Unlike software, a deep-tech hardware startup's first product is essentially a prototype, according to Cerebras CEO Andrew Feldman. The second iteration refines the technology, and only the third generation truly scales and achieves market traction. This necessitates a decade-plus timeline and immense capital before success.

The core architectural bet for Cerebras was that incremental improvements on an existing design (like a GPU) yield minimal gains because the incumbent has already optimized it. To achieve a step-change in performance, a fundamentally different approach is required, leading them to their massive, wafer-scale chip design.

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.

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.

Unlike traditional VCs, deep tech investors like Playground Global focus almost exclusively on underwriting technology risk. They bet on whether a scientific breakthrough is achievable, assuming that if the revolutionary technology (e.g., room-temperature superconductors) can be built, the market for it is virtually guaranteed.

Hardware innovation culture is fundamentally different from software. Founders must be intrinsically motivated by the slow, deliberate, and expensive process of creating physical things. The reward is not quick iteration but conquering the immense difficulty of a process where mistakes are very costly.

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.