Gray Smoke

  GenAI's shift from deterministic to non-deterministic systems breaks traditional development approaches. Having worked on scaling GenAI at big-tech, I've learned that success requires working backwards from desired outcomes—starting with precise problem boundaries and data quality requirements. These first two steps determine whether your GenAI product scales or remains an impressive demo. One of the biggest differences between existing computer systems and new GenAI systems is indeterminism—and it's breaking everything we thought we knew about building scalable products. For decades, computers excelled at doing the same task thousands of times with identical results. They weren't necessarily smarter than humans, but they had almost no variance or bias. GenAI deliberately introduces variance through "temperature" settings to enable creativity. Zero temperature produces identical, factual outputs. Higher temperature brings creativity and individuality. Th...