AI Feature Usage Inequality: The Reason Behind 1% of Functions Accounting for 99% of Use
In the realm of Artificial Intelligence (AI), a fascinating pattern has emerged that is universal across various AI products. Known as Zipf's Law, this phenomenon indicates a concentration of extreme usage at the top, with a rapid decay down the tail. This means that a few key features are used extensively, while the majority of features remain underutilised.
For product managers, this law carries significant implications. It's crucial to measure usage ruthlessly, invest accordingly, simplify aggressively, perfect the core, stop feature racing, and design for the reality of Zipf's Law. This approach ensures that resources are directed towards the features that truly matter to users, fostering a more efficient and effective product.
Marketers, too, must adapt their strategies to align with Zipf's Law. Instead of focusing on feature lists, they should market the head of the distribution, show depth, target use cases, and demonstrate habits. By highlighting the primary features that users value, marketers can effectively communicate the product's benefits to potential customers.
The winners in AI won't be those with the most features, but those who identify the vital few features that matter, perfect those features beyond all competition, resist the temptation to add complexity, build business models that align with usage reality, accept that most features are never used, and design for the reality of Zipf's Law.
In the AI market, large models may have thousands of capabilities, but users tap into only a few. Usage concentration leads to pricing power concentration, with core features being able to charge premiums and secondary features needing to be bundled. This trend is evident in products like ChatGPT, where 40% of all queries are for basic Q&A, 20% are for writing assistance, 15% are for code help, 8% are for translation, and 5% are for summarization.
As Zipf's Law becomes better understood, expect strategic responses such as the Ruthless Focus Strategy, the Progressive Disclosure Strategy, and the Modular Architecture Strategy. The AI interfaces are also converging to a few patterns, with the chat interface dominating. The future of AI may see a shift towards single-feature AI products, micro-apps for specific uses, dramatic simplification, the death of "all-in-one" AI, the specialization wave, and interface revolutions that embrace Zipf's Law.
However, it's important to note that companies cannot educate users out of Zipf's Law. Users economize by mastering a few high-value features and ignoring the rest, creating winner-take-all dynamics within product features. Great features can't overcome Zipf's Law, as users won't explore new features, habits are established, cognitive load is real, and switching costs dominate.
First discovered by linguist George Zipf in 1949, Zipf's Law states that in any given language, the most common word appears twice as often as the second most common, three times as often as the third, and so on. This pattern is now showing up in AI usage, revealing fundamental truths such as feature usage being extremely concentrated, human behavior following power laws, excellence beating breadth, habits dominating exploration, simplicity being a moat, and complexity being a liability.
As AI continues to evolve, understanding and adapting to Zipf's Law will be essential for businesses to create successful products, attract and retain users, and thrive in the competitive AI market.
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