🌍 Solid Foundations in Times of Artificial Intelligence

Thinking in systems before thinking in AI

We live in a world of systems: natural systems (the universe, ecosystems, climate, living beings), social systems (economics, politics, personal relationships), and productive systems (organizations, business processes, computers, software).

Donella Meadows, in her book Thinking in Systems, argues that to understand, create, transform, and improve existing systems, it is necessary to analyze them based on their most basic elements: their purpose, their relationships, and the feedback loops that balance them. She also notes that the complexity of their environment often makes our understanding of them counterintuitive. Human beings tend to think linearly, while the systems around us behave in complex ways, far from our linearity.

👉 In times of Artificial Intelligence, this perspective becomes highly relevant. Every day we have new opportunities to systematize our environment.

🧩 Fundamental concepts

Before venturing into designing intelligent agents or using advanced models, it is essential to have a clear knowledge base of how systems and computation work, so we can truly maximize their potential. Some key concepts include:

• Determinism vs. probabilism: "Same input = same output? Always?" A system is deterministic if its result is always the same given the same input parameters. Generative AI is a great example of the opposite: a probabilistic system.
• Computational efficiency: not everything that "works" is optimal for a given purpose. Knowing the computational cost of an algorithm allows us to select solutions that scale better in real-world environments. For example, some sorting algorithms—while less efficient in theory—perform better in practice under certain circumstances.
• Resource management: memory, processing time, energy. Systems constantly consume resources, and the way they are managed can drastically change the expected results.
• Synchronous vs. asynchronous: understanding how systems handle multiple processes (parallel or sequential) helps design smoother interactions between humans, machines, and data.
• Scalability: At what cost can I grow? This is an essential characteristic in the design of any system, right from the start.

✍️ The power of writing in the AI era

Beyond algorithms, AI depends on something deeply human: the clarity of instructions.

Structured, concise, and precise writing is the foundation of a good prompt and, in general, of effective interaction with probabilistic intelligent systems.

Just as in programming, where a poorly defined instruction can be the source of errors, in AI contradictory or ambiguous instructions can lead to very poor results.

Writing, then, is not just communication: it is the tool that shapes the behavior of models.

AI does not replace our understanding of systems: it amplifies it. The stronger our foundations, the more strategic our solutions will be.

🚀 Conclusion

Artificial Intelligence offers enormous potential, but its impact is greater when we are conscious of the elements that compose it:

• 📍 A clear purpose.
• 🔄 Systematic thinking.
• ⚙️ Applying computational principles.
• ✍️ Clear and precise writing in the form of instructions.

✨ These are the keys that turn AI into a true strategic ally rather than just a technological trend.