoring, claims—organized into a stack specifically for agentic AI, plus an AI CAT menu of mutuals, CAT bonds, liability regimes, and government backstops. Historical precedents like Underwriters Laboratories and the Closed Claims Project are used well. The authors are also refreshingly candid: they flag that the incident-to-usage index is too noisy and short for strong conclusions, that the GDP estimate is a linear approximation of an IMF model they don't have, and they disclose that AIUC employees helped write it while recommending the AIUC-1 standard. That transparency matters.

The soft spot is real and load-bearing. The pricing engine relies on performance-evaluation scores and red teaming as 'quasi-actuarial data' (§II.6.A), but there's no evidence these signals predict actual loss ratios. Test conditions aren't necessarily representative; once scores become rating inputs, Goodharting is likely. Red teaming bounds severity only if the discovered failures approximate real-world tail events. The report acknowledges the data are fragile but then treats the eval-based pricing as the foundation of the 2030 target. It's also not clear where the loss-severity priors come from for novel agent failure modes. This isn't fraud—it's an honest gap, and the authors say so implicitly—but it's the difference between a plausible case and a demonstrated one.

The conflict of interest is disclosed but worth weighing: three authors, including the lead, work for the company that maintains AIUC-1, and the report recommends that standard. That doesn't invalidate the analysis, but independent validation and public release of Appendix 1's data and code would raise my confidence substantially.

Bottom line: this is a genuinely useful read for insurers, policymakers, and AI-governance researchers. The stack is coherent and the recommendations are concrete. The empirical support for the headline claim is thin, but the paper is honest about many of its limits. I'd send it to peer review, flag the pricing premise for scrutiny, and ask for the supporting data. It's the kind of big-picture, actionable synthesis that gets more useful when people push on the weak points.

Recommendation: engage with it, but read the appendix caveats first.

Referee Report

4 major / 5 minor

Summary. The report argues that affirmative insurance coverage for AI agents, with limits reaching billions, is achievable by 2030 if the insurance industry coordinates to build an eight-component 'AI insurance stack.' The stack spans incident data collection, CAT modeling, standards, contract design, risk selection, pricing, monitoring, and claims management. The paper draws on historical analogies (UL, the Closed Claims Project, nuclear insurance, cyber insurance) and proposes technical underwriting tools such as performance evaluations, red teaming, and telemetry-based monitoring. It also sketches a separate 'AI CAT' layer for societal-scale risks, including mutuals, CAT bonds, and government backstops. The central claim is explicitly conditional on insurers being able to price and select risk without mature actuarial data, using evaluation scores and red-team results as substitutes. The report is transparent about some limitations, particularly in Appendix 1 and footnote 4, but those limitations bear directly on the load-bearing pricing premise.

Significance. This is a timely and unusually concrete policy/industry blueprint, with detailed recommendations assigned to specific actors (carriers, reinsurers, modelers, regulators). Its value lies in framing AI agent risk as an insurability problem and proposing a coordinated infrastructure response, rather than in new empirical results. The historical precedents are relevant and well chosen, and the report is honest about several of its own evidential weaknesses — the fragile incident/usage index, the absence of dollar-loss data in public incident reports, and the linear approximation of an unavailable IMF model. If the proposed stack were built and its pricing premise validated, the report could serve as a useful planning document for insurers and policymakers. However, the central 'achievable by 2030' claim rests on an unvalidated substitution of evaluation scores for actuarial data, and the GDP-loss estimate is based on an unverifiable approximation. These need to be either substantially supported or carefully de-emphasized before the report can carry its conclusions.

major comments (4)

[§II.6.A and §II.5.B] The pricing formula (expected loss = severity × probability × usage) relies on performance evaluations and red teaming to estimate probability and severity, with §II.6.A asserting these outputs are 'structurally similar to an actuarial table' and §II.5.B proposing them as substitutes for loss data. This is load-bearing for the 'billions by 2030' conclusion, yet the report provides no evidence that evaluation scores or red-team findings predict claim frequency or severity. Appendix 1 acknowledges the incident/usage index is 'too noisy and too short in temporal coverage to support strong conclusions' and that its construct validity is 'fragile,' and n.36 admits public incident data 'rarely if ever include exact dollar costs.' The report also does not address Goodhart dynamics: once scores become rating inputs, they will be optimized. A concrete validation strategy, pilot-study results, or
[Footnote 4 / Extended Summary] The $200 billion US GDP figure is presented in the Key Takeaways and Extended Summary as an estimate of what depends on insurers, but it is derived from a linear approximation of an IMF model to which the authors do not have access, under an explicit quasi-linearity assumption. The footnote discloses this, but the headline presentation does not. Because this number is used as a policy stake, it should be labeled as an illustrative sensitivity calculation, not an estimate, and should not appear without its caveats in the executive summary.
[§I.2, §II.3.D, author list] Several authors are employees of the Artificial Intelligence Underwriting Company (AIUC), which maintains AIUC-1, one of the four standards the report discusses and implicitly recommends for underwriting use. The same company produced the GDP estimate. Footnote 29 discloses the AIUC-1 conflict and states other authors take no position on its relative merits, which is good practice, but the report would benefit from a more prominent competing-interests statement and from making clear which specific claims and recommendations are authored by AIUC-affiliated authors. This is not a claim of misconduct, but a policy report recommending a standard with a direct financial stake should make the conflict visible to a reader who only reads the executive summary.
[§I.5 and §II.3.E] The historical analogies (UL, Closed Claims Project, INPO) are used as implicit support for the claim that a coordinated stack will reduce losses and enable profitable underwriting. The paper does not address a key disanalogy: those precedents involved physical systems with relatively stable failure modes, whereas AI agents are continuously updated, general-purpose, and susceptible to adversarial optimization of the very evaluation metrics proposed as pricing inputs. At minimum, the report should explicitly discuss why Goodharting of standardized evaluations is not expected to replicate the UL experience, and what safeguards (e.g., hidden test sets, surprise audits) would be deployed.

minor comments (5)

[Appendix 1] The appendix is commendably honest about biases, but the interpretation section could more explicitly say that the 80% decline in incident-to-usage ratio is not a reliable trend for underwriting. Consider adding a one-line summary box for practitioners.
[§II.1.B, footnote 14] The footnote beginning 'Reviewers note that...' appears to be an internal review note that was accidentally left in the manuscript. Please remove or convert to a normal authorial remark.
[§II.8.D] The paragraph beginning 'This stifles institutional learning...' appears to be cut off mid-sentence ('there is n...'). Complete the sentence and ensure no accidental truncations elsewhere.
[§II.2.A] The claim that 'we expect no more than fifty people are studying them full-time, worldwide' is an unsourced numerical claim. Either provide a citation or remove the specific number.
[Table 3] The comparison table would be stronger with a column or footnote explicitly listing the disclosure/conflict status of each standard's relationship to the authors. The current footnote is in the text, not the table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central argument rests on historical precedents and proposed infrastructure, not on a self-referential derivation.

full rationale

The report's central claim—that affirmative AI-agent coverage with billion-scale towers is achievable by 2030 through coordinated industry infrastructure—is a policy and market-building argument supported by external historical analogies (Underwriters Laboratories, the Closed Claims Project, IIHS, nuclear-pooling precedents) rather than by an equation or fitted parameter. The incident-to-usage ratio in Appendix 1 is explicitly flagged as too noisy and short to support strong conclusions, and it is not used to force the central conclusion. The report does recommend AIUC-1, a standard maintained by the lead author's employer, and it uses GDP and incident estimates produced by the Artificial Intelligence Underwriting Company; these are self-referential and disclosed, but they are not load-bearing for the central claim, which would stand with or without those particular estimates. The pricing section proposes using performance-evaluation scores as 'quasi-actuarial data' and red-teaming as a severity bound; this is an unvalidated empirical premise, not a circular derivation, because the report does not fit evaluation scores to realized losses and then present the same fitted values as independent predictions. The possible failure mode is lack of validation—a correctness risk—not definitional or constructional circularity. No quoted equation or cited prior result reduces to the paper's own inputs.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The report introduces no new physical or conceptual entities. Its free parameters are scenario choices in the GDP estimate and unspecified index construction choices. The most load-bearing axiom is that forward-looking evaluations can substitute for actuarial data, which is asserted rather than evidenced.

free parameters (2)

US AI Preparedness Index (AIPI) scenario adjustments = low: 0.71 (delta -0.06); high: 0.79 (delta +0.02)
Hand-chosen to create low/high institutional-readiness scenarios; multiplied through the IMF TFP shock linearly to derive the ~$200B GDP swing. The authors acknowledge they do not have access to the IMF's full model.
Incident/usage composite index weights = not specified
Appendix 1 constructs composite indices from heterogeneous public sources; the exact weighting, normalization, and inclusion criteria are not fully specified in the available text. The paper itself notes the construct validity is fragile.

assumptions (4)

domain assumption Historical insurance precedents (UL, IIHS, Closed Claims Project) transfer to AI agents.
Used throughout Part II to argue the stack can succeed; the analogy is argued, not established.
domain assumption Forward-looking performance evaluations and red teaming can substitute for mature actuarial data.
Central to pricing (Section II.6.A); no empirical validation that evaluation scores predict realized loss frequency or severity.
domain assumption Agentic AI is a general-purpose technology comparable to electricity.
Stated explicitly in Section I.3: 'This premise ultimately rests on our belief that agentic AI is a general-purpose technology, comparable in scope to electricity.'
domain assumption Agent task length doubles every ~4 months and reliability gains lag capability gains.
Cited from [19], [26], [27]; this is the basis for the claim that actuarial models will lag and that the stack must include continuous monitoring.

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Cite this review

Pith. "Pith review of Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack." pith.science (2026). https://pith.science/paper/U7KULTZJ

@misc{pith2026260711999,
  author       = {Pith},
  title        = {Pith review of: Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U7KULTZJ}},
  note         = {Machine review of arXiv:2607.11999}
}

read the original abstract

From maritime trade to commercial nuclear power, insurance has been the enabler of major economic and technological developments by pricing risk, limiting downside, and spreading best practices. The emerging AI agent economy, projected to handle trillions of dollars in transactions by 2030, looks to be the next such development. Yet insurers' exposure to AI agent risk currently sits largely unpriced across existing insurance lines; between this silent coverage and growing exclusions, coverage is not fit for purpose. Furthermore, insurability is trending the wrong way: AI agent capabilities appear to be outpacing reliability, leading to rising incident severity; concentration among a few foundation model providers threatens correlated losses; and traditional actuarial modeling will struggle to keep pace with a technology evolving as rapidly as frontier AI. This report argues that affirmative AI coverage with limits in the billions is achievable by 2030, but only with industry-wide coordination. Drawing on successful historical precedents such as Underwriters Laboratories, the Closed Claims Project, and others, we lay out an eight-component AI insurance stack spanning incident data collection, catastrophe modeling, standards, contract design, risk selection, pricing, monitoring, and claims management. Building out this infrastructure is what will enable insurers to cover and manage AI agent risk sustainably and at scale. Finally, we discuss coverage for catastrophic risk from frontier AI ("AI CAT"), including CBRN, critical infrastructure collapse, and loss of control scenarios. Addressing these tail risks will require purpose-built instruments, potentially including a frontier model developer mutual, catastrophe bonds, bespoke liability regimes, and government backstops.

Figures

Figures reproduced from arXiv: 2607.11999 by the authors.

**Figure 1.** ). The components are complements in important ways. Incident response and claims management, for example, are key sources of data, whose analysis then feeds into virtually every other layer, especially standard setting, risk evaluations, pricing, and accumulation risk research. Likewise, contractual exclusions of losses caused by upstream model failures, aimed at controlling accumulation risk, are unenforceable wit… view at source ↗

**Figure 2.** Note: Proportions are illustrative [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** US motor-vehicle-related deaths per million vehicle miles traveled (VMT) and annual VMT, by year. 1994- 2023. Source: Fatality Analysis Reporting System, National Highway Traffic Safety Administration [143]. Malpractice insurance for anesthesiology presents another successful precedent. In response to rising premiums in the 1970s [43], malpractice insurers and the American Society of Anesthesiologists (ASA) began th… view at source ↗

Figures from the paper (11 more)

**Figure 4.** Figure 4: Cyber has famously suffered from siloed incident data, a struggle to coordinate on minimum security controls, and a lack of standardized policy language which makes it difficult to compare products [21], [60]. These failings have contributed to the prevalence of narrow…

**Figure 5.** Figure 5: The AI Insurance Stack Foundational Product & Underwriting Supporting Functions Servicing Policies Legend: Foundational Supporting Functions Product & Underwriting Servicing Policies Incident Response + [PITH_FULL_IMAGE:figures/full_fig_p028_5.png]

**Figure 6.** Figure 6: Recommendations:  Insurers should lean into pricing based on system-specific stress-testing data (red teaming, performance evaluations etc.) to keep up with a rapidly evolving risk, and overcome the lack of actuarial data.  Insurers should feature-rate based on syste…

**Figure 7.** Figure 7: Note: Proportions of coverage are illustrative. [PITH_FULL_IMAGE:figures/full_fig_p069_7.png]

**Figure 8.** Figure 8: The incident index grows at roughly 100% year-over-year, while the usage index grows at roughly 350% year-over-year on average. The ratio of the two indices falls by over 80% from 2023 to 2025, suggesting that, on the whole, frontier AI systems are becoming more reliab…

**Figure 9.** Figure 9: Incident index: all leave-k-out compositions [PITH_FULL_IMAGE:figures/full_fig_p083_9.png]

**Figure 10.** Figure 10: Usage index: all leave-k-out compositions [PITH_FULL_IMAGE:figures/full_fig_p084_10.png]

**Figure 11.** Figure 11: Incident/usage ratio: all leave-k-out compositions [PITH_FULL_IMAGE:figures/full_fig_p084_11.png]

**Figure 12.** Figure 12: Public incidents (AIID) vs. enterprise API spend. [PITH_FULL_IMAGE:figures/full_fig_p085_12.png]

**Figure 13.** Figure 13: Frontier AI lawsuits vs. enterprise API spend. OpenAI Content Moderation vs OpenAI Daily Messages ( [PITH_FULL_IMAGE:figures/full_fig_p086_13.png]

**Figure 14.** Figure 14: OpenAI content moderation actions vs. OpenAI daily messages. [PITH_FULL_IMAGE:figures/full_fig_p087_14.png]

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[Online]. Available: https://www.mcki nsey.com/capabilities/quantumblack/our- insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for- consumers-and-merchants

Pith tools

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