The proliferation of AI leaderboards incentivizes companies to optimize models for specific benchmarks. This creates a risk of "acing the SATs" where models excel on tests but don't necessarily make progress on solving real-world problems. This focus on gaming metrics could diverge from creating genuine user value.

The unemployment rate for college-educated young men has surged to 7%, matching that of their peers without a degree. This parity indicates that a traditional degree's value in securing entry-level employment is eroding for this demographic, challenged by AI automation and increased competition from experienced workers.

Elite universities with massive endowments and shrinking acceptance rates are betraying their public service mission. By failing to expand enrollment, they function more like exclusive 'hedge funds offering classes' that manufacture scarcity to protect their brand prestige, rather than educational institutions aiming to maximize societal impact.

Don't trust academic benchmarks. Labs often "hill climb" or game them for marketing purposes, which doesn't translate to real-world capability. Furthermore, many of these benchmarks contain incorrect answers and messy data, making them an unreliable measure of true AI advancement.

ASU President Michael Crow argues that Ivy League schools are based on the colonial British model—small, elite, and fundamentally unscalable. This structure is insufficient for a large, modern democracy, which demands new university designs built for scale, speed, and broad accessibility.

The frenzy around elite college admissions is a systemic 'collective action trap.' Even parents and students who understand the limited value of prestige are forced to compete due to intense social pressure and status anxiety, amplified by social media. Opting out individually carries too high a social cost.

Instead of policing AI use, a novel strategy is for teachers to show students what AI produces on an assignment and grade it as a 'B-'. This sets a clear baseline, reframing AI as a starting point and challenging students to use human creativity and critical thinking to achieve a higher grade.

AI disproportionately benefits top performers, who use it to amplify their output significantly. This creates a widening skills and productivity gap, leading to workplace tension as "A-players" can increasingly perform tasks previously done by their less-motivated colleagues, which could cause resentment and organizational challenges.

A study of companies in the U.S. and Denmark found that while MBA-led firms achieved better short-term shareholder returns, this came at the expense of employees through suppressed wages. Critically, these leaders showed no evidence of increasing sales, productivity, or investment. The resulting wage declines led to higher-skilled employees leaving, crippling long-term company health.