Amazon deploys 1 million robots across warehouses

Amazon has announced it has deployed 1 million robots across its warehouses worldwide.

The milestone was marked with the delivery of the one millionth robot to a fulfillment center in Japan, confirmed by the company on June 30, 2025.

This achievement occurs as Amazon works toward balancing the use of robots and human workers in its warehouses.

According to The Wall Street Journal, 75% of Amazon’s global deliveries now involve robotic assistance.

In addition, Amazon introduced a new AI model called DeepFleet. This model is designed to optimize the routes for its warehouse robots and is expected to improve the speed of the robotic fleet by 10%.

Amazon has been enhancing its robotic capabilities since acquiring Kiva Systems in 2012.

The company continues to expand its automation efforts while integrating human and robotic workers into its logistics operations.

🔗 Source: TechCrunch

🧠 Food for thought

1️⃣ Amazon’s decade-long robotics journey reflects the industry’s automation curve

Amazon’s growth from its 2012 acquisition of Kiva Systems for $775 million to today’s milestone of 1 million robots demonstrates a methodical scaling approach to warehouse automation1.

This progression wasn’t overnight. It took 13 years to reach this milestone, showing how even tech giants must navigate the complexity of integrating robotics at scale2.

The company has systematically developed specialized robots for specific warehouse tasks, including the massive Robo Stow that lifts 3,000-pound pallets, Kiva robots that retrieve items, Pegasus robots that autonomously route parcels, and the modular Xanthus robots that handle various functions2.

This diversification strategy has enabled Amazon to automate different aspects of fulfillment gradually rather than attempting full automation at once, creating a blueprint that competitors like FedEx and UPS are now following with their own automation investments3.

The new DeepFleet AI model represents the next evolution, using generative AI to coordinate robot routes more efficiently and increase speeds by 10%, showing how machine learning is becoming critical to managing increasingly complex robotic systems.