Automation

Sadly there is truth in Manjo’s conclusion that many jobs will go away for good thanks to automation. As many liberal political activists and the OWS movement point out, productivity and corporate profits are booming, but while they often use this as a starting point to blame outsourcing and bonus-saving layoffs for a lack of jobs, they forget the role of automation. It’s not something we think about often and it’s not easy to make slogans to shame robots into quitting. You can fault an executive for laying off a thousand people to meet quarterly goals or deciding that hiring an American worker is too expensive and going overseas, but the uncomfortable reality is that a lot of companies are about as lean as they’re going to be after years of layoffs and belt-tightening and a number of smaller companies that used to outsource have been slowly weaning themselves off a reliance overseas factories citing increased labor costs, blatant theft of intellectual property aided and abetted by local bureaucrats, quality issues, and customs troubles. So how is productivity still up? Automation. How could you fault a company for increasing productivity not by simply getting rid of a job for questionable reasons, but to an automated tool? Of course the takeaway here is that some jobs will be completely unnecessary.

But just how many jobs will go the way of the dodo? Extending Manjo’s formula, we could even argue that one day not even programmers will be needed, only architects who run code generation tools as in an ironic twist, those who automated away tens of thousands of jobs now automate themselves away. But funny thing is that this approach has been tried before in IT and it did not end well. Model Driven Architecture, or MDA, attempted to create a kind of factory line for software where many steps could be fully automated, including generation of code. But lack of standards, incompatibilities with existing tools, and the many big and little issues in trying to turn an abstract model into a complete piece of software made the end products unmanageable. Why? While computers are great at repetitive tasks and crunching immense amounts of data, which is what they’re made to do, they’re not good at design or nuance. In programming, how does a machine know that object X needed to be encapsulated? Or that it could use less code to get the same behavior meaning less code to test? You need humans who know how to write code and define the rules to step in, roll up their sleeves and work on a creative problem like this. The MDA scholars tried to counter this issue by creating ever more abstract ways of designing logical models but abstraction doesn’t always yield lean, mean, performant applications.