Come July, Amazon will turn 25. By digital standards, that is old age. Recent data suggests that the average age of companies in the S&P 500 has dropped to under 20 years from 60 years in 1950s. The secret, it seems, to staying young is the ability to understand one’s core business and reinterpret it over and over again. We know the many avatars of Amazon – books, multi-category retail, grocery, cloud, physical stores, and content, among others. Amazon embodies within itself the very idea behind - “Creating Exponential Value.”
In this digital age, if you don’t reinterpret yourself, you risk becoming irrelevant. For example, fintech and insurtech firms have surfaced as stiff competitors to traditional banks and insurers. Transportation and hotel chains floundered while sharing-economy companies like Uber and Airbnb introduced massive amounts of inventory into the market riding on near zero marginal costs. Detroit couldn’t foresee the future of the car industry would be shaped in California and the future of automotive advancement was software innovation. Traditional retailers and media giants struggled as internet upstarts such as Amazon, Netflix, Google and Apple destroyed conventional business models evolved over decades. Industries are replete with examples of the same pattern - the upstarts saw abundance when the incumbents could see constraints.
The new entrants could grow exponentially as their business models were non-linear and followed the laws of software, whereas the incumbents relied on conventional wisdom. You could say software was seen as a game changer. Software has indeed been eating the world, as the General Partner of Andreessen Horowitz presciently, Mark Andreessen said, back in 2011. However, software too is on the threshold of a fundamental change. Programming, the creative process of software, is moving from telling machines what to do to showing machines what to do. This has ramifications not only for people who write software but also for those who use software. While traditional programming itself might not entirely disappear, it will certainly become niche. What will prevail is a new generation of programmers with the ability to manipulate data and coach the machines.
What about data itself? The largest stumbling block to large-scale AI adoption in enterprises is the availability of reliable, machine-consumable data. However, that seems to be changing as a newer class of AI technology can now function with much less data and can in some cases, generate the data needed to learn and become useful.
As AI evolves, the human factor within an enterprise is going to take on a newer dimension. Therefore, we are continually seeking answers to some fundamental questions regarding the future of enterprise adoption of technology. We are also working on AI applications in every area of industry: health care, manufacturing, media & advertising, banking and insurance. While the agenda of augmentation and automation unfolds, we are also looking into the larger ethical questions that face society in the coming age of machines. What is the right kind of AI? What is the role of humans in an AI pervasive world? We hope the coming months will provide some interesting and socially viable answers to these questions.