The real AI war isn't between OpenAI and Google.

It's between NVIDIA, AMD, Intel, Apple, Google TPU, Amazon Trainium, and a dozen startups making AI chips.

Why this matters for engineers: the hardware you target shapes the software you write.

NVIDIA dominates training today. CUDA is the default. But competition is intensifying fast. AMD's ROCm is getting better. Google's TPUs are competitive for specific workloads. Apple's M-series chips are surprisingly good for local inference. Groq's LPU promises incredibly fast inference.

Career implications: CUDA skills remain the safest bet for now. But engineers who can write hardware-agnostic ML code (using ONNX, OpenVINO, or framework-level abstractions) are increasingly valuable as companies diversify their chip portfolios.

A new job category is emerging: AI Hardware-Software Co-design Engineer. People who understand both the chip architecture and the ML workload, and can optimize the intersection.

These roles pay $200K-$400K because the impact of a 2x inference speedup at chip-level saves companies millions annually.

The AI chip market will exceed $300B by 2030. Every chip company, cloud provider, and major tech company is investing. The talent demand across this ecosystem is enormous.

If you find the intersection of hardware and ML fascinating, this is possibly the highest-ceiling career path in all of tech right now.