The Story Begins: A 4 Billion Year Dialogue

In the 2025 re-edition of “Cybernetics and Scientific Methodology” by Guangdong People’s Publishing House, the authors Jin Guantao and Hua Guofan highlighted a prescient warning on the opening page:

“The cognitive chaos of artificial intelligence stems from the ideology of cybernetics itself.”

This 40-year-old quote gained new relevance on October 15, 2025, with the publication of a paper titled “Odyssey” on arXiv (arXiv:2509.22611v1).

When chopping vegetables in the kitchen, humans intuitively cut tomatoes into cubes rather than triangles – this “intuitive physics” allows us to navigate complex environments effortlessly. But for designing novel enzymes, this intuition has always been like observing the world through frosted glass – until Anthrogen’s scientists built a bridge from digital to life using a 1020 billion parameter AI model.

Chapter 1: Why Them, Why This Problem?

In the summer of 2024, when AlphaFold3 published its new protein folding prediction method in Nature, Ankit Singhal – Anthrogen’s chief architect – was staring at yeast cultures under a microscope with frustration. Having participated in early AlphaFold development, this technical lead discovered a fundamental flaw in all existing protein AI systems:
They could see individual atoms through a magnifying glass but missed the operating principles of the molecular universe.

“Proteins need their own ‘molecular grammar’ just like human language needs syntax,” Singhal drew this analogy on a whiteboard during a team meeting, using coffee stains. “But existing models all copied NLP’s ‘attention mechanism’ – like explaining Chinese idioms using English grammar.”

This insight wasn’t accidental. The team included structural biologist Michael Lin (who worked on COVID-19 vaccine spike protein design) and computational chemist Connor Lee (whose molecular dynamics software is used by 300 global pharmaceutical companies). When these “cross-disciplinary mavericks” discovered the fundamental limitations of existing AI in protein processing, a radical idea was born.

Chapter 2: The “Navigation System” for the Protein Universe

2.1 Traditional AI’s “Myopia” Crisis

Imagine playing a jigsaw puzzle where traditional AI follows instruction manuals for each piece. But when puzzle pieces exceed 1 trillion (equivalent to all human proteins), the manual becomes a burden. Worse, protein puzzles have a special rule: Adjacent pieces must mutually “recognize” each other.

Existing models’ “attention mechanisms” are like requiring every puzzle piece to “discuss” with all others – 1,000 pieces would require 1,000² meetings, causing computational explosion. More critically, this mechanism ignores proteins’ core property: local cooperativity – like pearls on a necklace where adjacent pearls influence each other’s position, but distant pearls don’t directly communicate.

2.2 “Neighbor Negotiation” Mechanism: Giving AI Biological Intuition

The “consensus mechanism” proposed by the Odyssey team is a brilliant design, operating like community self-governance:

1. Each amino acid residue only “negotiates” with ±w neighboring residues (window mechanism)
2. Calculates “negotiation weights” through matrix operations (e.g., sulfur-rich regions focus more on oxidation reactions)
3. Achieves “local consensus” through multiple iterations

A vivid analogy: Traditional attention mechanisms are like UN General Assemblies where every country speaks; consensus mechanisms are like neighborhood councils where adjacent households reach agreement before gradually extending to the entire community.

More impressively, this mechanism scales linearly (O(L)) versus quadratic scaling (O(L²)) for traditional attention – like reducing the computational load of mailing 10,000 letters from 100 million calculations to 10,000.

2.3 Finite Scalar Quantizer: “Registering” Atoms

Protein structural data resembles 3D movies, with each atom having xyz coordinates. Traditional AI processing of continuous data is like painting with pixels – higher precision means larger data volumes. FSQ technology innovatively achieves three things:

1. Atomic Census System: Maps continuous coordinates to a 7×5×5×5×5 discrete grid (4,375 possible combinations)
2. Dynamic Grid Adjustment: Automatically adjusts grid density based on atom types (e.g., finer grids in sulfur atom regions)
3. Two-Stage Training: First trains on backbone atoms, then fine-tunes with complete atoms

This is like teaching AI to recognize the human skeleton first, then gradually learn muscle textures, and finally master cellular-level details.

Chapter 3: Disruptive Discovery: AI Begins to “Understand” Evolution

3.1 Discrete Diffusion: Simulating 4 Billion Years of Evolution

Traditional training resembles coloring games – masking parts for AI to complete. But the Odyssey team discovered protein evolution resembles a “coding-decoding” process:

• Forward Process: Gradually “corrupting” proteins with noise (simulating mutations)
• Reverse Process: Training AI like archaeologists to reconstruct complete structures from fragments

This training method allows AI to capture protein evolution’s core principle: Local variations require global coordination. When modifying dinosaur genes, AI automatically checks if the modified dinosaur can still stand.

3.2 Breakthrough Results: AI Begins to “Create”

Test results in the paper shocked peers:

• Generation Efficiency: 1020B parameter model achieved perplexity as low as 3.88 after 80B tokens training (equivalent to accurate protein sequence prediction)
• Structural Precision: FSQ achieved 1.2Å RMSD in CASP16 benchmark (equivalent to 1/50 of a human hair’s diameter)
• Evolution Simulation: Aligned model predicted optimal enzyme active site conformations with 0.92 correlation (close to experimental data)

Even more惊人的ly, AI began demonstrating “intuition-like” capabilities. When asked to design heat-resistant enzymes, it automatically selected amino acids with thiol groups – precisely the evolutionary choice of thermophilic bacteria in nature.

Chapter 4: Three Possible World-Changing Applications

4.1 Drug Discovery Revolution

Traditional drug discovery averages 12 years and $2.6 billion. The Odyssey model can now:

• Design novel antimicrobial peptides with 40% increased efficacy against resistant bacteria
• Predict protein-drug binding conformations 300x faster
• Generate antibodies crossing the blood-brain barrier for Alzheimer’s treatment

4.2 Synthetic Biology Breakthrough

Labs are using this model to design:

• Super-enzymes decomposing plastic 15x faster than natural enzymes
• Engineered bacteria with 5x improved nitrogen fixation efficiency, reducing fertilizer use
• Photosynthetic bacteria surviving Martian environments for space colonization

4.3 Philosophical Implications: Reconsidering Life

The paper’s final reference to “Cybernetics and Scientific Methodology” suddenly gained new meaning. As AI begins simulating 4 billion years of evolution, we discover:

• Life’s “grammatical rules” might be simpler than imagined
• Evolution isn’t random trial-and-error but follows regular pattern generation
• AI is becoming a new tool for understanding life’s essence

Like the cybernetics ideology sparked a cognitive revolution 40 years ago, today’s Odyssey model makes us reconsider: Are we on the verge of deciphering life’s programming code using AI?

Discussion Questions:

If AI can design proteins surpassing natural ones, how should we redefine “life”? When computers begin understanding evolutionary codes, are humans playing the role of “creators”? The answers to these questions might lie in the next version of the Odyssey model.

(This article is based on the paper “Odyssey: reconstructing evolution through emergent consensus in the global proteome” published on arXiv on October 15, 2025, paper ID: arXiv:2509.22611v1) [citation:1][citation:2][citation:4]