No one doubts that software engineering shapes every last facet of our 21st century existence. Given his vested interest in companies whose fortunes were built on software engineering, it was no surprise when Marc Andreessen declared that “software is eating the world.”
But what does that actually mean, and, just as important, does it still apply, if it ever did? These questions came to me recently when I reread Andreessen’s op-ed piece and noticed that he equated “software” with “programming.” Just as significant, he equated “eating” with industry takeovers by “Silicon Valley-style entrepreneurial technology companies” and then rattled through the usual honor roll of Amazon, Netflix, Apple, Google, and the like. What they, and others cited by Andreessen, have in common is that they built global-scale business models on the backs of programmers who bang out the code that drives web, mobile, social, cloud, and other 24/7 online channels.
Since the piece was published in the Wall Street Journal in 2011, we’ve had more than a half-decade to see whether Andreessen’s epic statement of Silicon Valley triumphalism proved either prescient or, perhaps, merely self-serving and misguided. I’d say it comes down more on the prescient end of the spectrum, due to the fact that most (but not all) of the success stories he cited have continued their momentum in growth, profitability, acquisitions, innovation, and so forth. People from programming backgrounds – such as Mark Zuckerberg – are indeed the multibillionaire rockstars of this new business era. In this way, Andreessen has so far been spared the fate of Tom Peters, who saw many of the exemplars he cited in his 1982 bestseller “In Search of Excellence” go on to be deconstructed by business rivals or blindsided by trends they didn’t see coming.
Rise of the learning machines
However, it has become clear to everyone, especially the old-school disruptors cited by Andreessen, that “software,” as it’s normally understood, is not the secret to future success. Going forward, the agent of disruption will be the data-driven ML (machine learning) algorithms that power AI. In this new era, more of the logic that powers intelligent applications won’t be explicitly programmed. The days of predominantly declarative, deterministic, and rules-based application development are fast drawing to a close. Instead, the probabilistic logic at the heart of chatbots, recommendation engines, self-driving vehicles, and other AI-powered applications is being harvested directly from source data.
The “next best action” logic permeating our lives is evolving inside of applications through continuous inference from data originating in the Internet of Things and other production applications. Consequently, there will be a diminishing need for programmers, in the traditional sense of people who build hard-and-fast application logic. In their place, the demand for a new breed of developer – the data scientist – will continue to grow. This term refers to the wide range of specialists who craft, train, and manage the regression models, neural networks, support vector machines, unsupervised learning models, and other ML algorithms upon which AI-centric apps depend.
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