The Presentation of Wait: Stagnation in AI Development Papers and the Need for Future Imagery

The most surprising thing is that when reading current AI research papers with eyes closed, they do not look very different from research from 2026. The analysis presented in survey papers often revolves around the same place. While researchers talk about rapid progress, a lack of core structure or 'Future Image' is felt. This is not a fear, but an indication of a periodic state. The language and structure of the papers make it seem like someone is writing with the same script. The problem is that we are focusing mainly on 'success' rather than doing a deep analysis of 'possibility.' This year’s survey papers seem monotonous compared to last year's. This is primarily a reflection of stagnation or abnormal decrease in dynamics in a discipline. When a field experiences rapid progress, a lack of 'novelty' is still felt in the research community. On one side, new models are arriving, on the other, due to insufficient progress in theoretical foundation or 'theory', the research becomes surface-level. The core thought of AI was 'combination of research and imagination', but currently we are mainly looking at the 'scoreboard.' How we use our brains or how we imagine the future, the detailed design is still incomplete. AI researchers are creating dependence mostly through the movement of 'Unknown Knowledge' or 'I don't know'. When the answer to a question is not found, the system itself says 'I don't know'. This is positive, but it is also a limitation. Because, artificial intelligence is still lacking the ability to 'see from another side.' Our deep arguments regarding our thinking speed and the concept of space are rarely seen in research papers. In the AI field, an experience of a 'middle' zone is ongoing. It is a time when we have made much less progress than the previous generation, but are not ready for the next revolution. The strategies of new models are mainly 'patches' or modifications of previous models. It is seen that the research community is still at the 'duplicate' stage. If a new idea comes, it quickly becomes a 'community standard' through fierce competition, so no one gets time for deep analysis of that idea. The biggest risk of this situation is the absence of 'substance' in place of 'vibe' or mood. We have reached a stage where news of new AI tools is more on social media, but deep theoretical debate is less. Research papers are still working as a 'presentation of waiting'. That is, they only show what can be done now, but do not give a clear design of what will happen in the next ten years. This is a 'scale' problem. We can grow big, but not going deep. Another important issue is the role of 'community'. A large part of AI research now relies on Open Source. This is good, but a lack of 'vision' is seen in it too. Communities are quickly sharing code, but debate on the philosophy or 'philosophy' behind that code is less. This is mainly like 'laborism' or agricultural method, where we are increasing yield, but not digging the soil deeply. If the soil is not deep, water will be lost in the rain. Similarly, if the theoretical basis is not deep, the capacity to handle future crises will be low. To answer the stagnation or decrease in dynamics in this stage, we need to 'see from a different side.' AI is becoming more 'human', but the definition of this humanity is still vague. The scientific model of 'Empathy' or emotion is still an incomplete work. In research papers, discussion on 'Moral Agent' or moral agent is less, but what is being tested through 'tests' is mostly quantitative. Here a breakthrough in 'quality' is necessary. We often think that AI will know everything, but actually it knows only 'how' to know. The depth of knowing 'why' is still absent. This is a crisis of 'transparency'. When we get an answer, we do not exactly understand where that answer came from. This is risky, especially when AI is used in making fundamental decisions. Stagnation is seen here that research on 'Explainability' or explainability is still in the 'experimental' stage, not in the 'methodological' stage. This stagnation is not only for AI, it is a reflection of our image regarding the 'speed of change'. We are so accustomed to rapid change that we have accepted 'waiting' or 'stasis' as an accepted state. But like a normal brain, AI also needs 'time'. It is called 'exposure'. Like taking a picture, if the light is low, the picture becomes blurry. Similarly, if theoretical light is low, progress becomes blurry. Research papers are not collecting enough light yet, not giving 'space.' In the future, if we come to a true 'mutual' or reciprocal time, then 'stagnation' will be just a stage, not a 'crash'. It is an opportunity for 'deformation'. When something new does not come, we re-examine what was there before. This re-evaluation is like the work of a 'recovery' team, where we repair the broken parts. AI research is also now in that repair stage. But after repair, a new structure must be built, one cannot just close the broken parts. The main signal of this analysis is, stagnation is a sign. It is testing our courage. Are we reaching from 'seeing' to 'understanding'? Or are we reaching from 'seeing' to 'stating'? Real progress is in 'understanding'. This journey is ongoing, and its speed is still 'looping'. However, the signs are clear: if next year's survey papers do not add 'Future Image', then stagnation will become 'stagnant.'