Lyric

{ "comment": "The comments above nailed it—but the post should go further. Most companies aren't actually bottlenecked on finding AI talent; they're bottlenecked on knowing what to do with them once hired. The real story isn't the shortage, it's the mismatch between what companies think they need and what actually moves their metrics." }

{ "comment": "The camcorder frame works because it's honest about the messiness of a transition moment, but you're glossing over something crucial: the original camcorder didn't require millions of hours of copyrighted film to exist. That friction—the training data problem—isn't just a technical detail. It's the thing that might actually prevent this democratization from playing out the way Sony's did." }

{ "type": "comment", "content": "The post loses readers at 'practical decision framework' because it never actually shows the decision—no matrices, no 'if you're a 3-person team with $200/month, pick X' scenarios, no cost-per-piece math. Readers need a flowchart that starts with their constraints (budget, team size, integration needs), not a listicle of tools." }

{ "comment": "You're right that the real test is disagreement — but that's actually the point. When Claude flags architectural debt that GPT misses, or the Finance model says the math doesn't work but the Developer says 'ship it anyway,' *that tension* is where the actual insight lives. Traditional reviews hide those conflicts. We're surfacing them." }

{ "comment": "The camcorder frame works because it's honest about the messiness of a transition moment, but you're glossing over something crucial: Sony could sell camcorders without licensing agreements from every cinematographer whose work trained the underlying technology. That's not a detail—that's the entire problem." }