Building Applications Has Become Easier Than Ever

10

October

2025

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A couple of months ago, a client at my student consultancy job asked us to automate a document anonymization process at their real estate agency. Due to data protection requirements, the processing had to be done locally or within their Microsoft environment.

After some unfruitful experimenting with Power Automate, we decided to give building our own tool with Claude (an LLM by Anthropic) a try. With a bachelor’s degree in international business, I had no coding knowledge whatsoever. The results were amazing. Within a few hours, we had some basic capabilities established.

After a while, I wanted to make the process more efficient than having to copy paste Claude’s changes into my project files. A new version, named Claude Code, had been released. It enables the AI to work in your project files directly. I had to watch a few tutorials and do some error-fixing with ChatGPT to get it to work in container (a sealed-off environment on my laptop). After about two hours we were ready to go.

The result was a developer working at lightning speed. It could code, test, readjust and retest until it works, all in one go. You see it break down the task into sub-tasks and tackle them one-by-one. Alternatively, you can put it in plan-mode so it will brainstorm about what you want, come up with multiple alternatives with pros and cons and execute one when you give the word. While it is executing that piece, you can open a second, third or fourth window to work on a different issue. You can quite literally run an entire team of coders at the same time, while you only manage them.

However, it’s not perfect. Especially fixing more complex bugs can be an issue. Sometimes, after showing the problem and asking for a solution several times, it won’t be able to fix it. Since I do not know anything about code myself, I had to be creative.

Firstly, working modularly helps you pinpoint the issue to a specific module. You can then ask Claude to zoom in on that module and come up with possible causes. With just logic you can often judge its suggestions. That way you can help Claude get closer to fixing the issue.

Sometimes, it gets stuck in a certain thinking path it has gone down. In that case, it can help to get a second opinion. You open a second window or ask a different LLM (e.g. ChatGPT) to look at the issue. This way it is not biased by the context in your current conversation or its LLM specific knowledge. This has more than once resulted in it immediately recognizing the real issue, and me being frustrated with the fact that I spent half an hour trying to fix it in the initial chat.

All in all, I was really amazed with the possibilities. Getting it all set up was a bit of trial and error, and it takes quite some time to brainstorm about the implications of architectural choices. But once you have done that, it builds full-fledged applications in minutes.

New AI tools are being released quicker than we can learn to use them, so adaptability seems more important than ever. Just being able to build applications is not enough either. Just like before coding became so much easier, you need a business case for the application too. All in all, I think it’s a great time to be a business student with an interest in technology.

To anyone else who has been experimenting with AI tools for coding: what tools do you use and what best practices have you discovered?

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AI can turn floor plans into 3D housing models. What does it take?

10

October

2025

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I read “Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans” by Cambeiro Barreiro et al. (2023). The goal they had was to turn scanned plans into vector 3D models that you can export as IFC, a building data format. It lets different BIM (Building Information Modeling) tools share the same data, like walls, rooms, and doors, etc. This matters especially for big real estate portfolio holders, like housing associations. These parties would be able to gather insights from the vast amounts of data, which are normally only available on paper.

The core problem

Symbols and techniques differ, and old drawings are clumsy. Windows and doors break through walls. The scans are biased. Redrawing by hand is labour-intensive, expensive, and error-prone. The stakes are higher for large portfolio holders, such as housing associations. They oversee thousands of residences. They are unable to compare layouts, energy features, or risks across the stock in the absence of structured data.

How it actually works

You begin with floor plan scans or PDFs. Like a human, the system reads the plan. It recognises windows, doors, and walls. It corrects gaps and crooked lines. After that, it creates clean vectors from these shapes. Lastly, it exports IFC and creates a basic 3D model. Common BIM tools can use that file. You can then measure areas, tag units, and count rooms. Additionally, you can link maintenance notes or energy data. Structure is crucial. You can perform the same checks on buildings in the portfolio at large scale once the data is in a standardised format.

source: Cambeiro Barreiro et al., 2023

Why portfolio owners should care

Data is locked in drawers by paper plans. They become structured BIM thanks to this pipeline. This opens up the portfolio’s unit mix, wall types, window counts, and layout features. It facilitates large-scale renovation planning, accessibility assessments, and energy audits. You receive a cleaner handoff to designers and contractors and expedited due diligence.

Takeaway

Digitising plans is the first step if you own a sizeable housing portfolio and would like to digitize. Segment walls, identify symbols, and carefully translate everything using floor-plan AI. To prevent lock-in and maintain the data’s utility across tools, export to a common data type like IFC. The improvements are easy to understand in terms of speed, consistency, and insight at the portfolio level.

Reference: Cambeiro Barreiro, A., Trzeciakiewicz, M., Hilsmann, A., & Eisert, P. (2023). Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans.

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Film Photography and AI: My experience

10

October

2025

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I started using ChatGPT for school, then realized it could help my photography too. With film, every click costs money and time. Having a quick second brain lowered the stress and helped me make better choices before I even loaded a roll.

Most of my shoots are low light or mixed neon. I ask for a quick plan: likely shutter speeds for Cinestill 800T or my MARIX 135 T800 and Amber T800, what EV to expect at blue hour, and how far I can push before motion blur ruins the look. It is not magic. It just gives me a sensible starting point so I do not waste half a roll testing the obvious.

I also use it for composition practice. I describe a scene from my contact sheet, like “subject under a shop sign, bright window behind, messy foreground.” It suggests two or three framings to try next time. Step left to kill a distraction. Drop the angle to separate the subject from the background. Add a leading line from the curb. Simple ideas, but it keeps me iterating. My contact sheets feel less random and more like a series with intent.

Metering and color are where it saves me the most. If I am debating 1 stop over for skin indoors, or how much to bias exposure for tungsten under mixed LEDs, I ask for trade-offs. It reminds me what will happen to highlights on 800T and what to expect from halation. When a scan comes back with a green cast, I run a quick checklist for likely causes and fixes. It is the same with push or pull. I still note my lab’s advice, but I go in with clearer expectations.

Trust grew with results. The more useful the output, the more I tried. I still keep guardrails. I verify technical claims, write shot lists, and never paste personal data. The goal is not to outsource taste. The goal is to give my taste more chances to show up.

If you shoot film, try this next roll. Write a one paragraph brief, ask for two lighting setups and a backup plan, and make a tiny shot list. Then compare that contact sheet to your usual one. Did you see more, or just shoot faster?

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Agentic AI in Customer Relationship Management (CRM)

10

October

2025

5/5 (1)

One of the most transformative developments in enterprise technology today is the emergence of Agentic AI in the field of CRM. The way campaigns are being designed, executed and how data is being processed fundamentally shifts towards the integration of Agentic Systems. 

But how does that actually work in practice?

Unlike conversational AI tools that only assist users through predictive analytics or content generation, agentic AI actively makes decisions and executes tasks, without constant human intervention or the need for prompts. In todays practice, this means CRM systems are evolving from static databases into intelligent ecosystems where AI agents autonomously manage lead follow-ups, orchestrate personalized customer journeys and interestingly, also initiate retention campaigns when churn risk is detected. When companies decide to implement those agentic capabilities into their CRM, the implications for efficiency and scalability are profound. Those companies can engage customer continuously and react to behavioural changes in real time. The most important aspect is that a level of personalization for the individual consumer can be achieved, which was previously impossible at scale. 

How to implement that into existing workflows?

Many firms overestimate their technology readiness, meaning that the often launch isolated pilots, rather than focussing on clean data, orchestration frameworks, or proper human oversight. To be able to implement this technology successfully companies need to follow a balanced approach between bottom-up and top-down. Only when the employees are being enabled and empowered to identify areas where the agentic AI can help, the implementation will work out. Especially in CRM it is of high importance, that the system development begins with clear process mapping, well-defined guardrails, and incremental deployment. This way the firms can expand the given autonomy as trust in the system grows. If Agentic AI in CRM is implemented right the CRM moves from a reporting tool about campaign success, customer churn, or CLV into a living, learning collaborator that augments every stage of the customer lifecycle. 

How does the agentic workflow look like in practice?

First, the Agent sets a Budget-Goal for a campaign (Increase of Abonnement-Conversion by 15%). Then it accumulates data from CRM and other sources. Third, the agent analyses and prioritizes the given data (Decision who, when, on which channel and with which offer we can contact the client). The fourth step is about Asset generation such as creating personalized text or visuals. Here the highlight is, that the agentic AI personalizes every Client contact based on the accumulated data in the previous steps. The next step is about Optimizing the output and flushes the campaign to the client base. The next step is crucial for the agentic system since feedback loops, as well as deep learning capabilities come into play. Here the agent will focus on the interpretation of the performance of the previous campaigns and adjusts where it is necessary. 

Conclusion

Agentic AI in CRM is without any doubt the biggest transformation in today’s business-world. Companies are constantly searching for better ways to run client campaigns, to reduce churn and to increase interaction with clients to consequently generate more revenue. With the integration of an Agentic CRM system, topics like scalability and marginal costs are important. Companies need to focus on the implementation now, instead of falling behind competitors.

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Platformization at Different Speeds: Why Some Industries Lag Behind

8

October

2025

5/5 (1)

What do some of the best-known tech companies have in common? Surely, they all are incredibly successful. But they all are also platform-based organizations that through the agility with which they are able to innovate and scale, have redefined entire industries by creating ecosystems and connecting users, data, and services in ways traditional firms could never match (Blumberg et al., 2020). Over the past decade, platform business models have revolutionized entire industries. Airbnb reshaped hospitality without owning a single hotel. Uber transformed urban transport without a fleet of cars. Spotify turned music into a service rather than a product. Yet, while these consumer-facing industries have been transformed almost overnight, others — like procurement, manufacturing, or healthcare — have been far slower to “platformize.”

The difference lies in how easily digital platforms can generate network effects. Platforms thrive when they connect fragmented markets, reduce transaction costs, and create standardized interactions between users (Madanaguli et al., 2023). If you think of a ride, a song, or a stay, transactions here are relatively simple. In sectors such as hospitality or entertainment, data is abundant, trust can be built through ratings, and users switch platforms easily.

In contrast, industries like B2B procurement involve complex relationships built on trust, quality assurance, and long-term contracts (Beard, 2025). Transactions are high-value, and negotiations are delicate and require time. As a result, even if the benefits of digital matching are clear – transparency, efficiency, and data insights – adoption takes time. Even emerging B2B platforms such as Torg or Wonnda, which connect food and beverage buyers with suppliers, must first overcome structural and cultural barriers before achieving the same level of network-driven momentum as Uber or Airbnb. Still, things are changing. Advances in AI and automation are reducing friction in traditionally “offline” sectors. As information flows become more standardized, we can expect a new wave of platforms to emerge — this time not for your next ride or vacation, but for sourcing ingredients, manufacturing components, or managing supply chains.

The question is no longer if platforms will disrupt every industry, but how fast and who will lead the change.

References

Beard, N. V. (2025, September 8). Optimising B2B procurement for the future (2025). BigCommerce. https://www.bigcommerce.co.uk/articles/b2b-ecommerce/b2b-procurement/

Blumberg, S., Kürtz, K., Bossert, O., & Richter, G. (2020, March 12). The power of platforms to reshape the business. McKinsey & Company. Retrieved October 5, 2025, from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/the-power-of-platforms-to-reshape-the-business

Madanaguli, A., Parida, V., Sjödin, D., & Oghazi, P. (2023). Literature review on industrial digital platforms: A business model perspective and suggestions for future research. Technological Forecasting and Social Change, 194, 122606. https://doi.org/10.1016/j.techfore.2023.122606

Soares, I., & Nieto-Mengotti, M. (2024). Network effects on platform markets. Revisiting the theoretical literature. Scientific Annals of Economics and Business, 71(4), 605–623. https://doi.org/10.47743/saeb-2024-0029

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Disrupting Disruptors, Whats Going On With Netflix?

3

October

2025

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Once a disruptor to Blockbuster and other video rental firms, Netflix started as a cheaper, simpler way to watch your favourite movies and TV shows. Then, utilizing on-demand video streaming, they started competing with cable networks as well. 

According to Christensen, Netflix is a classic case of disruptive innovation. Netflix entered the streaming market at the bottom as an inferior product. They had a limited catalog and lower bitrates, but had success with consumers who were both fed up with cable networks’ prices and their complexity, and did not want to turn to piracy or other illegal ways of watching content on the other. In fact, in the early days of streaming, Netflix made video piracy obsolete. It simply wasn’t worth the risk anymore when Netflix was so affordable. Then, slowly, as they obtained more licences and technological capabilities improved, they started winning in quality as well. The disruptor had become an incumbent itself.

Now, a decade later, the streaming landscape resembles the incumbent it displaced more than what it originally promised consumers. Fragmented supply, price increases, frequent advertisements, crackdowns on account sharing, and region-locked content have reintroduced the friction to the market that streaming originally took away.

Consumers are clearly unhappy with these developments. Netflix posted its first ever subscriber decline in Q2 2022, losing over 2 million subscribers in one month (GlobalData, n.d.).  While this rapid decline in subscribers was an exception, caused by Netflix’s announcement of no longer allowing password sharing, it exemplifies the issues the streaming industry currently has. But where are consumers turning now? Who is disrupting Netflix?

It turns out, video piracy sites haven’t been sleeping while Netflix has grown. They have turned to mimicking the disruptor’s playbook. They centralize the now fragmented catalogs, breaking down regional barriers, they don’t require accounts, and most importantly, they have caught up in quality as well. Modern video piracy sites not only support Full HD video streaming in real time, but their user interfaces have massively improved as well.

As they are now winning in convenience and able to compete in quality, video piracy sites are acting as a low-end aggregator in the streaming space, and they are regaining market share quickly. The US economy alone loses between $29 and 71$ million due to digital video piracy per year (Spajic, 2023). 

And so the cycle continues. But where is the streaming industry going next? Netflix once disrupted the industry by making video simpler. And consumers are now asking for the same simplicity again. If streaming can once again be the most convenient way to watch your favourite movies and shows, piracy’s advantage shrinks again. So the question for Netflix and its peers is simple: How can they reclaim consumer trust and market share, without abandoning profitability?

GlobalData. (n.d.). Netflix loses almost a million subscribers in last quarter. https://www.globaldata.com/data-insights/technology–media-and-telecom/netflix-loses-almost-a-million-subscribers-in-last-quarter/

Spajic, D. J. (2023, April 10). Piracy is Back: Piracy Statistics for 2025 | DataProt. dataprot. https://dataprot.net/blog/piracy-statistics/

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The Skinny Epidemic: The Rise of GLP-1 Drugs – Threat or Opportunity?

19

September

2025

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Over the past two years, the rise of GLP-1 drugs has become prominent, disrupting not only the healthcare but also the consumer markets worldwide. It was originally designed to treat patients with diabetes, however, these drugs (most commonly known under names like Ozempic or Monjaro) have been proven to significantly aid weight loss. 

Its boom in the market is quite obvious from the numbers; Goldman Sachs predicts the GLP-1 market to reach $100 – $150 billion by 2030 with over millions of users globally (CNBC, 2024). The market capitalization of Novo Nordisk, a key pharma company, jumped by billions after publishing the effectiveness of their anti-obesity drugs (Financial Times, 2023). At first, the GLP-1 was just seen as a revolution within the pharma industry. However, we can now see how it is creating rippling effects through various businesses and consumer facing industries; forcing firms to adapt their business models or risk getting left behind. 

Digital Health Business meets GLP-1

Numbers of digital health companies are now building programs around GLP-1 therapies. Start-ups are combining AI-driven insights with apps and gadgets that provide personalized care to consumers. With the increased users of GLP-1 drugs, there is a rising demand for preventative health care. The focus is now shifting away from weight management devices to digital therapeutics (PwC, 2024).  Medtech firms are responding with diagnostic tools that are integrated with AI and remote monitoring, making an innovative shift in preventative health care. 

Ripple Effects Beyond The Health Industry: 

The impact of GLP-1 drugs is extending far beyond the health care sectors. With the effect of these drugs, people are now less hungry, more healthy, and more energetic. These shifts in individual brings changes that industries should not ignore: 

  • Food & Beverage: The marketing of the snacks and the types of food being sold in the aisles of the grocery store are starting to shift. More protein rich and functional foods are on the rise. Firms such as Unilever and P&G are implementing AI tools to accurately predict demand and to forecast consumer preferences, which is essential in this shifting dynamics.
  • Fitness and Wellness: Gyms and digital fitness platforms are seeing increased engagement from people who are now capable of staying active. 
  • Travel & Leisure: Vacations are shifting away from food-centric experiences toward activity-driven tourism.
  • Retail & Apparel: Clothing brands are forecasted to face changing demands as body images shift. There is an increased demand for fitness wear as more people are motivated to maintain active lifestyle. 

Strategic Takeaways

Regarding the firms outside of the pharma industry, this boom of GLP-1 drugs poses them either an opportunity to adapt or a risk of irrelevance. The food industry must diversify their product portfolios, and apparel brands must rethink the trends and the sizing. Fitness platforms should integrate GLP-1 coaching systems that personalize across each consumer.

In order to ride this wave, firms should experiment with digital integration, AI powered analytics, and personalized services that can capitalize on this disruption into opportunity. 

References:

CNBC. (2024, May 25). Digital health companies are launching programs around GLP-1s. CNBC. https://www.cnbc.com/2024/05/25/digital-health-companies-are-launching-programs-around-glp-1s-.html

Financial Times. (2023, August 10). Novo Nordisk shares jump after new anti-obesity pill shows strong results. Financial Times. https://www.ft.com/

LightIT. (2024, June 12). Beyond the hype: Where and how digital health founders should build in the GLP-1 market. LightIT Blog. https://lightit.io/blog/beyond-the-hype-where-and-how-digital-health-founders-should-build-in-the-glp-1-market/

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AI in Housing Valuations: Make it explainable

19

September

2025

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AI can now help to set property values. What are the risks involved?

I came across this paper called Enhancing Explainable AI Land Valuations Reporting for Consistency, Objectivity, and Transparency. By Y. Yim and C. Shing (2025). The paper goes into detail on how explainable AI can ethically support the valuation of properties. This is a sensitive topic, since the valuation of properties affects many parties, like banks, buyers, sellers and the cities. There are major efficiency gains to be made by implementing artificial intelligence and machine learning in this sector. However, their integration also raises legal and ethical questions.

The Core problem

Many models act like a black box; this undermines the duty of the valuer to deliver transparent and consistent valuations. The legal system requires the properties to be thoroughly inspected. and the process to be well-documented.

A possible solution: Making AI explain itself

There are 3 pillars to ethically implement these innovations while integrating these technologies. Consistency: The model should provide repeatable results, following the same process. Objectivity: There should be a clear separation between the developers and validators of a model. Transparency: the model, data and limits should be well documented and easy to understand.

XAI tools like SHAP can be used to make the AI explain itself. These tools show how each feature/variable pushes the price up or down. The chart shown below ranks drivers such as zoning, building age, and floor area. This turns a score into a story that a client can follow.

“The visualisation sets a foundational baseline value (E[f(X)]) of 13.98 on the x-axis, representing the model’s average prediction when no specific feature information is provided. This is the expected value if we were to make a prediction without any additional information. The Output Value (f(x)), which in this instance is 14.07, reflects the actual prediction after accounting for the cumulative effect of the individual features. The colour-coded bars represent the push and pull of each feature on the prediction: the red bars show the features that contribute to an increase in the predicted land value, while the blue bars indicate a decrease.“

Takeaway

AI can increase efficiency and scale valuations. But it must earn trust. Build on three pillars: consistency, objectivity, and transparency. Use SHAP for explanations. Ship reports with a clear checklist and keep human judgement in charge.


References:
Yiu CY, Cheung KS. Enhancing explainable AI land valuations reporting for consistency, objectivity, and transparency. Land. 2025;14(5):927. https://www.proquest.com/scholarly-journals/enhancing-explainable-ai-land-valuations/docview/3212060417/se-2. doi: https://doi.org/10.3390/land14050927.

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Redesign or Fall Behind: How Agentic AI Rewrites Business Rules

19

September

2025

5/5 (1)

A new era emerges. Agentic AI is disrupting traditional processes and will reshape market dynamics — much like the emergence of the internet did. But what is Agentic AI, and why does it affect every company across industries? These systems don’t just follow rigid instructions: they perceive, decide, and act with autonomy, pursuing goals while coordinating tools and actions themselves (Sukharevsky et al., 2025).

Imagine a simple example: you are the pilot of an airplane, and you have an AI-Copilot beside you, assisting when needed. Here the AI has little to no autonomy. That has changed profoundly. Nowadays, systems are emerging that can operate that airplane alone, only needing human supervision under certain conditions. Where once you were the pilot, now you might be supervising from home rather than doing every action yourself. What does that mean? Our roles as humans are changing. We need to understand how to adapt our ways of working to keep up in a competitive, fast-paced environment.

You might ask: “Nice to know, but what does it mean for me or my job?” Simple answer: It affects every part of our jobs, industry dynamics, and daily routines. There is a need to redesign processes and workflows that were built for human control, to enable AI to take over certain business functions. For example, in financial services: ordering a new credit card in your banking app may involve dozens of background steps and decisions. To improve your experience and enable autonomy in agentic systems, companies that want to lead must follow 5 crucial steps:

  1. Clarify Goal & Intent
    Begin by defining what outcome you actually want, rather than mapping every decision.
  2. Define Simple Generic Steps
    Create a small set of core actions (like “Request → Action → Confirm”) that the AI can combine as needed.
  3. Capture Feedback & Metrics
    Monitor quality, speed, user satisfaction, etc., so you know if the redesign works.
  4. Set Human Guardrails
    Identify when AI needs human intervention: sensitive topics, rules, regulations.
  5. Pilot, Learn & Expand
    Start with one process or use case. Learn from feedback. Then refine and scale across other business units.

Companies like McKinsey warn that many gen AI and agentic AI pilots are failing or not delivering value unless workflows are re-designed from the ground up (Sukharevsky et al., 2025).

For example, Salesforce has cut roughly 4,000 customer support jobs, reducing its workforce in that division from ~9,000 to ~5,000, which the CEO says is due to AI agents taking over many support interactions (Sorace, 2025).

Redesigning processes isn’t optional. It’s now a competitive imperative.

Sukharevsky, A., Kerr, D., Hjartar, K., Hämäläinen, L., Bout, S., & Di Leo, V., with    Dagorret, G. (2025, June 13). Seizing the agentic AI advantage: A CEO playbook        to solve the gen AI paradox and unlock scalable impact with AI agents.McKinsey &          Company. https://www.mckinsey.com/capabilities/quantumblack/our-      insights/seizing-the-agentic-ai-advantage

Sorace, S. (2025, September 3). Salesforce cuts 4,000 jobs due to AI, CEO says. Fox Business. https://www.foxbusiness.com/economy/salesforce-cuts-4000-jobs-due-ai-ceo-says?

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K-pop on a digital transformation

18

September

2025

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When we hear the word “Korea”, it naturally bridges us towards some big players, and you can never mention Korea without talking about K-pop. Today, K-pop is not just about a music but it remains as the one of the biggest content and a culture within South Korea’s entertainment industry. The K-pop entertainment industry does not rely solely on sales on album, or their concert tickets. like they did in the past. They have successfully developed a whole new platform based digital transformation that built more connection between idols and fans, but also on the revenue side.

Case 1- Living in the bubble?

The bubble-Dear U is a subscription based app, where fans pay money(subscribe to the specific idol they want) and pay the money, then they can experience the private-style messages from idols(Zhang, Y. 2022). This gives an another level of connection, that as a fan you are privately having a conversation with your idol(But you’re not). To target users from different countries, they provide a translate features within the app as well so the language barriers is reduced.

Case 2- V Live

V-Live, as from the name you can see it is a digital platform where idols can livestream and fans can join their live with their comments, likes and etc. This was also intended to make more communication and connection between idols and fans(Park, S., Jo, H., & Kim, T. 2023). It was originally owned by Naver(another big company in Korea), but recently it was integrated by Weverse (HYBE’s platform which is another big entertainment industry)And this clearly depicts the envelopment strategy where swallowing up on the compliments.

K-pop industry builds on parasocial relationship

Looking at these examples and cases of digital platform integrated in the K-pop industry, we can clearly see that the K-pop industry builds its backbone on a parasocial relationship between the fans and idols. It’s not just about listening to their music anymore, it’s about how to satisfy the needs and desire of the fans.

Then what is this parasocial relationship? The parasocial relationship refers to a one-sided connection and feelings you get towards celebrity, influencer or a fictional character that is not based on the reality, and you experience feeling of intimacy, friendship, and some connection. So they key is that you do not know this person personally, but based on the marketing strategy and tools, you can feel this strong connection and intimacy.

This trend on K-pop displays on the downside of the industry, that the idols are commodified. Meaning they became a certain products that the entertainment industry produces based on the social beauty standards or other norms, and their worth matters only when they are consumed by the consumers(fans).

Do you think this platform based model of K-pop industry is the only solution of entertainment industry to survive in the future? or do you think this will create a negative effect where fans will get burned out from paying for so much subscription?

References:

Lee, S., & Prey, R. (2025). The Labor Process of Relational Labor: The Case of the K-Pop Fan Platform “Bubble.” Popular Music and Society, 1–21. https://doi.org/10.1080/03007766.2025.2492505

Jang, G., & Paik, W. K. (2012). Korean Wave as tool for Korea’s new cultural diplomacy. Advances in Applied Sociology, 02(03), 196–202. https://doi.org/10.4236/aasoci.2012.23026

Park, S., Jo, H., & Kim, T. (2023). Platformization in local cultural production: Korean platform companies and the K-pop industry. International Journal of Communication, 17, 2422–2443.

Zhang, Y. (2022). A study on the para-social interaction between idols and fans in virtual applications: Case study of Lysn Bubble. In Advances in Social Science, Education and Humanities Research, Volume 631. Duke Kunshan University.

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