Life Long Learning
Some of my favorite parts below.
There is a kind of Moore’s law at work, in which the capacities of these information tools are doubling every couple of years or less. You can’t “fire and hire” your way to success if you have to turn over people every 9 to 18 months to bring in new skills.
Soft skills > hard results is a powerful articulation, and one that ties into what Christian Madsbjerg writes about in Sensemaking. Humans filling in the important spaces between the machines.
When we talk about learning, the emphasis is often on “hard” skills, such as coding, analytics, and data science. While these skills will be critical, they are only part of the story.
The dynamics we described at the outset, in which information-rich tools become ubiquitous and people are a differentiator, paradoxically, increase the importance of such “soft” attributes as collaboration, empathy, and meaning making.
I spoke about meaning making in my TEDx talk, particularly in promoting the function of a system, as opposed to the data it captured. In a way going full circle to branding, and being driven by a purpose. When technology is ubiquitous and inexpensive, what makes someone pick one product over another? Meaning, mission, purpose.
You, and your people, can all be meaning seekers and meaning makers. Tapping into this fundamental human quality is your best strategy for winning hearts and minds, within and without.
Lastly, this idea that growth can counter automation is compelling and effective way of understanding the difference between humans and machines. In An Argument Against AGI I sketched the explicit idea of a machine’s operation on a single plain, and the human ability to connect different plains. Following the same logic, education and personal growth would allow us to bridge more and more domains, and dodge being automated by narrow algorithms.