The Specific Warning

MIT economist Andrew McAfee recently issued a pointed warning to executives: automating Gen Z entry-level positions to cut costs may produce short-term savings while destroying the pipeline through which organizations develop future senior talent. The warning comes as IBM and Salesforce have simultaneously doubled down on Gen Z recruitment, creating a visible split in corporate strategy. McAfee's concern is not primarily about fairness or youth unemployment. It is about organizational competence reproduction. If entry-level roles are the environments where people learn how organizations actually function, eliminating those roles eliminates the learning mechanism itself.

This is a sharper claim than it first appears. Most executive commentary on AI automation treats headcount reduction as a resource allocation question. McAfee is making a structural argument: the entry-level job is not just labor, it is a training context. Remove the context, and you remove what the context produces. That reframing has significant implications, and I think it maps onto something my own research has been circling around for some time.

Competence Does Not Transfer Without a Medium

The ALC framework I am developing distinguishes between two ways that expertise develops. Routine expertise - the procedural kind - is acquired by repeating tasks until they become automatic. Adaptive expertise, by contrast, develops when a learner encounters variation, encounters failure, and is forced to reason about underlying principles rather than surface procedures (Hatano and Inagaki, 1986). Entry-level roles, at their best, produce adaptive expertise precisely because they expose workers to the full, messy complexity of organizational life before those workers have the authority to avoid that complexity.

When organizations automate those roles, they are not just removing labor. They are removing the conditions under which adaptive expertise forms. The AI system that replaces an entry-level analyst can execute the procedure. It cannot develop a structural understanding of why the procedure exists, when it fails, and how to respond when the context shifts. That structural understanding is what McAfee is worried organizations will stop producing.

This connects directly to what Kellogg, Valentine, and Christin (2020) describe as algorithmic work: the irony is that as platforms and AI systems absorb routine tasks, the remaining human work becomes more cognitively demanding, not less. Organizations that hollow out entry-level pipelines are simultaneously increasing the cognitive demands on their senior workforce while eliminating the developmental pathway that produces people capable of meeting those demands.

The Awareness-Capability Gap at the Organizational Level

McAfee's warning also implies something the automation debate rarely addresses directly: executives understand that entry-level jobs are disappearing, but that awareness does not translate into effective organizational response. This mirrors what algorithmic literacy research consistently finds at the individual level - workers develop awareness of algorithmic constraints without developing the capability to respond to them effectively (Gagrain, Naab, and Grub, 2024). The same gap appears to operate at the organizational level when leadership recognizes a structural risk but continues optimizing for the metric that produces it.

IBM and Salesforce's contrarian move toward Gen Z investment suggests at least some firms are reasoning at the structural level rather than the procedural one. They are not asking "how do we reduce headcount costs this quarter?" They are asking "what does our talent pipeline look like in ten years if we make this decision now?" That is the difference between topographic optimization - finding the best path given current terrain - and topological reasoning, which asks how the terrain itself is being reshaped by the decisions being made.

What This Means for Organizational Theory

Rahman (2021) argues that algorithmic systems create invisible cages: constraint structures that workers navigate without fully perceiving. McAfee's warning suggests organizations are building a different kind of invisible cage for themselves. By automating the contexts that produce adaptive expertise, firms create a structural dependency on AI systems to perform functions that humans can no longer perform, not because humans lack the capacity, but because the organization eliminated the developmental environment that would have produced that capacity.

This is not an argument against automation. It is an argument for treating organizational learning environments as infrastructure rather than overhead. Schor et al. (2020) note that platform economies create new forms of worker precarity through structural dependence. The irony McAfee is identifying is that firms pursuing cost efficiency through automation may be engineering an equivalent precarity for themselves: structural dependence on AI systems for capabilities they no longer know how to develop internally. That is a coordination failure with a long lag, which is precisely why it is so easy to miss until it is expensive to reverse.