AI Agents, Big Tech Shakeup, Search, Shopping, Booking, Ads, Stocks

● AI Agents, Big Tech Shakeup, Search, Shopping, Booking, Ads, Stocks

AI Agents Are Redesigning the Internet: The Structural Shift Reshaping Search, Shopping, Booking, Advertising, and Big Tech Valuations

The core of this shift is not simply that ChatGPT has become smarter.

AI is now moving beyond answer generation into an intelligent agent era: systems that read email, identify tasks, access websites, make bookings, and carry out work through to the point of payment.

This report summarizes why personal AI agents such as Meta Muse, xAI-related agents, Grok, Open Interpreter, and Inflection could materially change how the internet is used.

It also examines why established internet gatekeepers such as Google Search, Amazon, Booking.com, Expedia, and Hotels.com may come under pressure, while human-attention platforms such as YouTube, Instagram, and Netflix may become more valuable.

From a global macro perspective, this is not only an AI trend but also the next stage of digital transformation and a factor that may require a fresh assessment of big tech valuations.

1. The AI agent now emerging is not a chatbot, but an internet proxy

Traditional ChatGPT has functioned primarily as a question-and-answer tool.

By contrast, an AI agent acts on a user objective by directly operating a computer and the internet.

In practical terms, it resembles Jarvis from Iron Man: a system that can open websites, compare information, draft emails, and complete reservations when asked to “take care of this.”

Chatbot: An AI that responds to questions.
AI agent: An AI that executes objectives.
Personal agent: An AI connected to email, calendar, browser, documents, and reservation systems to perform work on behalf of the user.

The key point is that AI agents are not simply text-generation tools.

The user is increasingly becoming the planner, while the agent performs the search, comparison, input, and execution steps.

2. The most important capability is proactivity

The central attribute emphasized in the source material is proactivity.

In this context, proactivity means identifying what needs to be done before the user explicitly requests it.

The important distinction is that this is not about sending more notifications.

The system must identify genuinely relevant tasks without becoming intrusive.

This is similar to a strong assistant or a strong team member.

Someone who prepares materials in advance, flags missed emails, and organizes documents for the next meeting can materially improve productivity.

AI agents are beginning to play that role.

3. Example use case: reading email and preemptively proposing lecture materials

The first notable case involves lecture preparation.

The user had been exchanging emails regarding a lecture.

The user sent a short message indicating they would prepare the lecture.

The agent then analyzed the email and proactively proposed the following:

“The lecture topic and recommended books are already set. Would you like me to draft an 80-minute presentation deck and a Q&A list?”

The agent identified the task without being explicitly instructed.

It then created a 27-slide presentation deck.

When the user provided links to prior lecture materials, the agent incorporated them and revised the deck accordingly.

The result reportedly matched the user’s intent by roughly 80%, leaving only final refinements for a human to complete.

The significance of this case is not simply that it generated slides well.

The key point is that the agent read email, identified an implied task, and made a recommendation before being asked.

This is the defining distinction between conventional generative AI and personal AI agents.

4. The Meta Muse case: identifying unanswered emails before the user does

The second case involves Meta Muse.

In the source material, the bot is referred to as “Karina.”

The user received an alert indicating that there were unread emails requiring action.

The message related to a request for original photos needed for promotional materials, and the user had not yet responded.

The important point is that the user had not asked the system to find unanswered emails.

The AI reviewed the inbox, identified items that required a reply, and surfaced them first.

This kind of experience materially changes the perceived value of workflow automation.

The system is no longer only searching for information; it is beginning to manage work.

5. Repetitive internet tasks are being automated: subscription cancellation, hotel booking, and outbound email

A second major advantage of AI agents is their ability to handle repetitive administrative tasks.

For example, canceling a paid subscription usually requires opening a website, logging in, navigating menus, and locating the billing page.

The process is often more cumbersome than expected.

In the source material, the agent handled a TVING subscription cancellation after the user said the current Chrome tab was on the TVING login page and asked for the paid subscription to be canceled.

The user also delegated the cancellation of a Smart Ring Oura subscription to the agent.

Hotel booking is another area where the difference is significant.

The agent reviewed Marriott Bonvoy availability for specific dates and compared whether point redemption or cash payment was more favorable.

It checked which hotels were available.
It compared cash rates with point redemption.
It recommended cash payment for some hotels.
It recommended point booking for others.
It reviewed hotel ratings and assessed the likelihood of late checkout.
It drafted English inquiry emails and sent them directly through Gmail.

This is not merely a convenience feature.

It indicates that the main constraint in internet usage is shifting from information access to execution friction.

AI agents are designed to reduce that friction.

6. Internet usage is splitting into two categories: task-driven and attention-driven

The most useful framework is to divide internet usage into two categories.

Task-driven internet: Use cases with a clear objective, such as shopping, flight booking, hotel booking, stock trading, or targeted information retrieval.
Attention-driven internet: Platforms such as YouTube, Instagram, TikTok, and Netflix where users spend time without a specific transactional goal.

AI agents are most likely to displace task-driven internet use.

Users may no longer need to open websites, navigate menus, compare options, and proceed to checkout manually.

Instead, they may simply say: “Book the earliest KTX ticket from Seoul to Busan after 9:00 a.m. next week.”

Or: “Find and compare hotel options for a family of four with breakfast and late checkout.”

This shift is comparable to the transition from PC-based internet to mobile internet.

PC-era internet required a desktop computer.

Smartphones removed that constraint and expanded internet usage significantly.

AI agents are now removing the burden of manually interacting with websites and apps.

7. Why Google, Amazon, and Booking.com may face pressure

If task-driven internet usage migrates to AI agents, established gateway companies may find themselves in a weaker position.

Google has long been the primary gateway for information discovery.

But users are increasingly asking AI systems directly rather than entering keywords into a search box.

Amazon faces a similar issue.

In the past, users went directly to Amazon to buy products.

With an agent in the middle, users may no longer need to visit Amazon themselves.

The agent can compare multiple retailers and present the best purchase options.

Booking.com, Hotels.com, and Expedia could also be affected.

These businesses monetize user traffic during the search, comparison, and booking process.

If an agent compares hotel websites, OTA platforms, reviews, and loyalty benefits on behalf of the user, time spent on a single platform may decline.

This does not imply that these companies will disappear.

Rather, internet power may shift from platforms visited directly by users to agents that mediate user decisions.

8. By contrast, YouTube, Instagram, and Netflix may become stronger

As AI agents take over task-driven usage, human-generated attention may become more valuable.

The value of platforms where people actually spend time and consume content may increase.

YouTube as a long-form video platform.
Instagram as a social platform built on relationships and taste.
TikTok as a recommendation-driven attention platform.
Netflix as a concentrated entertainment consumption platform.
Communities where users actively comment and debate.

If AI agent traffic grows, the advertising market may place greater value on genuine human traffic.

Advertisers may increasingly focus on actual dwell time, purchase intent, and community response rather than raw visit counts.

This could reshape the digital advertising market and the platform economy.

9. Why Meta Muse is drawing attention, and the associated risk

Meta Muse has attracted attention because of its rapid download growth and early user response.

Getting users to install a new app on a smartphone is increasingly difficult.

Its ability to overcome that barrier quickly is meaningful.

However, early momentum does not guarantee long-term success.

Clubhouse also experienced rapid initial adoption before losing relevance.

Whether Meta Muse ultimately becomes the leader in personal AI agents remains uncertain.

The key point is that Meta has moved quickly to reach consumers’ smartphones.

Early distribution is a major advantage in the personal agent market.

This is one reason Meta may be viewed differently from an investment perspective.

10. The most serious competitor may still be Google

The source material identifies Google as a potential dark horse.

The reason is straightforward.

AI competition is likely to become a contest involving token cost, compute efficiency, and infrastructure optimization.

Google is a vertically integrated AI platform spanning hardware and software.

It owns TPU-based AI infrastructure and a broad ecosystem including Search, YouTube, Android, Gmail, Chrome, Google Calendar, and Google Maps.

For an AI agent to perform real personal work, it must integrate deeply with email, calendar, browser, maps, payments, and document tools.

On that basis, Google has a substantial structural advantage.

OpenAI and Anthropic are strong on model capability.

Google is stronger on infrastructure efficiency and service integration.

If the AI agent market becomes a cost-intensive competitive race, the company with superior efficiency may ultimately be better positioned.

11. OpenAI, Anthropic, Adept, and Inflection have not yet entered the main phase

The personal AI agent market has no clear winner at this stage.

If OpenAI, Anthropic, and Google launch more powerful consumer-facing agents, the competitive landscape could change materially.

Startups such as Adept have also drawn attention in the computer-operation agent category.

Inflection has likewise attracted interest around personal assistant experiences.

In short, the direction is clear, but the winner remains undecided.

The most important mistake in AI analysis is to confuse technological direction with the success of a specific company.

The emergence of the AI agent era does not imply that Meta Muse will be the final winner.

12. The key insight missing from most coverage: the web is moving toward agent-friendly design

Most commentary stops at the idea that “AI can now book reservations too.”

The more important change is that the design of websites and apps themselves may need to evolve.

Until now, companies have optimized for human-facing UI and search-engine visibility.

Going forward, it may become more important to structure data in ways that AI agents can read and execute reliably.

For example, reservation availability, pricing, refund terms, point value, loyalty benefits, and review credibility must be accessible in a form that an agent can compare efficiently.

Competitive advantage may increasingly come from data structure rather than visual design.

Companies may need to optimize for what an AI selects, not just what a human sees.

13. The real bottleneck is not model quality, but permissions, payments, and trust

AI agents may become capable of operating computers effectively sooner rather than later.

However, mass adoption is likely to be constrained by other factors.

Permission: The extent to which an AI can access email, browser, payment details, and calendars.
Payment: How final approval is handled when an AI initiates a purchase.
Security: The risk of phishing sites or malicious links being processed incorrectly.
Liability: Who is responsible if an AI books or cancels incorrectly.
Trust: Whether users are willing to delegate important tasks to an AI.

For that reason, the eventual winners may not be the companies with the strongest models alone.

The most likely winners are those that combine user trust, security, payment controls, and ecosystem integration most effectively.

14. Economic implications: internet tolls and intermediaries may be repriced

AI agents may reshape the fee structure of the internet economy.

Historically, users visited platforms directly, and those platforms captured search advertising, brokerage fees, and ranking income.

If agents increasingly make optimized decisions on behalf of users, direct platform traffic may decline.

In that case, value may shift in three directions.

Agent operators: The platform that mediates user decisions becomes a new gateway.
Data providers: Companies that supply trusted pricing, inventory, and review data gain importance.
Verification infrastructure: Payment, identity, security, and audit-trail infrastructure may become more valuable.

At a macro level, this may raise productivity and reduce consumer search costs.

For platform companies, however, existing advertising and intermediation models may come under pressure.

From a global macro perspective, AI agents should be viewed not only as a labor-productivity catalyst but also as a force reshaping consumption and distribution channels.

15. Investment checklist

The spread of AI agents is a major issue for both big tech and the software sector.

However, it is risky to infer a stock thesis solely from the idea that “AI agents are the next big thing.”

The following factors should be monitored:

Distribution: The ability to reach large numbers of users across smartphones and PCs.
Integration: The depth of connection with email, calendar, browser, payments, documents, and maps.
Inference cost: The ability to reduce token cost and support scale.
Trust and security: Whether users are willing to allow real transactions and reservations.
Monetization model: Whether revenue comes from subscriptions, transaction fees, advertising, or enterprise automation.

Meta has an early advantage in distribution.

Google has an advantage in infrastructure and ecosystem breadth.

OpenAI has strong consumer awareness and model leadership.

Anthropic is positioned around trust and enterprise credibility.

Ultimately, the outcome is likely to depend on the full ecosystem rather than model performance alone.

16. The next 3 to 6 months may bring a more visible shift

The source material suggests that within 3 to 6 months, AI agents may outperform most people at using computers.

This does not mean the systems will be perfect across all use cases.

For example, certain payment flows and site interactions may still be imperfect.

But the direction of travel is already clear.

Projects such as Open Interpreter helped define the early direction for how AI should operate computers and interact with users.

If major AI companies accelerate product development in this direction, task-driven internet usage could move significantly toward agents.

Internet usage is unlikely to decline.

In fact, total usage may rise.

The difference is that more of it may be executed by AI agents rather than by people directly.

17. Core conclusion: the primary user of the internet is shifting from humans to AI

The internet as previously known was a system in which people searched, clicked, compared, and booked directly.

In the AI agent era, people set the objective and AI executes it.

This affects search, e-commerce, travel platforms, advertising, social media, cloud infrastructure, and AI semiconductors.

For individuals, this means less repetitive work.

For companies, it means the customer interface may be disintermediated.

For investors, it means established internet moats may need to be reassessed.

The next phase of the internet is likely to involve not more websites, but more capable personal AI agents.

Understanding this shift is likely to be central to interpreting the next phase of AI and digital transformation.

< Summary >

AI agents are not chatbots; they are personal proxies that perform internet tasks on behalf of users.

The key capability is proactivity: identifying needed work before the user explicitly requests it.

They are already being used for email review, lecture deck creation, subscription cancellation, hotel booking, review analysis, and outbound email drafting.

Task-driven internet use such as shopping, booking, and search is likely to face displacement by AI agents.

Google, Amazon, Booking.com, and Expedia may face structural pressure.

By contrast, YouTube, Instagram, and Netflix may become more valuable as human-attention platforms.

Meta Muse has gained attention through early distribution, but competition from Google, OpenAI, and Anthropic is still at an early stage.

The decisive factors are likely to be permissions, payments, security, trust, and service integration rather than model quality alone.

[Related Articles…]

*Source: [ 내일은 투자왕 – 김단테 ]

– 우리가 알던 인터넷 모두 끝났습니다.

● Bond Yield Shock, Nasdaq Defies Gravity, AI Rally Fights Back

U.S. Treasury Yields Near Multi-Decade Highs: Why Nasdaq Has Held Up, and What Inflation, Oil, and AI Leadership Mean

The key issue is not simply that Treasury yields have risen.

What matters more is why yields have climbed to multi-decade highs, why U.S. equities and the Nasdaq have remained resilient, and how leadership in the market may continue to separate by sector.

This move reflects a complex environment in which policy rates, inflation, oil, fiscal policy, and the AI investment cycle are all interacting at once.

On the surface, it appears to be a high-rate shock. In practice, capital is reversing direction within the market, with sharp divergence between areas seeing outflows and those attracting inflows.

Below is a consolidated review of the drivers behind the surge in Treasury yields, the reasons equities have held up, the capital concentration in the AI value chain, and the most important variables that are often overlooked in other coverage.

1. The First Driver of the Yield Surge: Treasury Selling Has Overwhelmed Buybacks

The most direct reason U.S. Treasury yields have moved higher is heavy selling in the Treasury market.

The U.S. Treasury has been operating a buyback program to repurchase longer-dated bonds.

In effect, this is designed to reduce upward pressure on yields by removing some supply from the market.

Even so, 10-year and 30-year yields remain elevated, indicating that selling pressure is stronger than the buyback effect.

U.S. Treasury selling has increased.
The buyback program is active, but it has not been sufficient to offset upward pressure.
Higher long-term yields suggest the market is pricing both sustained inflation and fiscal pressure.

Treasuries are typically considered safe-haven assets.

They also serve as the benchmark for global financing conditions.

When U.S. Treasury yields rise materially, yields in other sovereign markets tend to move higher as well.

As a result, this is not only a U.S. issue; it raises financing costs across global markets.

2. The Core Variable Behind Higher Yields: Geopolitical Risk and Oil

Geopolitical tension remains an important factor behind the recent rise in Treasury yields.

If Middle East risk does not ease, oil prices are unlikely to stabilize quickly.

Higher oil prices lift inflation expectations.

When inflation expectations rise, the Federal Reserve becomes more cautious about cutting rates.

As a result, higher oil prices translate into higher Treasury yields.

Under normal conditions, weaker global growth reduces oil demand.

Lower demand would typically stabilize or push down oil prices.

However, supply shocks from conflict in the Middle East can override weak demand.

Even with softer demand, tighter supply can still drive oil higher.

Global growth slowdown weighs on oil demand.
Middle East conflict constrains oil supply.
If supply disruption dominates, oil prices rise.
Higher oil prices lift inflation expectations.
Rising inflation expectations keep long-term yields elevated.

The broadcast also highlighted a possible peace proposal from Iran following the U.S.-China summit.

According to that interpretation, Iran reportedly sought frozen asset relief and sanctions easing on oil in exchange for reducing tension in the Strait of Hormuz and restraining Houthi-related activity.

Whether Washington would accept such terms remains uncertain.

If geopolitical tensions ease, oil and Treasury yields could decline.

If negotiations fail, high oil and high yields may persist longer.

3. The Trump Variable: Inflation Is the Strongest Driver of Political Support

One of the strongest points raised in the original discussion is that inflation materially affects political approval.

Historically, governments that allow inflation to remain elevated rarely benefit politically.

Rising prices reduce real income.

When living costs rise while wages do not, households feel materially worse off.

If rates also rise, debt servicing becomes more burdensome.

If asset prices weaken as well, consumers face a threefold squeeze.

Inflation reduces real purchasing power.
Higher rates increase debt service costs.
High rates pressure equities and real estate valuations.

This pattern was visible during the Biden administration after the Russia-Ukraine war began.

Sanctions on Russia were expected to shorten the conflict, but the war continued.

Energy prices rose, inflation strengthened, and higher living costs became a political burden.

A similar risk may apply to President Trump.

In the short term, a hardline geopolitical stance can support political positioning, but prolonged conflict and higher oil prices can reverse that effect.

Higher oil raises inflation, inflation reduces expectations for rate cuts, and that keeps long-term Treasury yields elevated.

In that sense, much of the recent yield increase can be viewed as a political and geopolitical backlash.

4. The U.S.-China Summit and the Possibility of a Taiwan-Iran Tradeoff

Another important interpretation discussed in the broadcast was the possible link between the U.S.-China summit and Iran negotiations.

China has repeatedly asked the United States not to interfere in Taiwan-related issues.

For Washington, Taiwan remains central to strategic competition with Beijing.

For that reason, a full concession on Taiwan is highly unlikely.

However, ahead of key political events, temporary restraint on Taiwan-related arms exports could be part of a broader negotiation framework.

In exchange, China could potentially play a mediating role on Iran or contribute to easing Middle East tensions.

The United States is unlikely to fully concede Taiwan.
Temporary adjustment of Taiwan-related actions may still be possible for political reasons.
China has the capacity to influence Iran and broader Middle East dynamics.
A Taiwan-Iran linkage could become a hidden market variable.

This is a strategic interpretation rather than a confirmed outcome.

However, markets often price probabilities before outcomes are finalized.

For that reason, U.S.-China talks, Taiwan policy, and Iran negotiations remain key events for Treasury yields and oil prices.

5. The World Is Moving Into a High-Cost Regime: A New High-Rate Normal

This period should not be viewed as a temporary rate spike.

The more important point is that the global economy is shifting into a higher-cost regime.

Conflict in the Middle East, energy supply risk, fiscal expansion, rising Treasury issuance, and a restrictive monetary stance are all raising the cost structure.

In the past, a 5% Treasury yield would have been considered exceptional.

Today, a long-end yield moving between the mid-4% and 5% range may no longer be unusual.

This is the essence of a higher-rate normal.

Higher oil prices increase production and logistics costs.
Inflation makes rate cuts more difficult.
Fiscal expansion increases Treasury issuance.
Greater Treasury supply pressures bond prices and lifts yields.
High-rate conditions may persist longer than in prior cycles.

Exchange rates follow a similar logic.

Where the long-run average USD/KRW rate once appeared closer to the 1,100 range, the 1,300 range has increasingly acted as a new reference point.

This can be understood as a stronger-dollar normal.

Likewise, Treasury yields may remain elevated rather than returning fully to historical averages.

6. Why Equities Have Not Collapsed: Policy Is Tight, but the Overall System Is Not Fully Tight

A common question is straightforward: if Treasury yields are this high, why has the Nasdaq not collapsed?

In general, higher Treasury yields are a headwind for equities.

When Treasuries offer higher risk-free returns, investors tend to reduce exposure to risk assets.

For that reason, Treasury yields act as a gravitational force on equities.

This cycle is different in one important respect.

Monetary policy is restrictive, but fiscal policy remains expansionary.

In other words, rates are tight, but government spending continues to support liquidity.

This reflects fiscal dominance.

In 2020–2021, both monetary and fiscal policy were expansionary.
In 2022–2023, both were restrictive.
Today, monetary policy is restrictive while fiscal policy remains expansionary.
For that reason, this is not a simple tightening regime.

This distinction is critical.

Yield levels alone would suggest broader equity weakness.

But when fiscal support is large, liquidity remains in the system.

That liquidity does not spread evenly across all names; it concentrates in the strongest growth themes.

7. Capital Is Flowing Into the AI Value Chain, Not the Broader Market

The strongest capital flow in the market is currently directed toward the AI value chain.

Even with elevated Treasury yields, AI semiconductors, memory chips, data centers, and cloud infrastructure names continue to benefit from strong earnings expectations.

The reason is that their expected growth and earnings expansion exceed the cost of capital implied by a 5% Treasury yield.

For example, AI data center-related revenue growth is running at several hundred percent year over year in some cases.

Cloud memory, automotive and embedded semiconductors, and mobile memory are also expected to post strong growth.

When global GDP growth is around 3%, some AI semiconductor companies are still generating earnings growth measured in multiples of that rate.

Global growth is modest, but AI-linked earnings are expanding rapidly.
When expected returns exceed the cost of capital, funds continue to flow into leadership names.
The Nasdaq is not uniformly strong; capital is concentrated in the AI value chain.
Memory and AI semiconductor companies such as Samsung Electronics, SK hynix, and Micron remain key beneficiaries.

Leadership changes over time.

In 2020–2021, ESG and renewable energy were leading themes.

Capital later shifted to electric vehicles and batteries.

In 2025, GPUs were central, and in 2026, DRAM and memory semiconductors have emerged as important leadership areas.

Leadership is therefore cyclical and determined by earnings and industry momentum.

8. A Key Point Often Missed: Higher Yields Do Not Weigh on All Stocks Equally

One of the most important takeaways is that higher yields do not suppress every stock in the same way.

High rates are a broad headwind.

But companies with exceptional earnings growth can absorb that pressure.

By contrast, weaker growth names become more vulnerable in a high-rate environment.

In other words, rising Treasury yields are more likely to create market divergence than a broad market collapse.

Capital does not spread evenly across the market.

Fiscal liquidity and investment flows concentrate in the strongest industries and companies.

High rates place greater pressure on non-leading stocks.
AI semiconductors and other fast-growing sectors can withstand higher rates.
Stock selection matters more than index direction.
If yields fall, leaders may rise further.
If yields remain elevated, the gap between leaders and laggards may widen.

In that context, the question is not whether the market as a whole can rise, but whether leadership names can continue to outperform.

In a high-rate environment, companies with clearly visible earnings growth receive the premium.

That is why sector-level capital flow is more important than broad index forecasting.

9. Stablecoins and Treasury Yields: A Potential Long-Term Support Factor

The broadcast also briefly mentioned stablecoins.

In the long run, stablecoins could increase structural demand for U.S. Treasuries.

Dollar-based stablecoins often hold short-term Treasuries as reserve assets.

As the stablecoin market expands, demand for Treasuries may increase structurally.

Greater demand for Treasuries would support bond prices and place downward pressure on yields.

In the short term, however, geopolitical risk and fiscal concerns can still dominate.

Over a longer horizon, stablecoins could contribute to Treasury market stability.

10. South Korea: The U.S.-Korea Rate Differential May Narrow

For South Korea, the U.S.-Korea rate differential remains an important variable.

The original discussion suggested that South Korea may maintain a tighter policy stance than the United States.

As a result, the gap between U.S. and Korean interest rates could narrow.

A narrower rate differential would be supportive for exchange rate stability.

At the same time, South Korea faces household debt, property market weakness, and slowing domestic demand.

The Bank of Korea therefore faces a difficult balance between inflation, the exchange rate, and growth.

11. The AI Slowdown Debate: Capital Constraints May Matter More Than Ethics

The debate around slowing AI development was also identified as an important trend.

On the surface, the argument is that AI regulation is needed because of social and ethical concerns.

From an economic perspective, however, capital constraints and investment competition may be more important.

AI companies are committing substantial capital to data centers and semiconductor procurement.

This is a classic capex competition.

For firms with limited financial flexibility, slowing the pace of investment can be rational.

By contrast, companies such as Nvidia benefit when AI investment continues to expand.

Political leaders also have incentives to avoid slowing AI investment too aggressively because it affects GDP, equity markets, employment, and political sentiment.

The AI slowdown debate is not only about ethics.
Balance-sheet capacity and investment pressure are key drivers.
GPU suppliers benefit from continued AI capex.
Policymakers are sensitive to the economic impact of AI investment.
AI regulation is therefore a complex issue involving technology, capital, and politics.

The key issue is not a conflict between AI and humans.

The real competition is likely to be between those who use AI effectively and those who are displaced by it.

The same applies to companies.

Firms that use AI to reduce cost and improve productivity are more likely to remain competitive, while others may fall behind.

12. Key Indicators to Watch Going Forward

In the current environment, investors should look beyond equity prices alone.

Treasury yields, oil, inflation, policy rates, and the AI investment cycle all need to be monitored together.

The following indicators are particularly important for assessing market direction.

U.S. 10-year and 30-year Treasury yield trends
Oil prices and Middle East geopolitical risk
U.S. inflation expectations
The Federal Reserve’s rate-cut or rate-hike stance
U.S. fiscal deficits and Treasury issuance
Earnings reports from AI semiconductor companies
Guidance from AI value-chain names such as Micron, Nvidia, Samsung Electronics, and SK hynix
Stablecoin market expansion and Treasury demand dynamics
Progress in U.S.-China talks, Taiwan policy, and Iran negotiations

The most important of these remains the direction of Treasury yields.

Yields are the gravitational force in financial markets.

When yields are high, valuations across asset classes come under pressure.

But leadership names with strong earnings growth can still rise despite that pressure.

13. Investment Conclusion: In a High-Rate Regime, Focus on Leadership Rather Than the Entire Market

The recent surge in Treasury yields may prove temporary, or it may mark the start of a higher-rate normal.

The key is not to assume a direction for rates in advance, but to identify the forces moving them.

If geopolitical risk eases and oil declines, Treasury yields could fall.

If conflict persists and fiscal deficits widen, elevated yields may remain in place longer.

In that environment, equities are more likely to move around leadership groups rather than rise uniformly.

Ultimately, the core questions are these:

Has the Treasury market entered a higher-rate normal?
How long can fiscal dominance support liquidity conditions?
Can AI value-chain leaders continue to generate earnings growth that exceeds the cost of capital?

In a high-rate regime, not every stock can outperform.

But sectors with strong earnings momentum can attract capital more forcefully.

For that reason, investors should focus less on broad market direction and more on where capital is concentrating.

< Summary >

The surge in U.S. Treasury yields reflects a combination of Treasury selling, geopolitical risk, higher oil prices, inflation, and widening fiscal deficits.

Equities have held up because monetary policy is restrictive while fiscal policy remains expansionary.

The broader market is not uniformly strong; capital is concentrated in AI semiconductors and the data center value chain.

In a high-rate environment, non-leading stocks face greater pressure, while companies with clear earnings momentum are more likely to outperform.

Going forward, investors should monitor Treasury yields, oil, inflation, policy rates, and the AI investment cycle together.

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*Source: [ 경제 읽어주는 남자(김광석TV) ]

– [생방송] 국채금리 수십년만의 최고치… 증시 버틸 수 있나? [즉시분석]

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