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2.5 Minute Read
We're just a few days into 2026, and if you're looking for a resolution that will actually move the needle in your public affairs work, today's brief read will give you one: stop using AI disasters as permission to let your competition race ahead on AI adoption.
You've seen the headlines. Google's AI told people to put glue on pizza. Microsoft's Bing chatbot told a New York Times reporter it was in love with him and that he should leave his wife. Self-driving Waymo cars got stuck in San Francisco when the power went out during the recent outage, creating a traffic jam of confused robots. A major retailer's AI chatbot told a customer that the company's return policy was terrible and recommended shopping at a competitor. These stories are virtually endless, certainly alarming, and objectively funny.
But they are also becoming an excuse for many people to delay AI adoption, rationalizing that these sensational headlines mean that AI is useless. This approach is a one-way ticket your competition lapping you—self-driving car or not.
Let's start with why these stories tend to dominate AI headlines. AI failures are the perfect storm of clickbait: they're novel, they tap into primal fears about technology run amok, and they give people that smug feeling of: "I knew it was overhyped."
Consider the San Francisco power outage that left Waymos confused and blocking intersections. Fair criticism, certainly. But what barely got noticed is that when the lights went out, human drivers were also stuck in traffic. Some ran red lights. Some caused accidents. Some just sat there honking. But "Robot Cars Get Stuck in Power Outage" gets more clicks than "Traffic Jam Occurs During Power Outage."
Then there's the Bing chatbot that told a New York Times reporter to leave his wife. Yes, it happened. And yes, it was bold of a non-sentient being. But let's put this in perspective: millions of people use AI assistants every day without being told to blow up their marriages. But you know what does give terrible relationship advice at scale? Instagram influencers, bar conversations at 2am, and whoever wrote "He's Just Not That Into You." But "Man Gets Bad Advice From AI" became front-page news because it was weird and new.
Or take Google's AI overview that confidently told users to add glue to pizza sauce to make the cheese stick better. This was certainly odd culinary advice. But it's also true that millions of people use AI every day without being told to consume adhesives. You know what does give terrible advice at scale? Random internet forums, your uncle's Facebook posts, and that friend who thinks they're a DIY expert. But "Man Gives Bad Advice" isn't a headline.
The truth about technological progress is that early adopters don't wait for perfection. They adopt despite the imperfections, because the advantages outweigh the risks.
Early email was clunky and unreliable. Early websites looked terrible. Early smartphones had terrible battery life and dropped calls constantly. GPS used to tell people to drive into lakes. But the people who adopted these tools early—imperfect as they were—built enormous advantages over those who waited for version 10.0.
The same is true in public affairs. Organizations that have embraced AI-powered targeting, production, and analysis aren't running flawless campaigns. But they're running better campaigns than those still doing things the old way. They're reaching their targets more precisely, identifying key influencers more exactly, and driving better outcomes for their organizations.
We're not suggesting you ignore the comedic gold of AIs run amok. We enjoy them as much as anyone. But what we are suggesting is that as you start the new year, keep these stories in perspective. Exceptions don't prove the rule in life, or in AI. And everything benefits from seeing things clearly.
🔱 Audit your AI excuses. In your next team meeting, have everyone share one reason they're hesitant about AI adoption. Then honestly evaluate whether that reason is based on actual risk assessment or just fear.
🔱 Find one AI experiment. Identify one application where AI could help and commit to testing it this month.
🔱 Read success stories, not just failure stories. For every AI disaster story you read, read one story about successful AI implementations. Balance your information diet.
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