Python has become the undisputed lingua franca of artificial intelligence, powering everything from LLMs to autonomous systems. However, its meteoric rise mirrors the dominance of COBOL in the 20th century, setting the stage for a massive wave of systemic technical debt that organizations are currently ignoring.
The Fragility of “Glue Code”
Python’s greatest strength—its low barrier to entry—is also its primary liability. Much of today’s AI infrastructure is built on “glue code,” a fragile web of dependencies and wrappers around low-level C++ libraries. This creates a maintenance nightmare for future engineers who must untangle complex version conflicts and undocumented abstractions once the original developers have moved on.
Performance and Portability Bottlenecks
As AI models scale, Python’s inherent performance limitations become glaring. To achieve efficiency, organizations are forced into expensive infrastructure workarounds or “refactoring hell” to bypass the Global Interpreter Lock (GIL). We are essentially building the high-speed future of computing on a foundation that wasn’t designed for modern hardware concurrency.
Summary: A Ticking Time Bomb
If we do not address these structural weaknesses now, today’s groundbreaking AI innovations will become tomorrow’s legacy burden. Python is becoming the new COBOL: essential, ubiquitous, and increasingly expensive to maintain. We must prioritize code sustainability before the debt comes due.