Ajay Agrawal’s welcome message from the 2017 conference:

It is my pleasure to welcome you to our third annual conference: “Machine Learning and the Market for Intelligence.”

As in prior years, our focus is not on the technical details of machine learning – the underlying mathematics, statistics, and algorithms – but rather on the economic and social implications of commercial AI. We encourage you to think about the impact of AI on business strategy. In other words, how will advances in AI influence resource allocation decisions that senior leadership make at large organizations?

I offer a thought experiment to illustrate. Most people are familiar with shopping at Amazon. As with most online retailers, you visit their website, shop for items, place them in your “basket,” pay for them, and then Amazon ships them to you. In summary, the business model is shopping-then-shipping.

Most shoppers have noticed Amazon’s recommendation engine (an AI) while they shop; that is the tool on their website that offers suggestions of items the AI predicts you will want to buy. At present, Amazon’s AI does a reasonable job. Considering the millions of items on offer, the recommendations it provides are significantly better than random.

However, they are far from perfect. In my case, the AI accurately predicts what I want to buy about 5% of the time. In other words, I actually purchase about one out of every twenty items it recommends. Not bad!

Now imagine the Amazon AI collects more information about us. In addition to our searching and purchasing behaviour on their website, it also collects other data it finds online (social media), as well as offline, such as our shopping behaviour at Whole Foods – not only what we buy, but also what time we go to the store, which location we shop at, how we pay, etc. Imagine the AI uses that data to improve its predictions.

The imagery I like to use when discussing this expanded potential for AI is turning up the volume knob on a speaker dial. But rather than volume, you’re turning up the AI’s prediction accuracy.

At some point, as you keep turning the knob, the AI’s prediction accuracy crosses some threshold such that it becomes in Amazon’s best interest to evolve their business model. The prediction becomes sufficiently accurate that it proves more profitable for Amazon to ship you the goods that it predicts you will want rather than wait for you to order them. Every week, Amazon ships you boxes of items it predicts you will want, and then you shop in the comfort and convenience of your own home by choosing the items from the boxes you wish to keep.

This approach offers two benefits. First, predictive shipping preempts the possibility that you purchase the items from a competing retailer. Second, predictive shipping nudges you to buy items that you were considering purchasing but might not have gotten around to. In both cases, Amazon gains a higher share of wallet. Turning the dial up far enough drives a change in Amazon’s business model from shopping-then-shipping to shipping-then-shopping.

Of course, shoppers would not welcome the hassle of returning all the items they don’t want. So, Amazon would invest in a collection infrastructure, perhaps a fleet of delivery-style trucks that do pick-ups once a week, conveniently collecting items that customers don’t want. 8 |

If this is a better business model, then why hasn’t Amazon adopted it already? Because if implemented today, the cost of collecting and handling returned items would outweigh the increase in revenue from a greater share of wallet. For example, I would return 95% of the items it ships to me. Amazon’s incentive to adopt the new model depends on the accuracy of its AI predictions.


That said, one can imagine a scenario where Amazon adopts the new strategy even before the prediction accuracy is good enough to make it profitable because the company anticipates that at some point it will be profitable. The sooner the new model is adopted, the faster the AI will learn because the training data will be better. Further, the sooner Amazon gets ahead, the harder it will be for competitors to catch up. Better predictions will attract more shoppers, more shoppers will generate more data to train the AI, more data will lead to better predictions, and so on, creating a virtuous circle. In other words, there are increasing returns to AI, and thus, the timing of adopting this kind of strategy matters. Adopting too early will be costly, but adopting too late will be fatal.

The key insight here is that turning the dial has significant impacts on strategy: 1) changing the business model from shopping-then-shipping to shipping-then-shopping, 2) vertically integrating a fleet of trucks to provide a collection service, and 3) accelerating the timing of investment due to path dependency and increasing returns – all this due to the single act of turning up the prediction accuracy dial.

At today’s conference, you will see several new applications of AI. Although most are early stage and the prediction accuracy is limited, they provide a starting point from which even an AI novice can extrapolate, simply by imagining turning up the dial.

Less than two months ago Russian President Vladimir Putin stated: “Artificial intelligence is the future, not only for Russia, but for all humankind… Whoever becomes the leader in this sphere will become the ruler of the world.” Russian mathematicians, statisticians, and computer scientists seem to have convinced President Putin that the dial can, and will, be turned way up.

One particularly interesting context for imagining turning the dial is our special segment on Artificial General Intelligence (AGI). Each of our speakers on this topic is not just pontificating, but rather, all are racing to build the world’s first AGI. They have distinct views on what else, in addition to prediction, will be required to achieve general intelligence. However, in every case, cranking the dial is a necessary condition for achieving this goal. And yes, the speakers will be asked to weigh in on their views of the possible implications for society as we hurtle toward a world that includes machines with human-like intelligence.

Today, I encourage you to utilize the presentations, debates, and lunchtime demos for two purposes: 1) to develop your own thesis of how fast and how far the dial will turn, and 2) to extrapolate from the early-stage AI applications you see to forecast what each means for the future of your organization.

Close your eyes, envision putting your fingers on the dial, and, in the immortal words of Spinal Tap, turn it to eleven.

Here’s to a productive conference,



Rotman School of Management,
University of Toronto

105 St George St,
Toronto, ON M5S 3E6

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Register here!