“Want to know the real secret to making money in the AI era?
Forget what you think you know about AI careers. The truth? It’s not about coding—It’s about mastering 3 subjects that will blow your mind! Are you ready to level up, or are you staying stuck?”
I’ve written before about the journey to medical school in the age of AI. In the comments, people were asking which field of study they should choose in the world of AI.
AI Secrets Unveiled: Master These 3 Skills to Dominate the Future (feat. Linear Algebra, Machine Learning, Deep Learning, ChatGPT, OpenAI, Grok, Gemini, field of study, college, university)
If you want to make money in AI-related jobs, there are three key areas you should focus on:
- Probability and Statistics: Probability distributions, statistical inference, hypothesis testing, regression analysis—don’t worry, it’s all doable!
- Linear Algebra and Calculus: Vectors, matrices, operations, multivariable calculus… it sounds heavy, but it’s your new best friend.
- Information Theory: Entropy, information quantity, channel capacity… not as complicated as it sounds, trust me.
If I had to pick just one, the most important of these is definitely Linear Algebra. Linear algebra is the backbone for AI development—vectors, matrices, and all that jazz.
Now, let’s talk about channel capacity in information theory. This concept explains how much information can be transmitted without errors in a system. You can think of it like a road. The bigger the road, the more cars can fit, right? It’s the same with information—the wider the bandwidth, the more data can pass through.
But, just like traffic speed depends on things like traffic lights or jams, noise affects channel capacity too. Noise messes with data accuracy and messes up a model’s learning ability. The goal? Separate the important information from the noise and make sure you’re learning accurately. The more information you have, the more room there is for errors. Reducing those errors is the key to optimizing AI.
Multivariable calculus, including partial derivatives, is used to optimize AI models that deal with multidimensional data. Geometric concepts like vectors and spatial coordinates are essential for understanding how AI works with space—think image processing, robotics, and data visualization.
The key concept here is Linear Algebra, which helps minimize information loss. Linear algebra also powers GPU acceleration, and large-scale matrix operations optimized through parallel processing can speed up your learning process. That’s why most machine learning and deep learning operations are built on linear algebra.
In summary, the three main mathematical pillars you need for AI are Linear Algebra, Multivariable Calculus, and Statistics. Probability distributions, statistical inference, and hypothesis testing form the theoretical base for machine learning and AI models that predict uncertain data.
If you’re a beginner, Gilbert Strang’s Introduction to Linear Algebra could be your go-to guide. You can find the author’s lectures on YouTube via MIT OCW.
Honestly, the above might have sounded like a foreign language, right? To put it simply, understanding that math and statistics are becoming increasingly important in AI careers is probably all you really need to know.
The truth is, most of us aren’t working in AI careers. We’re just occasional users of AI. As a user, you don’t need to dive deep into advanced math or statistics.
What’s more important when using AI is your ability to ask good questions and tell when AI is lying. Trust me, anyone who’s used AI will tell you the same. You quickly learn how important your questions are and how AI has a tendency to lie.
If you don’t catch AI’s lies, you’ll end up looking foolish—and the last thing you want is to be laughed at. These days, there are people who just copy-paste answers from ChatGPT and post them. When I see that, I want to ask them, “So what’s your opinion?” At the end of the day, what really matters is your opinion and your skills. AI is just a tool to help you, not a replacement for your brainpower.
Want to Spot Lies and Think for Yourself? It Starts with Simplicity!”
Want to be the person who spots a lie from a mile away? It’s not about being a genius—it’s about reading widely and thinking deeply. Forget chasing complicated answers. The truth often lies in the simplest places. The best part? Once you get it, you’ll be the one asking the right questions while everyone else is stuck in the noise.
Want to outsmart AI or anyone who thinks they can pull one over on you? Focus on building your own thinking. The power is in being able to cut through the chaos with a clear mind and a healthy dose of skepticism. The more you read and reflect, the sharper your instincts become. So, ditch the complexity—start with the basics, and let simplicity lead you to the answers that actually matter.
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