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🚀 Ex-Snap employees launch Ghost Angels fund for AI startups

Ghost Angels, an investment fund founded by former Snap employees, will focus on backing AI startups shaping the next generation of social media.

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Sources techcrunch.com

A group of 20 former Snap employees has officially launched an investment fund called Ghost Angels to support next-generation consumer and social AI startups. According to TechCrunch, while the fund declined to disclose its specific size, it has already invested in at least five companies and plans to back another 15 projects over the coming year.

Key developments

Max Rivera, Snap's former head of global partnerships (currently working at Microsoft's AI lab), initiated the fund in 2025 to formalize the angel investing community of Snap alumni. Ghost Angels brings together several prominent veterans, including Alexandra Levitt, who previously ran Snap's startup accelerator, and Will Wu, a founding member of Snap's product and design team.

The fund aims to combine a diverse mix of former senior executives and younger staff to bring multi-dimensional perspectives when evaluating projects and supporting founders at the pre-seed and seed stages. "Diversity of thought and experience is at the core of how we evaluate investments," Rivera shared.

Why it matters

According to Ghost Angels, the traditional ad-dependent social media model is increasingly disillusioning users. Current trends are seeing a clear split between the "social" and "media" elements. The fund aims to invest in AI tools that restore social media's original promise of genuine human connection, while also funding AI-native formats that lower the barrier to content creation.

Of note to the Vietnamese tech community is the shift in how modern startups operate. Instead of building bloated teams, today's founders run extremely lean operations, launch quickly, and constantly build in public. They are also moving away from purely ad-based revenue models, shifting instead toward subscriptions, tokens, or pay-as-you-go pricing based on actual usage.