Host: Nicola Marchese
Guest: Michael Stewart, Managing Partner @ M12, Microsoft's Venture Capital Fund
#DeepTechCatalyst
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KEY TAKEAWAYS:
1. The AI ecosystem is shifting from isolated hardware and software components to integrated global systems, emphasizing Systems Technology Co-Optimizations (STCOs). Current infrastructure, designed for simpler computing needs, must evolve to support more complex and specialized AI applications. Entrepreneurs can innovate by addressing inefficiencies in memory, logic, and interconnect technologies, enabling infrastructure to meet AI's growing demands.
2. Compute infrastructure faces three key challenges. First, edge computing requires balancing data processing between the cloud and edge for real-time needs and regulatory compliance, highlighting the need for memory-compute integration. Second, chip architectures are being redesigned to fit power and thermal constraints for specific applications like computer vision. Third, advancing memory technologies, such as high-bandwidth memory (HBM), is critical yet challenging due to unique material and process demands.
3. To design a customer-driven solution, start by understanding whether your target is an application provider or end-user, as their needs differ. Clearly define your value proposition as either incremental (10-30%) or exponential (10x, 100%) improvement. Be intellectually honest by evaluating if your innovation meets real customer needs and delivers transformative value.
4. Product-market fit is easier to identify in software through early adoption and scalability, while deep tech requires more effort due to capital-intensive technologies. Early-stage validation, like Letters of Intent (LOIs), can show market interest but aren't conclusive. Founders must collaborate with customers to articulate value and bridge the gap between innovation and practical application.
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