The Atlantic published an investigation into generative AI infrastructure spending, highlighting real concerns about resource efficiency in how companies are building out these systems. The article argues that tech firms may be spending enormous sums on models that aren't optimized for scalability — suggesting engineers have skipped the careful optimization work that made previous tech platforms efficient and cost-effective. There's solid evidence that training and operating large generative models is genuinely expensive and resource-intensive. However, whether this represents an "engineering disaster" is more debatable. Some analyses point to serious scalability and reliability challenges, but experts disagree on how fundamental these problems are and whether they undermine the overall enterprise. The article makes a specific claim about tech firms controlling around 70% of global high-end memory supply to feed current models. This figure is harder to verify and may be overstated. What matters here is the underlying point: current AI infrastructure spending is substantial and somewhat inefficient by design. But whether that's a temporary growing-pain issue or a sign of structurally wasteful spending — the kind that could crater investor confidence — remains genuinely uncertain. The article documents real cost concerns without settling the bigger question of whether the current AI build-out is fundamentally unsustainable.