AI Patents, Model Risk Insurance, and the New Skills of the AI Workforce

As 2025 closes, AI is reshaping everything from insurance patents and liability products to tech labor markets and the skills workers need to stay relevant. This week’s stories show how quickly power is concentrating—and where the real opportunities still are for humans in the loop.

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Three Insurers Now Own the AI Patent Game

In insurance, innovation leadership is no longer diffuse. New analysis from Evident shows that State Farm, USAA, and Allstate control 77% of all AI-related patents filed by insurers since 2014, with property/casualty carriers holding 89% of insurer AI patents overall
(Insurance Journal).

The patent mix is shifting, too. Generative AI filings focused on customer service and claims have jumped from 4% to 31% of all insurance AI patents since 2014, while genuinely agentic AI remains rare—only three insurers have filed agentic patents so far, led by USAA.

This creates a clear divide:

• Large P&C carriers are building moats around telematics, IoT, and end‑to‑end AI workflows for claims and service.
• Smaller carriers and MGAs will increasingly face a choice: license, partner with insurtechs, or accept that core AI capabilities become utilities rather than differentiators.

For anyone building AI in regulated industries, the signal is blunt: intellectual property strategy now matters as much as model performance. Patents are not a guarantee of commercial success, but they are an accurate map of where serious R&D dollars—and long‑term competitive advantages—are being placed.

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There’s Now Insurance for When Your AI Is Wrong

A parallel market is emerging to insure the models themselves. Deloitte projects that global AI insurance premiums will hit $4.8 billion by 2032, compounding at roughly 80% annually
(JD Supra). These products sit on top of traditional E&O and cyber cover, targeting failures unique to AI systems: biased outputs, hallucinations, IP violations, and model mispredictions.

Two developments stand out:

• Specialized AI liability cover is moving from theory to practice. Munich Re has written AI-specific policies since 2018. Start-up Armilla AI offers performance guarantees for ML models.
• Model error insurance is arriving as a distinct product. MKIII, which screens borrowers for U.S. mortgage lenders, bundles coverage for “excess errors” when defaults exceed the model’s predicted rates. Munich Re and Greenlight Re are backing the risk
  (Insurance Business).

Behind this sits a regulatory shift that makes AI risk more measurable and therefore more insurable. The EU AI Act began phasing in obligations for general‑purpose AI providers in August 2025, while over 25 U.S. states are adopting the NAIC’s AI Model Bulletin
(Model Bulletin PDF).

For enterprises deploying AI, the message is twofold:

• You now have a way to transfer part of your AI risk—if you can explain and quantify it.
• Underwriting AI risk is itself a new market, but one that demands real technical and regulatory depth, not generic cyber experience.

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Workers Fear Replacement Less—and Fear Skills Gaps More

A year ago, actuaries and underwriters were bracing to be automated out of existence. New survey data from hyperexponential shows a sharp turn in attitude: only about half now worry about being replaced by AI, down from 74–80% in 2024
(Carrier Management).

Two trends sit under that headline:

• Hands‑on exposure lowers existential fear. Professionals who actually use AI tools now see them as augmenting judgment rather than replacing it.
• Frustration is spiking. Just 1% say they’re satisfied with current AI tools, versus 22% two years ago. Ninety‑nine percent report struggles getting tools to work as hoped.

At the same time, AI‑driven restructuring has become explicit. Chubb plans to cut up to 20% of its 43,000‑person workforce over three to four years while automating 85% of major underwriting and claims processes
(Insurance Business). MIT’s Project Iceberg estimates today’s tools could already perform tasks worth 11.7% of U.S. wage value.

The pattern is now clear:

• Companies are hiring a relatively small cohort of people who build and manage automation—AI agent developers, forward‑deployed engineers, synthetic data specialists—while laying off far larger numbers whose work the automation replaces
  (Under the Radar).
• Surveys show actuaries and underwriters are less afraid of AI itself and more afraid of not having the right skills to work with it.

For individual workers, the durable advantage is shifting from “I use AI tools” to “I know how to design workflows, ensure compliance, and troubleshoot AI in production.” Foundation skills—governance, domain expertise, systems thinking, stakeholder translation—are outlasting job titles like “AI agent developer,” which may be commoditized within two years.

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Governance and Compliance: The Skill That Outlives Platforms

If models, tooling, and frameworks are changing every 6–18 months, what’s left that lasts a decade or more? This week’s deep dive from The Open Record argues the answer is governance and compliance
(full guide).

The contrast is stark:

• HIPAA has governed U.S. health data since 1996.
• GDPR has shaped European privacy since 2018.
• SOC 2 and ISO 27001 have anchored security programs for decades.

Over the same period, the industry has churned through wave after wave of platforms: on‑prem systems, cloud ERPs, mobile apps, SaaS, and now LLMs and agentic AI.

The implication: once you understand the regulatory frameworks—HIPAA, GDPR, SOC 2, the EU AI Act—you can apply that knowledge across every future AI platform that comes along. That makes governance and compliance a rare kind of AI skill: one that appreciates with each new model release instead of being reset by it.

A practical three‑tier path is emerging:

• Entry (0–6 months): Master one framework (e.g., HIPAA or SOC 2) well enough to implement checklists, basic controls, and breach response plans. Roles: HIPAA coordinator, privacy analyst.
• Intermediate (6–12 months): Map and manage multiple frameworks together; run internal audits; handle vendor risk. Roles: compliance manager, privacy engineer, SOC 2 program lead.
• Advanced (12–24 months): Design enterprise‑wide compliance and AI governance programs; advise the C‑suite on regulatory risk; handle crisis response. Roles: Chief Privacy Officer, VP of Compliance, regulatory strategy advisor.

As AI regulation hardens—via the EU AI Act, NAIC guidance, or future U.S. federal rules—the people who can translate between fast‑moving technical stacks and slow‑moving legal obligations will be the ones other teams depend on.

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The Takeaway

Across patents, liability cover, workforce shifts, and regulation, the pattern is consistent:

• Power and IP are consolidating around a small number of well‑capitalized players.
• Risk transfer is catching up to model failure, but only for organizations that can quantify what their systems actually do.
• The labor market is bifurcating between those who design, oversee, and govern AI systems and those whose tasks those systems can now perform.
• Skills that interface with law, trust, and context—governance, domain expertise, systems thinking—are proving far more durable than fluency with any single model or platform.

For organizations, the next 12–24 months will be about moving from pilots to production, and from ad‑hoc AI experiments to governed, insurable, and auditable systems. For individuals, it’s about shifting from “Can I use AI?” to “Can I make AI work safely, reliably, and profitably in the real world?”