Fetty Wap lost an eye as a child. Today, startups and Stanford scientists are rewriting what that loss could mean—and what the future of vision may become.

Many “AI-first” startups don’t fail because their models are weak—they fail when intelligence meets the physical world. Manufacturing constraints, reliability debt, and deployment realities quietly overwhelm promising demos, exposing blind spots that traditional investor diligence often misses. This article examines why models are not products, why execution determines survival, and how founders and investors can rethink risk before reality does it for them.

Most hardware startups make their first critical mistake with their first hire. By over-indexing on ML or firmware talent, early teams often overlook systems thinking, manufacturing reality, and long-term reliability. This article examines why early hardware teams become demo-optimized instead of product-ready, what the right first hires actually look like, and how founders can build teams capable of surviving real-world constraints—before costly mistakes are locked in.

In this episode of Silicon Valley Unplugged: Investing in Hard Tech, Nicolas Sauvage, President of TDK Ventures, joins host Jillian Sun for a candid conversation on what it really takes to build and fund deep-tech companies. From evaluating founders to scaling hardware, energy, AI, and climate technologies, this episode unpacks how breakthrough ideas move from the lab to real-world impact—and what separates promising demos from enduring companies.

Most hardware startups don’t fail because their ideas are wrong—they fail after the demo, when real-world constraints appear. Manufacturing assumptions collapse, suppliers misalign, thermal issues surface, and certification delays quietly drain runway. This article explores the “prototype trap,” where promising startups stall between proof and production, and explains how early engagement with Taiwan’s manufacturing ecosystem and programs like ICTGC can help founders turn fra