Physical AI is not just about "replacing humans with machines": Amazon adds 30% more high-skilled jobs.

Automation drove the development of the First Industrial Revolution and continues to evolve in today's Fourth Industrial Revolution. Although automation has become an important part of the manufacturing industry, the latest advancements in artificial intelligence, vision systems, and robot hardware are giving rise to a new generation of "smarter and more adaptable" machines.

The new white paper "Physical AI: Empowering a New Era of Industrial Operations" released by the World Economic Forum explores how these technological developments are expanding the boundaries of robots' roles. They not only improve efficiency but also bring greater flexibility and risk resistance to factory floors.

Previously, most industrial robots were designed to perform fixed and repetitive tasks in a controlled environment, but this situation is starting to change. With the help of physical AI, robots are gradually gaining the ability to perceive, learn, and respond to more complex environments, and they can support a wider range of task types.

This transformation comes at a critical juncture. Currently, manufacturers are facing multiple challenges such as rising costs, labor shortages, and changing customer demands, and the business environment is becoming increasingly complex.

But how did this situation come about? Understanding the evolutionary journey of industrial robots can provide important context for grasping future trends.

01. Evolution of Industrial Robots

The application of physical AI is the next step in the long - term evolution of industrial robots. We may think of robots as a product of the future, but the earliest industrial robots can be traced back to the 1960s. The term "robot" comes from the Czech word "robota", which means forced labor.

Early industrial robots were rule - based, that is, they performed repetitive tasks with high precision and speed through explicit programming, but they lacked flexibility. These systems have become standard in industries such as automotive and electronics, which have benefited from the increased productivity on the shop floor brought by robots.

For low - variable and high - volume tasks, these rule - based robots will still play a role, and their application scenarios and capabilities will continue to evolve.

Today, training - based robots are driving the rise of physical AI. Through AI and machine learning, robots can learn from experiences in simulated or real scenarios.

Different from their predecessors, they no longer rigidly follow specific programs but can handle tasks with certain variables, making them more suitable for "medium - volume" or even "non - repetitive" production tasks. The key is that their training can be achieved through virtualization, which significantly shortens the deployment time and expands the scope of tasks that can be automated.

Context - based robots are the next stage of intelligent automation. Similar to training - based robots, they are equipped with sensing tools, from high - resolution cameras to tactile sensors, which can "observe" and interpret their environment in real - time.

The core supporting these capabilities lies in powerful AI foundation models. These models can generate outputs through natural language prompts and integrate vision, language, and actions to understand the environment. They can grasp the context they are in, "think", make autonomous decisions, and even plan. The white paper compares the level of these skills to "human - level task intuition and planning ability".

Although these robots are still far from the humanoid appearance commonly seen in movies, their shapes are also changing: various forms such as quadruped robots, humanoid robots, and mobile robots have emerged one after another, further expanding the application scope of robots.

It should be emphasized that the three robot technologies, rule - based, training - based, and context - based, will continue to play a role in the manufacturing industry. As part of a diversified automation strategy, their deployment will be customized according to the needs of different production lines and task types.

02. Why Physical AI and Intelligent Robots Are Key to Manufacturing

For manufacturers alone, the assistance of robot technology comes at just the right time.

Currently, the supply chain is still very fragile, and problems such as geopolitical tensions, raw material shortages, and transportation bottlenecks have further exacerbated this situation. Market uncertainties have made these problems even worse, posing threats to productivity, profits, and risk resistance.

The rising costs of raw materials, energy prices, and salary levels, combined with labor shortages and an expanding skills gap, have jointly intensified the challenges in the manufacturing industry. At the same time, customer demands are also upgrading, emphasizing more on customization, faster delivery speeds, and sustainability.

Intelligent robots connect the digital world and the physical world and achieve the above goals by improving operational flexibility. However, manufacturers need to incorporate robot technology into their "long - term strategy" rather than just pursuing short - term benefits.

03. Building a Talent Team Capable of Harnessing Robot Automation

To achieve this transformation, "skilled labor" is crucial. According to the World Economic Forum's "Future of Jobs Report 2025", robots and autonomous systems will be the main source of job substitution. But as the latest physical AI white paper states, this "substitution" is not "job disappearance" but "job transformation". Like AI and other digital technologies, robot technology will also give rise to new high - skill jobs.

For example, machine operators will become robot technicians, logistics teams will coordinate mobile robots, maintenance teams will shift to predictive maintenance, and manufacturing engineers will focus on training and optimizing artificial intelligence and robot systems. In addition, automating previous manual jobs will free up human resources to perform more meaningful tasks.

To successfully integrate intelligent robots into the workflow, we need to focus on "labor force training and continuous learning". Skills retraining, skills upgrading, and long - term labor force planning are the keys to ensuring that intelligent robots "deliver value", which is not only related to corporate interests but also has social significance.

04. Real - World Application Cases of Physical AI

Although the field of intelligent robots is still in its early stages of development, early adopters have demonstrated the application value of this technology.

Amazon has deployed more than one million robots in its 300 distribution centers, collaborating with human employees to handle repetitive tasks such as sorting, moving, and transporting packages. The robotic packaging line can also minimize packaging waste, helping Amazon achieve its sustainability goals.

The overall management of these systems has achieved remarkable results in the pilot: the delivery time has been shortened, and the efficiency has been increased by 25%. The scheduling of all mobile robots on - site has improved the driving efficiency by 10%. At the test sites, Amazon has also added 30% more high - skill jobs.

At the same time, the electronics contract manufacturer Foxconn is transforming into what it calls a scalable AI - driven robot workforce to cope with rising labor costs and the trend of local manufacturing.

The company uses AI and digital twin technology to simulate and automate precise tasks such as "screwing and cable insertion", which were previously quite challenging for traditional rule - based robots.

The digital twin simulation has shortened the deployment time of the new system by 40%. The AI - driven robots have shortened the production cycle by 20% - 30% and reduced the error rate by 25%. The operating costs have decreased by 15%. Overall, in complex assembly tasks, the success rate of AI - driven robots is already higher than that of humans.

05. How Manufacturers Can Grasp the Value of Physical AI

Physical AI is not a distant future. Intelligent robots are already changing the manufacturing industry, and this trend will only intensify. As time goes by, we will see more and more "human - like capabilities" emerging, even if robots may not adopt a humanoid appearance.

Facing multiple challenges such as labor shortages, productivity improvement, and rapid response to market and economic changes, manufacturers need to act quickly to grasp the potential of this technology.

The World Economic Forum advocates that robots should not be used in isolation but a hierarchical automation strategy should be adopted to integrate various robot technologies to achieve system - level intelligence.

Although the speed of technological progress is amazing, enterprises should not blindly follow the trend but need to adhere to a "human - centered" strategy to ensure the sustainability and inclusiveness of robot integration. In addition, manufacturers also need to share experiences through collaborative projects and confidently enter this new era of automation.

Original source:

1.https://www.weforum.org/stories/2025/09/what-is-physical-ai-changing-manufacturing/

This article is from the WeChat official account "MetaverseHub". Author: MetaverseHub. Republished by 36Kr with permission.