Nuclear Engineers

So far, most nuclear‐engineering work still relies on experts, but AI tools are slowly helping with data-heavy tasks. For example, AI software has been tested to scan ultrasonic inspection data (from reactor vessel tests) far faster than a human can. In one trial, an AI “pre‐screened” crack‐detection data in minutes instead of hours, highlighting possible defects for engineers to review ^[1].

Similarly, researchers have built “virtual sensors” and machine‐learning models that can predict reactor system failures many times faster than traditional simulations ^{[2] [2]}. These AI tools don’t replace engineers – humans still make final calls – but they help spot problems earlier and focus human attention on the most important results.

Adoption of AI in nuclear engineering is growing, but it is cautious and slow. The IAEA notes that AI use in nuclear power plants is still mostly at the pilot stage ^[4]. There are only about 15,400 nuclear engineers in the U.S. and the field isn’t rapidly expanding ^[5].

This means there isn’t huge pressure to automate like there is in some other industries. Also, nuclear systems are safety‐critical, so companies can’t risk untested AI tools. Rigorous regulations and the need for expert oversight make development and approval of any AI system expensive.

At the same time, nuclear engineers are very well trained (and well paid), so businesses will balance the high cost of AI projects against the fact that current experts do the job well.