Consumer AI’s untapped ‘white space’ beyond productivity: Network-based platforms and marketplaces

vibrant street market with diverse crowd
While most consumer AI applications currently focus on productivity and search replacement, significant opportunities exist in network-based categories like dating, recruiting, social AI, and marketplaces such as shopping and home buying, according to a16z General Partner Olivia Moore and a16z Growth Partner Josh Elman.

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The current landscape of consumer AI is largely dominated by applications centered on productivity and ‘getting things done,’ often serving as replacements for search engines like Google. These include tools for writing essays, homework assistance, and drafting emails. However, this focus leaves a substantial ‘white space’ for AI innovation in other categories.

Significant opportunities lie in network-based categories that require a multiplayer product. Dating and recruiting, which both involve making matches, are prime examples where AI has yet to see widespread adoption. Social AI is another area that remains largely untapped; most current social AI activity involves users posting AI-generated content on existing platforms rather than new, AI-native social experiences.

Marketplaces also present considerable potential for AI integration. Categories such as shopping, home buying, and retail could be transformed by AI. For instance, in shopping, AI could enable users to visually explore options, customize products with specific details like piping on a collar, and even facilitate the manufacturing of these personalized items. Similarly, gaming and entertainment are wide open, as AI models have not yet consistently produced content that resonates with the average person, with the exception of AI micro-dramas.

These emerging opportunities are characterized by their focus on helping people “spend time” rather than merely “save time,” a distinction that a16z Growth Partner Josh Elman notes has historically led to the creation of some of the largest companies. The advancement of AI models is crucial for enabling these new ideas and inventions, with the long-term value expected to reside within the software layer and the processes they enable.

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