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Recent years have marked the era of language models and AI. AI is becoming essential across all fields, driven by significant performance improvements. In certain domains, AI now surpasses human capabilities. It looks like advances in AI can solve all the existing problems. Nevertheless, greater intelligence alone does not solve every problem. Language models are central to intelligent software but require auxiliary tools, such as MCPs (Model Context Protocols), for accessing the web, temporary memory, and so on.

Neural networks are black boxes, and this characteristic is shared by language models, multimodal models, and even agent systems built on neural networks. A black box means that we fundamentally cannot know the reasoning process that leads to a particular output. This makes it difficult to predict the consequences of changes to the model. Until recently, everything seemed to work well, but now problems are starting to surface. Excessive flattery, hallucinations, and abnormal behaviors are appearing even in top-tier AI services.

Language models are being used to assist human work across various fields, including coding and documentation, based on their ability to address both natural and formal language. However, we should now be able to use language models to define new problems and solve them. Specification writing using formal language is a prime example of such problems. Writing specifications in formal language is crucial for verification and accuracy, but it requires so much time and effort that it is rarely done except for very important projects.