[Special Contribution] ③ The Ecosystem of the AI Highway—Virtuous Cycle of Data, Models, and Community is Key

Jason Park, Full-Time Professor, Department of Digital Content Design, Osan University

▶ Who Will Operate That Road?

When the Gyeongbu Expressway opened in July 1970, people were not focused on the thickness of the asphalt. Their sole concern was one question: “Will this road truly get us there quickly and safely?” However, as time passed, the real question emerged: “Who will manage and develop this road?”

Fifty years later, we stand before another expressway: the AI Highway. In my previous contribution ①, we confirmed that competitiveness in the AI era depends not on the number of GPUs but on the speed of the infrastructure. In ②, we diagnosed the three walls blocking its construction: power and cooling, network and storage, and data and talent. Now, the final question remains: “Once all those walls are overcome and the expressway is completed, who will operate and develop it?”

The Gyeongbu Expressway became a true national artery not at the moment the pavement was laid, but when traffic control centers were established, speed limits and lane change signals were enforced, and accident response protocols were activated. An expressway without a control center is merely a lawless zone racing toward chaos at the highest speed. The AI Highway is no different. Even with the world’s best GPU cluster, if trained models fail to function properly in the field or user feedback is not reabsorbed as data, the infrastructure will stagnate, accumulating only costs. The true competitiveness in the AI era lies not in “building” but in “keeping it running.”

▶ The Heart of the Control Center: AI Governance Dashboard

Just as a traffic system cannot operate without signals, speed limits, and accident response protocols, the AI Highway requires clear operational principles and real-time monitoring systems. The first element to be established is the AI Governance Dashboard. This dashboard goes beyond simple performance metrics to integrate security status, model versions, data provenance, cost structures, and community contribution levels.

Global leading companies have already recognized this. OpenAI tracks all model changes through an internal governance dashboard, while Google automatically records data lineage on its cloud platform. They design operations not as “management” but as an “ecosystem.”

Our reality is different. A case from a public institution illustrates this: initial training succeeded, but when the model exhibited hallucinations in actual service, tracing the cause was impossible. There was no record of where the data came from or what preprocessing was applied. This is akin to driving on an expressway without traffic signals.

◆ Three Core Axes of Operations

First, Data Lineage Tracking

All data and models must have “versions.” In software development, code is managed with Git, and change histories are clearly recorded. Yet many organizations still treat AI models as “train once and deploy—done,” often struggling even to reproduce the same performance. Data lineage immutably records the provenance, transformation history, and usage of every dataset, much like a blockchain. This enables immediate tracing of which dataset a model was trained on or which parameters were recently changed if abnormal response rates suddenly appear. It is like erecting road signs on the expressway.

Second, Model Version Management

AI is no longer a domain where “the latest is the best.” Different models suit different tasks, and even the same model varies in character depending on hyperparameters and checkpoints. Thus, the unit of operation is not “one massive model” but “bundles of models with different versions.” The control center must display, for each model, the responsible owner, training data lineage, training and inference costs, deployment targets and methods, and failure rollback paths. In the field, unexpected inputs flood in immediately after deployment. Without lineage, root cause analysis takes days; without version management, rollback is impossible.

Third, RAG Optimization and Hallucination Control

Retrieval-Augmented Generation (RAG) is central to practical AI today. Referencing external knowledge in real time to generate responses offers flexibility but also amplifies the risk of error propagation. Many organizations currently blame hallucinations solely on the model. In reality, accuracy is determined by the knowledge graph, indexing strategy, chunk size, and reranking policy. Global companies maintain RAG hallucination rates below 1% and treat this as a public metric. Validation loops and automatic rollback mechanisms must be embedded in the operational system. Effective RAG is not merely “good retrieval technology” but the synthesis of “asking well and selecting well.”

▶ Virtuous Cycle Created by Transparency: Public Dashboard

The ultimate goal of operations is not closed management but an open virtuous cycle. To achieve this, a public governance dashboard is essential. It transparently displays real-time security status, model conditions, cost trends, and community contributions. Hugging Face in the United States has perfected this model: tens of thousands of models are open, the community raises issues, and improvements follow. Contributions reach thousands per quarter.

A domestic startup had a similar experience. Its initial model was used only internally, but after opening the dashboard and receiving feedback, reuse rates exceeded 70%. A virtuous cycle began where small and medium enterprises adopted existing models, refined them, and shared them again. When model deployment speed, most-reused datasets, and contribution methods are transparently disclosed, more people naturally join the ecosystem. This transcends mere “collaboration” to create an innovation cycle based on collective intelligence.

▶ Measurable Operations: New KPIs

All operations must be measurable. We must abandon outdated metrics like “how many GPUs.” Instead, the following KPIs should serve as the compass for the AI Highway:

Total Cost of Ownership (TCO): The true cost encompassing infrastructure, data preparation, and model maintenance—not just electricity bills. We must know the actual power and operational costs per AI computation.

Time-to-Deploy: The time from idea to field deployment of a new model. The target is not two days but two hours. One major company’s AI system, despite strong initial performance, was discontinued after six months due to lengthy deployment cycles that failed to keep pace with market changes.

Data and Model Reuse Rate: Target 70% or higher. This is not merely a technical issue but one of standardization and trust-building. On a national AI Highway, models or datasets created by one ministry must be easily usable by others, reducing redundant training and lowering TCO.

Community Contribution Count: Set at 100 or more per quarter. Like GitHub issues, anyone should be able to report bugs and propose improvements. Issue reports, documentation edits, and minor performance suggestions all become assets that enhance the expressway’s quality.

RAG Hallucination Rate: Strictly manage below 1%. This is not a slogan; it must appear daily on the dashboard with immediate response if targets are missed.

These KPIs are not mere numbers. They reshape organizational dialogue, realign priorities, and ultimately signal the ecosystem’s health.

▶ Ecosystem Built by the Community

What breathes life into all this is the community. The AI Highway must not become the exclusive domain of government or large corporations. Developers, researchers, startups, and even ordinary citizens must contribute and provide feedback on this road. China already realizes this on its national AI platform, where thousands of institutions share a common data lake and models, advanced by the community. We must move beyond fragmented pilot projects to a unified ecosystem.

A community feedback loop must be created where users become operators, forming a virtuous cycle. Official channels for reporting expressway issues and proposing improvements should be formalized. The answer to the final question in my contribution ②—“Who will drive on that road?”—is “all challengers in Korea,” and simultaneously, they must become joint owners of the road.

◆ What Must Be Done

First, establish a National AI Governance Center to standardize and operate data lineage, model versions, and RAG. It should serve as a traffic control center, monitoring and optimizing the entire expressway’s flow in real time.

Second, enact public dashboard standards. All participating institutions must share the same metrics, anonymizing sensitive items while making core signals—cost, performance, quality, and safety—visible to anyone.

Third, introduce community incentives. Prioritize resource allocation based on contribution volume and expand infrastructure access for outstanding contributors to encourage participation.

Fourth, legislate KPIs. Mandate reuse rates of 70%, hallucination rates below 1%, and deployment within 48 hours; only projects meeting these should receive government support.

Fifth, transform education programs. Train not only “scientists” who build AI models but also “operators” who deploy them in practice and “governance experts” who manage them efficiently.

▶ Investment, Not Cost

Of course, this operational system entails “costs.” Yet these are investments, not waste. Reducing model deployment cycles from two days to two hours enables faster responses to market changes and customer needs. Higher data reuse rates reduce power and time for training. Ultimately, the AI Highway’s efficiency must be judged not by “how quickly it was built” but by “how well it is running.” Perceived speed is born from operational design, not output.

◈ Like That Road 50 Years Ago

Fifty years ago, the Gyeongbu Expressway was more than paved asphalt. It became a true “national artery” because of the trucks and cars driving on it, rest areas and repair shops, traffic broadcasts, and accident response teams. The AI Highway we must design today is the same. GPUs and cables alone are insufficient. An operational system that orchestrates data flows, manages model evolution, and incorporates community voices—this is the ecosystem on the expressway.

The success of the AI Highway is determined not by the thickness of the asphalt but by the design of the traffic flow upon it. Under a robust governance with an integrated control center, sharing transparent dashboards and clear KPIs, and with the community paving the road together, this expressway will carry Korea’s next 50 years.

We must now change the question—from “Who will drive on that road?” to “Who will build and maintain that road together?” The answer is not the government, corporations, or developers alone, but all of us. If we have opened the road for them to drive, we must now ensure it breathes and grows on its own.

♣ I propose once again to the Presidential Office AI Chief: Elevate the operation and ecosystem of the AI Highway to a national strategy and commence designing the control center with all ministries, private sector, and academia. Just as we connected the road with cement 50 years ago, it is now time to connect a new ecosystem with data, models, and feedback. On that ecosystem, Korea’s next generation will prosper. Only then will Korea’s AI Highway become not a road merely following others but one the world watches as a leader.

 

[Editor’s Note] Author Jason Park graduated from the University of California, San Diego, taught at California high schools, and served as a counselor at the University of Illinois. He currently advises admissions at Eastern Illinois University, Southwest Minnesota State University, and University of Europe in Germany. He also operates the YouTube and TikTok channel “Jason Tube” and is a full-time professor at Osan University.