Uber has reported a number of issues recently, from its CEO Travis Kalanick berating one of its drivers and another exec resigning due to differences in “beliefs and approaches to leadership,” to accusations of what appears to be institutional sexism.
Yesterday, it released a report on its workforce entitled “Measuring What Matters: Diversity at Uber,” which revealed that there’s no diversity at the company. Liane Hornsey, its HR chief, has been promised unlimited resources to fix a culture variously described as cutthroat, political, aggressive, and intensely masculine.
In other words, it has a people problem.
Isn’t that the one thing its app fixes?
Think about it. The rocket science behind Uber isn’t its technology — the platform is a glorified calendar program — but rather the problem it solves: Efficiently matching drivers with riders.
To accomplish it, the app takes the variability of supply, location, scheduling, routing, and payment out of the equation. Its technology determines the most efficient way to actualize the intent of the human beings it pushes from here to there, whether as suppliers or customers. Behaviors that were once the purview of people are accomplished by the push of a button instead.
In fact, it reportedly wants to take human beings out of its supply chain entirely, replacing them with automated cars.
That’s the sad promise of tech these days; since people are so biased and imperfect, we prefer to let technology make decisions (humanity’s control over its own fate, however demonstrably imperfect, is what’s getting disrupted). A smartphone chip already “knows” more than most voters in America, and the reason and reliability of big data analyses follows logic rules that folks would find inscrutable when compared with their irrational, desperately flawed thinking.
It’s easier to trust tech because it’s gotten so hard to trust one another.
So why not use it to do for Uber’s employees what it does for its drivers and riders?
The technology already exists, as many decisions in a wide variety of industries are already made by computer. Supply chains have been getting optimized via operational controls for decades, and software can now do it automatically. Marketing expenditures can be allocated via complex ROI models that require no human judgement. Investment decisions, legal actions, and other risky moves are regularly analyzed with deep probability models.
If big data can determine what brand of toothpaste a website visitor would prefer, couldn’t it run a company?
It would solve Uber’s problems in an instant…
Read the entire essay at Linkedin