More hardware won't fix bad engineering

"As an industry, we've gotten good at buying our way out of bad decisions. Need more throughput? Add instances. Tail latencies get spiky? Add a cache in front of the cache. Kelly Sommers nails the root cause: Pattern-driven architectures can be organizationally tidy yet computationally wasteful. The fix isn't another layer-it's fundamentals. If you fund or run a backend team, data structures and algorithms aren't an interview hoop. They are operating leverage for service-level objectives (SLOs) and cost of goods sold (COGS)."

"Start with a simple premise: At scale, small inefficiencies become whole features' worth of cost and user pain. Jeff Dean's well-worn "latency numbers" cheat sheet exists for a reason. A main-memory access is hundreds of times slower than an L1 cache hit; a trip across a data center is orders of magnitude slower again. If your hot paths bounce around memory or the network without regard to locality, the user pays with time, and you pay with dollars."

Pattern-driven architectures often prioritize organizational simplicity over computational efficiency, leading teams to add resources instead of addressing root causes. Data structures and algorithms provide operating leverage for service-level objectives and cost of goods sold by reducing latency and resource consumption. Small inefficiencies at scale produce significant cost and user-impact, because memory and network latencies differ by orders of magnitude. Fan-out services amplify rare latency hiccups into SLA failures through tail latency effects. Engineering culture should treat data-structure choices and algorithmic trade-offs as first-class architectural decisions and measure them with ROI-like rigor. Developers should build systems that respect hardware locality and predictable performance.

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