LG to Start Making Money by Selling Its Exaone AI Externally
LG is selling its self-developed AI 'Exaone' externally. The full-scale commercialization of this AI, validated within the group, has begun. Lim Woo-hyung, co-Head of LG AI Research, presented LG's AI strategy and plans for utilizing Exaone at the 'LG AI Talk Concert 2026' held at LG Science Park in Magok, Gangseo-gu, Seoul, on the 14th. Hanwha Investment & Securities evaluated this as a strategy to develop 'expert AI' specialized for industrial sites rather than the commonly discussed general-purpose AI (AI usable for multiple purposes).
The Exaone product lineup LG has unveiled spans across industries. The in-house work tool 'Chat Exaone,' the 'Exaone Data Foundry' which automatically produces data for AI training, and the manufacturing inspection model 'Exaone Omni-Inspect' are already in operation. Also developed are 'Exaone Discovery,' which aids new materials development, 'Exaone Path,' a medical diagnosis model, and 'Exaone Forecast,' which predicts market trends.
External commercialization is taking shape first in the finance sector. LG AI Research jointly developed 'Exaone Business Intelligence,' which provides financial market outlooks and analysis, with the London Stock Exchange Group (LSEG) and commercialized it in September 2025, with its analysis results being provided to global institutional investors. It is also pursuing the development of financial AI solutions with Koscom. However, business viability indicators such as contract size, revenue performance, and the number of paying customers have yet to be disclosed.
In the global industrial AI market, not only big tech companies like Microsoft, Google, and Amazon but also specialized firms focused on manufacturing, healthcare, and finance are competing. It has been pointed out that LG must prove not only the accuracy of its AI but also system integration costs, data security, and maintenance capabilities.
LG is also expanding the scope of automation combining AI and robotics. Within this year, it plans to build an 'AI autonomous laboratory' where AI designs experiments, robots execute them, and AI analyzes the results again. Since it can operate 24 hours a day even after researchers leave work, automating repeated experiments in the development of new materials, batteries, and chemical and biopharmaceutical products can reduce research time and costs.
Whether Exaone can establish itself as an independent revenue-generating business beyond an 'in-house AI' is expected to be the next key challenge after technical validation.
