Why we’re at the beginning of the AI hardware boom | Caitlin Kalinowski (ex–OpenAI, Meta, Apple)

2026-05-10 · Source: Lenny's Newsletter · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

Caitlin Kalinowski, a prominent hardware leader from Apple, Meta, and OpenAI, discusses the evolving landscape of robotics, VR/AR, and hardware development. She highlights the shift towards physical AI as digital AI saturates, emphasizing the critical need for re-industrialization and a secure supply chain, particularly for components like actuators and magnets. Kalinowski reflects on the failure of consumer VR, attributing its underlying technologies to advancements in robotics, and expresses optimism for AR glasses. She also shares insights on hardware development principles learned from industry giants, including the importance of clear goals, tackling the hardest problems first, focusing on user interaction, and maintaining ruthless efficiency. The discussion also touches on the impact of memory price spikes due to AI demand and the future role of AI in CAD and PCB design.

Key takeaway

For CTOs and VPs of Engineering navigating the shift to physical AI, recognize that hardware development demands a fundamentally different, more conservative approach than software. Your teams must prioritize clear, unwavering goals and address the most challenging components early to manage the extended timelines and high costs of iteration. Focus on securing critical supply chain elements and consider vertical integration to build resilience against market shocks, ensuring your product roadmap remains viable amidst geopolitical and economic shifts.

Key insights

The next frontier for AI is the physical world, driving a critical re-industrialization effort and new hardware development paradigms.

Principles

• Define clear goals early and adhere to them in hardware development.
• Prioritize designing the hardest or riskiest parts first.
• Focus iteration on components with the most customer interaction.

Method

Hardware development requires a conservative approach with limited "compilations," necessitating extensive reliability checks and tests throughout the program, unlike software's rapid iteration cycles.

In practice

• Pre-buy critical components like memory to mitigate supply chain shocks.
• Verticalize supply chains to enhance adaptability to disruptions.
• Utilize AI for high-level planning, data analysis, and non-core engineering tasks.

Topics

• AI Hardware Boom
• Robotics and Physical AI
• VR/AR Technologies
• Hardware Supply Chain
• Memory Price Volatility

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Lenny's Newsletter.