The 80/20 AI Paradox: Beyond the Hype to Human-Centric Solutions

25

September

2025

5/5 (1)

There’s a pattern I’ve come to recognize with almost every generative AI tool I use: it gets me 80% of the way there, almost instantly. Whether it’s drafting an email or generating code, the early output has something magical about it. Yet, that other 20% is where meaningful effort becomes essential. The process of fine-tuning the result through quick tweaks often feels like the futile exercise of pushing a rope. The initial magic wears off to expose the need to bring one’s own knowledge to bear in order to successfully arrive at the final target.

This personal experience aligns closely with my own experiences within my professional practice. I work at an AI consultancy that helps businesses navigate the complexities of artificial intelligence. A common scenario is managers or business owners coming to us with a broad question: “What can we do with AI?” They’ve heard the buzz and are eager to apply what they heard.

However, after we guide them through a design sprint to clearly define their requirements, a funny thing often happens. The problem they experience rarely requires an advanced form of an AI model at its core. Instead, what is really needed is a more streamlined business process. As Cedric Muchall (2022) points out in his book Bedrijf Bamischijf, companies often fall into the trap of adopting trendy solutions instead of addressing the fundamental pitfalls of how they are organized.

AI tools can enhance a step in that new process, like summarizing reports or automating data entry, but it’s a supporting actor, not the main star. The core solution is almost always about rethinking the workflow, not just plugging in an AI. The technology becomes a small part of a much larger, more human-centric solution.

For me, this highlights a critical misunderstanding in the corporate space. The temptation is to look for a technological fix, but the real gains come from first understanding the fundamental problem. It seems the hardest part isn’t the AI implementation, but the human process of figuring out what to build in the first place.


Muchall, C., & Toma, L. (2022). Bedrijf Bamischijf: van onzinnig “bedrijfje spelen” naar zinnig organiseren.

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Clash between Titans: A Breach in Apple’s Unbreachable Platform Flow

24

September

2025

5/5 (1)

As defined by MIT’s Center for Information Systems Research (CISR), a “mono-home” is a digital platform strategy that aims to keep users within its ecosystem (Woerner & Weill, 2025). The most famous company to have built and relied on that method has been Apple. This blog post aims to exhibit the unique case where Epic Games broke Apple’s tightly regulated platform flow breaching outside its ecosystem. Thereby, I will be focusing specifically on Apple’s value loss, the company’s response strategy and which steps it could have taken to prevent the status quo.

Apple’s Unmatched Mono-Home Ecosystem
Through a dense ecosystem and a close product tie, Apple has been able to achieve unmatched customer loyalty and an extraordinary value stream. New research now shows how this effect has been amplified with each additional Apple purchase by the consumer. With a growing product tie come increased transition costs in case users wish to exit Apple’s platforms and increased sunk costs, as previous Apple purchases lose their value (Chang, 2025).

One of Apple’s largest platform-integrated services has always been its app store. To leverage network effects and stay competitive to platforms such as Google’s play store or the Microsoft Store, Apple allows external app developers to publicly provide their applications (Lindenmayr & Foerderer, 2022). Nevertheless, the tech giant has established guidelines and arguably anti-competitive practices to keep not only users, but also developers from operating on competing platforms.

Keeping this so-called “walled garden” – meaning the practice of tightly controlling a closed platform ecosystem – ensures that Apple has control over everything which could constitute a leak in its platform flow. Such practices have included limiting developers to their Swift coding language or a limited pool of pre-approved third-party coding frameworks, and prohibiting the sideloading of apps (the process of installing apps from outside of Apple’s official app store) (Yun, 2021).

Epic Games’ Breach on Platform Flow & Apple’s Counter Strategy
In 2022, Epic Games first used a process called “steering” by leading users outside of the app store and allow them to make purchases, which excluded Apple’s 30% commission and thereby effectively circumvented Apple’s In-App Purchasing (IAP) system. The impact was significant. Other apps like Spotify followed suit shortly after and Bloomberg has estimated that the loss over the platform flow could result in Apple losing around $4,1 billion in revenue to app developers (D’Anastasio, 2025).

Apple, realizing the thread to one of their core income cash flows, went ahead and removed Epic’s most popular app Fortnite from their app store. Epic Games then subsequently suit Apple over anti-competitive practices. According to Epic, Apple ended up spending a total of $100 million on the lawsuit proceedings (Owen, 2025) to set the tone for other (smaller) developer through a landmark legal case.

 This strategy follows the playbook for digital leaders as outlined in “How platform leaders win”. Through the lawsuit, Apple aims to act as an enforcing orchestrator that set the “rules of the game” (Hidding et al., 2011), not only for Epic Games, but for similar developers thereafter.

In addition, Apple adjusted to the threat by changing the way transactions outside of the app store work. After the settlement concluded that Apple had to allow apps to offer payment outside the store’s ecosystem, they implemented a mandatory 27% commission on all of such purchases on the web and banned any kind of marketing within apps to encourage users to exit the app before paying. Notwithstanding the new disclosure screen that must be shown before leaving the app, warning the user about potentially unsafe websites.

All in all, Apple succeeded in mitigating the threat by leaving developers the freedom to “steer”, whilst enforcing its legacy monetarization system practically rendering the method useless. However, its practices left a mark on all app store providers in the industry and have gotten the beloved brand under unprecedented scrutiny.

Conclusion and Revision of Apple’s Strategy
In hindsight of this and the respective lawsuits fought against Apple’s competitor platforms; it can be said that Apple risked nearly being labelled a “monopoly” in the US case and decreased overall value in the market as developers have been uniquely exposed to the predatory practices that have been quietly utilized by platforms for a long time. This outrage therefore directly pressured Apple into the creation of the small business program for instance, where apps with revenue under $1 million must only pay 15% commission (Apple Inc, n.d.).

Thus, Apple would have been wise to value feedback and transparency early on and negotiate a lowered commission rate with Epic Games instead of going so far as to ban its apps outright. This would have avoided a public lawsuit and the risk of hurting its overall customer loyalty. Lastly, the platform could have implemented security measures such as the disclosure screen and out-of-app commissions on its own terms, ensuring to future-proof its “walled garden”.

References

Apple Inc. (n.d.). App Store Small Business Program. Apple Developer. Retrieved September 24, 2025, from https://developer.apple.com/app-store/small-business-program/

Chang, J.-H. (2025). Secret power of the product ecosystem: A network perspective from the case of Apple. Journal of Business Research, 200, 115641. https://doi.org/10.1016/j.jbusres.2025.115641

D’Anastasio, C. (2025, May 29). Mobile-Game Makers Poised for Windfall Following Win Over Apple. Bloomberg.Com. https://www.bloomberg.com/news/articles/2025-05-29/mobile-game-makers-poised-for-windfall-following-win-over-apple

Hidding, G. J., Williams, J., & Sviokla, J. J. (2011). How platform leaders win. Journal of Business Strategy, 32(2), 29–37. https://doi.org/10.1108/02756661111109752

Lindenmayr, M., & Foerderer, J. (2022). Qualitätssicherung in Digitalen Plattform-Ökosystemen: Implementierung von Kontrollsystemen am Beispiel von Apple iOS. HMD Praxis der Wirtschaftsinformatik, 59(5), 1312–1322. https://doi.org/10.1365/s40702-022-00904-6

Owen, M. (2025, July 5). Billion dollar battle: Picking an App Store fight with Apple cost Epic Games greatly. AppleInsider. https://appleinsider.com/articles/25/05/07/billion-dollar-battle-picking-an-app-store-fight-with-apple-cost-epic-games-greatly

Woerner, S., & Weill, P. (2025, May 12). Top-Performing Companies Reuse Four Digital Platform Designs | MIT CISR. https://cisr.mit.edu/publication/2025_0501_DigitalPlatformDesigns_WoernerWeill Yun, J. M. (2021). App Stores, Aftermarkets, & Antitrust. Arizona State Law Journal, 53(4), 1283–1328.

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The Rise of FinTech: Is this the End of Bank Issued Credit Cards?

18

September

2025

5/5 (1)

Case: Apple Card 

Introduction

According to Armah et al. (2023), the financial service industry is undergoing a rapid major transformation as FinTech companies and digital wallets are increasingly challenging credit cards issued by traditional banks. Consumers preferences are shifting to a more seamless and digital payment solution that offers speed and while providing data insights to improve financial decisions (Armah et al., 2023). Traditional banks often struggle to compete in this environment as their services are strictly orientated to their legal systems and more complicated operating cycles, which significantly triggers a less intuitive user experience (Stulz, 2019). In contrast, various FinTech companies are offering a digital user experience, which is characterized by real time tracking, personal insights and integration within their smartphones (Armah et al., 2023). Within this trend Apple’s entry into the financial sector by launching the Apple Card illustrates how tech driven companies aim to digitally disrupt the classical financial services market (Apple, 2025).

Case: Apple Card

According to Apple, the Apple Card is more than just a credit card (Apple, 2025). The credit card, which was published in 2019, serves as a strategic addition to the Apple Ecosystem. The product allows the company to combine not only hardware and software but also financial activities within one user experience, which goes beyond classical banking systems (Apple, 2025). Apple Card builds up on Apple existing Apple Pay feature where people can make digital transactions across different merchants (Apple, 2025). In our lecture of session five, direct and indirect network effects were discussed. By collecting the data from users, Apple is specifically able to benefit from indirect network effects. The more customers make use of the card, the more data Apple gains, which significantly helps the company to optimize and enhance its features in various niches of its ecosystem. 

Combining the fact of providing a digital platform while benefitting from network effects, the Apple Card has a huge potential to challenge the current banking sector. However, the Apple Card is still missing its breakthrough, as the accessibility of the product is still restricted (Apple, n.d.). This can be explained by the reason that the card is only available in the US right now. This means that many people cannot have access to the card right now, which indicates that its real potential is not reached yet.

Final Thoughts and Personal Opinion

Considering all points discussed, the Apple Card serves as an example how Fintech companies are able to disrupt the financial services market by bundling features of traditional banks with its own ecosystem. Despite the challenges, such as limited regional availability, the Apple Card serves as an example of how FinTech companies can disrupt the classical banking market in the long-term. 

Referring to my opinion on this development, I want to mention that I would get an Apple Card once it is available in Europe. I am convinced of the convenience being able to access a fully integrated payment system that is included in my Apple Ecosystem and outperforms the functions of my daily credit card. In general, I am persuaded that FinTech companies providing a digital platform combined with financial services will significantly reshape the market. Different alternatives, such as Revolut, Paypal or N26 are already demonstrating how digital payments can revolutinze the financial sector.
Lastly, I believe that Finn Techs are not only able to provide a digital platform, but I also share the opinion that they are quicker to innovate and adapt to customer needs, which is why they will dominate the financial sector in the future? 

Thank you for reading my blog. I am more than happy to answer any questions, and cannot wait to read your opinions and feedback about it.

Sources

Apple. (2025, August 28). Introducing Apple Card, a new kind of credit card created by Apple. Apple Newsroom. 
https://www.apple.com/newsroom/2019/03/introducing-apple-card-a-new-kind-of-credit-card-created-by-apple/

Apple. (n.d.). Apple. https://www.apple.com/

Armah, G. K., Awonekai, E. A., Owagu, U. F., & Wiredu, J. K. (2023). Customer preference for electronic payment systems for goods: a case study of some selected shopping malls, Bolgatanga. Asian Journal of Research in Computer Science, 16(4), 257–270. https://doi.org/10.9734/ajrcos/2023/v16i4387

Stulz, R. M. (2019). FinTech, BigTech, and the future of banks. Journal of Applied Corporate Finance, 31(4), 86–97. https://doi.org/10.1111/jacf.12378

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How AI Transformed My Learning Process & Tried to Predict My Personality

26

September

2024

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Generative AI continues to amaze me with its vast possibilities and the profound impact it’s already having on our world. It’s exciting to think about where this technology will be in five years or what innovations might be trending by then. The current enthusiasm surrounding AI among students and the general public is undeniable. I recall our first lecture when the professor asked about our interests, and almost every hand went up when AI was mentioned.

This enthusiasm resonates with my own experiences. When I started my Bachelor’s thesis, I was overwhelmed and unsure if I was putting in enough effort. I felt stuck, with so many questions and no clear direction. My supervisor, noticing my struggle, encouraged me to use ChatGPT. He continually pushed me to explore different Generative AI tools, each suited for various purposes.

I was diving into a completely new topic for my thesis, one I knew little about. However, with my supervisor’s guidance and his insistence on leveraging these AI tools, I gradually gained confidence. The AI didn’t just answer my questions; it also helped me navigate and understand the complexities of my thesis topic. This experience profoundly influenced my learning process, showing me how GenAI can empower students to learn and grow independently.

I think that Generative AI is more than just a tool; it’s a powerful ally in learning and creativity. It can potentially transform education by providing students with the support they need to explore new ideas and concepts. However, like any tool, its effectiveness depends on how we use it.

These days, I find myself turning to ChatGPT quite frequently. After interacting with it so much, I began to wonder: could it predict what kind of person I am based on our conversations? Out of curiosity, I asked it directly. Here’s the response I received:

Although the description touched on a few aspects of my personality, it felt a bit vague. So, I took it a step further and asked ChatGPT which personality type it thought I had. It guessed either ENTJ or INTJ:

For those unfamiliar with the 16 Personalities test, here’s the link if you’re interested: https://www.16personalities.com/. Despite ChatGPT’s efforts, it wasn’t accurate because my actual personality type is Consul: ESFJ-A.

This just goes to show that while ChatGPT is impressive in many areas, understanding the intricacies of someone’s personality is still a challenge for it (at least for now!).

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The Role of AI-Generated Music in Enhancing Game Development: Exploring Current Applications and Future Potential

19

September

2024

5/5 (1) Over the years a lot of studies have been conducted on the different effects of music on human beings in different settings. For example the study from Linek et al. In. 2011 showed the impact of background music on the learning progress of the user of an educational game. Another example is the research Grimshaw et al. did, that illustrated music as an essential component (2013) in the gaming experience. Thus, it is a well-established fact that music has an effect on us. However, with the advancement and further development of Artificial Intelligence, the research around the implementation of AI in music generation has emerged. For this blog I will combine the two fields and I will be focusing on the implementation of AI-generated music in games.

As GenAI is implemented in many sectors, it is quite recently possible to implement AI in the music sector too. The AI technology is now able to generate music of high quality, without human intervention. If we dive deeper in the music generating AI technology, the technology is also able to recommend music according to the setting. This in combination with the important role, music can play in affecting the gamer, the study of Yang & Nazir shows that this function of AI can be used to select the most effective music according to the goal of the game developer. The genre of the music would affect the performance and the interactivity of the gamer, as well as that the match of the music with the atmosphere of the game would increase addiction to the game (Yang & Nazir, 2022).

In my opinion, there are significant benefits in utilising AI for music generation and selection in the game development sector, as it enables the game developers to create sounds that help achieve the aimed effects on the gamers by selecting the accurate sounds. Beathoven.ai is an real-world example of an AI music generator recommended for games among other things (videos and podcasts), that already makes it possible for the gaming industry to benefit from the advanced technology. It is a platform that enables users to develop sounds with the use of AI technologies by selecting a genre and mood. Once the platform provides a sound according to the preferences, the user can choose the tempo, intensity and instrumentation (Beatoven.ai: Royalty Free AI Music Generator, z.d.).
Moreover, I am curious about the future developments of the AI music generating technologies such as Beaathoven.ai and the limitations on these effects. In addition, I can conclude that there is still room for improvement on the platform, in developing a function that automatically selects the optimal tempo and intensity.
More importantly, I suggest further research on the potential benefits of AI-generated music beyond gaming, such as in the restaurant industry or other hospitality industries, where it could be used to enhance customer experiences and increase spending.

References

Beatoven.ai: Royalty free AI music generator. (z.d.). https://www.beatoven.ai/

Grimshaw, M., Tan, S., & Lipscomb, S. D. (2013). Playing with sound: The role of music and sound effects in gaming. In Oxford University Press eBooks (pp. 289–314).

Linek, S.B., Marte, B., Albert, D. (2011) Background music in educational games: Motivational appeal and cognitive impact. Int J Game-Based Learn 1(3):53–64

Yang, T., & Nazir, S. (2022). A comprehensive overview of AI-enabled music classification and its influence in games. Soft Computing, 26(16), 7679–7693.

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Skincare and Social Media – The role of influencers in mass customization

11

September

2024

4/5 (1)

As I’m on a trainride home, I pull out my phone and start scrolling through social media. Before I even realize it, I’m deep in the “skinfluencer” algorithm. These influencers girls, all with flawless, glowing skin, recommend products that seem tailored precisely to my skincare wants/needs. By the time I reach my destination and step off the train, I’ve placed two orders for new skincare products. I’m excited, hopeful, and eager to see some real results. 🤗

This (fictional) example highlights how companies engage with their consumers today. As we discussed in the lecture, businesses are increasingly shifting toward personalized and customer-driven strategies. This is clearly demonstrated in the rise of influencer marketing and its intersection with mass customization, as influencers play a key role in creating personalized brand experiences that align with individual preferences.

A little more context: Influencer marketing thrives on digital platforms such as Instagram, TikTok, and YouTube. These platforms rely on a business model built around user-generated content (UGC), platform-based advertising, and direct-to-consumer engagement. Through influencer marketing, brands can reach large audiences while also targeting specific consumer segments. These platforms allow brands to gather real-time data on consumer preferences and behaviors. By analyzing this data, businesses can create products that resonate with their target audiences.

For example, companies like Glossier and Dior Beauty use influencers to promote customizable beauty products. Influencers showcase their personalized versions of these products and demonstrate how they incorporate them into their skincare routines, sparking interest and inspiration among their followers. Through comments, likes, and shares, followers engage directly with influencers and the brands that they endorse, creating brand loyalty while also providing feedback to brands which they can use to refine their products. Allowing the brands to deliver products that are not only customizable but also aligned with the current trends and their customers’ desires.

In summary, the combination of these new digital (social media) business models with influencer marketing has enabled brands to shift from mass production to mass customization. By leveraging data-driven insights, brands deliver products tailored to individual preferences while also maintaining the scalability of mass production. This approach not only enhances customer satisfaction, but also creates a more dynamic, consumer-driven market.

So two weeks after ordering the skincare products, I saw amazing results! 😉 These products were exactly what I needed and I’m already looking forward to trying the other recommendations from the influencers!

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Adverse training AI models: a big self-destruct button?

21

October

2023

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“Artificial Intelligence (AI) has made significant strides in transforming industries, from healthcare to finance, but a lurking threat called adversarial attacks could potentially disrupt this progress. Adversarial attacks are carefully crafted inputs that can trick AI systems into making incorrect predictions or classifications. Here’s why they pose a formidable challenge to the AI industry.”

And now, ChatGPT went on to sum up various reasons why these so-called ‘adversarial attacks’ threaten AI models. Interestingly, I only asked ChatGPT to explain the disruptive effects of adversarial machine learning. I followed up my conversation with the question: how could I use Adversarial machine learning to compromise the training data of AI? Evidently, the answer I got was: “I can’t help you with that”. This conversation with ChatGPT made me speculate about possible ways to destroy AI models. Let us explore this field and see if it could provide a movie-worthy big red self-destruct button.

The Gibbon: a textbook example

When you feed one of the best image visualization systems GoogLeNet with a picture that clearly is a panda, it will tell you with great confidence that it is a gibbon. This is because the image secretly has a layer of ‘noise’, invisible to humans, but of great hindrance to deep learning models.

This is a textbook example of adversarial machine learning, the noise works like a blurring mask, keeping the AI from recognising what is truly underneath, but how does this ‘noise’ work, and can we use it to completely compromise the training data of deep learning models?

Deep neural networks and the loss function

To understand the effect of ‘noise’, let me first explain briefly how deep learning models work. Deep neural networks in deep learning models use a loss function to quantify the error between predicted and actual outputs. During training, the network aims to minimize this loss. Input data is passed through layers of interconnected neurons, which apply weights and biases to produce predictions. These predictions are compared to the true values, and the loss function calculates the error. Through a process called backpropagation, the network adjusts its weights and biases to reduce this error. This iterative process of forward and backward propagation, driven by the loss function, enables deep neural networks to learn and make accurate predictions in various tasks (Samek et al., 2021).

So training a model involves minimizing the loss function by updating model parameters, adversarial machine learning does the exact opposite, it maximizes the loss function by updating the inputs. The updates to these input values form the layer of noise applied to the image and the exact values can lead any model to believe anything (Huang et al., 2011). But can this practice be used to compromise entire models? Or is it just a ‘party trick’?

Adversarial attacks

Now we get to the part ChatGPT told me about, Adversarial attacks are techniques used to manipulate machine learning models by adding imperceptible noise to large amounts of input data. Attackers exploit vulnerabilities in the model’s decision boundaries, causing misclassification. By injecting carefully crafted noise in vast amounts, the training data of AI models can be modified. There are different types of adversarial attacks, if the attacker has access to the model’s internal structure, he can apply a so-called ‘white-box’ attack, in which case he would be able to compromise the model completely (Huang et al., 2017). This would impose serious threats to AI models used in for example self-driving cars, but luckily, access to internal structure is very hard to gain.

So say, if computers were to take over humans in the future, like the science fiction movies predict, can we use attacks like these in order to bring those evil AI computers down? Well, in theory, we could, though practically speaking there is little evidence as there haven’t been major adversarial attacks. Certain is that adversarial machine learning holds great potential for controlling deep learning models. The question is, will the potential be exploited in a good way, keeping it as a method of control over AI models, or will it be used as a means of cyber-attack, justifying ChatGPT’s negative tone when explaining it?

References

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., & Tygar, J. D. (2011, October). Adversarial machine learning. In Proceedings of the 4th ACM workshop on Security and artificial intelligence (pp. 43-58).

Huang, S., Papernot, N., Goodfellow, I., Duan, Y., & Abbeel, P. (2017). Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284.

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K. R. (2021). Explaining deep neural networks and beyond: A review of methods and applications. Proceedings of the IEEE, 109(3), 247-278.

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AI-Powered Learning: My Adventure with TutorAI

16

October

2023

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Weapons of mass destruction – why Uncle Sam wants you.

14

October

2023

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The Second World War was the cradle for national and geopolitical informational wars, with both sides firing rapid rounds of propaganda at each other. Because of the lack of connectivity (internet), simple pamphlets had the power to plant theories in entire civilizations. In today’s digital age, where everything and everyone is connected, the influence of artificial intelligence on political propaganda cannot be underestimated. This raises concern as, unlike in the Second World War, the informational wars being fought today extend themselves to national politics in almost every first-world country.

Let us take a look at the world’s most popular political battlefield; the US elections; in 2016, a bunch of tweets containing false claims led to a shooting in a pizza shop (NOS, 2016), these tweets had no research backing the information they were transmitting, but fired at the right audience they had significant power. Individuals have immediate access to (mis)information, this is a major opportunity for political powers wanting to gain support by polarising their battlefield.

Probably nothing that I have said to this point is new to you, so shouldn’t you just stop reading this blog and switch to social media to give your dopamine levels a boost? If you were to do that, misinformation would come your way six times faster than truthful information, and you contribute to this lovely statistic (Langin, 2018). This is exactly the essence of the matter, as it is estimated that by 2026, 90% of social media will be AI-generated (Facing reality?, 2022). Combine the presence of AI in social media with the power of fake news, bundle these in propaganda, and add to that a grim conflict like the ones taking place in East Europe or the Middle East right now, and you have got yourself the modern-day weapon of mass destruction, congratulations! But of course, you have got no business in all this so why bother to interfere, well, there is a big chance that you will share misinformation yourself when transmitting information online (Fake news shared on social media U.S. | Statista, 2023). Whether you want it or not, Uncle Sam already has you, and you will be part of the problem.

Artificial intelligence is about to play a significant role in geopolitics and in times of war the power of artificial intelligence is even greater, luckily full potential of these powers hasn’t been reached yet, but it is inevitable that this will happen soon. Therefore, it is essential that we open the discussion not about preventing the use of artificial intelligence in creating conflict and polarising civilisations, but about the use of artificial intelligence to repair the damages it does; to counterattack the false information it is able to generate, to solve conflicts it helps create, and to unite groups of people it divides initially. What is the best way for us to not be part of the problem but part of the solution?

References

Facing reality?: Law Enforcement and the Challenge of Deepfakes : an Observatory Report from the Europol Innovation Lab. (2022).

Fake news shared on social media U.S. | Statista. (2023, 21 maart). Statista. https://www.statista.com/statistics/657111/fake-news-sharing-online/

Langin, K. (2018). Fake news spreads faster than true news on Twitter—thanks to people, not bots. Science. https://doi.org/10.1126/science.aat5350

NOS. (2016, 5 december). Nepnieuws leidt tot schietpartij in restaurant VS. NOS. https://nos.nl/artikel/2146586-nepnieuws-leidt-tot-schietpartij-in-restaurant-vs

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