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Every other feature in this issue is about the mirage. This one is about the milestone — what an AI you can actually ship looks like, disguised as something that simply schedules your meetings.
Removing people is where the value is — and where the disasters are. The skill isn't choosing between speed and safety. It's knowing exactly where a human has to stand.
The engineering that shrinks the mirage — how grounding, retrieval, and verification turn a fluent guesser into a system you can check.
The danger isn't that AI is wrong. It's that AI is wrong fluently — and fluency is exactly what disarms the humans who were supposed to catch it.
You can't manage what you can't measure — and most teams still ship AI on a feeling. Here's how to turn "it seems good" into a number you can defend.
Thirty years ago, Geoffrey Moore explained why brilliant technologies stall right before the mainstream. His map explains exactly where enterprise AI is stuck today — and the mirage is what's holding it at the edge.
Why most enterprise AI value dies in the gap between an impressive prototype and a system you can actually ship — and what the 5% who cross it do differently.
Context is the working memory of an AI model — and one of its biggest bottlenecks. Why LLM responses degrade as chats grow, what "lost in the middle" means, and why unwritten human knowledge keeps software engineering a human-in-the-loop discipline.
AI answers everything with impressive confidence — and that may be exactly what makes it dangerous. Why an AI that admits uncertainty, instead of confidently guessing or eagerly agreeing with you, could become the more trustworthy one.
Why fiction is quietly one of the last cognitive workouts left standing — and what happens to the people who've stopped doing it. What a decade of empathy research, longitudinal brain studies, and falling reading rates say about the habit we're giving up.