When you think about AI today, it feels like
it's just everywhere. It's in your phone, recognizing
your face. It's in cars that are learning to
drive themselves, these huge language models
that can write poetry. It's completely woven
into the fabric of our lives now. Right. But
if you sort of peel back all those layers, you
know, the big tech companies, the flashy product
announcements, who's the person who actually
built the engine? Who is the architect of the
fundamental tech that... Let's a machine see.
That's the question we're digging into today.
And the person behind that curtain, our subject,
is Jan -André Lecun. He's a French -American
computer scientist. And his work really built
the bridge between these like abstract academic
ideas and AI that could actually be industrialized.
He's known for deep learning, of course, but
also computer vision, mobile robotics. He's foundational
in all those areas. And you mentioned he's French
-American. That dual citizenship is actually,
I think, really interesting. Well, his career
is this perfect blend of that European perdition
of deep theoretical work with this very American
drive for... massive industrial scale application.
Even his name, LeCun, has this deep history.
It comes from an old Breton word meaning John.
So a career that. really mirrors his own identity.
Exactly. But the big claim, the place you have
to start with him is his status. He is one of
the three figures who are, you know, universally
called the godfathers of AI. Or more specifically,
the godfathers of deep learning. Right. A more
accurate term. And that trio, that's him, Jan
LeCun, alongside Yoshua Bengio and Jeffrey Hinton.
And their work, it didn't just improve AI a little
bit. No, it was a complete paradigm shift. It
was a revolution. They took neural networks from
this, this kind of marginalized. almost forgotten
corner of academia in the 80s and 90s, and they
turned it into the single most important technological
engine of our time. Okay, let's unpack this.
So our mission for this deep dive is to trace
the history of his most famous invention, the
convolutional neural network, the CNN, and really
understand how he took that idea from a sketch
in a lab notebook to something that's driving
entire global industries. We've got a great stack
of source material for this. It covers his whole
career, from his education, his time at Bell
Labs, NYU, Meta, and a list of awards that is
just, well, it's pretty staggering. And that
path is so important for you, the listener, to
understand if you want to get a real handle on
AI's history and where it's going, his career
is like a perfect roadmap for how a technology
matures. What do you mean by that? Well, first
you have the fundamental research phase, which
for him was at a place like Bell Labs. Then you
have the academic phase where you institutionalize
that knowledge, which he did at NYU. And then
the final stage. The final stage is scaling it
up in a hyper competitive, massive scale environment,
which is exactly what he did at Meta. So, you
know, to understand his journey is to understand
the last 30 years of technology itself. All right.
Let's start at the very beginning. His early
academic life. He was born July 8, 1960, in a
suburb of Paris. In Soississou -Montmorency,
to be exact. And his education was very technical,
very rigorous from the start. He got his diplôme
d 'ingénieur and engineering degree from EZ Paris
in 1983. And then he went straight into the deep
theory, getting his Ph .D. in computer science
in 1987 from what's now part of Sorbonne University.
And that Ph .D. thesis from 87, it's a really
important historical document. It's called Modèle
Connexioniste de l 'Apprentissage, which is just
connectionist learning models. So even then,
he was focused on how these networks learned.
Right from the jump. And in that thesis, he actually
proposed an early version of the back propagation
algorithm. Which is, I mean, for anyone listening
who isn't a computer scientist, that's the absolute.
core mechanism for teaching a neural network,
right? It is the core mechanism. The problem
back then was all about efficiency. You have
a network with all these layers of artificial
neurons. You show it something, it makes a guess.
And if it's wrong, you need a way to send that
error signal all the way back through the network.
So every single connection can be adjusted just
a tiny bit. Exactly. And back propagation is
the elegant mathematical way to do that efficiently.
Before that, the methods were just too slow,
too clunky. They couldn't handle networks that
were more than a couple of layers deep. LeCun
was right there, working on the mathematical
tools that made deep learning even possible.
So he's tackling this fundamental problem, and
that work immediately connects him to the other
godfathers. Right after his PhD in 87, he does
a postdoc. At the University of Toronto. And
his mentor there was Geoffrey Hinton. The man
he'd later share the Turing Award with. It's
an amazing piece of foreshadowing. That year
solidified their intellectual bond. You have
to remember, in the late 80s, these guys were
not in the mainstream of AI research. Not at
all. The dominant idea was still symbolic AI,
logic -based systems. Right. This connectionist
stuff, these neural networks, were seen as kind
of a dead end by many. It was a small, tight
-knit group of researchers who were keeping the
flame alive during what we now call the AI winter,
when all the funding and enthusiasm had just
evaporated. And then, after that postdoc, he
makes the move that changes everything. The one
that proves this isn't just theory. Bell Labs.
In 1988, he joins the Adaptive Systems Research
Department at AT &T Bell Labs in New Jersey.
And you really can't overstate what Bell Labs
was at that time. It was the place to be, the
world's greatest innovation factor. Without a
doubt. And he spent eight years there from 88
to 96. And this is where it all comes together.
This is where he develops his signature invention.
The Convolutional Neural Network. The CNN. The
CNN. Specifically, the architecture that became
famous as Lynette. And the whole idea was described
as a biologically inspired model of image recognition.
He was literally looking at how the animal visual
cortex works. OK, so let's break that down because
this is the really critical part. Why did he
need a whole new type of network just for images?
What was wrong with the neural networks they
were already studying? That's the million dollar
question. And the answer is scale. Just pure
brute force scale. Think about. A simple black
and white image, maybe it's 200 pixels by 200
pixels. That's 40 ,000 inputs for your network
right there. 40 ,000. Now imagine connecting
every single one of those 40 ,000 pixels to every
neuron in the next layer. Even if that next layer
only has 100 neurons, you're suddenly looking
at 4 million connections, 4 million different
weights that the network has to learn. And on
the computers of the late 80s, that's just...
Completely impossible. The computational cost
was astronomical. You didn't have the memory.
And even if you did, the network would just overfit,
basically memorize the training photos instead
of learning the general concept of, say, what
a cat looks like. So the data itself was the
bottleneck. Precisely. And Lacoon's genius was
to say, OK, let's not do that. Let's build a
smarter architecture with some built in constraints
inspired by how vision actually works. And that's
where the three core ideas of CNN's come from.
Which are. Local connectivity, shared weights,
and pooling. Exactly. Let's take them one by
one. Local connectivity just means that a neuron
in one layer doesn't connect to the entire image.
It only connects to a small local patch of pixels
right in its neighborhood. So it's not trying
to see the whole picture at once. It's just looking
for a tiny feature, like an edge or a corner.
Just a little clue. And then comes the real magic
trick, shared weights. This means you use the
exact same little feature detector, the same
set of weights, which we call a filter, and you
slide it across the entire image. Ah, I see.
So if you have a filter that's good at finding
a vertical line, you use that same filter to
look for vertical lines in the top left, the
bottom right, everywhere. That's the key insight.
And what that does is, first, it dramatically,
I mean... It dramatically reduces the number
of parameters the network has to learn. Instead
of millions of weights, you might only be learning
a few dozen filters. And second, it gives the
network a built -in superpower. Translational
invariance. The ability to recognize an object
no matter where it appears in the frame. A cat
is a cat, whether it's on the left or the right.
And the last piece, pooling. Pooling, or subsampling,
is basically a way to shrink the data down. After
you found all these little features, you summarize
the information in a region, keeping the most
important signal and throwing away some of the
noise. It makes the network more robust to tiny
shifts and distortions. So it's this brilliant
hierarchical system. Find small local features,
then summarize them, then use those summaries
to find bigger features, and so on. That was
the architectural breakthrough that made processing
high -resolution images feasible for the very
first time. And his work at Bell Labs went beyond
just the architecture of Lynette. The sources
also mentioned things like optimal brain damage
regularization. Right, which is a very dramatic
name. It does. It sounds a little scary. What
was that trying to solve? It was another piece
of the puzzle. Remember, they were working with
small data sets in weak computers. Overfitting
was still a huge problem. Optimal brain damage,
or OBD, was a very clever way to make the network
simpler and more general. How did it work? After
you train the network, OBD would analyze all
the connections and figure out which ones were
least important for solving the problem. And
then it would preen them. It would cut them out.
Literally damaging the brain of the network.
Exactly. It was a way to keep the essential knowledge
while getting rid of the unnecessary complexity.
It made the models much more efficient and much
better at generalizing to new data they hadn't
seen before. A vital tool for that low resource
environment. And then there's something called
graph transformer networks. Yeah, the GTNs. That
was more focused on processing sequences of data,
not just static images. Think about trying to
read cursive handwriting, where one letter flows
into the next. It's not a single image. It's
a stream. GTNs were a way to apply these connectionist
ideas to that kind of structured sequential data.
Which all shows he was thinking about the whole
ecosystem of problems, not just one thing. But
here's where... For me, the story gets really
incredible. This isn't just a theory that sat
in a lab. Bell Labs immediately gave him a way
to apply this on a massive industrial scale.
This is the killer app. The proof that this wasn't
just an academic curiosity. The application was
handwriting recognition. specifically for reading
the numbers on bank checks. And this wasn't some
small pilot program. Lacan and his team built
a system that was widely deployed by NCR and
other big companies that made banking hardware.
It's hard to imagine the scale of that now. Every
day, millions and millions of paper checks were
moving through the financial system. It was a
huge, messy, complex problem. You have all these
different handwriting styles, different pens,
smudges, and the system had to be incredibly
accurate. You can't make mistakes when you're
reading the dollar amount on a check. And the
sources claim that this system, built on Lynette,
was reading, what was it, over 10%. Of all checks
in the United States in the late 90s and early
2000s. 10%. Just stop and think about that number.
That's not a research project. That is a core
piece of the national financial infrastructure.
Billions of transactions processed by a deep
learning network that the public had never even
heard of. It's the ultimate proof of concept.
It proved decades before the current AI boom
that neural networks were accurate, reliable
and scalable enough for mission critical industrial
work. It answered the big question. Can you trust
these things? And the banking industry implicitly
said, absolutely yes. Which brings up a really
fascinating question. If CNNs were so successful
that they were reading 10 % of U .S. checks in
the 90s, why did the AI winter continue? Why
did it take another 15 years for the rest of
the world to catch on to deep learning? That's
the million -dollar question, and it's so important
for understanding the history. Lacan had provided
the blueprint. He had the algorithm. But two
other essential ingredients were missing at scale.
Hardware and data. Hardware and data. Training
Lynette to read digits was possible on 90s computers.
But trying to use those same ideas on much bigger,
full -color images from the Internet or training
networks that were 10 times deeper, it was just
computationally out of reach. So we needed two
things to happen. We needed massive labeled data
sets, which we eventually got with things like
ImageNet. And we needed cheap, massively parallel
computers, which we got, ironically, from the
video game industry. GPUs. Gravix Processing
Unit. Exactly. The algorithm was ready and waiting
in 1995. The rest of the ecosystem just took
another decade and a half to catch up to LeCun's
vision. That makes perfect sense. The blueprint
for the car existed, but the factory and the
highways hadn't been built yet. Yeah. So after
that huge success in 1996, he makes a move within
AT &T. He goes to AT &T Labs Research and becomes
head of the Image Processing Research Department.
And here, his focus shifts a little bit, but
he's still working on these huge infrastructural
problems. He and his collaborators, Lambo2 and
Patrick Hafner, developed this technology called
D -View. Which is an image compression technology.
And a very, very smart one. It was designed specifically
for scanned documents, especially ones from libraries
and archives that have a mix of text, drawings,
and photos. What was the big idea behind it?
How is it better than, say, a JPEG? The genius
of DZ was that it separated the image into layers.
It would analyze the page and create a background
layer for the paper texture, a high -resolution
foreground layer for the crisp text and lines,
and another layer for any photos. And then compress
each layer separately using the best method for
that type of content. Exactly. So you could aggressively
compress the background and the photos, but you
could keep the text perfectly sharp and readable,
which is crucial for archives. It was so effective
that the Internet Archive and other big digitization
projects used it for millions of documents. So
again, it's him. him tackling these fundamental
problems of how we manage and access vast amounts
of information at scale. First recognition with
CNN's, now dissemination with DJVU. It's a consistent
theme throughout his entire career. He's always
thinking about the infrastructure. So after this
incredibly productive period in corporate research,
first at Bell Labs, then a short time at NEC
Research Institute, Lacan makes a really big
shift in 2003. He goes back to academia. He joins
New York University, NYU, where he still is today.
He's the Jacob T. Schwartz Chaired Professor
of Computer Science and Neuroscience. And this
feels like a very deliberate move. After proving
the industrial viability of his ideas, he seems
to be stepping back to focus on the next wave
of fundamental research. Absolutely. It was a
chance to dig deeper into the theory, to explore
ideas that weren't quite ready for commercial
application, and really importantly, to start
building the institutions that would train the
next generation of AI researchers. And the sources
highlight a few key areas of his research at
NYU. One of them is something called energy -based
models. That sounds very abstract. It is, but
it's a hugely important idea if you want to get
to human -like intelligence. You see, most of
the AI we use today, including the early CNNs,
relies on supervised learning. You need massive
data sets with labels. You need a million pictures
of cats, each one labeled cat. Which is not how
humans learn. Not at all. We learn mostly by
just observing the world. Unsupervised learning.
Energy -based models, or EBMs, are a framework
for getting machines to do that. How do they
work, conceptually? The basic idea is that the
model assigns a score, which it calls energy,
to any piece of data. A plausible, correct piece
of data, like an image of a real cat, gets a
low energy score. An impossible piece of data,
like a garbled, nonsensical image, gets a very
high energy score. Okay, so learning becomes
about trying to find the low -energy configurations.
Exactly. It's like a landscape with hills and
valleys. The model learns to shape that landscape
so that all the real -world data points settle
into the deep valleys. It's a way for the machine
to learn the underlying structure of reality,
to figure out what's possible and what's not
without needing a label for everything. That
feels like a necessary step toward common sense.
It is. It's the conceptual groundwork for the
world models he's focused on today. It's about
moving beyond just recognizing patterns and starting
to build an actual understanding of how the world
works. And alongside that theoretical work, he's
also applying these ideas to the physical world,
specifically to mobile robotics. The forces mention
work on autonomous off -road driving. Yeah, and
that's a great example. Driving on a perfectly
marked highway is one thing. Driving through
a forest or across a field where there are no
lanes and the terrain is unpredictable, that's
a much harder problem. It requires a much deeper
level of visual understanding. It requires robustness.
It shows he was always committed to taking these
ideas out of the clean digital world and testing
them in the messy, complicated physical world.
And at the same time he's doing all this research,
he's also becoming a major institution builder
at NYU. In 2012, he becomes the founding director
of the NYU Center for Data Science. A huge move.
That helped formalize data science as its own
serious academic discipline. It gave it a home.
Before that, it was kind of scattered between
computer science, statistics, and other departments.
He helped put it at the center of the university.
And that drive to build and shape the field went
way beyond just NYU. In 2013, he and Yoshua Bengio
co -found a new conference. The International
Conference on Learning Representations, or ICLR.
Which is now one of the top three most important
AI conferences in the world. Easily. It's an
absolute must -attend for anyone in the field.
And it was born out of necessity. The field of
deep learning was just exploding so fast that
the older, broader machine learning conferences
couldn't keep up. They needed their own dedicated
venue. But ICLR was different. It wasn't just
another conference. It had this radical new structure
that looked... had been pushing for, an open
review process. Yes, and this is so key to his
philosophy. The traditional peer review process
for scientific papers is slow and secretive.
You submit a paper, it disappears for months,
and you get anonymous reviews back. And LeCun
argued that was holding the field back. He argued
it was an obstacle to progress. With the ICLR
model, Papers are published online first on a
site like ArtFiev, and then the reviews and discussions
happen out in the public. It's faster, it's more
transparent, and it allows the entire community
to participate in vetting new ideas. He was trying
to build a scientific communication system that
moved at the speed of the technology itself.
Exactly. It's all about openness and acceleration.
It's a theme you see again and again in his career.
He also ran this legendary annual workshop in
Snowbird, Utah for decades, just constantly building
that community. Which all leads to this massive
turning point for him and for the whole field.
December 9th, 2013. A date that should be in
the history books of AI. That's the day Jan LeCun
became the first director of Meta AI Research,
or FAIR. He joined what was then Facebook as
their chief AI scientist. This was a seismic
event. This was one of the godfathers of the
field who had just built a major academic center,
making a definitive move to big industry. And
it signaled that the era of deep learning as
a purely academic pursuit was over. This was
the moment it moved into the hyperscale engine
room of one of the biggest tech companies on
the planet. So what was the crucial difference?
What could he do at Meta that he just couldn't
do at NYU, even with all his resources there?
Three things that no university can ever provide
at that level. Scale, hardware, and the feedback
loop. Let's break those down. At Fifth Fire,
he suddenly had access to data from billions
of users flowing in real time. That's a scale
of data that's just unimaginable in academia.
Second, he had access to giant custom -built
data centers full of GPUs designed for one purpose,
training massive AI models. And third, the feedback
loop. The ability to actually deploy a new model
to millions of users overnight and see instantly
how it performs in the real world. That cycle
of idea, experiment, deployment, and feedback
could happen in weeks at Meta, whereas in academia
it might take years. So all of his foundational
ideas, the CNNs, the energy -based models, they
now had the perfect greenhouse to grow at an
exponential rate. And they did. Everything from
the news feed to ad targeting to content moderation
to the language and vision models Meta uses today.
It was all built on the foundation that Lacoon
and the FAIR team established. It was the decade
where deep learning became completely, undeniably
industrialized. Which, of course, led to this
incredible cascade of global recognition for
him and his collaborators. We have to talk about
the 2018 Turing Award. You do. I mean, the Turing
is the Nobel Prize of computing. Getting that
award was. It was the ultimate validation. It
was the computing establishment officially declaring
that the connectionists had won. He shared it,
of course, with Bengio and Hinton. And the award
citations specifically mentioned their conceptual
and engineering breakthroughs. I love that they
included engineering. It wasn't just for the
ideas. It was for making the ideas work. Hinton
for the core learning mechanisms, Bengio for
sequences and generation, and Lacoon for the
CNN architecture that finally unlocked. And it
really was a celebration of their persistence,
wasn't it? Sticking with these ideas through
the AI winter when almost everyone else had given
up. It was a total vindication. The Turing Award
was the moment that deep learning officially
moved from the fringe of computer science to
its absolute center. And the awards just kept
coming. And what's interesting is how they connect
his work to the broader tech ecosystem. Take
the 2022 Princess of Asturias award. He shared
that one with Benjio and Hinton again, but also
with Demis Hassabis from DeepMind. Which is a
very significant pairing. It explicitly links
the foundational academic work of the godfathers
to the leader of one of the most successful industrial
AI labs today. It draws a straight line from
the theory to the application. And then the 2024
VinFuture prize paints an even clearer picture.
Because that one was shared with an even bigger
group. the three godfathers, plus Fei -Fei Lai,
and Jensen Huang, the CEO of NVIDIA. And that's
the whole story right there. That award recognizes
the complete system that was necessary for the
deep learning revolution. You have the algorithms
from Lacoon, Hinton, and Bengio. You have the
massive dataset ImageNet from Fei -Fei Lai's
team. And you have the hardware, the GPUs, from
Jensen Huang's NVIDIA. Algorithm, data, and hardware.
The holy trinity of modern AI. You can't have
the revolution with just one or two of those.
You need all three. And that prize was the ultimate
recognition of that entire collaborative ecosystem.
The recognition also came from the highest levels
of government and engineering. The Queen Elizabeth
Prize for Engineering in 2025. Again, framing
his work not just as science, but as a world
changing piece of engineering. And in 2023, he
was named a Chevalier of the French Legion of
Honor. which is the highest order of merit in
France. It's his home country, recognizing that
one of their own has fundamentally reshaped the
global technological landscape. It's a beautiful
moment that brings his French -American identity
full circle. It all just paints this picture
of a career that has had this profound, sustained,
and universally recognized impact on the world.
So after all that... After inventing the CNN,
industrializing it at Bell Labs, building an
academic powerhouse at NYU, guiding Meta's AI
strategy for a decade, and winning every major
award you can possibly win, what's left to do?
That is the question. And according to our sources,
we have an answer. The news from November 2025
is that Yann LeCun is leaving Meta. After 10
years as chief scientist, he's leaving to launch
his own startup. And this isn't just him retiring
or moving to a new company. This is a statement.
He's pivoting to what he sees as the next great
unsolved problem in AI. So what does this all
mean? The focus of this new venture is very specific,
and I think the wording is important. It's world
model architectures and human -like artificial
intelligence. That phrase, world models, is key.
It signals a complete shift from the kind of
AI that dominates today. How so? Think about
what current AI, even the most advanced large
language models, really does. At its core, it's
a hyper -sophisticated pattern matching machine.
It's incredibly good at recognizing what's in
a picture or predicting the most statistically
likely next word in a sentence. But it doesn't
really understand what it's saying or seeing.
Exactly. It lacks a model of how the world actually
works. It doesn't grasp cause and effect or what
you might call intuitive physics. It has no common
sense. So give me an example. If you show a current
AI. A video of a glass tipping over on a table.
It could write a perfect caption. A glass is
falling off a table. But a world model would
do something more. It would anticipate. It would
understand gravity and momentum and fragility.
It would know, without having to be told, that
the glass is going to hit the floor and shatter
into a thousand pieces. That's about building
an internal simulator of reality inside the AI.
That's the perfect way to put it. It's moving
from prediction of words to prediction of the
future state of the world. It's what would allow
an AI to plan, to reason about the consequences
of actions, to handle situations it has never
seen before. It's the leap from recognition to
cognition. From system one, that fast, intuitive
pattern matching. System two, the slower, more
deliberate reasoning part of intelligence. That's
the mountain he's setting out to climb now. And
you can see his commitment to this direction.
in his other ongoing work. He's still a co -director
of the Learning and Machines and Brain program
at CIFAR. Which is all about that intersection
between neuroscience and AI, trying to learn
from the ultimate world model, the human brain.
And maybe most importantly, given his history,
he's a scientific advisor to this French research
group called QTAI. Yes, and QTAI is a very interesting
organization. It's backed by some huge names
in European tech and finance. Even Eric Schmidt.
Right. And their entire mission, their founding
principle, is that all of their research, all
of their models will be completely open source.
That feels like a direct challenge to the current
trend in AI, where the most powerful models are
kept proprietary and locked away inside a few
giant tech companies. It's the ultimate expression
of the philosophy he started with ICLR. He believes
that this next generation of foundational AI
is too important to be controlled by any one
entity. His involvement with Kigatai shows a
deep commitment to making sure that the tools
to build human -like intelligence are available
to everyone. So he's not just trying to invent
the future of AI. He's trying to shape the culture
of how it's built and shared. Absolutely. He's
pushing the scientific frontier and the philosophical
one at the same time. Okay, so to wrap this all
up, to summarize this incredible journey, Yann
LeCun's career is really the story of modern
AI in miniature. It is. He starts in the mid
-80s working on the core math of back propagation.
Then at Bell Labs, he has his watershed moment
creating the convolutional neural network. And
he doesn't just create it. He proves its value
in the real world with that system that read
more than 10 % of all checks in the U .S., the
first huge industrial success for deep learning.
Then he moves to NYU, where he solidifies the
academic field, helps create the Center for Data
Science, and changes how research is shared with
ICLR. And then the decade at Meta, taking all
those ideas to planetary scale, and in the process,
collecting every major award in science and engineering,
from the Turing to the VinFuture Prize, which
recognized that whole ecosystem of hardware,
software, and data that he helped create. His
career is the thread that connects those abstract
connectionist ideas from the 80s to the powerful
industrialized AI that shapes our world today.
And now, the man who built so much of the present
is leaving it all behind to focus purely on the
future. The person who taught machines how to
recognize things is now setting out to teach
them how to understand things. That's it, exactly.
And that brings us to the final thought we want
to leave you with. Lacan's early career was all
about solving the problem of perception. of recognition
that the generation of ai he built the cnn's
mastered the question of what what is in this
image and now he's focused entirely on world
models Right. So the question for the future
is this. If that last generation of deep learning
was about mastering the what, how profoundly
will this next generation, the one focused on
world models, master the why and the what if?
If CNNs gave machines eyes, will world models
give them imagination? That's the challenge that
will define the next decade of AI, and Yann LeCun
has put himself right at the heart of it all
over again.