The impact of AI on software production is vast, but it is crucial to recognize that not all software markets will be transformed in the same way. While Chris Loy's analysis of "industrial software" highlights significant changes in software quantity and quality, it falls short by treating software as a uniform entity. Instead, the landscape of software can be divided into at least eight distinct markets, each with unique constraints and demand characteristics. Understanding these variances is essential, as changes in production capabilities due to AI can lead to different outcomes depending on the underlying constraints of each market. For instance, in the realm of enterprise internal tools, AI can help alleviate the bottleneck created by the high costs of developer time, allowing for a backlog of useful internal projects to be addressed. This market is poised for substantial growth as demand is strong and largely unmet. Conversely, in the enterprise SaaS market, while AI may expedite production, the competitive landscape remains fixed regarding customer acquisition and market size, leading to a scenario where rapid iteration intensifies competition without expanding the overall market. Here, quality may suffer as companies rush to release features, emphasizing speed over correctness. Additionally, the rise of personal and hobbyist software illustrates a democratization of software creation, where non-programmers can easily build tools for their own use. The barrier to entry is drastically reduced as AI enables anyone to code, leading to an explosion of vernacular software. This shift mirrors historical changes in creative fields, such as photography, where accessibility transformed the landscape. As software creation becomes more approachable, the quality expectations may become irrelevant, as users prioritize functionality over longevity, highlighting a significant shift in how software is perceived and utilized in everyday life.