Counterintuitively, the "move fast and break things" mantra fails in hardware. Mock Industries achieved a 71-day aircraft development cycle not by rushing tests, but by investing heavily in software and hardware-in-the-loop simulation to run thousands of virtual cases before the first physical flight.

Instead of waiting for a formal requirements document, Pika developed its Dropship drone based on direct, actionable feedback from military users (Air Force, Army) on a prior model, accelerating the design cycle.

Instead of building its final passenger jet, Boom first developed a smaller, sub-scale prototype to prove its Mach 2.2 technology. This startup-like, sequential approach proves the core concept at a much lower cost, making the capital-intensive project more manageable and fundable.

The founders initially focused on building the autonomous aircraft. They soon realized the vehicle was only 15% of the problem's complexity. The real challenge was creating the entire logistics ecosystem around it, from inventory and fulfillment software to new procedures for rural hospitals.

Hardware founders often fixate on the core device. Zipline learned the hard way that their aircraft was only 15% of the total system complexity. The truly difficult challenges lay in the surrounding logistics: inventory management, cold chain, maintenance, air traffic control, and ground infrastructure.

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.

Unlike software's daily compilations, hardware development allows only a few "compiles" (builds) in total. This necessitates a more conservative, upfront process focused on reliability and planning, as you can't ship over-the-air updates to fix physical products.

The software-centric Minimum Viable Product (MVP) model is ill-suited for hardware. Instead of aiming for a 'viable' product, focus on a 'testable' one. This allows for controlled pilot deployments to gather real-world data and iterate before committing to expensive, hard-to-change physical designs.

Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.