Architecture of
Meaning
We are witnessing a deadlock in the development of artificial intelligence. The race for parameter counts and data volume has led to the creation of monstrous models that require the energy of a small state but remain fundamentally fragile and inefficient.
Our vision is that the solution lies not in extensive growth, but in a paradigm shift. We are moving from the "brute force" of computing to the elegance of architecture. The future of AI is systems capable of deep latent reasoning, operating orders of magnitude more efficiently than existing solutions.
We are building intelligence that does not just predict the next token, but truly thinks — efficiently, safely, and transparently.
Breaking Barriers
Energy
We are developing "latent reasoning" methods that allow models to think in vector space without generating intermediate tokens. This reduces computational costs and energy consumption by orders of magnitude, making powerful AI accessible and sustainable.
Rationality
Modern LLMs inherit human cognitive errors. We implement architectural mechanisms for attention control and verification (G-factor), which allow the system to maintain rationality and logical integrity even in complex, ambiguous situations.
Safety
Instead of trying to restrict the model after training, we create "Neural Bytecode" — a deterministic execution environment where safety is built in at the level of fundamental instructions.
Igor Petrenko
Independent Researcher,
Entrepreneur,
Author and Public Figure
AIFUSION is the result of years of research into computational efficiency and the nature of intelligence. I founded this lab with the conviction that the current path of AI development leads to a dead end, and we need radically new approaches.
My background combines deep technical research and entrepreneurial experience. I am the author of 7 books, several papers on optimizing neural network architectures and the creator of the platform IN4U — an information system for BRICS countries cooperation.
My goal is to optimize technologies to achieve a balance between humans and technology, avoiding a collapse driven by the demand for computational resources. To improve the performance of artificial intelligence, we must understand our own cognitive limitations and problems. Only then can we make this technology a transparent, efficient, and safe tool for humanity.