AI 2027 looks shakier after scrutiny

Hot take: this looks less like “the forecast is wrong” and more like “the confidence interval got flattened into certainty.”

• –The critique focuses on the gap between the paper’s polished presentation and the fragility of its assumptions.
• –It argues the 2027 timeline depends heavily on specific modeling choices rather than robust evidence.
• –The main pressure points are uncertainty propagation, internal consistency, and sensitivity to inputs.
• –The post positions the revised forecast as a more cautious read: interesting directionally, but too aggressive on timing.