AI software models advance every few months, creating exponential demand. However, the hardware infrastructure like chip fabs operates on two-to-four-year development cycles. This timeline disconnect between software's rapid pace and hardware's slow build-out creates a persistent supply crunch that money alone cannot instantly solve.

Unlike software, hard tech involves long scale-up timelines and high capital costs. Founders must specifically seek the small subset of investors and partners who understand the market context and have the risk appetite for massive, world-changing opportunities, rather than trying to appeal to all VCs.

Zipline's journey highlights a mismatch between standard VC fund timelines (10-12 years) and the longer development cycles of "real-world tech" like robotics. Founders in these spaces must be prepared for a 15-20 year journey and communicate this reality to investors from the start.

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.

Companies pursuing revolutionary technologies like autonomous driving (Waymo) or VR (Reality Labs) must endure over a decade of massive capital burn before profitability. This affirms venture capital's core role in funding these long-term, high-risk, high-reward endeavors.

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.

Shkreli argues that revolutionary hardware ventures require exceptionally long time horizons, making traditional VCs unsuitable partners due to their fund cycles. He suggests targeting corporate investors who understand and can stomach a 15-20 year development runway.

Unlike pure software, the value in physical AI and hard tech comes from long-term compounding of technology. Startups often fail because they don't survive long enough to see these returns. This makes early commercial discipline and constraints crucial for longevity.

Companies tackling moonshots like autonomous vehicles (Waymo) or AGI (OpenAI) face a decade or more of massive capital burn before reaching profitability. Success depends as much on financial engineering to maintain capital flow as it does on technological breakthroughs.