Insights

Building a sustainable manufacturing ecosystem with Physical AI and a unified factory OS

DAIM Young Jae Jang CEO

In the global AI landscape, the United States possesses formidable IT capabilities but a weakened manufacturing base, while China boasts massive manufacturing infrastructure but faces limits in IT competitiveness. In contrast, Korea—equipped with world-class manufacturing infrastructure, process know-how, and a complete ecosystem spanning sensors, robotics, and communications—holds a unique and powerful position to take the lead in the new era of Physical AI.

However, to translate this opportunity into real industrial competitiveness, three fundamental mindset shifts are required to break through the limitations of legacy smart factories.

1. Unstructured manufacturing floors: Moving beyond LLMs to reinforcement learning and digital twins

[Image generated by AI | Source: KAIST Physical AI Research Center]

While language AI derives answers from public textual patterns, factories involve endless environmental variables, making it nearly impossible to reproduce identical results through simple data collection and training. Thus, the core of Physical AI lies in combining Reinforcement Learning (RL)—where systems self-optimize through trial and error—with digital twins.

Conducting repeated real-world factory experiments causes immense resource waste and costs; however, virtual environments allow thousands of simulations to run repeatedly without operational risk.

2. Breaking down $150k cost barriers: Domestic unified factory OS and uncrewed operations (Dark Factories)

[Source: DAIM Research, KAIST Physical AI Testbed Site]

The biggest reason small and medium-sized regional factories hesitate to automate is high cost and complexity. Foreign factory design and operation software, such as Siemens, costs up to $150k in annual licensing fees alone—a prohibitive barrier for mid-sized manufacturers. Furthermore, adopting equipment and robotics piecemeal led to severe cross-system incompatibility.

What manufacturing needs now is a unified factory OS that allows AI to monitor facilities and logistics in real time and autonomously control operations without manual intervention.

Only when this standardized OS framework is established can entire factories transition into autonomously operating dark factories (uncrewed manufacturing).

3. Evolving from single-unit component sales to turnkey factory package exports

Domestic robotics and equipment providers cannot win individual survival battles against US or Chinese competitors by selling standalone hardware. Just as designing and building a turnkey structure yields far higher value than selling raw materials, the same principle applies to manufacturing AI.

This is proven by the field results at the Jeonbuk Testbed and KAIST Testbed, where applying Physical AI to actual automotive component manufacturing significantly boosted productivity and quality. We must finalize a Team Korea strategy that bundles domestic manufacturing OS, robotic hardware, and IT infrastructure into complete turnkey factory packages for global export.

Ultimately, robotics adoption and uncrewed operations are not about replacing workers, but providing a vital lifeline to regional manufacturing suffering from labor shortages. Factories are massive, long-term markets requiring continuous maintenance and expansion rather than one-off builds.

A Korean-style Physical AI factory package unifying hardware and software represents a sustainable survival strategy for domestic manufacturing—and our most powerful export asset to reshape global markets.

[Recommended Lecture] Factory & Logistics Automation Systems Using Physical AI (CEO Chang Young-jae)

https://www.youtube.com/watch?v=4icd1YqPxSM