From Factory Data to On-Chain Trading, Web3 Firms Seek Opportunities in South Korea

"South Korean Manufacturing Data Can Become an AI Training Asset"

Lighter Targets Korean Market With Easier-to-Use Platform

Global blockchain companies are eyeing South Korea's manufacturing data and crypto trading market as new business opportunities. They said manufacturing and robotics data could become a new source of revenue if used for AI training, while decentralized exchanges, or DEXs, will need both trade-verification technology and the ease of use of mainstream financial apps to expand their user base in South Korea.

Andrea Muttoni, chief executive officer of the Data Foundation, and Vladimir Novakovski, CEO of Lighter, outlined that view at the a16z Crypto Korea Summit held at Josun Palace in Seoul's Gangnam district on Oct. 1. Both said verification systems that can confirm the source of training data and how transactions are processed will become more important as AI adoption increases.

"South Korean Manufacturing Data a New Revenue Source in the AI Era"

Muttoni said South Korea's strengths in manufacturing and robotics could make it a key supplier in the market for AI training data. Robots need large volumes of real-world data showing how people and machines move in actual workplaces in order to learn physical tasks. Information collected online alone cannot meet that demand.

"U.S. AI companies can keep scraping the internet, but that will not give them access to production processes at LG factories or manufacturing data from Hyundai Motor," Muttoni said. "If South Korean companies organize the data they hold into usable formats and grant usage rights, that can become a new source of revenue."

He added that the same data could also be used to develop in-house AI models and robots at South Korean companies.

The key issue is building a system that can verify where data came from and how it has been used. AI companies often obtain data through brokers, but it remains unclear what data came from where and how it was used in training, he said.

The Data Foundation is building infrastructure to track that process. More than 250 million "data receipts" have been recorded on its network, Muttoni said. Instead of storing raw data on a blockchain, the system records when the data was created, how it was collected and how it has been used.

Large datasets remain in conventional storage services, while records needed for verification are managed on-chain, he added.

Muttoni said the system should eventually compensate data providers. AI should be able to identify how much each dataset contributed to a given output and reward those contributors, he said, adding that such a structure is needed to make the data economy sustainable.

He also pushed back on the idea that synthetic data will fully replace real-world data. AI-generated data is useful, but weather, market conditions, human language and behavior keep changing. AI will need a steady supply of up-to-date real-world data, and distinguishing between collected and synthetic data will become increasingly important, he said.

Lighter Says DEXs Must Be Easy to Use to Gain Users

Novakovski said user experience will determine whether decentralized exchanges achieve broader adoption. South Korean users accustomed to centralized exchanges will not switch to new services if they have to accept more friction in the trading process.

"Users should not feel like they are using a DEX," he said. "It should feel like using a modern trading app or a neobanking app."

The complex technology should operate behind the scenes, while users get practical benefits such as lower costs and faster execution.

Lighter is an Ethereum-based DEX that uses zero-knowledge proofs to verify whether orders were processed according to predetermined rules. The platform handles about 500 million orders a day while proving the outcome of each one, Novakovski said. It was designed so users can verify that the system worked properly rather than simply trust the exchange's explanation.

He said that kind of verification will become more important as AI-based trading expands. If something goes wrong while AI is executing orders, users need to be able to inspect the trading process and how the system operated. It is also possible to prove that preset risk limits were followed without disclosing the underlying investment strategy, he said.

Still, Novakovski was skeptical that AI will replace all human investment decisions. Asset allocation and trading based on fixed rules can be automated, but top-tier professional trading firms already use advanced AI and massive computing power. AI is more likely to improve traders' capabilities by a notch or two than eliminate them entirely, he said.

In South Korea, Lighter is expanding its reach among local users. The company added Korean-language support about a week after launch, Novakovski said. The number of Korean-language users grew from about 30 initially to the thousands afterward.

He added that Lighter has formed an Asia team and is meeting South Korean traders directly.

Over the longer term, computing resources, data and financial products will be linked through blockchain-based financial systems, he said. Before any future in which AI makes every decision on behalf of people, the first shift is likely to be toward giving users better information and broader access to products to support their own decisions.