Research no longer relies on genius scientists, but on GPUs ---Yesterday's plot continues to reverse! Openai officially announced! More importantly, this was completed within a few days using an unpublished model from opanai. (The new model has significantly improved capabilities compared to Astra, as shown in the attached image) The significance of this matter itself is very significant, belonging to the level of a Fields Medal discovery, but its significance goes far beyond the event itself. Science is being restructured as a high-speed industrial process driven by computing power. First, let's take a blind look: The Millennium Problem and its' explosion point ' Navier Stokes equation (N-S equation): The fundamental equation in physics that describes the motion of fluids such as water, air, and ocean currents. Its status is equivalent to Newton's laws of motion in the field of fluids. However, in mathematics, no one has been able to prove for a century whether the smooth motion equation of a fluid always has a well-defined solution in all initial states. This is one of the seven famous "Millennium Prize Problems" in the field of mathematics. Finite time singularity: can be vividly understood as the "rampage of physical quantities". At the beginning of the system, the wind is calm and the waves are smooth everywhere. However, after a finite period of time, some key indicators (such as velocity gradient or vorticity) soar to infinity, and mathematically, there appears an uncountable point similar to "dividing by zero". This means that the classical continuum model completely fails at this point, mathematically known as a 'blow up'. Lean formal proof: In the past, mathematical papers were manually reviewed by peer experts, which inevitably overlooked hidden deduction loopholes. Lean is a computer code proof language that requires mathematical deductions to be written line by line into rigorous logical code, with machine compilers verifying each axiom step to completely eliminate logical jumps and human errors. The key to this breakthrough lies in the fact that it is a massive system composed of 10000 collaborative intelligent agents, which can handle 130 billion tokens and exchange 2.7 million pieces of information within 88 hours, completing a high-intensity scientific research team operation. OpenAI presents a 3D Scaling architecture that has not been fully validated before: Training computing power: endowing individual models with basic scientific intuition and logical depth; Test time Compute: allowing a single agent to deeply deduce and repeatedly verify drafts on complex nodes; Multi agent computing power: supporting thousands of nodes to explore different hypothesis branches in parallel and share intermediate results in real-time. In the experiment, this system first mobilized 100 intelligent agents to solve the relatively low difficulty Euler equation (simplified equation ignoring fluid viscosity) within 50 hours. The system autonomously recognized the value of this achievement and then automatically adjusted its computing power, using the results of the Euler equation as a springboard to focus on the N-S equation. This organizational process of proposing hypotheses, parallel exploration, precipitation of conclusions, and dynamic scheduling of computing power is highly similar to a well organized and clearly defined "virtual science team" that operates autonomously. To truly understand the long-term impact of this breakthrough, it is necessary to strictly distinguish between two levels: The first layer (single point breakthrough): A famous problem is solved. The scientific value of solving the N-S equation or Riemann hypothesis is enormous, but still limited. The second layer (ability transition): AI has the ability to stably and batch solve such high difficulty scientific and engineering abstract problems. The truly disruptive aspect is the second layer. Universal solving ability can leverage highly valuable scientific research. For example, breaking through high-temperature plasma turbulence control in controllable nuclear fusion, acting as a computing power amplifier for the development of new materials, catalysts, and drugs in materials and life sciences, transforming complex physical processes into computable models in high-tech manufacturing, and significantly reducing physical experiments for aircraft, batteries, and heat dissipation design. Designing an aircraft engine or verifying the heat dissipation of a new chip requires thousands of costly physical experiments; If mathematical theory and AI simulation can compress the number of experiments to dozens, the development logic of hard technology will move towards a software development model of "writing code simulation testing rapid refactoring". But the most important and valuable aspect is AI driven AI model development: AI is used to optimize its own network architecture, training algorithms, and data flow design, which will directly trigger Recursive Self Improvement (RSI). Similar to proving the Stokes equation, AI development operates almost entirely in the digital world, where agents can autonomously write code, initiate training, monitor loss function curves, and analyze and evaluate them without waiting for the slow material preparation and physical manufacturing cycles of the physical world. And as AI begins to drive breakthroughs in multiple fields of science, breakthroughs in different fields will tightly interlock, forming self reinforcing multi flywheel systems. In the traditional perspective, the rate of progress in science is often regarded as an exogenous constant - for example, developing a new drug takes an average of 10 to 15 years, while commercial fusion often takes "decades" to complete. But when the scientific research system is fully automated, the iteration speed of the technology cycle itself will become an endogenous variable that can be accelerated. This shift in research paradigm will be a typical gradual, then sudden leap in macroeconomic driving force: In the initial stage, a large number of papers were solved, complex mathematical theorems were formally verified, and new material candidates emerged in the laboratory, but the macro GDP remained almost unchanged; Only when these technological inventories accumulate to a critical point, accompanied by the popularization of low-cost industrial robots, the construction of new clean energy networks, and the production of advanced manufacturing factories, will the huge underlying scientific assets be concentrated and transformed into physical productivity, forming a sudden leap in macroeconomic development. The breakthrough of the Navier Stokes equation has provided a cutting-edge observation window for the entire scientific and technological community. What it truly reminds us of is not just fluid mechanics taking another step forward, but also reminding us to examine how many trillions of dollars in long-term value are being locked in those "problems that humans temporarily cannot figure out due to limited brain computing power" in civilized society. When computing power officially jumps from the assembly line factory that manufactures models to the "super research institute" that can autonomously explore the unknown 24/7, the rate at which humans extract scientific answers from these "unknown inventories" will be completely rewritten.