Okay, let's unpack this. Imagine for a second
that you are in just the most deeply toxic, on
again, off again relationship imaginable. Oh
boy. You know exactly the kind I'm talking about,
right? First, there is this wild, breathless
infatuation where they promise you the absolute
moon. I mean, they tell you they are going to
change your entire life tomorrow. Right, the
honeymoon phase. Exactly. But then comes the
bitter, devastating disappointment when they
completely fail to deliver on any of it. And
finally, you enter this freezing cold period
where everyone in your friend group simply refuses
to even mention the ex's name out loud. Yeah,
you just pretend they don't exist. Right. And
today we are taking a deep dive into the history
of artificial intelligence. And what is so striking
to me is that for the last 70 years, humanity
has basically been trapped in that exact toxic
relationship with AI technology. That is honestly
that's a highly accurate way to frame it. That
cold period of the relationship you mentioned,
that's actually what the technology industry
literally calls an AI winter. An AI winter. Yeah.
It describes these cyclical historical periods
where funding, public interest, and academic
research in artificial intelligence essentially
just froze over. completely dried up. And the
term itself was actually coined during one of
those really bitter breakups. Really? When was
that? It first topped up in 1984. There was this
public debate at the American Association of
Artificial Intelligence. And two leading researchers,
Roger Schenck and Marvin Minsky, they were looking
around at the massive corporate hype of the 1980s.
And they just started sounding the alarm. Because
they had seen it before, right? Exactly. They
had already survived an earlier funding freeze
back in the 1970s. So they explicitly warned
the business community that the enthusiasm was
you know spiraling completely out of control
they saw the writing on the wall they did I mean
they predicted a massive chain reaction they
actually compared it to a nuclear winter Wow
nuclear winter that's bleak it is but they were
warning that when the technology inevitably failed
to meet these utopian promises Pessimism would
infect the research community. It would bleed
into the mainstream press and trigger severe
funding cuts that would effectively end serious
research. And spoiler alert, they were right.
They were entirely right. Yeah. Just three years
after they coined the term, a billion -dollar
AI industry began to spectacularly collapse.
Which is just wild to think about. I mean, to
understand how you even get to a billion -dollar
collapse in the 1980s, we have to go back to
the beginning. We have to look at how this promise
started and why the very first bubble burst so
incredibly fast. Yeah, back to the 1950s. Yeah,
because when you look at the origins, it all
kicks off with this intense Cold War optimism.
But what fascinates me is that the spark that
lit the very first boom was essentially a well
it was a heavily curated parlor trick you're
referring to the 1954 Georgetown IBM machine
translation experiment and it absolutely was
a parlor trick but the hype was instantaneous
I mean the media absolutely lost its mind over
this you had headlines screaming things like
robot brain translates Russian into King's English
right making it sound like sci -fi was already
here Exactly. The implication was that a flawless
automatic translator was just a few years away.
But wait, if you actually look under the hood
of that 1954 machine, what was it genuinely doing?
Because it certainly wasn't reading Tolstoy.
No, not even close. The sober reality was that
the machine didn't actually know Russian at all.
It was only translating 49 carefully selected
sentences. 49 sentences? Yeah. And it was operating
with a minuscule vocabulary of just 250 words.
Wait, 250 words? That is nothing. It's nothing.
To put that in perspective, human beings need
a vocabulary of around 8 ,000 to 9 ,000 word
families just to comprehend written text with
98 % accuracy. Oh, wow. So the researchers in
the 1950s, they ran head first into what became
known as the common sense knowledge problem.
OK, explain that for me. What is the common sense
knowledge problem in this context? It fundamentally
comes down to the profound difficulty of word
sense disambiguation. That is a mouthful. It
is, yeah. But basically, for a machine, translating
a word isn't just about looking it up in a dictionary.
It's about understanding the context of the real
world. Think of the word pen. Like a writing
pen. Right. Are we talking about a writing instrument
or are we talking about a pig pen? Yeah. As humans,
our common sense knowledge of the world lets
us instantly know which one makes sense in a
sentence. Oh, sure. I don't even have to think
about it. Exactly. But for early computers, which
had absolutely no experience of the physical
world, determining that context was a mathematical
nightmare. Yes. And there is this absolutely
amazing, though possibly apocryphal, anecdote
from that era that perfectly illustrates this.
The translation game. Right, so they supposedly
fed the classic biblical phrase, the spirit is
willing but the flesh is weak, into an early
translation system. It translated it into Russian,
and then they translated it back into English
to see how it did. And the result is legendary.
It's spin -out. The vodka is good, but the meat
is rotten. It's such a hilarious result, but
it perfectly encapsulates the exact technical
hurdle they couldn't clear. The machine has absolutely
no idea what a spirit or flesh actually is in
a metaphorical context. It just maps words. But
here's my genuine question. If the system was
that limited, if it was basically just a giant
mechanical flash card reader matching 250 words
without any actual understanding, how did the
researchers convince the U .S. government to
pour millions and millions of dollars into it?
Well, it comes down to geopolitical desperation.
You have to remember the context of the Cold
War. The CIA and the U .S. military were incredibly
anxious. They were terrified of the Soviets.
Right. They desperately wanted a way to instantly
and automatically translate intercepted Russian
scientific reports and communications. Yeah.
So they saw that 1954 demonstration and they
wanted to believe that an imminent breakthrough
was right around the corner. They wanted a silver
bullet. Exactly. And they were also heavily influenced
by new, highly theoretical work in grammar by
linguists like Noam Chomsky, which made them
think language could be easily reduced to pure
mathematics. But eventually the bill comes due,
right? The optimism hits a brick wall. Precisely.
reality caught up. By 1964, the government started
getting suspicious about the total lack of actual
usable progress. Like, where's our translator,
guys? Yeah. So the National Research Council
formed the Automatic Language Processing Advisory
Committee, known as ALPAC, to investigate. And
their 1966 report... was utterly devastating.
What did it say? They systematically proved that
machine translation was slower, less accurate,
and fundamentally more expensive than just paying
human translators. Ouch. I mean, when your multi
-million dollar supercomputer is actually worse
for the budget than just hiring a guy with a
dictionary. Exactly. And the fallout was immediate.
The government pulled all support after spending
some 20 million dollars. Careers were destroyed
overnight. Research simply ended. The first winter.
Yeah, it was the first true A .I. winter, proving
the hard way that curating a few sentences in
a pristine lab environment does not magically
scale up to the messy, ambiguous complexities
of natural human language. So if the government
realizes they are paying 20 million dollars for
a parlor trick, what happens to all these researchers
who promise the moon? Because that realization,
that lab experiments couldn't just scale to the
real world, that must have forced a massive reckoning
across the entire field. Oh, it completely shifted
the entire landscape of how research was funded.
So in the 1960s, the Defense Advanced Research
Projects Agency, or DARPA, had been throwing
massive grants at AI with almost no strings attached.
This blank checks. Pretty much. You had directors
like J .C .R. Licklider, whose whole philosophy
was, quote, funding people, not projects. They
were basically handing brilliant scientists money
to dream. That sounds like a pretty sweet gig.
It was, until the 1969 Mansfield Amendment was
passed by Congress. That forced DARPA to pivot.
Legally, they could only fund projects that had
strict, mission -oriented military applications.
Ah! No more blank checks for philosophy. You
have to build something the military can use
today. Exactly. Researchers now had to prove
their work would produce immediate technology.
and their previous habit of making wildly optimistic
promises to secure those grants, well, it began
to backfire dramatically. Right, because now
they have to actually deliver. A perfect example
is the Speech Understanding Research Program,
or SRR, at Carnegie Mellon. DARPA wanted a system
that could respond to voice commands from a military
pilot in real time. Okay, high stakes. Very.
So Carnegie Mellon developed a system called
Harpy. Now, Harpy could indeed recognize spoken
English, which sounds like a massive success.
This was a catch. There was a devastating catch.
It could only understand the words if the human
pilot spoke them in a highly specific, pre -programmed
grammatical order. Which is practically useless.
I mean, it's like buying an insanely expensive,
voice -controlled, autonomous military tank.
But it only follows your instructions if you
speak to it in a perfect Shakespearean sonnet.
That's a great way to put it. If you are in the
middle of a firefight and you stutter... or you
use the wrong syntax, the tank just sits there
and does nothing. And DARPA was furious. They
felt completely duped by the academic community.
They canceled a $3 million a year contract over
it. Wow. And this exact same frustration was
boiling over internationally too. Over in the
UK, Parliament commissioned Professor Sir James
Lighthill in 1973 to evaluate the actual state
of AI. The famous Lighthill Report? Yeah. And
the resulting Lighthill report was essentially
a targeted lethal strike on the entire field.
He criticized AI for its grandiose objectives
and pointed out that nothing being done in AI
couldn't just be achieved in other, more traditional
sciences. He really went for the throat. He did.
But his most devastating critique, the one that
really froze the industry, was his identification
of the combinatorial explosion. The combinatorial
explosion. Let's make sure we really unpack that
for you listening, because it sounds like a Michael
Bay movie title, but it's actually a mathematical
nightmare that killed the industry. How exactly
does it work? Think of it this way. Early AI
algorithms worked perfectly fine on simplified
toy versions of problems. If you ask a computer
to calculate every possible move in a game of
tic -tac -toe, it's easy. There are a very limited
number of combinations. Right. A kid could do
it. But as soon as you introduce the variables
of the real world, say, calculating the best
route for a delivery truck, navigating a city
with traffic, construction, weather, the number
of possible combinations the computer has to
calculate doesn't just add up. It explodes. It
explodes exponentially. It goes from a few hundred
possibilities to trillions upon trillions. The
computers of the 1970s simply didn't have the
processing power or memory to handle it. The
machines would grind to an absolute halt. It
just freeze. Yeah, the problems were mathematically
intractable. And once Lighthill proved that these
machines fundamentally couldn't handle real -world
math, the money vanished. I mean, following that
report, AI research in the UK was completely
dismantled, surviving in only a tiny handful
of universities. It was a purge. And what's crazy
to me is that the researchers themselves knew
they were in trouble. There's this brilliant
observation from AI researcher Hans Moravec.
He blamed the crisis entirely on his own colleagues.
Oh, the exaggeration web. Yes. He said they were
caught in a web of increasing exaggeration. To
get the first grant, they promised DARPA the
moon. When they only delivered a rock, they had
to promise the moon and the stars just to get
the next grant to fix the first project. It was
essentially an academic pondy scheme of hype.
What's fascinating here is that despite this
narrative of total devastating collapse, historians
like Thomas Hay argue that the winter wasn't
entirely dead. Really? How so? If you look beneath
the surface of the headlines and the massive
funding cuts, professional interest was quietly
thriving. Hay looked at the membership of the
Association for Computing Machinery's Special
Interest Group on Artificial Intelligence. Okay,
SIGART. Right. Between 1973, when the light -hole
report dropped, and 1978, which is conventionally
considered the darkest, coldest part of the winter,
their membership didn't shrink. It almost tripled
to 3 ,500 members. Oh, wow. So even while the
government money dried up and the press moved
on to other things, this dedicated community
of professionals was quietly growing, sharing
ideas and building the foundation for the next
boom. Exactly. But that raises a big question.
If the military closes its checkbook, these researchers
have to go somewhere else to keep the lights
on in their labs. Who was willing to fund them
in the 1980s? The corporate sector. Ah, big business.
Yes. Stripped of government defense funding,
the AI community pivoted hard toward commercial
enterprise. And this created a highly specialized
corporate bubble that set the stage for an even
more spectacular crash. We enter the 1980s era.
of the expert system. The expert system. By 1985,
corporations around the world were spending over
a billion dollars on these systems. And to be
completely fair, for a brief minute, they actually
worked. You look at something like the XCon system
developed at Carnegie Mellon for the Digital
Equipment Corporation. A massive success story.
Huge. It was designed to automatically select
computer components based on customer orders,
and it saved the company $40 million over just
six years. That is massive, undeniable ROI. Suddenly,
every Fortune 500 company wanted an expert system.
They did. But this corporate boom led directly
into a massive hardware trap. How so? Because
the dominant programming language for AI in the
United States at the time was LISP. companies
started building physical machines optimized
specifically to run that one singular language.
Wait, building entirely new hardware just for
one language? Yes, we were talking about companies
like Symbolics and LISP Machines, Inc. selling
massive, highly specialized, incredibly expensive
computer workstations. Okay, the best analogy
I can think of for this is it sounds exactly
like buying a massive $10 ,000 heavy -duty stove
for your kitchen that has been biologically and
mechanically engineered to only cook omelets.
That is a perfect analogy. Like, it makes the
absolute best omelet in the world incredibly
fast. But what happens when you want to make
pasta? Your expensive stove is entirely useless.
And to build on your analogy, it wasn't just
that it only knew the recipe for an omelet. The
very pipes and heating elements of the stove
were physically constructed in a way that couldn't
boil water. Oh, man. The memory and processors
of these LISP machines were hardwired specifically
to read LISP code. And that extreme specialization
is exactly what killed the market in 1987. What
happened in 87? The general computing landscape
shifted dramatically. General purpose desktop
computers from companies like Apple, IBM, and
Sun Microsystems became faster, cheaper, and
powerful enough to run LISP environments purely
through software on standard Unix operating systems.
So suddenly there was absolutely no reason for
a corporation to buy a massive specialized omelet
maker when a standard cheap kitchen stove could
do it all. Exactly. An entire specialized industry
worth half a billion dollars was replaced by
general purpose computers in a single year. Consumers
stopped buying and the LISP hardware market completely
evaporated. Half a billion dollars vanished in
12 months. But it wasn't just the hardware that
failed, right? The software itself, these expert
systems that corporations were betting everything
on, eventually hit a massive wall, too. Yes.
The software suffered from a fatal flaw that
researchers called brittleness. Brittleness?
Yeah. These expert systems were essentially...
giant rule -based engines. They couldn't learn
on their own. Human experts had to manually type
in every single line of logic. Okay, let's break
that down with an example. How exactly does brittleness
break a system? Imagine you build a medical diagnosis
system. You write a rule that says, if the patient
has a fever, give them aspirin. That works fine.
Seems simple enough. But then you realize, what
if the patient is allergic to aspirin? Okay,
you have to write another rule. What if you're
pregnant? Another rule. What if they have a rare
blood condition? Another rule. Oh, I see where
this is going. Eventually, your rule book is
millions of pages long. If you give the system
an unusual input that it hasn't explicitly been
programmed for, it doesn't just fail gracefully
or ask for clarification. It crashes. It makes
grotesque, catastrophic mistakes. And worse,
changing one rule might accidentally contradict
10 other rules. As the knowledge base grew, managing
the truth of the system became computationally
and financially grueling. It's like building
a house out of toothpicks. Eventually, adding
one more piece brings the whole thing crashing
down. And once the corporate world realized this,
the dominoes just kept falling. They absolutely
did. The US government had briefly started funding
AI again in the 80s through the Strategic Computing
Initiative, but a new director named Jack Schwartz
came in and absolute gutted it. He was not a
fan. Not at all. He looked at expert systems,
dismissed them as just clever programming, and
famously argued that DARPA should surf rather
than dog paddle. Ouch. He meant they should ride
the massive waves of promising technologies,
and he definitively believed AI was a sinking
ship. Meanwhile, over in Japan, their government
had poured $850 million into a massive fifth
-generation computer project aiming for machines
that could naturally converse and reason. And
how did that go? By 1992, that project ended.
Not with a roar, but with an absolute whimper.
Expectations had, once again, vastly outpaced
reality. Which forces the entire industry underground.
I mean, the crashes of the 1980s and 90s were
so financially devastating, and the public embarrassment
was so profound that researchers literally had
to change the name of what they were doing just
to survive. The Stealth Era. By the early 2000s,
the term artificial intelligence carried such
a deep toxic stigma of false promises that no
venture capitalists would go near it. Not a chance.
Computer scientists actively avoided the term
for fear being viewed as wild -eyed dreamers
or snake oil salesmen. So they completely rebranded.
They called their work machine learning or advanced
analytics or cognitive systems. Anything but
AI. Anything to avoid the dreaded AI label so
they could quietly secure funding for their labs.
If we connect this to the bigger picture, this
stealth period was actually incredibly productive.
The philosopher Nick Bostrom made a brilliant
observation about this era. What did he say?
He pointed out that a lot of cutting edge AI
was actually succeeding and filtering into everyday
applications during this time. But the great
irony is the moment an algorithm becomes genuinely
useful and common, like the pathfinding logic
in a video game or logistic software for shipping
companies. we immediately stop calling it AI.
We just call it software. Exactly. The goalposts
constantly shift. Here's where it gets really
interesting, because all of that quiet, stealthy,
foundational work, hiding under different names,
set the stage for the current AI spring. The
deep freeze finally thaws and the world changes
entirely around 2012. The turning point. That
was the year a deep learning network called AlexNet
entered a major visual recognition challenge
and absolutely dominated the competition blowing
traditional algorithms out of the water. And
from there the modern era just explodes. The
modern metrics are staggering by every possible
measure. We went from that visual recognition
breakthrough to systems like AlphaGo Mastering
Complex Strategy games to massive leaps in Google
Translate. It's everywhere now. By 2022, total
global investment hit $50 billion. In that same
year, there were 800 ,000 U .S. job openings
related to the field. And, of course, the release
of ChatGPT, which hit 100 million users in just
a matter of months. We're in an era of absolutely
unprecedented scale and public adoption. Okay,
but here is my big pushback for you. We just
spent this entire deep dive looking at the cyclical
history. We saw the massive translation hype
in the 60s crash and burn. We saw the expert
systems of the 80s wipe out half a billion dollars
in a single year. Right. With today's billions
pouring in and everyone on earth talking about
chat GPT, are we just inflating the absolute
biggest bubble yet? Or have we actually solved
Sir James Lightfield's combinatorial explosion?
Have we fundamentally broken the cycle? This
raises the most important question of our current
era. And to answer it, we have look at how today's
AI actually works. It turns out the engine powering
today's AI spring is built on the very ideas
that were mocked and discarded during the winters.
For instance, back in 1969, Marvin Minsky published
a critique of early artificial neural networks,
known as single layer perceptrons. That critique
was so devastating, it helped kill funding for
neural networks for over a decade. So Minsky
killed his own field's funding. Basically, yeah.
He was mathematically right about the limitations
of a single layer, but researchers knew that
multi -layer networks could theoretically solve
complex problems. The roadblock was that nobody
in the 1960s or 70s knew how to train a multi
-layer network. Oh, I see. They had the blueprint
for the engine, but they didn't have the fuel
or the spark plug. Exactly. It took decades for
two crucial things to happen. First, computing
power had to catch up. The raw processing power
of modern warehouse -sized data centers has effectively
bulldozed through the combinatorial explosion
that Lighthill warned about. Just brute -forcing
it. Exactly. We now have the brute force to calculate
those trillions of possibilities. Second, researchers
fully realized a training method called backpropagation.
Okay, backpropagation. How does that actually
work? Why was that the missing spark plug? Imagine
a student taking a complex calculus test. In
the old days, the system would just get an F
and not know why. Which doesn't help you learn
at all. Right. Backpropagation is a feedback
loop. The network makes a guess, compares it
to the correct answer, calculates exactly how
wrong it was, and then mathematically sends that
error signal backward through all its layers.
Yeah, backwards. Backpropagation. Got it. It
minutely adjusts the weight and importance of
every single digital neuron so that next time...
the guess is slightly more accurate. That makes
sense. And when you do that millions of times
a second across billions of data points using
modern supercomputers, the system effectively
learns. The deep learning networks powering today's
massive language models are the direct evolutionary
descendants of those early dismissed neural networks.
The seeds that were planted and buried during
the long winters have finally bloomed. So what
does this all mean for you listening right now?
Why does knowing the history of LISP machines
and Cold War translation actually matter? It's
vital context. I think understanding this cyclical
history acts as a kind of superpower. When you
open your phone today and see breathless headlines
promising that a new AI is going to instantly
revolutionize the world or replace all human
jobs by next Tuesday, you can spot the patterns.
The web of increasing exaggeration. Exactly.
More of ex web. You can see the venture capital
frenzies and you can anticipate the inevitable
collision with real world complexity. You know
that making a parlor trick work in a highly curated
demo video is very, very different from deploying
a flawless scalable tool in the messy, unpredictable
real world. That is the essential takeaway. True
technological progress is never magic. It is
hard, iterative, and often grueling work. It
takes decades. Navigating this current boom without
getting burned requires a skeptical eye, critical
thinking, and a willingness to look past the
marketing hype to rigorously evaluate what a
system actually understands versus what it's
just mimicking. And speaking of what a system
can genuinely understand, I want to leave you
with a final provocative thought to mull over.
Our sources included a fascinating point from
a review by James Gleick regarding the fundamental
nature of intelligence. That's a profound point.
It really is. We know that modern AI, like these
incredible large language models, can ace standardized
medical exams and generate beautiful complex
essays. But as Gleick points out, they remain
fundamentally disembodied. No physical presence.
Right. They are strangers to blood, sweat, and
tears. For biological creatures like you and
me, agency, reason, and purpose don't just come
from reading text. They come from acting in the
physical world and directly experiencing the
physical consequences of our actions. The real
stakes. A scraped knee, a broken heart, the feeling
of absolute exhaustion. So the next time you
are interacting with one of these incredibly
smart chatbots, ask yourself this. Can an intelligence
that has never once experienced the physical
world ever truly understand it, or is it just
the most advanced, brilliant translation trick
humanity has ever built? Thank you so much for
taking this deep dive with us.