Speaker 1: Welcome to the debate. So imagine signing a multimillion dollar
Speaker 1: corporate IT contract. Okay, but you pay absolutely nothing in
Speaker 1: year one.
Speaker 2: Wait, nothing like zero zero.
Speaker 1: Your payment in here two depends entirely on whether the
Speaker 1: artificial intelligence actually saves you money.
Speaker 2: Oh right, the outcome based.
Speaker 1: Model, exactly, And that exact deal is happening right now
Speaker 1: in Germany. But while AI is driving the software costs
Speaker 1: to near zero, on the ground, it's simultaneously triggering this
Speaker 1: massive I mean, it's a two hundred and twenty billion
Speaker 1: dollar debt tsunami. Yeah, it's huge, and that threatens to
Speaker 1: hike the cost of borrowing for you know, the entire
Speaker 1: global economy. So the core question we're looking at today
Speaker 1: is are we entering an era of unprecedented deflationary productivity
Speaker 1: or are we just building an inflationary debt bomb.
Speaker 2: And the tension there is I mean, that is exactly
Speaker 2: what we are unpacking today. We're looking at this fundamental
Speaker 2: divergence in macroeconomic analysis. Right We're drawing on recent warnings
Speaker 2: from the Swiss National Bank and the IMF alongside the
Speaker 2: real time structural upheaval in India's IT services sector, which
Speaker 2: is what a three hundred and fifteen billion dollar industry.
Speaker 1: Yeah, three hundred and fifteen it's massive.
Speaker 2: Right, And of course we have to look at the
Speaker 2: record breaking surge in US corporate debt right now.
Speaker 1: Definitely, And so to lay out where I stand, my
Speaker 1: position is that AI is fundamentally a profound deflationary force. Okay,
Speaker 1: we are witnessing an unprecedented wave of productivity gains. That
Speaker 1: is well, it's already restructuring global service delivery. When you
Speaker 1: decouple human labor hours from economic output, you create this
Speaker 1: long term structural mechanism that drives costs down across the board.
Speaker 2: See, I think you're hyper focusing on the end product
Speaker 2: while completely ignoring what it takes to build the factory.
Speaker 2: How so well, I contend that the massive redirection of capital,
Speaker 2: the strain on physical resources, and just the sheer scale
Speaker 2: of debt required to actually build this AI infrastructure, it
Speaker 2: creates undeniable systemic inflationary pressures in the short to medium term.
Speaker 2: I hear that, But and the assumption that short term
Speaker 2: capital exhaustion just magically guarantees long term structural deflation that
Speaker 2: is a massive leap of faith that the data simply
Speaker 2: doesn't support right now.
Speaker 1: Well, let's break down exactly how this productivity engine is
Speaker 1: functioning in the real world, because it's not theoretical. Fair enough,
Speaker 1: Look at the Indian IT industry. For decades, this sector
Speaker 1: operated on a very specific mechanism, which was labor.
Speaker 2: Arbitrage, right hiring overseas.
Speaker 1: Exactly, you hired one thousand developers because paying someone in
Speaker 1: Bangalore was structurally cheaper than paying someone in New York.
Speaker 1: The business model was literally just selling human time by
Speaker 1: the hour.
Speaker 2: The billable hours model.
Speaker 1: Yeah right, But today that entire paradigm is being dismantled.
Speaker 1: Clients are breaking the traditional reliance on those billable.
Speaker 2: Hours because they want more for less.
Speaker 1: Exactly, they are demanding more for less. Major players are
Speaker 1: restructuring their business models toward measurable performance outcomes. So when
Speaker 1: you decouple human labor from output, meaning you know you
Speaker 1: can deliver five times the software analysis using an AI
Speaker 1: model without adding a single human headcount, you have achieved
Speaker 1: the literal textbook definition of structural productivity.
Speaker 2: I see what you're saying.
Speaker 1: But you lower the baseline cost of operations for the
Speaker 1: broader economy.
Speaker 2: That is deflationary, okay, but lowering a baseline cost in
Speaker 2: one specific sector does not equal macroeconomic deflation.
Speaker 1: Why not if it's a massive sector, because you're.
Speaker 2: Looking at downstream IT efficiency, which completely obscures the upstream
Speaker 2: macro costs of the AI buildout. We need to look
Speaker 2: closely at the framework laid out by Petrachudin from the
Speaker 2: Swiss National Banks Governing Board.
Speaker 1: Okay, yeah, Chudin's report.
Speaker 2: And also Savannah Tenrero at the IMF. Redirecting these massive
Speaker 2: global investment flows to build out AI, it creates immediate
Speaker 2: severe physical shortages. You mean like the chips and things, ships, Absolutely,
Speaker 2: but we're also talking about energy grids, cooling systems, specialized silicon.
Speaker 2: When you drain resources to fund one specific technological boom,
Speaker 2: prices for those underlying resources skyrocket. Well sure in the
Speaker 2: short term, but the capital markets are flashing red right now.
Speaker 2: By August of this year, AI hyperscalers. So the massive
Speaker 2: tech companies building this infrastructure, they issued a record two
Speaker 2: hundred and twenty billion dollars in corporate debt right that
Speaker 2: is up from just twelve point five billion in the
Speaker 2: same period last year.
Speaker 1: It's a big jump. I admit it's.
Speaker 2: An aggressive capital drain. And that strains credit markets, pushes
Speaker 2: up yields, and acts as a present undeniable inflationary force today,
Speaker 2: not in some theoretical future.
Speaker 1: But I think you are treating a temporary supply bottleneck
Speaker 1: like it's a permanent feature of the economy.
Speaker 2: Two hundred and twenty billion is a pretty big bottleneck.
Speaker 1: It is, But let's look at what that debt is
Speaker 1: actually buying and how it changes corporate contracts on the ground.
Speaker 1: You mentioned the downstream efficiency as if it's just a
Speaker 1: minor detail.
Speaker 2: I didn't say it was minor.
Speaker 1: I just it is the entire point. Historically, outsourcing was
Speaker 1: a numbers game. Now, as Jim at Arroya of the
Speaker 1: Everest Group points out, the leverage has completely flipped to
Speaker 1: the clients. Okay, they do not care how many human
Speaker 1: hours go into a project anymore. They are paying for
Speaker 1: algorithmic certainty.
Speaker 2: Which is exactly why the Nifty IT index tumbled by
Speaker 2: a fifth this year. Yes, it wiped out seventy three
Speaker 2: billion dollars in market value for its top ten constituents.
Speaker 2: The old human heavy model isn't just shifting, it's collapsing
Speaker 2: under its own weight, exactly because human labor is suddenly
Speaker 2: viewed as a liability, not an asset.
Speaker 1: It is collapsing and what is replacing it is vastly
Speaker 1: more efficient for the ends consumer. Let's look at the
Speaker 1: mechanics of these new deals, all right. Tcs's chief executive
Speaker 1: recently confirmed that roughly eighty percent of their finance, HR
Speaker 1: and business services contracts are now based on outcome performance measures.
Speaker 2: Eighty percent. Wow, yeah, that figure has.
Speaker 1: Doubled since late twenty twenty three. We are seeing deals
Speaker 1: like Cognizant partnering with Dailer Truck where they are literally
Speaker 1: splitting the AI related cost savings on the balance sheet, right,
Speaker 1: or HCl Tech's cloud management deal with Eon. That's the
Speaker 1: one I mentioned. At the start, HCl tech takes on
Speaker 1: all the upfront risk, they don't get paid until year
Speaker 1: two and only if the efficiency gains actually materialize.
Speaker 2: It's a bold strategy, it is.
Speaker 1: This isn't just a slight discount. This is a brutal,
Speaker 1: hyper efficient market mechanism that forces price reduction. The provider
Speaker 1: takes the risk, the AI does the heavy lifting, and
Speaker 1: the broader economy gets cheaper services.
Speaker 2: At the micro level. Sure Gamler Truck and Eon or
Speaker 2: outside sslutely saving money on their IT contracts. But let's
Speaker 2: apply Petrachudin's macroeconomic lens here again. Okay, she explicitly points
Speaker 2: out that productivity gains are not a new phenomenon. They
Speaker 2: happen regularly throughout history. True, but they do not automatically
Speaker 2: lead an economy into structural deflation. Deflation is a macroeconomic calculation.
Speaker 2: It is a rate of change, right. It's not a single.
Speaker 1: Event, right, It's annualized exactly.
Speaker 2: For AI to have a true deflationary effect on the
Speaker 2: global economy, those price declines would have to repeat themselves
Speaker 2: regularly on an annual basis across all sectors.
Speaker 1: But if inflation is the annualized rate of change, a
Speaker 1: permanent restructuring of how a service is delivered permanently alters
Speaker 1: that trajectory.
Speaker 2: It shifts the baseline once. A one time thirty percent
Speaker 2: drop in IT servicing costs is a shock to the system. Sure,
Speaker 2: but the following year, if that cost stays flat at
Speaker 2: the new lower level, inflation is zero not negative.
Speaker 1: Okay, point there.
Speaker 2: It doesn't create systemic compounding deflation. Meanwhile, the mechanism required
Speaker 2: to achieve that one time drop.
Speaker 1: Is just staggering the debt buildout.
Speaker 2: The investment flows. Yes, they are being aggressively redirected away
Speaker 2: from other productive sectors of the economy to build out
Speaker 2: server farms secure vast amounts of energy by silicon Right.
Speaker 2: Shootin warns this exact dynamic creates shortages and painful adjustments
Speaker 2: for the rest of the economy. The upward inflationary pressure
Speaker 2: of building the infrastructure entirely offsets the downstream software savings
Speaker 2: in the short and medium term.
Speaker 1: Okay, but you are evaluating this transition as a standard
Speaker 1: capital expenditure cycle rather than a fundamental we wiring of
Speaker 1: economic production.
Speaker 2: How is it different from any other major infrastructure build.
Speaker 1: Think about the introduction of the assembly line. But let's
Speaker 1: push the mechanics of that analogy a bit further.
Speaker 2: Okay, I'm listening.
Speaker 1: The assembly line didn't just lower the cost of a
Speaker 1: model T once it destroyed bloated legacy manufacturing models. True,
Speaker 1: but crucially, the assembly line still required you to pay
Speaker 1: a human and hourly wage to stand there and turn
Speaker 1: a wrench.
Speaker 2: Right.
Speaker 1: What TCS, Cognizant and eonor doing with outcome based pricing
Speaker 1: is the equivalent of buying the cars without having to
Speaker 1: pay for the factory floor time. They have entirely severed
Speaker 1: human time from economic value.
Speaker 2: I mean, that's a nice analogy, but.
Speaker 1: Wait, just look at the firms. Massive legacy firms like
Speaker 1: Infosis and whip Row are seeing subdued growth of you know,
Speaker 1: one to three percent because they're stuck in the old model.
Speaker 1: But look at the agile tier two firms right Persistent
Speaker 1: saw revenue serge sixteen percent in a single quarter. Coforge
Speaker 1: is sales jumped by a third. They are deploying AI
Speaker 1: for rabid pilot programs that automate millions of manual data
Speaker 1: entry hours.
Speaker 2: Yeah, they're moving fast.
Speaker 1: That brutal market efficiency is exactly the mechanism that guarantees
Speaker 1: compounding law long term price reductions across every industry that
Speaker 1: uses software.
Speaker 2: The assembly line analogy falls apart, though, when you look
Speaker 2: at the macro financial plumbing required to sustain it. How
Speaker 2: because the assembly line didn't require the entire global debt
Speaker 2: market to bend to its will before a single car
Speaker 2: rolled off the floor.
Speaker 1: That's a bit dramatic, don't you think?
Speaker 2: Not really, you cannot separate the software efficiencies you're praising
Speaker 2: from the hardware and energy realities required to run them.
Speaker 2: The hyperscalers, you know, the Amazons, Googles, Microsofts. They are
Speaker 2: triggering a debt surge that is testing the absolute structural
Speaker 2: limits of investor demand.
Speaker 1: It's a lot of debt.
Speaker 2: Yes, but let's examine Amazon's recent long dated bond sale.
Speaker 2: They raised twenty five billion dollars, right, but it priced
Speaker 2: at roughly one hundred and twenty basis points over treasuries
Speaker 2: last year that spread would have been half of that.
Speaker 1: Okay, but you're citing one hundred and twenty basis points
Speaker 1: spread as if it's an economic crice.
Speaker 2: It's a significant indicator.
Speaker 1: A wider spread just means investors want a slightly higher premium.
Speaker 1: It doesn't indicate a broken capital market. It just indicates
Speaker 1: standard supply and demand.
Speaker 2: It indicates severe investor fatigue and a rising fear premium.
Speaker 2: Let's translate what those basis points actually mean for the
Speaker 2: broader market. Sure Tech corporate bond spreads are currently at
Speaker 2: eighty nine basis points. That is, nine basis points wider
Speaker 2: than the overall investment grade market.
Speaker 1: Okay.
Speaker 2: Neil Sutherland at Schroeders pointed this out. Clearly, tech has
Speaker 2: historically traded materially tighter, meaning it was cheaper for them
Speaker 2: to borrow than almost anyone else because they were seen
Speaker 2: as incredibly safe.
Speaker 1: Right, they had a built in advantage exactly.
Speaker 2: But now they are trading wider than the average corporation.
Speaker 2: Alphabet had to offer a concession of ten to fifteen
Speaker 2: basis points just to get their recent offering.
Speaker 1: Absorbed, a small concession.
Speaker 2: But investors are demanding a penalty because the sheer volume
Speaker 2: of this two hundred and twenty billion dollar debt issuance
Speaker 2: is overwhelming the market's plumbing.
Speaker 1: Okay, but you're describing a temporary resupply dynamic, not a
Speaker 1: permanent structural flaw. The hyperscalers still carry incredibly strong corporate ratings.
Speaker 2: I'm not saying they're going bankrupt.
Speaker 1: They have fortress like balance sheets and they generate massive,
Speaker 1: reliable cash flows. Karen Choi, a capital group, noted that
Speaker 1: the investment grade corporate bond index is yielding around five
Speaker 1: point four percent. Right, that yield is perfectly in line
Speaker 1: with long term historical averages. Foreign investors, pension funds, insurance companies.
Speaker 1: They are still eagerly absorbing this issuance because the fundamentals
Speaker 1: of the underlying businesses are solid.
Speaker 2: They are solid. Yes, they are.
Speaker 1: Investing in infrastructure that has a clear, very obvious path
Speaker 1: to profitability.
Speaker 2: Like I said, no one is arguing that Amazon is
Speaker 2: going to default on its debt. This isn't a credit
Speaker 2: risk issue in the traditional sense. It's a capacity issue
Speaker 2: capacity in what way? Think of the institutional bond market
Speaker 2: as a standard garden hose. It has a rigid, fixed capacity.
Speaker 1: Okay, I follow what.
Speaker 2: The AI hyph what for scalers are doing right now
Speaker 2: is trying to force a fire hose volume of debt
Speaker 2: through that garden hose. It doesn't matter how pure the
Speaker 2: water is, meaning how strong their credit ratings are. The
Speaker 2: hose can only take so much pressure before it backs up.
Speaker 1: Wait, let's clarify that for a moment. Why is the
Speaker 1: hose rigid? Capital markets are literally designed to be dynamic,
Speaker 1: not entirely. If there is a profitable opportunity, the market
Speaker 1: expands to fund it. Why are you treating institutional investor
Speaker 1: demand as a hard physical limit.
Speaker 2: Because of strict regulatory and portfolio mandates. Karen Choi highlighted
Speaker 2: this exact friction point, the concentration limits. Yes, many pension
Speaker 2: and insurance funds have hard rules that cap their exposure
Speaker 2: to individual issuers at like two or three percent of
Speaker 2: their total assets. Right, They literally are not allowed to
Speaker 2: hold ten percent of their portfolio and Amazon bonds, no
Speaker 2: matter how attractive the yield is, the hose is rigid
Speaker 2: because of regulation and risk management. Okay, So as the
Speaker 2: same handful of tech giants repeatedly tap the market issuing
Speaker 2: that two hundred and twenty billion in just eight months,
Speaker 2: they hit those hard portfolio constraints. Right, As George Katrombone
Speaker 2: at DWS stated, it's not a blank check. If hyperscalers
Speaker 2: keep demanding capital at this rate, they have to offer
Speaker 2: higher yields to attract buyers outside of those traditional.
Speaker 1: Channels, which they're doing.
Speaker 2: But when their yields rise universally, it causes widespread inflation
Speaker 2: in the cost of capital for everyone else.
Speaker 1: Okay, we need some definitional precision here regarding what we
Speaker 1: call inflation. All right, what you just detailed, you know,
Speaker 1: temporary spikes in chip prices or short term portfolio constraints
Speaker 1: causing wider spreads in the bond market. Those are front
Speaker 1: loaded capital expenditures.
Speaker 2: They're still costs.
Speaker 1: They are the necessary friction cost of building out a
Speaker 1: new technological paradigm. It is not systemic long term macroeconomic inflation.
Speaker 2: I disagree. I think they bleed into the macroeconomy.
Speaker 1: But when the global labor market shifts structurally, replacing millions
Speaker 1: of billable human hours with automated AI driven outcomes, you
Speaker 1: create a permanent deflationary shock. That fundamental shift in how
Speaker 1: work is priced will easily outlive this current, admittedly aggressive
Speaker 1: cycle of hyperscaler debt issuance.
Speaker 2: You are vastly underestimating how short term capital friction becomes
Speaker 2: embedded into long term systemic inflation.
Speaker 1: How so walk me through it.
Speaker 2: Let's return to Petrachudin's core warning. When you redirect hundreds
Speaker 2: of billions of dollars into AI infrastructure, you cause immediate
Speaker 2: cascade difficulties for the rest of the economy.
Speaker 1: Okay, let's trace the mechanism.
Speaker 2: Then, right now, governments are still running massive deficits, meaning
Speaker 2: they are issuing a lot of government debt. Yes, add
Speaker 2: this absolute flood of AI borrowing to the market. When
Speaker 2: hyperscalers soak up the available capital, it pushes up treasury
Speaker 2: yields because the competition for every investment dollar is fierce.
Speaker 1: Yes, competition for capital drives up yields temporarily, that is
Speaker 1: exactly how markets prioritize the most vital infrastructure projects.
Speaker 2: But look at the collateral damage. When treasury yields are
Speaker 2: pushed up, the benchmark cost of borrowing rises for the
Speaker 2: entire global economy. Right Suppose an agricultural company needs a
Speaker 2: loan to build a new tractor factory. Because the hyperscalers
Speaker 2: have soaked up so much capital, that agricultural company has
Speaker 2: to offer a significantly higher interest rate just to secure
Speaker 2: their loan.
Speaker 3: So their borrowing cost goes up, their operating cost surge.
Speaker 3: How do they recover that cost They raise the price
Speaker 3: of the tractors, which raises the price of food production,
Speaker 3: which gets passed directly to the consumer at the grocery store.
Speaker 1: I see the chain you're building.
Speaker 2: So even if a corporate HR department is saving money
Speaker 2: on their IT services down the line, the cost of housing,
Speaker 2: food production and traditional manufacturing goes up right now because
Speaker 2: capital is so expened. Okay, the immediate inflationary pressure neutralizes
Speaker 2: those promised future efficiencies. Silvana Tenrero's research at the IMF
Speaker 2: confirms this. Boosting productivity does not automatically lower consumer inflation
Speaker 2: if the macroeconomic environment is heavily distorted by the cost
Speaker 2: of achieving that productivity.
Speaker 1: But the crowding out effect you're describing assumes that AI
Speaker 1: exists in a vacuum, completely separate from the agricultural company
Speaker 1: or the manufacturing plant.
Speaker 2: I'm not saying it's in a vacuum.
Speaker 1: History shows us that the dividends of a general purpose
Speaker 1: technology always spill over to benefit those exact sectors.
Speaker 2: Eventually.
Speaker 1: Maybe that tractor factory is precisely the kind of business
Speaker 1: that will benefit when the cost of enterprise software supply, chain, logistics,
Speaker 1: and back office administration plummets to mere zero thanks to AI.
Speaker 2: But they have to survive the capital drought. First.
Speaker 1: You are looking at the two hundred and twenty billion
Speaker 1: in tech debt and seeing a dram the system. I
Speaker 1: look at that exact saying two hundred and twenty billion
Speaker 1: and see the necessary down payment on a global operating
Speaker 1: system that makes every subsequent physical and digital transaction cheaper.
Speaker 2: A down payment that is currently straining the physical limits
Speaker 2: of our infrastructure.
Speaker 1: Well, growth requires strength.
Speaker 2: When tech spreads widened by nine basis points over the
Speaker 2: investment grade market, that is, the market's mechanism of screaming
Speaker 2: for a breather. The debt issuance in August looked very
Speaker 2: different from January. How So, in January AI linked deals
Speaker 2: were absorbed easily. By August, fatigue had deeply set in.
Speaker 2: Companies that historically issued shorter term debt are now locking
Speaker 2: in long dated bonds just to finance these server farms.
Speaker 1: Right to secure the runway.
Speaker 2: They are intentionally locking in higher capital costs for the
Speaker 2: next decade because they know the buildout is going to
Speaker 2: be brutally expensive for a long time.
Speaker 1: They are locking in those costs because the projected return
Speaker 1: on invested care capital for AI compute is completely unprecedented.
Speaker 1: Let's trace this macro theory back to the micro evidence. Okay,
Speaker 1: why would a massive German utility like Eon sign a
Speaker 1: multi year deal with HCl Tech where they pay nothing
Speaker 1: in the first.
Speaker 2: Year because HCl Tech took the risk, right?
Speaker 1: But why because the underlying technology, the exact AI models
Speaker 1: running on those expensive debt finance server farms, is capable
Speaker 1: of delivering such massive verifiable efficiency gains in year two
Speaker 1: that the provider is willing to take on all the
Speaker 1: upfront risk.
Speaker 2: It's still a huge gamble.
Speaker 1: That represents a fundamental structural change in how economic value
Speaker 1: is generated. We are entirely moving away from paying for
Speaker 1: human time well.
Speaker 2: Algorithmic certainty is a highly optimistic phrase for a technology
Speaker 2: that is still wildly expensive to train, run.
Speaker 1: And cool, but the capabilities are there.
Speaker 2: And again, if we look at the macro picture, a
Speaker 2: utility company saving money on its internal software management does
Speaker 2: not solve the structural inflation caused by real world resource bottlenecks.
Speaker 1: The resource bottlenecks will sort themselves out.
Speaker 2: There are physical limits on our energy grids to power
Speaker 2: these massive data centers. There are hard limits on the
Speaker 2: TSMC foundries producing the specialized silicon. The prices for those
Speaker 2: physical inputs will remain structurally high. That is the upward
Speaker 2: inflationary pressure to Chewton is warning central banks about. This
Speaker 2: isn't just a capital market phenomenon. It is a physical
Speaker 2: resource phenomenon.
Speaker 1: But bottlenecks and physical resources are exactly what the free
Speaker 1: market is exceptionally good at solving when the price incentive
Speaker 1: is right.
Speaker 2: By raising prices, the high yields.
Speaker 1: And widespreads you are pointing to in the debt markets
Speaker 1: are the exact signal needed to attract more capital to
Speaker 1: solve the energy and chip constraints.
Speaker 2: But the act of solving those constraints is inherently inflationary.
Speaker 1: Not in the long run.
Speaker 2: When you have to build new nuclear power plants or
Speaker 2: reactivate dormant ones just to feed data centers, the cost
Speaker 2: of concrete, steel and specialized labor goes up for everyone
Speaker 2: else in the economy.
Speaker 1: But they're producing more power overall.
Speaker 2: The Swiss National Bank hasn't changed its long term inflation
Speaker 2: forecast yet they still project zero to two percent through
Speaker 2: twenty twenty nine. But Shuden made it explicitly clear that
Speaker 2: their policy rate is not set in stone right.
Speaker 1: They have to remain flexible.
Speaker 4: If this AI investment cycle continues to warp the broader
Speaker 4: economy and suck up physical resources, central banks will be
Speaker 4: forced to adjust monetary policy to contain the inflation it
Speaker 4: is causing.
Speaker 1: Look, if we synthesize this incredibly complex dynamic, my position
Speaker 1: remains rooted in the fundamental restructuring of global service contracts.
Speaker 2: Okay, the three hundred.
Speaker 1: And fifteen billion dollar Indian IT sector is actively proving
Speaker 1: that the shift from billable hours to measurable outcomes is real.
Speaker 1: It's happening today.
Speaker 2: I don't deny it's happening in software.
Speaker 1: It is an undeniable demonstration of AI's unprecedented deflationary and
Speaker 1: productive power. The market is mercilessly efficient. The agile time
Speaker 1: two firms are dismantling legacy giants by offering faster, cheaper,
Speaker 1: AI driven pilots that decouple time from output. Right that
Speaker 1: reality validates the massive front loaded costs. The productivity gains
Speaker 1: are structural, and they will ultimately exert a compounding deflationary
Speaker 1: force on the global economy.
Speaker 2: And my position remains grounded in the immediate, unavoidable macroeconomic
Speaker 2: reality of the physical buildout. You simply cannot ignore a
Speaker 2: staggering two hundred and twenty billion dollars drain on capital
Speaker 2: markets in just eight months.
Speaker 1: It's a big number.
Speaker 2: The resulting resource bottlenecks, the upward pressure on treasury yields,
Speaker 2: and the rigid portfolio constraints of major institutional investors are
Speaker 2: creating severe systemic inflationary pressures right.
Speaker 1: Now in the short term.
Speaker 2: Yes, economic history, along with the current warnings from central bankers,
Speaker 2: proves that isolated productivity gains do not guarantee systemic deflation.
Speaker 2: If the cost to achieve those software gains per per
Speaker 2: minutely elevates the cost of capital, concrete, and energy, the
Speaker 2: net result for the average consumer might just be higher
Speaker 2: prices across the board.
Speaker 1: Well, the clearest point of convergence here is the sheer
Speaker 1: scale of the disruption. Absolutely, we both acknowledge that the
Speaker 1: global economy is currently undergoing a massive turbulent reallocation of resources.
Speaker 1: AI is not merely a software update. It is a
Speaker 1: structural force, fundamentally altering both traditional service business models and
Speaker 1: corporate debt markets simultaneously.
Speaker 2: The scale really is the defining feature. This transition is
Speaker 2: touching everything from the structure of a multi year cloud
Speaker 2: management contract in Germany to the yield on a ten
Speaker 2: year treasury note in the United States.
Speaker 1: It's everywhere.
Speaker 2: The upstream costs and the downstream efficiencies are locked in
Speaker 2: a tug of war.
Speaker 1: The true macroeconomic legacy of AI will depend entirely on
Speaker 1: whether those downstream corporate efficiencies, the ones severing the link
Speaker 1: between human labor and economic output, can ultimately outpace the
Speaker 1: massive upstream costs of building the physical infrastructure.
Speaker 2: That's the real test.
Speaker 1: We highly encourage you to continue exploring the source material
Speaker 1: from the Swiss National Bank, the IMF, and the ongoing
Speaker 1: shifts in the IT sector to trace how these opposing
Speaker 1: economic forces unfold over the coming years.
Speaker 2: It is one of the most fascinating structural shifts in
Speaker 2: modern economic history, and we are living right in the
Speaker 2: middle of it.
Speaker 1: So when you look at the economic data tomorrow, ask yourself,
Speaker 1: are we witnessing an era of unprecedented deflationary productivity or
Speaker 1: are we staring into the inflationary pressures of macroeconomic muddy waters.
Speaker 1: Thank you for listening.