The Distribution of AI Applications: The Focal Point of 1% Features Accounting for 99% of Usage
In the world of artificial intelligence (AI), a fascinating pattern has emerged that reveals a lot about how users interact with these advanced tools. Known as Zipf's Law, this principle, first observed in various domains, suggests that a few features dominate usage in AI products, while most are barely used.
According to recent studies, in image AI, for instance, portrait enhancement accounts for 35% of usage, background removal for 25%, style transfer for 15%, object removal for 10%, and advanced features for less than 15%. This trend is not unique to image AI; it extends across various AI products.
The ChatGPT usage pattern mirrors this trend, with basic Q&A (40% of all queries), writing assistance (20%), code help (15%), translation (8%), and summarization (5%) making up the majority of interactions. The rest, less than 12%, includes a variety of other tasks.
This extreme concentration at the top and rapid decay down the tail is a universal pattern in AI usage. This pattern has significant implications for AI product development and strategy.
One such strategy is the Ruthless Focus Strategy, which involves identifying power features, investing heavily in dominant features, aggressively pruning underused features, and focusing on depth over breadth. This strategy aligns with Zipf's Law, as it prioritises the few features that generate the majority of usage.
Another strategic response to Zipf's Law is the Progressive Disclosure Strategy, which hides complexity from most users by layering features. Core features are visible to all, power features are available on request, advanced features are hidden by default, and API/developer features are in separate documentation.
The Modular Architecture Strategy separates the core product from peripheral features, offering a minimal, perfect, fast core product, an optional plugin ecosystem, a feature marketplace for third-party extensions, and an API platform for building custom features.
These strategies aim to simplify AI products, making them more user-friendly and efficient. As Zipf's Law becomes understood, expect single-feature AI products, micro-apps for specific uses, dramatic simplification, death of "all-in-one" AI, product specialization, new interfaces that embrace Zipf's Law, and the rise of single-button products, zero-learning curve designs, habit-first interfaces, and invisible AI.
However, great features can't overcome Zipf's Law. Users won't explore, habits are established, cognitive load is real, and switching costs dominate. This is known as the Core Feature Paradox, where users choose products based on feature breadth but use them for feature depth.
Moats exist only in high-usage features. A strong moat for excellence at the #1 used feature, a weak moat for breadth of rarely-used features, and no moat for me-too implementations of everything. Companies can't educate users out of Zipf's Law, as usage still follows Zipfian distribution despite education attempts.
For product managers, it is important to measure ruthlessly, invest accordingly, simplify aggressively, perfect the core, stop feature racing, and design for the reality of Zipf's Law. The long tail strategy, which suggests serving niche needs profitably, is not effective in AI due to the near-zero usage of long tail features, significant maintenance costs, complexity degrading the core experience, and disproportionate support burden.
In conclusion, understanding and designing for Zipf's Law is crucial for success in the AI industry. The wisdom is knowing which features matter and making them absolutely perfect. In AI, as in language, a few words do most of the work. The challenge is knowing which ones.
Read also:
- Overcoming Yielding Regulations Hurdles in Indian Export Sector for EU Markets
- Shaping production and consumption tendencies via cosmetic certification
- Increased Investment in AI, Machine Learning, Internet of Things, and Quantum Computing to Propel Growth in the Digital Economy (2020 Budget)
- Deteriorated residence designed for trainees
