Insuring the Synthetic Future: How AI Is Redrawing Risk, Expanding TAM, and Empowering the MGU Renaissance

As artificial intelligence advances from software concepts to physical forms—in humanoids, autonomous vehicles, and generative agents—the insurance industry stands on the verge of radical change. We are not just witnessing a technological shift; we are entering what Aldous Huxley might call a “Brave New World”—not dystopian, but indeed confusing for those still clinging to traditional insurance models designed for the industrial age rather than the algorithmic one.

Yet amidst uncertainty lies opportunity. At the core of this transformation is a dramatic shift in the addressable market for insurance—its Total Addressable Market (TAM)—and a demand for product innovation that traditional carriers are structurally ill-equipped to fulfill. It is here that Managing General Underwriters (MGUs) can—and must—step into the breach.

To understand the scope of change, consider how artificial intelligence is redefining the parameters of insurable risk. Unlike natural catastrophes or human behavior, AI-driven systems generate emergent, probabilistic risks with opaque causality. Consider the following:

• AI copilots in commercial software and professional services: Who is liable when a financial advisor uses an LLM to build a portfolio that underperforms or violates regulations?
• Autonomous vehicles (AVs) and robots in mobility and labor: How do you underwrite kinetic and decision-making risk when software controls the machine?
• Synthetic labor and humanoids: What happens when embodied AI makes decisions that create physical or reputational harm?

Goldman Sachs estimates that generative AI alone could increase global GDP by 7% and improve productivity by 1.5% over the next decade. This growth not only adds value but also creates trillions in new risk exposures—from IP infringement and algorithmic bias to machine behavior that extends beyond its training data. The total addressable market for insurance no longer stops at workers’ comp, cyber, or D&O—it now must include AI-driven liability, non-human actors, and hybrid-intelligence operational systems.

As one underwriter recently described, “We’re no longer insuring humans doing work. We’re insuring work being done by non-deterministic agents.” The actuarial tail doesn’t wag this dog—it gets buried entirely.

Brave New Programs: MGUs as the New Vanguard

To adapt, the insurance market must assume a role more akin to that of the World Controllers in “Brave New World”: architects of societal systems, rather than mere respondents to random events. And that means shifting from risk transfer to risk creation: not just pricing the future but designing it.

This is where MGUs are uniquely positioned.

Unlike large carriers encumbered by legacy tech, embedded bureaucracies, and risk-averse cultures, MGUs operate with nimbleness, focus, and a builder’s mindset. The MGU model allows for rapid experimentation, specialized underwriting, and the development of programs that address vertical or emerging risks. In a world where risk is dynamic and software-defined, static products and ISO-coded lines no longer suffice.

Here are the four areas where MGUs can redefine insurance in the AI economy:

1. Insuring AI Itself – AI models are IP-rich, highly trained assets. Whether in fintech, medtech, or proptech, the models themselves require novel first-party coverage structures, including digital asset protection, performance warranties, and training data contamination coverages. MGUs can create these policies from scratch—partnering with tech companies and reinsurers willing to underwrite tail risk in exchange for data insights and premium margins.
2. Liability from AI-Augmented Decision-Making – Professional liability will shift as AI assists (and sometimes overrides) human judgment. A physician who uses an AI diagnostic tool, a lawyer who drafts briefs using LLMs, or a financial advisor relying on algorithmic insights all exist in a new gray zone of shared liability. MGUs are better suited to rapidly segment these risk pools, tailor coverage triggers, and build integrated risk-service ecosystems (claims, monitoring, indemnity).
3. New Mobility: Autonomous Vehicles and Humanoids – AVs and humanoids blur the line between product and operator liability. Tesla, Waymo, and Optimus-type devices will create edge-case behaviors not foreseen in standard GL or auto coverage. MGUs can work alongside mobility companies, capture telemetry, and use real-time data for usage-based pricing, coverage bundling, and AI-native claims triage.
4. Utopian Risk Pools: Embedded, Affinity, and Distributed Intelligence – In “A Modern Utopia”, H.G. Wells envisioned a world managed by a voluntary aristocracy of intelligence. In today’s AI-centric economy, intelligent software agents may coordinate labor, logistics, or decisions in ways akin to that vision. MGUs can build embedded insurance products aligned to distributed agent ecosystems—insurance for AI marketplaces, open-source contributions, or swarms of collaborative bots managing infrastructure or commerce.

One of the most profound implications of AI is the inversion of the insurance value chain: from retroactive indemnity to proactive risk partnership.

With AI systems emitting massive quantities of behavioral data, insurers can shift toward:

• Continuous underwriting, based on behavioral analytics and telemetry
• Claims automation, where AI flags anomalies, estimates loss, and initiates payout
• Risk engineering, using AI to simulate, test, and de-risk processes before a claim arises

In this model, MGUs need to collaborate with software-centric companies—such as AI risk observability platforms, cyber posture rating services, and model interpretability engines—to provide services alongside insurance. Risk capital becomes only one part of a three-part system: insurance, prevention, and recovery.

This evolution is not without risk. In “Brave New World,” Huxley warned against unchecked technological control, where progress equates to pacification. As AI takes on more decisions—and underwriting becomes less human—we must ensure that the MGU-led innovation doesn’t become opaque or create moral hazards. Risk pricing must stay transparent, auditable, and fair.

Conversely, “A Modern Utopia” offers hope: a society where intellectual and moral excellence, not brute capital, shapes governance. MGUs must embody that role—curators of risk, not exploiters of ignorance. By integrating data science, ethics, and market incentives, MGUs can build not just profitable products but resilient systems. The insurance industry must confront a simple truth: AI is not a substitute for underwriting expertise. It is the foundation of the modern economy. The total addressable market (TAM) for insuring that economy is expanding exponentially. Those who cling to outdated models or risk-averse cultures will lose relevance. MGUs who adopt forward-looking architectures—data-native, software-aligned, and morally grounded—will not only lead the market but also shape its very future.

As H.G. Wells put it: “A man of imagination among painters will use his imagination to paint.” So too must we, in the insurance industry, imagine boldly—and insure accordingly.