AI is entering a new phase. The challenge is no longer building intelligence, but delivering it economically at scale. As AI adoption moves beyond model development and into enterprise deployment, inference is reshaping AI economics, infrastructure architectures, and investment priorities across the semiconductor ecosystem.
In Scaling AI Inference, a new publication from McKinsey and GSA, industry leaders outline five shifts expected to shape the next era of AI infrastructure and value creation.
Key Insights
- Cost per outcome will matter more than benchmark performance
- Specialized silicon will emerge to address increasingly diverse AI workloads
- Inference will be distributed across cloud, enterprise, edge, and devices
- Performance gains will depend on optimizing the entire stack, from silicon to software
- Open standards will become increasingly important as AI ecosystems fragment and scale
Why This Matters
By 2030, AI inference is expected to account for the majority of AI demand, shifting industry focus from training models to serving AI workloads efficiently and cost-effectively. This transition creates new opportunities across compute, memory, networking, software, packaging, power delivery, and cooling technologies.
Download the Report
Gain insights into the technology, economics, and semiconductor strategies that could define the next decade of AI deployment.
View the Webinar
View the webinar recording of McKinsey and GSA discussing the report’s findings and what they mean for semiconductor companies, technology providers, investors, and enterprise leaders. View the Webinar
