Joshua Gans and Colleagues Discuss How to Build a Competitive Advantage with Machine Learning

By TAP Staff Blogger

Posted on October 29, 2020


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The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine learning. This technique for taking data inputs and turning them into predictions has enabled tech giants such as Amazon, Apple, Facebook, and Google to dramatically improve their products. It has also spurred start-ups to launch new products and platforms, sometimes even in competition with Big Tech.
- from “How to Win with Machine Learning” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

 

In their article, “How to Win with Machine Learning,” Rotman School of Management professors Ajay Agrawal, Joshua Gans, and Avi Goldfarb explain how companies entering industries with an AI-enabled product or service can build a sustainable competitive advantage and raise entry barriers against latecomers. The professors show how “late adopters of the new technology can still advance—or at least recover some lost ground—by finding a niche.”

 

Below are a few excerpts from “How to Win with Machine Learning” (Harvard Business Review, September–October 2020 Issue).

 

BUILDING COMPETITIVE ADVANTAGE IN PREDICTION

 

In many ways, building a sustainable business in machine learning is much like building a sustainable business in any industry. You have to come in with a sellable product, carve out a defensible early position, and make it harder for anyone to come in behind you. Whether you can do that depends on your answers to three questions:

 

1. Do you have enough training data?
One barrier to entry, therefore, is the amount of time and effort involved in creating or accessing sufficient training data to make good-enough predictions.

 

… training-data entry requirements are subject to the economics of scale, like so much else. High-growth markets attract investments, and over time this raises the threshold for the next new entrant (and forces everyone already in the sector to spend more on developing or marketing their products). Thus the more data you can train your machines on, the bigger the hurdle for anyone coming after you, which brings us to the second question.

 

2. How fast are your feedback loops?
Prediction machines exploit what has traditionally been the human advantage—they learn. If they can incorporate feedback data, then they can learn from outcomes and improve the quality of the next prediction.

 

The extent of this advantage, however, depends on the time it takes to get feedback. With a radiology scan, if an autopsy is required to assess whether a machine-learning algorithm correctly predicted cancer, then feedback will be slow, and although a company may have an early lead in collecting and reading scans, it will be limited in its ability to learn and thus sustain its lead. By contrast, if feedback data can be generated quickly after obtaining the prediction, then an early lead will translate into a sustained competitive advantage, because the minimum efficient scale will soon be out of the reach of even the biggest companies.

 

3. How good are your predictions?
The success of any product ultimately depends on what you get for what you pay.

 

Because AI is software-based, a low-quality prediction is as expensive to produce as a high-quality one, making discount pricing unrealistic. And if the better prediction is priced the same as the worse one, there is no reason to purchase the lower-quality one.

 

CATCHING UP

 

The bottom line is that in AI, an early mover can build a scale-based competitive advantage if feedback loops are fast and performance quality is clear. So what does this mean for late movers? Buried in the three questions are clues to two ways in which a late entrant can carve out its own space in the market. Would-be contenders needn’t choose between these approaches; they can try both.

 

Identify and secure alternative data sources.
In some markets for prediction tools, there may be reservoirs of potential training data that incumbents have not already captured.

 

Alternatively, instead of trying to find untapped sources of training data, latecomers could look for new sources of feedback data that enable faster learning than what incumbents are using.

 

Differentiate the prediction.
Another tactic that can help late entrants become competitive is to redefine what makes a prediction “better,” even if only for some customers. … By using training data (and then feedback data) from another system or another country, the newcomer could customize its AI for that user segment if it is sufficiently distinct.

 

CONCLUSION

 

The potential of prediction machines is immense, and there is no doubt that the tech giants have a head start. But it’s worth remembering that predictions are like precisely engineered products, highly adapted for specific purposes and contexts. If you can differentiate the purposes and contexts even a little, you can create a defensible space for your own product.

 

Read the full article: “How to Win with Machine Learning” by Ajay Agrawal, Joshua Gans and Avi Goldfarb (Harvard Business Review, September–October 2020 Issue).

 


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Joshua Gans is the Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto, and the chief economist at the Creative Destruction Lab. He is the author of The Disruption Dilemma (MIT Press, March 2016) and a co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

 

Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management. He is the founder of the Creative Destruction Lab, co-founder of The Next AI, and co-founder of Kindred. He is the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

 

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School of Management, University of Toronto. He is also the chief data scientist at the Creative Destruction Lab and the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

 
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