Courtney Fahnhorst, a wound care and hyperbaric medicine specialist, found a unique side hustle through a LinkedIn ad: training artificial intelligence models. The mother of four and former ER doctor now dedicates spare time to evaluating how well AI handles clinical situations, pointing out errors and misconceptions.
Fahnhorst took the role to supplement her income while saving for a private practice in Florida. She describes the pay as competitive with clinic-based work, though exact figures are not disclosed. The position offers flexibility, allowing her to set her own hours around a full-time job.
The gig involves posing clinical scenarios to AI software and assessing its responses. Fahnhorst draws on 15 years of medical experience to identify where the model falls short, providing corrective feedback. This contractor role has grown common as AI companies seek domain experts to refine their systems.
Beyond the paycheck, Fahnhorst notes unexpected professional value from the work. She continues to evaluate AI while building capital for her future practice, illustrating how the AI training economy is drawing on specialized human knowledge to improve model accuracy.
Critics argue that such piecemeal contractor roles lack stability, benefits, and career progression compared to traditional employment, potentially exploiting expert labor for one-off training tasks without long-term commitment from AI firms.