If a logistics company still has hundreds of thousands of people delivering, moving, and sorting goods,
the natural thought might be:
First, use AI to help these workers improve efficiency.
But JD is preparing to do something much bigger.
On September 9, 2026,
JD Logistics announced at the JDD Global Technology Explorers Conference:
Over the next five years,
it plans to purchase:
3 million robots.
1 million autonomous vehicles.
100,000 drones.
This is not a small-scale test in a single warehouse.
What JD aims to do is:
integrate various types of machines
into one logistics system, covering warehousing, sorting, transportation,
all the way to last-mile delivery.
On the other hand,
JD and its ecosystem currently still employ about 700,000 delivery and logistics personnel.
So the real interesting question isn’t:
"Is JD going to replace all human workers with machines?"
But rather:
When machines outnumber logistics workers, what roles will people take on?
JD Isn’t Buying Just One Type of Robot
When many think of Physical AI,
the first image is often:
a humanoid robot.
One that walks,
lifts boxes,
and picks up items.
It seems like a universal robot could handle all tasks.
But JD’s announced approach is almost the opposite.
Their "Wolf" series robots are not just one type.
Some handle warehouse movement.
Some handle picking.
Some handle sorting.
Some operate in cold chains.
Some manage unmanned pharmacies.
They include L4 autonomous delivery vehicles.
And even logistics drones.
JD Logistics states that their "Wolf" lineup now includes 9 product categories and 11 robot models
covering warehousing, sorting, transportation, and delivery with various devices.
In other words,
they’re not waiting for a single robot to learn to do everything.
Instead,
different machines each do what they are best at.
The Real Key Is the "Super Brain"
If there are 10 machines in a warehouse,
the task isn’t too difficult.
But if the future truly involves:
millions of robots,
millions of autonomous vehicles,
and hundreds of thousands of drones,
the main challenge isn’t just:
"Will each machine work?"
It's:
Who decides what each machine should do at any given time?
JD Logistics refers to the scheduling system behind this as the:
"Super Brain."
Its role isn’t to move boxes itself,
but to handle higher-level decisions and coordination in the supply chain.
For example:
Where do orders come from now?
Which warehouse is best suited to ship?
Which batch of goods should be picked first?
Which machine is available?
Which delivery route is most suitable?
Where are bottlenecks starting?
If different machines just "do their own thing,"
the more there are,
the more chaotic the operation might become.
So JD’s next move is to have these different machines
work in a coordinated manner.
This Might Be What Scaled Physical AI Actually Looks Like
We often imagine Physical AI as:
an AI growing a body
and working like a human.
But logistics offers a different answer.
Moving goods
doesn’t necessarily need two legs.
Delivering packages
doesn’t necessarily need two hands.
In warehouses, a wheeled robot can be faster than one with legs.
For city deliveries, a low-profile autonomous vehicle can be cheaper.
For mountainous or hard-to-access areas,
drones may be more suitable.
So large-scale automation
is probably not:
a universal humanoid robot.
But rather:
many specialized machines, each handling a specific task,
combined into one system coordinated by AI.
JD Has Already Been Testing Autonomous Delivery
This isn’t just a vision on a presentation slide.
JD announced in Q2 this year
that JD Logistics already operates thousands of autonomous vehicles
in daily operations across more than 20 provinces in China.
Shenzhen has even started night-time autonomous delivery routes,
allowing some vehicles to operate 24 hours a day.
This means the real question now isn’t:
"Can autonomous delivery be done?"
but:
When will it scale up from thousands of vehicles to much larger numbers?
JD’s latest answer:
Targeting millions of units.
What About the 700,000 Logistics Workers?
This is where the story gets more sensitive.
According to the South China Morning Post,
JD and its ecosystem currently employ approximately 700,000 delivery and logistics personnel.
If automated handling, sorting, autonomous vehicles, and drones become more mature,
the frontline repetitive jobs
will obviously be most impacted.
Earlier this year, JD founder Liu Qiangdong proposed a retraining program
to help some couriers and warehouse staff transition into
robot maintenance,
equipment service,
and technical roles.
In other words,
JD’s stated direction is not:
"Getting rid of all 700,000 employees."
It’s:
as machines take on more frontline work,
some human roles will evolve accordingly.
But "Retraining" Doesn’t Solve Everything
This story isn’t all rosy.
A company saying it will retrain staff for higher-skilled jobs
doesn’t mean every employee can easily transition.
Delivering, moving, and sorting is different from repairing robots and managing automation.
Some people can learn quickly.
Some will need longer.
And the number of roles needed may not match the current workforce size.
The questions to watch are not whether companies say:
"We will retrain workers,"
but rather years later:
How many actually complete the transition?
Are salaries increasing?
Are these new jobs sustainable?
And how many original roles truly disappear?
These answers are still unknown.
One Thing Is Becoming Clear
Previously, a logistics company’s core assets were:
warehouses,
trucks,
distribution centers,
and delivery personnel.
In the future, another key asset might be:
a fleet of machines.
More importantly,
who can effectively manage these machines.
Owning 1 million autonomous vehicles
doesn’t mean having an efficient logistics system.
Just like a company with 1,000 employees
isn’t necessarily more efficient.
The real difficulty is:
Who does what?
When?
Where?
Who handles problems?
How does the system coordinate?
This marks a major shift as AI moves from "answering questions"
to "managing the real world."
AI Is No Longer Just Helping Humans with Tasks
Over the past year, when we talk about AI agents,
it’s often about:
helping write emails,
organizing data,
doing research,
or navigating websites.
These mostly happen in the digital realm.
But when AI starts controlling:
robots,
autonomous vehicles,
drones,
and automated warehouses,
it’s a whole different level.
AI’s decisions
actually cause physical machines to move,
pick up packages,
deliver goods to other cities,
and even get medicines to the next person.
AI is crossing from the digital world into the physical world.
So The Key Isn’t the Number "3 Million" Robots
3 million robots are eye-catching,
but this number represents JD’s procurement plan for the next five years,
not the number already deployed.
The real importance lies in the direction this signals.
JD is not betting on waiting for:
"The perfect humanoid robot to emerge."
But rather:
As soon as a type of machine can handle one logistics step well, put it to work.
Let robots handle moving,
robots handle picking,
autonomous vehicles handle road delivery,
drones handle aerial delivery,
all coordinated by AI.
This may bring large-scale automation
to reality faster than waiting for one "do-it-all" robot.
And Human Roles Will Change Alongside
Future logistics sites
may still be staffed with many people.
But workers’ roles may gradually shift from:
doing the manual tasks themselves—
moving,
delivering,
and sorting—to:
monitoring equipment,
handling exceptions,
repairing machines,
managing multiple devices,
and solving problems unanticipated by AI and automation.
In other words,
the real question isn’t:
"Will AI take my job?"
But:
"When AI and machines take over the most repetitive parts of the job, what will be left for me to do?"
JD’s plan to acquire 3 million robots magnifies this question.
The real thing to watch in the future is not:
how many people remain in warehouses,
but
how many machines those people end up managing.
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