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How Amazon’s Next-Generation Robotic Warehouses Work—and What Tye Brady Says Is Changing

Amazon’s warehouse strategy is moving beyond mobile robots: Sequoia, robotic arms, AI coordination and human workers now operate as one integrated system.
From TheFinanceBase Team10 min to read
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Amazon’s next-generation fulfillment center is not a warehouse staffed entirely by humanoid robots. It is a coordinated industrial system in which mobile robots, robotic arms, automated storage, computer vision, artificial intelligence, and human workers share the job of moving inventory through the building.

That strategy was outlined by Tye Brady, Amazon Robotics’ chief technologist, in a 2024 TechCrunch interview. Since then, Amazon has announced its one-millionth robot, introduced the DeepFleet fleet-coordination model, expanded work on tactile and agentic systems, and removed at least one announced system, Blue Jay, from operations. The evidence points to a gradual shift toward integrated, human-supervised automation—not an imminent fully autonomous or humanoid-only warehouse.

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The direct answer: Amazon is integrating many specialized systems

The familiar image of Amazon warehouse automation is a low-profile mobile robot carrying an inventory pod. That remains important, but it is no longer the whole strategy.

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Amazon’s newer facilities combine:

  • mobile robots that move inventory, carts, and packages;
  • automated storage-and-retrieval systems;
  • robotic arms for picking, sorting, stacking, and consolidating;
  • computer vision and sensors;
  • software that coordinates robot traffic and warehouse workflows;
  • ergonomic workstations where employees handle items; and
  • maintenance, engineering, and human exception-handling teams.

The central idea is to select the technology for a specific operational problem rather than wait for one general-purpose machine that can do everything. Brady’s argument is practical: wheels are usually better than legs on smooth warehouse floors, robotic arms are useful for manipulation, and people remain valuable when products or situations fall outside predictable patterns.

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Inside Amazon’s Shreveport Gen 12 fulfillment center

Amazon’s facility in Shreveport, Louisiana, is the clearest public example of this approach. Amazon describes it as its first “Gen 12” fulfillment center. The building covers more than 3 million square feet—about 55 football fields—and is expected to employ 2,500 people when fully ramped up.

The site uses robotics and technology throughout its major production areas. Its central inventory system is Sequoia, a multilevel, containerized storage-and-retrieval system that combines mobile robots, gantry systems, robotic arms, computer vision, and employee workstations.

Amazon says Sequoia can store more than 30 million items at the Shreveport facility. The company also says the deployment is five times larger than its first Sequoia installation in Houston. That comparison refers to the size of the Sequoia deployment—not necessarily five times the speed, capacity, or productivity.

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In a typical flow, inventory is stored in containers, mobile robots move those containers through the system, and robotic equipment presents items to employees at workstations designed to keep products within a more comfortable mid-thigh-to-mid-chest “power zone.” Other robots and software then help move, sort, pack, and dispatch orders.

A fulfillment center should not be confused with a delivery station. Fulfillment centers store inventory and handle activities such as picking, packing, and sorting. Delivery stations are later points in the network where packages are organized for last-mile delivery.

How the main robot systems fit together

Sequoia: automated storage and retrieval

Sequoia is best understood as the facility’s inventory-storage backbone. Rather than requiring employees to walk through aisles searching for products, the system brings inventory toward the people and machines that need to process it.

Amazon says Sequoia can identify and store inventory up to 75% faster and can reduce fulfillment processing time by up to 25%. Those are Amazon-reported claims, not independently audited benchmarks, and the processing figure should not be treated as a guarantee for every facility or order type.

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Proteus: autonomous movement through open areas

Proteus is Amazon’s first fully autonomous mobile robot. It is designed to move carts or packages through open areas while navigating around employees.

Proteus illustrates why mobile platforms remain central to Amazon’s strategy. Warehouses generally offer broad, smooth floors, and wheeled systems can move efficiently through that environment without the mechanical complexity of walking.

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Robin, Cardinal, and Sparrow: robotic manipulation

Amazon uses robotic arms under names including Robin, Cardinal, and Sparrow. Their roles include sorting, stacking, consolidating, and handling products.

Amazon says the latest version of Sparrow can handle more than 200 million unique products with different shapes, sizes, and weights. That is a company product-handling claim, not an independently measured success rate. It also does not mean that every item is equally easy for the arm to grasp or place.

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Irregular, flexible, reflective, transparent, fragile, slippery, damaged, or poorly packaged items remain difficult because a robot must recognize the object, determine where to grasp it, apply appropriate force, and place it without damaging the product or surrounding inventory.

DeepFleet: coordinating the mobile fleet

Amazon announced DeepFleet on June 30, 2025. It describes the system as a generative-AI foundation model that coordinates the movement of mobile robots across its fulfillment network.

Amazon says DeepFleet can reduce robot travel time by 10%. That is a fleet-efficiency claim—not a claim that total order throughput rises by 10%, or that robots become 10% faster in every movement. The system is also not a humanoid intelligence or a general-purpose warehouse brain. Its reported role is fleet-level coordination.

Vulcan: contact-sensitive handling

Amazon describes Vulcan as its first robot with a sense of touch. In practical terms, this refers to contact-sensitive capabilities intended to help with ergonomically challenging tasks.

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“Sense of touch” should not be read as human-like tactile understanding. It is Amazon’s description of a robotic system using contact information as part of its handling process.

Project Eluna: operational decision support

Project Eluna is better described as an operational-support system than as a robot. Amazon says it uses agentic AI to help operators identify bottlenecks and make workflow decisions.

This distinction matters. The modern warehouse is becoming a combination of physical equipment and software systems: robots move and manipulate goods, while AI tools may help coordinate traffic, detect problems, or recommend operational responses.

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Why “10 times more robots” does not mean “10 times more automated”

Amazon says next-generation facilities use 10 times more robotics than earlier buildings. The phrase is easy to overinterpret.

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It does not necessarily mean:

  • 10 times more robots per employee;
  • 10 times more robots per square foot;
  • 10 times higher productivity;
  • 10 times less human work; or
  • 10 times more automation in every task.

Amazon and TechCrunch have not publicly specified the precise denominator and methodology for that comparison in the cited material. It is safest to understand the statement as Amazon’s comparison of robotics deployment in newer buildings with predecessor facilities, not as a universal measure of warehouse autonomy.

Why Amazon is retrofitting existing buildings

Brady’s discussion emphasized “brownfield” automation: installing new systems in facilities that already exist and may already be operating.

That approach can spread technology faster than waiting for every new warehouse to be designed from scratch. It can also allow Amazon to use its existing network, land, labor markets, transportation connections, and delivery infrastructure.

But retrofitting is difficult. New robots must work alongside existing conveyors, inventory flows, employees, safety procedures, power systems, and software. Installation may require what Brady characterized as fixing the airplane while it is flying: improving the operation without stopping the business for a complete rebuild.

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The trade-off is flexibility versus complexity. Modular systems can be added in stages, but every added robot, sensor, software layer, and charging area creates more integration and maintenance work.

Amazon’s one-million-robot milestone

On June 30, 2025, Amazon announced that it had deployed its one-millionth robot across a network spanning more than 300 facilities.

This is a company-reported operational milestone, not an independently audited count. It also does not mean Amazon has one million identical autonomous machines independently completing customer orders. The figure covers multiple categories of equipment, including mobile robots, robotic arms, and specialized systems with narrow roles.

The important change is therefore not simply the number of robots. It is the increasing coordination among different types of robots, warehouse software, storage infrastructure, and employees.

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Why Amazon is not betting everything on humanoid robots

Amazon has explored humanoid robotics, including work associated with Agility Robotics’ Digit robot. In the 2024 TechCrunch interview, Brady described that relationship as still being in an R&D phase.

The practical question is where a human-shaped machine offers an advantage. Humanoid legs may make more sense in environments with stairs, uneven terrain, or infrastructure designed for people. In a fulfillment center with smooth concrete floors, wheels are often simpler, faster, and more energy-efficient.

That does not support the claim that Amazon has abandoned humanoids. It supports a narrower conclusion: Amazon has continued to explore them, while conventional mobile robots, storage systems, robotic arms, and fleet software remain the core of its warehouse strategy.

The role of AI is narrower—and more important—than the marketing suggests

“AI-powered warehouse” is not a complete technical explanation. Different systems use intelligence for different tasks:

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Function What the technology does
Fleet coordination Plans and adjusts routes for large numbers of mobile robots.
Computer vision Recognizes products, containers, locations, and potential obstacles.
Robotic manipulation Helps select grasp points and move products with robotic arms.
Contact sensing Provides information when a robot is touching or manipulating an item.
Workflow support Helps operators identify bottlenecks and decide how to respond.
Exception handling Escalates unusual, damaged, ambiguous, or failed transactions to people.

Amazon and Brady describe this convergence of software intelligence and physical machines as “physical AI.” Its real value depends on the quality of the entire system: sensors, motion planning, warehouse-management software, network reliability, maintenance, safety controls, and human oversight.

What happened to Covariant’s technology and talent?

In August 2024, Amazon announced that it had hired Covariant’s founders and approximately 25% of the startup’s employees. TechCrunch connected the move with Amazon’s effort to expand foundational models for industrial robotics and difficult pick-and-place tasks.

This was an acqui-hire-style talent and technology move, not proof that Amazon acquired Covariant as an entire company. It also should not be confused with immediate large-scale deployment. The relevance is the long tail of warehouse exceptions: products vary in size, shape, packaging, fragility, and orientation, and models trained for broader manipulation tasks may help robots handle more of those cases over time.

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What happens to warehouse workers?

Amazon presents automation as a human-machine system rather than a plan to remove people from fulfillment centers. Robots can handle transport, storage, lifting, sorting, and repetitive movement, while employees continue to perform picking, packing, maintenance, supervision, judgment, troubleshooting, and exception handling.

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Amazon says next-generation sites require 30% more reliability, maintenance, and engineering employees. That differs from the approximately 25% increase quoted in the 2024 TechCrunch interview. The figures should not be blended; they may reflect different facilities, estimates, or points in the rollout.

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Amazon also says its mechatronics and robotics apprenticeship offers hourly wages up to 40% higher than entry-level roles. That is a company-reported program claim, not evidence that every warehouse worker will receive a higher wage or that technical jobs are equally available in every location.

The employment effects remain more complicated than “robots create jobs” or “robots eliminate jobs.” Key questions include:

  • Does employment rise because the facility processes more volume, rather than because automation inherently creates jobs?
  • Can existing employees access technical roles without substantial retraining?
  • Does automation reduce physical strain while increasing monitoring or productivity pressure?
  • Do injury rates improve consistently across facilities and job categories?
  • Does automation change the mix of jobs more than it changes total employment?

Amazon says it has achieved more than 30% safety improvements over several years and expects next-generation facilities to build on that progress. That wording should not be converted into a specific Shreveport injury-rate result. The company’s safety and employment claims require facility-level and independent labor evidence before they can be generalized across the industry.

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Reported operational benefits—and their limits

Amazon reports several benefits from its newer automation systems:

  • up to 25% faster fulfillment processing;
  • up to 25% improvement in cost to serve during peak periods at the next-generation facility;
  • more items eligible for Same-Day and Next-Day Delivery;
  • more than 30% safety improvement over several years, according to Amazon’s description; and
  • a 10% reduction in robot travel time from DeepFleet.

These figures describe different things. A facility-level processing estimate is not the same as a network-wide result. A projected improvement is not the same as a measured result. A reduction in robot travel time is not automatically a matching reduction in delivery time. And a safety trend across several years is not the same as a verified injury-rate result for one building.

Why the warehouse can still fail

More automation does not eliminate failure modes; it changes them. Possible problems include:

  • robot congestion or poor fleet routing;
  • a failed robotic arm or gantry creating a downstream bottleneck;
  • computer-vision errors involving unusual packaging;
  • inventory placed in the wrong tote or location;
  • damaged, leaking, tangled, or deformed products;
  • network, cloud, sensor, or power failures;
  • human-robot interaction incidents;
  • insufficient maintenance capacity during peak season; and
  • software integration failures between old and new equipment.

Highly automated systems can make some failures more systemic. A single employee error may affect one package; a software, network, or traffic-management problem can affect a much larger area.

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Amazon’s public robotics history also shows why announcements should not be treated as permanent deployments. Amazon said Blue Jay was no longer being used in operations as of February 25, 2026, although underlying technology continued to support its network. That does not make the project irrelevant, but it is a reminder that pilots can be redesigned, paused, or withdrawn.

What has changed since the Tye Brady interview?

The 2024 interview described a transition from primarily autonomous mobile robots moving inventory pods toward integrated robotic facilities. By August 2026, the public story had expanded in several directions:

  1. Scale: Amazon announced its one-millionth robot and a network of more than 300 facilities.
  2. Coordination: DeepFleet added a foundation-model approach to mobile-robot traffic management.
  3. Physical capability: Vulcan added contact-sensitive handling capabilities, in Amazon’s terminology.
  4. Operational intelligence: Project Eluna was positioned as an agentic system for identifying bottlenecks and supporting workflow decisions.
  5. Qualification: Blue Jay’s removal from operations showed that not every publicized robotics system becomes a permanent production tool.

Meanwhile, humanoids remain an area of exploration rather than the defining technology of Amazon’s fulfillment network.

Bottom line

Amazon’s next-generation warehouse is best understood as a human-supervised, software-coordinated industrial system. Sequoia organizes and stores inventory; mobile robots transport it; robotic arms manipulate it; AI coordinates fleets and supports decisions; and people handle exceptions, maintenance, judgment, and work that remains difficult to automate.

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The central story is not that Amazon has replaced warehouse workers with humanoids. It is that Amazon is combining specialized machines and software across both new and existing facilities. The benefits Amazon reports—faster processing, lower peak-period costs, improved ergonomics, and more delivery options—are significant but should be read as company claims with different methodologies and levels of verification.

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