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• Case | HBS Case Collection | September 2020

  Apple Bets on Augmented Reality

  Rory McDonald, David Lane and Mel Martin

  In 2020, augmented reality (AR) was still a nascent technology with blockbuster potential, one which Apple was actively developing as its iPhone franchised waned. But the emergence of AR was uneven, including the disappointing Google Glass and the unexpected viral success of Pokémon Go. AR technology was complex—involving integration of numerous advances in hardware and software—and expensive, dominated by Big Tech incumbents such as Facebook, Google, and Microsoft, whose AR applications thus far belied the breadth of vision articulated by their leaders. This left the door open for Apple, whose work on AR headsets had generated both internal conflict and continuing questions about where in the value chain the company should play, and how to win amid competing industry visions of the future of AR.

  Keywords: Industry Structures; Product Development; Commercialization; Competition; Corporate Strategy; Diversification; Technology; Technology Adoption; Information Technology Industry; Technology Industry; Video Game Industry;

  Citation:

  McDonald, Rory, David Lane, and Mel Martin. "Apple Bets on Augmented Reality." Harvard Business School Case 621-007, September 2020.  View Details

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• Working Paper | HBS Working Paper Series | 2020

  Time and the Value of Data

  Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

  This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and, thereby, a decline in created business value. We model time-dependency as a shift in the probability distribution and derive several counter-intuitive results.
  We, theoretically, prove that even an infinite amount of data collected over time may have limited substance for predicting the future, and an algorithm that is trained on a current dataset of bounded size can attain a similar performance. Moreover, we prove that increasing data volume by including older datasets may put a company in a disadvantageous position.
  Having these results, we answer questions on how data volume creates a competitive advantage. We argue that time-dependency weakens the barrier to entry that data volume creates for a business. So much that competing firms equipped with a limited, but sufficient, amount of current data can attain better performance. This result, together with the fact that older datasets may deteriorate algorithms' performance, casts doubt on the significance of first-mover advantage in AI-based markets.
  We complement our theoretical results with an experiment. In the experiment, we empirically measure the value loss in text data for the next word prediction task. The empirical measurements confirm the significance of time-dependency and value depreciation in AI-based businesses. For example, after seven years, 100MB of text data becomes as useful as 50MB of current data for the next word prediction task.

  Keywords: economics of AI; Machine learning; non-stationarity; perishability; value depreciation;

  Citation:

  Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020.  View Details

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• Case | HBS Case Collection | December 2019 (Revised July 2020)

  Karin Vinik at South Lake Hospital (A)

  Joseph L. Badaracco and Kim B. Clark

  A newly appointed hospital CEO must decide how aggressively she should pursue a sexual harassment accusation against a long-time senior hospital executive, who was also a rival for the CEO position.

  Keywords: sexual harassment; Leadership; Decision Making;

  Citation:

  Badaracco, Joseph L., and Kim B. Clark. "Karin Vinik at South Lake Hospital (A)." Harvard Business School Case 320-071, December 2019. (Revised July 2020.)  View Details

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