How do we grow and scale, build process and systems, while also maintaining wonder, serendipity, and innovation at the same time? Is it even possible or does one have to sacrifice the other? Today’s guest shares some light on this very topic.

Edward Tenner is a writer, a speaker, as well as a Distinguished Scholar in the Smithsonian’s Lemelson Center for the Study of Invention and Innovation. His latest book, The Efficiency Paradox, dives into the sharing economy, life hacks, our efficient culture, and how to benefit from serendipity.

One of the lessons Edward learned about efficiency was in the 1980s when he saw that there were more and more computers being installed and more and more paper being thrown in the recycle bin. The Efficiency Paradox gives a look into new technology that has emerged with mobile computing, artificial intelligence, big data, and the cloud. It takes a look at both the productive use of it as well as some of its underlying problems.

Artificial Intelligence recognizes patterns and thus helps us become more efficient. This information is perfect to determine what items are in your inventory and which customers have been the most profitable. However, with prediction, there are also weaknesses in these algorithms.

One of these weaknesses being that AI algorithms detect changing patterns and so you can’t count on your recent experience to be a complete guide on what to do next if you don’t also supplement it with imagination and serendipity, as well as making controlled mistakes.

Another problem with AI is that it’s not very good at achieving diversity, especially among different gender and ethnic groups. Edward personally sees these errors as a systematic issue of a much more fundamental problem. By keeping within our respective lanes, we are less likely to venture out and bring in diversity, bring in new ideas, and bring in thoughts that might challenge the status quo.

Interview Links:

Edwardtenner .com

The Efficiency Paradox : What Big Data Can ’t Do , by

144: Edward Tenner Profit — What Big Data Can’t Do

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