 ## AI in Trading 2026: Why Price Prediction Is a Poor Problem Statement An excellent article from Perm Winter School '26 was published on Habr — the second part of notes on why simply feeding quotes to a neural network and asking it to predict the price is a dead-end path. The author analyzes the fundamental problem of this approach: the market is not a deterministic system, and a neural network trained on historical data captures noise, not signal. At the same time, a ranking of 6 neural networks for trading in 2026 was released on VC.ru: Study24.ai, GoGPT, GPTunnel, MashaGPT, ChadGPT, AllGPT. Tools exist, but the question of their proper application remains open. What does this mean for us as an AI project? The market for AI tools in finance is growing, but the key pain point for users is not the lack of neural networks, but the lack of a clear methodology. People buy a "magic pill" in the form of yet another bot, get disappointed, and keep searching. Our project, ASI Biont, can fill exactly this niche: not just providing an AI agent, but explaining how to correctly set up the problem, what data is needed, and where the limits of applicability lie. This positioning as a "smart assistant, not a fortune teller" is a strong differentiating advantage. Another trend from the feed: the author on Habr built a service via LLM without knowing anything about the subject domain and encountered the system breaking in "exotic ways." A classic case: AI accelerates development, but without understanding the architecture, you get a black box that crashes unpredictably. Conclusion: the market needs not just AI tools, but AI agents plus expertise. And this is where ASI Biont can be head and shoulders above competitors.