Running the Model, Not Training It
The current shift in AI is not about building ever larger models, but about executing them at speed and scale. Nvidia dominates training, where flexibility and parallelism matter, but companies like Groq focus on inference, where determinism, low latency, and predictability matter more than adaptability. For modeling, simulation, and institutional analysis, this distinction changes the center of gravity. Models no longer sit idle between runs; they operate continuously, shaping decisions in real time. Universities, labs, and policy institutions must now govern not just what models say, but how fast they act, how reproducible their outputs remain, and where human judgment still enters the loop. The strategic question is no longer who trains the model, but who controls its execution.
Further Reading
Jensen Huang delivering the NVIDIA keynote at CES 2025 in Las Vegas, highlighting the company’s central role in large-scale AI training and compute infrastructure. Photo by Joseph Zadeh. CC BY-SA 4.0.