Next-Gen AI Chip Company Challenges NVIDIA's Dominance in Six Ways, Shifting Focus from Peak Compute to Data Movement Efficiency Reconstitution

BlockBeats News, July 26th, Anthropic investor Deedy pointed out that all startups developing next-generation AI chips are trying to break NVIDIA's dominant position in different ways, aiming to tackle the fundamental issue of "data movement." Six differentiated challenge approaches include: eliminating DRAM (Groq, acquired by NVIDIA for $20 billion), eliminating interconnects (Cerebras, listed with a market value of approximately $48 billion), eliminating compute/memory separation (d-Matrix), eliminating server-centric architectures (Majestic), eliminating universality (Etched, Taalas, MatX), and eliminating a $400 million lithography machine (Substrate).

The listings and acquisition prices of Cerebras and Groq have set a pricing benchmark for the entire AI chip track. Etched's valuation doubled to $10.3 billion this week, erupting from a low-key state in just three weeks, further confirming that capital is rapidly reassessing the pricing of this track. Among 18 major startup companies, private companies have a combined paper value of approximately $58 billion, with a public market value of around $48 billion, covering inference, training/new architectures, systems, fabs, lithography, and other segmented directions.

Each approach is challenging the core assumption of NVIDIA's GPU architecture — the movement of data between computing units and memory consumes both time and energy. Groq completely eliminates memory hierarchy latency by using SRAM instead of DRAM, Cerebras eliminates chip-to-chip interconnect bottlenecks at the wafer level, d-Matrix performs calculations directly in memory, and Etched sacrifices universality to design hardware specifically for the Transformer architecture. The AI chip startup ecosystem has evolved from a single narrative of replacing GPUs to a multi-pronged attack at the architecture level. NVIDIA's acquisition of Groq actively engages in this process, indicating its deep understanding that the core of next-generation competition is no longer peak computing power but the thorough reconstruction of data movement efficiency.