Goldman Sachs Says Inference Is Driving the Next AI Wave

For much of the generative AI boom, the conversation revolved around training ever-larger models. Every new release from OpenAI, Anthropic, Google, Meta and xAI pushed the industry toward more powerful GPUs, larger clusters and record-breaking capital expenditure from hyperscalers.

That phase is far from over. But another shift is beginning to emerge.

As enterprises move beyond pilots and start deploying AI agents, copilots and customer-facing applications at scale, the infrastructure challenge is no longer just about building smarter models. It is increasingly about running them reliably, cheaply and with low latency.

Goldman Sachs Asset Management says this transition was one of the clearest takeaways from its recent meetings with technology companies in Silicon Valley. The firm's investment team believes enterprise AI demand is steadily shifting from model training to inference, the process of serving AI responses in production.