This is so true, but to follow this advice takes a respectable amount of personal discipline, combined with picking a very specific niche to focus on. A business with an R&D core such as AI should only be considering generalizing (if at all) after developing concrete confidence that you are on, and will stay on the bleeding edge of your initial niche. It's easy to feel ashamed when you tell someone you're doing X, and enough lay (tech) persons ask if it uses the trending research technology, and you're not. It may also make you think like you should use that tech, but it's likely a costly distraction more often than not.

Our company [0] developed a cutting edge computer vision system focused on detecting cardio machine exercise cadence (hyper specific!) and became the only reliable camera-based solution to do so. We then tried to generalize to all exercise motion (rep tracking, a still unsolved problem), achieved mediocre success, and put the exploration to sleep later because we think waiting for other technologies to mature would be easier and faster (better 3D cameras, AI pose models, etc). On the other hand we've picked other niches that meet our business needs to expand our CV R&D into, with pretty good success but mostly just for internal use (video content creation tools). More importantly, we're still the best camera-based indoor cardio detection tech out there, and that's a big part of why we're still alive as a bootstrapped business founded in 2010.

Quadruple down on your niche first!

[0] https://www.activetheoryinc.com/