Artificial Intelligence: Forget the Job-Killing Hype. Get Ready for a new era of Better, Faster, Cheaper Predictions (Part 1)
With the blistering pace of digital disruption, competitive advantage can vanish in a flash.
And large companies are getting killed off faster than anyone ever imagined.
(Note: The average age of a company on the S&P 500 has plummeted from 60 years in the 1950s to less than 20 years today [Credit Suisse]).
But of all the technologies that are driving disruption, artificial intelligence (AI) may be the most disruptive of all.
All you have to do is type “artificial intelligence” into your search bar to see truckloads of hype about workforce disruption.
This is an important conversation.
But it misses another transformative trend: The astonishing evolution of AI is taking uncertainty out of strategy and helping the most successful companies anticipate new opportunities and risks.
Which is precisely why AI expert Avi Goldfarb (@avicgoldfarb) is urging senior execs to look beyond workforce horror stories, and focus on the AI trend as a drop in the “cost of prediction.”
And here’s the best part.
Turns out that all of the hype around AI is not as scary as it seems.
So says Goldfarb, who recently sat down with Appian to talk about his new book—”Prediction Machines: The Simple Economics of Artificial Intelligence”.
In this thought-provoking interview, Goldfarb gives us a simple framework for understanding what the explosion of AI means for your organization.
Hope you enjoy the conversation.
What’s it all About When you Sort it Out, AI?
Appian: Some reviewers have called your book the best book yet on the topic of artificial intelligence. That’s pretty high praise. So, what inspired you to write the book?
At the university of Toronto, I run a program to help science-based startups scale up. I’ve been doing that since 2012, when we had a startup come through our lab that called itself an AI company. The next year, there were a few more. And by 2015, there was this flood of companies coming through our lab calling themselves AI companies.
Appian: So, what do you think was behind that trend?
Goldfarb: When we dug into it, we discovered a few things. A lot of the technologies we were seeing were invented at the University of Toronto. That’s why we started seeing it so early. But, more importantly, we realized that this trend was going to be a big deal. It looked like a technology that had the potential to change the way we work and live, just like the internet and electricity.
Appian: So, what happened next?
Goldfarb: We tried to think through, what this technology is all about.
And what has changed in the last few years is that machines have gotten much better at prediction. And what I mean by prediction is reducing uncertainty—filling in missing information.
Strategy Without Uncertainty. Hmm, Imagine That!
Appian: So, was that revelation the motivation for “Prediction Machines”?
Goldfarb: …We wanted to get our heads around what this exciting new technology was all about. So, we put our economist framework on it.
And once we realized that it was all about prediction, we could re-frame the importance of the technology as a drop in the cost of making better, faster, cheaper predictions.
And once you understand that, you start to see all of the consequences. In business, there’s lots of uncertainty.
There are lots of decisions that you make that are fundamentally compromises, because you don’t really know what’s going to happen.
There are all sorts of things that companies do to deal with the uncertainty they face. They hedge and play it safe.
But if you can make good predictions about what will happen in the future, then you can make much better decisions.
Appian: What’s the biggest takeaway for senior exes?
Goldfarb: Some see it as an opportunity to save costs. I don’t want to say that the idea of saving labor costs is wrong.
(As smart technology diffuses, you’re going to have machines do things that people use to do.)
But in the process, you’re also going to create a whole new set of opportunities to improve organizational processes and also serve customers better.
And when you serve customers better, you’re going to get more customers. And that’s going to create a different set of things for people to do.
The Flip Side of Killing Jobs: Creating Better Ones
Appian: Which will create new jobs that didn’t exist before. Which is the flip side of the job-killing debate.
Goldfarb: Fifteen years ago, it would’ve been very difficult to foresee that hundreds of thousands of people would have the job of social media marketer.
And yet they do. And 150 years ago, when most people worked in agriculture, almost all of the jobs that exist today would have been unimaginable.
— Avi Goldfarb (@avicgoldfarb) October 3, 2017
Appian: There’s a ton of hype out there about AI. What are some of the biggest misconceptions about it?
Goldfarb: The first one is that true AI—or a machine that thinks like a human—is just around the corner.
I don’t think that this is a crazy notion. But it has very little to do with the technology that’s driving the current excitement around AI.
Jobs Aren’t the Issue. Economic Inequality Is
Appian: Which brings us back to the prediction capability of AI.
Goldfarb: The current excitement is around prediction technology. And prediction is a big deal because it helps us make better decisions.
Something else that is also misunderstood is the anxiety that has been focused on jobs. But jobs aren’t the issue so much as economic equality and inequality (is).
Artificial intelligence is a technology of low-cost prediction and discovery, writes @SteveLohr via @nytimes w/ comment by @professor_ajay + Prediction Machines w/ co-authors @avicgoldfarb @joshgans https://t.co/nwdVlMu323
— Rotman School (@rotmanschool) October 21, 2018
Appian: What do you mean by economic equality and inequality?
Goldfarb: Have you seen the movie, The Matrix? Well in the movie, every human has a job. And every human’s job is to serve as a battery.
The point is, they are terrible jobs. The flip side of that is, 100 years ago, most people worked many hours a week.
They didn’t get weekends off. They would start work as children. And they’d work through old age—if they were able to live that long—because there was no structured retirement.
Today, we have more leisure time. We have 40-hour work weeks. We stay in school longer, and don’t start work until much later. We get to retire. We get vacation. And it’s hard to think of any of that as bad.
So, the point is not: Are we going to have jobs? The point is: Are those jobs going to be good jobs?
And are the benefits of AI going to be distributed in a way that people think is fair and just? And that’s a much harder question.
(Tune in next week for the next episode of this 2-part series with AI expert and “Prediction Machines” co-author, Avi Goldfarb.)