In the field of data science, many people believe that the key to success is to pursue the most advanced and complex AI models to achieve the best predictions. However, this is not entirely true.

It's interesting to note that a lot of data scientists, especially those with an academic background, tend to prioritize model prediction performance above everything else. But when you move from academia to industry, the rules change significantly.

In an industrial setting, we need to consider factors that go beyond just making accurate predictions. Here, factors like maintaining, scaling, and explaining the model's decisions become equally important. It's crucial to keep the model working well, ensure it can handle increasing demands, and understand why it makes the predictions it does.

The consequence of this shift in perspective is quite clear. In most practical situations, the marginal improvement in performance that comes from using a complex model doesn't necessarily outweigh the challenges it brings. Complexity adds difficulties – it's harder to maintain, trickier to scale, and often more mysterious in how it makes decisions. So, what does this mean?

In essence, we must strike a careful balance between predictive power and these practical considerations in an industrial context. Sometimes, and surprisingly, the most practical and effective choice in real-world scenarios is a simple model. This reminds us that in the world of AI, bigger and more complex doesn't always equate to better. It's not just about achieving the highest prediction accuracy; it's about creating AI that functions effectively in the real world, where simplicity often works best.