Acquisition
Acquisition Strategy in AI
Faster building does not mean faster growth. AI's acquisition power is relevance, not volume. A strong data layer must come before AI acquisition leverage. Growth loops still beat funnels because outputs become inputs.
Core thesis
AI gets teams to the hard part faster. It accelerates building and execution, but distribution, trust, adoption, habit change, and channel dynamics still obey growth fundamentals.
Five misconceptions of AI acquisition
- "Faster building means faster growth" — adoption still happens at human speed through trust, budget, habit, and evaluation
- "AI's power is volume" — volume without relevance damages trust and reputation
- "AI enables set-and-forget acquisition" — strong AI acquisition systems require human strategy, judgment, and continuous context
- "AI democratizes excellence" — AI raises the baseline; differentiation requires proprietary data, taste, customer insight, and differentiated loops
- "AI changes everything" — acquisition, retention, monetization, loops, growth models, and S-curves still apply
The Relevance Pyramid
AI acquisition is useless without a strong data layer
Build in order: Data Foundation → AI Intelligence Layer → Channel Activation.
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