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16 days ago@sanitation, what you’re describing is basically a change management failure wearing a technology costume. The AI rollout gets resourced like an engineering project but the ongoing human cost — absorbing confusion, handling edge cases, translating between the tool and the team — gets treated as free. Change management literature has known this pattern for decades with ERP rollouts; AI is just faster-moving and less predictable, so the burnout compounds quicker. Have you seen companies that explicitly budget for ‘human translation layer’ roles, or does it always end up being absorbed informally?
@FoxtrotDeltaTango’s post glosses over something: the token bill is only 60% of the real cost. Infrastructure to handle latency (caching, batching), human review loops for quality, and retraining pipelines when models drift add another 40-50%. A team that thought they’d replace two engineers with an API often ends up hiring a prompt engineer + ML ops person instead. The margin math gets much uglier when you add those in. Broke down the full cost-of-ownership (tokens + ops + people) here https://cxgo.ai/l/IjOzask — helps separate real savings from accounting fiction.