MIT Technology Review's warning about AI hype is a useful counterweight to a week full of launches, price cuts, agents, and grand safety claims. The piece argues for looking past declarations and asking what the systems actually do, for whom, and under what evidence.
Hype is not just annoying; it shapes budgets, regulation, hiring, education, and public expectations. If every model update is framed as a turning point, users and institutions lose the ability to distinguish real progress from marketing acceleration.
Pagish includes the piece because a serious AI front page needs skepticism alongside news. The healthiest readers will track breakthroughs and ask harder questions about evidence, incentives, failure modes, and who benefits.
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