Large-scale language models (LLMs) have already moved beyond the experimental stage and become integral to corporate operations and social infrastructure. However, as LLM performance continues to improve, a new question emerges: not "Which model is smarter?" but "Which model should I choose?" A key aspect of this choice lies in the contrast between open and closed models.

Current Trends in the LLM Ecosystem

The LLM market is currently divided into two distinct halves. One is expansion centered on open models, where code, weights, and learning methods are relatively open. The other is advancement centered on closed models, where performance is enhanced by massive computational resources and data. Interestingly, while both camps talk about the "future," their definitions of that future are completely different.

The evolution and philosophy of the open model

The core values of an open model are transparency, reproducibility, and controllability. Model architecture and operating principles can be understood, fine-tuned for specific purposes, or deployed directly within internal environments. This evolution leads to a structure where more people can understand and improve models. For businesses, this is attractive in terms of data sovereignty and security, and AI can be designed to meet specific industry regulations or internal policies. However, the training costs and operational burden of maintaining up-to-date performance remain challenges.

Evolutionary direction and strategy of closed models

Closed models focus on performance, stability, and immediacy. They evolve rapidly based on massive user feedback, vast data, and dedicated infrastructure, allowing users to experience top-tier performance without complex setup. The advantages of this approach are clear: high language understanding, versatility for diverse tasks, and rapid feature updates. However, they also present limitations: the model's internals remain a black box, and it's difficult to fully control how data is processed or the underlying learning base.

Differences in 'operating philosophy', not a battle of technology

The debate between open and closed models is often reduced to a comparison of performance, but the essence is different. It's not about technological superiority, but rather a difference in philosophy regarding how to integrate AI within an organization. The open model seeks to integrate AI as part of internal systems, while the closed model is closer to utilizing AI as external infrastructure. The former favors control and customization, while the latter favors speed and versatility.

Impact on Businesses and Brands

From a corporate perspective, this choice isn't a matter of short-term cost, but rather a matter of long-term strategy. Organizations opting for an open model must simultaneously develop internal capabilities and data management systems. Conversely, those opting for a closed model face the risk of dependence on a specific platform in exchange for rapid adoption and expansion. At the brand level, explainability and accountability for AI are becoming increasingly important, and this choice directly impacts future trust strategies.

In practical use, the 'dichotomy' is collapsing.

Many organizations in the real world no longer adopt a single solution. They employ open models for core decision-making and sensitive data areas, while closed models are used for customer service and general-purpose operations. In other words, the direction of evolution is not competition, but rather blending and division of roles.

Insight Summary

The future of LLM isn't simply a matter of open models winning or closed models dominating. What matters is which model fits our organization's mindset and operational structure. The evolution of LLM is not only an evolution of the model itself, but also an evolution of how we trust and govern AI. Only organizations that first answer this question will take the lead in the next stage of the AI race.