In 1949, linguist George Zipf discovered something strange: in any 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 – where a few items dominate while most barely register – appears everywhere from city sizes to wealth distribution. Now it’s showing up in AI usage, and it’s breaking everyone’s product strategies.
Zipf’s Law reveals an uncomfortable truth about AI products: while companies race to add hundreds of features and capabilities, users stubbornly stick to using the same few functions over and over. The distribution is so extreme it seems impossible – until you look at the data.
“Good enough” dominates “optimal”This creates winner-take-all dynamics within product features.
The Product Strategy Implications
The Feature Bloat Trap
Companies keep adding features because:
Competition Pressure: Match competitor feature lists
Marketing Needs: New features for announcements
User Requests: Vocal minorities demand edge cases
Engineering Pride: Technical capability demonstrationsBut Zipf’s Law means most features are waste.
The Core Feature Paradox
The paradox: Users choose products based on feature breadth but use them for feature depth.
Selection Criteria: “Can it do everything?”
Usage Reality: “I only use it for one thing” Retention Driver: Excellence at core features Churn Reason: Core feature degradation
Companies optimize for selection (breadth) but should optimize for retention (depth).
The Long Tail Illusion
The “long tail” strategy suggests serving nicheneeds profitably. But in AI:
Long tail features have near-zero usage
Maintenance costs are significant
Complexity degrades core experience
Support burden is disproportionateThe long tail is a cost center, not profit center.
Strategic Responses to Zipf’s Law
The Ruthless Focus Strategy
Accept Zipf’s Law and optimize for it:
Identify Power Features: Find your 1% that drives 50% usage
10x Investment: Put all resources into dominant features Aggressive Pruning: Remove underused features Depth Over Breadth: Better to do one thing perfectly
Layer 1: Core features (visible to all) Layer 2: Power features (available on request) Layer 3: Advanced features (hidden by default) Layer 4: API/Developer features (separate documentation)
This respects Zipf’s Law while serving edge cases.
The Modular Architecture Strategy
Separate core from periphery:
Core Product: Minimal, perfect, fast Plugin Ecosystem: Optional additions Feature Marketplace: Third-party extensions API Platform: Build your own features
Let Zipf’s Law work for you – most users get simplicity, power users get everything.
The AI-Specific Manifestations
Prompt Engineering Concentration
Even in “open-ended” AI, usage concentrates:
Top Prompts (variations of):
1. “Explain this to me”
2. “Write a [document type] about [topic]”
3. “Fix this [code/text]”
4. “Summarize this”
5. “Translate to [language]”
Despite infinite possibilities, users converge on a few patterns.
The Model Capability Waste
Large models have thousands of capabilities, but users tap into few:
GPT-4 Capabilities: Poetry, analysis, coding, translation, reasoning, math… Actual Usage: 90% is basic text generation Capability Utilization: <5% of model potential Economic Implication: Overpaying for unused capability
1. Measure ruthlessly – Know your true distribution
2. Invest accordingly – Resources should follow usage
3. Simplify aggressively – Remove the tail
4. Perfect the core – Excellence at the head matters most
5. Stop feature racing – Breadth is a losing game
For Marketers
Market the head, not the tail:
1. Lead with power features – What people actually use
2. Avoid feature lists – They don’t drive decisions
3. Show depth, not breadth – Excellence over options
4. Target use cases – Specific problems, not capabilities
5. Demonstrate habits – Show repeated use, not one-time tricks
1. Price the head – That’s where value lives
2. Bundle the middle – Package secondary features
3. Abandon the tail – Or make it community-supported
4. Compete on core – That’s where battles are won
5. Differentiate on excellence – Not feature count
The Future of AI Products Under Zipf’s Law
The Great Unbundling
As Zipf’s Law becomes understood, expect:
Single-feature AI products
Micro-apps for specific uses
Dramatic simplification
Death of “all-in-one” AI
The Specialization Wave
Products will choose their Zipfian peak:
Writing AI (only writing)
Code AI (only coding)
Image AI (only images)
Analysis AI (only data)Generalist AI will lose to specialists.
The Interface Revolution
New interfaces that embrace Zipf’s Law:
Single-button products
Zero-learning curve designs
Habit-first interfaces
Invisible AI (no interface at all)
Key Takeaways
Zipf’s Law in AI usage reveals fundamental truths:
1. Feature usage is extremely concentrated – A few features dominate completely
2. Human behavior follows power laws – This isn’t changeable through design
3. Excellence beats breadth – Better to perfect one feature than add ten
4. Habits dominate exploration – Users stick with what works
5. Simplicity is a moat – Complexity is a liability
The winners in AI won’t be those with the most features, but those who:
Accept that most features are never used – and that’s okayZipf’s Law isn’t a problem to solve – it’s a reality to design for. The question isn’t how to get users to use more features, but how to make the features they do use absolutely perfect. In AI, as in language, a few words do most of the work. The wisdom is knowing which ones.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
