This is not a particularly good take and is indicative of a fundamental misunderstanding of what a top-tier technical college education is suppose to offer. Preparing to understand modern AI as a Harvard or Stanford undergrad is not about learning "prompt engineering", vibe coding, or building Slop Domain-Specific Wrapper Agent #1000, all of which can be picked up in a few days if not hours. To the contrary, the best way for a smart 18-22 year-old to understand AI is to develop a very solid intuition for undergraduate and graduate level probability, linear algebra, and classical ML. If you actually know how foundational RL topics like Q-learning work, you are 95% of the way there, and if you can't even learn that from Harvard or Stanford then this is probably a skill issue on your end. In @boazbaraktcs's excellent ML theory seminar in 2021, I don't think I wrote more than 200 lines of code cumulatively in the entire semester yet I learned an immense amount and credit that class for sparking my interest in modern AI. A year ago I couldn't coherently tell you what a transformer was, but it doesn't matter, because when you develop proper quantitative foundations in college you can figure it out in a couple of weeks. None of this stuff is really that complicated, people just like to pretend that it is.