Within Amazon, the Nova family of AI models has earned the derisive nickname "Amazon Basics," a reference to the company's cheap private-label brand. This highlights internal sentiment that the models are reliable and cheap but not state-of-the-art, forcing many of Amazon's own AI products to rely on partner models.
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Box CEO Aaron Levie advises against building complex workarounds for the limitations of cheaper, older AI models. This "scaffolding" becomes obsolete with each new model release. To stay competitive, companies must absorb the cost of using the best available model, as competitors will certainly do so.
LLMs are becoming commoditized. Like gas from different stations, models can be swapped based on price or marginal performance. This means competitive advantage doesn't come from the model itself, but how you use it with proprietary data.
AWS leaders are concerned that building flagship products on third-party models like Anthropic's creates no sustainable advantage. They are therefore pressuring internal teams to use Amazon's own, often less capable, "Nova" models to develop a unique "special sauce" and differentiate their offerings from competitors.
While custom silicon is important, Amazon's core competitive edge is its flawless execution in building and powering data centers at massive scale. Competitors face delays, making Amazon's reliability and available power a critical asset for power-constrained AI companies.
Amazon's internal use of an AI tool to help write its mandatory six-page product documents subverts the exercise's core purpose. The process was designed to force deep, rigorous thought through the 'pain' of writing. Using AI as a shortcut risks leading to shallower strategic thinking.
Internal documents reveal Amazon's strategy to avoid words like "automation" and "robot," opting instead for "advanced technology" or "cobot." This linguistic choice is a deliberate attempt to manipulate perception and downplay the reality that its technology is designed to replace human workers, not just assist them.
Overshadowed by NVIDIA, Amazon's proprietary AI chip, Tranium 2, has become a multi-billion dollar business. Its staggering 150% quarter-over-quarter growth signals a major shift as Big Tech develops its own silicon to reduce dependency.
Beyond capital, Amazon's deal with OpenAI includes a crucial stipulation: OpenAI must use Amazon's proprietary Trainium AI chips. This forces adoption by a leading AI firm, providing a powerful proof point for Trainium as a viable competitor to Nvidia's market-dominant chips and creating a captive customer for Amazon's hardware.
Alexa's architecture is a model-agnostic system using over 70 different models. This allows them to use the best tool for any given task, focusing on the customer's goal rather than the underlying model brand, which is what most competitors focus on.
The narrative of NVIDIA's untouchable dominance is undermined by a critical fact: the world's leading models, including Google's Gemini 3 and Anthropic's Claude 4.5, are primarily trained on Google's TPUs and Amazon's Tranium chips. This proves that viable, high-performance alternatives already exist at the highest level of AI development.