Managing Our Hub Economy

Marco   Iansiti; Karim R. Lakhani

Article | Harvard Business Review | September–October 2017

Managing Our Hub Economy: Strategy, Ethics, and Network Competition in the Age of Digital Superpowers

Abstract

A small number of digital superpowers—Alibaba, Amazon, Microsoft, and others—have become “hub firms” because they control access to billions of mobile customers coveted by all kinds of product and service providers. These hubs drive increasing returns to scale and claim a disproportionate share of the value being created in the global economy. The authors argue that the hub economy will continue to spread across more industries, concentrating more power in the hands of a few. As an example, they take an in-depth look at the auto industry and how Apple and Alphabet/Google are poised to become the main beneficiaries as cars turn into digitally connected spaces for work, entertainment, and shopping. As hubs proliferate and expand their reach, the danger is that they will exacerbate economic inequality and threaten social stability. It is thus incumbent on all stakeholders—traditional companies, start-ups, institutions, and communities—to make certain changes in the ways they do business. Moreover, hub firms themselves must do a better job leading responsibly for the good of all, not just creating and capturing value but doing more to sustain other players in the ecosystem.

Keywords: Competition; Strategic Planning; Auto Industry; Technology Industry;

Format: Print

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Citation:

Iansiti, Marco, and Karim R. Lakhani. "Managing Our Hub Economy: Strategy, Ethics, and Network Competition in the Age of Digital Superpowers." Harvard Business Review 95, no. 5 (September–October 2017): 84–92.

True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented businesses. Then, in 2007, True North invested in Meru Cabs, a ride-sharing service with a growing fleet of cabs that had to change its business model with the launch of tech-based platforms like Uber. This experience led True North to significantly alter its stance on the importance of technology-powered business models. The team learned that all businesses needed to effectively become technology businesses to remain relevant. True North initiated the “Analytics, Algorithms and Artificial Intelligence” (3A) project. A system, Kelp, was designed and developed internally to facilitate the transformation of True North into an AI-first firm based on the introduction of data-driven decision making and productivity-enhancing digital tools. In 2020, as different elements of Kelp were launched, the leadership team met to discuss the future of this technology.

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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Managing Our Hub Economy: Strategy, Ethics, and Network Competition in the Age of Digital Superpowers

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