Ilya Sutskever: “If you learn all of these, you’ll know 90% of what matters”

Authenticity and Possible Product Tie‑in

• Multiple commenters question whether the list is genuinely from the cited researcher or just “someone’s bookmarks.”
• Some evidence is cited (interviews, social posts, an ex-employee’s onboarding notes), but others note there is no direct, explicit confirmation that this exact list is authentic.
• A few see it as subtle marketing for a VC‑backed browser; others push back, saying the content looks legitimate and useful.
• Overall status: plausible but unverified; several people explicitly flag that it may not be the actual list.

Scope and Content of the List

• Despite being described as ~30 “papers,” it includes a full CNN course, a ~500‑page Kolmogorov complexity book, and other long texts.
• Core topics include classic deep learning, RNNs/LSTMs, attention/transformers, and some foundational theory (e.g., Kolmogorov complexity).
• One commenter notes a specific chapter range in the Kolmogorov book as especially recommended.

Relevance, Coverage, and Obsolescence

• Some argue the list omits major modern areas: reinforcement learning, diffusion models, graph neural networks, low‑bit networks, and LLM‑era engineering (in‑context learning, RAG, tools, multimodality).
• The claim that this covers “90% of what matters today” is viewed as bold and “very opinionated.”
• Several note the list is years old and may be dated relative to current LLM practice.
• Others suggest the missing topics might reasonably be the remaining “10%.”

Effort Required and Intended Audience

• Commenters stress that “reading” ≠ “learning”: true understanding would take substantial time.
• Estimates range from a year of full‑time effort to several years part‑time, depending on prior math/CS background.
• For someone with no relevant background, some suggest 5+ years of full‑time study to reach the level assumed.
• Many emphasize that the list was reportedly tailored for a highly experienced engineer, not for beginners.

Learning Strategies, Constraints, and Tools

• Discussion on prerequisites: calculus, linear algebra, statistics, algorithms, and some learning theory are recommended.
• Strategies: follow bibliographies forward and backward, look for survey papers, build a “narrative arc” across seminal works.
• Several stress having a concrete project or goal; otherwise the material is likely to be forgotten and used only for “armchair” commentary.
• Some suggest using modern language models as tutors: read, then ask questions when stuck.

Time, Life Responsibilities, and Trade‑offs

• A long subthread debates the feasibility of “locking yourself in a hotel for a week” to study.
• One side argues that most professionals could carve out such time if they truly prioritize it; others counter that family, health, and financial constraints make this unrealistic for many.
• There is reflection on prioritization, ambition, and the tension between career advancement and other life responsibilities.

Practical Handling and Community Resources

• Commenters share backups and alternative mirrors (simple HTML lists, wget one‑liners), and note the original site sometimes behaves oddly.
• One person printed the combined PDFs as a spiral‑bound volume (~360 pages, including one extra paper).
• Another created a public reference manager group and reports basic stats: items span from the early 1990s to 2020, and include a mix of papers, preprints, a course, a dissertation, a book, and blog posts.
• Some ask how to filter out AI/LLM content from the site entirely; suggestions include browser extensions and ML‑based classifiers, with the aside that simple regex filters might cover most cases.