As we enter the second machine age, it’s easy to assume that coding skills will be in ever increasing demand. But this TechCrunch feature suggests both that the skills shortage will likely prove fleeting, due to the impending automation of much coding, as well as that bullshit abounds in schemes which aim to address the contemporary shortfall:
It’s an ingenious business model. There’s a dearth of skilled coders in the marketplace to fill the five million computing jobs available in this country. For somewhere between free and $36,000, you learn to program computers in less than a year. If you’re one of the lucky few, you will hit your aha moment with programming and develop a personal passion for it, as well land a real job.
In 15 years, those hard-won skills will be obsolete — if they ever stuck in the first place. Despite their promises, coding academies don’t manufacture coders. They cast wide nets to discover new talent that has not yet been exposed to code. Most people don’t find coding enthralling or interesting enough to continue to pursue it as a career. Given the changing nature of software, they probably shouldn’t.
I see coding shrinking as a widespread profession. Not because software is going away, but because the way we build software will fundamentally change. Technology for software creation without code is already edging toward mainstream use. Visual content creation tools such as Scratch, DWNLD and Telerik will continue to improve until all functionality required to build apps is available to consumers — without having to write a line of code.
Who needs to code when you can use visual building blocks or even plain English to describe intent? Advances in natural-language processing and conceptual modeling will remove the need for traditional coding from app development. Software development tools will soon understand what you mean versus what you say. Even small advances in disambiguating intent will pay huge dividends. The seeds are already planted, from the OpenCog project to NLTK natural-language processing to MIT’s proof that you can order around a computer in your human language instead of code.
Granted, the author has a vested interest as the developer of one of these automation tools. But he makes an interesting point about the growth of what might be conceived as a coding skills bubble, with 175 percent growth in US academies between 2013-14 and an estimated $2.2 million revenue per school. There’s a lot of money to be made here, with a growing stream of people gravitating towards one of the few occupations popularly understood to be entering a boom time. But those willing and able to make money from providing such an education are disproportionally able to shape public perceptions of coding as an occupation, as well as having an obvious financial incentive to do so. Why entertain challenging questions about the future of coding as an occupation when there’s so much money to be made now?
The way myths about coding spread seems strikingly similar to other spheres of the digital economy. The TechCrunch author has a wonderfully succinct phrase: publicize successful outliers to propagate the illusion. The visibility of those who have ‘made it’, who are living the dream and doing what they love, licenses commitment and investment by a much greater cohort who have no prospect of doing so. Sound familiar? I’d love to explore this more rigorously at a later date, because the prospect of a homologous opportunity structure between coding and much of the creative industries is a fascinating hypothesis. Now what about data science? Is there an equivalent data science bubble developing?