Amazon’s Power Move: Making AI Profitable by Bringing It In-House

Amazon (NASDAQ:AMZN | AMZN Price Prediction) has long established itself as a pioneer in artificial intelligence, integrating sophisticated AI capabilities across its vast ecosystem. Through Amazon Web Services (AWS), the company provides foundational tools that power countless businesses worldwide, while internally deploying AI to enhance customer experiences — from Alexa’s voice recognition and conversational abilities to personalized product recommendations, supply chain optimization, and fraud detection in its e-commerce operations. 

These deployments have driven efficiency and innovation, cementing Amazon’s reputation as an AI leader. However, heavy reliance on third-party foundation models and hardware, particularly expensive Nvidia (NASDAQ:NVDA) GPUs for training and inference, has proven less cost-effective amid surging AI development expenses. This dependency has limited scalability and profitability in a market where compute costs can dominate budgets.

In a strategic pivot toward greater control and efficiency, Amazon is shifting to develop its own AI models in-house. Powered by its proprietary Trainium and Inferentia chips, the company aims to slash costs dramatically — potentially to a fraction of what rivals pay when depending solely on external hardware. In short, it is looking to make AI more profitable.

Cutting Loose the Apron Strings

The Wall Street Journal first reported this ambitious plan, highlighting Amazon’s new AI chief Pete DeSantis’s conviction that in-house chip usage could transform AI economics. By building models on Trainium (for training large-scale generative AI) and Inferentia (for efficient deployment), Amazon seeks to reduce dependency on costly third-party suppliers. 

This not only addresses the high barriers of chip scarcity and pricing but also enables more affordable AI offerings on AWS, attracting enterprises hesitant about skyrocketing expenses from pure-play AI providers.

Amazon’s Thrifty Ambitions

For Amazon itself, the implications are significant. Success here could supercharge AWS profitability. The cloud services operations are where Amazon already makes most of its profits. By bringing the modeling in-house, it could turn the AI infrastructure into a high-margin growth engine rather than a cost center by becoming a huge driver of future revenue rather than a drag or lower-margin activity due to external chip dependency and high capex intensity.

Lower training and inference expenses would allow competitive pricing for services like Amazon Bedrock and the Nova foundation models, drawing more customers and increasing cloud market share. Internally, optimized in-house models could further enhance Amazon’s consumer products — making Alexa smarter, recommendations more precise, and logistics even more efficient — while reducing overall operational costs. 

Amazon has seen strong demand for Trainium2, the rollout of Trainium3 (touted for up to 40% to 50% cost savings over GPUs in certain workloads, with doubled compute performance and improved efficiency), and early commitments that signal it will realize full supply allocation by mid-2026. Partnerships and iterative advancements in custom silicon further strengthen this vertical integration, positioning AWS as a cost-effective alternative in a GPU-dominated landscape.

Key Takeaway

While promising, Amazon faces notable risks in this endeavor. The primary challenge is whether Trainium and Inferentia can consistently match or exceed the performance of industry-leading chips like Nvidia’s GPUs in raw speed, latency, ecosystem maturity — such as software tools like CUDA — and broad developer support. Some early feedback from startups has suggested Trainium instances may lag in certain metrics, potentially hindering adoption for cutting-edge applications. Talent competition in AI also remains fierce, and execution risks could delay Amazon’s timelines.

That said, Amazon’s chances of achieving its goal appear solid. Its massive scale, deep engineering resources, and AWS’s position as the leading cloud provider provide a strong foundation. With chips like Trainium3 promising significant efficiency gains — up to 4.4x performance improvements in some configurations — and growing customer commitments, including large-scale deployments, the strategy leverages Amazon’s strengths in cost leadership and infrastructure. 

If Amazon’s AI chips narrow performance gaps — as iterative generations suggest — it could redefine AI profitability, making in-house development a sustainable advantage in the long run.