Intel (x86) and ARM built decades-long monopolies by controlling instruction sets.
Licensing or compatibility dictated who could build chips, and locked the ecosystem to their terms.
AI’s Shift
In AI, the model (Claude, GPT, Gemini) becomes the functional equivalent of the instruction set.
The architecture of inference chips is defined by the model (layer sizes, precision, memory bandwidth).
This makes the model owner the new monopoly power. You cannot build an independent inference chip for GPT without OpenAI’s cooperation.
Blocking Copycats
Closed weights: The core IP is the model itself, not the chip. Without access to weights, no competitor can “copy” GPT’s instruction set.
Ecosystem lock-in: Software runtimes, quantization methods, and compilers become proprietary extensions—like CUDA for NVIDIA.
Vertical integration: Model makers who build custom inference chips tie the hardware directly to their model, blocking substitution.
Strategic Result
Chip vendors: Reduced to subcontractors unless they align with a model owner.
Model owners: Achieve monopoly status equivalent to Intel/ARM in the last era, but with stronger lock-in because the instruction set (model weights) is closed IP.
Barrier to copying: High — not from semiconductor know-how, but from legal/IP control over model architectures and trained weights.