The question of verification has become urgent because AI has disrupted the traditional signals that evaluation systems relied on.

When a machine can produce a polished essay, a clean portfolio, or a structured case study in minutes, the outputs that hiring managers once used to assess capability become unreliable.

Verification in the AI era needs to do something different from what traditional credentialing does. It needs to assess the process, not just the product. The judgment behind the work, not just the work itself. The consistency of a person's approach across time and context, not just one curated sample.

Several approaches are emerging in response. Skills-based hiring frameworks attempt to assess capability directly rather than relying on credentials. Work samples and structured evaluation are being introduced earlier in hiring processes. And a smaller number of practitioners are building what might be called proof systems that allow people to document, structure, and share their professional evidence in a way that travels with them.

Mitch Chibundu's company, Hourze, is designed specifically as this kind of infrastructure. It functions as a verification layer where time invested, skills developed, and work completed become structured, trusted proof. The product is built on the premise that verification is most valuable when it is built into the workflow of work itself, not applied retroactively at the point of evaluation.

Her argument, developed through Hourze, her writing, and her advisory practice, is that the professionals and institutions who build verification into their processes now will have a structural advantage as AI-era trust systems mature.