● SpaceX-xAI Mega-Merger, Orbital AI Data-Centers, Power Grab, Cloud Disruption
Why a SpaceX–xAI “Merger” Matters: A Scenario That Could Reshape AI Infrastructure, Energy Leverage, and Cost Structure via Orbital Data Centers
This analysis examines the potential for relocating key AI infrastructure components (power, cooling, data processing, communications) from terrestrial facilities to space-based systems, and the implications for total cost of ownership (TCO) and market structure.
Key points:1) Why a SpaceX–xAI combination is commercially coherent (structure and economics)
2) The economic rationale behind claims that “AI compute could be cheaper in space within 2–3 years” (power, cooling, land, grid constraints)
3) New markets created by integrating Starlink, Starship, and satellite constellations with AI data centers (“space cloud”)
4) Near-term constraints (heat rejection, radiation, maintenance, launch costs) and a realistic timeline
5) Core considerations often omitted in mainstream coverage
1) News Brief: What “SpaceX Bringing xAI In-House” Would Imply
Core event
- SpaceX and xAI are reported to be combining via merger or acquisition structure.
- Combined implied valuation is cited at approximately the low-trillion-dollar range (around $1.25T referenced).
Market positioning
- The strategic significance is not “an AI startup scaling,” but a structural shift toward vertically integrated capabilities spanning defense, communications, launch services, and AI infrastructure.
IPO / financing indications
- IPO timing speculation centers on late 2026.
- SpaceX is referenced as potentially raising up to $50B.
- This appears aligned less with operating liquidity and more with AI-infrastructure capex requirements.
2) Why the Combination Can Be Rational: Complementary Constraints
(1) xAI’s primary constraint: compute and capital
- Competitive advantage in AI increasingly depends on securing large-scale compute and power at lower unit cost.
- Large technology companies invest aggressively in data centers to reduce TCO.
- xAI’s scaling path is capital-intensive; SpaceX has meaningful cash-generation potential from launch services and Starlink.
(2) SpaceX’s next growth vector: from satellite connectivity to satellite compute
- Starlink has established a global low-Earth-orbit communications network.
- A logical extension is processing data in orbit prior to downlink to reduce transmission needs and enable faster decision loops.
- With accelerating AI data center demand, “orbital data center deployment and operations” could become a new revenue category.
(3) Outcome: vertical integration across the full stack
- The stated portfolio direction implies consolidating:
- Data generation (platforms)
- Model training and inference (AI)
- Transport (communications)
- Compute hosting (data centers)
- Logistics (launch and deployment)
3) “Compute Could Be Cheaper in Space”: TCO Decomposition
The assertion can be assessed by decomposing data center TCO rather than treating it as speculative.
Five recurring terrestrial data center bottlenecks1) Power procurement (generation, transmission, substations)
2) Cooling (cost escalation driven by heat density)
3) Land and siting (zoning, permitting, community constraints)
4) Construction lead time (including grid interconnect)
5) Electricity price volatility (energy and policy risk)
Where space can be structurally advantaged (in theory)
- Power: direct solar generation in orbit; orbital profiles can mitigate terrestrial intermittency constraints.
- Cooling: absence of atmospheric heating and potential for lower-equivalent cooling burdens.
- Siting: reduced terrestrial permitting and local opposition pressures.
Critical qualification
- “Space is cold, therefore cooling is free” is incomplete.
- Without convection, heat must be rejected via radiation (radiators).
- Larger radiator area increases mass and volume, directly increasing launch and structure costs.
- Orbital data centers may trade lower operating cooling cost for higher capex and launch cost driven by thermal management hardware.
4) Why “Up to 1 Million Satellites” Matters: Starlink as AI Infrastructure
The scale discussion is material if interpreted as a long-term distributed infrastructure concept rather than purely connectivity.
(1) Starlink is fundamentally a low-Earth-orbit mesh network
- Functionally, the constellation is a distributed network of routing and relay nodes, not only a consumer broadband service.
(2) Adding compute modules enables orbital edge computing
- Integrating communications payloads with inference, preprocessing, compression, and encryption capabilities creates a space-based edge layer.
- At sufficient scale, this could evolve into a “space cloud” market focused on global coverage and specialized missions.
(3) Starship’s role: lowering the logistics cost of orbital infrastructure
- The feasibility of orbital compute is strongly conditioned by launch cost per kilogram.
- If Starship achieves targeted reuse and cost metrics, the economic threshold for deploying server-class payloads in orbit improves materially.
5) Technical Constraints: Why This Is Not Immediately Deployable
Constraint A: heat rejection (radiator sizing)
- High-density accelerator clusters require substantial radiator area for radiative heat disposal.
- Radiator growth increases launch cost, mechanical complexity, and exposure to micrometeoroid risk.
Constraint B: maintenance and replacement
- Failed modules require a servicing model: autonomous robotics, modular design, and mature on-orbit servicing capabilities.
Constraint C: radiation and component durability
- Radiation increases memory and logic upset rates (e.g., single-event effects).
- Shielding increases mass and therefore launch cost.
Constraint D: latency and network architecture
- Training requires intensive synchronization and high-bandwidth interconnects, making distributed orbital training challenging.
- Early adoption is more plausible for inference and data preprocessing, where distribution and fault tolerance are easier.
6) Market and Macro Implications: Three Areas of Potential Impact
Impact 1: AI infrastructure competition shifts from grid access to energy sourcing
- Current constraints emphasize interconnect queues, substations, and generation build-out.
- If orbital power becomes viable for specific workloads, grid bottlenecks may become less determinative in some segments.
Impact 2: data center and cloud competition could broaden beyond terrestrial regions
- Terrestrial clouds are region-centric.
- An orbital cloud would likely first address coverage-intensive and mission-specific use cases (defense, maritime, remote operations) rather than mainstream enterprise workloads.
Impact 3: defense and space assets may be re-rated by AI-driven demand
- ISR and communications satellites are already strategic; adding onboard compute increases the value of real-time detect-analyze-transmit decision chains.
7) Core Points Often Underweighted in Public Coverage
Key point 1: initial customers are likely sovereign and defense
- The early value proposition emphasizes coverage in denied environments, security, and operational independence rather than lowest-cost compute.
- These buyers exhibit higher willingness to pay for mission-critical capabilities.
Key point 2: inference is more likely than training as the first orbital workload
- Training imposes the highest requirements on power, networking, and maintenance.
- Inference supports distributed deployment and graceful degradation, aligning better with constellation architectures.
Key point 3: the strategic center is TCO competition, not model benchmarking
- Durable advantage may accrue to the entity that can deliver AI capacity at the lowest end-to-end cost.
- Vertical integration across launch, connectivity, platforms, and compute is a direct attempt to compress system-level TCO.
Key point 4: “1 million satellites” can be interpreted as a long-horizon distributed compute grid
- The scale is consistent with a vision of densely distributed compute nodes, not solely incremental connectivity.
8) Signals to Monitor
1) Starship launch cost and reuse reliability metrics (primary driver of orbital data center economics)
2) Starlink satellite generation changes: increasing share of onboard compute versus communications-only payloads
3) xAI infrastructure strategy: indications of reduced terrestrial capex or explicit hybrid/orbital positioning
4) Regulatory trajectory: orbital congestion, debris constraints, spectrum, and geopolitical risk
5) Contracts and M&A activity in radiation-tolerant semiconductors, memory, and on-orbit servicing robotics
< Summary >
- A SpaceX–xAI combination implies an attempt to alter AI infrastructure TCO through vertical integration, not merely an AI corporate transaction.
- Orbital data centers could reduce terrestrial constraints in power, cooling, and siting, while introducing new constraints in radiators, maintenance, radiation tolerance, and launch economics.
- Early deployment is more likely in inference and preprocessing than in large-scale training.
- Sovereign and defense buyers may catalyze the initial market due to security, coverage, and autonomy requirements.
- Competitive positioning in AI will increasingly depend on system-level cost delivery, not only model performance.
[Related Links…]
- SpaceX: Latest insights collection — https://NextGenInsight.net?s=SpaceX
- Data centers: Investment and power issues — https://NextGenInsight.net?s=DataCenter
*Source: [ 내일은 투자왕 – 김단테 ]
– 인류 문명의 역사를 바꿀 합병
● Weak-Dollar Shock, Emerging-Market Surge, Uranium Boom, AI-Power Crunch
Signals That Emerging (Non-U.S.) Markets Could Become the New Leadership Trade vs. U.S. Equities: A Consolidated View Across a Weaker Dollar, Commodities, and AI Infrastructure
This report covers:
1) A structural explanation of why a weaker dollar tends to favor non-U.S. assets (especially emerging markets), using the U.S. Dollar Index (DXY) as the core framework.
2) Why prominent Wall Street bulls (including Yardeni) have shifted toward increasing non-U.S. exposure, and what Bank of America means by a potential “dollar bear market.”
3) Key pillars that could persist into 2026: emerging markets (Korea/Taiwan/Latin America), commodities (notably uranium), geopolitics (energy/defense), and AI infrastructure (higher sensitivity to big-tech earnings).
4) A practical checklist for portfolio monitoring and rebalancing beyond generic “EM is attractive” narratives.
1) Global Market Setup: “U.S. Range-Bound, Non-U.S. Decoupling”
Key takeaways
– The three major U.S. equity indices are up roughly low-single digits year-to-date: stable but not leadership.
– In contrast, non-U.S. equities (including Korea) have advanced more strongly, reinforcing a perceived decoupling.
– Market attention may tilt from earnings to the U.S. Dollar Index (DXY) as a primary driver of relative performance.
Why DXY matters most in this phase
– A weaker dollar typically reduces friction for global liquidity to rotate into non-U.S. assets.
– Flows often favor regions with lower valuations, higher cyclicality, and greater exposure to commodities and manufacturing, commonly including emerging markets and select developed ex-U.S. markets.
– This environment can produce U.S. gains alongside stronger relative performance outside the U.S.
2) Core Catalyst: Policy Signaling That Favors a Weaker Dollar
Market-relevant headlines
– Public comments supportive of a weaker dollar triggered an immediate market response.
– Gold and commodities surged, followed by short-term volatility and partial retracement, consistent with rapid repricing of the “weak-dollar trade.”
Focus on DXY rather than bilateral USD/KRW
– USD/KRW embeds Korea-specific variables (exports, trade balance, domestic rates, geopolitical risk).
– DXY more directly reflects broad dollar strength and is more relevant for assessing U.S. vs. non-U.S. asset allocation flows.
3) The Big Picture: Long-Run Link Between Dollar Weakness and Non-U.S. Outperformance
Chart interpretation (conceptual)
– Upper series: U.S. Dollar Index (DXY)
– Lower series: relative strength of U.S. vs. non-U.S. equities
Summary
– As DXY declines, the probability of non-U.S. (Europe/Japan/emerging markets) relative outperformance tends to rise.
– As DXY strengthens, U.S. equities (particularly large-cap growth/mega-cap tech) tend to benefit on a relative basis.
2000s vs. 2010s to early 2020s
– Early 2000s: weak-dollar conditions coincided with strong emerging-market leadership (including BRICs).
– 2010s to early 2020s: strong dollar and mega-cap tech leadership reinforced a prolonged U.S. dominance regime.
– Current conditions may indicate an inflection toward reduced U.S. relative leadership, contingent on macro evolution.
In this regime, inflation, rate-cut expectations, and global slowdown risk can interact to increase dollar sensitivity and volatility.
4) Wall Street Narrative Shift: Yardeni and Bank of America
1) Yardeni: increasing emphasis on non-U.S. exposure
– The message is not “the U.S. is structurally uninvestable,” but that U.S. weights may warrant adjustment.
– U.S. equities can continue to rise, while the relative opportunity set may broaden outside the U.S.
2) Bank of America: potential “fifth dollar bear market”
– “Bear market” refers to the dollar, not equities.
– Historically, the dollar has experienced multi-year downcycles with declines of ~20% or more; BofA frames the current setup as a candidate for another such cycle.
– Assets that have statistically benefited during weak-dollar regimes include emerging-market equities and gold/commodities.
Implementation note
– These are probabilistic frameworks, not certainties.
– The coordinated shift in institutional language can itself influence positioning and flows.
5) Two Primary Non-U.S. Regime Themes: Semiconductor Supply Chain vs. Commodity Producers
Theme A: semiconductor and manufacturing supply-chain beneficiaries
– Korea and Taiwan are frequently highlighted due to concentrated exposure to critical hardware.
– As AI scales, hardware constraints (semiconductors, power, servers, networking) can become binding bottlenecks.
Theme B: commodity-linked beneficiaries
– A weaker dollar often supports commodity pricing dynamics.
– Commodity-intensive markets (including Brazil and parts of Latin America) can become candidates for valuation catch-up.
Investment framework
– The relevant question is less “U.S. vs. non-U.S.” and more “which factors typically outperform in weak-dollar regimes” (commodities, emerging markets, value, manufacturing, energy).
6) Uranium Re-Entering Leadership: A 2026-Oriented View
Key points
– Uranium has ranked among the stronger commodity performers year-to-date.
– Fuel supply tightness has supported pricing.
Why uranium is regaining narrative momentum
– AI data-center buildouts increase power demand structurally.
– As grid stability and baseload supply become more critical, nuclear power can be re-evaluated as an implementable option regardless of political debate.
– This positions uranium as both a commodity theme and an AI-linked power infrastructure theme.
7) Geopolitics as Ongoing Support for Energy and Defense
Current flow of risk factors
– Tariffs, sanctions, and elevated tensions across multiple regions continue to accumulate as headline risk.
– Even if markets become less reactive to tariff news, energy supply uncertainty can remain supportive for oil and the energy complex.
Why energy “range break” signals matter
– Energy is often under-owned; when trend shifts occur, inflows can accelerate.
– A revitalized commodity/energy cycle can drive pronounced sector rotation.
Defense as earnings- and policy-driven
– Air defense and missile-related buildouts can translate into order flow rather than one-off sentiment catalysts.
– This dynamic can increase buy-the-dip behavior during drawdowns.
8) U.S. Mega-Cap Tech: Rising Sensitivity to AI Investment Economics
Current market asymmetry
– “Investing in AI” is no longer automatically interpreted as bullish.
– If AI spend pressures margins or weakens guidance, it can be priced as a negative.
Mechanism
– Expectations and valuations remain elevated for mega-cap technology.
– Without credible evidence of monetization velocity, incremental capex can reduce tolerance for execution risk.
Key checkpoints
– For names such as Alphabet, Amazon, and Palantir, the focus is less on AI mention frequency and more on:
1) cloud growth rates 2) margin trajectory 3) credibility of monetization relative to capex growth
9) February–March Volatility: Statistics as Reference, Strategy as Preparation
Key point
– Historical seasonality suggests that positive January performance has correlated with favorable full-year outcomes at higher-than-random frequencies.
– However, February–March often features higher volatility.
Practical approach
– For January leadership themes (commodities, emerging markets, select sectors), a February pullback can function as de-risking and overheating relief.
– The primary question is not whether price momentum pauses, but whether DXY reverses into a renewed uptrend (invalidating the core premise).
Across these themes, DXY remains the common linkage.
If dollar weakness persists, the non-U.S./commodities/emerging narrative remains supported; if the dollar turns higher, the strategy requires prompt reassessment.
10) Under-Emphasized Points for Portfolio Decision-Making
Point 1: The core of “EM leadership” is not country selection but rebalancing within the dollar regime
– Interpreting Korea’s outperformance purely as idiosyncratic can miss the broader allocation driver.
– Understanding the recurring weak-dollar pattern (U.S.-to-non-U.S. flow rotation; factor leadership in value/commodities/manufacturing) improves cycle-to-cycle positioning.
Point 2: For mega-cap tech, “AI investment” is increasingly treated first as a cost line
– This can enable non-U.S. and commodity-linked assets to outperform even while U.S. indices remain range-bound.
– This mechanism contributes to the perceived “difficulty inversion” between U.S. and non-U.S. allocations.
Point 3: Uranium is both a commodity theme and an AI power-infrastructure theme
– The linkage to data-center-driven power demand adds duration to the thesis beyond a generic nuclear cycle narrative.
Point 4: The operational action is not “reduce the U.S.,” but moving ex-U.S. exposure from zero to a meaningful weight
– Implementation is primarily rebalancing rather than all-in/all-out rotation.
– Early in regime shifts, this distinction can materially affect outcomes.
11) One-Line Checklist: What to Monitor Daily/Weekly
– Is DXY maintaining a downward trend (premise validation)?
– Are commodities (gold, silver, copper, uranium) showing “leader-to-laggard” breadth expansion?
– Is emerging-market strength broadening regionally rather than remaining concentrated in a single-country theme?
– Are mega-cap earnings being priced primarily through margins and guidance rather than AI narrative?
– Are geopolitical events translating into energy supply uncertainty (supporting durable sector rotation)?
< Summary >
– The primary market variable may shift from rates to DXY; sustained dollar weakness can favor non-U.S. leadership across emerging markets, Europe, and Japan.
– Yardeni and Bank of America have begun to frame a potential inflection in the multi-year U.S.-dominance regime.
– The core non-U.S. pillars include semiconductors (Korea/Taiwan) and commodities (notably uranium/energy), with geopolitics providing structural support for energy and defense.
– U.S. mega-cap tech is increasingly evaluated through margin and guidance sensitivity to AI capex, which can reinforce a range-bound index tape alongside higher dispersion at the stock level.
[Related links…]
- Capital Flows to 2026 Through the Lens of DXY: U.S. vs. Emerging Markets
- Uranium, Nuclear, and AI Power Infrastructure: Core Takeaways for the 2026 Commodity Cycle
*Source: [ 소수몽키 ]
– 앞으로 미국 증시보다 이머징이 대세? 줄줄이 태세전환하는 월가의 보고서들
● Wash Hawk Panic Triggers Black Monday Liquidation Not Fundamentals
Kevin Warsh “Hawkish Shock” and the Black Monday Sell-Off: A Reassessment of the Warsh Narrative
This report addresses four points:1) The misinterpretation mechanism that led markets to label “Warsh = hawk.”2) Why the administration’s midterm-election strategy (rate cuts and liquidity expansion) is a core variable.3) How AI-driven disinflation can reshape the inflation framework itself.4) Scenario-based implications of the Senate confirmation process (the Tillis variable) for global equities, U.S. Treasury yields, and the U.S. dollar.
1) Issue Summary (Headline Style)
“Expectations around Kevin Warsh shifted abruptly from ‘ultra-dovish’ to ‘hawkish fear,’ triggering broad risk-asset declines; however, the move appears more consistent with position unwinds driven by a framing error than with a structural breakdown.”
The central point is not whether Warsh is objectively hawkish, but that markets were positioned for aggressive liquidity expansion; when that expectation was challenged, risk was reduced rapidly. This pattern is consistent with liquidity-driven regime corrections.
2) Surface Cause vs. Primary Driver
2-1. Surface Cause: Fear of a “Hawkish” Next Fed Chair
As Warsh emerged as a leading candidate (or was perceived as such), markets reacted as if an accommodative regime would end. Gold, silver, equities, and broader risk assets sold off in tandem.
2-2. Primary Driver: Reversal of Trades Priced for an “Ultra-Dovish” Outcome
Markets had already priced a scenario in which the next Fed leadership would be more accommodative, centered on rate cuts and expanded liquidity.
Once the “Warsh may be the most hawkish” framing gained traction, positioning was unwound rapidly (deleveraging), amplifying the drawdown.
The move is better interpreted as expectation- and positioning-driven rather than as confirmation of recessionary inevitability.
3) Why Markets Misread Warsh: The “Two-Decade-Old Quote” Problem
3-1. Applying 2008-Era Remarks Directly to 2026
During his tenure as a Fed Governor (2006–2011), particularly around the Global Financial Crisis, Warsh expressed caution that excessive quantitative easing could fuel inflation.
Markets isolated that episode and extrapolated it into a current “tightening instinct” narrative.
3-2. The Policy Environment Has Changed
In 2008, policy was emergency stabilization amid systemic financial stress. In 2026, the debate increasingly centers on AI-driven productivity, expanding supply capacity, and disinflation dynamics.
Policy preferences can shift materially depending on the prevailing constraint and macro regime. A key market error was ignoring this time-context shift.
4) Midterm Election Incentives: Why “Rate Cuts and Liquidity Expansion” Matter
The dominant political macro event for 2026 is the midterm election cycle. Typical policy incentives include:
- Defending growth
- Managing unemployment
- Supporting asset markets
- Improving household sentiment
In this context, rate-cut expectations and liquidity conditions are primary levers.
This creates a practical question: whether an administration would prefer a materially hawkish Fed chair into the midterm cycle. This is central to reassessing the Warsh narrative.
5) The Case for Warsh as a “Rules-Based Dove,” Not an Unconditional Dove
5-1. The Objective Is Not “Cuts at Any Cost,” but “Cuts That Can Be Justified”
The Fed is committee-driven; the chair must persuade a majority of the FOMC.
From the executive branch perspective, appointing a purely compliant figure risks intensifying independence concerns and increasing the probability of adverse Treasury-market reactions.
A candidate who can provide a coherent rationale for easing may enable policy pivoting with lower institutional and market friction.
5-2. Warsh’s Recent Emphasis: AI Adoption -> Cost Reduction -> Disinflation
Warsh’s recent messaging can be summarized as:
- AI can raise productivity and lower costs
- This can reduce medium-term inflation pressures
This reframes inflation from a default risk posture to a supply-side productivity narrative, potentially altering the expected policy-rate path and the broader debate on neutral rates.
6) AI-Driven Disinflation: Economic Mechanism
AI diffusion can stimulate demand, but its primary macro channel may be increased supply capacity and lower unit costs.
Service sectors with high white-collar intensity (legal, accounting, customer support, software development, design) are particularly exposed:
- Tasks previously requiring large teams can be handled by AI plus smaller headcount
- Unit costs decline
- Competitive dynamics can translate cost declines into pricing pressure
If markets begin to treat this as a structural trend rather than a transient effect, inflation-risk premia may compress.
7) Core Forward Variable: Senate Confirmation and the Tillis Constraint
7-1. Confirmation Bottleneck: Senate Banking Committee Dynamics
The process runs from the Senate Banking Committee to the full Senate. If committee arithmetic becomes unfavorable, confirmation can stall.
7-2. The Tillis Condition: No Confirmation Before Resolution of Powell-Related Investigation Issues
This is less about personal preference toward Warsh and more about procedural legitimacy and independence framing.
Regardless of Warsh’s stance, a delayed confirmation shifts the timing of policy expectations. Timing risk can be sufficient to reprice assets.
8) Scenario Framework: Market Responses (Equities, Bonds, USD)
8-1. Scenario A: Investigation Ends or Is Withdrawn -> Confirmation Obstacle Clears
Markets may interpret this as reopening the liquidity path. Risk appetite can recover quickly, with global equities typically responding positively to reduced policy uncertainty.
8-2. Scenario B: Tillis Reverses Position -> Higher Probability of Committee Passage
Less dramatic than Scenario A, but removal of a bottleneck can restore a policy-forward narrative and support risk sentiment.
8-3. Scenario C: Independence Controversy Intensifies During Hearings -> Long-End Yield Shock (Steepening Risk)
This is the highest-impact operational risk:
- Rate-cut expectations can pull front-end yields lower
- Simultaneous independence concerns can raise term premia and push long-end yields higher
The result may be curve steepening, a configuration that can pressure growth equities and leveraged positions.
9) Underemphasized Core Points
1) The drawdown may have been driven more by crowded positioning than by a confirmed policy regime shift. In liquidity-driven environments, small framing changes can trigger outsized sell-offs without implying trend failure.
2) AI-driven disinflation functions as a potential policy-justification framework, not merely a technology theme. Once embedded into central-bank reaction functions, second-order effects can be large.
3) The confirmation process affects the speed of expectations. In certain regimes, timing dominates direction in determining price action.
10) Investor Checklist (Decision-Focused)
- Prioritize confirmation timeline and vote counts over simplified “hawk vs. dove” labels.
- In hearings, focus on the consistency of the AI-inflation-productivity framework more than on headline rate commentary.
- Monitor long-end yield spikes and curve steepening as signals for higher risk-asset volatility.
- In liquidity-led regimes, distinguish deleveraging-driven sell-offs from structural trend breaks.
Summary
The Black Monday adjustment associated with Warsh was more consistent with a framing-driven, positioning-heavy liquidation than with a structural crisis. The “Warsh = hawk” conclusion relied heavily on out-of-context, decades-old remarks. In a midterm-election backdrop, the more relevant question is whether policymakers prefer a chair capable of providing a defensible rationale for easing. Warsh’s AI-disinflation framework could reduce inflation-risk premia and shift expectations for the policy path. The principal near-term market variable is not ideology but Senate confirmation risk (including the Tillis constraint) and whether independence controversy pressures the long end of the Treasury curve.
Related
- Fed policy shifts and implications for global asset markets (latest): https://NextGenInsight.net?s=Fed
- Stablecoin competition and 2026 liquidity dynamics: the next variable in dollar dominance: https://NextGenInsight.net?s=stablecoin
*Source: [ 경제 읽어주는 남자(김광석TV) ]
– 시장은 왜 케빈 워시를 오해했나? 블랙먼데이 조정의 진짜 원인 | 클로즈업 – 워시 쇼크
