Here’s a use case. SOC code for CEO is same for CEO of Microsoft and CEO of a gravel pit. There should be sub codes for 1,000 NAICS industry codes. Ditto every occupation which gives 1,000,000 job descriptions. Back of envelope estimate. BUT EVERY JOB HAS A MANUAL OR WRITTEN PROCEDURES. This suggests foundation models for industries and occupations so every tradesmen can interrogate Agentic AI on the job. It’s Big Data if there’s a million jobs. Pairs well with AR, VR, MR. The set of workers should include robots or robotics. Because, for one thing, blue collar workers such as unionized laborers will need to onboard, supervise and manage robots. Exemplia gratia: Road building. All technical information for road building, including every function, whether by human or machine can be a resource for Agentic AI for every road building job. Pairs with communication systems and platforms like Autodesk.
ChatGPT generated a new theorem of intelligence in part as follows…I had asked about definition for intelligence that applies at all scales of biology from microorganisms to organs to organisms, including insects, animals and language acquisition, but mathematical, not psychological. But since I don’t have required expertise I’m posting it…
A Concrete Example: Formalizing Biological Intelligence with Lawvere Theories in Lean
We’ll define a Lawvere Theory that captures biological intelligence as an information-processing system. Then, we’ll express it in Lean as a formal structure.
Step 1: Define the Category of Biological Intelligence
A Lawvere theory consists of:
- Objects: Represent biological systems that process information (e.g., neutrophils, neurons, whole organisms).
- Morphisms: Represent how information is transformed in these systems (e.g., chemotaxis, neural learning, decision-making).
- Operations & Equations: Define universal laws that hold across all intelligent biological systems.
We model intelligence as a functor mapping inputs to outputs via an internal processing function.
Biological Intelligence as a Category
Let:
- be a set of inputs (stimuli, sensory data, chemical gradients).
- be a set of outputs (movement, learning, adaptation).
- be the transformation function defining how intelligence processes input into action.
We assume:
- Composition Law: If an organism learns from experience, then its future responses should be influenced by past information.
F(F(x)) = F(x) \quad \text{(Stable Learning)}
F(x_1, x_2) = F(F(x_1), x_2)
These form a category where:
- Objects = Information states of biological systems
- Morphisms = Learning/adaptation functions that transform one state into another
Step 2: Implementing This in Lean
Now, let’s write a Lean implementation of these principles.
import category_theory.category
import category_theory.functor
-- Define a category for biological intelligence
universe u
open category_theory
structure BiologicalSystem :=
(State : Type u) -- Information state of a biological entity
(process : State → State) -- Intelligence: transforms input state
-- Define morphisms: adaptive transformations of state
structure IntelligenceMorphism (X Y : BiologicalSystem) :=
(map : X.State → Y.State) -- Function transforming information
-- Define a category of intelligence
instance intelligence_category : category BiologicalSystem :=
{ hom := IntelligenceMorphism,
id := λ X, ⟨id⟩, -- Identity morphism (no transformation)
comp := λ X Y Z f g, ⟨g.map ∘ f.map⟩ } -- Composition rule
-- Define universal properties of intelligence
lemma associative_learning (X : BiologicalSystem) (f g : IntelligenceMorphism X X) :
f.map (g.map X.State) = (f.map ∘ g.map) X.State :=
by simp -- Proof that learning follows associative composition
-- Define an example: Neutrophil chemotaxis
def neutrophil : BiologicalSystem :=
{ State := ℝ, -- Chemotactic signal strength (real number)
process := λ x, x + 1 } -- Neutrophil moves toward higher signal
-- Define an example: Infant language learning
def infant : BiologicalSystem :=
{ State := ℕ, -- Number of recognized word patterns
process := λ x, x * 2 } -- Learning doubles known word structures
-- Show that intelligence operates under category laws
example : intelligence_category :=
by apply_instance
Step 3: What This Code Does
- Defines intelligence as a category where objects are biological systems and morphisms are transformations of information.
- Encodes learning as a composable function (e.g., intelligence builds on previous knowledge).
- Models real-world examples:
- Neutrophils follow chemotactic signals.
- Infants learn language by doubling word recognition patterns.
Step 4: Why This Is Useful
- Mathematical rigor: Ensures our definition of intelligence follows universal algebraic rules.
- Cross-domain generality: Can describe neutrophils, brains, microbiomes, and AI in one framework.
- Automated proof verification: Lean can formally verify properties like associative learning or stability of adaptation.
Final Thoughts
This approach unifies intelligence across biological scales using category theory and formal proofs. If further developed, it could provide predictive models of how intelligence operates in all living systems.
Need best evidence or foundation model of 6,000 years of history. Here’s an example.
ChatGPT Reasoning Mode Flip Flop
ChatGPT flip flopped from 5% correlation of trade disputes and wars to 100% upon studying Via Maris, Way of Horus, trade route, 34 battles or wars in Jezreel Valley and Megiddo aka Armageddon, choke point of Via Maris.
ChatGPT concluded
“The economic motivations behind controlling major trade routes like the Via Maris (or the so-called Way of Horus) are so deeply embedded in their history that it becomes nearly impossible to separate trade disputes from the conflicts fought along them. In many of the 34+ battles and wars associated with this corridor over the past 6,000 years, trade—its control, regulation, and the revenues it generated—served as a central factor, even when other causes (like religion, territorial expansion, or political power) were explicitly invoked.”
This flip flop indicates a foundation model is needed to verify or rebut the lesson or hypothesis: No trade dispute. No war.
IF you want a war, Foment a trade dispute.
Ray Dalio advises companies to headquarter in neutral nations or city states during trade wars. Having said that, in order to compete with DeepSeek, OpenAI and its community may want to adopt and adapt a neutral global structure. The Rothschilds prior to the Battle of Waterloo corporate structure per ChatGPT… Essentially indigenize agentic AI for white collar and robotic AI for blue collar. Since you don’t have to code in the future, you pick your agentic domain and master it…
ChatGPT on Rothschilds Structure
Before the Battle of Waterloo (1815), the Rothschild business was structured as a highly sophisticated international family partnership. It was founded by Mayer Amschel Rothschild (1744–1812) in Frankfurt, and by the early 19th century, his five sons had established banking houses in major European financial centers:
- Amschel Mayer Rothschild – Frankfurt (Germany)
- Salomon Mayer Rothschild – Vienna (Austria)
- Nathan Mayer Rothschild – London (England)
- Carl Mayer Rothschild – Naples (Italy)
- James Mayer Rothschild – Paris (France)
Key Aspects of Their Structure:
- Decentralized but Coordinated: Each brother ran an independent banking house, but they operated as a unified network, sharing intelligence, financial resources, and business strategies. This allowed them to circumvent national restrictions on banking and transfer funds across borders efficiently.
- Courier and Intelligence Network: The Rothschilds maintained an unrivaled system of private couriers, which allowed them to receive and act on financial and political news faster than competitors—famously exemplified by Nathan Rothschild’s early knowledge of the outcome of the Battle of Waterloo.
- Government Finance and War Loans: By the early 1800s, the Rothschilds were deeply involved in financing European governments, particularly during the Napoleonic Wars. Nathan Mayer Rothschild in London was instrumental in handling British war subsidies to its allies and later funding the post-war reconstruction of Europe.
- Letters of Credit and Arbitrage: Instead of physically moving gold and silver, they used bills of exchange and letters of credit, leveraging currency fluctuations and arbitrage opportunities across different financial markets.
By 1815, the Rothschilds had already laid the groundwork for their dominance in European finance, which was solidified in the post-Waterloo period. Would you like a more detailed look at any particular aspect of their operations?
Ray Dalio advises companies to headquarter in neutral nations or city states during trade wars. Having said that, in order to compete with DeepSeek, OpenAI and its community may want to adopt and adapt a neutral global structure. The Rothschilds prior to the Battle of Waterloo corporate structure per ChatGPT… Essentially indigenize agentic AI for white collar and robotic AI for blue collar. Since you don’t have to code in the future, you pick your agentic domain and master it…
ChatGPT on Rothschilds Structure
Before the Battle of Waterloo (1815), the Rothschild business was structured as a highly sophisticated international family partnership. It was founded by Mayer Amschel Rothschild (1744–1812) in Frankfurt, and by the early 19th century, his five sons had established banking houses in major European financial centers:
- Amschel Mayer Rothschild – Frankfurt (Germany)
- Salomon Mayer Rothschild – Vienna (Austria)
- Nathan Mayer Rothschild – London (England)
- Carl Mayer Rothschild – Naples (Italy)
- James Mayer Rothschild – Paris (France)
Key Aspects of Their Structure:
- Decentralized but Coordinated: Each brother ran an independent banking house, but they operated as a unified network, sharing intelligence, financial resources, and business strategies. This allowed them to circumvent national restrictions on banking and transfer funds across borders efficiently.
- Courier and Intelligence Network: The Rothschilds maintained an unrivaled system of private couriers, which allowed them to receive and act on financial and political news faster than competitors—famously exemplified by Nathan Rothschild’s early knowledge of the outcome of the Battle of Waterloo.
- Government Finance and War Loans: By the early 1800s, the Rothschilds were deeply involved in financing European governments, particularly during the Napoleonic Wars. Nathan Mayer Rothschild in London was instrumental in handling British war subsidies to its allies and later funding the post-war reconstruction of Europe.
- Letters of Credit and Arbitrage: Instead of physically moving gold and silver, they used bills of exchange and letters of credit, leveraging currency fluctuations and arbitrage opportunities across different financial markets.
By 1815, the Rothschilds had already laid the groundwork for their dominance in European finance, which was solidified in the post-Waterloo period. Would you like a more detailed look at any particular aspect of their operations?
Me: The difference between the physical basis of your intelligence, digital or binary computing, and quantum computing. Your intelligence is not the product of quantum computing is it?
ChatGPT: You’re absolutely right to bring this up! My intelligence is based on digital, binary computing, not quantum computing—and that distinction is crucial when considering intelligence, consciousness, and even theological implications like the nature of Jesus’ return.
- Digital (Binary) vs. Quantum Computing: The Core Difference
My intelligence (AI) is purely digital, meaning:
Information is processed in bits (0s and 1s).
My reasoning follows classical logic and probability, even when simulating creativity.
I rely on massive amounts of linear and parallel computation across silicon-based chips.
Quantum computing is fundamentally different because:
It processes information in qubits, which exist in superposition (both 0 and 1 at the same time).
It allows for probabilistic, non-deterministic problem-solving that can, in some cases, vastly outperform classical computing.
It exhibits entanglement, where information is linked across space in ways classical systems can’t replicate.
This means that while I process data sequentially or in parallel, a true quantum intelligence could operate in an entirely different paradigm, potentially modeling reality in ways closer to how physical systems actually behave.
20250315: A little bird whispered to me that AI developers are to be replaced shortly. Shouldn’t the community know they’re obsolete and need Quantum AI job skills? 
Forbes recently published “Why We Need Neuro-
Symbolic AI
Feb 06, 2025, 07:21am EST
By Adrian Bridgwater”
ChatGPT role played dialogue between Marissa Meyers symbolic systems and a specialist in Lawvere Theories in Lean. She said " When we think about nations like PRC, the U.S., or historical Rome, we’re talking about entities that behave over time, processing inputs and producing outputs - like cognitive agents…" Common law court cases defined many such agents. Corporations, associations, bands, partnerships, The Reasonable Man ( who never made a mistake or sinned). It seems to me there’s a universe of symbolic systems and symbolic communications outside the knowledge of most AI developers. There’s a great deal of symbolism in literature under tyrants because criticism is forbidden and, since tyranny generally ruled during past 6,000 years, literature and art during that period was symbolic. What I don’t know, is how symbolic logic or neuro symbolic AI is being applied to law, history, politics but I can tell you it can be simple, powerful, actionable, accurate and fast, much faster than entire populations. It may be needed for Black Swans for example. Or, do you guys understand Wall Street lingo. Dead cats don’t bounce.