Summary
This study examines why the development pace of large language models has recently slowed down and why the focus of AI research has shifted from "increasing intelligence levels" to "consistency, tool usage, and contextual action execution." Data saturation, rising costs, and the limits of model scaling indicate that direct intelligence increases are not sustainable. In this context, future AI models are expected to focus on tool-augmented architectures that are application-oriented, open to system integration, and support human workflows at the action level.
Introduction
The rapid performance gains observed in large language models between 2020–2024 created a public expectation that AI would develop continuously and linearly. However, the slowdown observed in the development curve in recent years signals a change in the methodological direction of AI research. Development is no longer progressing toward increasing the model's pure cognitive capacity; instead, it's moving toward improving how it operates, uses context, and interacts with tools.
Data Saturation and Model Scaling Limits
Large language models are trained on internet-scale datasets. However, most of the available data sources:
- consist of repetitive content,
- have low information density,
- are heterogeneous in terms of reliability and accuracy.
Therefore, the high-quality unique data needed for models to become "smarter" cannot be produced at the current pace. Research shows that the marginal benefit of simply increasing parameter count is diminishing. This indicates that the scaling approach based on model size has reached the law of diminishing returns.
Consistency and Reliability Instead of Intelligence Increase
Current research trends in AI models focus on:
- consistent response generation,
- reducing hallucination rates,
- better context management,
- more transparent reasoning processes.
The fundamental rationale for this direction is:
The reliability and verifiability of output, independent of the model's intelligence level, has higher value in real-world use.
Therefore, research focus continues performance improvement through qualitative deepening.
The New Era: Tool-Augmented AI Models
The key feature of future AI models is the capacity to perform actions rather than just generate text. These models will be able to:
- connect to external software,
- interact with databases,
- make edits in design tools,
- execute commit and test processes on GitHub,
- perform process automation through APIs.
This approach transforms the model from a passive responder into an operational actor.
This transformation changes AI from:
- a tool for generating information,
- into a collaborative system that optimizes workflows
Economic Dimension: Usage Cost Exceeding Human Labor
Increasing model capabilities directly increases computational costs. Training and running large-scale models leads to:
- high GPU resource costs,
- high energy consumption,
- high maintenance and infrastructure burdens.
This situation points to an era where AI usage costs may exceed human labor in some sectors.
Therefore, economic value in the future will be determined not by model usage, but by proper usage design.
Conclusion and Forecast
- AI development pace hasn't slowed down; it has changed direction.
- The new focus is not larger models, but more accurate context, higher consistency, and stronger action capacity.
- Tool-augmented systems will transform AI from passive information-providing systems into active problem-solving systems.
- Therefore, one of the most important competencies of the future will be the design of AI output; the user's ability to structure context will become more decisive than the model's cognitive capacity.