This time is different! From mathematical explosion to treating all diseases From the birth of pure abstract mathematics, to the construction of physical reality, to the transformation of complex molecules (chemistry) and emergent systems (life sciences), the transmission chain of such fundamental paradigm shifts in the history of human science typically has a traditional cycle of between 50 and 150 years. This long transmission chain is essentially a linear accumulation of experiments, delays in information transmission, generalization of conceptual frameworks, and bottom-up reconstruction of complex systems. Typical transmission cycles in history: 1. Calculus ->Classical Mechanics ->Molecular Thermodynamics ->Biological Metabolism and Enzyme Dynamics (~140 years) Mathematical starting point (1670s – 1680s): Newton and Leibniz invented calculus. Physical Breakthrough (1687): "Mathematical Principles of Natural Philosophy", where differential equations became the universal language for describing classical dynamics and planetary orbits. Chemical Leap (1870s): J. Willard Gibbs introduced calculus and partial differential thermodynamics into chemical systems, establishing the criteria for free energy and the theory of chemical equilibrium. Life Science Transformation (1913): Michaelis Menten established differential equations for enzymatic reaction kinetics, giving birth to modern quantitative biochemistry. Cycle span: approximately 130-140 years. 2. Non Euclidean geometry/linear algebra ->Quantum mechanics ->Quantum chemistry/chemical bonding ->Molecular biology/Structural biology (~70-90 years) Mathematical starting point (1854): Riemann proposed Riemann geometry; Matrix theory and Hilbert space gradually formalized from the mid to late 19th century to the early 20th century. Physical Breakthrough (1925-1926): Heisenberg established matrix mechanics, Schr ö dinger proposed wave dynamics, and quantum mechanics was fully established. Chemical Crossing (1927-1939): The Heitler London Model and Linus Pauling's publication of "The Nature of Chemical Bonds" used quantum mechanics to elucidate covalent bonds, hybrid orbitals, and resonance theory. Life Science Transformation (1953): Watson, Crick, and Franklin used Pauling's structural chemistry model and X-ray crystallography to analyze the DNA double helix; In 1958, Perutz analyzed the crystal structure of hemoglobin. Cycle span: approximately 70-100 years. The core reason why the transmission chain is so long is the knowledge island effect: mathematicians' abstract tools often "lead" physicists by decades. Einstein spent years searching for mathematical tools while searching for the equations of the gravitational field in general relativity, until Grossman reminded him that Riemannian geometry and tensor analysis were already complete. and The calculation of complex problems is irreducible: Dirac had a famous saying in 1929: "The underlying physical laws that make up most of physics and all of chemistry are fully known, and the difficulty lies only in the fact that the exact equations of these laws are too complex to solve." From the Schr ö dinger equation of quantum mechanics to macromolecular reactions, microscopic accuracy quickly fails in the face of macroscopic complexity. AI will drastically compress the flow of information, search, and modeling cycles, from years and months to days, hours, and even minutes and seconds. Mathematics ->Physics: Compressing from 'intergenerational waiting' to 'near real time bridging' The big language model and interdisciplinary neural representation can instantly align physicists' vague phenomenological assumptions with obscure algebraic topology, differential geometry, or functional analysis tools in mathematical libraries. With the help of interactive theorem provers such as Lean and cutting-edge inference models, the formalization of complex mathematical conjectures and the verification of the explosive characteristics of partial differential equations are being compressed from the calculations of classical computers by human mathematical masters in years and months to parallel searches in hours or even shorter. This enables physics to obtain strict expression tools in a very short period of time once breakthroughs are made in underlying mathematics. Physics ->Chemistry: Using "Neural Operators/Proxy Models" to Break First Principles Computing Bottlenecks Traditional density functional theory (DFT) is limited by computational complexity O (N ^ 3) or even higher, and cannot handle real chemical systems. AI can directly learn the mapping solution of the Schr ö dinger equation for electronic structure, and increase the speed of molecular dynamics simulation by several orders of magnitude while maintaining quantum level accuracy. The laws of physics are transformed into actionable chemical molecular interactions, with periods ranging from decades suppressed in daily iterations of computing power. Chemistry ->Biology: Transitioning from "accidental blind screening" to "generative reverse design" Traditional biology heavily relies on phenomenological and reductionist assumptions; AI structural biology, such as AlphaFold/ESMFold and protein language models, directly internalizes physical and chemical laws as latent spatial priors, reducing the prediction time from DNA sequences to three-dimensional functional proteins from years to seconds. The research and development paradigm is also transitioning directly from "natural separation and screening of enzymes and molecules" to "reverse generation of non natural catalysts and complexes based on reaction kinetics and target pocket structures". Overall, AI has compressed the "theoretical stage" of the actual life science (and other applied sciences) cycle by nearly 90% by transmitting mathematical breakthroughs Empirical implementation will certainly involve various factors such as wet experiments, real environments, and computational irreducibility. However, even based on the above compression, the transmission of scientific progress will be accelerated by at least 10 times or even a hundred times. Moreover, AI's continuously enhancing ability to transform informal problems into formal problems will promote the transformation of a large number of computational irreducible problems into computable problems. When irreducible complex life systems are constantly transformed into computable high-dimensional geometry by AI, from mathematical breakthroughs to what Anthropic CEO Dario said about curing all diseases in 5-7 years, and even the millennium dream of human "elixir of life", it is entirely possible to be compressed into solvable problems by the brute force of computing power.