In his reflective piece on OpenAI’s forum “Making AI Work for Everyone”, Cezary Gesikowski, AI & Innovation Capability Development Lead at the Government of Canada, shares a personal and professional awakening sparked by the dialogue between Kevin Weil (OpenAI) and Erik Brynjolfsson (Stanford). He highlights how AI is reconfiguring our understanding of productivity, value, and governance, urging a shift from cost-centered to benefit-centered frameworks like GDP-B. Gesikowski aligns his frontline experiences in the Canadian public sector with Brynjolfsson’s “productivity J-curve,” observing that cultural rigidity, not technology, often impedes AI adoption. Drawing parallels between historical technological revolutions and today’s AI transformation, he stresses the need for reimagined workflows and adaptive ecosystems. The piece also applauds emerging global AI leadership, such as Canada’s policy strides and the UAE’s universal ChatGPT Plus access, as signs of inclusive innovation. He advocates for embracing human-AI complementarity, organizational ambidexterity, and global metrics that better capture digital value. Ultimately, Gesikowski sees AI not just as a technical tool, but as a societal lever that, if used wisely, can foster equity, dynamism, and human flourishing.

Dr. Anton Maximov delivered a compelling presentation on how artificial intelligence is revolutionizing neuroscience by enabling the analysis of complex brain structures at an unprecedented scale and speed. His work uses AI-driven tools—particularly convolutional neural networks integrated with 3D electron microscopy—to uncover the nanoscale architecture of long-term memory. This research, previously impossible with manual techniques, showcases how AI transforms the pace, precision, and possibility of scientific discovery. Dr. Maximov also reflected on how AI is streamlining everyday scientific tasks, democratizing hypothesis generation, and setting the stage for more dynamic, scalable, and biologically inspired AI systems. His talk illustrated a future where AI not only accelerates research but redefines how science is conceived, conducted, and shared.

This groundbreaking research reveals the physical architecture of memory engrams in the mouse hippocampus, pushing the boundaries of neuroscience through the integration of artificial intelligence with advanced 3D electron microscopy. By combining chemogenetic tagging of neurons with AI-driven image segmentation, the researchers achieved an unprecedented nanoscale reconstruction of hippocampal circuits involved in memory. Contrary to classical Hebbian theory, they found that neurons active during memory formation do not preferentially wire with each other; instead, they expand their connectivity via complex multisynaptic boutons (MSBs), enabling broader and more flexible information encoding. These structural changes were accompanied by synapse-specific enlargement, mitochondrial remodeling, and altered interactions with astrocytes—signatures linked specifically to negative-valence associative learning. The findings challenge traditional models of memory storage and suggest a novel mechanism where synaptic complexity, not just strength or density, enhances memory capacity. Overall, this study provides a structural foundation for understanding how the brain encodes, generalizes, and maintains long-term memories.