In this episode of The Macro AI Podcast, Gary Sloper and Scott Bryan break down why Amazon’s Trainium chip is not just a hardware announcement, but a signal that the economics of AI are fundamentally changing.

They explore how Amazon Web Services is using custom silicon like Trainium to shift enterprises from renting AI to building and owning it—and why that strategy only works when customers go deeper into the AWS ecosystem. This isn’t about winning benchmark battles; it’s about creating economic gravity around where AI gets built.

The conversation also tackles the question every executive is asking: How does this compare to Nvidia? While NVIDIA continues to dominate AI innovation and experimentation, AWS is focused on industrial-scale economics—making large, repeatable training workloads cheaper, more predictable, and easier to operationalize inside its cloud.

Gary and Scott then connect the dots to real enterprise strategy, including:

• Why AI infrastructure decisions are becoming long-term financial commitments
• How custom chips influence cloud pricing power and cost curves
• The rise of multi-cloud strategies that separate AI innovation from AI economics, including the role of Oracle Cloud Infrastructure as a cost-efficient execution layer
• Why FinOps is becoming essential as AI training, retraining, and inference costs compound over time

The key takeaway for business leaders: AI advantage won’t come from simply adopting the latest models. It will come from who controls the economics of building, scaling, and evolving AI over the next decade.

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About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog