The article outlines a fundamental shift in the artificial-intelligence value chain: for many years the focus was on training large models, but now the real economic frontier lies in inference—that is, running those models in real time, responding to user queries, making predictions, generating outputs. Training remains critical, but the cost, demand and business opportunity of inference are growing rapidly.
In the second part, the piece explains why inference is becoming so consequential: as more businesses deploy AI in live settings (chatbots, recommendation engines, agentic systems), each user interaction triggers compute, memory and latency demands. The infrastructure required is increasingly substantial; therefore, the cost of inference becomes a key margin-driver. It places new pressure on hardware, software stacks, optimization and business models.
Thirdly, the article identifies how this shift affects stakeholders: chip makers, cloud providers, enterprise users and model developers all stand to gain or lose depending on how well they adapt. Specialized inference hardware, model-optimisation techniques and inference-centric infrastructure are becoming strategic assets. For enterprises, efficiency in inference (faster responses, lower cost per query, scalability) will determine the ROI of AI deployments.
Finally, the article projects future implications: as inference demand continues to scale, we should expect new business models built around “inference as a service”, competition for inference-optimised hardware, and further innovation in model architectures (e.g., sparse, efficient, reasoning-capable) that reduce cost per output. There are also broader economic consequences—cost structures shifting, new winners and losers, and inference becoming a battleground for AI leadership.
