In this episode of the DataStorage.com podcast, John sits down with Lu Zhang, Founder and Managing Partner of Fusion Fund, to talk about where AI infrastructure, data quality, cloud architecture, and venture capital are headed next.
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.
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What is The Datastorage.com Podcast: Building for Tomorrow's Cloud Infrastructure?
The Data Storage Podcast explores the infrastructure powering AI, cloud computing, and the next generation of data-driven companies.
Each episode features deep conversations with founders, engineers, investors, and infrastructure leaders building the future of AI workloads, multi-cloud architecture, synthetic data, storage economics, and distributed systems.
We go beyond surface-level cloud talk to examine:
• AI infrastructure strategy
• Multi-cloud architecture decisions
• Cloud cost optimization
• Data storage economics
• Synthetic data & robotics
• Emerging “neocloud” providers
• Enterprise storage modernization
If you’re a technical founder, infrastructure leader, system architect, or investor tracking the evolution of cloud and AI, this podcast is built for you.
Produced by Datastorage.com.