Why Testing AI for Safety Is Necessary — But Still Not Enough

Entrepreneur

by Neel Somani

February 19, 2026

AI-Generated Deep Dive Summary

Testing AI systems may seem like a reliable way to ensure safety, but relying solely on testing is insufficient to address potential risks. The article highlights that while testing can reveal what has happened in specific cases, it does not guarantee what cannot happen. This limitation is particularly concerning for AI systems, as they are complex and adaptive, making failures unpredictable even with extensive testing. For example, Australia's Robodebt system, which seemed functional during testing, failed due to a single design flaw, leading to significant harm. The article emphasizes the importance of formal methods, which can define what failures are impossible by design. Unlike testing, these methods ensure that certain outcomes cannot occur, regardless of input variations. This approach is crucial for managing tail risks—those rare but high-impact events that leaders must prioritize. AI safety is not just an engineering challenge but a leadership decision, requiring proactive strategies to prevent failures rather than merely identifying them after the fact. For startups, this matters significantly. Deploying unsafe AI systems can lead to reputational damage, legal consequences, and loss of trust. Startups often operate in competitive environments where a single failure could have long-lasting repercussions. By adopting formal methods alongside testing, they can better ensure reliability and mitigate risks, ultimately fostering innovation while maintaining accountability. In conclusion, while testing is an essential part of AI safety, it cannot be the sole solution. Startups must integrate formal methods and prioritize leadership decisions to manage risks effectively. This balanced approach ensures that AI systems are not only tested but also designed to prevent failures from occurring in the first place.

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Originally published on Entrepreneur on 2/19/2026