Grandmasters of Work

Garry Kasparov lost to IBM’s Deep Blue computer in 1997. The press saw it as the turning point where man lost his edge to machine.

Kasparov saw it differently. Not because he needed to justify his loss, but because he recognized the nuance of what computers were capable of.

Deep Blue was unbeatable – Kasparov once called it “godlike perfection” – at a select number of board configurations. But there were instances, both then and now, where a chess game is won by creativity, which is the one thing computers couldn’t offer. Kasparov once wrote: “In chess, as in so many things, what computers are good at is where humans are weak, and vice versa.”

The real value of chess’s man-vs.-machine showdown wasn’t pitting one against the other. It was combining the two. So chess players began using computers to assist their own judgement.

And they were phenomenally successful.

“Freestyle chess,” or letting humans use computers to guide their decisions, is now the most advanced method of competitive chess. Man plus machine beats both man and machine.

Kasparov wrote:

Having a computer partner meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations … since [he and his opponent] both had equal access to the same database, the advantage still came down to creating a new idea at some point.

All the press saw with Deep Blue was a black and white, one winner, one loser. The idea that humans and computers complimented each other didn’t register, because it wasn’t as exciting. But collaborating with humans was Deep Blue’s biggest contribution to chess, by far.

Which is good example of what’s happening to a lot of fields.

Automation will replace millions of jobs. It has been for hundreds of years. But just like Deep Blue, our tendency to view a job as won by either man or machine overlooks how much is gained by combining the two.

Take financial advisors. Most asset allocation can now be automated. But that doesn’t mean financial advisors will be replaced by computers. Adding value as a financial advisor now means taking the computer’s numbers and explaining them to clients in ways they understand, with equal parts technical skill and empathy. Holding their hands when the market is tanking. Offering context when a client tries to fight the computer’s suggestions. Financial advisors will thrive in the coming years, so long as they recognize they are neither Kasparov or Deep Blue. They’re freestyle chess players.

Same for tax preparation, which is as automatable as it gets. There will always be value in explaining to people in clear language how tax law works and reassuring them that they’ve done the right thing. The company that will win tax preparation is the one that can automate 90% of our taxes with the ability to talk to someone knowledgeable who can walk you through the 10% that requires nuance, context, and reassurance.

Most customer service jobs are like this. Self-driving cars may one day replace professional drivers, but not before those drivers become staggeringly more reliable with things like Uber, Lyft, and GPS maps. Ride-hailing apps upending the taxi business is the perfect example of what sits between pure human work and pure automation. They’re playing freestyle chess. Same with medicine. I can look up an incredible amount of reputable, evidence-based medical information online. But I still need a doctor to look me in the eye, tell me what it means, what I should do, and actually execute the procedure.

The irony of Deep Blue is that it made the human element of chess more important. By taking the need to calculate off players’ minds, they could focus on creativity and strategy. That’s true for most jobs these days. The grandmasters of work are the ones who realize that automation doesn’t make human skills irrelevant. More often, it leverages their work and frees up time to double down on things humans are good at, like context, communication, and empathy.

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