When a useful metric like "average handling time" becomes a performance target, employees game the system. Reps may hang up on customers to meet quotas, destroying the metric's ability to reflect actual customer satisfaction.

When hitting a target is the only path to reward, truth becomes the first casualty. Individuals feel pressure to fabricate data, cherry-pick metrics, and hide negative findings to achieve their goals. The system begins to actively reward dishonesty and punish transparency.

According to Goodhart's Law, when a measure becomes a target, it ceases to be a good measure. If you incentivize employees on AI-driven metrics like 'emails sent,' they will optimize for the number, not quality, corrupting the data and giving false signals of productivity.

Setting rigid targets incentivizes employees to present favorable numbers, even subconsciously. This "performance theater" discourages them from investigating negative results, which are often the source of valuable learning. The muscle for detective work atrophies, and real problems remain hidden beneath good-looking metrics.

Charles Goodhart's Law states that when a metric becomes a target, its value as an indicator is destroyed because people will manipulate it. For example, support teams might merge tickets to artificially lower resolution times, hitting their target without actually improving service.

Alan Chang argues that incentivizing metrics can have negative second-order effects. For example, a recruiter bonused on 'hires per month' may be motivated to convince hiring managers to lower the talent bar just to hit their target, which is detrimental to the company's long-term goals.

When teams are singularly focused on hitting a number (e.g., engagement, account openings), they may rationalize unethical methods, as seen with Facebook's platform issues and Wells Fargo's fraudulent accounts. The relentless pursuit of a metric can justify evil outcomes.

When complex entities like universities are judged by simplified rankings (e.g., U.S. News), they learn to manipulate the specific inputs to the ranking formula. This optimizes their score without necessarily making them better institutions, substituting genuine improvement for the appearance of it.

A Soviet nail factory, first incentivized by the number of nails, produced millions of uselessly tiny nails. When the incentive changed to total weight, they produced uselessly giant nails. This is a classic example of Goodhart's Law: when a measure becomes a target, it ceases to be a good measure.