Today’s post continues the Mistakes of Mainstream Management (MMM) series after a long break and explores how Silicon Valley executives and managers overindex on specialists over generalists and not only fail to develop their people to their fullest potential but also lose tremendous productivity gains due to handoffs and territorialism.
I’ve observed this phenomenon not only in software development teams but also cybersecurity organizations. Don’t mistake me - specialists are needed, but my point is that we have gone to the extreme, completely ignoring the human potential to learn and develop.
Today’s post is also a dedication to Charlie Munger, who passed away Thanksgiving week, two years ago (Nov 28, 2023). Munger took a multidisciplinary approach when it came to understanding businesses outside in (for investment purposes).
Similarly, my blogposts take a multidisciplinary approach to understanding businesses, as an insider and provide leaders with a fundamentally different approach to view the corporation in order to dissolve their problems and deliver effective outcomes. Let’s dive in…
Table of Contents
The “Why” from Feynman
I'll start with a quote from the physicist Richard Feynman that got me thinking at a very early age about the importance of multidisciplinary/interdisciplinary learning.
If our small minds, for some convenience, divide this universe, into parts - physics, biology, geology, astronomy, psychology, and so on - remember that nature does not know it!
Then I stumbled upon Mungers’ works who put forward his fundamental organizing ethos:
1. You must both rank and use disciplines in order of fundamentalness.
2. You must, like it or not, master to tested fluency and routinely use the truly essential parts of all four constituents of the fundamental four-discipline combination (math, physics, chemistry, and engineering), with particularly intense attention given to disciplines more fundamental than your own.
3. You may never practice either cross-disciplinary absorption without attribution or departure from a principle of economy that forbids explaining in any other way anything readily explainable from more fundamental material in your own or any other discipline.
4. But when the step 3 approach doesn’t produce much new and useful insight, you should hypothesize and test to establishment new principles, ordinarily by using methods similar to those that created successful old principles. But you may not use any new principle inconsistent with an old one unless you can now prove that the old principle is not true.
Here is the best part: You don't have to learn for several years, be a "know it all" or be the "expert" in all fields to improve your effectiveness. Most fields only have a few essential principles that you need to integrate into your thinking process - even incorporating simple insights from just one other field can significantly improve your effectiveness as a leader.
The quickest return on investment is not doing new things with this approach, but avoiding the errors that you are making now, which in itself delivers profound long-term benefits.
The Downfall of Nokia
Most of executives and practitioners in Tech. corporation and Silicon Valley have their training in STEM fields. They always go for the “logical” sounding, “big data” backed decisions. But, when new ideas start, they start as anecdotes - there is no data. They completely forget that they need to pay attention to human psychology at play - not just internally within the corporation (socio-technical system), but more importantly the psychology of your (potential) customers.
Many counterintuitive 'psychological' ideas are totally worth testing in complex systems. By sticking solely to "logical", "rational", or "data-driven" or "quantified" ideas and decision making, your corporation may go extinct one day in the future.
This real-life story comes from Tricia Wang who recounts the downfall of Nokia (when Apple stepped into the smart phone business) due to its "big-data" driven decision making and quantification bias. Nokia completely missed the early signals on how much the Chinese (one of its largest markets) desired the smart phone - but, by living with them, Tricia observed how low-income people were saving up to 50% of their salaries to buy one. That deep understanding of human nature, even if it was a small sample size, is very important.
Tricia got it - but, Nokia completely missed the insight that even the poorest in China would want a smartphone and that they would do almost anything to get their hands on one. This is what happens when you miss early signals, which'll obviously appear weak when quantified - they suffered a rapid destruction of market share:
It is comparable to how the Titanic missed its sole data point that really mattered.
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