I've noticed a major trend lately where tech giants are prioritizing inference speed. As we move further into 2026, I keep seeing reports that is now the dominant part of the AI lifecycle. Is it just about user experience, or is there a deeper technical reason why it’s becoming more important than the initial training phase?
3 answers
From a production standpoint, training is essentially a one-time sunk cost, whereas inference is an ongoing operational expense. Recent data suggests that up to 90% of the total cost of ownership for an AI model comes from running it in the real world rather than building it. We are seeing a shift where "test-time compute"—giving the model more time to "think" during the output phase—is yielding better accuracy than just feeding more data into training. This shift makes high-throughput systems crucial for sustaining modern agentic workflows.
Do you think the rise of edge computing is the primary driver here, or is it mostly about the sheer volume of API calls in the cloud?
It's mainly because training builds the potential, but inference realizes the actual value for the end-user. Without speed, the model is useless.
Spot on, Jeffrey. Even the most sophisticated model won't succeed if the latency makes it feel sluggish to the person using it.
It's actually a bit of both, Gregory. While edge devices need efficiency, the cloud is where the massive "test-time compute" scaling happens. Companies like Nvidia and Broadcom are now designing chips specifically to handle the "decode" phase of inference more efficiently because that is where the bottleneck currently sits for real-time user interactions.