Speaker 1: I want you to imagine a campfire. Like you're sitting
Speaker 1: out in the woods, right, Yeah, it's a freezing night.
Speaker 1: You desperately need to keep this fire going just to
Speaker 1: stay warm. But instead of grabbing a log or some
Speaker 1: dry branches, right, you reach into this heavy canvas duffelback
Speaker 1: and you pull out a thick stack of CRISP one
Speaker 1: hundred dollars bills.
Speaker 2: Just straight cash, straight cash.
Speaker 1: And you toss them directly into the flames. Wow. They
Speaker 1: curl up, turn black and disappear. Wow, But you don't stop.
Speaker 1: You keep tossing those stacks in second after second, minute
Speaker 1: after minute.
Speaker 3: I mean, the physical logistics of that are it's almost
Speaker 3: impossible to even visualize, you know, right, to burn through
Speaker 3: that much money you're talking about setting fire to like
Speaker 3: hundreds of thousands of pounds of pure currency, you'd need
Speaker 3: an industrial incinerator running around the.
Speaker 1: Clock, exactly. It's a completely absurd mental image. But here's
Speaker 1: the thing. You are going to keep tossing those hundred
Speaker 1: dollar bills into the fire until you have burned through
Speaker 1: exactly twenty point nine billion dollars in a single year,
Speaker 1: in a single year, and that twenty point nine billion
Speaker 1: dollar cash burn. That is not a hypothetical number. That
Speaker 1: is the audited financial reality of open AI for the year.
Speaker 2: Twenty twenty five, which is just staggering.
Speaker 1: It really is so welcome to Thrilling Threads today, we
Speaker 1: are pulling apart a truly fascinating and frankly deeply alarming
Speaker 1: piece of source material. We're looking at a comprehensive breakdown
Speaker 1: by the entrepreneur Tom Billu.
Speaker 2: Yeah, his video on this is intense.
Speaker 1: It's titled, uh, Big Tech ran out of ideas and
Speaker 1: AI is the cover story. We had to react.
Speaker 3: And you know, his analysis is so critical right now
Speaker 3: because it really forces us to look past the I
Speaker 3: guess you'd call it the dazzling magic tricks we see
Speaker 3: on our screens every day.
Speaker 1: Oh, for sure, the shiny object syndrome.
Speaker 2: Exactly.
Speaker 3: We're being asked to stare directly into this terrifying financial
Speaker 3: engine that is actually powering the entire artificial intelligence boom.
Speaker 3: And he pulls from these incredibly dense financial reports insider
Speaker 3: interviews and he builds a very very unsettling picture.
Speaker 1: So our mission for today's Thrilling Threads is to untangle
Speaker 1: this massive, just glaring contradiction sitting right at the very
Speaker 1: heart of the AI industry, right, because on one hand,
Speaker 1: you have these mind bending, world changing technological leaps happening
Speaker 1: like almost weekly at this point.
Speaker 3: Yeah, curing diseases, writing code, driving cars.
Speaker 1: Right. But on the other hand, the industry is quietly
Speaker 1: building what looks like an apocalyptic, multi trillion dollar financial bubble.
Speaker 2: Yeah, it's a bubble that could pop at any minute.
Speaker 1: So to set the table for you, the listener, our
Speaker 1: source material presents two very distinct viewpoints on this contradiction.
Speaker 2: Right, we have ed Zitron anchoring one end of the spectrum.
Speaker 2: He's the bear, right, the ultimate bear case.
Speaker 1: Yeah.
Speaker 3: Zitron is this fierce, just unrelenting critic who looks at
Speaker 3: the current AI industry and literally calls it a nothing burger.
Speaker 1: A nothing burger. Wow.
Speaker 2: Yeah.
Speaker 3: His core argument is that artificial intelligence, at least how
Speaker 3: it's currently monetized and packaged for enterprise use, is really
Speaker 3: just a ten to thirty billion dollar industry. Okay, But
Speaker 3: the problem is that it has put on this very elaborate,
Speaker 3: very expensive costume, and it's masquerading as a trillion dollar industry.
Speaker 3: He believes the fundamentals simply do not exist to support
Speaker 3: these massive valuations.
Speaker 1: And then providing the counterweight to that is Tom Billy
Speaker 1: himself kind of acting as the cautious bull here. Right.
Speaker 1: He looks at the exact same data as Zitron, but
Speaker 1: he comes away to a totally different conclusion. Like Billy,
Speaker 1: you genuinely believes that AI is a revolutionary technology. He
Speaker 1: thinks it's already fundamentally changing the fabric of human society.
Speaker 2: Which is hard to argue against when you see it
Speaker 2: in action.
Speaker 1: Right, And this is the massive caveat here. Even as
Speaker 1: a believer in the technology, he openly admits that the
Speaker 1: financial debt being accumulated to build it is absolutely terrifying.
Speaker 3: Yeah, it creates this fascinating tension for our whole discussion today.
Speaker 3: We have one prominent voice arguing the technologies business model
Speaker 3: is a total illusion, like a scam basically, yeah, and
Speaker 3: another saying the tech is an absolute miracle, but the
Speaker 3: sheer cost of it might just destroy the broader economy.
Speaker 2: Anyway.
Speaker 1: It makes me think of a Buddy mine a few
Speaker 1: years back. He got into this crazy situation. He went
Speaker 1: to a high end dealership and saw this absolute spaceship
Speaker 1: of a luxury sports car.
Speaker 2: Oh no, I see where this is going.
Speaker 1: Right. He was so blinded by the stitched leather seats,
Speaker 1: the zero to sixty speed, and just the sheer social
Speaker 1: status of driving the thing that he signed the financing
Speaker 1: papers without actually reading them.
Speaker 2: Did he even look at the maintenance schedule?
Speaker 1: He definitely didn't look at the natance schedule. He drives
Speaker 1: it off a lot, feeling like, you know, the smartest
Speaker 1: guy in town, naturally, But a year later he realizes
Speaker 1: the car requires this special type of imported synthetic oil
Speaker 1: that costs like one thousand dollars a gallon. Oh my god,
Speaker 1: the brake pads need replacing every three thousand miles, and
Speaker 1: his loan interest is compounding daily. So the car is real,
Speaker 1: the speed is real, the incredible speed is completely real.
Speaker 1: But the math dictates that he's going to lose his
Speaker 1: house to pay for it.
Speaker 2: Right.
Speaker 3: The utility of a product doesn't just like rewrite the
Speaker 3: laws of mathematics exactly. A miraculous product can still bankrupt
Speaker 3: you if the cost to produce it exceeds what anyone
Speaker 3: is actually willing to pay. And we are talking about
Speaker 3: that exact same dynamic today, just scaled up to a
Speaker 3: global multi trillion dollar level.
Speaker 1: Okay, let's unpack this. Let's ground that campfire image in
Speaker 1: the hard numbers. Our source brings up that audited FTI
Speaker 1: report detailing the open AI twenty twenty five cash burn.
Speaker 1: I want to really understand the mechanics of that burn rate,
Speaker 1: because this isn't just a tech startup spending too much
Speaker 1: venture capital on like super Bowl ads or luxurious office campuses, right.
Speaker 2: No, not at all.
Speaker 3: There is a fundamental flaw in the actual economic model
Speaker 3: of the software itself. The core thesis ed Xyitron lays out,
Speaker 3: which Billu heavily highlights in the video, hinges on one
Speaker 3: incredibly dangerous reality.
Speaker 2: For Silicon Valley.
Speaker 1: What's the reality?
Speaker 3: It boils down to a single sentence. AI costs increase
Speaker 3: linearly with.
Speaker 1: Revenues, linearly with revenues.
Speaker 3: Yes. And to understand why that is a literal death
Speaker 3: sentence for a tech company, you have to look at
Speaker 3: the golden rule of the digital age, the marginal cost
Speaker 3: to software. Precisely, it is the exact opposite of how
Speaker 3: AI works. Think about a traditional software product like a
Speaker 3: word processor or a photo sharing app. Okay, a company
Speaker 3: might spend fifty million dollars paying engineers to write the
Speaker 3: original code.
Speaker 1: That's a huge upfront cost.
Speaker 3: Huge, But once the code is finished, compiling and sending
Speaker 3: the second copy of that software to a customer costs
Speaker 3: practically zero. Sending the two billionth copy costs practically zero.
Speaker 3: You build it once and you sell it infinitely. The
Speaker 3: profit margins approached ninety nine percent over time.
Speaker 1: And that mechanism is why companies like Microsoft and Apple
Speaker 1: became the most valuable entities in human history.
Speaker 3: Exactly, they achieved infinite scale and zero marginal cost.
Speaker 1: I'm seeing the disconnect here because when I open a
Speaker 1: traditional app, my my own phone or my own laptop
Speaker 1: is doing the processing work to run it right. The
Speaker 1: software company isn't paying for my device's electricity or as processor.
Speaker 2: Nope, that's on you.
Speaker 1: But when I use a large language model like chad GPT,
Speaker 1: my phone isn't doing the heavy lifting at all.
Speaker 2: Not even a little bit.
Speaker 3: The heavy lifting is being done miles miles away every
Speaker 3: single time a user types of prompt and hits enter.
Speaker 3: The AI doesn't just like retrieve a pre written file.
Speaker 3: It's not a search engine exactly, a massive array of
Speaker 3: highly specialized hardware, specifically these incredibly expensive in Vidia GPU
Speaker 3: sitting in a giant data center has to physically spin
Speaker 3: up and perform billions of mathematical calculations in real time
Speaker 3: just to generate that specific unique string of text. Wow,
Speaker 3: it requires raw physical computing power. It requires massive amounts
Speaker 3: of electricity to run the chips, and then massive amounts
Speaker 3: of water and power to cool the chips down so
Speaker 3: they literally don't melt.
Speaker 1: So every single time someone uses it, they are burning
Speaker 1: physical resources.
Speaker 2: Every single time.
Speaker 1: So as your user base grows, and as those users
Speaker 1: start prompting the AI more frequently, your server costs and
Speaker 1: your electricity bills go up and lockstep with your revenue. Right,
Speaker 1: you never reached that software sweet spot of infinite free replication.
Speaker 1: It's like you are essentially running a heavy manufacturing plant,
Speaker 1: but your product happens.
Speaker 3: To be text, and the source material points out that
Speaker 3: it's actually getting worse worse. Well, as these models get
Speaker 3: more complex, you know, moving from GBT three to GPT
Speaker 3: four and whatever comes next, they require exponentially more compute
Speaker 3: to train and to run, so the margins are deteriorating.
Speaker 3: The more successful the product becomes in terms of user engagement,
Speaker 3: the more money the company actually loses.
Speaker 1: But the tech industry's standard defense mechanism to this argument.
Speaker 1: I hear it constantly from the engineers. They always say, Oh,
Speaker 1: don't worry about the power draw today, the specialist silicon
Speaker 1: will save us tomorrow.
Speaker 2: The magic chip argument.
Speaker 1: Yes, They point to Nvidia's roadmap, things like the upcoming
Speaker 1: via Reuben's chips, promising that the next generation of hardware
Speaker 1: will be so fast and so efficient that the influence
Speaker 1: costs will just drop to zero.
Speaker 3: Yeah, but our source pushes back aggressively on that hardware narrative.
Speaker 3: Xetron argues there is zero historical or mathmatal proof that
Speaker 3: hardware upgrades can magically fix these inverted margins.
Speaker 2: Zero proof none, Because while.
Speaker 3: It is true that in Nvidia is making chiffs that
Speaker 3: are say, twice as efficient, the AI companies are simultaneously
Speaker 3: building models that are ten times large.
Speaker 1: I see the.
Speaker 3: Software's demand for power vastly outpaces the hardware's efficiency gains.
Speaker 3: It's like if your car engine gets twenty percent better
Speaker 3: gas mileage, but you suddenly decide tow a battleship instead
Speaker 3: of a rowboat, you are still going to run out
Speaker 3: of fuel.
Speaker 1: Right.
Speaker 2: The math is not.
Speaker 3: Trending toward profitability. It's trending toward a hard physical ceiling.
Speaker 1: It's like inventing a futuristic, fully automated bakery. And this
Speaker 1: bakery produces the most incredible, life changing bread you have
Speaker 1: ever tasted in your life.
Speaker 2: Okay, I like this right.
Speaker 1: It cures ailments, it gives you energy for day. It's
Speaker 1: a literal miracle. But the catch is the oven only
Speaker 1: runs if you shovel flawless one carrot diamonds into the furnace.
Speaker 3: The fuel cost is just astronomical and inescapable.
Speaker 1: Exactly everyone wants the bread. You have millions of people
Speaker 1: lining up around the block to buy a loaf, but
Speaker 1: every single time you bake one, you have to go
Speaker 1: buy another diamond to burn.
Speaker 2: You can't just scale up the recipe.
Speaker 1: No, you can't scale it up without scaling up your
Speaker 1: diamond purchases. So no matter how much you charge for
Speaker 1: the bread, unless you start charging millions of dollars a loaf,
Speaker 1: which no one can afford, the business model is inherently
Speaker 1: doomed because the fuel cost scales directly with the output.
Speaker 3: That analogy perfectly captures the panic that's kind of bubbling
Speaker 3: under the surface in Silicon Valley right now. Because if
Speaker 3: the costs are this astronomically high, if they are burning
Speaker 3: through twenty point nine billion dollars just to keep the
Speaker 3: diamond furnace running, someone has to be footing that bill.
Speaker 2: And retail consumer is paying.
Speaker 3: Like twenty bucks a month for a premium chare, but
Speaker 3: subscription that barely puts a debt in that number. The
Speaker 3: true financial targets for these companies are corporate.
Speaker 1: Enterprises, which is where the entire narrative starts to fracture,
Speaker 1: right because according to the source material, those massive corporate enterprises,
Speaker 1: the Fortune five hundred companies that are supposed to be
Speaker 1: injecting hundreds of billions of dollars into this ecosystem, they
Speaker 1: are suddenly slamming on the brakes. The video pulls this
Speaker 1: incredible clip of Alex Karp. He's the CEO of Palenteer,
Speaker 1: and he's being interviewed on CNBC's squawk Box, and he
Speaker 1: issues a very very blunt warning about enterprise adoption.
Speaker 3: And Karp is uniquely positioned to see this right because
Speaker 3: Palenteer builds data integration software for massive institutions. We're talking
Speaker 3: from the military to.
Speaker 1: Global banks, heaby hitters.
Speaker 3: Yeah, and he notes that corporate enterprises are realizing they
Speaker 3: are getting absolutely zero measurable value out of AI tokens.
Speaker 3: Karp literally labeled the current enterprise AI push as a
Speaker 3: waste of time.
Speaker 1: Wow.
Speaker 3: He said, the basic view among these massive companies is, quote,
Speaker 3: I'm going to chilax and waste my time with tokens.
Speaker 3: I'm going to get no value and they're going to
Speaker 3: get my IP.
Speaker 1: That is a brutal public assessment from a major industry insider.
Speaker 1: Waste my time with tokens. I really want to break
Speaker 1: down why this is happening, Like, why are sophisticated companies
Speaker 1: paying huge sums of money to integrate these large language
Speaker 1: models and then feeling like they were sold a bill
Speaker 1: of goods.
Speaker 3: Well, it comes down to the fundamental architecture of the
Speaker 3: models themselves. As Ziitron details in the source, large language
Speaker 3: models are inherently hallucination.
Speaker 1: Prone, right, the hallucinaty.
Speaker 3: Yeah. And this isn't just a bug that can be
Speaker 3: patched out in the next update. It is a core
Speaker 3: feature of how the technology operates. It's a reality that
Speaker 3: even companies like open ai have openly acknowledged in their
Speaker 3: own technical.
Speaker 1: Papers because they aren't actually looking up facts in a database. OK.
Speaker 1: I think a lot of people misunderstand this.
Speaker 2: They completely misunderstand it.
Speaker 1: When you ask a chatbout a question, it's not like
Speaker 1: googling the answer and reading a Wikipedia page back to you.
Speaker 1: It's just predicting the next most statistically likely word in
Speaker 1: a sequence based on all the billions of texts that
Speaker 1: read during its training phase.
Speaker 3: Exactly, they operate in a probabilistic vector space. They don't
Speaker 3: actually know anything, They just know patterns of language. So
Speaker 3: if an enterprise asks an AI to summarize a highly
Speaker 3: complex proprietary financial audit, the AI might encounter a gap
Speaker 3: in its pattern recognition, and instead of saying I don't know,
Speaker 3: it's programming basically compels it to generate the most plausible
Speaker 3: sounding next word, Oh I am, So it will confidently
Speaker 3: invent a completely fictional set of numbers, format them perfectly
Speaker 3: in a beautiful table, and present them to the executive
Speaker 3: as absolute fact.
Speaker 1: And in a corporate environment, that is a tipping time bomb.
Speaker 1: You cannot run a global supply chain, or a hospital's
Speaker 1: legal department, or an airline's maintenance schedule using a tool
Speaker 1: that occasionally just hallucinates a fictional reality.
Speaker 2: You'd be insane to try.
Speaker 1: Precision is everything. A ninety nine percent accuracy rate sounds
Speaker 1: great if you're at a high school essay, but that
Speaker 1: one percent failure rate will bankrupt a fortune five hundred
Speaker 1: company if they act on bad data.
Speaker 3: Exactly, and because of this hallucination problem, zyschron points out
Speaker 3: an incredible limitation for the entire AI industry. They cannot
Speaker 3: charge their clients based on outcomes or success.
Speaker 2: We explain that think about a consulting firm.
Speaker 3: A consulting firm can charge a client based on how
Speaker 3: much money they save them, right, a percentage of the savings. Sure,
Speaker 3: AI companies can't do that because they can't guarantee the
Speaker 3: output is even real. So they are forced to charge
Speaker 3: based purely on usage, on how many compute tokens you
Speaker 3: burn through, regardless of whether the answer actually solved your
Speaker 3: problem or you know, created a massive legal liability.
Speaker 1: So they actually have a perverse incentive to encourage waste. Exactly,
Speaker 1: they want the enterprise employees to spin their wheels, go
Speaker 1: back and forth with the chatbot all day long, tweaking prompts,
Speaker 1: try and get a usable answer. Because the AI company
Speaker 1: gets paid for every single keystroke.
Speaker 3: Yep.
Speaker 1: But Karp's warning on CNBC went beyond just wasting money.
Speaker 1: Let's dig into the second half of his quote. They're
Speaker 1: going to get my IP. This introduces what we're calling
Speaker 1: the Great IP heist.
Speaker 3: This is one of the most shocking allegations discussed in
Speaker 3: the source material. The accusation here is that AI companies
Speaker 3: are effectively pillaging their own clients intellectual property to launch
Speaker 3: direct competitors against them.
Speaker 1: It's wild. Our source gives a very specific, devastating example
Speaker 1: of this dynamic in action. There's this massive collaborative design
Speaker 1: platform called Pigma used by designers all over the world,
Speaker 1: very popular. Yeah and Anthropic, which is one of the
Speaker 1: leading AI labs, allegedly trained their foundational models by scraping
Speaker 1: and absorbing massive amounts of data from Pigma, and then
Speaker 1: armed with Pigma's own underlying logic and design patterns and
Speaker 1: Tropic turnaround and launched a tool called clotted Design. Dylan Field,
Speaker 1: the CEO of Figma, was reportedly blindsided and just absolutely
Speaker 1: shocked by this move.
Speaker 3: It sets a terrifying precedent for literally any corporate executive.
Speaker 3: Imagine you are the CEO of a highly specialized logistics
Speaker 3: company rap Your only competitive moat is your proprietary data,
Speaker 3: the decades of routing information, supplier negotiations, efficiency metrics you've
Speaker 3: built up over years. If you feed all of that
Speaker 3: into an off the shelf AI model to try and
Speaker 3: like optimize your routes.
Speaker 1: You're training the AI on your secret sauce.
Speaker 2: You're handing it over.
Speaker 3: The AI company can then absorb that knowledge, generalize it,
Speaker 3: and sell a cheap automated logistics expert tool to everyone
Speaker 3: else in.
Speaker 1: The world, including your direct competitors.
Speaker 2: Including your direct competitors.
Speaker 3: You essentially paid the AI company to automate your own
Speaker 3: business model and sell it to the highest bedder.
Speaker 1: It's like inviting a famous chef into your family restaurant
Speaker 1: to help you organize your kitchen. The chef takes detailed
Speaker 1: notes on your grandmother's secret recipe, walks right next door,
Speaker 1: and opens a massive chain restaurant selling the exact same
Speaker 1: dish for half the price.
Speaker 2: That is exactly it. Of course, enterprises are slamming on
Speaker 2: the brakes.
Speaker 1: Right, But Alex Karp isn't just complaining on television about this.
Speaker 1: Palenteer actually proposes a technical solution to this IP theft problem,
Speaker 1: don't they?
Speaker 2: They do?
Speaker 3: Karp argues that the only way enterprises will ever safely
Speaker 3: adopt this technology is through what the source describes as
Speaker 3: an obfuscation layer or a middle layer. Palenteer calls their
Speaker 3: version of this an ontology.
Speaker 1: Okay, explain the mechanics of an ontology. How does a
Speaker 1: middle layer actually protect the data?
Speaker 3: You can visualize it as a secure walled garden. So
Speaker 3: enterprises desperately want the analytical reasoning power of an LM. Right,
Speaker 3: they have mountains of unstructured data that humans just can't
Speaker 3: process fast enough, but they absolutely cannot allow that data
Speaker 3: to be sent back to the AI's core training lervers.
Speaker 1: Right, keep the chef out of the recipe book exactly.
Speaker 3: So the ontology acts as a secure translator and a barrier.
Speaker 3: It utilizes technologies like vector databases and something called retrieval
Speaker 3: augmented generation or.
Speaker 1: RAG a RAG. Okay, So the enterprise keeps its data
Speaker 1: completely inside its own locked house exactly.
Speaker 3: When an employee asks a question, the ontology searches the
Speaker 3: company's internal highly secure database, retrieves the highly specific proprietary facts,
Speaker 3: and then hands those facts to the AI model in
Speaker 3: a sort.
Speaker 2: Of temporary workspace. Let's a sandbox, Yeah, sandbox.
Speaker 3: The AI uses its reasoning capability to formulate an answer
Speaker 3: based only on those provided facts, and then the workspace
Speaker 3: is just wiped clean, deleted. Oh well, the AI model
Speaker 3: itself remains frozen. It does not learn from the interaction,
Speaker 3: and it cannot send the data back to its creators.
Speaker 3: It allows the enterprise to benefit from the intelligence without
Speaker 3: surrendering the intellectual property.
Speaker 1: As Bill U heavily stresses in the video, The Future
Speaker 1: of Corporate AI guarantees that you must somehow turn that
Speaker 1: generic AI into something proprietary to you. You have to
Speaker 1: because if your company is just using the exact same
Speaker 1: off the shelf chat GPT that your competitor across the
Speaker 1: street is using, there is zero competitive advantage. If the
Speaker 1: AI running your strategy gives the exact same answers as
Speaker 1: the A running your competitor strategy, the entire industry just
Speaker 1: commoditizes and everybody loses their edge.
Speaker 3: Right, and the struggle over application and utility really raises
Speaker 3: a glaring question about the current leadership of these major
Speaker 3: AI labs. The source points out something very very telling
Speaker 3: about figures like open ayes Sam Altman and anthropics Daryo Miday.
Speaker 1: This part of the deep Sorry, this part of our
Speaker 1: analysis truly blew my mind. Both of these men who
Speaker 1: are currently perceived as like the grand architects of the future,
Speaker 1: they have publicly gone on record speaking to software developers
Speaker 1: and said variations of we can't wait to see what
Speaker 1: you build with this.
Speaker 3: Yeah, And in the context of standard Silicon Valley culture,
Speaker 3: that sounds like optimistic cheerleading, right, It's meant to sound empowering,
Speaker 3: like they are handing the tools of creation directly to
Speaker 3: the masses.
Speaker 1: What I have to push back hard on that framing. Yeah,
Speaker 1: if you look at it objectively, it is deeply concerning.
Speaker 1: The creators of this technology just burn twenty billion dollars
Speaker 1: building the most complex, energy intensive artificial brain in human history.
Speaker 1: They are raising hundreds of billions more. If they are
Speaker 1: standing on a stage openly saying we can't wait to
Speaker 1: see what you figure out how to do with it,
Speaker 1: does that actually mean they have absolutely no idea what
Speaker 1: their own product is fundamentally good for.
Speaker 3: Ed zischron seizes on that exact point. His argument is
Speaker 3: that they don't know what to build because the technology
Speaker 3: lacks intrinsic business utility in its raw form interesting, So
Speaker 3: their strategy is just to release it to the public,
Speaker 3: encourage thousands of startups to spend venture capital on compute
Speaker 3: tokens trying to find a use case, and then the
Speaker 3: AI monopolies will just sit back, observe what works and
Speaker 3: steal the best ideas.
Speaker 1: They're outsourcing the R and D.
Speaker 3: They're outsourcing the innovation because they don't have the answers themselves.
Speaker 1: It's staggering. It is the equivalent of building the most expensive,
Speaker 1: complicated hammer in the universe, a hammer that costs twenty
Speaker 1: billion dollars a year just to hold in your hand,
Speaker 1: and then staring blankly at the public waiting for someone
Speaker 1: else to invent the nail.
Speaker 2: That's a great way to put it.
Speaker 1: But in fairness to the source material, Tom Billy pushes
Speaker 1: back against Ittron. Here. Billy doesn't see this as a
Speaker 1: unique failure of AI leadership. He looks at technological.
Speaker 3: History, right, Bill, you argues, quote, in what universe would
Speaker 3: you expect the telecoms who built out the Internet to
Speaker 3: know what was going to be born of this? And
Speaker 3: that is a vital historical counterpoint, the people laying copper
Speaker 3: telephone wires didn't predict the fax machine. The companies laying
Speaker 3: fiber optic cables in the late nineties could not have
Speaker 3: conceptualized a platform like.
Speaker 1: Uber because Uber relies on a real time global GPS
Speaker 1: network interacting with pocket supercomputers right exactly.
Speaker 3: Foundational infrastructure almost always predates its most valuable applications.
Speaker 2: You build the roads before you know where the cards
Speaker 2: are going.
Speaker 1: Okay, but knowing that the current AI business model is
Speaker 1: bleeding cash at an unprecedented rate, and that the margins
Speaker 1: are physically inverted, and that corporate clients are actively terrified
Speaker 1: of IP theF how does this industry survive long enough
Speaker 1: to find its Uber or its iPhone?
Speaker 2: It's the multi trillion dollar question.
Speaker 1: To figure that out. Our source forces us to look backwards.
Speaker 1: Here's where it gets really interesting. We need to explore
Speaker 1: the ghosts of the dot com bubble and what Tom
Speaker 1: Billu calls the inheritance generation.
Speaker 3: Yes, the historical framework here is so essential to understanding
Speaker 3: the current panic. If we connect this to the bigger picture,
Speaker 3: when we look back at the last two centuries of
Speaker 3: industrial development, whenever a truly revolutionary technology arrives that requires
Speaker 3: a massive physical infrastructure build out, like what we're talking about,
Speaker 3: the laying of the transcontinental railroads, the digging of the
Speaker 3: global canal systems, or the massive fiber optic cable boom
Speaker 3: of the late nineties Internet. Okay, there is a nearly
Speaker 3: universal economic pattern. The infrastructure build out almost always bankrupts
Speaker 3: the very first generation of investors.
Speaker 1: Let's explore the mechanics of that destruction. Why does the
Speaker 1: first generation always get wiped out if the technology is
Speaker 1: actually good and.
Speaker 3: Changes the world Because of the debt to adoption ratio,
Speaker 3: the sheer capital cost of laying that physical infrastructure is
Speaker 3: massively front.
Speaker 1: Loaded, right, buying all the steel.
Speaker 3: Buying the steel for the tracks, or, in the case
Speaker 3: of the nineties telecom boom, companies like Global Crossing spending
Speaker 3: billions of borrowed dollars to lay millions of miles of
Speaker 3: fiber optic cables across the floor of the Atlantic Ocean.
Speaker 3: The upfront cost is astronomical, but the public's adoption of
Speaker 3: the technology is incredibly slow.
Speaker 1: Right, because the Internet existed in nineteen ninety nine, but
Speaker 1: you couldn't stream a four K movie on it yet
Speaker 1: there simply weren't enough users paying for the bandwidth to
Speaker 1: cover the debt payments on those cables exactly.
Speaker 3: The revenue just trickles in while the multi billion dollar
Speaker 3: loan payments are due immediately, so the debt crushes the
Speaker 3: pioneer companies before the revenue curve can catch up. The
Speaker 3: companies default on their loans and they file for Chapter
Speaker 3: eleven bankruptcy.
Speaker 1: But and this is the key, the railroads don't magically
Speaker 1: disappear when the bank forecloses on the holding company. The
Speaker 1: physical tracks are still sitting there in the dirt. The
Speaker 1: fiber optic cables don't dissolve in the ocean. They are
Speaker 1: still sitting on the seafloor, fully operational.
Speaker 3: Enter the inheritance generation. These are the second wave entrepreneurs
Speaker 3: and investors who arrive.
Speaker 2: Right after the bloodbath.
Speaker 3: They look around the economic wreckage and see this amazing,
Speaker 3: world changing infrastructure already built. And because the original prey
Speaker 3: in your company went bankrupt, all the original debt was
Speaker 3: wiped out in the court's restructuring process.
Speaker 1: So the new generation gets to walk in and buy
Speaker 1: these massive dark fiber networks for pennies on the dollar,
Speaker 1: completely unburdened by the massive debt required to actually build them.
Speaker 3: And they are the ones who build the wild historic fortunes.
Speaker 3: The source points out that the entire web two point
Speaker 3: zero revolution, netflix, streaming, the mobile app ecosystem, cloud computing,
Speaker 3: it only exists and only became profitable because it was
Speaker 3: built on top of the heavily discounted telecomunityation's infrastructure that
Speaker 3: ruin the previous generation of investors in the dot com crash.
Speaker 1: It is the creative destruction of capitalism functioning exactly as intended.
Speaker 1: Applying this historical cycle directly to AI today is where
Speaker 1: Billio and Zitron are having this massive, high stakes debate.
Speaker 1: They are looking at the current darlings of Silicon Valley
Speaker 1: companies like open Ai and Thropic, even the hardware divisions
Speaker 1: of major tech firms, and asking if they are destined
Speaker 1: to be the bankrupt railroad layers.
Speaker 3: Of our era, and Billu argues that yes, the current
Speaker 3: trajectory suggests it is almost certainly going to play out
Speaker 3: like the telecombust The debt weight being accumulator right now
Speaker 3: to build these massive compute clusters is just too high.
Speaker 3: The linear cost problem is too severe. The math simply
Speaker 3: dictates a breaking point that will likely bankrupt this first
Speaker 3: generation of investors.
Speaker 1: But he doesn't think the tech is fake.
Speaker 3: No, he stresses a vital point, Just because the initial
Speaker 3: business model collapses does not mean the artificial intelligence itself
Speaker 3: is mirage.
Speaker 1: Right. Billia used a great phrase in the video, the
Speaker 1: toothpaste isn't going back in the bottle.
Speaker 2: I love that line.
Speaker 1: Yeah. Even if the large language models hit an esumptope tomorrow,
Speaker 1: meaning the scaling laws break down and they stop getting smarter,
Speaker 1: they just freeze at their current intelligence level, the capabilities
Speaker 1: they already possess today are extraordinary. The foundational infrastructure is real,
Speaker 1: the miracle is real, but the financial structure currently holding
Speaker 1: it up is completely doomed.
Speaker 3: Which brings us to the most urgent question for anyone
Speaker 3: listening to thrilling threads right now. If we accept the
Speaker 3: premise that this debt is eventually going to crush the
Speaker 3: first movers, we need to trace exactly where that colossal,
Speaker 3: unpayable debt is currently hiding in the global.
Speaker 1: Economy and just how unfathomably big it actually is. Yes,
Speaker 1: that takes us into the sheer physical scale of what
Speaker 1: is being built right now. When you hear the word gigawatt,
Speaker 1: usually think of Doc Brown and Back to the Future, right,
Speaker 1: one point twenty one gigawats to power the DeLorean, Right.
Speaker 1: But the numbers being discussed in this source material make
Speaker 1: time Track look like a high school science fare project.
Speaker 1: Let's talk about the Stargate Data Center, and the jaw
Speaker 1: dropping risk Oracle is taking.
Speaker 3: On the hardware footprint discussed in the source is staggering.
Speaker 3: We are talking about a specific project where Oracle is
Speaker 3: reportedly building seven point one gigawats of new data center
Speaker 3: capacity for just one single customer.
Speaker 2: Open AI, Open AI.
Speaker 1: I want to make sure you, the listener, understand the
Speaker 1: sheer physical magnitude of seven point one gigawatts. A standard
Speaker 1: massive nuclear power plant produces about one gigawatt of electricity.
Speaker 1: Oracle is trying to secure the power equivalent of seven
Speaker 1: nuclear reactors just to run the cooling fans and power
Speaker 1: the GPUs for one company's artificial intelligence models.
Speaker 2: It's almost incomprehensible the.
Speaker 1: Amount of copper wire, the land rights, the water required
Speaker 1: for the cooling towers. It is an infrastructure project on
Speaker 1: the scale of building a new city.
Speaker 3: And here is the math of doom that the source
Speaker 3: lays out regarding that specific project. To pay for the
Speaker 3: construction and operation of just this one stargate level data center,
Speaker 3: it is estimated that open ai will need to generate
Speaker 3: roughly seventy five billion dollars in annual compute revenue.
Speaker 1: Seventy five billion dollars a year from a company that
Speaker 1: we established at the very beginning of this conversation is
Speaker 1: currently losing over twenty billion dollars a year with inverted
Speaker 1: profit margins.
Speaker 3: Yep.
Speaker 1: So what happens when, not if, but when open ai
Speaker 1: inevitably cannot pay that seventy five billion dollar bill to
Speaker 1: Larry Ellison an Oracle.
Speaker 3: This is where a localized tech sector failure has the
Speaker 3: potential to cascade into a systemic macroeconomic threat. If open
Speaker 3: ai cannot generate the revenue to pay Oracle, then Oracle
Speaker 3: cannot pay back them.
Speaker 1: If Oracle stock tanks because of a massive default, the
Speaker 1: banks will issue a margin call, forcing Ellison or other
Speaker 1: major holders to execute a massive sell loss to cover
Speaker 1: the loans, which crashes the stock even further, wiping out
Speaker 1: billions in market cap in a matter of days.
Speaker 2: A house of cards.
Speaker 1: But that's just the billionaire class taking a haircut. Why
Speaker 1: should you, the average person listening to this on your
Speaker 1: commute to work, care if Oracle stock drops.
Speaker 3: Because this is tom Billy's gravest, most urgent warning in
Speaker 3: the entire video. He states that the financial system is
Speaker 3: currently running the two thousand and eight playbook all over again.
Speaker 1: Okay, stop right there, the two thousand and eight playbook.
Speaker 1: Are you saying the banks are doing to data centers
Speaker 1: what they did to housing? Walk me through how a
Speaker 1: tech center in the desert becomes a subprime mortgage crisis.
Speaker 3: Okay, So, in the lead up to the two thousand
Speaker 3: and eight financial crisis, the banks had a massive problem.
Speaker 3: They were issuing terrible, highly toxic subprime mortgages to people
Speaker 3: who could never pay them back. To hide this risk,
Speaker 3: the banks use a mechanism called securitization. They took thousands
Speaker 3: of these toxic mortgages, slice them up into tiny pieces,
Speaker 3: mixed them in a blender with a few good mortgages,
Speaker 3: and created mortgage backed securities a dreaded MBS exactly, And
Speaker 3: then the rating agencies stamp these toxic bundles with a
Speaker 3: triple A safety rating, declaring them as safe as government bonds.
Speaker 1: They hid the poison inside a seemingly safe, diversified investment
Speaker 1: and then sold it to everyone's pension fund.
Speaker 3: Exactly, and according to our source, the exact same financial
Speaker 3: engineering is happening right now with this AI infrastructure debt.
Speaker 1: You're kidding.
Speaker 3: No, The major banks are financing hundreds of billions of
Speaker 3: dollars to build these speculative data centers. They know the
Speaker 3: debt is highly risky because companies like.
Speaker 2: Open ai are burning cash.
Speaker 3: So to mitigate their own risk and get the debt
Speaker 3: off their balance sheets, they are packaging these massive data
Speaker 3: center loans into complex financial instruments, diversifying that debt out
Speaker 3: into the broader economy.
Speaker 1: So they are taking this highly volatile, extremely risky AI
Speaker 1: infrastructure debt and hiding it inside corporate bond ETFs, inside
Speaker 1: insurance company portfolios, inside standard retirement programs.
Speaker 2: That is the fear. It's entirely possible.
Speaker 3: The source warns that this debt is being packaged up
Speaker 3: given a high credit rating because it's technically backed by
Speaker 3: massive tech conglomerates and hidden in place as the average
Speaker 3: investor would never ever think to look.
Speaker 1: I want to speak directly to you for a second.
Speaker 1: You really have to wonder exactly what is hiding inside
Speaker 1: your own four to one k right now. Truly, when
Speaker 1: you put your money into a broad tech ETF or
Speaker 1: a standard S and P five hundred index fund, you
Speaker 1: probably think you are just broadly inventing in the steady
Speaker 1: growth of the American economy. You think you're being responsible
Speaker 1: sase right, But you might actually be holding the bag
Speaker 1: for a seven point one gigawat data center that requires
Speaker 1: an impossible seventy five billion dollars a year just to
Speaker 1: break even. If that domino falls, if that debt defaults,
Speaker 1: it's not just tech executives losing their bonuses, it's your
Speaker 1: retirement account taking a twenty percent hit overnight.
Speaker 3: The market is hyper aware of this fragile tightrope, even
Speaker 3: if the retail investor isn't. The source quotes a Goldman
Speaker 3: Sachs analyst who pinpointed exactly what the trigger for the
Speaker 3: collapse will be. The analyst noted quote, the first hyperscaler
Speaker 3: to pull CAPEX will get rewarded by the markets.
Speaker 1: Let's break down that terminology. CAPEX stands for a capital expenditure.
Speaker 1: It's the money a company spends to buy, maintain, or
Speaker 1: improve its fixed physical assets. In this context, it's the
Speaker 1: tens of billions of dollars Amazon, Google, and Microsoft are
Speaker 1: spending to buy in Nvidia chips and build these massive
Speaker 1: data centers.
Speaker 3: Right now, everyone is caught in a prisoner's dilemma. It's
Speaker 3: an arms race. They are spending blindly because they are
Speaker 3: terrified that if they stop, their competitors will achieve artificial
Speaker 3: general intelligence first and just wipe them off the map.
Speaker 3: But the moment one of the giants, say Google or Microsoft, blinks,
Speaker 3: the moment they step back, look at the inverted margins
Speaker 3: and announce, we are stopping our capital expenditures. We aren't
Speaker 3: building the next data center because the math doesn't work.
Speaker 2: The market might actually cheer for them.
Speaker 3: Yes, the stock might jump because investors will be relieved
Speaker 3: they finally stop the bleeding.
Speaker 1: But Zitron argues that a pullback in capex or a
Speaker 1: freeze on data center debt issuance will signal bedtime for
Speaker 1: this industry. Is the entire illusion. The entire multi trillion
Speaker 1: dollar valuation of the AI sector relies on the narrative
Speaker 1: of hypergrowth and constant, unstoppable forward momentum. Exactly the moment
Speaker 1: the music stops. The moment one major player admits the
Speaker 1: economics are flawed, everyone looks around and realizes there aren't
Speaker 1: nearly enough chairs.
Speaker 3: The bubble pops, which logically forces us to ask if
Speaker 3: the financials are truly this toxic, If the margins are inverted,
Speaker 3: the debt is astronomical, and the enterprise clients are revolting
Speaker 3: over ip theft, why on earth do companies like Microsoft,
Speaker 3: Meta and Google keep spending these tens of billions of
Speaker 3: dollars quarter after quarter? R What is their actual motivation
Speaker 3: if they know the math is broken?
Speaker 1: Ed Zitron has a brutally simple, almost cynical explanation for
Speaker 1: why big tech keeps throwing cash into the furnace. He
Speaker 1: claims they are completely out of ideas.
Speaker 3: It's a fascinating psychological and financial diagnosis of the tech giants.
Speaker 3: Let's examine the shell game big tech is pulling here.
Speaker 3: According to the source, companies like Meta, Microsoft, Google, and
Speaker 3: Amazon currently hold massive, unassailable monopolies in legacy digital areas.
Speaker 1: Sure Meta owns global social media and digital advertising, Google
Speaker 1: essentially owned search, Amazon dominates e commerce and basic cloud hosting.
Speaker 3: Right, and these legacy businesses are still highly profitable, and
Speaker 3: they are still generating massive amounts of free cash flow.
Speaker 1: But they're massive, mature companies. You can't show eighty percent
Speaker 1: year over year growth when you already own the entire earth.
Speaker 1: Wall Street demands a growth story always, so they hide
Speaker 1: their massive AI financial losses behind the revenue growth of
Speaker 1: those legacy businesses. The source specifically points out a major
Speaker 1: red flag. Microsoft and Amazon refuse to disclose their true
Speaker 1: isolated AI run rate revenues. They will not separate the
Speaker 1: numbers out for investors to see, which is crazy, and
Speaker 1: they just roll the AI costs in the AI revenue
Speaker 1: into one big, happy combined cloud services number.
Speaker 3: And as the source rightly notes, public companies absolutely love
Speaker 3: sharing good news. If Microsoft's A I division was generating
Speaker 3: billions in pure profit, they would be screaming it from
Speaker 3: the rooftops on every single quarterly earnings call.
Speaker 2: Oh totally, they would print.
Speaker 3: It in bold font on the first page of the prospectus.
Speaker 3: The fact that they are intentionally burying the AI numbers
Speaker 3: inside their general cloud revenue tells you they have something
Speaker 3: to hide. Zitron argues it is a trillion dollar.
Speaker 1: Facade, a shell game.
Speaker 3: Yes, they are using the massive profits from search and
Speaker 3: social media to subsidize the catastrophic AI losses, all designed
Speaker 3: to maintain the illusion that these companies are still capable
Speaker 3: of hypergrowth. They desperately need investors to believe there is
Speaker 3: a next iPhone or a next Google Search right around
Speaker 3: the corner, because that is the only way to justify
Speaker 3: their astronomical, historically unprecedented stock valuations.
Speaker 1: If you just listen to Zitron's side of the argument,
Speaker 1: you walk away thinking artificial intelligence is literally just an
Speaker 1: elaborate financial scam designed to sell in Nvidia computer chips
Speaker 1: to gullible executives.
Speaker 2: Right, it sounds incredibly bleak.
Speaker 1: But I have to step in here and forcefully pushed
Speaker 1: back against Zitron's pure, unadulterated pessimism because Tom bill U
Speaker 1: provides massive, concrete, real world evidence of AI's power that
Speaker 1: we simply cannot ignore. The financial model might be a
Speaker 1: house of cards, but the utility of the software is undeniable.
Speaker 3: The rebuttal is crucial for a balanced understanding. We are
Speaker 3: not just talking about chatbots writing bad slam poetry or
Speaker 3: generating weird pictures of hands with six fingers. All right,
Speaker 3: The real world miracles are already happening, and they are
Speaker 3: generating massive tangible value.
Speaker 1: Let's talk about the specific examples bill you brought up
Speaker 1: to counter the bear case. First, he looked at his
Speaker 1: own company, which develops video games. He stated that a
Speaker 1: massive amount of their C plus plus programming code, the
Speaker 1: foundational logic of the software, is now being written directly by.
Speaker 2: AI, which is huge for efficiency.
Speaker 1: It has fundamentally transformed the speed and cost of their
Speaker 1: software development. What used to take a team of engineers
Speaker 1: weeks can now be prototyped in an afternoon.
Speaker 3: Then he points to the medical and scientific industry, which
Speaker 3: is perhaps the most profound application of this technology to date.
Speaker 3: Billiu explicitly mentions AI's ability to fold tens of thousands
Speaker 3: of proteins.
Speaker 1: I want to pause and highlight that because I think
Speaker 1: a lot of people don't realize how massive that breakthrough is.
Speaker 1: Protein folding used to be one of the most computationally difficult,
Speaker 1: time consuming tasks in all of biology decades of work. Right,
Speaker 1: a proteins function is entirely determined by its three dimensional shape.
Speaker 1: Figuring out how a one dimensional string of amino acids
Speaker 1: folds into that three D shape is so mathematically complex
Speaker 1: that it could take a human researcher armed with a
Speaker 1: PhD and a lab full of equipment five years just
Speaker 1: to map the shape of one single.
Speaker 2: Protein five years for one.
Speaker 1: But AI systems, specifically Deep minds Alpha fold, have successfully
Speaker 1: predicted the structure of hundreds of millions of proteins essentially overnight.
Speaker 1: It solved the fifty year Grand challenge in biology in
Speaker 1: a matter of months.
Speaker 2: Mind blowing.
Speaker 1: It is in sustantaneously revolutionizing drug discovery and disease treatment.
Speaker 1: That is not a nothing burger. That is a paradigm
Speaker 1: shift in human medicine that will save millions of lives.
Speaker 3: Absolutely, And when you look at the physical world, Bill
Speaker 3: Yu points to Elon Musk's Tesla. Billy recounts a personal
Speaker 3: experience writing in a Tesla with the full self driving
Speaker 3: software activated.
Speaker 1: Oh yeah, this story was wild.
Speaker 3: He describes navigating five or six miles up through the
Speaker 3: winding complex, unstructured roads of the Hollywood Hills construction zones, pedestrians,
Speaker 3: unpredictable traffic, and the human driver never touched the steering
Speaker 3: wheel once. That level of spatial awareness, object permanence, and
Speaker 3: real time decision making in a high stakes physical environment
Speaker 3: is entirely driven by neural networks, So we.
Speaker 1: Have this incredible tension. The financials are a disaster, the
Speaker 1: margins are upside down, but the technological output is practically miraculous.
Speaker 1: How do we reconcile that?
Speaker 3: Tom Billy Use synthesizes this contradiction by defining our current
Speaker 3: era as the Golden Age of AI.
Speaker 1: Why call it the golden age if the models are
Speaker 1: flawed and hallucinating.
Speaker 3: Because the flaws are exactly what keep humans in the
Speaker 3: loop Right now, the AI is incredibly powerful, but it
Speaker 3: still falls down. It still hits limits, It still hallucinates
Speaker 3: fake case law or writes a line of code with
Speaker 3: a critical security bug, and because of those flaws, humans
Speaker 3: are still totally one hundred percent necessary. The AI makes
Speaker 3: us immensely more productive. It writes the bulk of the
Speaker 3: C plus plus code, It predicts the protein structures, It
Speaker 3: steers the car, but it still requires a human hand
Speaker 3: on the tiller to guide it, verify its output, and
Speaker 3: fix its mistakes.
Speaker 1: It's augmenting us.
Speaker 3: It hasn't erased human labor yet. It is a tool
Speaker 3: of unprecedented leverage. Rather than an autonomous replacement. We get
Speaker 3: the massive boost and productivity without being rendered obsolete.
Speaker 1: But that Golden Age leverage still requires massive amounts of electricity,
Speaker 1: It still requires seven point one gig a lot data centers,
Speaker 1: It still requires hundreds of billions in funding. So if
Speaker 1: Big tech is playing a shell game with their earnings
Speaker 1: to hide the losses private Wall Street investors might eventually
Speaker 1: get spooked by the massive debt and trigger a two
Speaker 1: thousand and eight style crash. Is there a force big
Speaker 1: enough to prop this bubble up even if the free
Speaker 1: market completely rejects it.
Speaker 3: The short answer to that question is yes, and it
Speaker 3: introduces perhaps the most chilling element of this entire analysis.
Speaker 2: There is one entity with enough.
Speaker 3: Capital and more importantly, enough strategic motivation to ignore the
Speaker 3: rules of the free market entirely, the United States government.
Speaker 1: This was the part of the source material that genuinely
Speaker 1: made the hair on the back of my next stand up. Yeah,
Speaker 1: we've been talking about AI as software, as chatbots and
Speaker 1: code assistants, but the video reveals some shocking details about
Speaker 1: the true classified capabilities of these models. Behind closed doors,
Speaker 1: we are introduced to two specific models, anthropics Fable five
Speaker 1: and open AI's mythos and.
Speaker 3: The anecdote surrounding these specific models completely reframe the conversation
Speaker 3: from economics to national security. According to the source, the internal,
Speaker 3: unrestricted version of Anthropics model, allegedly code named Fable five,
Speaker 3: was profoundly capable of offensive cyber operations like hacking. Yes,
Speaker 3: it was reportedly able to hack into legacy computer systems,
Speaker 3: autonomously finding and exploiting zero day bugs that human engineers
Speaker 3: didn't even know existed. It was so potent that Anthropic
Speaker 3: voluntarily went to the US government to report its capabilities.
Speaker 1: They basically walked into a secure facility and said, we
Speaker 1: built something too dangerous to release exactly, and the version
Speaker 1: of the AI that was eventually released to the public,
Speaker 1: the claud model you or I can use on our phones,
Speaker 1: was heavily nerved, meaning its capabilities were intentionally restricted, downgraded,
Speaker 1: and lobotomized to make it safe for public consumption.
Speaker 3: The source mentioned similar rumors regarding open AI's Mythos model.
Speaker 3: It is reportedly so advanced at coding and potential cyber
Speaker 3: warfare operations that it is essentially being viewed by defense
Speaker 3: intelligence as a weapon system. A weapon system a weapon
Speaker 3: system that under current regulations cannot be freely exported to
Speaker 3: certain allies for fear of the underlying model weights being
Speaker 3: stolen or reversed engineered by adversaries.
Speaker 1: If the federal government is stepping in behind closed doors
Speaker 1: and saying this software is equivalent to a cruise missile
Speaker 1: or a stealth bomber, that changes the economic reality of
Speaker 1: the industry entirely completely, because you do not let the
Speaker 1: company building your stealth bombers go bankrupt just because their
Speaker 1: consumer division had a bad quarter. Lockheed Martin doesn't go
Speaker 1: out of business because the free market didn't buy enough
Speaker 1: fighter jets.
Speaker 3: It introduces the ultimate wildcard the geopolitical arms race. The
Speaker 3: source explicitly mentions the market shockwave caused by the launch
Speaker 3: of China's deep Seat ai model oh.
Speaker 1: The Deep Seak launch was a massive market event. For
Speaker 1: a long time, the narrative was that American tech giants
Speaker 1: had an insurmountable lead right a massive moat built on
Speaker 1: Nvidia chips and endless capital. But when Deep Seak launched,
Speaker 1: it demonstrated that China was not only catching up, but
Speaker 1: potentially matching or surpassing American AIQ capabilities using significantly less
Speaker 1: compute power. The reaction to the free market was pure
Speaker 1: unadulterated panic.
Speaker 3: The source notes that the realization of the deepseek threat
Speaker 3: wiped out nearly a trillion dollars in US tech market
Speaker 3: value in a single day.
Speaker 1: A trillion dollars in one day.
Speaker 3: Investors were terrified that America was losing the AI race,
Speaker 3: and all the projected future dominance of US tech giants
Speaker 3: evaporated instantly.
Speaker 2: It proved the moat was an illusion.
Speaker 1: Put yourself in the shoes of a defense strategist. You
Speaker 1: have a technology that is recognized as a strategic military
Speaker 1: asset capable of taking down power grids or hacking financial systems,
Speaker 1: and you have a hostile foreign power actively trying to
Speaker 1: beat you to artificial superintelligence. In that scenario, does the
Speaker 1: free market even apply anymore?
Speaker 3: The source explicitly poses this exact question. Tom Billieu asks
Speaker 3: aloud whether these companies will eventually be nationalized. There are
Speaker 3: already heavy rumors cited in the video that the US
Speaker 3: administration or perhaps a US backed sovereign wealth fund is
Speaker 3: considering taking a five percent direct ownership stake in Open
Speaker 3: AI to ensure it has the capital to continue operating
Speaker 3: regardless of its cash burn.
Speaker 1: Billy says he finds that kind of government intervention economically disruptive.
Speaker 1: He's a free market guy. But from a geopolitical standpoint,
Speaker 1: if AI is truly a weapon system, the government literally
Speaker 1: cannot let these companies fail.
Speaker 3: And this is where the historical inheritance generation cycle we
Speaker 3: discussed earlier might actually get broken.
Speaker 1: All right.
Speaker 3: Historically, the government stood by and let the railroad companies
Speaker 3: go bankrupt. They let the internet telecom companies go bankrupt
Speaker 3: in the nineties. The free market was allowed to destroy
Speaker 3: the first movers so the second wave could rebuild efficiently.
Speaker 3: But if OpenAI is holding the keys to national cybersecurity
Speaker 3: and global dominance, the government might be forced to step
Speaker 3: in and bail them.
Speaker 1: Out, bail out the Stargate Data center.
Speaker 3: They might absorb that massive debt directly onto the national
Speaker 3: balance sheet, entirely altering the pure free market destruction cycle.
Speaker 1: It becomes too big to fail, but not just for
Speaker 1: the economy, too big to fail for national survival. It
Speaker 1: is a wildly complex, terrifying web. We've gone from throwing
Speaker 1: twenty billion dollars into a campfire to stealing corporate IP
Speaker 1: to the ghosts of the railroad barons, to hiding toxic
Speaker 1: data center debt in your retirement accounts, and finally to
Speaker 1: AI as a classified military weapon system.
Speaker 3: The share volume of variables is overwhelming, But if we
Speaker 3: try to synthesize this entire journey, what we see is
Speaker 3: a landscape defined by absolute extremes. We are undeniably writing
Speaker 3: a rocketship of world altering technological progress. It's predicting protein structures,
Speaker 3: driving cars, and rewriting software. But that rocketship is currently
Speaker 3: fueled entirely by an unsustainable, systemically dangerous financial debt model
Speaker 3: that defies basic economic gravity and relies on the assumption
Speaker 3: of infinite capital.
Speaker 1: It's brilliant and broken at the exact same time. And
Speaker 1: as we wrap up this thrilling threads, I want to
Speaker 1: leave you with a final provocative thought tom all Over,
Speaker 1: something that builds on what Alex Karp and Edzitra were
Speaker 1: arguing about utility. If they are right, and the massive
Speaker 1: godlike intelligence of these large language models eventually just asymptotes.
Speaker 1: If it hits a wall and becomes a cheap, boring,
Speaker 1: everyday commodity like electricity, then the true winners of this
Speaker 1: era won't be the companies burning twenty billion dollars to
Speaker 1: build the biggest brains today.
Speaker 3: History proves that exact point. The people who made the
Speaker 3: most money off the discovery of electricity weren't necessarily the
Speaker 3: people building the power plants and stringing the high ritage lines.
Speaker 3: The massive fortunes were made by the people who invented
Speaker 3: the light bulb, the washing machine, the television, the appliance makers.
Speaker 1: If AHI just becomes cheap electricity, the winners will be
Speaker 1: the quiet focused companies who learn how to plug that
Speaker 1: cheap intelligence into everyday objects. The company's transforming how we
Speaker 1: discover medicine, how we manage a global supply chain, or
Speaker 1: even just how we interact with the cherry you're sitting
Speaker 1: on right now. The true revolution won't be the giant
Speaker 1: AI brain in the desert. It will be what the
Speaker 1: inheritance generated figures out how to plug into it exactly.
Speaker 1: So what do you think? Are you looking at your
Speaker 1: index funds and your four to one K a little
Speaker 1: differently today? Do you believe we are heading for a
Speaker 1: catastrophic two thousand and eight style AI debt crash? Or
Speaker 1: will the technology advance fast enough and prove useful enough
Speaker 1: to save itself from the math. Drop down into the
Speaker 1: comments and tell us exactly where you stand. We read
Speaker 1: every single one. Thank you for joining us, for throwing threads.
Speaker 1: We'll see you next time.