Ricardo Amper, CEO of the $1.25 billion AI identity verification company Incode, recently stated that he preferentially hires Gen Z workers because they are "less biased" than older generations, explicitly arguing that "too much knowledge is actually bad." This is not a casual hiring preference. It represents a fundamental misunderstanding of how competence develops in algorithmically-mediated environments, with implications that extend far beyond one company's talent strategy.

The claim conflates tabula rasa with adaptive expertise. Amper appears to be arguing that the absence of domain knowledge produces superior judgment in AI development contexts. But the research on expertise development tells a different story. Hatano and Inagaki (1986) distinguished between routine expertise, which optimizes performance in stable contexts through procedural knowledge, and adaptive expertise, which enables performance across novel contexts through principled understanding of underlying structures. What Amper is describing is neither. He is describing inexperience and labeling it adaptability.

The Schema Vacuum in AI Organizations

This preference for "unbiased" workers reveals what I have been calling the schema vacuum problem in AI deployment contexts. Organizations implementing algorithmic systems face a choice: invest in developing structural schemas that enable workers to understand the topology of algorithmic constraints, or select for workers who lack competing frameworks entirely. Amper has chosen the latter, apparently believing that the absence of knowledge structures is equivalent to flexibility.

The problem is that platforms and algorithmic systems do not reward blank slates. They reward the development of accurate mental models of system behavior (Kellogg, Valentine, & Christin, 2020). The variance puzzle in platform work demonstrates this clearly. Workers with identical access to platform affordances show dramatically different outcomes, and these differences emerge from their capacity to develop functional schemas about how algorithmic systems operate. Power-law distributions in platform outcomes do not result from natural talent. They result from differential schema development, often through trial and error that organizations are unwilling to support systematically.

The Transfer Failure Embedded in the Hiring Logic

What makes Amper's statement particularly concerning is that it institutionalizes a training approach guaranteed to produce routine rather than adaptive expertise. If organizations select specifically for workers without domain knowledge or competing frameworks, they must then train these workers through platform-specific procedural instruction. Learn these tools, follow these workflows, optimize these metrics. This produces exactly the kind of context-dependent competence that fails to transfer when the platform changes, when the algorithm updates, or when the regulatory environment shifts.

The counterintuitive finding from schema induction research is that general training targeting structural features of a domain produces better far transfer than specific procedural training, even when specific training produces faster initial performance (Gentner, 1983). A worker who understands the structural features of how identity verification algorithms handle edge cases can adapt when the specific algorithm changes. A worker trained only on the current procedural implementation cannot. By selecting for workers without "too much knowledge," Amper is optimizing for immediate productivity at the expense of organizational adaptability.

The Governance Vacuum

This hiring philosophy also reveals the absence of organizational structures for algorithmic governance. If the CEO of an AI identity verification platform believes that domain expertise is a liability, what does that signal about the organization's capacity to anticipate algorithmic harms, respond to audit findings, or adapt to regulatory requirements? The awareness-capability gap that Kellogg and colleagues documented in platform work applies equally to platform design. Organizations can be aware that their systems produce differential outcomes without possessing the structural schemas necessary to intervene effectively.

The broader pattern here is that AI organizations are recreating the coordination failures visible in platform labor markets within their own workforce development. They are selecting for legibility and compliance rather than adaptive capacity, then expressing surprise when their systems fail in novel contexts or when regulatory requirements demand principled rather than procedural responses.

The Gen Z workers Amper is hiring deserve better than to be valued explicitly for what they do not know. They deserve organizations willing to invest in the development of transferable schemas rather than platform-specific routines. The alternative is a workforce optimized for today's systems with no capacity to adapt to tomorrow's, led by executives who have mistaken inexperience for plasticity.