According to a recent LinkedIn post from CarbonSix, the company is positioning “Physical AI” as a near-term, practical technology for manufacturing rather than a distant concept. The post highlights commentary by CTO HJ Terry Suh in the Economist Specialist View, emphasizing his experience at MIT CSAIL, NASA JPL, Toyota Research Institute, and Boston Dynamics AI Institute as the basis for CarbonSix’s approach.
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The LinkedIn post suggests that Suh views the core manufacturing challenge as shifting from “where to produce” to “how to produce,” underscoring the limits of traditional rule-based automation in variable, real-world environments. Instead, the company’s perspective centers on Physical AI systems that learn from data and encode human tacit knowledge, potentially enabling more adaptive automation on the factory floor.
According to the post, CarbonSix appears to advocate incremental deployment of Physical AI, starting with high-impact processes that can be addressed even with limited data rather than pursuing broad “big bang” rollouts. For investors, this staged implementation approach may reduce adoption risk for customers and could support a more predictable revenue ramp if the company succeeds in converting pilot projects into scaled deployments.
The post also frames Physical AI as a differentiator for manufacturing competitiveness, implying that firms able to operationalize such systems may gain efficiency and flexibility advantages over those relying on conventional automation. If CarbonSix can turn this positioning into commercially viable, production-grade solutions, it could benefit from growing demand for AI-enabled industrial automation and strengthen its role in the advanced manufacturing ecosystem.