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Growing up, we all have days where we would want someone else to choose for us or tell us what to do and how to do it. I decided to make this dream a reality and allow ChatGPT to take over my daily decisions for one day. So, for 24 hours, big or small decisions, I asked AI for advice and ultimately AI had the final say.

When I woke up in the morning the first thing I did was to ask for a focused study schedule. ChatGPT did surprisingly at this task, as it generated a realistic plan for me with breaks and clear goals for what I needed to get done. There were even suggestions for breathing exercises during the break that would help me focus. After having tried many different structures for approaching studying over the year of my studies, this plan surprised me as I was more productive than usual.

However, during the day AI did not perform as well. When it came to asking for lunch recommendations, it suggested for me to have a quinoa salad with roasted vegetables, a healthy option but not one with common ingredients that I would have in my house. Later, when I got offered to hang out with some friends, AI advised me to stick to the plan it had constructed for me to be able to “maintain consistency”. Even though this made me more productive, it did make me feel a bit disconnected.

After a long and productive day following the carefully curated plan made from ChatGPT, I was quite tired and had some personal worries. To continue my 24 hour mission, I asked AI for advice with my personal matter, and to my surprise, the response I got made me more calm, as it gave me structured steps on how to handle what I was feeling. The response was quite comforting, making me feel a bit eerie about how easy it was for the software to mimic emotional support.

Looking back at the day I spent using AI as my personal decision maker, I realized it can be a great tool for planning, getting motivation and even emotional support, but should never have this much control. I saw that I missed using my own intuition to make decisions, which made me feel more disconnected with the people around me. I believe this level of spontaneity cannot be replicated by an algorithm. For this reason, I would not outsource my daily choices to AI but I would use it as a digital advisor as part of my toolkit.

Would you ever let an AI software guide you day?

References

Google. (2025). A split image showing a person interacting with a holographic study schedule on one side, and a stylized wireframe brain with glowing connections and decision-making icons on the other, separated by a prominent question mark, symbolizing AI-guided decisions and human intuition. [Generative AI image].

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The Freemium Challenge

In the digital world nowadays, the use of the freemium business model has become the standard. From professional networks such as LinkedIn to streaming services such as Netflix, companies offer their basic services for free to customers to create the initial attraction, hoping that they will then choose to upgrade to have access to premium features (Anderson, 2009). This strategy is very successful at helping a company grow, but it doesn’t come without any obstacles. Companies that employ have to ask themselves: How do you balance attracting a large user base for free while actually generating revenue?

Social Referral Programs

A common tactic used by companies is social referral programs. More specifically, incentives, such as unlocking premium features, are offered to existing users to bring in new users to the platform (Schmitt et al., 2011). This way the company leverages the most valuable asset they have, their existing customer base, to exponentially grow and keep generating revenue. However, even though in theory this tactic is successful, recent research on a freemium dating platform show that this strategy does not come without risks (Belo & Li, 2022)

What Research Shows

The study by Belo and Li (2022) on a european dating platform that used the freemium model found that there is a clear and positive correlation between the referral requirement and a the financial performance of a firm. More specifically, when the number of referrals needed to unlock a premium feature increases, there was a significant increase in the total number of referrals but also the overall revenue of the company. This signals that the strategy is successful and that more aggressive referral requirements would directly lead to more users and more revenue.

The Trade-off

Nevertheless, the story does not end there, as the same research showed that there is a double-edged nature to this strategy. Even though the user bases of the platform were growing exponentially and revenues were increasing, signalling winning, the company was losing on another crucial part: user engagement. As the number of referrals increased, users took longer to unlock the premium features, which in turn reduced their social utility and their engagement with the application. Thus, as a result of this strategy the company was gaining new users at the cost of existing customer churn.

My Take

Whether or not as a company you want to engage in this trade-off should depend on your time horizon. If you are focused on boosting short-term revenues for a funding round or to become more profitable, tightening referral requirements could be the way to go. However, in the long term such a tactic will undermine retention and growth that’s based on word-of-mouth, which are critical for the success of the freemium business model (Oestreicher-Singer & Zalmanson, 2013).

A smarter approach would to tier the rewards, meaning referrals would unlock some premium features but not all, keeping top-tier ones pay-only, as the study suggests (Belo & Li, 2022). Another alternative is making incentives time-limited, so engagement would be restored once new users settle in.

Takeaway

Social Referral Programs are a powerful tool when it comes to growth, but the growth does not come for free. Raising the bar of referrals might lead to higher revenues today, but may discourage the community that fuels tomorrow’s growth. The best strategy likely lies in carefully navigating the freemium equation by finding balance and using both sides.

References

Anderson, C. (2009). Free: The future of a radical price. Hyperion.

Belo, R., & Li, T. (2022). Social referral programs for freemium platforms. Management Science, 68(12), 8933–8962. https://doi.org/10.1287/mnsc.2022.4301

Google. (2025). A symbolic image representing the trade-off between user engagement and revenue in a freemium business model, with a prominent balancing scale at its center. The scale has two pans. The left pan holds a large, gleaming dollar sign ($), stylized to look heavy, and is tilted slightly downwards. The right pan holds a simplified, generic person icon, also stylized to appear lighter, and is tilted slightly upwards. The pivot point of the scale is perfectly centered. The background is a gradient of muted blues and greens, suggesting a professional and analytical context. Subtle, almost transparent lines and bar graphs are faintly visible in the background, hinting at data and business metrics. The overall image has a clean, modern aesthetic with clear, crisp lines and soft shadows to give depth. [Generative AI image].

Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? A digital business strategy for content providers in the social age. MIS Quarterly, 37(2), 591–616. https://doi.org/10.25300/MISQ/2013/37.2.09

Schmitt, P., Skiera, B., & Van den Bulte, C. (2011). Referral programs and customer value. Journal of Marketing, 75(1), 46–59. https://doi.org/10.1509/jmkg.75.1.46