How Some of the Brightest Minds Get Things Wrong

Even the Smartest Scientists (and CEOs) Make Blunders. So, what to do?

One of the things I like most about subscribing to Scientific American magazine (SciAm) is that very often they talk about errors that really smart people make. I still have my September 2020 issue which celebrated 175 years of the publication. It contains an article entitled, “Reckoning With Our Mistakes.” The deep historical disrespect of women in science is one the more disheartening facts to read about in the article. Saying things like, “women can never be good at math.”

The current September 2025 issue has a section about 180 degree turns in thinking and knowledge. This is when what people believed and touted as facts and truth turned about to be completely wrong and the opposite being the actual truth of the matter. A prominent example in the section is debunking the belief that the expansion of the Universe was slowing down and might eventually reverse itself. The truth turns out to be that the Universe is expanding at an accelerating rate (getting bigger faster!).

In my personal life I have also been completely wrong about somethings: breast feeding babies was a recent one. I was blessed to have my first grandchild and immediately put my foot in my mouth. My view was that under all circumstances an infant must be breast fed for at least 6 months. Now true enough; many pediatricians recommend this, but it is not a drop-dead requirement that if not done the world comes to an end! We live in an age of all manner of baby formulas and other nutritional options that can lead to perfectly healthy children. My mistake captured both of the key ways which the folks at SciAm pointed to as leading to errors: incomplete information and analysis, and political bias. Here is how Charles Mann, the writer of the lead story, explains the two ways things go really wrong:

“The first is when research disciplines are young thinly populated and just developing instruments of sufficient power to test their initial beliefs.” The second, possibly more consequential situation is when scientific findings lead to so much public interests that they become the concern of political authorities.”

Mann also takes note of a great book, Thomas Kuhn’s The Structure of Scientific Revolutions. He points out that,

In Kuhn’s view, there are periods of “normal science” in which researchers have a shared view consensus—a paradigm, in his terms—about how nature works. Then a new theory or experiment shatters the paradigm. Believers in the old paradigm resist furiously, but eventually the old ideas are rejected. From the reversal emerges a new paradigm, which will be thrown over in turn.

As I thought about the observations in SciAm, it brought me back to one of the reasons I wrote my book, The Art of Quantum Planning.” I say in my introduction that. “I have seen a tendency to get stuck in old patterns, unhealthy group think, and narrow safe zones.” Those issues occurred as I led corporate teams in scenario analyses. This echoes Mann’s points about inadequate analysis, sticking to old paradigms, and political bias/influence. The objective of my book was to pull some core ideas from science (and physics) and to use them as metaphors to open up a more learning-oriented way of planning for organizations. My intention was to reduce the likelihood of being completely wrong in planning work, especially if good scenario thinking (multiple views of the future) could be incorporated. The seven core ideas in the book could be used as tools to challenge thinking.

In a lead chapter on false dualities, I use the particle/wave nature at the quantum level to move us away from us-versus-them thinking. I am seeing a lot of this in the techno-optimist versus AI-doomsayers debate. We are far too early in the development AI, and still in learning mode on its capabilities and use cases. Unfortunately, politics has entered the development of AI, and we have to now wait on the errors to come. Recent developments related to government ownership of Intel Corporation and taking revenue from Nvidia’s chip sales to China are worth pondering for long term effects (see Bloomberg on Socialist policies in the United States).

Where I stand today is in the same place that SciAm seems to be standing where people are still making some of the same errors. My writing in past posts here point some of these out. See:

What is different now is that we are having a revolution in what Mann says are our “instruments of sufficient power to test our beliefs.” Clearly as artificial intelligence tools get more powerful and widespread in their use, we are on the precipice of a leap forward in our instruments. Add in quantum computing, deep sea exploration, the James Webb telescope, and bio- genetic engineering, and my head starts spinning. I am likely leaving some other areas of innovation out. What might the future hold in terms of complete reversals in our thinking? How can we prepare for what might be coming?

Here are some things I will do (and would recommend to people leading organizations):

1. Become a lot more careful in believing what I think I know. There is a good chance I could be out of date with recent studies and research. Be more inquisitive about the depth of evidence.

2. Ask more questions, especially when I have some attachment to what I am thinking (also known as bias). Listen more deeply to ideas that conflict with my own. Identify and release some of my biases.

3. Look for more information about emerging ideas and discoveries. Think about how they might have systemic effects (even in what might appear to be unrelated areas). Thank you SciAm for covering some of this!

4. Talk to and listen to people from varied backgrounds and experiences. Travel helps here as my recent trip to Greece can attest.

5. Continue to play around with different scenarios and push them harder into areas of disbelief. (What might I believe that may be possible that is not currently?) Create, share and discuss scenarios with a diverse group.