Zuzanna Stamirowska

Zuzanna Stamirowska, CEO and cofounder of Pathway, dives into the revolutionary BDH architecture designed for continual learning and brain-like reasoning. She explains why current AI lacks memory and how BDH introduces real-time context and adaptability. Discover how this architecture fosters genuine learning, allowing models to 'get bored' and strengthen connections. Zuzanna emphasizes that reasoning is the core of intelligence, paving the way for significant innovations in fields like Formula 1 and beyond.

Zuzanna Stamirowska, CEO of Pathway (building post-transformer, memory-first AI); Victor Szczerba, CCO at Pathway (enterprise product and observability lead); Rowan McNamee, Co-founder and COO of Mary Technology (legal fact-management for litigators). They discuss memory-inspired models and long attention spans, continual learning and efficient architecture, and using AI to extract, organize, and verify legal facts.

Zuzanna Stamirowska, CEO and co‑founder of Pathway and researcher in post‑transformer AI, discusses Dragon Hatchling, a new architecture adding native memory and continual learning. She explains why transformers hit limits in memory and reasoning. The conversation covers interpretability, reduced compute via sparsity, safety implications, and how her complex‑systems background shaped this work.

Zuzanna Stamirowska, CEO and co-founder of Pathway, explores AI beyond transformers. She discusses latent reasoning, continual learning without catastrophic forgetting, internal versus external memory, and models that track state across long horizons. The conversation also covers small, efficient reasoning systems, ARC-AGI, training economics, hardware, open models, and the path toward truly autonomous agents.

Zuzanna Stamirowska, co-founder and CEO at Pathway and complexity scientist, discusses brain-inspired BDH architecture for memory-enabled AI. She explains why current transformers reset, how sparse graph-based memories and Hebbian-like updates enable real-time, continuously updating models. They cover scaling, resilience, interpretability, productization with NVIDIA/AWS, and use cases for changing-data and regulated industries.