Saad Ullah
Amazon's push to dominate AI infrastructure is running into problems. Tech commentator recently called out AWS for losing AI customers because of its decision to build its own chips rather than using NVIDIA's. He argues the strategy is backfiring badly, putting Amazon behind in the most critical tech race of our time.
The Custom Chip Plan That Didn't Work
AWS launched its Inferentia and Trainium chips to cut costs and reduce dependence on NVIDIA. The idea made sense on paper: build cheaper hardware under Amazon's full control and save money long-term. But according to Oguz O. | 𝕏 Capitalist , this missed a critical point—the developer ecosystem.
AWS's chips might be cost-efficient in theory, but they lack the massive community support that makes NVIDIA GPUs the industry standard. Developers using popular frameworks like PyTorch, TensorFlow, or JAX build everything around NVIDIA's CUDA platform. Moving AI workloads to AWS's chips takes extra time, specialized knowledge, and often means accepting performance trade-offs. Those hassles wipe out any potential savings.
NVIDIA's CUDA platform has become one of tech's strongest competitive moats, and it's not just about raw hardware power—it's about how easy everything is to use. Thousands of pre-built libraries, open-source tools, and pretrained models work seamlessly with NVIDIA's ecosystem. Developers can deploy AI models faster and with less risk. AWS's chips offer limited compatibility and a much smaller community, which slows everything down. For companies rushing to implement AI solutions, getting to market quickly matters more than marginal cost savings—and that's where AWS is losing.
Oracle's Winning Strategy
While AWS pushes its proprietary chips, competitors like Oracle took a different approach. Oracle positioned itself as NVIDIA's partner rather than trying to compete. By offering the latest NVIDIA GPUs directly through Oracle Cloud Infrastructure, they deliver cutting-edge performance without the massive investment in chip design.
This "work with NVIDIA, don't fight them" strategy is paying off. Oracle's AI infrastructure revenue has jumped in 2025, driven by demand for NVIDIA-powered systems and high-performance training environments. Meanwhile, AWS risks falling further behind as customers flock to platforms where NVIDIA's hardware and software just work out of the box.
The takeaway is simple: in AI infrastructure, ecosystem trumps efficiency. Custom hardware without developer support becomes a problem, not an advantage. Even if AWS's chips cost less per unit, the total development cost—including time, maintenance, and retraining—tips the scale back to NVIDIA.
Worse still, once customers build their AI systems on rival clouds like Microsoft Azure, Google Cloud, or Oracle using NVIDIA infrastructure, it's extremely hard for AWS to win them back. Each deployment deepens the lock-in, making AWS's challenge even steeper.
Saad Ullah
