The systemic risk created when multiple business units or competitors rely on the exact same model architecture for critical decision-making.
You think you are diversifying your risk by using the industry standard model, but you are actually building a single point of failure. When everyone uses the same underlying logic for credit scoring or hiring, a single edge case or bias in that model ripples across the entire market simultaneously.
This creates correlated failures that no amount of internal hedging can fix. If the model breaks, your entire sector breaks with it. You are not competing on intelligence, you are competing on who can fail the hardest when the shared algorithm hits a wall.
Three major lenders adopted the same high-performance underwriting model to optimize approval rates. When a sudden shift in consumer debt patterns occurred, all three models rejected the same demographic simultaneously, causing a localized liquidity freeze.
I told my team that using the same predictive engine as our biggest rival was a mistake. We were just mirroring their blind spots instead of finding our own market edge.
We used to have varied, manual screening processes that yielded diverse talent pools. After switching to a single industry-standard AI screening tool, our hiring outcomes became identical to every other firm in the city.
We ignored the warning signs of using a popular open-source routing model. When a global supply chain disruption hit, the model's shared logic forced every company to reroute through the same bottleneck, creating a massive, avoidable gridlock.
