Software companies struggle to build their own chips because their agile, sprint-based culture clashes with hardware development's demands. Chip design requires a "measure twice, cut once" mentality, as mistakes cost months and millions. This cultural mismatch is a primary reason for failure, even with immense resources.
Next-generation hardware companies like SpaceX now operate like software firms, with designs and requirements changing daily. This departure from the rigid, top-down 'waterfall' process creates a new market for agile collaboration tools, analogous to how GitHub emerged to serve agile software teams.
Building the next generation of industrial technology requires a specific cultural and talent synthesis. Success demands combining Silicon Valley’s software-first culture and talent with the deep, domain-specific knowledge of industrial veterans who understand real-world constraints and past failures.
Unlike software, where customer acquisition is the main risk, the primary diligence question for transformative hardware is technical feasibility. If a team can prove they can build the product (e.g., a cheaper missile system), the market demand is often a given, simplifying the investment thesis.
While capital and talent are necessary, the key differentiator of innovation hubs like Silicon Valley is the cultural mindset. The acceptance of failure as a learning experience, rather than a permanent mark of shame, encourages the high-risk experimentation necessary for breakthroughs.
The conventional wisdom that you must sacrifice one of quality, price, or speed is flawed. High-performance teams reject this trade-off, understanding that improving quality is the primary lever. Higher quality reduces rework and defects, which naturally leads to lower long-term costs and faster delivery, creating a virtuous cycle.
For a hyperscaler, the main benefit of designing a custom AI chip isn't necessarily superior performance, but gaining control. It allows them to escape the supply allocations dictated by NVIDIA and chart their own course, even if their chip is slightly less performant or more expensive to deploy.
Unlike software, hardware iteration is slow and costly. A better approach is to resist building immediately and instead spend the majority of time on deep problem discovery. This allows you to "one-shot" a much better first version, minimizing wasted cycles on flawed prototypes.
Boom Supersonic accelerates development by manufacturing its own parts. This shrinks the iteration cycle for a component like a turbine blade from 6-9 months (via an external supplier) to just 24 hours. This rapid feedback loop liberates engineers from "analysis paralysis" and allows them to move faster.
Returning founder Jamie Siminoff cut an 18-month hardware development cycle to under 7 months. He did this by challenging the "why" behind every process step and eliminating generous time buffers, arguing that excess time guarantees that delays will fill it.
The current 2-3 year chip design cycle is a major bottleneck for AI progress, as hardware is always chasing outdated software needs. By using AI to slash this timeline, companies can enable a massive expansion of custom chips, optimizing performance for many at-scale software workloads.