There are video lectures you can use to accompany the book: https://youtube.com/playlist?list=PLE18841CABEA24090
Also, I highly recommend the courses How to Code: Simple Data amd How to Code: Complex Data on edX that are based on HtDP.
That was my journey as a self-taught programmer who at one point realized had to learn the timeless foundations.
And then MIT's 6.005 [1], where you will apply all this with a realistic language (Java, although the concepts carry to any language), and learn how to design, code and test programs that "have no bugs, are easy to understand and ready for change".
And also learn a bit about algorithms, I don't think that one can design and understand properly without. And what is O(2^n) today will still be that in 100 years. MIT's 6.006 is amazing, both professor and TA [2].
I've seen Knuth's TAOCP recommended around here. Don't even consider that, do a course like 6.006 first, the Everest shouldn't be the first mountain you climb. Likewise favour HtDP over SICP at first, I've been there.
If after all this you are still interested in LLMs, I recommend EdX's "Large Language Models: Application through Production" [3]
[1] https://ocw.mit.edu/courses/6-005-software-construction-spri...
[2] https://ocw.mit.edu/courses/6-006-introduction-to-algorithms...
[3] https://www.edx.org/course/large-language-models-application...
I took the course in 1999.
And yes the material is just as relevant today.
Not everyone uses geometry every day, but engineers do. And we all depend on engineers, so the understanding of geometry is a fundamental underpinning of our entire society. The same can be said of computer science. We don’t all program for a living, but the structure and interpretation of computer programs (the literal structure and interpretation, not the book) is becoming an ever larger factor in how our society functions.
I think Sussman's answer is more of an observation of the result of the switch and not an explanation why. MIT is a slave to fundraising, and I suspect the industry trends of programming languages, funding sources, job market, and other marketing and political decisions led to the change over anything actually technical.