Disruption at the edge
One of the best ways to understand the future is to study industries that have artificials constraints and work their way on figuring out a way around these constraints. These industries often tend to be most innovative and define new technology use cases way before those use cases get mainstream adoption. There are four industries that regularly demonstrate these characteristics:
1) Terrorism and related crime
2) Adult entertainment
3) Drug trafficking
I am, of course, using the term ‘industry’ loosely here. These industries have always stayed a few steps ahead of the rest. For example, the primary use case of drug trafficking on Silk Road was one of the first large scale implementations of the blockchain, much more before it fascinated the financial servcies industry. The adult entertainment industry was the first to monetize content effectively on the internet, something that the traditional media still struggles to do. Terrorism used technology to manage remote decentralized teams using mobile networks way before fleet managers and distributed sales teams figured out the mechanics of doing that. We’ve repeatedly seen these industries leverage technology to figure out use cases and discover mechanics way before traditional industries do.
Gambling, in particular is a very interesting industry. Casinos, themselves, have always served as multi-sided interaction environments where the platform (casino) always wins by knowing more about the interactions than any individual participant.
The gambling industry was the first to formalized and implement behaviour design. Casinos have repeatedly worked on creating behavior design schedules so that users keep coming back and participating further. These principles subsequently found their way into other industries like advertising and gaming, and subsequently into social media.
The gambling industry also instituted one of the first large scale implementations of mass personalization, using data. Casinos have always built specific strategies to target whales – gamblers who spend extraordinarily high amounts and are accordingly treated with different incentives. With a wealth of data, casinos have been working on scaling whale-type incentives across a larger base of players, and on automating their experience journey with the casino. Data-driven mass personalization is increasingly entering other industries. Personalised experiences that were once served only to the top 1% are moving onto a larger base. Wealth management is one such industry where a handful of high net worth individuals get highly personalized advice but the middle layer of investors is served with products and services, not necessarily with personalized investment advice. With the advent of robo advisors, we will see technology augment human workers enabling a larger base of advisors to scale their investment advisory with lower skill requirements and on better economics, allowing them to cater to a much larger group of investors.
I believe we will see something similar in the early adoption of VR and automation, and will be keeping an eye out for unexpected use cases in these spaces.
Can making product usage difficult for users actually be a good idea? In some cases, yes!
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