The Overconfidence Epidemic, feat. AI
Originally published on LinkedIn on April 23, 2026. Cross-posting here for readers who prefer to receive my writing in this format.
Earlier this week I had the privilege of joining some pretty incredible leaders at Chief to discuss where AI truly creates leverage and how leadership teams are navigating AI adoption today. The discussion covered executive decision-making, organizational readiness, and a handful of surgical tactics each of us has successfully tried.
There were some very smart perspectives shared on one concept I have been elaborating on recently. It is what I'd call an epidemic of overconfidence - not in AI itself, but in our mastery of it.
The New York Times recently published a piece by Savannah Sobrevilla about "toxic confidence," the cultural shift from humble brags and impostor syndrome to unshakable self-assurance and self-appointed expertise. The under-qualified declare themselves authorities, and I think we are all seeing the line between conviction and performance getting thinner by the day.
This is showing up in the AI space in a way that isn't just cultural and has a tangible operational cost.
People confidently claim decades of expertise in a use case that has been broadly consumer-accessible for only over three years (OpenAI launched ChatGPT in late 2022). LinkedIn has become a place where, increasingly, you can't tell if there's a person thinking behind the post or if it's AI writing for AI to read. I am not against the use of AI to help refine the delivery of ideas, though, and this very post went through many iterations supported by Claude. The issue is not that. It is that everyone is a GenAI strategist now, and everyone has cracked the framework.
Research is starting to show that the perception that AI has made us more productive is not always accurate. A Harvard Business School study with Boston Consulting Group (BCG) found that for tasks outside AI's sweet spot, people using AI were actually 19% less likely to produce correct solutions. And a recent piece in the Harvard Business Review by researchers at University of California, Berkeley, Haas School of Business found that AI didn't reduce work: it intensified it, with employees taking on broader scope and longer hours without being asked. The efficiency created its own pressure cycle.
When you outsource the thinking, the prompting quality drops. When the prompting quality drops, the output quality drops. And then you spend more time iterating than you would have spent thinking and putting pen to paper. The output looks polished (think nice words, clean structure), but the thinking either isn't there or is barely there.
The output is not the writing. The output is a reflection of the thinking. And I feel like we can't always tell the difference anymore.
When that gets lost, and leaders start managing their teams based on what a model told them rather than what their experience tells them, we've unconsciously moved from leveraging a tool to being contained by one. Has anyone here ever had a leader walk into a room and say, "AI told me this is how we should do it, so this is what I want to see"? That moment, when a model's output overrides years of earned judgment, is the one we should be paying attention to.
The ones who can show that there is reasoning behind what they produce, that there is a question behind the answer, are the ones who will be best positioned as this technology matures.
A practical way to try and not be overwhelmed by the speed of feature launches and new tools to implement is to create a RACI framework for your goals and priorities. If you are the accountable person (i.e. the one who is ultimately answerable for the outcome and has final decision authority) on any given topic, that is where your time and mental bandwidth should go. Do use AI to leave no stone unturned. But try to narrow. Context switching is driving a real cost: research from UC Irvine shows it takes over 23 minutes to regain deep focus after an interruption, and high-frequency switching has been linked to burnout independent of workload. Though that is something to discuss at another time.
The humble brag may be dead, but honest thinking doesn't need one.