A Foxconn engineer in Shenzhen spent six months writing code to make a robot insert cables into iPhone charging ports.

She mapped 21 connector types, wrote conditional logic for stiff versus flexible cables, and programmed angle adjustments for tight tolerances.

The robot failed on cable type 22. 

She patched the code, but then a variation of cable type 12 broke it in a new configuration.

After six months and thousands of lines of code, the task stayed manual.

In 2024, Foxconn gave her a different tool: a digital twin where the robot could practice.

She stopped writing code entirely.

👋🏻 I'm Leonardo Ubbiali. This week we're looking at a framework from a new WEF report that explains why tasks that couldn't be automated for decades are finally getting automated, and what comes after.

The WEF report, written with BCG, identifies three types of robotics that will coexist on factory floors.

Getting the distinctions right matters because each one automates different work and requires different investment.

Rule-based robots have been around since the 1960s.

You program them to do one thing with precision: weld this joint, stamp this part, move this pallet.

They work when the task is predictable and the volume is high.

These aren't going away.

Training-based robots are what Foxconn deployed.

Instead of writing code for every scenario, you put the robot in a simulated environment and let it learn through practice.

Foxconn used NVIDIA's digital twin platform to train robots on tasks like screw-tightening, pick-and-place, and cable insertion i.e, tasks where too much variability made rule-based programming fail.

According to the WEF report, Foxconn's digital twin simulations cut deployment times by 40%, improved cycle times by 20-30%, and lowered error rates by 25%.

Amazon followed a similar path.

Their DeepFleet AI coordinates over a million warehouse robots, learning from data across 300+ facilities.

Every failure at one site trains robots everywhere else.

Packages shipped per worker climbed from 175 in 2015 to 3,870 in 2025.

Training-based robots are why tasks that stayed manual for decades are now getting automated.

Context-based robots are the next tier, and most manufacturers haven't heard of them yet.

These use AI foundation models that can take natural language instructions, perceive their surroundings through cameras and tactile sensors, and reason through unfamiliar tasks.

The WEF report compares their capabilities to "human-level task intuition and planning."

They learn from practice, AND think about what to do in situations they've never encountered.

This is where NVIDIA's GR00T N1.6 and Boston Dynamics' partnership with Google DeepMind are pointed.

It’s about training robots to do known tasks better, but also giving them the ability to handle tasks nobody anticipated.

Context-based robots are early.

But manufacturers who understand the trajectory will make better investment decisions than those treating "AI robotics" as one thing.

The WEF report is specific about what happens to people.

It maps the transitions:

Amazon has already upskilled 700,000+ employees since 2019.

Their Mechatronics and Robotics Apprenticeship graduates earn up to $21,500 more annually than entry-level fulfillment roles. 

And their next-generation fulfillment center in Shreveport requires 30% more engineers and technicians than traditional warehouses.

The Foxconn engineer who spent six months writing code now trains robots in simulation.

So they still remain employed, just with a different job description.

The WEF report warns that this only works if companies invest in workforce transition before deploying robots, not after.

The Full Report: The WEF report is the best single document I've read on where manufacturing robotics is headed. It covers the technology stack, the case studies, and the workforce playbook. Worth the read.

How Foxconn uses Omniverse to create physics-accurate simulations where robots learn before touching real equipment. Includes detail on deployment time reduction and the Houston factory build.

How long did your engineers spend programming robots for tasks that stayed manual?

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Leo