In the early 1990s, the brilliant minds at an MIT lab were disappointed. They were trying to add predictive functionality to a robotic arm. The objective was to autonomously catch a small ball tossed in its general direction. But this functionality was still out of reach. Thanks to Moore’s Law, they had expected there would be enough computing power to identify, track and predict the flight path of the ball. Identifying and tracking was working, prediction was not. It was failing to calculate the flight path in real time reliably enough for the arm to intercept the ball.
Looking for a different way to solve the problem.
They reached out to their academic colleagues in a relatively new field that was taking off at all the major universities: cognitive neuroscience. How does the human brain handle this challenge? Computers were just starting to beat the occasional championship chess player, but in this instance, they were too slow to achieve a real-time task that the average three-year-old could perform. Surprised, they learned our brains can’t predict the trajectory either. As a matter of fact, at the speed many balls travel in sports, our visual input devices – a.k.a eyes – provide too few data points per second to perform an accurate calculation anyways. Instead, the brain only makes one binary prediction. Is the ball catchable? If yes, it triggers a routine, and we catch the ball. If no, we don’t even try.
This small insight, led to a big breakthrough.
They stopped trying to predict the exact flight path of the ball. Instead, they predicted with great confidence the areas where the ball was not going. They then used this information to reposition the arm in real-time away from those areas, ultimately being able to arrive in the same location as the ball in time to catch it.
This strategy can be adopted in businesses. Where do you invest today’s returns to secure the future of your company?
Instead of trying to predict the unknowable — where your industry or market is precisely going to be in a decade — You can ask the inverse: “Where is it not going? Or, more simply, what is not at risk of changing in the next 10 years?” and confidently build a strategy around that which is true and stable over time.
Two Examples:
1) In the early 2000’s, while under intense criticism for not returning a profit, Amazon asked a similar question, and leadership concluded:
It’s impossible to imagine a future 10 years from now where a customer comes up and says, I just wish your prices were a little higher, I just wish you’d deliver a little more slowly… I wish you had a smaller selection. — Impossible.
2) The stalwarts of the energy sector have illuminated over and over again, most recently in the spring of 2020, how well you capture the boom is always secondary to being well positioned to survive the next bust. Markets cycles are unlikely to change in the next 10 years. If you are in an industry subject to major swings, such as commodities or many service sectors, asking what’s not going to change keeps present the fact that your growth trajectory will almost always be shaped like a sine waves over the long run. Investing in what’s required to ensure you stay profitable at the bottom of the next cycle is not only prudent but proves reliable.
Take Away
Our cognitive focus naturally attends to things that are changing or have the potential to change. We begin to stop noticing the overwhelming majority of things that remain the same. But when you invest in something that you know is stable for the long term, you can confidently afford to put sustained energy towards achieving it.
One True Question:
What is one thing worth investing in that won’t change in your business over the next 10 years?
Reference: The Nonlinear Systems Laboratory - Massachusetts Institute of Technology | Hawkins, Jeff (2004). On Intelligence (1st ed.). Times Books. ISBN 978-0805074567 | AWS 2012 re:Invent Day 2: Fireside Chat with Jeff Bezos & Werner Vogels
Kommentare