● Tesla 400 Collapse, Nvidia Shock Reversal, Fed Cut Doubt Sparks AI Bloodbath

Tesla 400 Collapse, Nvidia Reversal, and Rate Cut Controversies: Wall Street’s Real Signals at a Glance

This article contains the precise triggers for Tesla’s 400 collapse, the mechanism behind Nvidia’s earnings rally reversal, changes in the probability of a Fed rate cut, Dan Ives’ reasoning on why this isn’t an “AI bubble,” and key variables of AI infrastructure, power, and ROI that other news outlets have overlooked.
It also outlines, from a practical standpoint, how Tesla’s robo-taxi, FSD, and Optimus could defend their valuations amid risks from rates, inflation, currency, and recession.
Amid the confusion over market direction, it provides a checklist and action framework that you must review now.

Today’s Market at a Glance: A Swift Shift from “AI Festival” to “Risk Reassessment”

Nvidia’s excellent results and guidance brought early-day AI optimism to its peak.
The Nasdaq attempted a 2% surge during the session, and the S&P 500 opened strongly.
However, the robust job gains in the U.S. September employment report quickly dampened expectations for a rate cut.
The probability of a year-end rate cut dropped to about 32% by market standards, with some pushing expectations to January next year (around 54%).
With expectations of higher rates and a stronger dollar overlapping, exchange rate volatility increased, and profit-taking was concentrated in overvalued AI-related stocks as growth stock discount rates rose.
Nvidia initially surged over 5% before falling to -3% after the announcement, and AI-themed stocks like AMD and Oracle ended the day weakly.
Tesla, having broken its psychological support level at 400, closed at 395.23, falling below this symbolic level.

Elon Musk Issue Briefing: “Counter Fake News Immediately” and “Diner is a Genuine Business”

Musk immediately countered exaggerated and distorted reports regarding his compensation package on X.
He reaffirmed the principle that if there are no achievements, there will be no rewards, and that the compensation is directly linked to Tesla’s growth.
He also denied the Daily Mail’s claim of “reading lips,” emphasizing that the conversation at that time was about a different topic.
X has become a real-time fact-checking channel for Musk to constantly correct media narratives.
In Los Angeles, “Tesla Diner” is transitioning from a concept to a full-fledged commercial operation with full-time chefs, full service, and a team developing branded menus.
The experiment to convert the 20–30 minute “waiting time” at charging stops into revenue is now in full swing offline.
This is an important signal, especially when combined with robo-taxi, as it creates a value chain of “transportation + waiting time + consumption.”

Key Takeaways from Dan Ives’ Interview: “AI Takes Off in Three Phases, Not a Bubble”

This round of Nvidia’s results was hailed as a “masterpiece,” with data center investment and demand viewed as structurally strong.
Only about 3% of U.S. companies and less than 1% in Europe and Asia (excluding China) have fully adopted AI, indicating early stages of integration.
Nvidia’s demand-to-supply ratio is as tight as 12:1, and the investment scale over the next 2–3 years could exceed the total of the past 8–10 years.
With the number of applications increasing for companies like Palantir, MongoDB, and Snowflake, concerns over “ROI uncertainty” have been absorbed by actual usage.
Google is entering a revaluation phase with Gemini and strengthening its own chips, and even within Nvidia’s dominant position, collaboration and parallel strategies are expected to accelerate.
Apple, with 2.4 billion iOS devices and 1.5 billion iPhones as its “largest user base,” has significant leverage in the consumer AI space.
Some analysts suggest that if Apple chooses to partner (e.g., linking with Gemini), there could be considerable upside in its stock re-rating.
In conclusion, the AI investment cycle remains intact, and pessimists are likely to remain on the sidelines.

What Wall Street Really Worries About: Interest Rates, Inflation, Exchange Rates, and ROI

The robust employment numbers spark concerns over a reheating of inflation, bringing to mind a “higher for longer” rate scenario.
Higher rates increase the discount rate for growth stocks, making their valuations more sensitive.
A stronger dollar expands exchange rate volatility, impacting the unit costs of exports and imports and the earnings outlook of multinational companies.
With a surge in AI capital expenditure, if megacaps delay revealing visible ROI, stock volatility could increase.
Although some believe the risk of recession has diminished, the “lagging effect” of rate stickiness dampening consumption and employment remains a key surveillance point.

Tesla’s Decline Interpreted: “Just an AI Premium Adjustment, No Change in Fundamentals”

Tesla did not face direct negative factors such as changes to its robo-taxi schedule, delays in FSD updates, recalls, or shifts in earnings guidance.
This decline is more akin to an external shock during an “AI valuation discount” that spread across growth stocks.
Tesla’s valuation includes not only its EV business but also AI option values like FSD, robo-taxi, and Optimus, making it highly sensitive to interest rates.
Even if a rate cut by year-end fails to materialize, if expectations are deferred to early next year, there is room for the risk premium on growth stocks to normalize once again.
Tesla has fundamental drivers in place such as one of the lowest debt ratios in the industry, strong cash reserves, preparedness for unsupervised robo-taxi transition in Austin, pilot production of Optimus, and accelerated plant automation.
Expansions of Dojo and FSD learning infrastructure position the company to ride the structural growth of the AI cycle alongside sustained Nvidia demand.

Critical Points Other News Does Not Cover: AI Growth Hinges on “Power, Network, and ROI”

GPUs alone are not enough.
The bottlenecks in power permits and the expansion of transmission and distribution for data centers will practically determine the pace of AI investments in 2025–2026.
HBM, CPO/active optics, and Ethernet/InfiniBand networking are the true bottlenecks in training performance, with prices and lead times ultimately affecting ROI.
From 2025, budgets will shift from training to inference.
The inference architecture that reduces power consumption, latency, and total cost of ownership (TCO) will be the winner in the stock market.
The AI ROI of megacaps should be measured by the sum of “new revenue + cost savings + improvements in dwell time/conversion rates.”
The key to monetization lies not only in simple model performance but also in the redesign of business models.
Tesla’s Diner is experimenting with monetizing the waiting “charging time.”
When integrated with robo-taxi, it creates a new KPI of “service revenue per kWh.”
If successful, automotive companies could be revalued not as car makers but as “mobility and retail platforms.”
FSD is driven by data network effects.
The accumulation of operational data, automated labeling, and training pipelines makes it structurally difficult for competitors to catch up.
Regulation acts as both a risk and a moat.
The more local permits are expanded, the more the operational data of the early movers accumulates exponentially, widening the gap.

Checklist and Scenarios: 10 Things to Review Immediately

1) Check changes in interest rates, the FOMC path, and dot plots to see if there are signals of “higher for longer” rates.
2) Monitor core price trends from CPI and PCE and wage growth to determine if inflation could reheat.
3) Keep an eye on the direction of the dollar index and exchange rates for their sensitivity to emerging markets and export company earnings.
4) Look into detailed components of the U.S. employment report (such as participation rate and hours worked per week) for potential recession signals.
5) Monitor Nvidia’s backlog, lead times, and guidance on HBM supply.
6) Track news related to power permits, substation expansion, and changes in data center power pricing structures.
7) Watch for announcements of AI ROI cases from hyperscalers (improvements in search, advertising, and commerce conversion rates).
8) Observe trends in Tesla’s FSD subscription rates, ARPU, and accident rate data.
9) Check for the expansion of regulatory permits and pilot operation indicators for robo-taxi in various cities.
10) Await details on the actual scope of plant automation investments and cost savings from Optimus.

The Current Position of the Tesla Investment Story: “It’s the Cycle, Not the Price”

Although the stock market may be swayed in the short term by rates and news, long-term value will be determined by cash flow cycles.
Tesla’s long-term drivers include the commercialization of FSD, the robo-taxi network, productivity innovations from Optimus, and the expansion of revenue sources across its ecosystem with charging and diner services.
As long as the AI cycle persists, this adjustment is more likely a matter of “cycle adjustment versus price adjustment.”
Ultimately, as the interest rate path stabilizes, the risk premium on growth stocks is expected to readjust.

< Summary >

After Nvidia’s strong results, the employment report diminished rate cut expectations, leading to profit-taking in overvalued AI stocks.
Dan Ives diagnosed the situation as “not an AI bubble” based on adoption rates and the demand-to-supply ratio.
Tesla’s decline was more about an “AI premium adjustment” rather than a change in fundamentals.
The real variables are power, network, HBM, and ROI, and in 2025 the optimization of inference and the redesign of business models will be decisive.
Tesla’s robo-taxi, FSD, Optimus, and diner experiments leave room for a revaluation of multiples as they evolve into “mobility + waiting time + consumption” platforms.
Keep a close watch on indicators for rates, inflation, and exchange rates, as well as on bottlenecks in AI infrastructure, FSD, and robo-taxi operations to navigate the cycle.

[Related Articles…]

Tesla Robo-Taxi Timeline and FSD Monetization Prospects

Impact of the Fed’s Rate Cut Scenario on Growth Stocks

*Source: [ 오늘의 테슬라 뉴스 ]

– 테슬라 400 붕괴! 엔비디아 급반전·금리 컷 논란… AI 거품 재점화? 댄 아이브스 인터뷰로 본 월가의 진짜 생각은?

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● AI Gold Rush, Robo-Taxi Boom, Teslas Rent Frenzy

Tesla’s Clear Hint: Federal AI Regulation Integration, FSD Rental, SAM3 & Next-Generation Gemini, and the Real Value of AI as Indicated by Multiples

Today’s article contains three points.First, how Trump’s push for integrated federal AI regulation is shortening the timeline for robo-taxi commercialization.Second, why Tesla’s ultra-low-cost FSD trial rental is considered a “Customer Acquisition Cost (CAC) innovation.”Third, how Meta’s SAM3 and Google’s next-generation Gemini demonstrate a vision- and ASIC-focused transition that hints at an industry restructuring.Additionally, the news format covers how the S&P 500 multiples and Bitcoin volatility contributed to today’s stock market fluctuations.The key points will be highlighted from a perspective of the global economic outlook, inflation, interest rates, stock market, and AI investments.

Today’s Market Briefing: Bitcoin Volatility → Simultaneous Shaking of Risk Assets

Bitcoin’s preliminary decline has transferred risk-off sentiment to the U.S. stock market overall, causing tech stocks to close lower.Even though the previous day’s Nvidia earnings and guidance, along with the performance of a newly released foundation model, reaffirmed that “this is not an AI bubble,” intraday volatility increased due to liquidity reduction and profit-taking.The two main points are:

• When Bitcoin’s volatility as a liquidity proxy increases, the beta of tech stocks amplifies—a recurring correlation.
• However, the fact that the S&P 500 multiple has hardly expanded on a YTD basis indicates that this is fundamentally different from a typical bubble where prices surge independent of earnings.In conclusion, in a phase where the real value creation of AI is being confirmed, sudden market fluctuations like today may actually present attractive valuation opportunities.

Valuation Check: What the Stagnant Multiples Indicate

The observation that the S&P 500’s P/E multiple has not significantly changed since the beginning of the year is valid.This implies two key points:

• The rise at the index level was supported by earnings upgrades or increased expectations, not by indiscriminate expansion of multiples.
• For stocks like Tesla with high multiples, it reflects not “one year” but “five years” of earnings power that has been priced in, making it difficult to label them as overvalued or undervalued solely by sector averages.Ultimately, in phases like today’s sudden market movements, there could be an enhanced attractiveness relative to valuation when AI’s actual value creation is confirmed.

Policy Changes: Trump’s Push for Integrated Federal AI Regulation

According to reports, the Trump administration is preparing and pushing an executive order to consolidate the varying state-level AI regulations into a single federal framework.The key elements are “federal preemption” and “compliance tied to subsidies.”

• Compliance with federal rules can largely offset the differing state requirements.
• There is even discussion of imposing federal subsidy restrictions on states that fail to comply.From Tesla’s perspective, the impact is significant.
• If the existing state-by-state permits and operations for robo-taxis pass the federal standards, it will open up a path for simultaneous nationwide expansion.
• While mapping-dependent companies like Waymo are preparing for city-by-city launches that take longer, Tesla, whose FSD is already operational over a wide area, can “decide on deployment and expand immediately.”Some cautionary points include:
• Actual legislative and administrative procedures, state DMV authorities, and standardization of insurance and liability systems will require time to construct detailed governance.
• Nationwide expansion will only materialize concurrently with safety metrics, recall procedures, and an OTA regulatory framework.

Tesla On-Site Signal: The CAC Innovation of the FSD v14 Trial Rental

In some regions in the U.S., there have been observations of aggressive promotions such as ultra-low-cost Tesla rentals on a weekly basis (for the Cybertruck, around $75 per day), including free Supercharging and FSD trials.The meaning is clear.

• It is a strategy that converts Customer Acquisition Costs (CAC) from advertising into trials. The higher the tangible value of FSD, the steeper the conversion rate.
• With safety metrics of v14 continuously improving via OTA updates, a full-stack revenue model is completed through “trial → purchase → subscription (FSD/insurance).”
• The trial rental serves a dual purpose by enhancing both data collection (edge cases) and a usability feedback loop.This trend has also caught the attention of Wall Street. Piper Sandler presented a positive viewpoint and set a high target price based on the robo-taxi and FSD momentum.

European Data: Accelerated Q4 Sales in Norway and Europe

In weekly registration statistics from Norway and other European regions, there is a discernible trend of Tesla sales rapidly recovering after entering Q4.The background includes finalized subsidy schemes, a renewed expansion in overall EV demand, and the normalization of production.Since Europe is a region where subsidies and regulations have been pre-adjusted, signals of bolstering demand are likely to appear there first.

Meta SAM3: Proof of Scalability for a Vision-Centered Approach

Meta’s next-generation segment model, ‘SAM3,’ has been unveiled, and it is evaluated as having enhanced capabilities in quickly and accurately segmenting objects within images and videos and linking them to text.The core aspects are twofold:

• Large-scale auto-labeling: Accelerates the generalization of “vision-based automatic labeling,” a method Tesla pioneered for labeling autonomous driving data. It simultaneously improves the cost, speed, and quality curve of data.
• Multimodal bridge: Serves as an interface that seamlessly connects vision-specialized models with language models, simplifying the “instruction-understanding-action” pipeline.The pure vision approach, as opposed to reliance on LiDAR, has significant software scaling benefits.
• The cycle from camera to labeling to training to deployment is fast, and foundational components (such as SAM3) are quickly improved within the open ecosystem.

Google’s Next-Generation Gemini and the ASIC Transition: What the TPU Indicates

Google highlighted that its next-generation Gemini (referred to by some as “Gemini 3”) was trained and served using TPU.The key point is that “dedicated ASICs can outperform general-purpose GPUs in terms of performance, power efficiency, and bandwidth efficiency,” which serves as an industry signal.

• Advantages of TPU/ASIC: Optimization of on-chip interconnects, precise memory scheduling, and maximization of tokens processed per unit of power.
• Risks: Vendor lock-in due to dedicated stacks, software ecosystem compatibility, and supply chain flexibility.Tesla’s roadmap for robotics and autonomous driving chips (HW4 → next-generation HW5/‘AI5’) follows a similar philosophy.
• Elon Musk has emphasized efficiency by saying, “Even if Nvidia chips were free, we would still use our dedicated chips.”
• The goal is to achieve a TCO innovation that significantly reduces costs and power consumption while delivering equivalent performance.While Nvidia remains the standard for data center training/serving, the era of ASIC for edge, vehicle, and on-device AI is likely to prevail for a long time.

Intelligence-Centered AI Architecture: Why Customer Focus and Computing Concentration Are Inevitable

We are currently in the early to mid-phase of the “intelligence race.”

• To achieve the highest level of intelligence, the greatest computing power is required. Therefore, it is structurally inevitable for hyperscalers that have pre-emptively secured GPUs/TPUs/ASICs to adopt a customer-centric approach.
• The key is “proof of value creation.” The models revealed today are already showing commercially viable performance in translation, search, copilot functions, creative tasks, and simulation.Ultimately, the focus should be less on “whether it’s a bubble” and more on “which company can turn it into cash flow.” The winners will be those who close the loop from pipeline to product, subscription, and API.

AI as a Strategic Asset: Geopolitical and Investment Perspectives

AI is no longer merely a technology; it is a strategic asset on a national level.

• The United States, conscious of China’s pursuit, has a strong incentive to standardize regulations and accelerate investments.
• As AI becomes increasingly securitized, regulations will converge into a two-track system of “safety-centric guardrails + innovation promotion.”The investment framework is clear.
• Consistent regulations → accelerated commercialization → enhanced data/network effects → strengthened barriers to entry (not through monopolies, but through economies of scale and data).

Investment Checklist (Decision-Making Hints)

• Federal AI executive order text: safety standards, responsibilities & insurance, data usage rules, division of authority between state and federal.
• Tesla FSD safety metrics: accidents/interventions per mile, OTA release notes, performance in city and complex intersections.
• Robo-taxi commercialization track: commercial insurance, fare structure, operational scope (geofence), and performance in nighttime/adverse weather.
• Speed of the ASIC transition: Google TPU, Tesla AI chips, and the custom silicon roadmaps of Amazon/Microsoft.
• S&P 500 earnings outlook (2025–2026): earnings upgrade in AI beneficiary sectors vs. stagnant earnings in non-beneficiary sectors.
• Crypto–stock correlation: liquidity events (Fed interest rate, QE/QT, net inflow of stablecoins).

Today’s Key News Summary

• Preliminary Bitcoin decline → tech stock weakness; despite increased volatility, multiples have not changed significantly.
• Reports of Trump’s push for integrated federal AI regulation → highlighted possibility of nationwide simultaneous expansion of robo-taxis.
• Expansion of Tesla FSD v14 trial rental → CAC innovation and enhanced data loop.
• Growing upward reassessment on Wall Street (e.g., Piper Sandler) → revaluation of the robotics and subscription model.
• Accelerated Q4 sales in Europe (including Norway) → a signal of renewed demand.
• Meta SAM3 & Google’s next-generation Gemini → industry signals for vision-language integration and the ASIC transition.

The Most Crucial Point Often Overlooked Elsewhere

• Should federal preemption be enforced, robo-taxis will shift from “city-by-city regulatory hurdles” to a competition based on “platform operating capability.” Once network effects take hold, the speed of expansion accelerates.
• The combination of Pure Vision + dedicated ASICs simultaneously lowers the combined marginal costs of data, computing, and algorithms. This is key to long-term profitability.
• Tesla’s ultra-low-cost FSD trial rental converts advertising expenses into trial costs through a CAC arbitrage. As conversion rates and LTV improve, multiple expansion follows naturally.
• AI infrastructure CAPEX is transitioning from an initial GPU cycle to a secondary ASIC cycle, simultaneously increasing both supplier and user surplus.
• Stagnant index multiples can obscure the earnings slowdown in non-AI sectors. Portfolios should be designed to separately address AI beta and earnings defense.
• Bitcoin volatility is a shadow of liquidity. Using the systematic beta expansion that spreads from crypto to big tech as a signal for risk management is effective.

< Summary >

• The market is experiencing heightened volatility, yet the multiples remain distant from bubble signals.
• The push for integrated federal AI regulation represents a quantum leap point for nationwide robo-taxi commercialization.
• Tesla’s FSD trial rental is both a CAC innovation and a data loop accelerator.
• SAM3 & next-generation Gemini drive accelerated vision-language integration and the ASIC transition.
• AI is a national strategic asset, and regulation is converging to “guardrails + promotion.”
• The global economic outlook, inflation, interest rates, stock market, and AI investments should be viewed within one framework.

[Related Articles…]

• The Impact of Unified U.S. Federal Regulation on Robo-Taxi Commercialization
• From GPU to ASIC: The Transition in AI Infrastructure CAPEX Cycles

*Source: [ 허니잼의 테슬라와 일론 ]

– 테슬라의 확실한 힌트를 오늘 확인했습니다. AI 산업의 발전 방향을 보면 정답이 보입니다. 일론은 또 옳았다!

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● Liquidity Meltdown, Fake AI Bubble

Wall Street Short & Long Simultaneously Shout “The Resilience of the Liquidity Market” and “Fake AI Risk” Analysis

Key Issue Alerts Included in Today’s Article

This article explains in detail the connection between the crypto market makers’ deleveraging, which is the real cause of the short-term plunge, and the volatility in the U.S. stock market.

It outlines from a supply-demand perspective how Minervini’s logic for maintaining short positions and Scott Lutner’s year-end rally scenario can simultaneously hold.

The article translates into investment strategies Carson Block’s warning about the collapse mechanism of the “fake AI” bubble and why shorting large tech stocks like Nvidia is dangerous.

It interprets the actual impact on interest rates and inflation expectations from Federal Reserve’s Lisa Cook’s statement about “overvalued assets.”

A checklist often overlooked by other channels (stablecoin issuance, options gamma position, system fund flows) is provided as an indicator for timing real-world position shifts.

News Summary: When Volatility Widens, the Conclusion is “Supply-Demand Imbalance”

Despite strong earnings from Nvidia, the Nasdaq experienced intraday swings before closing lower, and the S&P 500 passed through a phase of increased volatility.

Bitcoin and Ethereum also saw significant declines, simultaneously sending signals of a contraction in liquidity across all risk assets.

Lisa Cook’s remarks on the possibility of “overvalued asset adjustments” added selling pressure to interest rate-sensitive sectors.

Nonfarm payrolls were distorted due to revisions and shutdown effects, causing effective signals to be weak, and the market reacted more to “flows” than the data itself.

In summary, the weakness in the U.S. stock market is driven by “supply-demand shocks” rather than fundamentals, and short-term changes can be rapid.

Expert Views: Looking at the Same Market from a Different Angle

Minervini: He assesses that technical damage remains in the short term and suggests maintaining short positions while being ready to reverse positions at any time with December seasonality in mind.

The key is to simultaneously prepare “short positions with risk limits near the stop loss” and “switching to individual stocks when trending upward.”

Carson Block: He advises against shorting large tech stocks (especially Nvidia) due to their low survival rate and recommends targeting “fake AI” and abnormally high tech premium stocks.

He reaffirms that the true trigger for bubble collapse is not fundamentals but rather “excess supply of speculative assets and the packaging of financial products.”

Tom Lee: He observes that after the massive liquidation in early October, the recovery of liquidity from crypto market makers has been delayed, and a deleveraging pattern of approximately 6–8 weeks is repeating.

The weak liquidity in crypto is interpreted as a leading signal of weakness in U.S. stock market risk assets.

Scott Lutner: He predicts that the current weakness is due to the forced reduction of systematic and rules-based funds, and if retail buying and institutional risk-on activity for the year-end occur together, a significant rebound could follow.

Crypto-Stock Link: Why This Downturn Is About “Supply-Demand”

The massive leveraged liquidations in crypto trigger the need for market makers to recapitalize, widening spreads and reducing order book depth.

At this time, the perceived liquidity across risk assets declines, and even in the U.S. stock market, volatility increases asymmetrically.

Typically, this phase subsides within 6–8 weeks, with a recovery in stablecoin net issuance and normalization of the derivatives market basis serving as leading signals.

Therefore, Tom Lee’s “short-term correction followed by a recovery” framework aligns with historical patterns.

Policy Variables: Implications of Lisa Cook’s Remarks

The mention of “overvalued asset adjustments” leads to a reevaluation of risk premiums rather than a change in the interest rate trajectory.

The internal stance of the Fed shows confidence in system stability and considers systemic risks like those in 2008 to be low.

In short, it is a market where it is the adjustment of premiums rather than interest rates themselves, and valuation multiples play a central role in volatility more than the pace of inflation easing.

AI Sector Details: How to Differentiate “Real AI” from “Fake AI”

Real AI involves companies connected to high-performance computing infrastructure, model operations, and revenue-generating pipelines, with order flows and customer lock-in confirmed by data.

Fake AI, on the other hand, only carries AI narrative keywords, with minimal contribution to revenue and margins, relying heavily on large-cap vendor dependency.

Carson Block’s short targets are the latter, and the classic peak of a bubble is characterized by “countless small-cap AI stories” and the spread of packaged financial products.

Core companies like Nvidia, with proven revenue models and ecosystem power, carry significant structural shorting risks.

Supply-Demand and Derivatives: Why a Year-End Rally Seems “Possible”

Systematic, CTA, and risk parity funds automatically reduced their exposure in response to rising volatility and falling prices, and the exit of supply amplified the short-term shock.

In contrast, retail investors absorbed downside risk through spot buying and call option purchases, and institutions, under year-end performance pressure, have substantial room to revert to risk-on positions.

The point at which options market gamma positions shift from negative to positive could quickly herald a reversal in spot trends.

Data Check: The Signal-to-Noise Ratio of Employment and Macro Data

Recent employment data has been heavily distorted by revisions and shutdown effects, making it an unreliable signal to alter the interest rate path.

The market is currently more sensitive to “liquidity and supply-demand” factors than to economic forecasts, with policy and data events playing merely a triggering role.

Portfolio Strategy: Balancing Defense and Opportunity Capture

Core: Maintain positions that can withstand volatility, such as large-scale AI infrastructure, consumer staples, and healthcare.

Selective Beta: Diversify with energy and quality growth stocks to construct a bet that is neutral to the path of interest rates and inflation.

Tactical: For fake AI and narrative-based themes, reduce exposure during rally phases through partial scaling down, using shorts or put-call spreads to maximize risk-adjusted returns amid excessive volatility.

Crypto: Refrain from expanding leverage until the recovery in stablecoin net issuance, normalization of derivatives basis, and confirmation of net spot inflows to exchanges are all observed.

Points Rarely Covered by Other Channels

When the deleveraging of crypto market makers acts as a leading indicator for volatility in U.S. stocks, the fastest recovery signal is the rebound in net stablecoin issuance.

A weekly shift of options gamma positions from negative to positive indicates the quality and sustainability of a spot rebound.

Fake AI can be identified by four metrics: the AI revenue contribution rate, improvement in gross margins, customer attrition rate, and concentration of large customers.

The key driver of the year-end rally is not expectations of an interest rate cut but rather portfolio rebuilding under institutional performance pressures, marking a distinctive element of this cycle.

Calendar and Scenarios

Base Scenario: Volatility peaks within one to two weeks, supply-demand restoration in early to mid-December, and a rebound in risk assets at year-end appear likely.

Risk Scenario: Excessive proliferation of fake AI, a surge in new supply, further reduction in systematic funds, and resurfacing policy uncertainties could result in renewed volatility expansion.

Trigger Checklist: A rebound in stablecoin net issuance, a flip in options gamma, renewed ETF inflows, and stabilization of credit spreads must be confirmed simultaneously.

Keyword Scan: Meeting SEO and Investment Points Simultaneously

The recovery in U.S. stock market supply-demand is driven by portfolio rebuilding rather than interest rate expectations, and inflation remains a secondary variable in current volatility.

For the AI sector, it is effective to differentiate between core and peripheral companies and prioritize those with visible cash flows amid economic forecast uncertainties.

Reframe your portfolio narrative around the five key terms: interest rates, inflation, AI, U.S. stocks, and economic outlook.

< Summary >

The short-term weakness is fundamentally about supply-demand shocks rather than fundamentals, with crypto deleveraging acting as a leading indicator.

Minervini maintains short positions amid technical weakness, while Lutner anticipates a recovery in systemic funds leading to a year-end rally.

As Carson Block warns, fake AI is at the heart of the bubble, and shorting large tech stocks carries significant risk.

Lisa Cook’s remarks are merely signals of premium adjustments, with systemic risks remaining low and potential for rebound if supply-demand is restored.

The checklist includes stablecoin issuance, options gamma, ETF inflows, and credit spreads, suggesting that expanding leverage before confirmation is unwise.

[Related Articles…]

Bitcoin Cycle and Year-End Rally Checklist

Post-Nvidia AI Value Chain Investment Map

*Source: [ Maeil Business Newspaper ]

– [홍장원의 불앤베어] 상승론자 톰리가 말하는 코인 하락 이유. 미네르비니 “숏포지션 유지” 럽너 “조정장 후 상승할 것”

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