Venturing into AI Infrastructure: VC Insights for Deep Tech Startups. A chat with Michael Stewart, Managing Partner @ M12, Microsoft's Venture Capital Fund

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Nov 15, 2024

In this engaging discussion, Michael Stewart, Managing Partner at M12, shares his expertise in AI investments and materials science. He highlights the shift toward integrated global systems for AI and the urgent need for evolving compute infrastructure. Key challenges include balancing edge computing with cloud needs and the redesign of chip architectures. Stewart also emphasizes the importance of understanding customer needs for tech entrepreneurs and navigating the complexities of securing Series A funding in the deep tech space.

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INSIGHT

AI Systems Shift

The AI ecosystem is shifting from isolated components to integrated global systems.
This emphasizes Systems Technology Co-Optimizations (STCOs) across entire networks.

ADVICE

Edge AI Development

Focus on compute and memory integration for edge AI technologies.
Consider the power envelope and thermal design parameters when designing chip architectures.

ADVICE

Customer-Driven Solutions

Understand your target customer, whether application provider or end-user, as their needs differ.
Clearly define your value proposition as either incremental or exponential improvement.

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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.

#Fundraising #Startups #DeepTech #VentureCapital #CorporateVentureCapital #CVC #Quantum #Semiconductors #Computing #VCs #HardTech #industry #tech #strategy #founders #technology #entrepreneurship #phd

Disclaimer: This content is provided solely for educational and informational purposes and should not be interpreted as financial or legal advice. Given the complexity and potential impact of these terms, it is crucial to consult with an attorney. Legal expertise is invaluable in navigating these technical aspects and ensuring that the terms are in your best interests before finalizing any agreements.