Amazon (AMZN) Stock Upgraded to Overweight on AWS Growth Outlook

Core Viewpoint - Amazon.com, Inc. is identified as a key AI stock to monitor, with an upgrade from Wells Fargo analyst Ken Gawrelski from Equal Weight to Overweight, raising the price target from $245.00 to $280.00, driven by anticipated AWS revenue acceleration in 2026 due to Project Rainier [1][4] AWS Revenue Acceleration - Project Rainier, a dedicated AWS data center for Anthropic compute in Indiana, is expected to begin operations in January 2026, contributing an estimated $14 billion in annual AWS revenue at full capacity of 2.2GW [2][4] - AWS growth estimates have been raised to +22% from prior estimates of +19% and consensus of +18%, with Project Rainier expected to contribute 5% and 4% to AWS growth in 2026 and 2027 respectively [4] - AWS market share losses are projected to peak in 2025 at -470 basis points year-over-year, with improvements expected in 2026 and beyond as AWS accelerates [4] Capacity and Utilization - Phase 1 of Project Rainier (1.3GW) is anticipated to come online in January 2026, with a 6-month utilization ramp, while Phase 2 is expected to start in Q4 2026 [4] - The Indiana campus at full capacity (2.2GW) is projected to contribute approximately $14 billion in annual revenues to AWS [4] Market Share Projections - AWS market share is expected to modestly improve to -420 basis points year-over-year in 2026, -260 basis points in 2027, and -180 basis points in 2028, with a forecast of 32% share by 2029, down from 47% in 2024 [4] - The cloud industry is projected to grow at a 31% CAGR to $870 billion from $230 billion in 2024, providing a favorable backdrop for AWS despite market share losses [4] Risks and Challenges - Risks include execution challenges with Project Rainier, performance of Trainium chips, slow core workload demand, and potential AI-related margin pressures [3][4] - Operational execution of Project Rainier and data center capacity scaling are identified as key risks due to ongoing supply constraints and uncertainty around large Trainium deployments [4]