Ajay Agrawal

Founder, Creative Destruction Lab and Machine Learning and the Market for Intelligence Conference; Geoffrey Taber Chair in Entrepreneurship and Innovation, Rotman School of Management

“YOU CAN SEE THE COMPUTER AGE everywhere but in the productivity statistics.” So stated Nobel Laureate and MIT economics professor Robert Solow in 1987. Eventually, economists found where the productivity gains from the computer age were hiding: in the future. While they eventually showed up, they took longer than expected because they were tied to investments in ‘complements’ — all of the things other than algorithms/models that are necessary to make commercial-grade AI work (data, redesigned workflows, training, regulation, human judgment, infrastructure, etc.).

As in the computer age, the widespread productivity gains associated with machine intelligence will depend on investments in complements. As we shift from technical achievements in AI (‘Look everyone! The AI can read a handwritten address on an envelope!’ ‘The AI can drive a car!’ ‘The AI can classify a medical image!’) to large-scale commercial deployment, the design and implementation of complements will be paramount.

The computer scientists designing AIS are far ahead of those building the complements — industry practitioners, social scientists, regulators and the like. Now that everyone has realized the sweeping potential of AI, companies and countries are racing to create and control the complements. While the algorithms are software and thus have low barriers to entry (notwithstanding scale advantages with respect to training data), many complements require significant capital expenditure and thus have higher entry barriers. Therefore, competition policy and market dynamics will move even further onto centre stage.

In other words, we are entering the next phase of the AI revolution: competition in the market for AI complements. This will feel different from what we’ve experienced so far. The genteel competition among computer scientists on display at

conferences like NIPS that is based on the performance of new AI algorithms against well-specified technical benchmarks like Imagenet will give way to competition among firms over the ownership and control of scarce complements such as data, infrastructure, talent and relationships.

For enterprises, competition in the semi-scientific culture of algorithmic performance against benchmarks was curious and novel. However, competition over complements is familiar territory. And given the size of the prize, this competition is likely to get rough and tumble, as corporate AI strategies depend at least as much on complements as algorithms. Intensified competition will increase the pressure on companies to deliver results. Internal debates like the one at Google regarding whether to abandon Project Maven — a collaboration with the U.S. Department of Defence to utilize AI for image analysis that could potentially be used to improve drone strikes — will seem quaint. Furthermore, competition will not only intensify at the company level. In recent months, one country after another has announced its national AI Strategy — and most of them read more like industrial than science policy. Competition over complements is about to become fierce.