AI Hardware & Chips

AI hardware and chips encompasses the specialized processors, accelerators, and computing architectures designed specifically for artificial intelligence workloads, including GPU alternatives, AI inference chips, neuromorphic processors, and the EDA tools that enable their design. The sector is defined by NVIDIA's dominance in AI training hardware (commanding 80%+ market share in data center GPUs) and the enormous opportunity to challenge that dominance through specialized architectures optimized for specific AI workloads. Companies like Cerebras (wafer-scale computing), Groq (deterministic inference), Tenstorrent (RISC-V AI processors), and SambaNova (dataflow architecture) are pursuing fundamentally different approaches to AI compute. The CHIPS Act has committed $52 billion to domestic semiconductor R&D, creating non-dilutive funding that de-risks private investment in AI chip design. The key tension in the sector is between NVIDIA's CUDA software ecosystem lock-in and the potential for specialized chips to deliver 10-100x efficiency improvements for specific AI workloads like inference, edge AI, and domain-specific models.