Mark Cuban Predicts AI Job Simulators Will Transform Onboarding

Billionaire entrepreneur Mark Cuban believes artificial intelligence is about to transform how new employees learn the ropes at work. In a recent post on X, the former Shark Tank investor predicted that AI-driven “job simulators” would become one of the most significant AI applications in the corporate world, replacing much of what companies currently handle through traditional onboarding.

From Coworkers to Code

Cuban explained that as AI reshapes workplaces, employees will have fewer chances to learn on the job by observing and working alongside experienced coworkers. He suggested that organizations with deep institutional knowledge will build custom simulators encoding their most experienced employees’ judgment and decision-making. New hires could then rehearse everything from routine tasks to high-pressure decisions without any real-world stakes attached.

Borrowing From Pilots and Race Car Drivers

The concept mirrors training methods already used in other high-stakes fields. Commercial airline pilots regularly complete flight-simulator sessions to safely practice emergency procedures, while professional race car drivers rely on racing simulators to test strategy and sharpen skills before ever reaching the track. Cuban envisions a similar model spreading into corporate training, potentially turning onboarding into an interactive, scenario-based experience built by the people who understand the work best.

Cambridge organizational sociology professor Thomas Roulet noted that many companies and business schools already use virtual reality to train staff on topics like unconscious bias, suggesting the shift Cuban describes extends an existing trend rather than representing something entirely new. He added that AI tools are well suited to generating realistic scenarios that support learning.

Possible real-world applications include junior lawyers rehearsing depositions and courtroom appearances, or medical trainees practicing diagnoses and emergency responses in safe, simulated settings before facing them with real patients.