AI PMs need eval frameworks, not just PM experience

Wild Rise's argument, writing in The Dipper Magazine, is that the split between AI product managers and traditional PMs comes down to determinism versus probability: a traditional feature returns the same output for the same input, while an AI feature can drift as the underlying data shifts, so requirements have to be written as acceptable ranges rather than fixed rules. The sharpest consequence is quality assurance, where pass or fail testing gives way to building and maintaining an evaluation framework, benchmark datasets included, because no one else in the company can define what "good" means for a probabilistic feature. The piece cites AI PM salaries reaching $165,000 to $238,000 base, then pivots to recommending Uplers' two-stage vetting process, complete with a 48-hour shortlist and replacement guarantee, as the fix for hiring mismatches.