Summary of "The future of computer science"

High-level predictions

“AI slop” — superficial AI features added to products without real value — will be filtered out. Companies that merely tack AI onto products will fail or pivot to meaningful uses.
Employers will expect demonstrable, useful outcomes from AI use, not just AI-generated prototypes. The ability to leverage AI to ship real, usable products will become a baseline expectation.

Companies and candidates will be judged by real outcomes, not gimmicks.

Generalist developers are most at risk because AI can handle broad tasks quickly. The durable moat is specialization: deep domain expertise where human judgment, debugging, benchmarking, low-level design, or nuanced UX/design are required.
Schools often produce breadth but little depth. Use AI to skip irrelevant basics and spend the saved time developing deep skills and real experience.
Evidence of competency should be practical and verifiable:
- Reviewed/merged pull requests
- Projects with real users (even a small group)
- Work that stands up to code review Avoid relying on flashy or AI-generated demos that can’t be independently verified.

Data structures & algorithms interviews won’t disappear — they test thinking and communication under pressure, not just memorized answers.
Expect more practical, collaborative interviews (pair-programming or building with an engineer) from small-to-mid companies.
Prepare for both kinds of assessments:
1. Algorithmic problem solving (e.g., LeetCode-style practice)
2. Hands-on build tasks and collaborative work

Actionable prep tips:

Complete “LeetCode 150” early.
Practice weekly build exercises: start from an AI prompt but then close off external help to expose gaps.
Be ready with at least two specific stories for each resume bullet.
Rehearse the STAR method (Situation, Task, Action, Result).

Frontend: implement a Wordle-like game in React (or equivalent) to show core competence.
Backend: build a basic API server from scratch.
Systems: implement a simple shell or file explorer to understand program runtime and systems concepts.
Weekly self-tests: ask AI for a small project that demonstrates baseline competency, then build it solo in your editor to surface knowledge gaps.

As AI automates boring or tedious learning tasks, the “fun” parts of CS (hackathons, club projects, collaborative builds) become more important and engaging.
Find collaborators via:
- University clubs
- Hackathons
- r/programmingbuddies subreddit
- In-person study groups

Brilliant (sponsor): interactive problem-based learning platform for math, CS, and programming that emphasizes active problem solving over passive videos.
- Recommended courses: Algorithmic Thinking and Thinking in Python (authors include contributors from MIT/Harvard/Stanford).
- Claimed benefits: concepts stick better because users solve problems interactively.
- Offers noted: 30-day free trial and a discount on annual premium (via the creator’s link).

Primary speaker: a graduating computer science student / video creator (unnamed in subtitles), providing personal analysis and advice.
Sponsor/source: Brilliant (interactive learning platform; course authors from MIT/Harvard/Stanford).
Other referenced sources: LeetCode (platform), r/programmingbuddies (community).