Microsoft teams up with AI start-up to simulate brain reasoning in a groundbreaking collaboration aimed at advancing artificial intelligence through neuroscience-inspired technology. The tech giant has partnered with Swiss start-up inait to develop AI models that mimic the reasoning powers of mammal brains. This innovation is expected to transform fields ranging from financial trading to robotics by enabling AI systems to learn from real-world experiences rather than just analyzing pre-existing data.
The partnership, announced on March 19, 2025, leverages two decades of digital neuroscience research to push AI capabilities beyond traditional machine learning. By drawing inspiration from biological intelligence, Microsoft teams up with AI start-up to simulate brain reasoning, creating AI models capable of adapting dynamically to new situations.
A New AI Paradigm Inspired by the Brain
Unlike conventional AI models that rely on vast datasets and pattern recognition, inait’s approach focuses on true cognition and decision-making. According to Richard Frey, CEO of inait, the start-up was founded in 2018 with the belief that “the only proven form of intelligence is in the brain.” He emphasized that mastering brain functionality could lead to an entirely new generation of AI—one that is more powerful, efficient, and adaptable.
This collaboration allows Microsoft to integrate inait’s digital brain technology into its AI model portfolio, providing enterprises with highly intelligent, real-time learning AI systems.
Transforming Finance and Robotics with Brain-Based AI
One of the key applications of this partnership is in financial trading, where AI-powered trading algorithms, risk management tools, and personalized financial advisory systems will offer more adaptive and intelligent decision-making capabilities.
Meanwhile, in robotics, the technology will contribute to industrial automation, making machines more responsive to changing environments. This could revolutionize manufacturing, logistics, and autonomous robotics by enabling machines to make decisions similar to those of biological organisms.
Adir Ron, Microsoft’s EMEA Cloud and AI Director, highlighted the shift from data-driven AI models to cognition-based digital brains, calling it “a transformative step toward real artificial intelligence.”
Decades of Neuroscience Research Powering AI Innovation
The AI models being developed by Microsoft and inait are built on a Swiss government-funded neuroscience initiative spanning 20 years. This project successfully created biologically accurate digital replicas of mammal brains, generating over 18 million lines of code to simulate brain functions.
Henry Markram, co-founder of inait and lead scientist behind the Swiss brain research project, stated that the models can be adapted for different species, from mice to humans, allowing for scalable AI applications.
By incorporating brain-simulation-based AI, Microsoft aims to develop models that:
- Consume less energy than traditional deep learning models
- Learn faster and adapt in real-time to changing environments
- Enhance AI-driven decision-making across multiple industries
Challenges and the Future of Brain-Inspired AI
Despite the potential breakthroughs, brain-based AI faces challenges such as the complexity and resource intensity required to simulate human cognitive functions. However, Markram noted that not all business applications require full-scale human brain simulations.
To support global research in this field, inait is offering simulation technology through the Open Brain Institute, a non-profit providing both free and subscription-based access to brain models. This could pave the way for neurological research advancements, including studies on autism and other cognitive disorders.
A Step Closer to True Artificial Intelligence
As AI research advances, Microsoft teams up with AI start-up to simulate brain reasoning, marking a critical step toward developing AI systems that think and learn like humans. The fusion of neuroscience and artificial intelligence is set to redefine how machines understand and interact with the world, making AI more intuitive, adaptive, and efficient.
With growing interest in connectome mapping and dynamic simulations, researchers hope this technology will unlock new possibilities for AI-driven automation, decision-making, and problem-solving in the years to come.