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Sports are one of the main forms of entertainment for Dutch people. Over 55% of the Dutch population older than six watches at least one sporting event each week (RIVM, 2022). Additionally, 73% has watched at least one event of the 2024 Olympics (NOS, 2024). It indicates that sporting events, for many, are an integral part of their lives.

However, the focus of broadcasters in the Netherlands mainly lies on broadcasting popular sports such as football, formula 1 and cycling. Which is logical. It does not make sense for large broadcast platforms to put a focus of resources such as expensive human capital on sports that receive little attention. Broadcasting less popular sports such as badminton, fencing and curling for example do not deliver enough views to justify high fixed broadcasting expenses. This is where our Generative AI-model will make a difference. Our company, GameChanger, will transform raw footage and data of sporting events into high-quality summaries, both textual and visual. This allows for a new consumer segment to be served in the broadcasting industry: the niche sports fan. This fan will be able to read about and watch highlights of his/her favorite sport. In addition to providing products for a new consumer segment, existing customers receive the opportunity to explore new sports and potentially find new hobbies and interests.

Our model will not only be beneficial for consumers, but also for our potential partners. GameChanger provides products that are of high-quality, easy to produce, requires low-resource input and most importantly, will be available in large numbers. Existing broadcasters such as ESPN, Viaplay and NOS Online can provide content to their customers with a large catalogue, tailored to their own preferences.

This personalization has proven to be effective in retail and social media for example. According to a McKinsey & Company report (2021), two-thirds of customers found personalization in the form of tailored messaging and recommendations was an important factor in their purchasing. GameChanger will aim to leverage this personalization needs to create customer value.

A prosperous business model is not without challenges, and for our mission these also exist. Our solution needs to carefully consider legal issues by ensuring the licensing of data is secure for example. In addition, the collected data needs to be of the highest quality and produced content needs to be consistent overtime.  To mitigate this, human gatekeepers are needed, and this will eventually ensure iterative improvement of the model.

To conclude, GameChanger has the possibility to revolutionize the sports broadcasting industry through effectively using GenAI in providing long-tail content in the form of both textual- and visual summaries. Once the model is properly trained and the initial challenges are mitigated. With promising partnerships in combination with serving new and existing consumer segments, the future looks promising.

References:

McKinsey & Company. (2021). The value of getting personalization right—or wrong—is multiplying. McKinsey & Company.

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

NOS. (2024). Kijkcijfers Olympische Spelen.NOS. https://over.nos.nl/nieuws/7-op-de-10-nederlanders-beleeft-de-spelen-bij-de-nos/#:~:text=12%2C2%20miljoen%20Nederlanders%20hebben,gemiddeld%202%2C7%20miljoen%20kijkers.

RIVM. (2022). Sportfan via media. RIVM. https://www.sportenbewegenincijfers.nl/kernindicatoren/sportfan-media

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Generative AI has already given us an abundance of opportunities which were not deemed possible only 3 years ago. From having a crappy version of ChatGPT (which was already groundbreaking at the time) to generated videos which can hardly be recognized as AI. The technologies are being improved regularly, leading to better versions of LLMs (such as ChatGPT and Google’s Gemini) which ultimately makes many tasks easier and faster to perform for humans.

If I relate it to my personal situation regarding using these text generating forms of AI, I can say it has made studying easier. From simply using it to improve my writing in formal language and grammar, to asking for solutions on specific assignment-related questions. Especially the latter has made tasks easier to do as the GenAI can provide with a starting point to use as a thought. If I for example ask Gemini to give me three examples of firms that use data monetization and how these firms do so, almost instantly I receive an answer which I can use to start in my answer. The time it saves me doing my own research on these simple questions might look marginal, but once added up can really make a difference. Therefore, one can argue that these forms of GenAI increase a student’s productivity quite significantly.

However, a potential downside I have noticed in this process, is the passive learning I experience. With using AI, I hand the thinking process to Gemini, after which I only have to read and reason which response best suits the question I need to answer. If an answer is not to my liking, I can simply ask the LLM to give a new answer, or slightly change my prompt in the hope it changes the answer accordingly. My own learning might become more passive than actively having to think on my own answers. This can be considered as becoming lazy in my opinion. During the guest lecture, Stefan van Duin also mentioned that one of his biggest concerns regarding GenAI in the future, is that it can make us lazy human beings. In the context of this thought, I feel the process of making us lazy has definitely already began.

In conclusion, I argue that the LLMs have made us more productive and gave us opportunities of enriching ourselves much easier and faster than we might have ever could have imagined. However, we should be careful in the usage of AI and our own development. We should not become lazy in our own thinking process and fully utilize LLMs for our tasks. Perhaps universities should focus on making assignments that are not possible to do with these LLMs. All in all, It is a double-edged sword for which we need to be careful in the increasingly better LLMs that we will see in the, foreseeable, future.

(I couldn’t come up with a saying for the last sentence. So, I intuitively asked Gemini, who quickly came up with ‘double-edged sword’. Just an example on how embedded it might already be in my system!)

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Digital platforms have become a major player in today’s society. Most people can’t imagine life without Google, Facebook, LinkedIn or WhatsApp to name a few. Many may argue it has made our existence and ways of communication easier and more fun. Most of these platforms are characterized by having a monopoly. The network effects that the companies experience, allows them to grow exponentially and outperform competitors with their growth model. This has created an ease for consumers as their entire network is active on a few platforms which makes communication relatively easy.

However, there lays some ‘dark’ implications behind these platform monopolies. First and foremost, large quantities of personal data are absorbed by digital platforms. Rather than several firms having small pieces of data, companies such as Facebook and Google have an abundance of personal data of a large part of the global population. Facebook is famously known for the example of the scandal with Cambridge Analytica in the previous decade. Personal data of 87 million Facebook users was mined and scraped without consent (McIntosh, 2019). This highlights the vulnerability of large storage of data in one place such as these platform monopolies.

Another potential danger from digital monopolies is the political influence it can exert on its users. Large scale advertisers can easily influence viewed content of users of a platform. Google and Facebook effectively control the online advertising (Epstein, 2017), and this can turn them in decisive political players regarding advertisements of political nature (Matz et al., 2018). This should be quite concerning, given the relative neutrality large platforms like these should possess in my opinion.

Admittedly, since the General Data Protection Regulation (GDPR) was launched in 2016 by the European Union, some concerns have decreased in terms of data privacy and saw an increase in consumer welfare (Heine, 2021). This, as of 2024, turned out to not be enough, however. Major challenges still exist. One of those is the lack of consistency and effectiveness of authorizing resources (Heine, 2021; Burgess, 2022 & Saqr, 2022). These bureaucratic delays and inefficiencies ultimately lead to little protection of users from digital platform monopolies.

Altogether, I argue that more needs to be done to limit the influence platform monopolies either directly or indirectly impose on our lives. Data privacy and platform neutrality are two principles that have become increasingly important and will only grow in importance in the future. Stronger measures from governments and global institutions need to address these concerns accordingly, where a focus needs to lay on effectively tackling current problems as we can see with the regulatory quality of the GDPR for example.

References:

Burgess, M. (2022). How GDPR Is Failing. Wired. https://www.wired.com/story/gdpr-2022/

Epstein, M. (2017). The Google-Facebook Duopoly Threatens Diversity of Thought. Wall Str Journal.  https://www.wsj.com/articles/the-google-facebook-duopoly-threatens-diversity-of-thought-1513642519.

Heine, I. (2021). 3 Years Later: An Analysis of GDPR Enforcement. CSIS. https://www.csis.org/blogs/strategic-technologies-blog/3-years-later-analysis-gdpr-enforcement

Matz, S., Cerf, M. & Rolnik, G. (2018). Solutions to the Threats of Digital Monopolies. Promarket. https://www.promarket.org/2018/04/10/solutions-threats-digital-monopolies/

McIntosh, D. (2019). We Need to Talk About Data: How Digital Monopolies Arise and Why They Have Power and Influence. Journal of Technology Law & Policy 23(2): 185-213 https://scholarship.law.ufl.edu/jtlp/vol23/iss2/2

Saqr M. (2022). Is GDPR failing? a tale of the many challenges in interpretations, applications, and enforcement. International journal of health sciences, 16(5), 1–2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441646/