And, for the nitty-gritty breakdown, here's a chart of the ten jobs with a 99-percent likelihood of being replaced by machines and software. They are mostly routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers, and insurance underwriters). At the bottom, I've also listed the dozen jobs they consider least likely to be automated. Health care workers, people entrusted with our safety, and management positions dominate the list.

If you wanted to use this graph as a guide to the future of automation, your upshot would be: Machines are better at rules and routines; people are better at directing and diagnosing. But it doesn't have to stay that way.

The Next Big Thing

Predicting the future typically means extrapolating the past. It often fails to anticipate breakthroughs. But it's precisely those unpredictable breakthroughs in computing that could have the biggest impact on the workforce.

For example, imagine somebody in 2004 forecasting the next ten years in mobile technology. In 2004, three years before the introduction of the iPhone, the best-selling mobile device, the Nokia 2600, looked like this:

Many extrapolations of phones from the early 2000s were just "the same thing, but smaller." It hasn't turned out that way at all: Smartphones are hardly phones, and they're bigger than the Nokia 2600. If you think wearable technology or the "Internet of Things" seem kind of stupid today, well, fine. But remember that ten years ago, the future of mobile appeared to be a minuscule cordless landline phone with Tetris, and now smartphones sales are about to overtake computers. Breakthroughs can be fast.

We might be on the edge of a breakthrough moment in robotics and artificial intelligence. Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats. Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they're bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do. This is why some people think managers are, for the moment, one of the largest categories immune to the rushing wave of AI.

Meanwhile, lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can't answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.

But perhaps we've hit an inflection point. As Erik Brynjolfsson and Andrew McAfee pointed out in their book Race Against the Machine (and in their new book The Second Machine Age), robots are finally crossing these moats by moving and thinking like people. Amazon has bought robots to work its warehouses. Narrative Science can write earnings summaries that are indistinguishable from wire reports. We can say to our phones I'm lost, help and our phones can tell us how to get home.