Knowledge @ Future of Work

Recently I watched the TV series — The Men Who Built America (originally broadcasted on History Channel and now available on Amazon). This was not my first time watching the series, but the first-time binge watching it. The fascinating docu-series captures the life, purpose, battles & conquests of prominent personalities of the American industrial era — Vanderbilt, JP Morgan, Andrew Carnegie, John Rockefeller, Henry Ford, Thomas Edison, Nikola Tesla and many more. There were several interesting facts in the series that I did not know before — the rivalry between many of these personalities (e.g. Vanderbilt vs. Rockefeller, Edison vs. Tesla), JP Morgan’s home was the first private residence to have electricity in the world. Watching the series prompted me to think about one important, but understated fact about the era — knowledge & resource development was a key driver of the industrial era! As I researched deeper, I realized that knowledge & intellectual enhancement has been at the core of most economic revolutions since early 18th century.

The Business Dictionary defines Knowledge as Human Faculty resulting from interpreted information; understanding that germinates from combination of data, information, experience, & individual interpretation. The foundation of modern knowledge concepts was laid by Sir Francis Bacon, also known as Father of empiricism or scientific method, in the early 1600s. His work in many ways inspired the technological discoveries in the enlightenment era and thereafter. ‘The Intellectual Origins of Modern Economic Growth’ by Joel Mokyr discusses the impact of the Baconian philosophy on the industrial revolution. The growth of “useful knowledge” as a function of counting, classifying and cataloging information and its understanding, reduced access costs tothis knowledge and its distribution was critical to developments in the revolutionary era.

Fast forward to today, we are experiencing another revolution — Industry 4.0, AI, digital or intelligent automation (whatever we may call it). I studied over 150 papers on Future of Work in the past two years and the key underlying theme in most of them focuses on proliferation of data and information and how that is likely to change the operating mechanisms of most economies, cities, industries, and workforce. The Kensho Index, for instance, is a good example to visually imagine how we might think of industries of the future.

While data and information intensity in many industries will grow exponentially, one must question its impact on knowledge & resource development and how industries and economies can exploit this to their benefit. There are several lessons from the past and although I don’t feel able or prepared to answer them all, following are some foundational thoughts based on research. The premise is simple — development of progressive knowledge ecosystem is critical to economic and institutional growth.

## An aspiring knowledge ecosystem requires many participants & their collaboration: The European & British enlightenment era is a classic example of how varied disciplines contributed to the Age of Reason or Enlightenment during 1600–1850. The era featured prominent personalities such as Rene Descartes (Philosopher), Adam Smith (Economist), James Watt (Inventor), Sir Isaac Newton (Scientist), John Locke (Governance & Political Thoughts) and many more. Five distinct personas emerge:

The diversity, organization and collaboration of these personas contributed significantly to the innovation in the industrial era and development of a robust knowledge ecosystem.

Interestingly, this was also the phase when coffee gained popularity and coffeehouses became popular place to meet for many great thinkers of the era. Coffee acted as a great stimulant while people were mostly addicted to alcohol before that (a depressant).

The implications for the current revolution should be no different. Each participant has a role to play whether we consider global, economic or company level knowledge ecosystems. Let’s consider an institution looking to make steady progress on its charter in the new information and data world. Based on the above, what questions should we be looking to answer:

• What is our greater purpose in the modern AI era?
• Do we have a diverse representation in our ecosystem to facilitate robust knowledge development?
• How do various personas interact and collaborate with each other or are we too siloed / fragmented?
• How do we define and develop useful knowledge?

## Discovery of useful knowledge and its applicability may not be instantly visible but steady progress will deliver results: An article by Kevin Hartnett on ‘Foundations Built for a General Theory of Neural Networks’ provides a great example for this: At first, steam engines weren’t good for much more than pumping water. Then they powered trains, which is maybe the level of sophistication neural networks have reached. Then scientists and mathematicians developed a theory of thermodynamics, which let them understand exactly what was going on inside engines of any kind. Eventually, that knowledge took us to the moon.

Another paper, “Trade, Knowledge, And The Industrial Revolution” by Kevin H. O’Rourke, Ahmed S. Rahman, Alan M. Taylor looks at two distinct elements of technological process: basic knowledge (what) and applied knowledge (how) (based on Baconian principles that suggested that basic knowledge drives applied knowledge that leads to material benefits). In its conclusion it states — “One such force was the continuing growth in Baconian knowledge, which would eventually lead to the growth of the science-based and skill-intensive sectors of the Second Industrial Revolution where, we conjecture, such knowledge was ultimately more stimulative of innovation”.

This explains that knowledge development is iterative and there can be a lag between analyzing the what and the how. Institutions, therefore, need to think steady and long term development while capitalizing on intermediate discoveries:

• How do we define a knowledge program for our institution?
• How do we structure efficient mechanism to develop knowledge and classify knowledge — one explaining the “what” and other explaining the “how”?
• How do we develop programs for lateral or analogous learning to speed up the process of discovery to results?
• How do we harness the collective & useful knowledge of our system?

## Scaling knowledge systems requires building of knowledge supply chains: Knowledge is sometimes understood as a theoretical or passive concept, however, it’s far more actionable and applicable if we study it as a supply chain & economics topic — as source raw material (data), structuring (information), packaging (supplier inference), storage (systems), distribution modes (books, events, websites, etc.), and consumption (recipient inference). Many of the early systems for knowledge development and management were developed in the first industrial era and as the methods grew in modes and sophistication, the speed of innovation grew. Joel Mokyr in his paper talks about how access costs to knowledge reduced rapidly in the industrial era due to various technological, cultural and social reasons and its positive impact on knowledge distribution. There were also commercial institutions and individuals dedicated to bridging the gaps by sharing prescriptive knowledge. On the other hand, the people who invented and commercialized technologies were often different (e.g. Thomas Edison developed electric light, but it was monetized by JP Morgan). In summary, knowledge operates within a supply chain environment. Given that the current era is all about data and information, it is not hard to imagine that knowledge is the key currency. Thinking of institutional relevance, some key questions:

• How do we define a knowledge ecosystem in an applicable context?
• How do we build a lean, scalable and efficient knowledge supply chain?
• Who should operate at each level of the supply chain?
• How do we derive economic benefit from the knowledge supply chain?

In my own view, answers to many questions raised above are missing in current thinking, not due to lack of ability but due to constrained thinking. The current focus for everyone seems to be on data and there may very well be a point soon when data will commoditize (some of it is already happening). The key difference then, between under performing / out performing institutions, laggard / forward economies will be the ability to think, prepare, develop, leverage and govern their knowledge eco-systems.

Author - Amit Shanker

Once a person asked a luminary, what is the future path and the luminary said, the future is the path!!