Schooling has become a victim of Goodhart's Law. When a measure (grades, test scores) becomes a target, it ceases to be a good measure. Students become experts at 'doing school' — maximizing the signal — which is a separate skill from the actual creative and intellectual capabilities the system is supposed to foster.

Despite average test scores on a consistent exam dropping by 10 points over 20 years, 60% of all grades at Harvard are now A's, up from 25%. This trend suggests a significant devaluation of academic credentials, where grades no longer accurately reflect student mastery.

Historically, one-on-one tutoring—proven to boost student outcomes by two standard deviations (the "Bloom Two Sigma effect")—was reserved for the elite. AI now makes this highly effective, personalized educational model scalable and accessible to all.

Traditional education is IQ-coded. By using AI tutors that require mastery of concepts before advancing, learning becomes a function of effort, not innate intelligence. This model allows any student, regardless of their starting point, to achieve 100% proficiency by systematically filling their knowledge gaps.

AI makes cheating easier, undermining grades as a motivator. More importantly, it enables continuous, nuanced assessment that renders one-off standardized tests obsolete. This forces a necessary shift from a grade-driven to a learning-driven education system.

While cheating is a concern, a more insidious danger is students using AI to bypass deep cognitive engagement. They can produce correct answers without retaining knowledge, creating a cumulative learning deficit that is difficult to detect and remedy.

The traditional school year allocates hundreds of hours to each subject. Data from Alpha School shows that with a mastery-based AI tutor, students can master an entire K-8 grade-level curriculum in only 20-30 hours. This 10x improvement highlights the massive inefficiency of the teacher-led classroom model.

National tests in Sweden revealed human evaluators for oral exams were shockingly inconsistent, sometimes performing worse than random chance. While AI grading has its own biases, they can be identified and systematically adjusted, unlike hidden human subjectivity.

Generative AI's appeal highlights a systemic issue in education. When grades—impacting financial aid and job prospects—are tied solely to finished products, students rationally use tools that shortcut the learning process to achieve the desired outcome under immense pressure from other life stressors.