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Finding these optimization opportunities can itself be a significant undertaking. It requires end-to-end understanding of the spec to identify which behaviors are observable and which can safely be elided. Even then, whether a given optimization is actually spec-compliant is often unclear. Implementers must make judgment calls about which semantics they can relax without breaking compatibility. This puts enormous pressure on runtime teams to become spec experts just to achieve acceptable performance.

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Tech job openings are falling. Hiring rates broadly are weak. And layoffs across the economy recently hit their highest level since 2009—although Zandi makes the distinction AI’s weighing effect on the job market “is due to weaker hiring, not layoffs.” Meanwhile, the National Bureau of Economic Research reports over 80% of firms in recent surveys say there is no impact from AI on employment or productivity over the past three years—yet those same firms forecast AI will boost productivity by 1.4% over the next three years. That disconnect between falling hiring numbers and rising productivity is precisely what worries Zandi and why he considers this a watershed Cortés moment.

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