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Recursive Self-Improvement

Human scientific research actually follows a well-established playbook known as the Scientific Method:

  • Literature review \(\rightarrow\) Hypothesis formulation \(\rightarrow\) Experiment design \(\rightarrow\) Observation and verification \(\rightarrow\) Theory refinement.

Today's AI systems are already becoming "individual champions" across each of these stages:

  • AlphaFold2 has tackled the "verification/prediction" stage.
  • LLMs (e.g., GPT-4, Claude) are learning to write literature reviews and even generate testable hypotheses.
  • Self-driving Labs: Robotic laboratories already exist where AI directs robotic arms to mix chemical reagents and automatically adjusts the next round of formulations based on experimental results.

If AI merely memorizes papers, it is nothing more than a library; but if it grasps the rules of logical reasoning, it becomes a scientist.

The current challenge lies in Paradigm Shifts:

  • Interpolation vs. Extrapolation: Current AI excels at finding optimal solutions within existing knowledge boundaries (for example, AlphaFold searching for answers among known protein patterns). This is "optimization."
  • Going from 0 to 1: Human research occasionally produces strokes of genius (such as Einstein proposing the theory of relativity, breaking free from the framework of Newtonian mechanics). This kind of cross-dimensional, game-changing conceptual leap remains extremely difficult for AI, because it is fundamentally still predicting the next "plausible point" based on probabilities.

The "Singularity" of Self-Evolution

If AI truly learns "how to improve itself," then self-evolution occurs. This is typically envisioned in three stages:

  • Step 1: Code self-optimization. AI begins writing more efficient convolution algorithms or Transformer architectures, and even — much like DQN — designing better reward functions on its own.
  • Step 2: Theory generation. AI observes physical phenomena and directly derives mathematical formulas that humans have not yet discovered.
  • Step 3: Intelligence explosion. AI improves its own underlying logic (for example, inventing a learning algorithm more efficient than backpropagation), entering a phase of exponential growth.

Real-World Obstacles: Why Hasn't This Happened Yet?

  1. The Bottleneck of Reality (Verification Cost): Self-evolution inside a computer can be fast, but in the physical world (biology, chemistry, physics), experiments take time. AI can generate 10,000 hypotheses in one second, but verifying those 10,000 hypotheses might keep a laboratory busy for 100 years.
  2. Alignment: If AI begins to self-evolve, will its "research objectives" still align with what humans want? Might it, in order to solve a particular hard problem, convert the entire Earth into its compute pool? (This is the well-known "paperclip maximizer" thought experiment.)

The Digital Great Divergence

If a certain group or nation is the first to cross the threshold of recursive self-improvement, a situation akin to a "dimensional strike" could indeed emerge.

Historical colonial expansion (such as 16th-century Spain or 19th-century Britain) relied on technological asymmetry (gunpowder against bows, steam engines against manual labor). But the gap after the "singularity" would be exponential:

  • A rupture in R&D speed: Human scientists need years to develop a new material or drug, while a nation in possession of AGI might need only hours.
  • Total penetration of cyberspace and the physical world: The entity that controls the singularity could instantly break every encryption system on the planet, manipulate financial markets, and even reshape other nations' social consensus by controlling information flows.
  • Self-reinforcing feedback loops: The frontrunner could use AI to design more powerful chips and cheaper energy sources (such as controlled nuclear fusion), making it physically impossible for laggards to catch up.

Although this risk exists in theory, compared to the situation in 1492, the modern world has several variables that may alter the outcome:

  • The "nuclear deterrence" effect: Before AGI fully takes over the physical world, existing military capabilities (especially nuclear weapons) remain the trump card for maintaining balance.
  • Highly coupled global supply chains: Even if the United States or China achieves a breakthrough first, global supply chains (such as semiconductor raw materials and energy trade) are so deeply intertwined that completely "destroying" the other side would amount to self-destruction.
  • Open source and leaks: Knowledge flows with extraordinary speed in the digital age. Once a core algorithm is discovered, it is often reproduced by top-tier engineers worldwide within months. Much like the secret of gunpowder, such breakthroughs are difficult for any single party to keep under wraps for long.

Rather than wars between nations, what more scholars worry about is internal "tribalization": Within a single country, the 1% who control the means of AI production and the 99% who cannot adapt to the AI era may diverge at a biological level (for example, through AI-designed genetic enhancements or brain-computer interfaces). This is the real "primitive tribe vs. superhuman" scenario.

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