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Nowadays, generative AI tools have evolved so much that they allow you to materialise almost any idea into concrete user interfaces, either websites or prototypes. In my case, I want to reflect on my use of v0 to prototype a company-designed AI tool.

For another class called Validation & Pivoting, we undertake an entrepreneurial project. In this context, we had to prototype our idea, and we could use AI tools to achieve a qualitative prototype. To optimise the use of v0, I previously transferred the main idea to ChatGPT which helped me write a precise prompt for v0 to create the prototype closest to the final idea. I was very impressed by how v0 manages to transform your idea given in basic words into a very detailed, user friendly and well thought prototype draft. Not only is this draft very close to what I imagined, but it is also very easy to use the chatbot included in v0 to make modifications and adapt your prototype toward its final version. It feels just like chatting with a designer or developer, except that it’s more of a monologue than a dialogue, and that the chatbot doesn’t take initiative or sometimes lacks creativity. Another major advantage is that it also gives you access to the actual frontend code, which you can inspect or even modify. It is also very helpful if you consider connecting a backend to this frontend, allowing you to turn your prototype into a concrete MVP.

It also comes with some drawbacks and limitations. Firstly, there is a risk of over-reliance on AI tools for designing user interfaces. Moreover, it does not perform full user simulations or test dynamic workflows. Lastly, the code sometimes needs to be verified, and the backend creation could be more optimised.

Generative AI tools are revolutionary to create user interfaces in minutes, but how much more transformative will they become once the corresponding backend can also be generated?

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Information goods such as ChatGPT or Spotify are emerging nowadays. Pricing them represents a key strategic challenge for one main reason. In contrast with traditional goods, they have a near to zero marginal cost and different perceived values according to customers (individually/segment). There are multiple pricing strategies for information goods such as versioning, bundling or group pricing but they are limited because mainly static and they do not take into consideration the individual willingness to pay. But how is the use of AI going to impact pricing strategies of information goods?

To illustrate this question, let’s talk about the Amazon case. Amazon uses algorithms that adjust prices in real time settings. Around 2.5 million prices are modified every day, which means an average price that changes every 10 minutes (Mattes, 2023). Pricing algorithms consider multiple factors such as competitors’ prices, supply and demand through estimated demand and inventory levels or timing through time of the day/year or special events. Since many sellers on Amazon use those algorithmic pricing tools, some interactions can lead to tacit algorithmic collusions such as a collective price increase. Even if this system increased profitability, it also comes with drawbacks. Indeed, even if it tends to decrease when customers get used to it, frequent fluctuations can undermine trust, and this is amplified when prices are perceived to be unfair or arbitrary. It also presents regulatory challenges to determine how those algorithms must be regulated. Moreover, the main challenge it faces is this strategic dilemma of how far personalisation should go without alienating users.

This leaves us room for reflecting: are pricing algorithms ethical and where should we draw the line? And how should platforms balance personalisation and fairness when pricing information goods?

Mattes, Thomas (2023). Algorithmic price administration: How Amazon hijacks competition through automation. Berkeley Technology Law Journal, 37(4), 1179–1240. https://btlj.org/wp-content/uploads/2023/08/0008-37-4-Mattes.pdf