𝗧𝗵𝗲 𝗡𝗲𝘂𝗿𝗮𝗹 𝗕𝗹𝗼𝗮𝘁: 𝗪𝗵𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗔𝗜 𝗥𝗢𝗜

The massive hardware “cathedrals” we recently analyzed—those towering monuments of compute—are now being filled with increasingly complex software architectures. However, as we scale, we are noticing a shift: the neural network is often being used as a “universal solvent” to dissolve engineering problems that actually require better data standards and cleaner logic.

At Ambiente Ingegneria, we develop integrated Machine Learning solutions, from RAG-based assistants to image recognition. Yet, our experience in web development with Python and PostgreSQL has taught us that a neural-first approach can lead to “Neural Bloat”—a state where we throw more parameters at a problem instead of refining the underlying engineering.

Consider the current landscape:
1.