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Hardware

What Tesla and SpaceX Teach Founders About Building Hardware | a16z

What Tesla and SpaceX Teach Founders About Building Hardware | a16z

Ex-Tesla and SpaceX engineers Chandler Luzsicza and Turner Caldwell share the practical lessons they learned about building complex hardware and how they apply them at their own startups. They cover core principles like decision velocity, strategic vertical integration, managing the critical path without creating new bottlenecks, and using aggressive milestones as a forcing function to reveal true priorities. The discussion also delves into the value of a 'factory mindset,' the importance of a rigorous hiring process, and advice for young engineers.

Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World

Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World

Nominal's co-founders discuss the new age of reindustrialization and the critical need for a modern data infrastructure in hardware engineering. They explain how their platform acts as a 'GitHub for hardware data,' providing a system of record for testing that bridges the gap between simulation and reality, and serves as the essential verification layer for the future of 'Physical AI'.

Inference at Scale:Breaking the Memory Wall

Inference at Scale:Breaking the Memory Wall

Sid Sheth, CEO of d-matrix, details their memory-centric approach to AI inference hardware, focusing on their Digital In-Memory Compute (DIMC) architecture. He explains how DIMC, an augmented SRAM technology, minimizes data movement to solve the memory bottleneck, delivering significant gains in latency and energy efficiency, particularly for the 'decode' phase of large language models.

20+ Years in Tech: Things We Wish We Knew Sooner • Daniel Terhorst-North & Kevlin Henney

20+ Years in Tech: Things We Wish We Knew Sooner • Daniel Terhorst-North & Kevlin Henney

In a reflective and forward-looking conversation, Daniel Terhorst-North and Kevlin Henney explore the evolution of software development over the past 20 years and predict the key challenges and innovations for the next 20. They delve into the philosophy of programming language design, the critical need for hardware-sympathetic programming, the untapped potential of concurrency models like CSP and the Actor Model, and the future of user interfaces and decentralized technology.

Flipping the Inference Stack — Robert Wachen, Etched

Flipping the Inference Stack — Robert Wachen, Etched

The current AI inference stack, reliant on general-purpose GPUs, is economically and technically unsustainable for real-time AI at scale. AI hardware expert Robert Wachen argues that the future is specialized hardware, like Transformer-specific ASICs, which can unlock currently bottlenecked applications such as real-time video, code generation, and large-scale enterprise deployments by solving critical latency and cost-per-user challenges.

Moonshot Podcast Deep Dive: André Prager on Prototyping at Wing

Moonshot Podcast Deep Dive: André Prager on Prototyping at Wing

André Prager, former Chief Engineer at Wing, discusses the core engineering philosophy of simplicity and cost-effectiveness that enabled the drone delivery service. He covers the design of key systems like the passive charging pad, the intelligent winch, the non-powered autoloader, and the iterative process of making the drones acoustically unobtrusive.