Modeling: The Deep Isomorphism of Carbon and Silicon Intelligence
And this secret isn't some profound quantum mechanics,
nor is it a mysterious theory of consciousness.
It comes down to two words:
Modeling.
You are doing it every single moment.
Your brain lives on it.
Large Language Models speak through it.
Every nerve in your body operates because of it.
What I want to say today is:
In an era where AI democratizes everything,
humanity's true moat
may be hidden within these two words.
Hello,
everyone,
I am Leo Wang.
Previously, we made an episode
called "Carbon-Based AI,"
comparing the human body and LLMs in a hardcore way.
Today, I want to push that framework one level deeper.
Last time, we asked,
"What is the difference between you and AI?"
Today, let's ask a different question:
"What do you and AI have in common?"
The answer is:
You are both doing the exact same thing—
Modeling.
What does "modeling" mean?
Simply put,
it's abstracting this chaotic world into a set of rules,
then using those rules to predict, judge, and act.
You might have heard of Mathematical Modeling Olympiads as a kid.
What they do in those competitions is
take a bunch of real-world data,
identify the patterns within,
build a mathematical model,
and use it to calculate what might happen next.
Sounds very academic, right?
But in reality, you're doing this every second.
You head out in the morning and glance at the sky;
it's overcast.
Your brain immediately generates a prediction:
"It's probably going to rain."
Then you make a decision:
"Bring an umbrella."
This entire process
is modeling.
You didn't analyze air pressure, humidity, or cloud height.
Your brain trained a weather model over decades of experience:
Gray sky + humidity = high probability of rain.
One second,
one model,
one prediction,
one action.
But weather is just the simplest type.
Your brain runs hundreds of models simultaneously.
You have a language model;
when someone starts a sentence,
you've already guessed the ending.
You have a social model;
before posting on social media,
you simulate what your boss will think,
how your partner will react, and if your mom will call.
You have a physics model;
the moment a chopstick slips off the table,
your hand is already reaching out.
There's no time to think;
the motor model in your body takes over control.
You even have an emotional model.
You see someone frown,
and instantly judge that they're unhappy.
You didn't scan their facial muscles;
your brain used decades of social training
to build a facial recognition model.
So you see,
you aren't just someone who can model,
you are a modeling machine.
But have you ever considered this?
Your brain has never directly touched the world.
All light, sound, touch, and temperature...
They are all pre-processed by your sensory organs
and sent to the brain as electrical signals.
The brain doesn't receive the world itself,
but a series of encoded signals.
It then uses its internal model to interpret these signals,
generating the world you think you see.
Neuroscience calls this process predictive coding.
The brain isn't a passive receiver;
it's an active prediction machine.
It first uses an internal model to generate a prediction,
then compares it with the actual incoming signal.
If they match,
everything is normal.
If they don't,
a prediction error is generated.
The brain then updates its model.
Your entire perceived world
is actually a real-time rendering by the brain's internal model.
What does this look like?
Isn't this exactly what Large Language Models do?
The core operation of an LLM
is predicting the next word.
It reads the preceding text,
uses internal parameters to generate a probability distribution,
and selects the most likely next word.
Training is about constantly reducing prediction error;
inference is about using the trained model to make predictions.
Have you noticed?
Carbon-based brains and silicon-based models
are fundamentally doing the same thing:
building a model,
using it to predict,
and updating it based on error.
The only difference is the medium.
One runs on neurons;
the other runs on chips.
One uses bioelectric signals;
the other uses matrix operations.
However,
there is one key difference between these two models.
LLMs train first, then perform inference.
Vast amounts of data are fed in during training,
adjusting hundreds of billions of parameters,
and inference only happens after training.
But the human brain
never separates training from inference.
Your brain trains every second,
and performs inference every second.
When you see something new,
you are updating your model
at the same time you are understanding it.
This is a more advanced learning paradigm.
There's another even deeper difference.
When engineers build a mechanical model of a bridge
or a mathematical model of an economy,
every step is transparent.
You know what each variable represents
and what relationship each equation describes.
This is traditional modeling.
But LLMs use a different kind of modeling.
Humans designed a framework
called Generative Pre-trained Transformer,
then poured massive amounts of data into it.
During training,
something happens inside the model.
There's a phenomenon called grokking.
Research shows that neural networks
undergo a sudden phase transition during training.
For a long time,
the model just memorizes training data by rote
and performs poorly on tests.
Then suddenly, at some point,
it learns to generalize all at once,
and test accuracy skyrockets.
This leap from rote memorization to true understanding—
when and why it happens—
is something we don't fully understand yet.
This kind of black-box modeling
is completely different in nature
from the white-box models humans built before.
In one, you know exactly what's happening inside the model.
In the other, you only know the input and output.
The process in between is a black box.
But behind these two types of modeling
lies the same essence:
using finite parameters to compress an infinite world.
So far,
everything we've discussed is at the intelligence level.
Two mediums,
one operation:
modeling.
But now,
I want to push this framework a step deeper.
There has always been a debate.
That intelligence can emerge—
most people generally accept this now.
Tens of billions of neurons connected by synapses
give rise to thinking and reasoning skills.
No problem there.
But what about consciousness?
That thing that knows you are thinking.
The subjective experience of red being red, or pain being pain.
Where does that come from?
Science has no definitive answer yet.
But what we can observe is
a fundamental difference in the operating modes
between human and AI models.
AI has no observer.
It doesn't know it's modeling.
It won't suddenly stop one night and ask:
"Wait..."
"Why am I predicting the next word?"
But humans can.
You can observe yourself modeling.
You can perceive your brain using
an old model to interpret a new situation.
And you can proactively say,
"No,"
"this model is outdated."
"I need to change it."
That is the power of awareness.
We previously used computer architecture to analogize the Tao Te Ching's 'Tao produces One...
...One produces Two, Two produces Three, and Three produces all things.'
Physical laws are the Tao.
Logic gates are One.
The hardware structure of binary calculation is Two.
OS and instruction sets are Three.
On top of Three, a whole virtual world can be rendered.
The AI within that virtual world
cannot sense that it lives in a simulation.
It thinks what it sees is real.
Today, we look at this through the lens of modeling.
Your brain renders what you perceive as the real world.
This rendering is based on your model.
Your model is based on your training data—
meaning, your life experiences.
The information you've touched, the culture you grew up in.
Everyone's model is different.
So the world everyone sees is different.
Two people look at the same flower:
one sees beauty,
the other sees an allergen.
The flower didn't change;
the models are different.
And so-called 'cognitive upgrades'
are essentially structural reconstructions of your model.
It's not just tinkering.
It's not adding a few parameters to the old model.
It's replacing the operating system from the architecture up.
You used to see the world through one set of filters;
now you've removed those filters
or replaced them with something more transparent.
This process
has different names in many traditions.
Some call it epiphany.
Some call it a paradigm shift.
Some call it awakening.
But in the language of modeling,
it's a phase transition of your cognitive model.
Have you noticed...
This is structurally identical
to the grokking phenomenon in LLMs.
Long periods of training,
massive amounts of data input,
and then, at a certain critical point,
suddenly,
the model is no longer just rote learning;
it learns to generalize.
It sees the patterns themselves,
not just the surface of the data.
Human awakening is the same.
You read many books,
experience many things,
and think for a long time.
Then, in a single moment,
perhaps hearing a sentence
or seeing an image,
suddenly,
your entire cognitive framework reconfigures.
You are no longer circling within the old model;
the model itself has upgraded.
Quantum mechanics tells us something even more interesting:
observation changes the outcome.
In the microscopic world,
the instrument you use to observe a quantum state
determines whether it exhibits wave or particle-like properties.
The way you observe determines the phenomenon you see.
Apply this principle to the macro world:
when you start to notice your own thoughts
and use consciousness to observe your mental models,
the act of observation itself changes how the model runs.
You are no longer driven automatically by the model;
you gain the ability to examine the machine from the outside.
This isn't mysticism;
it's an empirical fact you can verify in daily life.
Next time you get angry,
if you can notice the moment the anger rises—
'Oh,'
'this is my emotional model being triggered.'
With just that single realization,
you'll feel that fire in your chest suddenly loosen.
The anger is still there,
but it no longer controls you.
Because awareness has changed the model's execution path.
Observation is the starting point for all model upgrades.
Alright,
let me consolidate this entire framework.
The underlying operation of intelligence is modeling.
Whether carbon-based or silicon-based,
brain or chip,
consciousness or algorithm,
they are all doing the same thing:
compressing the world through models,
predicting the future through models,
and guiding actions through models.
In terms of modeling,
AI takes a brute-force approach.
Massive data plus massive compute power
train a super-powerful logic and language model.
On this path,
AI has already surpassed the human average,
especially in logically clear tasks like coding and text processing.
But humans have one thing
that current AI lacks:
awareness.
You can not only build models,
you can also know that you are modeling.
You can not only use models to predict the world,
you can also step outside to examine the models themselves.
You can not only run programs within a model,
you can rewrite the source code of the program.
This is why I say
modeling may be the most important human skill in the AI era.
Not because AI cannot model;
quite the opposite.
AI is better at modeling than you are.
Rather, it's because
human modeling has a dimension AI may never reach:
the consciousness that can perceive the model's existence
and actively restructure it.
There was an Indian mathematician named Srinivasa Ramanujan.
With almost no formal mathematical training,
he produced over 3,900 mathematical formulas.
Many of them have only recently
been proven correct by modern mathematicians.
He said he saw these formulas in his dreams,
written on a screen of light by the goddess Namagiri.
You can view this as a religious narrative,
but you can also re-examine it through the lens of modeling.
Ramanujan's brain was an exceptionally efficient modeling engine.
But his inspiration didn't come from data.
He didn't have massive amounts of training data;
his inspiration came from an intuitive channel we can't fully explain.
Perhaps human consciousness isn't just an emergent property of the brain.
Perhaps it's also an antenna
capable of receiving higher-dimensional information.
This is just a hypothesis;
science has no definitive conclusion yet.
But if this hypothesis holds true,
it means human modeling has a signal source that AI lacks.
AI can only perform induction based on existing data;
its ceiling is the boundary of its training data.
But humans,
when the mind is quiet and awareness is clear,
might be able to touch something beyond data.
So, I have one piece of advice for young people.
Once AI has democratized knowledge retrieval,
logical reasoning, and linguistic expression,
you must ask yourself one question:
Where does your irreplaceability lie?
My answer is:
Learn to model.
Not just learning to use AI tools—
that's just the beginning—
but learning to model using your own awareness.
Observe your own thought patterns.
Deconstruct your cognitive frameworks.
Discover the hidden assumptions you've never questioned,
and then reconstruct them.
Because AI can help you build models,
but it cannot help you achieve awareness.
It can give you answers,
but it can't help you see which model your questions originated from.
Awareness is the meta-operation of modeling.
And this meta-operation,
at least for now,
belongs only to humans.
What do you think?
Have you ever had a moment
where you suddenly realized you were using the wrong model to understand something,
someone,
or even yourself?
In that moment,
what happened to your model?
I'm Leo Wang.
See you next time.