This commentary critically evaluates the evolving landscape of software development influenced by artificial intelligence. The author posits that the prevailing sentiment suggests AI can generate 100% of code, leading to developer satisfaction, yet highlights significant unaddressed challenges, particularly concerning validation and quality assurance.

A core contention is the often-overlooked aspect of thorough testing and end-user satisfaction. While AI demonstrably accelerates the initial coding and prototyping phases, the expectation of a commensurate 100x increase in release velocity is deemed unrealistic. The author argues that if traditional software development effort is conceptualized as a sum of development ($D$) and testing ($T$) efforts, where $D \approx T$, then $Release\ Time \propto D + T$. If AI reduces $D$ to nearly zero, the overall release speed improvement is at best approximately $2x$, as $T$ remains a significant component ($Release\ Time \propto 0 + T$). This implies that the reduction in coding effort does not eliminate the necessity for rigorous testing.

The current industry culture is described as shifting the burden of quality assurance onto the end-users. Developers, having potentially used AI for testing and a perfunctory manual check, are perceived to adopt a "release now, fix later" mentality, often expecting users to report bugs which can then be addressed by AI-driven debugging. While this might be acceptable in collaborative open-source environments, the author finds it problematic for closed-source commercial solutions, citing an unnamed "CC" product line as an example of consistently buggy releases due to rushed cycles. This trend, it is argued, erodes the significance of semantic versioning and renders the expectation of long-term support (LTS) increasingly unrealistic.

The piece further delineates the trade-offs between two development paradigms in the AI era:

1. Rapid AI-Driven Development: This approach prioritizes speed, allowing for quick scaffolding and proof-of-concept (PoC) validation of core ideas. However, it often commences without adequately solidified conceptualization, leading to potential loss of direction, wasted effort on misaligned features, and a high probability of having to refactor or discard initial implementations.
2. Meticulous Upfront Design followed by AI-Assisted Implementation: This method involves a significant initial investment in detailed architectural design and planning. While the initiation phase is prolonged, once development commences, AI's assistance can then be leveraged to achieve a higher quality outcome with greater efficiency, as the AI operates within a clearly defined and well-structured framework.

A critical observation is made regarding the nature of AI's assistance: "Vibe coding" (implying AI-driven code generation) does not inherently enhance the developer's foundational knowledge or expertise. Instead, it serves to entirely replace the manual effort of implementation. The ultimate responsibility for determining the correctness and alignment of the implemented solution still rests with the human who initiated the task. The author concludes by noting a profound challenge: it is increasingly rare, if not impossible, for any single human expert globally to possess the full-stack knowledge required to comprehensively judge the correctness of AI-generated implementations across all layers. The implicit statement is that while delegating everything to AI is an option, ultimate accountability for the outcomes remains with the human stakeholder.