Bermuda's AI Moment

PerspectiveBy Jorai Laurenceo, Founder & Executive Director of Laurenceo
Laurenceo's Bermuda AI GuidesAll Writing

Bermuda does not need to build frontier AI models to matter in artificial intelligence. It needs to do what it has always done with risk: understand it earlier than everyone else, price it properly and write the rules that serious capital trusts. That is Bermuda's AI moment, and it is happening now.

Why Bermuda, Why Now

Bermuda is where a large share of the world's catastrophe and specialty risk is underwritten. In a single statement last year, the Bermuda Monetary Authority noted that Bermuda's commercial insurers and reinsurers paid $1.1 trillion in gross claims costs between 2016 and 2024. When AI changes how that market underwrites, reserves and pays claims, the consequences travel to every country that buys protection here.

That is why the BMA's work matters beyond the island. Its July 2025 discussion paper, its February 2026 stakeholder letter and its August 2026 consultation on a Guidance Note on the Responsible Use of AI make Bermuda the most advanced offshore financial centre on AI supervision. Cayman, by comparison, is reported to be targeting an AI legislative framework for mid-2027.

Bermuda's advantage is not size. It is that its regulator, its industry and its people sit close enough together to turn principles into practice quickly.

The BMA Chose the Right Design

The BMA could have copied the EU AI Act. It did not. Its proposal explains how existing obligations (governance, risk, conduct, cyber, outsourcing, AML) apply when a firm uses AI, built on eight principles and five outcomes, with no new licence. For a market as varied as Bermuda's, from captives to digital asset businesses, that is the right call.

But principles are the easy part. Every firm will agree with accountability, proportionality and explainability. The test is the fifth outcome: adequate evidence for internal assurance and supervisory review. Can a firm prove, on any given day, that its AI controls are working?

Evidence Is Where Bermuda Can Lead

In Laurenceo's public response to the consultation, I argued for one idea above all: evidence by default. In a well-engineered AI system, every control produces its own evidence as a by-product of running. A supervisory review then becomes a query against records that already exist, rather than a project to reconstruct them.

If Bermuda makes that its standard, it offers something no large jurisdiction has managed: AI supervision that is rigorous without being slow. That is exactly what global capital looks for when it chooses a domicile.

What It Will Take

  1. A final Guidance Note that defines sufficient evidence, including an illustrative minimum evidence set by materiality.
  2. Agentic AI boundaries enforced outside the model, because a model told not to use a tool, but able to, is not bounded.
  3. Talent. AI governance should become a recognised profession in Bermuda, with training at Bermuda College and in industry.
  4. Public-sector example. Government should publish a register of its own AI uses, as it asks of others.
  5. Measurement. Bermuda should measure AI's effect on its own workforce rather than borrow foreign estimates.

The Window

Jurisdictions that define the standard early tend to keep the business. Bermuda did it with catastrophe reinsurance, with insurance-linked securities and with digital assets. AI governance is the next opportunity, and the window is the next few years, while the rest of the world is still deciding what good looks like. Laurenceo exists to help Bermuda take it.

About the Author

Jorai Laurenceo is the Founder and Executive Director of Laurenceo, Bermuda's AI governance think tank, based in Hamilton, Bermuda. He wrote Laurenceo's public response to the Bermuda Monetary Authority's AI consultation and leads Laurenceo Intelligence. More about Jorai.

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