"As someone who spends a lot of time writing x86_64 Assembly and optimizing pure-JAX code for TPU clusters, this recent obsession with LLM-generated 'Lines of Code' metrics feels like a massive step backwards. In High-Performance Computing (and especially things like quantum simulation, which I work on), the entire goal is reducing complexity and overhead. The magic of frameworks like JAX/XLA isn't how many lines of code you write, but how elegantly a few purely functional lines can compile down to highly parallelized hardware instructions. If an LLM writes 100,000 lines of boilerplate for a project, someone eventually has to maintain, debug, and pay for the compute to execute that bloat. The real value of AI in engineering shouldn't be churning out a million lines of CRUD per month; it should be helping us build better differentiable systems, grokking complex mathematical landscapes, or spotting inefficiencies in low-level execution. We spent decades learning that Goodhart's Law applies heavily to software engineering (more code != better software). It’s strange seeing leadership forget that just because the code is now generated by an agent."