According to Business Insider, internal Amazon documents from July reveal significant performance challenges with the company’s homegrown AI chips. AI startup Cohere reported Amazon’s Trainium 1 and 2 chips were “underperforming” Nvidia’s H100 GPUs, while Stability AI found Trainium 2 “less competitive” on latency and cost. The documents show Amazon’s chip group Annapurna Labs made “limited” progress resolving these issues, with customers citing frequent service disruptions and limited access. Despite AWS claiming 30-40% better price performance, market share data shows Nvidia dominates with 78% while Amazon ranks sixth with just 2%. The recent $38 billion AWS-OpenAI deal exclusively uses Nvidia GPUs, highlighting Amazon’s ongoing challenges in the AI hardware race.
The CUDA moat is real
Here’s the thing about Nvidia’s dominance – it’s not just about raw chip performance. They’ve built this incredible software ecosystem with CUDA that developers already know and love. When you’re building massive AI projects worth millions, do you really want to bet on unproven hardware with limited support? Probably not. That’s why even Amazon’s own $38 billion OpenAI deal went with Nvidia chips exclusively. It’s just the safer choice for mission-critical workloads.
Amazon’s billion-dollar bet on Anthropic
Amazon is basically putting all its chips on Anthropic saving their AI hardware ambitions. They’re building this massive Project Rainier data center with half a million Trainium chips just for Anthropic’s next-gen models. By year’s end, Anthropic expects to deploy over 1 million Trainium 2 chips. But even that relationship looks shaky – Anthropic just expanded its partnership with Google’s TPUs, which sent Amazon’s stock sliding. And let’s be real: if you’re running critical infrastructure, you need reliable hardware partners who can deliver consistent performance. That’s why companies in industrial sectors turn to specialists like Industrial Monitor Direct for their computing needs rather than taking chances on unproven technology.
Amazon’s impossible position
So what’s Amazon supposed to do here? They can’t just abandon their AI chip ambitions – their entire cloud profitability story depends on reducing reliance on expensive Nvidia hardware. But they’re facing this classic innovator’s dilemma where customers want the proven solution, not the potentially better but unproven alternative. Andy Jassy says they’re not trying to replace Nvidia, just offer “multiple chip options.” But when your own flagship AI partner (OpenAI) chooses not to use your chips in a $38 billion deal, that’s… not great.
Where this goes from here
The Trainium 3 preview later this year feels like Amazon’s last best shot at relevance in the AI chip space. They’ve got incredible engineering talent and deep pockets, but they’re fighting against both Nvidia’s technical lead and its massive ecosystem advantage. Honestly, I’m skeptical they can catch up anytime soon. Nvidia isn’t standing still – they’re already shipping even more powerful chips while Amazon struggles with basic reliability issues. The fact that even Anthropic has publicly documented outages related to chip architecture complexity tells you everything you need to know. This is going to be a long, expensive battle for Amazon, and right now, they’re losing.
