● AI Deflation Shock – Rates Crash, Jobs Burn, Debt Bites
A Potential “AI Deflation” Within the Next 3 Years? Reframing Musk’s Interview Through a Macroeconomic Lens
This report evaluates the realistic conditions under which a “deflation inflection point within three years” could occur and the likely transmission sequence across interest rates, asset markets, employment, and sovereign debt. It also clarifies that “money disappears” is not a claim about the end of currency, but a shift in price-setting power. The final section provides an actionable checklist for near-term positioning.
1) Key Statements (Condensed)
1-1. Claim A: Over the long term, “money” weakens
Money is not an intrinsic value, but a numerical database used to allocate human labor.
If AI and robotics fulfill a large share of production and services, the labor-allocation function of money diminishes.
1-2. Claim B: The future “base currency” is energy (electric power)
Energy cannot be created by statute.
Energy generation, storage, and transmission capacity becomes a primary measure of civilizational capability.
If solar, batteries, and robotic production become self-sustaining, segments of the economy may partially decouple from traditional monetary dynamics.
1-3. Claim C: AI/robotics may be the only path to addressing the U.S. debt burden
Interest expense is increasingly constraining, and the current structure may be unsustainable without a productivity shock.
If AI/robotics sharply raise productivity, the growth and tax base could change materially.
1-4. Claim D: A deflation “turning point” could occur in ~3 years
Inflation/deflation is simplified as the relative pace of money supply growth versus real output growth.
If AI-driven output growth exceeds monetary expansion, deflationary pressure emerges.
The proposed inflection could be observable within three years.
2) Translating This Into a Macro Scenario: Two Types of Deflation
2-1. Demand-collapse deflation (historically feared)
Depression-like cases where income falls, consumption contracts, firms fail, and unemployment rises, pushing prices down.
This is associated with broad economic dysfunction.
2-2. Supply-expansion deflation (the AI/automation case)
Prices decline because production costs fall and output expands via AI, robotics, and automation.
The key driver is lower marginal cost rather than macro contraction.
However, if job displacement and distributional shocks accompany it, household-level outcomes can still deteriorate.
3) Is “3 Years” Realistic? Four Necessary Conditions
3-1. Condition ①: Speed of AI embedding into production, not pilots
Not tool adoption, but structural labor substitution across manufacturing, logistics, customer service, sales, software, and back office functions.
The binding constraint is integration across internal processes, enterprise data, and decision rights, not model capability.
3-2. Condition ②: Robotics/automation bottlenecks (hardware diffuses slowly)
Software scales via updates; robotics requires factories, line redesign, safety compliance, and maintenance.
A more probable sequence is wave-like diffusion:
digital products/services → white-collar productivity → selective manufacturing/logistics.
3-3. Condition ③: Electricity prices must decline materially
AI is power-intensive.
For AI to lower economy-wide costs, power, semiconductors, and data-center costs must ease; in the near term, they may rise.
3-4. Condition ④: Monetary/fiscal pacing and the interest-rate transmission channel
Even if output expands, the path depends on central bank rate policy.
“AI deflation” is driven roughly 50% by technology and 50% by policy/interest rates.
(Connected variables: interest rates, inflation, deflation, recession risk, global supply chains.)
4) Asset-Market Implications: Deflation Is Not Uniformly Positive
4-1. First to reprice downward: “replicable” goods and services
Software, content, basic design, advertising production, translation, routine coding, and document drafting.
These categories approach near-zero marginal cost under AI; pricing pressure is already observable.
4-2. Most resilient pricing: “scarcity + regulation + physical constraints”
Power grids (transmission/distribution), data-center land, critical minerals, prime urban land, regulated healthcare/insurance, and licensed defense/space sectors.
Supply cannot scale quickly even with better technology.
Near-term outcomes are likely asymmetric: sharp declines in some prices and persistence or increases in others.
4-3. Debt regime shifts: nominal GDP growth versus interest rates
A view that deflation mechanically drives rates toward zero and eases debt service is incomplete.
Deflation can increase real debt burdens if prices and wages fall while nominal liabilities remain fixed.
Policy responses may prioritize either inflationary erosion of debt or AI-driven productivity to defend nominal GDP.
5) The Core Meaning of “Money Disappears”: Not Currency Extinction, but a Shift in Price-Setting Power
5-1. Money has three functions; only one is likely to weaken
Money serves:
① medium of exchange (payments)
② store of value (savings/wealth)
③ unit of account (pricing/accounting)
AI/robotics do not eliminate ① or ③ and may increase reliance on them.
The function most exposed is ②: the ability to accumulate numeric balances as enduring power.
5-2. Future power shifts from account balances to “access rights”
“Energy access” generalizes to access to electricity, compute, production capacity, data, and operating rights for robotics.
Capital alone may not secure access when constrained by regulation, licensing, or infrastructure.
Conversely, entities with access via platforms, networks, open ecosystems, or public support can remain competitive.
The locus of inequality may shift from cash/real estate toward electricity, compute, data, and permits.
6) A Practical 36-Month “AI Deflation” Timeline (Base Case)
6-1. 0–12 months: Wage disinflation/deflation in white-collar segments
Labor pricing adjusts before headline CPI.
Pressure concentrates on repetitive office work, junior roles, and outsourced unit pricing.
Consumer sentiment may weaken despite lower unit costs.
6-2. 12–24 months: Service-price compression and corporate margin redistribution
Price competition intensifies across content, marketing, customer service, translation, and parts of education.
A key question is whether efficiency gains accrue to consumers, shareholders, or platform intermediaries.
6-3. 24–36 months: Entry into a plausible window for broader price deflation
Requires easing constraints in power, data centers, and semiconductors.
The speed and extent of rate cuts remain a major variable.
The more likely pattern is sector-specific deflation, not synchronized economy-wide deflation.
7) Undercovered High-Impact Points
7-1. Primary beneficiaries may be price-controlling platforms, not consumers
Even if production costs decline, distribution/search/advertising/marketplace bottlenecks can limit pass-through.
Platforms may absorb gains through margin expansion.
7-2. If energy becomes the binding “currency,” national competitiveness shifts from FX to grids and permitting
Power permitting, transmission/distribution investment, data-center attraction, and industrial electricity tariff design become de facto industrial policy.
Policy and infrastructure signals may matter more than traditional financial headlines.
7-3. The core deflation risk is not CPI, but rigidity of fixed costs
In a falling-price environment, the most destabilizing elements are sticky fixed costs:
rent, interest, education fees, insurance premiums.
Even if AI lowers some prices, households may not benefit if fixed costs remain elevated.
The central issue becomes feasibility of fixed-cost restructuring.
8) Action Checklist for Individuals and Companies
8-1. Individuals: deflation preparedness prioritizes fixed-cost reduction, not purchase timing
Review debt structure (floating vs fixed), housing costs, subscription spending, and insurance/education expenses.
Lower fixed costs improves resilience under both inflationary and deflationary regimes.
8-2. Careers: advantage shifts from “AI tool users” to owners of AI-enabled processes
More important than tool proficiency: data access, decision authority, and process design capability.
Automation adoption typically requires governance and control rights.
8-3. Investing/operations: replicable value implies baseline unit-price erosion
Content, commoditized SaaS, and agency models should be structured assuming ongoing price compression.
More defensive areas may include power, data centers, semiconductor supply chains, robotics maintenance, and security/compliance.
(This is a structural framework, not a security-level recommendation.)
< Summary >
AI and robotics can produce a “supply-expansion deflation” regime distinct from recession-driven deflation.
A three-year window is more plausible as sector-by-sector dynamics than synchronized economy-wide deflation.
The primary shift is not the end of money, but the reallocation of power from account balances toward access to electricity, compute, data, and permits.
Individuals should prioritize fixed-cost reduction; companies should treat AI deployment as a process and governance issue rather than a purely technical one.
[Related Articles…]
Why asset-market regimes shift under deflationary conditions
Early-stage rate-cut cycle: key indicators to monitor
*Source: [ 오늘의 테슬라 뉴스 ]
– 앞으로 3년, 디플레이션이 온다? 일론 머스크가 말한 돈의 미래와 우리가 처음 겪게 될 세상은 ?
● CPI Shock, Micron HBM Sellout, Tesla Robotaxi Spotted
Tesla Cybercab Public-Road Testing Sighting + CPI Surprise + Micron HBM “Sold Out Through 2026”: Why Markets Are Re-Accelerating
This report consolidates three developments:1) What the Cybercab public-road testing sighting in Austin, Texas implies for the robotaxi commercialization timeline
2) Why a downside CPI surprise immediately re-rated equities (particularly growth and Tesla)
3) Why Micron’s results and HBM commentary represent confirmation of an AI infrastructure cycle (not a one-off earnings beat)
1) One-line market summary: Disinflation + AI earnings confirmation + tangible robotaxi evidence aligned
The primary driver of early-session strength in U.S. equities was a CPI print below expectations, interpreted as incremental disinflation. Lower inflation reduces perceived rate pressure, which typically supports higher valuation multiples for growth equities. Tesla is structurally rate-sensitive given the long-duration nature of its cash-flow expectations.
In parallel, Micron’s earnings provided quantitative evidence that AI demand is translating into revenue and forward commitments. Separately, sightings of a Cybercab operating on public roads reinforced investor focus on robotaxi execution.
2) CPI surprise: Why Tesla is highly responsive (rates, valuation, consumer demand)
A below-consensus CPI print is commonly translated into:“Less hawkish Fed trajectory -> peak-rate expectations -> growth-multiple re-expansion.”
Tesla often exhibits amplified sensitivity because it combines cyclical exposure (vehicle demand and consumer conditions) with long-duration optionality (autonomy, robotics). When U.S. rates function as a key risk-on/risk-off signal, the equity response can be outsized.
3) Why Micron’s earnings matter for the broader AI complex: “HBM demand is effectively committed through 2026”
Micron’s move was not solely an idiosyncratic earnings reaction. The core signal was management commentary indicating HBM (high-bandwidth memory) demand is effectively sold out through 2026.
This implies AI infrastructure spending is being formalized through supply constraints, purchase commitments, and capex planning, consistent with an industrial cycle rather than a short-lived theme. “AI bubble” narratives often reference price action; however, tight supply and forward capacity lock-ins increase the probability that fundamentals will continue to support revenue visibility.
A related datapoint noted in market discussion: Elon Musk reportedly followed Micron’s CEO on X shortly after the earnings release, consistent with rising strategic importance of compute-and-memory supply chains for autonomy, robotics, and in-vehicle AI.
4) Cybercab public-road testing sighting: Austin as an operational staging area, not a simple prototype loop
A Cybercab was reportedly sighted on public roads in Austin, Texas, with location details shared by the source, strengthening credibility. Additional sightings were referenced in the Palo Alto area, suggesting multi-region testing and broader data collection.
A key point emphasized by observers was a robotaxi-specific design (no steering wheel, no pedals). Public-road operation is generally interpreted as a step beyond concept disclosure, implying progress toward regulatory, safety, and operational readiness.
5) Why Cybercab unit economics can structurally compress: Designed for fleet operations, not private ownership
The economic thesis centers on product intent. Model Y is designed for private ownership, where consumer preferences (fit-and-finish, options, performance, brand attributes) structurally add cost.
Cybercab is positioned as a fleet asset. The relevant KPIs shift to cost per mile, maintenance simplicity, and utilization rate. Targets cited in discussion include:
- Production cadence: 1 unit per 10 seconds
- Operating cost objective: approximately $0.20 per mile
Waymo’s cost-reduction efforts were referenced as still implying a vehicle cost above $60,000 in some analyses. If Cybercab can achieve a $20,000–$25,000 range, competitive dynamics may increasingly hinge on cost structure and scalability rather than marginal technology differences.
6) Tesla’s operational groundwork: “Autonomous driving supervisors” hiring as a deployment indicator
Tesla has reportedly posted roles for autonomous driving supervisors (safety/monitoring) across Texas, California, Arizona, Florida, and Nevada.
These postings can be interpreted as operational signals regarding where and how Tesla intends to scale data collection and early operations. Commentary referenced potential expansion beyond initial Texas/California operations toward a five-state footprint, implying a service deployment plan rather than a purely experimental program.
7) Musk’s “perception” framing: From technical capability to social driving nuance
The “perception” claim associated with FSD versions was framed as two observable behaviors:
- “Social judgment” in ambiguous scenarios
- “Acknowledgment” of other road users’ risk and intent through yielding decisions
7-1) Perception stage 1: Social judgment (selecting the least-risk action under ambiguity)
Examples referenced include:
- Navigating around an obstruction requiring temporary lane crossing rather than stalling under rigid rule constraints
- Treating a malfunctioning traffic signal as a stop-controlled intersection without proliferating hard-coded edge cases
- Handling unclear stop-line geometry by stopping, creeping for visibility, stopping again, and then turning
- Interpreting another driver’s yielding intent during merges/turns and proceeding accordingly
These behaviors are presented as outputs of learned driving policy rather than brittle rule-based logic.
7-2) Perception stage 2: Acknowledgment (preemptive yielding to reduce conflict risk)
Examples referenced include:
- Slowing and yielding when a pedestrian unexpectedly appears near a lane deviation around parked vehicles
- Reducing speed to create recovery time for vulnerable road users (e.g., cyclists) entering a risky position
- Waiting through crosswalk clearance beyond minimum compliance, aligned with social safety signaling
8) Safety: Areas where software systems can outperform humans (attention persistence, reaction time, expanded sensing)
Referenced scenarios included:
- Detecting and responding to wrong-way vehicles in low-visibility highway conditions
- Anticipating pedestrian incursions (e.g., chasing a ball) and executing early lane or speed adjustments
- Preemptively avoiding adjacent vehicles drifting across lanes
The central argument is that human attention is inherently variable (fatigue, distraction, blind spots), whereas camera-based systems do not experience attention decay. Commentary also referenced vehicle structural safety in severe incidents as part of the overall safety case.
9) Investment framing of today’s catalysts
- Macro: CPI disinflation reduces rate pressure and supports growth-equity valuation expansion
- AI cycle: Micron’s earnings and HBM “sold out through 2026” commentary indicate demand is locked via commitments amid supply tightness, improving durability of AI infrastructure spending
- Tesla robotaxi: Cybercab public-road sightings support a shift from announcement to productization and operational testing
- Unit economics: Cybercab’s fleet-first design enables structurally lower capex per vehicle and improved utilization economics
- Product maturity: FSD demonstrations emphasize learned-policy handling of edge cases and social driving cues, relevant to public and regulatory acceptance
10) Key points underemphasized in mainstream coverage
1) The primary battleground is fleet unit economics (vehicle capex + utilization), not autonomy demos
Robotaxi is a service business. As technology converges, cost per mile, maintenance load, and utilization increasingly determine competitive outcomes. Cybercab is positioned as an asset optimized around these metrics.
2) “Social judgment” in edge cases is a practical milestone and a regulatory communication tool
Public skepticism concentrates on exceptions: ambiguous intersections, informal yielding, irregular pedestrian behavior. Consistent real-world handling and accumulated evidence can become a persuasive dataset for regulators and the public.
3) HBM “sold out through 2026” reframes the AI bubble debate
Price appreciation is observable, but forward capacity lock-ins indicate demand is being translated into contracted supply, consistent with a multi-year cycle.
4) CPI affects not only Tesla’s equity multiple but also robotaxi financing and expansion conditions
Lower rates influence capital costs for fleet build-out and multi-region operational scaling, impacting the broader robotaxi ecosystem.
< Summary >
CPI disinflation reduced rate pressure and improved the growth-equity backdrop. Micron’s results and commentary indicating HBM capacity is effectively committed through 2026 reinforced the durability of the AI infrastructure cycle. Concurrently, Cybercab public-road sightings in Austin supported the interpretation that robotaxi efforts are progressing from announcement toward pre-deployment operations. Cybercab’s fleet-optimized design provides a structural rationale for aggressive cost reduction, while FSD demonstrations emphasize learned-policy handling of social driving nuance relevant to perceived safety and adoption.
[Related Articles…]
- https://NextGenInsight.net?s=Tesla
- https://NextGenInsight.net?s=HBM
*Source: [ 허니잼의 테슬라와 일론 ]
– [테슬라] 사이버캡 공도 테스트 확인! 사이버캡의 원가가 극단적으로 낮을 수밖에 없는 이유 / 일론 머스크가 말한 자율주행 ‘지각’의 의미를 깨달았습니다!
● BOJ Rate Shock, Yen Carry Unwind, FX War, KRW Whiplash
Bank of Japan Raises Policy Rate to 0.75% (“Highest in ~30 Years”) — Yen Carry Unwind, FX-Pressure Dynamics, and Implications for USD/KRW (0.5% → 0.75%)
This note consolidates four items:First, why a 0.75% policy rate is significant domestically for Japan.
Second, why the yen carry trade unwind is primarily a question of pace rather than existence.
Third, how a post-tariff “currency pressure” framework (Plaza Accord–style constraints) can operate in practice.
Fourth, scenario linkages to USD/KRW, global equities, and Korea’s exports and capital flows.
1) News Briefing: BOJ Policy Rate to 0.75%… Raised from 0.5% to 0.75%
The Bank of Japan raised its policy rate to 0.75%.
This places Japan back in a range that is effectively the highest since 1995.
The key is not the “0.75%” level itself, but the pace of Japan’s transition from negative/zero rates toward a normalized rate regime. This move is better viewed as part of a longer path toward an estimated neutral rate range (approximately 1.0%–1.5%), rather than a one-off action.
Market impact appears partially pre-priced via prior guidance; however, the decision remains a source of incremental volatility.
2) Rationale: Domestic Drivers in Japan (Inflation, Wages, and Policy Ammunition)
A central domestic consideration is that Japan has operated with limited room to cut rates in downturns. With policy rates near zero or negative, Japan had materially less conventional easing capacity during shocks relative to other economies.
Raising rates in normal times can be interpreted as an effort to rebuild policy flexibility.
Domestic conditions have also improved versus prior cycles:
- Inflation has remained above the 2% target range for an extended period.
- Wage growth has been observed in the 4%–5% range, particularly among large corporates, supporting the policy normalization rationale.
This rate increase reflects a restart of a conventional monetary policy cycle rather than a purely FX-driven action.
3) Key Macro Context: Post-Tariff Phase and FX Pressure (“Second Plaza Accord” Narrative)
A recurring macro framework links the tariff phase to a currency phase. If tariffs constrain exports but a counterparty currency depreciates, tariff effects can be diluted. In that context, aligning FX outcomes can increase the effectiveness of trade policy.
Japan is a relevant focal point:
- The yen has a large weight in the U.S. dollar index.
- Yen appreciation can contribute to downward pressure on the dollar index.
- A softer dollar may improve U.S. export competitiveness at the margin.
Accordingly, the BOJ hike can be interpreted as both a domestic normalization step and a component within broader external FX-pressure dynamics.
4) Yen Carry Trade Unwind: Pace Matters More Than the Concept
The primary risk channel is the potential unwind of yen-funded carry positions: borrowing in low-yielding yen and allocating to higher-yielding or higher-risk assets (e.g., U.S. Treasuries, emerging market assets, equities). Repatriation or deleveraging can tighten global financial conditions.
In this cycle, the critical variable is the speed of repositioning rather than whether unwinds occur at all.
The BOJ typically provides extensive forward communication, allowing markets time to adjust. Prior episodes of volatility associated with yen carry positioning have also reduced the novelty of the risk.
However, even with a gradual pace, positioning may remain structurally biased toward reduction, implying recurring volatility rather than a single discrete shock.
5) FX Transmission: Yen Appreciation Pressure → Potential Dollar Index Weakness → Medium-Term Downside Bias for USD/KRW
A stronger yen can exert structural downward pressure on the dollar index, which can influence USD/KRW over the medium term.
Near-term moves should not be inferred mechanically, as FX outcomes reflect multiple drivers beyond rate differentials, including growth, risk appetite, trade balances, and official communication or intervention.
Directionally, a Japan normalization cycle (yen-supportive) combined with an eventual U.S. easing cycle (dollar-negative) increases the probability of USD/KRW attempting to peak and soften over a medium-term horizon.
6) Global Market Checkpoints: Divergent “Pivot” Cycles and Renewed Volatility
A defining feature is policy divergence:
- U.S. and Europe: generally shifting toward rate cuts (easing).
- Japan: shifting toward rate hikes (tightening).
This divergence can compress or reprice interest-rate differentials, a key determinant of cross-border carry and leverage. Faster changes in differentials can transmit volatility across equities, bonds, and FX simultaneously.
The BOJ move is therefore better framed as a signal of ongoing global liquidity and capital-flow reallocation rather than a standalone event.
7) AI Trend Implication: Rates and FX Can Reprice AI Valuations
AI sector fundamentals may be driven by technology adoption, but equity valuation multiples remain sensitive to discount rates and global dollar liquidity.
The BOJ hike does not directly impair AI demand; however, an accelerated carry unwind or instability in dollar liquidity can weaken risk appetite and pressure growth-asset valuations. Monitoring should include the pace of U.S. rate cuts, the pace of Japan rate hikes, and the direction of the dollar.
8) Core Points Often Underemphasized
Many discussions focus narrowly on whether a yen carry unwind will occur. The more durable considerations are:
- Japan’s rate hikes are both yen-supportive and a process of restoring monetary policy flexibility; the regime shift is structural.
- Post-tariff policy effectiveness can incentivize FX outcomes that prevent currency depreciation from offsetting tariff impacts; potential pressure points extend beyond Japan to other export-oriented economies.
- Market stress is driven more by the speed of positioning adjustment than by event occurrence; recurring volatility patterns are plausible through 2025–2026 if carry positioning continues to normalize.
< Summary >
The BOJ raised its policy rate to 0.75%, returning to a level effectively last seen in the mid-1990s. The move reflects domestic inflation and wage normalization and a strategic effort to restore monetary policy flexibility. In parallel, a post-tariff currency-pressure framework is gaining relevance. The yen carry trade unwind is primarily a function of pace, with a bias toward episodic volatility rather than a single collapse. Yen strength can contribute to dollar-index weakness and, over a medium-term horizon, may add downward pressure to USD/KRW.
[Related Articles…]
- https://NextGenInsight.net?s=FX
- https://NextGenInsight.net?s=rates
*Source: [ 경제 읽어주는 남자(김광석TV) ]
– [속보] 일본 30년만의 최고 금리. 일본은행의 금리인상(0.5%→0.75%). 엔케리 청산 공포 다시오나? [즉시분석]
