Speaker 1: Twenty twenty six, twenty twenty seven, and twenty twenty eight.
Speaker 2: Three years. It sounds like a standard lease agreement, doesn't it.
But it's not.
Speaker 1: No, it certainly doesn't feel like just another three years.
You know, Usually when we sit down to unravel these threads,
looking at the tech landscape, the future of humanity, the
usual light dinner table conversation, we are casting our gays.
Speaker 2: Pretty far out decades out.
Speaker 1: Usually, yeah, exactly, we talked to twenty forty. We're talking
about fusion energy coming in twenty years like it always has,
or you know, colonies on Mars. But the intelligence deck
we have on the desk today, this isn't about someday.
Speaker 2: It is entirely different. We aren't talking about a distant horizon.
We were talking about a window of time that is
staring us right in the face.
Speaker 1: The next thirty six months.
Speaker 2: The next thirty six months, and according to the analysis
we've pulled, specifically looking at the first mover's intelligence reports
and the breakdown from doctor Julia McCoy, these aren't just
sequential years. They are designated as a phase shift.
Speaker 1: A phase shift I want to on that term because
it sounds cool, but it also implies something volatile, unpredictable.
Speaker 2: It is volatile. In physics, a phase shift isn't a
gradual slope. It's a cliff.
Speaker 1: Right.
Speaker 2: Think about water. You heat it up, it gets hotter hotter.
That's linear, you can predict it. But at one hundred
degrees celsius, it doesn't just get.
Speaker 1: Hotter, It becomes something else entirely.
Speaker 2: It turns to steam. It expands sixteen hundred times in volume,
It changes its fundamental state of matter. That is what
human capability is about to undergo.
Speaker 1: So we aren't looking at incremental improvements. This isn't a
slightly faster phone, a slightly brighter screen.
Speaker 2: No, not at all. We are looking at mastering complexity
at a level our species has literally never achieved before.
Speaker 1: And the catalyst for this steam explosion, it's the holy crap,
it's here moment that I think a lot of people
missed while they were scrolling through social.
Speaker 2: Media, exactly the moment where quantum computing went from a
physics experiment and maybe someday thing in a lab to
a reality. It's happening right now.
Speaker 1: Welcome to thrilling Threads. I'm your host, here to pull
on the loose ends of the future until the whole picture.
Speaker 2: Unravels, and I'm here to help make sense of the
tangled mess we find underneath.
Speaker 1: Today, we have a massive thread to pull. It's a collision,
really a head on crash between two titans that are
currently dominating the headlines, but rarely in the same sentence.
In one corner, we have the energy hungry beast that
has taken over our lives, our work, and our stock
markets artificial.
Speaker 2: Intelligence, and in the other corner, the physics bending solution
that might be the only thing that can save in
quantum computing.
Speaker 1: And just to set the table, we are working off
some incredible intelligence reports. Today. We've got analysis on breakthroughs
from Google and Microsoft that are fresh, I mean early
twenty twenty five, twenty twenty six fresh. This isn't old news.
Speaker 2: No, this is happening as we speak. And the mission
for this thread is critical. It's not just academic. We
need to figure out if Google's recent quantum breakthrough has
actually solve the AI energy crisis, or if Microsoft has
a secret weapon hidden up its sleeve that's about to
steal the entire show.
Speaker 1: Okay, let's unpack this because I think most people hear
AI and quantum and the default too cool faster computers.
Speaker 2: Right, my games will look better exactly, Maybe Syria will
finally understand what I'm asking. But you're telling me this
is actually an energy story at its core.
Speaker 1: It is fundamentally an energy story. In fact, if you
don't look at it through the lens of thermodynamics, you
miss the entire economic reality of what's happening. You missed
the whole point.
Speaker 2: So lay up the scene for us. Let's start with
the problem. We know. AI is blowing up chat GPT, Gemini, Claude,
the image generators. It feels like magic, but magic usually
has a cost. What is the cost of that magic?
Speaker 1: The cost is electricity, massive, gluttonous, almost unbelievable amounts of it.
Speaker 2: Put in perspective.
Speaker 1: Okay, so you look at the trajectory. The big picture
number is that data centers are set to double their
electricity consumption by twenty thirty double. That's already huge, it is,
But that's just the aggregate number. It's a bit misleading
because it smooths things out. The scary part is the density.
Speaker 2: The density, what do you mean? I mean the power
draw of a single piece of equipment a single high
end AI chip like the Nvidia H one hundreds or
the new Blackwall architecture we're seeing roll out, running at
full training capacity, draws an incredible amount of power.
Speaker 1: How much?
Speaker 2: We are talking about a single chip using as much
power as an entire American household?
Speaker 1: Wait, hold on, let me just process that one chip
a piece of silicon the size of a dinner plate.
Speaker 2: Roughly. Yes, When you factor in the chip itself, the
memory transfer, and crucially the cooling required to keep it
from melting into slag, you are looking at kilowatts of
draw for one unit. Now imagine a data center.
Speaker 1: Do not just using one.
Speaker 2: We aren't building them with a few hundred chips anymore.
We are building what they call superclusters with tens of
thousands of them. Some plans call for hundreds of thousands.
Speaker 1: So we are essentially building small cities that do nothing
but sit there, hum and eat electricity to run a
chat butt.
Speaker 2: That is exactly what we're doing, and we are hitting
a wall, a physical energy based wall. We call it
the consumption crisis.
Speaker 1: The consumption crisis.
Speaker 2: Yeah, classical computing, the silicon transistors we've used for fifty
years cannot keep growing exponentially without literally destroying the energy grid.
There just isn't enough power to go around.
Speaker 1: So we're driving a Ferrari, but we're running out of
gas stations.
Speaker 2: It's worse. It's much worse than that. We're driving a
Ferrari and the engine is getting so hot it's about
to melt the chassis, and the only way to cool
it down is to pour more gas on it.
Speaker 1: That sounds problematic. It sounds like a death spiral.
Speaker 2: It's a thermodynamic death spiral. And that's not hyperbole.
Speaker 1: Okay, this brings us to the physics of why. This
is the part that I think is so fascinating. I
saw Landauer's principle in our source notes. This sounds like
the kind of thing that gave me nightmares in college physics.
But it seems to be the villain of this story.
Speaker 2: It is the fundamental villain.
Speaker 1: Yes, break it down for us. Why are our computers
so ridiculously inefficient?
Speaker 2: It's fascinating, Actually, Rolf Landauer back in nineteen sixty one
at IBM, realized something profound.
Speaker 1: About information sixty years ago.
Speaker 2: Over sixty years ago, he realized that information is physical.
It's not just an abstract concept floating in the ether.
It has to be stored represented by something real.
Speaker 1: Okay, that makes sense, a magnetic bit, a voltage in
a transistor exactly.
Speaker 2: And Landauer's principle states that there is a minimum energy
cost to erasing information. This is tied to entropy, the
second law of thermodynamics.
Speaker 1: Entropy, the tendency towards chaos and disorder.
Speaker 2: Precisely, every time a classical computer deletes a bit, turns
a one to a zero, or wipes a memory block,
it has to dissipate energy as heat.
Speaker 1: It must, so the act of forgetting creates heat.
Speaker 2: That is a perfect way to put it. You cannot
destroy information without increasing the entropy of the universe.
Speaker 1: That feels so profound it is.
Speaker 2: Think about us standard logic gait in your computer like
an A and D gate. It takes two inputs, say
a one in a zero, and gives one output a zero. Okay,
but what happened to the information about the two separate inputs?
You had two bits of information, Now you have one.
Where did the other one go?
Speaker 1: It was erased, It's gone, it's gone.
Speaker 2: And that erasure manifests physically as heat every single.
Speaker 1: Time, and classical computers do this how often, billions trillions
of time to second in every single chip. It's a
relentless cycle of right erase right erase. As the problems
we try to solve, like training a massive AI model
with trillions of parameters, get more complex that cycle accelerates.
Speaker 2: So more complexity means more erasing.
Speaker 1: Which means more energy use, which means more heat. The
heat output becomes unmanageable. That's why you see these crazy
stories about data centers being built under water or in
the Arctic circle. They're not doing it for fun. They're
doing it because of Landauer's principle.
Speaker 2: It's the entropy tax. We have to pay it every
time we compute the.
Speaker 1: Entropy tax exactly, and with classical chips we are paying
a higher and higher tax rate. The faster we try
to go, we're getting taxed into oblivion.
Speaker 2: So enter the hero of our story, the tax evator,
quantum computing.
Speaker 1: The ultimate tax shelter.
Speaker 2: Now, I've heard about superposition and entanglement, the spooky stuff
where things are in two places at once. But you're saying,
the real killer feature for the energy grid isn't the
spooky stuff. It's reversibility. This is the mind explosion moment
for most people. Yes, the spooky stuff is cool, it's
what gives it the power, But the efficiency comes from
something else.
Speaker 1: Okay.
Speaker 2: Quantum computers handle information in a fundamentally different way. In
quantum mechanics, operations are modeled by what we call unitary transformations.
Speaker 1: Whoa hold on unitary transformations? You lost me unpack that, okay?
Speaker 2: Okay. To put simply, it just means the operations are reversible.
You can run the computation forward and then you can
run it backward to get to the exact starting state.
Speaker 1: Reversible meaning they don't erase.
Speaker 2: Meaning they can uncompute.
Speaker 1: Okay. An analogy. Uncomputing sounds like time travel. Doesn't sound real.
Speaker 2: Think of it like walking through a pristine field of snow.
Classical computing is like stomping through the snow, digging holes,
kicking it around. It's messy. To reset the path or
hide your tracks, you have to shovel fresh snow over it.
That's a lot of work.
Speaker 1: Right, lots of sweaty work shoveling snow. And that shoveling
is the.
Speaker 2: Heat generation exactly. That's Landauer's principle and action reversible computing.
Quantum computing is like carefully walking backward, stepping exactly into
your own footprints until you're back at the start.
Speaker 1: You haven't disturbed the snowfield.
Speaker 2: You haven't increased the chaos or entropy of the system.
You've left it exactly as you found it.
Speaker 1: So because you aren't destroying the state of the pit,
you aren't triggering Landauer's penalty. You aren't paying the tax.
Speaker 2: Exactly, you evade the tax for the core computation. At least,
there's still energy needed to run the machine. But the
computation itself is in principle nearly energy free.
Speaker 1: That is wild. So it's not it's just that they
are faster. Everyone says quantum is faster. Yeah, but for
the energy grid, the headline is that they are sustainable.
Speaker 2: For complex problems. Yes, we aren't talking about ten percent
better battery life on your laptop. We are talking about
doing calculations that would require a nuclear power plant's worth
of energy on a classical supercomputer, okay, and doing that
same calculation using the energy of a light bulb on
a quantum machine. It's a different universe of efficiency.
Speaker 1: That is the miracle we need. I mean, if we
want AI to keep getting smarter, to reach that personal
intelligence level. The reports talk about where it's actually useful
without burning down the planet.
Speaker 2: We need this switch, we do, and that's why the
race between the tech giants has suddenly shifted from R
and D science projects to survival mode. This is no
longer a choice, It's a necessity.
Speaker 1: Which brings us to the first giant Google.
Speaker 2: Google Quantum AI, the behemoth, the one that's been making
the most noise for the longest time.
Speaker 1: They've been at this for a while. I remember the
quantum supremacy announcement back in twenty tine nineteen with the
Sycamore processor.
Speaker 2: I remember that.
Speaker 1: They claimed they did a calculation in two hundred seconds
that would take a supercomputer ten thousand years. IBM disputed it,
of course.
Speaker 2: It was a whole thing, a lot of academic squabbling, yes,
but it put the world on notice.
Speaker 1: But the first mover's report says they just hit a
much more significant milestone in this twenty twenty five twenty
twenty six window, something that makes a premacy look like
a warm up act.
Speaker 2: They demonstrated the first logical quibit prototype.
Speaker 1: Okay, jargon alert, we need to beat precise here logical quibbit?
How is that different from the quibits we've heard about
for the last decade. This sounds like a marketing term.
Speaker 2: It's not marketing, it's well, it's everything. This is the
single biggest hurdle in the field. Think of a regular
physical quibbot as a very fidgety, anxious child.
Speaker 1: Okay, I can picture that.
Speaker 2: It's easily distracted. A tiny fluctuation in temperature, a stray
electromagnetic wave from your phone, a cosmic ray from a
distant supernova. Anything makes it lose its memory. This is
called decoherence. It's incredibly error prone.
Speaker 1: So up until now quantum computers were like really fast
calculators that got the answer wrong fifty percent of the time.
Speaker 2: Or the signal would just dissolve into noise before you
could even finish the math. You'd ask it what's two
plus two? And before it could answer, it would forget
the question.
Speaker 1: Not very useful. So what's the logical quibit? How does
it fix a fidgety child problem?
Speaker 2: A logical quibit is error correction. Imagine you take that
one fidgety child and you surround him with one hundred
other children. A whole classroom of them a whole classroom,
and they all hold hands. They form a collective. If
the one in the middle trips or gets distracted, the
others hold them up. They keep them in line. The
information isn't stored in one kid. It's stored in the
pattern of the group.
Speaker 1: It's redundancy like a rate array for hard drives, but
for quantum bits.
Speaker 2: It's an active error correction system, specifically something called surface code.
By weaving many physical quibets together, Google created a single
logical unit that stays stable. They prove that making the
group bigger actually reduced the error rate.
Speaker 1: So the more kids holding hands, the more stable the
group becomes.
Speaker 2: Exactly and that was the Holy Grail moment. It proved
that this wasn't an insurmountable physics problem.
Speaker 1: So demonstrating this means they crack the code on reliability.
It's no longer just theoretical physics. It works. It's an
engineering problem. Now, it's a scaling problem.
Speaker 2: It is. It's the transistor moment. It's the jump from
vacuum tubes to solid state and Google isn't stopping there.
Their public roadmap is aggressive. They're planning to achieve one
million quibuts in the coming years.
Speaker 1: One million physical quibbits just for context. Right now we're
dealing with what a few hundred maybe a thousand in
the best lab systems.
Speaker 2: In terms of high quality physical equibitts, yes, but in
terms of logical quibits, the currency that actually matters for
running useful programs, we are barely at step one. We
have maybe one two a handful.
Speaker 1: So jumping to a million physical equibits to support thousands
of logic ones is I mean, that's not a step,
that's a leap to another planet.
Speaker 2: It's a total shift in processing capability. It's the difference
between counting on your fingers and simulating a galaxy.
Speaker 1: But there's always a butt. There has to be a catch.
Speaker 2: There is always a butt.
Speaker 1: And here's where it gets really interesting for our energy story.
The report points out a massive achilles heel for Google.
The big chill yay.
Speaker 2: The reality check. Google uses what are called superconducting quantum computers.
That's their chosen architecture. It means it means they rely
on materials that have zero electrical resistance, an amazing property.
But to get that property super conductivity, you need to
cool the chips down.
Speaker 1: How cool all we talk and put it into the
fridge cool?
Speaker 2: I wish, no, you need to get them to about
fifteen millikelvin.
Speaker 1: Put that in perspective for me, I don't know what
a mil kelvin is.
Speaker 2: Okay, so absolute zero. The coldest possible temperature in the
universe is zero kelvin deep space. The vacuum between galaxies
is about two point seven.
Speaker 1: Kelvin, colder than deep space orders.
Speaker 2: Of magnet colder than deep space. It is one of
the coldest places in the known universe.
Speaker 1: Inside that machine, we're talking about those giant gold and
chandelier looking things we see in the press photos right exactly.
Speaker 2: Those are called dilution refrigerators. They use a mix of
helium three and helium four isotopes to literally pump heat
out of the system stage by stage. They are incredibly complex,
incredibly expensive, and crucially for our conversation, they are power hungry.
Speaker 1: How power hungry? The report says running one of these
systems consumes around twenty five kilowatts of power.
Speaker 2: Twenty five kilowatts, and crucially, most of that electricity isn't
doing any math, is not computing anything.
Speaker 1: It's just running the freezer.
Speaker 2: It's just keeping the system frozen. Its overhead, it's the
cost of entry before you even do a single calculation.
Speaker 1: It's massive overhead. If you want to scale this up
to millions of quivts, you need acres of these fridges.
You're building a warehouse of quigentic freezers.
Speaker 2: That's the problem. You need a power substation just for
the refrigeration plant for your computer.
Speaker 1: So we're solving the AI energy crisis by building giant
freezers that use a ton of energy. There's an irony,
there's a huge irony.
Speaker 2: It's a trade off. It's still better than classical for
certain specific problems, but it's not the perfect free lunch
we might hope for. And this is where the source
draws are really sharp.
Speaker 1: Contrast, right, it compares this to neutral atom quantum computers.
Speaker 2: Neutral atom a completely different approach.
Speaker 1: How do those work?
Speaker 2: They work at room temperature. Well, the atoms themselves are
laser cool to be almost motionless. But the machine itself
sits in a standard server rack in a normal room,
no giant golden.
Speaker 1: Chandeliers, and the power draw the.
Speaker 2: Report pegs them at roughly ten kilowatts.
Speaker 1: So less than half that's already a huge difference in
efficiency right out of the gate, a huge difference.
Speaker 2: So you have this dynamic where Google is the clear
leader in crevit quality and scale. They have the logical quibit,
but they are hauling around this massive energy guzzling refrigeration baggage.
Speaker 1: They are they are betting that their speed of development
and their software ecosystem will outweigh the infrastructure cost. But
that vulnerability, that massive energy bill, that's exactly where the
wild cart steps in. Microsoft is sleeping giant. I feel
like Microsoft has been the quiet kid in the back
of the class on this one for a while. They've
been talking about this topological stuff for a decade, and honestly,
a lot of people in the field thought it was vaporware,
a dead end.
Speaker 2: A lot of people wrote them off completely, and.
Speaker 1: Then suddenly, boom, February twenty twenty five, they dropped the mic.
Speaker 2: They really did. They announced the Majorina one quantum chip.
Speaker 1: Majorana sounds like a spell from Harry Potter, yeah, or
apasta sauce.
Speaker 2: It's named after a Tori Majorana, a brilliant, mysterious Italian
physicist who theorized a particle that is its own antiparticle
back in the nineteen thirties and then vanag vanished. Yeah,
just disappeared off a boat. It's a whole other story.
But the tech is what matters, they claim, and we
have to emphasize claim because this is cutting edge to
be able to fit over one million quivots on a
chip the size of your palm.
Speaker 1: Whoa stop rewind that Google needs a warehouse of fridges
for a million kubits. Microsoft says the size of my palm.
Speaker 2: The size of your palm?
Speaker 1: Oh, I mean that defies the sense of scale we
just established. That sounds like science fiction.
Speaker 2: It comes down to the physics. It's a completely different paradigm.
They are using what are called topological superconductor material.
Speaker 1: Okay, expert mode, help me out. Topological. I know what
a topographical map is with the mountains and valleys, but
what is a topological computer.
Speaker 2: Okay, Think of a knot in a piece of string.
If you have a simple loop of string lying on
the table, it's just a circle. If you tie a
knot in it, that knot is topologically protected me meaning
you can twist the string, stretch it, wiggle it around,
vibrate the table. But the knot stays a knot. The
fundamental property of knottedness doesn't change, right.
Speaker 1: The only way to remove the knot is to.
Speaker 2: Cut the exactly. Microsoft is using that principle to store information.
In Google's machine, the information is fragile. It's like balancing
a pencil on its tip. One tiny bump and it
falls over. It decoheres, okay. In Microsoft's theoretical machine, the
data is stored in the knot, specifically in the way
these exotic quasi particles, these majorana particles, are braided around
each other. The information is woven into the very physics
of the material.
Speaker 1: So the information is naturally robust. It's protected by.
Speaker 2: The knot exactly. Local noise, a little bit of heat,
a vibration, It doesn't untie the knot. So you don't
need that massive army of one hundred physical quibits holding
hands to make one logical quibbet. Their physical cuibbits are
in theory already logical quibits.
Speaker 1: That's the game changer. They don't need the error correction overhead.
Speaker 2: They don't need the massive overhead. And if and it
is a colossal texas sized I, this works at scale.
Microsoft leapfrogs everyone.
Speaker 1: It wouldn't just be a player. They would dominate the
quantum space.
Speaker 2: They'd own it. It would be a monopoly because of
the form factor.
Speaker 1: A palm sized chip versus a warehouse of freezers.
Speaker 2: If you can put a million kubits on a palm
sized chip that were except you know, slightly less extreme temperatures,
you can put it in a server rack, You can
put it in a satellite, you can put on a
fighter jet. You don't need to build a specialized frozen
cathedral to house it.
Speaker 1: This reshapes the next decade entirely. If Microsoft pulls this off.
Google's massive fridge farms look like dinosaurs.
Speaker 2: They look like the eni At computer from the nineteen forties. Big, impressive,
room sized, took a whole team to run, but completely
and utterly obsolete the moment.
Speaker 1: The microchip arrived. But I have to play Dell's advocate
here sounds too good to be true. Microsoft has claimed
to have found these midrona particles before, and then had
to retract papers. The scientific community is, let's say.
Speaker 2: Skeptical, extremely skeptical, and for good reason. In twenty eighteen
and again in twenty twenty one, there were major retractions
from research groups they were working with. It was embarrassing
for the field and for Microsoft. But this time they've
published data on something called the fractional quantum Hall effect,
which is supposed to be the definitive fingerprint of this
topological state.
Speaker 1: So they think they really have it. This time.
Speaker 2: They seem confident enough to put a product name on
it and announce a roadmap. That's a big step, but
it's high risk, high.
Speaker 1: Reward, the highest reward in computing history. Probably.
Speaker 2: I don't think that's an exaggeration, but we.
Speaker 1: Can't count out the others. There are more than two
horses in this race. The report mentions a third player,
a dark horse.
Speaker 2: Ion Q ion Q, Yes, the pure play.
Speaker 1: They aren't Google or Microsoft. They're not a trillion dollar
behemoth with a quantum division.
Speaker 2: This is all they do right, market cap around what
eighteen billion dollars, which is huge for a startup, but
a drop in the bucket for their competitors. They use
trapped ion architecture trapped ions.
Speaker 1: Okay, so this is a third way. This isn't super
conducting loops or topological knots. This is where they take
single atoms, charged atoms and hold them in place with
electromagnetic fields.
Speaker 2: Correct, they use uurbium atoms. Usually they levitate a little
line of them in a perfect vacuum chamber, and then
they zap them with precisely tuned lasers to do the computing.
Speaker 1: What's the advantage there? Why do it that way?
Speaker 2: The advantage is that nature makes the quibbets. Every single
uturbium atom is perfectly identical to every other uturbium atom
in the universe. Ah, no manufacturing defense exactly In Google's approach,
you have to build the quibbit on a chip using lithography.
No two are ever perfectly alike. You get variations. Ion
q uses nature's perfection. Their equipots are incredibly high quality
and stable.
Speaker 1: And what's their status? Where are they in the race?
Speaker 2: They are currently unprofitable, which is expected, but they are
shipping hardware. You can access their machines on the cloud
right now. The key for them is connectivity and activity.
In a chip like Google's equibit can usually only talk
to its immediate neighbors. It's like a game of telephone.
To get a message from one side to the other
takes a lot of stuf. In an ion trap, you
can wiggle the whole line of atoms so that any
quibit can talk directly to any other quibit. It's like
a conference call versus a game of telephone.
Speaker 1: That sounds much more efficient for running software the actual
algorithms it is.
Speaker 2: It dramatically simplifies the algorithms, but they struggle with scaling up.
It's hard to trap and control millions of individual atoms.
So they're great on quality but behind on quantity.
Speaker 1: So you have three competing philosophies. Google's scale at all
costs freezer farm, Microsoft's high risk, high reward magic chip,
and ion Que's slow and steady quality first approach.
Speaker 2: That's a great summary. And if Google's cold approach hits
an energy wall and Microsoft's magic not physics doesn't pan out,
ion q is sitting there with a system that just works.
Speaker 1: It's the hedging strategy for the market.
Speaker 2: It is, and for investors that's the key. This isn't
a one horse race.
Speaker 1: Let's talk about that. The investment angle. The source. Doctor
McCoy calls the twenty twenties the investment opportunity of a
lifetime for this convergence. That's a bold claim.
Speaker 2: It's hard to argue with that, we are looking at
a fundamental platform shift. This is like the invention of
the Internet or the microchip. It changes everything. You have AI,
which is a world changing application that needs quantum, which
is a world changing platform. The valuation of the winner
here isn't going to be billions. It's going to be
in the trillions.
Speaker 1: It's a winner take all or winner take most dynamic.
Speaker 2: Isn't it. It usually is in tech infrastructure. Look at
cloud computing. You've got Aws, Azure, Google Cloud. That's basically it.
The company is investing early, Google, Microsoft, IONQ. They will
either be worth ten x their current.
Speaker 1: Value or they get acquired by the one who wins. Yeah,
there's no prize for second place precisely.
Speaker 2: But here is my advice, and it echoes the report.
Don't just look for the fastest computer. Everyone chases speed.
That's the sexy.
Speaker 1: Metric, right, how many operations per second?
Speaker 2: Yeah, but that's a trap. Look for the most efficient one.
Speaker 1: Because of the energy wall. We're coming full circle.
Speaker 2: Because of the energy wall. The winner is the one
who solves the energy problem. If your computer is a
million times faster but requires the power output of Chicago
to run, nobody can afford to turn it on, the
economics of energy will dictate the winner of the quantum race,
not raw speed.
Speaker 1: That is a crucial distinction. It's not a drag race.
It's an efficiency race. It's la masse, not a quarter mile.
Speaker 2: Correct, and the company that builds the most fuel efficient
engine is the one that will win the twenty four
hour race.
Speaker 1: So what does this all mean for us, the listeners,
the people not buying billion dollars server farms or trading
stock options. We've got this hardware war happening, But what
happens when the software starts running on these things?
Speaker 2: This is where we move beyond speed and talk about
solving the impossible.
Speaker 1: The impossible. I like this ount of that.
Speaker 2: I mean it literally. Quantum isn't for better graphics on
your video games or faster emails. Our phones are already
great at that. It's for problems that are literally impossible
for classical computers to solve because the number of variables
explodes exponentially, the combinatorial explosion exactly. Give me an example
from the.
Speaker 1: Source, let's see it mentions energy systems optimizing wind farms.
Speaker 2: Perfect example, think about a huge offshore wind farm hundreds
of turbines.
Speaker 1: Okay, I see wind turbines all the time. They spin,
they make power. What's the problem. It seems pretty simple.
Speaker 2: The problem is fluid dynamics. The wind is chaotic. If
the front row of turbines faces the wind directly, they
extract energy, but they also create a wake, like the
wake behind a boat. It's turbulent, messy.
Speaker 1: Air, right, and that messy air hits the turbines.
Speaker 2: In the back row, and it kills their efficiency. The
back row is drafting like a Nascar But in this case,
that's bad for generating power.
Speaker 1: So what's the solution. Just space them farther apart.
Speaker 2: You could, but that costs a fortune in real estate
and undersea cables. The optimal solution is to angle every
blade on every turbine slightly differently in.
Speaker 1: Real time to steer the wake.
Speaker 2: To steer the wake around the other turbines, But the
wind changes every second, the direction, the speed. Calculating the
optimal angle for hundreds of turbines thousands of blades in
real time that involves so many variables that a classical
supercomputer just chokes on it. It can't keep up.
Speaker 1: Too many possibilities to check an impossible number.
Speaker 2: But a quantum computer can model that chaos. It can
look at all the possibilities at once and find the
global optimum, the perfect setting, instantly. The source estimates this
could boost the efficiency of a wind farm by twenty
percent or more.
Speaker 1: Twenty percent. That's massive. That's like building a whole new
power plant for free.
Speaker 2: It is. And here's the beautiful irony. The energy needed
to run that quantum optimization algorithm is tiny compared to
the gigawatts of extra renewable energy you gain from the
wind farm.
Speaker 1: So you spanned a little bit of quantum energy to
save massive amounts of real world energy.
Speaker 2: Exactly. It's leverage, a tiny push on a very big level.
Speaker 1: What else where else can we apply that lever?
Speaker 2: Material science? This is the big one for me. This
is where we stop discussing materials by accident like we
have for most of human history, and start designing them
atom by atom.
Speaker 1: Better batteries, that's the one everyone talks about.
Speaker 2: Better batteries, absolutely lighter, more powerful, no rare earth metals.
But let's go deeper. Fertilizer.
Speaker 1: Fertilizer, You're going from quantum computing to farming. That feels
like a leap.
Speaker 2: Stick with me. The way we make ammonia for fertilizer
right now is a process called haberbosh. It involves incredibly
high heat and high pressure to rip stable nitrogen molecules
out of the air. It's brute force.
Speaker 1: Chemistry, and it must use a lot of energy.
Speaker 2: It consumes about one percent two percent of the world's
entire energy supply one process, one single industrial process. It's
one of the largest single sources of carbon emissions on
the planet.
Speaker 1: That is staggering. I had no idea.
Speaker 2: Now consider this. Bacteria in the soil do this every
single day at room temperature and normal pressure, using an
enzyme called nitrogenase.
Speaker 1: So nature has better tech than we do.
Speaker 2: Much better, infinitely more elegant. But we can't replicate it
because we can't fully simulate exactly how that complex enzyme works.
The electron interactions are fundamentally quantum mechanical.
Speaker 1: And classical computers can't do quantum mechanics. It seems like
they should be able to.
Speaker 2: The physicist Richard Feinman said it best decades ago. He said,
nature isn't classical, damn it. And if you want to
make a simulation of nature, you'd better make it.
Speaker 1: Quantum mechanical nature runs on quantum physics, electrons, chemical bonds,
molecular orbitals.
Speaker 2: Right, Trying to simulate a complex molecule on a silicon
chip is like trying to describe a Rembrandt painting using
only the words yes and no. You just can't capture
the nuance. A quantum computer, on the other hand, is
the quantum system. It models the problem faithfully.
Speaker 1: So if we unlock that, we could produce fertilizer with
a tiny fraction of the energy cost. We could design
room temperature superconductors.
Speaker 2: All on the table. If we solve the catalyst for
nitrogen fixation, we could cut global carbon emissions by a huge,
huge amount, almost overnight. That is the scale of application
we are talking about. It's civilization altering.
Speaker 1: And this can expect to AI. The report uses the
phrase quantum inspired optimization.
Speaker 2: That sounds like buzzword salad, doesn't it like something from
a marketing burcher.
Speaker 1: It really does. What does it actually mean?
Speaker 2: But it's real. It means using these quantum principles to
train our AI models more efficiently. Right now, training AI
is a brute force search through a massive possibility space
to find the right setting.
Speaker 1: Right, turning all the little knobs until it works.
Speaker 2: And it costs hundreds of millions of dollars in electricity.
Quantum optimization algorithms could find the best path through that
space much more efficiently. The goal is to make AI
deliver better performance with fewer.
Speaker 1: Operations, less energy per task.
Speaker 2: Making AI sustainable. That is the holy grail. Instead of
brute forcing intelligence with more and more power. We find
the elegant path. We make it smarter, not just bigger.
Speaker 1: So we're at the convergence point. We have the hardware
race Google versus Microsoft versus ion Q. We have the
application explosion batteries, energy grids, fertilizer AI itself. Now, let's
look at the calendar again. That three year window twenty
twenty six to twenty twenty.
Speaker 2: Eight, the phase shift window.
Speaker 1: The source highlighted a really critical statistic, almost a warning
about the power demand discrepancy.
Speaker 2: Yes, this is why governments and national labs are sweating
right now. The electrical power demand for a full scale,
fault tolerant quantum computer could differ by up to two
orders of magnitude.
Speaker 1: Two orders of magnitude, so one hundred times.
Speaker 2: One hundred times different depending on which architecture wins.
Speaker 1: So if we bet on the wrong horse, if a
country builds its national quantum strategy around one technology, if we.
Speaker 2: Build our national infrastructure around a technology that requires one
hundred x more power, say we go all in on
the giant fridges, and then Microsoft's low power topological chip
actually works. We've wasted billions and locked ourselves into an
unsustainable grid for decades.
Speaker 1: You'd have the quantum equivalent of a country full of
Betamax players in a VHS.
Speaker 2: World exactly, but a Betamax player that requires its own
power plant to run. Governments and industries have to decide
now which path to back, and it's a huge gamble.
Speaker 1: It's high stakes poker with the national grid. Is the
buy in?
Speaker 2: It is And by twenty twenty eight, the prediction in
the report is that we will see the first commercially
viable large scale quantum computers solving real world problems, not
just lab demas real valuable commercial problem.
Speaker 1: That's just around the corner. That's not science fiction anymore.
Speaker 2: It's a business plan.
Speaker 1: And the source mentions Google's vision of personal intelligence. What
is that.
Speaker 2: It's the next step beyond large language models. It's an
AI that knows you better than you know yourself.
Speaker 1: That's both terrifying and incredibly exciting.
Speaker 2: Yes, it is. Imagine an AI that has a perfect
memory of every conversation you've ever had, every document you've
ever read. It understands your context, anticipates your health needs
based on your biometrics, manages your finances with perfect foresight,
like a.
Speaker 1: True digital assistant. Not the dumbsby we have.
Speaker 2: Now exactly, but that level of complexity, that constant personalized
computation is impossible to run cheaply on today's hardware. It
would be too slow and too expensive.
Speaker 1: So the quantum breakthrough is the prerequisite for that AI breakthrough.
Speaker 2: Precisely, the convergence of AI and quantum is the phase shift.
It's the moment we stop struggling against complexity and start
mastering it.
Speaker 1: It's a lot to take in. The scale of this
is immense.
Speaker 2: It is. It touches everything.
Speaker 1: So let's try to synthesize this. We've covered the energy crisis,
the physics of why the corporate players, the global stakes.
When you step back from all the details, what's the
one thing that keeps you up at night about this?
Speaker 2: You know, usually with these topics. It's the risks, the
terminator scenarios, the AI safety questions. But here what keeps
me up is the sheer magnitude of the shift in logic.
How so, we are building computers that work on fundamentally
different principles of physics than anything we have ever built.
We are stepping away from the deterministic, binary world of
zeros in one black and white yes and no, and
we are entering the probabilistic, complex, entangled world of nature itself.
Speaker 1: It feels profound, it's philosophical almost. We're building a computer
that thinks more like the universe does.
Speaker 2: It is, but on a very practical level, it's the
talent gap. The shortage of people who understand this stuff
is severe. We have maybe a few thousand real experts
in the world. We are trying to build the engine
of the twenty first century, and we don't have enough
mechanics who speak the language.
Speaker 1: That's a real bottleneck. The tech could be ready, but
we won't have the people to use it.
Speaker 2: It is, But my optimism comes from that idea of
reversibility we talked about the beginning, the idea that we
can compute without destruction, that we can be efficient. That
feels like a mature step for civilization.
Speaker 1: And my takeaway is that the race is on between
the tech giants, but the real winner, as you said,
is whoever solves the energy efficiency puzzle. It's not about
who gets to the finish line first. It's about who
has enough gas left in the tank to keep going beautifully.
Speaker 2: Put that's the whole story in a nutshell.
Speaker 1: So let's leave our listeners with some actionable advice. We
don't just want to blow their minds. We want to
give them a toolkit for this new world.
Speaker 2: Right, what do you do on Monday morning?
Speaker 1: For the investors listening, what's the play?
Speaker 2: Watch who is solving the energy problem? Don't get distracted
by press releases about quibet counts alone. If a company
says we have ten thousand kibbuts ask how much power
does it take to run them? Look at the cooling requirements,
look at the total system cost of ownership. The efficiency
is the key metric.
Speaker 1: Okay. For the tech workers, the coders, the engineers.
Speaker 2: Start learning quantum principles immediately. You don't need a PhD
in physics, but you need to understand the logic, understand
the new types of algorithms. Learn the basic languages like
qushtab from Microsoft or a circ from Google. The talent
gap is a massive opportunity. If you can bridge the
world to classical coding with quantum concepts, you are writing
your own tip get for the next twenty years.
Speaker 1: Be the translator between the two worlds.
Speaker 2: Be the translator. You'll be invaluable.
Speaker 1: And finally, for the business leaders, the CEOs, the strategists.
Speaker 2: Look at your own industry and identify the problems that
are currently too complex. The ones your supercomputers choke on,
the supply chain logistics that are always messy, the drug
discovery simulations that take years, the material designs that are
too expensive to test in the.
Speaker 1: Real world, the impossible problems.
Speaker 2: Those are your quantum opportunities. Start cataloging them now. Be
ready to deploy when the tech lands in twenty twenty eight,
because your competitors will be.
Speaker 1: Twenty twenty eight. Mark your calendars. Everyone, The age of
quantum abundance is coming. It's coming fast, and that brings
us to the end of this thread. But we really
want to hear from you, the listener. We've painted a
picture of a future where AI could consume the power
of nations, but this new form of computing might just
save it.
Speaker 2: It's the ultimate ds X machina, a solution appearing from
the very fabric.
Speaker 1: Of physics exactly. So here is the direct question for you.
We're facing a future where AI consumes the power of nations,
but quantum might save it. Do you trust big tech
to prioritize the energy efficient solution or will the race
for raw speed win out.
Speaker 2: Will they choose the Ferrari engine that melts the chassis
because it looks good in a demo or the efficient
engine that saves the planet but is maybe a little
less flashy.
Speaker 1: It's a question of profit versus prudence. Leave a comment
and let us know your stand We really want to
know what you think about this.
Speaker 2: We do this conversation needs to happen out in the open.
Speaker 1: Thanks for joining us on thrilling Threads.
Speaker 2: Thank you.
Speaker 1: Welcome to the future.
Speaker 2: It's already here, you just have to know where to look.