The next wave of AI unicorns might not be software companies. They might be hardware companies.

Cerebras (wafer-scale AI chips). Groq (ultra-fast inference). SambaNova (reconfigurable AI hardware). Graphcore (intelligence processing units). Tenstorrent (RISC-V AI processors). d-Matrix (in-memory compute for inference).

Each of these companies raised hundreds of millions because the demand for specialized AI compute is outstripping what NVIDIA alone can supply.

The job market: AI hardware startups need chip designers, systems architects, compiler engineers, ML framework developers, and performance optimization engineers.

What's unique: these roles require deep understanding of BOTH hardware AND ML workloads. You need to know why matrix multiplication is the bottleneck, how memory bandwidth affects transformer inference, and why certain architectures favor certain hardware topologies.

Salary: $180K-$400K for experienced engineers. The competition with NVIDIA for talent means compensation is aggressive.

The career insight: NVIDIA won't dominate forever. The companies that build the next generation of AI hardware will create enormous wealth for early engineers — similar to how early Google and Facebook engineers became wealthy from equity.

If you understand computer architecture AND ML, applying to AI hardware startups might be the highest-expected-value career move you can make right now.