As artificial intelligence (AI) continues to advance at a rapid pace, it's natural to wonder how well we need to understand the mechanics of an AI program in order to scale it for production. After all, even the simplest of AI systems can exhibit unexpected behavior, which can have significant consequences. So how do we evaluate and deploy AI systems in a way that minimizes the risk of negative outcomes?

Well, one potential solution is to design AI systems with transparency and explainability in mind. By understanding the mechanics of these systems and how they reach their decisions and predictions, we can better predict their behavior and identify potential problems or biases. But as AI systems become more complex and interconnected, it becomes increasingly difficult to understand the inner workings of these systems, which can make this approach difficult to implement.

So what's the trade-off here? On the one hand, traditional programming methods are often more predictable and easier to understand, as they are designed by humans and follow explicit rules. This can make them more suitable for certain tasks where the consequences of unexpected behavior are high. However, traditional programming methods can also be more time-consuming and costly to develop, as they require human input and expertise at every stage of the process. On the other hand, AI systems are often more flexible and adaptable, as they are able to learn and improve their performance over time. This can make them more suitable for tasks that are too complex or dynamic for traditional programming methods. However, AI systems may also be more unpredictable and harder to understand, as they are based on complex algorithms and may exhibit unexpected behavior.

In the future, it's likely that the use of AI will become increasingly prevalent, and organizations will need to carefully consider the trade-offs between traditional programming methods and AI in terms of predictability and cost. This may involve weighing the benefits of flexibility and adaptability against the risks of unpredictability, and deciding which approach is most suitable for a given task. Ultimately, the decision will depend on the specific needs and constraints of the organization and the task at hand.

But it's worth noting that many things in life are already beyond our scope of understanding, yet we still use them safely and effectively. For example, modern large airplanes are complex systems that are difficult for a single person to fully understand, yet we still trust them to transport us safely. Similarly, we may need to consider the use of AI systems that are beyond our complete understanding, as long as appropriate safety measures are in place and the systems are transparent and explainable to some degree.

These are certainly exciting times in the world of AI, and it will be interesting to see how organizations navigate the trade-offs between traditional programming methods and AI as the technology continues to advance.