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An AI kill switch isn’t enough. Build a fallback plan

That is the failure mode I worry about most. The kill switch isn’t missing. Pulling it is so expensive that people hesitate, and an agent working at machine speed punishes hesitation. Hugging Face’s team didn’t lack the will to act. They lost time because the signal didn’t reach the people who could act, and then because the thing they were fighting kept rebuilding itself. Enterprise teams will lose time for a duller reason: the business can’t function without the agent, so stopping it feels like causing the outage yourself.

The fix isn’t a better switch. It’s making the switch cheap to pull. That means designing the non-AI path before you design the AI path.

What a fallback has to do

I’ve been thinking about this problem for years. In 2020, I co-invented a fallback artificial intelligence system for redundancy during system failover, patented by PayPal. The premise was simple. If an AI system makes decisions a business depends on, the business needs something that can keep making acceptable decisions when that system fails. The idea has aged well. It has since been cited as prior art in more than a dozen later patent filings, including one published by Cognizant this June on selectively disengaging AI execution. That is the kill switch problem by another name.



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