By Guest commentary Tuesday, September 8th, 2026

By Gleb Tsipursky

Central Coast employers are investing in the physical infrastructure of innovation. They should make an equally deliberate investment in the human infrastructure that turns new technology into durable capability.

AeroVironment’s recently announced $100 million Moorpark campus is designed to consolidate research, engineering, design, prototyping and production. 

That kind of proximity can accelerate product development. It can also accelerate the development of people if employers resist the temptation to let AI absorb the routine work through which younger employees once learned how the business actually functions.

The latest Stanford Digital Economy Lab employment update makes the stakes concrete. 

Using ADP payroll data covering millions of U.S. workers through June 2026, the researchers found that employment among workers ages 22–25 in highly AI-exposed occupations was about 19% below where it would have been if it had kept pace with similarly aged workers in less-exposed occupations. 

The comparable gap was 15% in the July 2025 data vintage. The adjustment appears mainly through reduced hiring, and it is concentrated where AI use tends to automate human tasks. Experienced workers show no comparable gap.

For a region competing for engineers, analysts, technical specialists and future managers, that pattern should change how AI return on investment is measured.

The easy metric is hours saved. 

A better metric asks what happens to the saved hours. If software drafts the first analysis, does a junior employee spend the recovered time checking assumptions and testing edge cases? 

If AI produces a first-pass design note, does the new engineer compare it with production constraints and review the discrepancies with a senior colleague? 

If a model summarizes customer or supplier information, does the analyst learn to identify missing context before anyone acts on it?

That is an apprenticeship return: AI removes low-value repetition while the organization deliberately increases exposure to judgment.

Central Coast employers should establish three operating rules. 

First, every automated junior task should be paired with a higher-skill responsibility the employee begins practicing sooner. 

Second, experienced employees should receive explicit coaching and review responsibilities instead of being expected to mentor around the edges of an already full workload. 

Third, teams should track time to independent competence alongside cost, cycle time and output.

Those rules matter especially in workplaces where design, manufacturing, software, customer work and field operations interact. 

A young employee who only sees an AI-generated answer learns less about the system. A young employee who must verify the answer against a test result, production constraint, customer requirement, or failure mode starts learning the system faster.

The Central Coast does not need to choose between automation and apprenticeship. It needs to demand both from the same investment. The strongest AI deployment is the one that makes the organization more productive today and produces more capable people for tomorrow.

Dr. Gleb Tsipursky is a behavioral scientist, CEO of Disaster Avoidance Experts and author of The Psychology of AI Adoption at Work: From Resistance to Results.