This conclusion made it easier to write the role guideline, but likely leaves you, our SDEs and SDMs, with an important question about how you should be using AI, and how you should be measuring AI usage in your teams and organizations. We believe that it is best to measure, and be measured by, long-term outcomes. But these outcomes can be time delayed, difficult to measure, and hard to isolate from other trends. In some cases, we have tackled those problems by using input metrics instead, like token usage or adoption of AI tools, or throughput metrics like code changes and reviews. We are all aware of how deeply flawed these metrics are, primarily because they are disconnected from what matters. We know that using these metrics could, if used as a target instead of a measure, drive negative outcomes, increasing costs (tokenmaxxing is at odds with frugality), and increasing complexity (measuring LOC is at odds with invent and simplify). We believe that this community, with your strong ownership and focus on delivering results, will help us avoid these negative outcomes.
