The Skill You've Never Been Taught: How to Think and Make Better Decisions

The Skill You've Never Been Taught: How to Think and Make Better Decisions

No skill is more valuable and harder to come by than the ability to critically think through problems. Unfortunately, schools don't teach us a method of thinking. It's something that can be learned, but not taught. However, the ability to think critically is essential for making good decisions and creating more time for ourselves.

One common misconception is the idea that multitasking is an efficient way to think and work. However, research shows that the more people multitask, the worse they are at not only other mental abilities but also multitasking itself. Multitasking impairs our ability to think because it prevents us from concentrating on one thing long enough to develop an idea about it. In fact, the best writers write much more slowly than everyone else because they understand the importance of slowing down and concentrating.

By slowing down and concentrating, we give our brains a chance to make associations, draw connections, and arrive at original ideas. This is what thinking is all about. William Deresiewicz, a writer, emphasizes the importance of concentration and patience in his creative process. He says, "It's only by concentrating, sticking to the question, being patient, letting all the parts of my mind come into play, that I arrive at an original idea." This notion of slowing down and focusing applies not only to writing but also to any problem-solving activity.

Interestingly, the busiest people are often the ones who make the worst decisions. This is because they don't allocate enough time to think and understand the problem at hand. Busy people spend a lot of time correcting poor decisions that could have been avoided with proper thinking. Therefore, if we want to think better and make better decisions, it's crucial to schedule dedicated time for thinking and honing our understanding of the problem.

Now, let's shift our focus to predicting machine learning moats. A moat refers to a lasting advantage that allows a business to have excellent returns on invested capital. In the context of machine learning, the moat lies in the interface between scaling laws and products. While software scales with zero marginal costs, machine learning scales with nonlinear emergent behaviors. This means that a truly great business must have a moat that protects its returns on invested capital.

When it comes to machine learning systems, data is the moat. Well-defined and curated training data cannot be easily taken away by an employee leaving or a simple leak. It provides a lasting advantage because it is diverse and not repeated when scaling. Adding new data can lead to new abilities and extremely concentrated usage, giving companies a significant advantage. Companies like Runway and Jasper are crafting moats in specific verticals by being the best-in-class and establishing themselves as brand names.

However, it's important to note that the model itself is not the moat. Models can be easily replaced with fine-tuning or procedural changes. The real moat lies in the dataset, infrastructure, and processes that create structural advantages for the machine learning system. Lensa, for example, may not have a moat because it won by being the first, not by establishing a lasting advantage through its data.

In conclusion, the ability to think critically is a skill that is often overlooked but is essential for making better decisions and creating more time for ourselves. Slowing down, concentrating, and allocating dedicated time for thinking are crucial steps in improving our thinking abilities. When it comes to machine learning, data is the moat that provides lasting advantages and protects excellent returns on invested capital. Understanding the interface between scaling laws and products is key to predicting machine learning moats and identifying the elements that create structural advantages.