AI News Monitor

Despite the emergence of more efficient AI models like DeepSeek, demand for data centers and processing power continues to surge, according to Andy Jassy, CEO of Amazon.

In a recent discussion, Andy Jassy emphasized that the rapid growth of artificial intelligence (AI) has not shown any signs of slowing, even with advancements that reduce computational costs.

No Slowdown in AI Infrastructure Demand

When asked whether breakthroughs like DeepSeek—a new large language model (LLM) praised for its efficiency—could reduce the need for massive data centers, the Andy Jassy dismissed the idea.

“No,” he stated. “We would like nothing better than to make the cost of AI meaningfully less expensive for customers.”

While efficiency improvements are welcome, the Amazon top executive argued that lowering costs actually fuels more innovation rather than reducing overall spending.

“Customers love when you take the cost per unit of something down,” he said. “It allows them to save money in what they build, but they don’t spend less—it unleashes them to do more innovation, and they spend more.”

Amazon Pushing to Reduce AI Costs

Amazon is actively working to drive down AI expenses, particularly in two key areas:

1. Chip Performance – The company has developed its own custom AI chips, claiming a 30-40% improvement in price performance compared to existing GPU-based solutions.
2. Inference Costs – While most AI spending today goes toward training models, the executive predicts that future costs will shift toward inference (running AI models in production). Reducing these costs is critical for broader adoption.

Competition with NVIDIA

Though not explicitly naming NVIDIA, the Amazon top executive acknowledged the competitive landscape in AI hardware.

“It is going to happen,” he said regarding price reductions. “If you sat in the meetings with the AWS team right now, they feel like it is their responsibility to make the cost of AI meaningfully less than today.”

Looking Ahead

Andy Jassy compared the current AI boom to the early days of AWS, where lowering compute and storage costs led to more spending, not less, as businesses expanded their capabilities.

“Same thing when we started AWS—we significantly changed the cost of compute, storage, and database. Customers loved it, but they spent more on infrastructure because it allowed them to build more for their customers.”

As AI continues to evolve, AWS remains committed to driving down costs while scaling infrastructure to meet relentless demand.