New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

While competitors chased cutting-edge physics, AI chip company Groq used a more conservative process technology but loaded its chip with on-die memory (SRAM). This seemingly less advanced but different architectural choice proved perfectly suited for the "decode" phase of AI inference, a critical bottleneck that led to its licensing deal with NVIDIA.

AI chip startup Talos takes a contrarian approach by casting models "straight into silicon," creating inflexible, model-specific hardware. This trades flexibility for massive gains in speed and cost, betting that frontier models will remain stable for periods of 3-12 months, making the "cartridge-swap" model economically viable.

NVIDIA's commitment to CUDA's backward compatibility prevents it from making fundamental changes to its chip architecture. This creates an opportunity for new players like MatX to build chips from a blank slate, optimized purely for modern LLM workloads without being tied to a decade-old programming model.

GPUs were designed for graphics, not AI. It was a "twist of fate" that their massively parallel architecture suited AI workloads. Chips designed from scratch for AI would be much more efficient, opening the door for new startups to build better, more specialized hardware and challenge incumbents.

Incumbents face the innovator's dilemma; they can't afford to scrap existing infrastructure for AI. Startups can build "AI-native" from a clean sheet, creating a fundamental advantage that legacy players can't replicate by just bolting on features.

NVIDIA's financing and demand guarantees for its chips are not just to spur sales, which are already high. The strategic goal is to reduce customer concentration by helping smaller players and startups build compute capacity, ensuring NVIDIA isn't solely reliant on a few hyperscalers for revenue.

Product managers at large AI labs are incentivized to ship safe, incremental features rather than risky, opinionated products. This structural aversion to risk creates a permanent market opportunity for startups to build bold, niche applications that incumbents are organizationally unable to pursue.

Google's TPUv1 was a minimal viable product built in a year by a skeleton crew. This lean approach is now impossible for new AI chips because the market has matured, and the "table stakes" for features, performance, and reliability are much higher, requiring a more complete initial product.