The End Of Knowledge - Vault 7/344 - xHubAI - 25/07/2025 - 🔴UNA NUEVA ERA EN AI ： Futuro- de la AI- Simulación de la realidad- física y videojuegos (Parte 1⧸2)

graph LR classDef learn fill:#ffe0b2,font-weight:bold,font-size:14px classDef bio fill:#c8e6c9,font-weight:bold,font-size:14px classDef phys fill:#bbdefb,font-weight:bold,font-size:14px classDef game fill:#e1bee7,font-weight:bold,font-size:14px classDef evo fill:#ffccbc,font-weight:bold,font-size:14px classDef agi fill:#d1c4e9,font-weight:bold,font-size:14px classDef ener fill:#fff9c4,font-weight:bold,font-size:14px Main[Vault7-344] Main --> L1[All nature patterns
classically learnable 1] L1 -.-> G1[Learning] Main --> B1[Protein folding
tractable via evolution 2] B1 -.-> G2[Biology] Main --> P1[VO3 reverse-engineers
fluids without physics 3] P1 -.-> G3[Physics] Main --> L2[Non-randomness from
evolutionary pressure 4] L2 -.-> G1 Main --> L3[Classical models
replace quantum need 5] L3 -.-> G1 Main --> L4[Understanding via
prediction not awareness 6] L4 -.-> G1 Main --> P2[Passive view
suffices for physics 7] P2 -.-> G3 Main --> B2[Virtual cells
100x faster trials 8] B2 -.-> G2 Main --> B3[Yeast ideal
for whole-cell sim 9] B3 -.-> G2 Main --> B4[AlphaFold3 maps
dynamic interactions 10] B4 -.-> G2 Main --> B5[Multi-timescale cells
need hierarchy 11] B5 -.-> G2 Main --> O1[AI searches
chemical soup for life 12] O1 -.-> G2 Main --> L5[Life continuum
not binary 13] L5 -.-> G1 Main --> A1[NN AGI is
classical computing peak 14] A1 -.-> G4[AGI] Main --> C1[P vs NP ties
to nature patterns 15] C1 -.-> G3 Main --> L6[Cellular automata
classically emergent 16] L6 -.-> G1 Main --> P3[Chaos resists
efficient modeling 17] P3 -.-> G3 Main --> P4[VO3 hints
low-D manifolds 18] P4 -.-> G3 Main --> P5[AI physics like
toddler intuition 19] P5 -.-> G3 Main --> G1a[Games generate
personalized narratives 20] G1a -.-> G5[Games] Main --> G1b[Open worlds
co-created with AI 21] G1b -.-> G5 Main --> G1c[Black & White
early RL morality 22] G1c -.-> G5 Main --> G1d[Infinite content
on demand via AI 23] G1d -.-> G5 Main --> G1e[Hassabis dreams
post-AGI physics game 24] G1e -.-> G5 Main --> G1f[Games become
post-scarcity meaning 25] G1f -.-> G5 Main --> G1g[Civ series
fav for strategy 26] G1g -.-> G5 Main --> G1h[Gaming fuses
code art systems 27] G1h -.-> G5 Main --> E1[LLM guides
evolutionary search 28] E1 -.-> G6[Evolution] Main --> E2[Evolution combines
parts for emergence 29] E2 -.-> G6 Main --> E3[Old EA never
truly novel 30] E3 -.-> G6 Main --> E4[Hybrid LLM-evolution
beats limits 31] E4 -.-> G6 Main --> R1[Taste hardest
to replicate 32] R1 -.-> G7[Research] Main --> R2[Conjecturing harder
than solving 33] R2 -.-> G7 Main --> R3[AI cant ask
transformative questions 34] R3 -.-> G7 Main --> R4[Einstein creativity
needs data leap 35] R4 -.-> G7 Main --> T1[1900 cutoff
test for AGI 36] T1 -.-> G4 Main --> T2[Inventing Go-level
games signals AGI 37] T2 -.-> G4 Main --> T3[Alpha Evolve self
improves narrowly 38] T3 -.-> G4 Main --> S1[Hard takeoff
risky uncontrollable 39] S1 -.-> G4 Main --> S2[Many S-curves
not single leap 40] S2 -.-> G4 Main --> S3[Scaling laws
hold everywhere 41] S3 -.-> G4 Main --> S4[DeepMind ready
for plateau 42] S4 -.-> G4 Main --> D1[Synthetic data
eases human scarcity 43] D1 -.-> G4 Main --> C2[Compute demand
keeps rising 44] C2 -.-> G4 Main --> E5[Fusion solar
AI grid solve energy 45] E5 -.-> G8[Energy] Main --> E6[Room-temp superconductors
revolutionize grids 46] E6 -.-> G8 Main --> E7[AI designs
next-gen solar batteries 47] E7 -.-> G8 Main --> E8[Energy solves
desalination space mining 48] E8 -.-> G8 Main --> E9[Post-scarcity ends
zero-sum fights 49] E9 -.-> G8 Main --> E10[Fair sharing
next challenge 50] E10 -.-> G8 Main --> W1[AI beats
classic weather models 51] W1 -.-> G9[Weather] Main --> W2[AI forecasts
near-chaotic cyclones 52] W2 -.-> G9 Main --> W3[Storm chasers
fuse human AI 53] W3 -.-> G9 Main --> A2[50 % chance
AGI by 2030 54] A2 -.-> G4 Main --> A3[AGI needs
10k task consistency 55] A3 -.-> G4 Main --> A4[Europe vague
loses AI race 56] A4 -.-> G4 G1[Learning] --> L1 G1 --> L2 G1 --> L3 G1 --> L4 G1 --> L6 G2[Biology] --> B1 G2 --> B2 G2 --> B3 G2 --> B4 G2 --> B5 G2 --> O1 G2 --> L5 G3[Physics] --> P1 G3 --> P2 G3 --> C1 G3 --> P3 G3 --> P4 G3 --> P5 G4[AGI] --> A1 G4 --> T1 G4 --> T2 G4 --> T3 G4 --> S1 G4 --> S2 G4 --> S3 G4 --> S4 G4 --> D1 G4 --> C2 G4 --> A2 G4 --> A3 G4 --> A4 G5[Games] --> G1a G5 --> G1b G5 --> G1c G5 --> G1d G5 --> G1e G5 --> G1f G5 --> G1g G5 --> G1h G6[Evolution] --> E1 G6 --> E2 G6 --> E3 G6 --> E4 G7[Research] --> R1 G7 --> R2 G7 --> R3 G7 --> R4 G8[Energy] --> E5 G8 --> E6 G8 --> E7 G8 --> E8 G8 --> E9 G8 --> E10 G9[Weather] --> W1 G9 --> W2 G9 --> W3 class L1,L2,L3,L4,L6 learn class B1,B2,B3,B4,B5,O1,L5 bio class P1,P2,C1,P3,P4,P5 phys class G1a,G1b,G1c,G1d,G1e,G1f,G1g,G1h game class E1,E2,E3,E4 evo class R1,R2,R3,R4 agi class E5,E6,E7,E8,E9,E10 ener class W1,W2,W3 phys

Resume:

The session centers on analyzing the implications of the U.S. AI Action Plan, particularly how it positions America in the global AI race against China and Europe. Domenech introduces the topic by referencing a recent interview between Lex Fridman and Demis Hassabis, co-founder of Google DeepMind and Nobel laureate. The conversation explores Hassabis’s provocative conjecture that any pattern found in nature can be efficiently modeled by classical learning algorithms, suggesting that biological, physical, and even cosmological systems are learnable due to their evolutionary structure.
The discussion then shifts to the capabilities of modern AI systems, particularly DeepMind’s AlphaFold and VO3 video generation model, which demonstrate an intuitive understanding of physics, materials, and fluid dynamics. Hassabis argues that these systems reverse-engineer reality by extracting lower-dimensional manifolds from data, implying that classical computers can model complex natural phenomena without needing quantum systems. The conversation also touches on the philosophical implications of AI understanding, questioning whether passive observation can yield genuine comprehension of the physical world, and how this challenges traditional views on embodied cognition.
Finally, the dialogue explores the future of AI in scientific discovery, gaming, and energy. Hassabis envisions AI-driven “virtual cells” that simulate entire biological systems, accelerating medical research. He also discusses the potential for AI to create immersive, personalized video games and to solve grand challenges like fusion energy and climate change. The session concludes with reflections on the geopolitical stakes of AI development, emphasizing that while the U.S. and China are aggressively advancing, Europe risks falling behind due to lack of strategic clarity.
The core thesis advanced by Hassabis is that nature is not random but shaped by evolutionary pressures, creating patterns that neural networks can efficiently learn. This idea underpins DeepMind’s successes in protein folding, game playing, and video generation, and suggests that classical systems can model much more of reality than previously thought. The conversation repeatedly returns to the notion that if something has evolved, it can be modeled, and that AI systems are becoming increasingly adept at reverse-engineering these natural processes. This has profound implications for science, where AI could accelerate discovery by simulating complex systems like cells or even the origin of life.
The discussion also delves into the philosophical and technical limits of AI understanding. While systems like VO3 can generate realistic videos of liquids and lighting, the question remains whether they truly “understand” physics or are merely mimicking patterns. Hassabis suggests that their predictive accuracy constitutes a form of understanding, albeit not human-like consciousness. This leads to broader questions about the nature of reality, the continuum between living and non-living systems, and whether AI could eventually simulate the emergence of life from chemical soups. The conversation emphasizes that AI is not just a tool but a new lens through which to explore the fundamental structure of the universe.
Looking forward, Hassabis outlines a vision where AI transforms not just science but also entertainment and energy. He imagines games that dynamically generate stories and worlds tailored to each player, creating unprecedented levels of personalization. He also foresees AI solving energy scarcity by optimizing fusion reactors and solar grids, ushering in an era of radical abundance. However, he warns that achieving AGI will require more than scaling current systems—it will need breakthroughs in creativity, taste, and hypothesis generation. The session ends with a geopolitical note: while the U.S. and China are racing ahead, Europe must urgently adapt or risk obsolescence.

Key Ideas:

1.- Hassabis conjectures all natural patterns are efficiently learnable by classical algorithms.

2.- AlphaFold success proves protein folding is computationally tractable via evolutionary structure.

3.- VO3 video model reverse-engineers fluid dynamics without explicit physics programming.

4.- Nature’s non-randomness arises from evolutionary selection pressures over time.

5.- Classical systems can model complex systems without quantum computers.

6.- AI understanding emerges from predictive accuracy, not consciousness.

7.- Passive observation suffices for intuitive physics, challenging embodied cognition theories.

8.- Virtual cells could simulate entire biological systems for 100x faster experiments.

9.- Yeast cells are ideal starting models for full single-organism simulation.

10.- AlphaFold3 models protein-RNA-DNA interactions, moving beyond static structures.

11.- Multi-timescale cellular processes require hierarchical simulation architectures.

12.- AI could simulate origin of life by searching chemical soup parameter spaces.

13.- Life/non-life distinction is a continuum, not a binary threshold.

14.- AGI built on neural networks will ultimate expression of classical computing power.

15.- P vs NP question linked to whether nature’s patterns enable polynomial-time solutions.

16.- Emergent phenomena like cellular automata are likely classically modelable.

17.- Chaotic systems with sensitive initial conditions may resist efficient modeling.

18.- VO3’s liquid simulations suggest lower-dimensional manifolds govern reality.

19.- Intuitive physics understanding in AI mirrors human child-level comprehension.

20.- Next-gen games will generate personalized narratives dynamically around player choices.

21.- Open-world games will become true co-creation between player and AI simulation.

22.- Black & White’s creature AI reflected player morality via early reinforcement learning.

23.- Future games may use AI to create infinite, compelling content on demand.

24.- Hassabis dreams of post-AGI sabbatical to build open-world physics simulation game.

25.- Video games may become primary medium for human meaning in post-scarcity society.

26.- Civilization series remains Hassabis’s favorite game due to strategic depth.

27.- Gaming teaches programming, art fusion, and systems thinking simultaneously.

28.- Alpha Evolve uses LLM-guided evolutionary search to discover novel algorithms.

29.- Evolutionary computing can combine components to create emergent capabilities.

30.- Traditional evolutionary algorithms failed to generate truly novel properties.

31.- Hybrid LLM-evolution systems may overcome historical emergence limitations.

32.- Research “taste” is hardest AI capability to replicate, separating good from great scientists.

33.- Formulating falsifiable, insightful conjectures is harder than solving them.

34.- AI systems currently lack ability to ask transformative scientific questions.

35.- Einstein-level creativity requires leap of imagination beyond current data.

36.- AGI testing should include 1900-knowledge cutoff to simulate relativity discovery.

37.- Inventing deep, elegant games like Go would signal cross-domain AGI creativity.

38.- Alpha Evolve enables recursive self-improvement in narrow domains like matrix multiplication.

39.- Hard takeoff scenarios are possible but not desirable due to control risks.

40.- Incremental improvements may lead to AGI via many small S-curves rather than single leap.

41.- Scaling laws still hold across pre-training, post-training, and test-time compute.

42.- Google DeepMind positions itself to make breakthroughs when scaling plateaus.

43.- High-quality human data scarcity is mitigated by synthetic data generators.

44.- Compute demand will grow due to training, inference, and test-time scaling needs.

45.- Energy scarcity solutions include fusion, solar, and AI-optimized grid systems.

46.- Room-temperature superconductors would revolutionize energy transmission.

47.- AI-designed materials could enable next-gen solar panels and batteries.

48.- Solving energy enables desalination, space access, and asteroid mining.

49.- Post-scarcity economy could eliminate zero-sum resource competition.

50.- Fair distribution of abundant resources becomes next major societal challenge.

51.- Weather prediction AI surpasses traditional fluid dynamics simulations.

52.- Cyclone path forecasting demonstrates AI modeling of near-chaotic systems.

53.- Storm chasers exemplify fusion of human experience and AI prediction models.

54.- 50% probability assigned to achieving AGI by 2030 based on current trajectories.

55.- AGI must demonstrate consistency across tens of thousands of cognitive tasks.

56.- Europe risks strategic failure in AI race compared to U.S. and China clarity.

Interviews by Plácido Doménech Espí & Guests - Knowledge Vault built byDavid Vivancos 2025