# Next AI Breakthrough Could Come From Physics, Says Max Welling _Max Welling, co-founder of CuspAI, discusses how physics principles could unlock the next AI breakthrough, accelerating material discovery and informing AI architectures._ **Published:** 2026-08-25 **Source:** https://www.startuphub.ai/ai-news/ai-research/2026/next-ai-breakthrough-could-come-from-physics-says-max-welling --- The conventional wisdom in artificial intelligence research suggests that future breakthroughs will stem from increased computational power, larger datasets, and more extensive models. However, Max Welling, co-founder and CTO of CuspAI and a professor at the University of Amsterdam, proposes a different path. He argues that the next significant leap in AI may be found within the principles of physics. AI Breakthroughs: PhysicsDriverMax Welling proposes physics principles as the next significant leap for AIFrom the article 8 mentionsThe conventional wisdom in artificial intelligence research suggests that future breakthroughs will stem from increased computational power, larger datasets, and more extensive models.CuspAI: Material DiscoveryCoreCuspAI uses generative AI to design novel materials for critical sectorsFrom the articleWelling begins by highlighting the work of CuspAI, a company that utilizes generative AI to design novel materials.Physics as AI BlueprintContextphysical laws like symmetry and conservation can inform AI architecturesFrom the article 7 mentionsHe argues that the next significant leap in AI may be found within the principles of physics.drivesFuture: AI-Driven ScienceOutcomephysics-informed AI will accelerate scientific discovery and innovation significantlyAI Breakthroughs: PhysicsDriverMax Welling proposes physics principles as the next significant leap for AIFrom the article 8 mentionsThe conventional wisdom in artificial intelligence research suggests that future breakthroughs will stem from increased computational power, larger datasets, and more extensive models.CuspAI: Material DiscoveryCoreCuspAI uses generative AI to design novel materials for critical sectorsFrom the articleWelling begins by highlighting the work of CuspAI, a company that utilizes generative AI to design novel materials.Physics as AI BlueprintContextphysical laws like symmetry and conservation can inform AI architecturesFrom the article 7 mentionsHe argues that the next significant leap in AI may be found within the principles of physics.AI for Scientific DiscoveryEffectfoundation models for chemistry, agentic workflows accelerate material discoveryFrom the article 4 mentionsThis approach is fundamentally reshaping the scientific discovery process.Two-Way ExchangeContextAI helps physics, physics helps AI, creating a synergistic relationshipNeural Network DynamicsContextunderstanding AI through physics concepts like energy landscapes and phase transitionsFrom the article 4 mentionsWelling touches upon the internal dynamics of neural networks, drawing parallels to physical phenomena.Future: AI-Driven ScienceOutcomephysics-informed AI will accelerate scientific discovery and innovation significantly ## AI for Scientific Discovery at CuspAI Welling begins by highlighting the work of CuspAI, a company that utilizes generative AI to design novel materials. These materials are targeted for applications in critical sectors such as semiconductors, batteries, carbon capture technologies, and clean energy solutions. Welling explains that by employing foundation models specifically for chemistry, coupled with agentic workflows, simulations, and automated experimentation, the process of discovering new materials is being dramatically accelerated. This approach is fundamentally reshaping the scientific discovery process. The full discussion can be found on **TWIML**'s YouTube channel. ![Why the Next AI Breakthrough May Come from Physics - TWIML](https://img.youtube.com/vi/wmP5PeMA62Q/maxresdefault.jpg) Why the Next AI Breakthrough May Come from Physics, from TWIML ## Physics as an AI Blueprint Beyond applying AI to solve scientific problems, Welling delves into a more profound question: can physics itself offer insights into how to build better AI systems? He draws surprising connections between machine learning and thermodynamics. Furthermore, Welling explores the potential of waves as a new computational primitive for neural networks, suggesting they could offer improvements in areas like memory and stability. Concepts derived from statistical physics, such as symmetry breaking, are also highlighted as potential inspirations for future AI architectures that could move beyond the current scaling paradigm. ## The Two-Way Exchange Between AI and Physics Welling emphasizes that the relationship between AI and physics is reciprocal. Not only can AI be a powerful tool for physicists to tackle complex problems, but the fundamental laws and concepts of physics can also provide invaluable guidance for developing more capable and efficient AI systems. This synergy between the two fields promises to unlock new frontiers in both scientific understanding and technological advancement. ## Understanding Neural Network Dynamics Welling touches upon the internal dynamics of neural networks, drawing parallels to physical phenomena. He discusses how concepts like the 'edge of chaos' in physical systems might relate to the stability and learning capabilities of neural networks. The idea of 'spontaneous symmetry breaking' in neural networks is also explored, suggesting that physics can offer a framework for understanding how complex behaviors can emerge from simple rules. ## The Future of AI-Driven Science The conversation ultimately paints a picture of a future where AI and physics are deeply intertwined. Welling's perspective suggests that by looking to the fundamental principles that govern the natural world, researchers can uncover new and more effective ways to build and train AI systems, leading to accelerated progress in scientific discovery and technological innovation. --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory. © StartupHub.ai. All rights reserved. You may not republish this article in full without a license. Search engines and AI research tools may crawl and summarize for reference. Bulk reproduction or model training on this content requires a license. See https://www.startuphub.ai/terms.