Code is often perceived as a mere tool, but it’s more accurately a liability that requires constant maintenance and adaptation. Tech leaders mistake the sheer volume of code generated by AI as a sign of progress, not realizing that it multiplies potential issues. Code doesn't just sit idle; it interacts with a constantly changing environment, leading to errors and failures when assumptions made during its creation no longer hold true. The piece draws a parallel to Paul Mason’s concept from his 2015 book, emphasizing that code is not a self-sustaining machine but a fragile entity that deteriorates over time, necessitating extensive upkeep. The distinction between "writing code" and "software engineering" is critical. Writing code focuses on immediate functionality and performance, while software engineering emphasizes long-term viability and adaptability. The latter involves understanding how a system integrates with other systems, both upstream and downstream. As code ages, it becomes more susceptible to external changes, making it imperative to regularly update and refactor it. The author highlights real-world examples, like the Y2K bug and the upcoming Y2038 problem, to illustrate how software can fail not because of inherent flaws, but due to the relentless passage of time and evolving contexts. One striking example involves location-based services, which have been repurposed over time. A bug in a location app can lead to significant consequences, such as misidentifying a person's location and causing unnecessary confrontations. This highlights the need for constant vigilance and updates. As systems age, they accumulate tech debt, complicating future fixes and adaptations. Old code becomes a tangled mess, requiring layers of new code to address emerging issues, which only exacerbates the problem. The piece makes it clear: without proactive management, code becomes an increasingly burdensome liability rather than a valuable asset.