A lot of people who make over $350,000 are about to get replaced by software

Artificial intelligence is poised to automate lots of service jobs. The White House has estimated there's an 83% chance that someone making less than $20 will eventually lose their job to a computer. That means gigs like customer service rep could soon be extinct.

But it's not just low-paying positions that will get replaced. AI also could cause high earning (like top 5% of American salaries) jobs to disappear.

Fast.

That's the theme of New York Times reporter Nathaniel Popper's new feature, The Robots Are Coming for Wall Street.

The piece is framed around Daniel Nadler, the founder of Kensho, an analytics company that's transforming finance. By 2026, Nadler thinks somewhere between 33% and 50% of finance employees will lose their jobs to automation software. As a result, mega-firms like Goldman Sachs will be getting "significantly smaller." 

That's because Kensho does analytics work - previously an artisanal skill within Wall Street - at high speeds. Instead of sorting through news clippings to create a report, Kensho generates them from its database of finance analytics - essentially doing the work of researchers and analysts algorithmically. 

Type in "Syrian Civil War" into Kensho and you'll get a number of data sets showing how major assets like oil and currencies reacted to events in the conflict, Popper reports. The minutes-long search "'would have taken days, probably 40 man-hours, from people who were making an average of $350,000 to $500,000 a year," says Nadler, the Kensho founder. 

Goldman is actually a huge investor in Kensho. It will be interesting, to say the least, to see how that investment pays off. 

When speaking with Tech Insider about Google's huge AI victory in the game of Go, Brown University computer scientist Michael L. Littman explained that in any game with fixed rules, computers will win. 

"What we're finding is that any kind of computational challenge that is sufficiently well defined, we can build a machine that can do better," Littman says. "We can build machines that are optimized to that one task, and people are not optimized to one task. Once you narrow the task to playing Go, the machine is going to be better, ultimately."

Perhaps machines are just more optimized for certain types of white collar finance work, too.