Job Merckens

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After almost one year since the outbreak of COVID-19, the world is still in a race to find a vaccine or remedy against it. Since the Spanish flu, the Corona-virus is the latest pandemic to hold it’s grip on the world as we know it. It affected the way we are living and how we humans interact overall. Therefore, more and more creative solutions have been up for the test against this virus. In order to win the race, lots of research now also calls in the aid of Artificial Intelligence. However, even if well-intended, how far should we take AI in taking on COVID-19? How much of our personal data should we allow AI to process?

Potential uses of AI

First of all, Artificial Intelligence could be used to improve the diagnosing process. As it turns out AI can not replace an entire process, especially in a sector as sensitive as health care where human intervention is vital. Baidu, a Chinese technology company announced last march that it could use infrared sensor systems in order to single out infected persons in crowds (Johnson, 2020).

Another application of AI for COVID-19 diagnosis is the use of AI-driven CT-scan interpreters in order to take time of radiologists’ hands in diagnosing and analyzing the virus. Some hospitals are already using this application, especially in regions where the virus is spreading at a faster rate than hospitals can manage (Wittbold et al., 2020) .

However, finding a quick, efficient and effective way to diagnose people who got infected by the virus, is only a solution to a part of the problem. Luckily, there is also a potential use of AI in research for a vaccine on COVID-19. This can be done by using AI through machine learning into the research of drug discovery for treating diseases. Traditionally drug discovery is a slow process, even without having to adhere to the strict drug-regulations like we have in the European Union. However, through the use of AI one can quickly sift through the large amount of data available on the virus, study the structure and how it affects human beings and consider the suitability of various drugs (Wakefield, 2020).

“Now more than ever, there is a need to unify these disparate drug discovery data sources to allow AI researchers to apply their novel machine-learning techniques to generate new treatments for Covid-19 as soon as possible.” – Prof Ara Darzi, director of the Institute of Global Health Innovation, at Imperial College

AI and personal data

As outlined above, AI has great potential uses for diagnosing and even treating our pandemic, so that we might soon get a glimpse of our social lives as humans before the virus. Now governments and large research institutions are stepping up to introduce apps for the public to download in order to monitor the spread of the virus. With AI and machine learning, lots of data is often required for the system to function at a sufficient level of accuracy (Brynjolfsson and McAfee, 2017). So with the Corona-app launching in the Netherlands, lots of personal data, like health records and how long you had contact with who, is required from the public.

I am really interested in how you think about this development. Would you download the app? Why or why not? Do you think such application is useful for fighting the pandemic? Please let me know what you think in the comments below.

References

– Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence: What It Can — and Cannot — Do for Your Organization. Harvard Business Review Digital Articles, 3–11.

– Johnson, K. (2020). How people are using AI to detect and fight the coronavirus. VentureBeat. https://venturebeat.com/2020/03/03/how-people-are-using-ai-to-detect-and-fight-the-coronavirus/

– NOS. (2020, October 6). Kan de corona-app helpen? Deze deskundigen denken van wel. https://nos.nl/artikel/2351218-kan-de-corona-app-helpen-deze-deskundigen-denken-van-wel.html

– Wakefield, B. J. (2020, April 17). Coronavirus: AI steps up in battle against Covid-19. BBC News. https://www.bbc.com/news/technology-52120747

– Wittbold, K. A., Carroll, C., Iansiti, M., Zhang, H. M., & Landma, A. B. (2020). How Hospitals Are Using AI to Battle Covid-19. Harvard Business Review. https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19

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When organizations enter in a platform competition, the most dominant strategy to use is the “Get-Big-Fast” (GBF-) strategy (Oliva et al., 2003). If the strategy succeeds in its effect to take on the competition, platforms often end up having to defend their market share from envelopment attacks. The goal is to reach such a market dominance that a winner-take-all result will be obtained. However, GBF-strategies are very costly and because technological developments enter the markets more and more rapidly, there is little time for management to consider alternative effective strategies themselves. But when the platforms grow and platform competition evolves through envelopments would it not be strange to expect that platform competition will always end in a winner-take-all result?

“The more people searched, the more data they gave Google to make its index better, smarter, faster, and, eventually, more personal. In short: as Google got bigger, it got better, which made it bigger still. Google is a winner that has taken it all.” – Om Malik, founder of GigaOm

Driver of winner-take-all outcomes

We speak of a winner-take-all outcome when a market demonstrates positive network effects (van Alstyne et al., 2016), a technology or firm (or platform for that matter) that somehow gets ahead, increases its market share and corners the market, thereby driving out the firms or technology that lag behind who lose their market share.

In particular, are the indirect network effects considered to be the most important driver. These indirect network effects can also be considered as having a ‘focality advantage’ where a low-quality platform with strong indirect network effects or ‘local bias’ are still able to win the market or let the market tip when the consumer believes the network size or -intensity is more important. Therefore, consumer beliefs could shape the platform competition outcome in winner-take-all.

“In some industries, digitalization and globalization have created a winner-take-all game in which the company that wins the game accrues almost all the pay-off. How? By attaining either a scale or a network advantage.”– Dan Neiweem, co-founder and principal at Avionos

Winner-take-some outcome

However, there is a considerate amount of literature that challenge the general winner-take-all outcome in platform competition. There are four ways in which a winner-take-all outcome is not obtained in platform competition:

First, winner-take-all results are not obtained when multihoming is available on both sides. This happens mostly through a combination of indirect network effects and the development of low-cost conversion technologies that enable more multihoming (Iansiti and Lakhani, 2018).

Second, when platform business model complexity is low, then a winner-take-all result will not be obtained because the openness of its architecture is easily adapted and perfected by competitors (Zhao et al., 2019).

Third, it could also happen that multiple platforms enter the platform competition early in a platform life-cycle. In that case there is a higher chance that they will coexist considered they have the same network and business opportunities. Thus a winner-take-all result not to be obtained.

Finally, platform path dependency can determine whether or not a winner-take-all result can be obtained. This means that if a platform has a successful history in the market, it is more likely that a winner-take-all result will be obtained, and if less successful, this likelihood is reduced. Therefore when the platform has some downfalls in it’s market-performance, it could indicate negative spiral downwards (Schilling, 2002).

References:

– van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, Platforms, and the New Rules of Strategy. Harvard Business Review. https://hbr.org/2016/04/pipelines-platforms-and-the-new-rules-of-strategy

– Iansiti, M., & Lakhani, K. R. (2018). Managing Our Hub Economy. Harvard Business Review. https://hbr.org/2017/09/managing-our-hub-economy

– Malik, O. (2017, June 19). In Silicon Valley Now, It’s Almost Always Winner Takes All. The New Yorker. https://www.newyorker.com/tech/annals-of-technology/in-silicon-valley-now-its-almost-always-winner-takes-all

– Mourdoukoutas, P. (2019, February 16). How To Compete In A Winner-Takes-All Digital Global Economy. Forbes. https://www.forbes.com/sites/panosmourdoukoutas/2019/02/16/how-to-compete-in-a-winner-takes-all-digital-global-economy/#25420386c22d

– Oliva, R., Sterman, J. D., & Giese, M. (2003). Limits to growth in the new economy: exploring the ‘get big fast’ strategy in e-commerce. System Dynamics Review, 19(2), 83–117. https://doi.org/10.1002/sdr.271

– Schilling, M. A. (2002). Technology Success and Failure in Winner-Take-All Markets: The Impact of Learning Orientation, Timing, and Network Externalities. Academy of Management Journal, 45(2), 387–398. https://doi.org/10.5465/3069353

– Zhao, Y., von Delft, S., Morgan-Thomas, A., & Buck, T. (2019). The evolution of platform business models: Exploring competitive battles in the world of platforms. Long Range Planning, 101892. https://doi.org/10.1016/j.lrp.2019.101892

AI as a weapon against Covid-19: How far can we go?

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Platform Competition: When Winner-Take-All turns into Winner-Take-Some

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