The engineering process evolved from physical prototypes to digital simulations. AI models now represent a third leap, accelerating design iterations from days to minutes. This allows for exploring thousands of options instead of dozens, drastically shortening development cycles.

The AI revolution isn't just about software. For the first time in years, venture capital is flowing into hardware like specialized semis and even into energy generation, because power is the core bottleneck for all AI progress.

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.

For decades, hardware startups failed because building the necessary bespoke software was too difficult and expensive. The rise of general-purpose AI provides a powerful, adaptable software layer "out of the box." This dramatically lowers the barrier to scaling for hardware-intensive businesses like robotics and drones, making them more attractive for creative financing.

As AI commoditizes software, hardware is re-emerging as a key defensibility layer for startups. A decade ago, VCs avoided hardware, but now a physical device tied to a software subscription creates powerful stickiness and justifies high valuations, representing a major shift in investment strategy.

Investing in deeply technical hardware, like Cerebras's wafer-scale chip, requires a degree of ignorance about the true difficulty. Unlike software, where a core technical insight gets you 80% of the way, in hardware it's only 2%. The rest is a brutal, multi-year battle against physics and complex supply chains that experts might avoid entirely.

Building a foundational deep tech company is like restarting a video game, but with all the advanced power-ups unlocked. Founders today can leverage powerful AI models and agents to accelerate progress that took the last generation of scientists over a decade to achieve, essentially speedrunning scientific discovery.

The most significant aspect of OpenAI's Jalapeno chip isn't its performance but its rapid nine-month 'tape out' time. This demonstrates that using AI models to design hardware can dramatically shorten development cycles, creating a new competitive advantage based on iteration speed.

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.