The 'manufacturing AX polarization' issue raised by Byeng-Dong Youn, CEO of OnePredict (and professor in the Department of Mechanical Engineering at Seoul National University), is a familiar story to anyone who knows the field firsthand. While large enterprises already have their own data centers and dedicated AI organizations, having pushed predictive maintenance and automated quality inspection beyond the pilot stage, small and mid-sized manufacturers are often still stuck at the stage of deciding whether to adopt AI at all. The problem is that this gap tends to widen rather than narrow over time.
The two keywords mentioned in the presentation, 'education' and 'GPU,' are actually the most basic infrastructure for AX transformation. Computing resources such as GPUs require large upfront investment, and AI workforce training doesn't produce short-term results, making both easy to deprioritize for smaller companies. However, without these two pillars in place, even a good AI model or solution can fail to take root on the shop floor. This is exactly the point TECH2030 repeatedly confirms while supporting various manufacturing sites. Success or failure often hinges less on the technology itself and more on the organizational capability and infrastructure needed to adopt it.
That said, the specific policy proposals, scale of support, or implementation timeline from this presentation are difficult to confirm clearly from the summary alone. We recommend checking the original article for the full context and detailed discussion.