Every Technology Wave Builds A Trust Layer. AI Is Building Its Now.

The future of innovation is global. We discuss it here.

Photo by Alex Shute on Unsplash

This piece was first published on my Forbes column here.

‘Lloyd’s of London is over 300 years old. It was built to insure ships in oceans no one trusted. The Lloyd’s of AI hasn’t been built yet, but it’s coming.

Trust institutions rarely show up first. They arrive late, after the technology has already broken something.

For example, the Big Four accounting firms were all founded around 1850. That timing is not an accident: the industrial revolution had produced firms too large and too complex for owners to verify directly, and a new profession emerged to do the verifying. Independent auditors became the trust layer that made modern capital markets possible.

Underwriters Laboratories emerged because electrification was powerful, useful, and occasionally catastrophic. The certificate authorities that quietly run SSL only existed because e-commerce demanded a way to know the site asking for your credit card was real. And of course, Lloyd’s of London began as a coffeehouse where shipowners tried to price risks they could not see.

AI is presently in this gap. Yes, the technology is being adopted. But it is creating a new surface area of risk. To remedy it, a new verification layer is emerging, and I think it’s the most important venture category nobody is talking about yet.

The early wave verified what AI said

An early wave of AI verification focused on confirming models were safe, accurate and claims were real. This ran from late 2022 through roughly the end of 2024, and solved a discrete problem for models at the time.

That gave (some amount of) hallucination detection, prompt injection defense and deepfake detection.

Implicit in the first wave was that a human, or a deterministic system, would read output and act on it. The verification layer just needs to flag the bad ones.

Agents are changing everything.

The next wave verifies what AI does

In 2025, AI stopped producing text for humans to read and started doing work for itself. Trades were booked. Refunds were processed. Articles were filed. Code shipped to production. The verification question changed shape.

It is no longer is this output accurate.

It is now can someone stand behind the work that just happened?

The most widely adopted use case of AI is arguably coding. Unsurprisingly, this has been a big area of investment for verification. But the use cases are expanding, and so too are the verification styles of companies, including in fintech and insuretech.

The Artificial Intelligence Underwriting Company launched in July 2025, among a few startups in this category. Its product is insurance for AI agents, priced against a new audit standard called AIUC-1, plus an underwriting process that ties premium to evidence.

Objection is building a journalist ranking and verification network to “adjudicate the truth of journalism. If the next decade of frontier AI is trained on the public corpus of news, verifying news verifies the foundation.

Oath (a Fluent incubation) launched this week, built a licensed audit firm built natively for AI-generated financial work. The thesis: as AI increasingly completes bookkeeping and tax preparation, someone still has to sign off on the financials.

At the end of the day, the same idea is present in each one: take a verification function that used to sit inside a human professional category and rebuild it for a world where the underlying work is autonomous.

The Verification Surface Area is Increasing

The obvious objection: if AI does the verification, the verifiers disappear. We believe the historical record argues the opposite.

Start with developers. ChatGPT shipped in November 2022. Since then, AI coding tools have moved from novelty to default. A March 2026 Boston University report finds 84% of developers now use or plan to use them. Over the same period, U.S. software developer employment hit a record 2.5 million, up more than 400,000 since 2022. Headcount went up, not down.

Accountants tell the same story over a longer arc. The profession has absorbed mainframes, ERP systems, the cloud, and now AI. The Bureau of Labor Statistics counted over 1.5 million accountants and auditors in 2024, a record, and projects above-average growth over the next decade. BLS’s own framing: automation of routine tasks is expected to increase demand for advisory and analytical work, not reduce it.

This is Jevons. Cut the unit cost of something useful and demand outruns the cost cut. Benedict Evans made a similar argument about AI job exposure. When you cut the unit cost of something useful, the world tends to ask for more of it.

Verification is in that bucket.

Where are we heading?

The first wave verified what AI said. The second wave is verifying what AI did.

The space is very much dynamic. Standards will emerge, including some that machines can issue to other machines, with a human institution somewhere in the chain that is willing to be legally liable when the chain breaks. We are not there yet.

I also expect a wave of players that “guarantee” the outcome of the work or bear the risk of failure, insuring it much like the Lloyds example for insurance.