// Insights

What we're thinking about.

Essays on AI, insurance, and the gap between tools and transformation.

You Bought More AI Than You Think

Ask a carrier CEO where AI touches a decision about a policyholder, and you'll get the list of things the company bought as AI. That list is accurate — and incomplete, because some of the AI reaching your customers arrived bundled inside software bought for a different job: a fraud score inside the claims platform, a propensity model inside the retention tool. Each one shapes a rate, a coverage decision, or a claim, and none of them are on the list. You can't govern, explain, or answer for a decision path you haven't found — and someone is eventually going to ask you to produce the real list. The fix isn't a governance program; it's a mapping exercise you can run on one workflow, on your own clock, before someone else asks on theirs.

August 13, 2026·6 min read

Every Verb Makes You Choose

Naming the verbs in a workflow tells you what each step could point to — a rule, a model, a language model, or a person. It doesn't tell you how much room to give that step once you know, and that second question is a separate decision most teams skip. Rules are predictable going in and rigid once written; agents are flexible and only traceable after the fact. Most shops make that trade once, at the project level, so the vendor's default configuration ends up deciding it for them. The fix is smaller than it sounds: for each verb you've already named, ask whether you chose where it sits on that line, or whether something else chose it for you.

August 6, 2026·7 min read

Read Your Own Workflow, One Verb at a Time

Most carrier AI conversations start with the wrong question. "Where should we use AI?" gets answered at the altitude of departments and product categories, where nothing operational ever happens. The useful question is smaller: what are the verbs? Any piece of work is a chain of verbs, and each one points at exactly one capability — a rule, a model trained on your own data, a language model, or a person. Name them and you can read any workflow in your shop yourself, in about the time it takes to read this post — and the verbs that point at a person are the half of the map a rented system can't touch.

August 5, 2026·11 min read

What Walks Out With Them

The retirement wave is a deadline on your AI program, not an HR problem. What walks out with a thirty-year underwriter is judgment — the tells, the exception paths, and the accounts she treats differently for reasons she never said out loud — and judgment was never written down. The practical move is to sequence the AI program against the succession plan, starting with the workflows where the honest answer to "who else can do this" is one person's name.

July 29, 2026·10 min read

Why Launchpad

Great MGAs run a lot of business with lean teams, but the AI build that would compound their advantage is too expensive and slow to ever get to. So we built Launchpad: we fund and build the AI infrastructure a small cohort of MGAs own, and we get paid only when it produces results. The difference from every workflow vendor isn't the demo — it's the business model underneath it. You rent a shared workflow and your competitor rents the same edge; you own the build and the advantage stays yours.

July 22, 2026·5 min read

The Ownership Line

Carriers are right to rent most of the AI stack — nobody builds their own cloud. But the agentic workforce built around your book and your underwriters' judgment is the one layer you can't afford to rent, because that's where carrier-specific advantage compounds. Rent that from a vendor and you've rented your own moat back from a business whose model depends on you never owning it.

July 9, 2026·5 min read

The Process You Wrote Down Is Not the Process You Run

Why carrier AI pilots break on the cases that matter most — and the one step that prevents it. Every operation drifts from its documentation the moment it meets reality. Build automation against the paperwork and you automate the routine majority while breaking on the exception-dense minority, which is exactly where the loss dollars, the litigation exposure, and the senior judgment already live.

July 1, 2026·9 min read

Where to Point AI: A Deployment Map for Carriers

Accuracy tells you whether a model works. It doesn't tell you where it's safe to deploy. Two questions — how reversible a wrong answer is, and whether a human is still making the call — sort your entire AI roadmap onto a grid that shows where to deploy now and where pilots go to die.

June 24, 2026·8 min read

Where Insurance AI Compounds, and Where It Stalls

The models are good now. Pilots stall because of where you point them. Four structural properties of insurance work decide where AI pays off and where it won't — and telling the compounding layer from the judgment layer is what a decade inside a carrier teaches you.

June 10, 2026·9 min read

Insurance Has an AI Last-Mile Problem

The first mile — recognizing AI can materially improve insurance operations — is done. The last mile is everything between that recognition and production technology delivering tangible business impact. Most carriers are stuck there, and the gap is widening.

April 30, 2026·5 min read

Context Is the Moat You're Not Building

Every AI vendor can plug into your data. None of them can plug into your context. The institutional knowledge locked in your senior employees' heads is the real competitive advantage — and it's decaying every day it stays unstructured.

April 17, 2026·6 min read

No Memos, No Code: How Our Exec Team Spontaneously Built an AI Leadership Layer

Everyone on our leadership team built their own team of specialized AI agents. None of them were asked to build one and most have never coded before (or since).

March 25, 2026·7 min read

We Built an AI-Native Insurer. Here's Why Incumbents Can Win Too.

The organizational immune system is real. But so is the operational advantage incumbent carriers have—if they move fast enough.

March 6, 2026·9 min read