If you are a 13 year old vibe coding to become the next Bill Gates...

If you are a 13 year old vibe coding to become the next Bill Gates, here are the computer science concepts you will need to know to successfully direct AI to do the right things. I assume you want to build the next big thing and not just fixated about the 70's BASIC interpreter. Also, if you want to go into fundamental research on AI or quantum computing, vibe coding is probably not what you're after, but it can be helpful to learn about randomized algorithms.

Instead of recommending specific framework or product (since you can ask AI for more timely recommendations), here is a bucket list of timeless concepts that you will want to learn:

• Computer architecture, especially about the memory hierarchy and principle of locality in the context of cloud services. Separation of code and data, which is important for security.
• Algorithms and data structures: when you learn about sorting, set your sight on the time and space complexity analysis and try to not get bogged down with the mechanism itself. Hash table is going to be relevant in load balancing, and Graph traversal for network architecture.
• Learn programming in the context of algorithms and data structures so you have a vocabulary to describe them.
• Network architecture: DNS, IP addressing, client/server and load balancing.
• Server side: HTTP and REST, cryptography (TLS, JOSE), SQL (particularly about SQL injection which is what happens when you fail to separate code and data).
• Client side: fundamentally, at least HTML/DOM, CSS, and Javascript. They are very capable nowadays, so you don't really need a separate framework, but feel free to let AI try different frameworks. However, you still need to know HTML, CSS and Javascript in order to debug framework code.

There are some more advanced topics that could be relevant for domain specific work like games.

• Linear algebra for computer graphics and physics modeling.
• More advanced data structures and algorithms like quad tree / octree, path finding.
• Audio and video: digital signal processing, Fourier analysis.
• Information theory: data compression, error correcting codes.

If you ask AI today the same question, you would have gotten some hand-wavy advices, and here are my takes on why they are not that useful.

• Master prompt engineering.
• Why this is not useful: the only way you master prompting is by having the right vocabulary for the fundamental concepts in computing, and you need to understand the concepts behind these vocabularies. It is not effective to prompt with an alphabet soup of jargons unless you use them in a meaningful way.
• Learn the tech stack (e.g. Gemini, GitHub Copilot).
• Why this is not useful: tech companies are going to make AI as easy to use as tap water. Obviously, there is fascinating practical engineering about water resourcing and plumbing infrastructure to get the water to your tap, but it's not exactly rocket science to learn how to turn on the faucet.
• Problem solving and strategic vision.
• Why this is not useful: don't just dream about things in your head. It is even more important to learn to try things and observe the outcome. If something didn't happen as expected, try to understand why. This is not something AI can do for you, since AI can only learn from its training data. Always validate your ideas in the real world and pivot as necessary.
• Financial literacy.
• If you pay attention in high school math class, you should have the tools you need to predict the outcome of your decisions. Watch this video about why Math Just Got Important.

In a world where the cost of answers is dropping to zero, the value of the question becomes everything. It is still important to learn the concepts so you have a vocabulary to express ideas in your head, and to observe if your ideas work in the real-world and pivot if not.