Fusion Fund’s Lu Zhang on AI Infrastructure, Data Quality, Edge AI & the Future of Venture

Lu shares her perspective as both a former founder and an early-stage investor backing deep tech, healthcare AI, infrastructure, and enterprise startups. The conversation explores what actually matters in AI today: high-quality data, inference cost, architecture design, multimodal data, edge AI, governance, and the growing importance of specialized agents over one-size-fits-all models. They also discuss how startups should think about hyperscalers vs. neo-cloud providers, why data curation is becoming mission-critical, how regulated industries like healthcare and finance are approaching AI adoption, and what Lu learned from this year’s NVIDIA GTC. Topics covered: • What Lu Zhang looks for in early-stage AI founders • Why data quality matters more than data quantity • AI infrastructure vs. AI applications • Inference cost, model efficiency, and architecture design • Edge AI and the future of AI deployment • Data governance, compliance, and regulated industries • Multimodal data and enterprise memory graphs • Otter, MCP, conversational data, and AI agents • Hyperscalers vs. alternative cloud and storage providers • The future of consolidation in cloud infrastructure • AI’s environmental impact and the role of efficient data systems • NVIDIA GTC, healthcare AI, and where venture is heading next If you’re a founder, investor, infrastructure leader, or enterprise operator trying to understand where AI is really going, this episode is packed with practical insights.