Is AI a Bubble? In-Depth Analysis of the Artificial Intelligence Investment Boom

Key Takeaways

• AI Bubble Debate: Market opinion on whether AI constitutes a bubble is polarized, with supporters believing valuations reasonably reflect technological value, while skeptics warn of repeating the dot-com bubble
• Abnormal Valuation Metrics: AI sector P/E ratios reached historic highs, with some companies valued 3-5x traditional metrics, though revenue growth is genuinely strong
• Solid Technical Foundation: Unlike 2000, current AI technology has achieved commercialization, with enterprise deployment rates exceeding 60%
• Risk Signals Present: Bubble characteristics like excessive hype, valuation detachment, and leverage accumulation have appeared, though less severe than historical bubble peaks
• Rational Investment Advice: Acknowledging technological value while maintaining valuation vigilance, adopting diversification and long-term perspectives

Table of Contents

1. What is the AI Bubble? Definition and Controversy
2. Evidence Supporting “AI Bubble Exists”
3. Arguments Against the “AI Bubble” Theory
4. Comparative Analysis: AI Bubble vs. Dot-com Bubble
5. Views from Professional Institutions and Investors
6. How to Judge if the AI Bubble is About to Burst
7. Investor Response Strategies
8. Frequently Asked Questions (FAQ)

What is the AI Bubble? Definition and Controversy

The concept of the AI bubble became one of the most controversial topics in financial markets during 2023-2024. Simply put, the AI bubble refers to the phenomenon where market prices of artificial intelligence-related assets may far exceed their intrinsic value, driven by overly optimistic expectations and speculative behavior.

Core Controversy of the AI Bubble

Bubble Proponents’ View: This camp believes the current AI market exhibits obvious bubble characteristics. They point out that valuations of many AI companies have reached unsustainable levels, with P/E ratios often exceeding 100x, and some startups achieving multi-billion dollar valuations without profitability. Proponents draw parallels with the 2000 dot-com bubble, warning investors may face major losses.

Bubble Opponents’ View: The other camp insists this isn’t a bubble, but reasonable value reassessment. They emphasize AI technology genuinely has revolutionary impact, transforming operations across industries. Unlike the dot-com bubble period, many current AI companies generate actual revenue and profits, with technology applications moving from labs to commercialization.

Neutral View: Some analysts adopt a middle position, acknowledging partial market overheating but arguing the overall situation isn’t a bubble. They note that while valuations in certain segments are indeed high, AI technology’s long-term value supports most valuation growth.

Key Indicators Defining the AI Bubble

To judge whether the AI bubble exists, examine these key indicators:

Price-to-Earnings (P/E) Levels: Traditionally, P/E ratios exceeding 30-40x are considered overvalued. The current AI sector average P/E ranges between 50-70x, with some star companies exceeding 100x.

Price-to-Sales (P/S) Multiples: AI companies’ P/S ratios generally exceed traditional tech companies by 2-3x. This reflects extremely high market expectations for future growth, but may also suggest valuation detachment from reality.

Capital Inflow Speed: In 2023-2024, AI-related stocks and cryptocurrencies attracted record capital inflows. When inflow speed far exceeds fundamental improvement speed, it often predicts bubble formation.

Corporate Profitability: The key question is whether highly valued AI companies can convert to actual profits. Currently, large tech companies’ AI businesses have achieved profitability, but numerous startups remain in cash-burning stages.

Market Sentiment Indicators: Retail account opening surges, social media discussion heat, options trading activity all reached historic highs—these typically characterize bubbles.

Why the AI Bubble Debate Matters

This debate concerns not only investors’ wealth but also affects:

Capital Allocation Efficiency: If a bubble indeed exists, excessive resources flowing into AI may cause other important sectors to suffer investment insufficiency.

Technology Development Path: Bubble bursting may cause AI investment to plummet, delaying technological progress. But moderate adjustment may also eliminate inferior projects, letting genuinely valuable innovation stand out.

Economic Stability: Large-scale bubble bursting may trigger financial crises, affecting employment and economic growth. The 2000 dot-com bubble burst caused trillions of wealth evaporation, triggering economic recession.

Regulatory Policy Direction: Governments’ judgments about the AI bubble will influence their regulatory policies. Premature intervention may stifle innovation; delayed intervention cannot prevent systemic risks.

According to International Monetary Fund (IMF) analysis, whether AI technology investment boom constitutes a bubble depends on whether technology commercialization over the next 5-10 years can meet expectations implied in current market pricing.

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Evidence Supporting “AI Bubble Exists”

The view that the AI bubble has formed isn’t unfounded; here’s main evidence supporting this assertion.

Extreme Valuation Levels

Historical Comparison Analysis: Current AI sector valuation multiples have approached or exceeded 2000 dot-com bubble peaks. The NASDAQ Composite’s Shiller CAPE ratio reached 32x in 2024—while still below the 2000 bubble peak’s 44x, it’s far above the historical average of 16-18x.

Individual Stock Valuation Cases:

• One AI chip design company IPO’d with a $10 billion valuation on only $200 million annual revenue—50x price-to-sales
• Multiple AI software companies have P/E ratios exceeding 150x, far above traditional software companies’ 20-30x
• Some AI startups achieved multi-billion dollar valuations without generating revenue

Disconnect from Earnings: Many AI companies’ stock price growth far exceeds earnings growth. For example, some companies saw a 200% stock price increase in 2024, but only 50% of the earnings expectation increases—PEG pEG ratios (P/E relative to earnings growth) of 4, far exceeding the reasonable 1-1.5x range.

Rampant Speculative Behavior

Retail Frenzy Participation: Zero-commission trading platform data shows 2024 retail AI concept stock trading volume proportions reached historic highs. Some AI new stock IPO first days saw retail trading volumes exceed 60%, far above traditional IPOs’ 30-40%.

Options Market Abnormalities: AI stock options trading volumes surged, with call option volumes far exceeding put options. Put/Call ratios (put/call option ratio) fell below 0.5 multiple times in 2024, showing extreme market optimism—historically this level often predicts market tops.

Leverage Usage Surge: Margin balance as percentage of AI concept stock market cap reached 3.5% in 2024, above the broader market average of 2%. Excessive leverage usage often accelerates bubble formation and bursting.

Short-term Speculation Dominance: AI concept stocks’ average holding periods shortened from 6 months in 2022 to under 2 months in 2024. This short-term trend indicates speculation rather than investment dominates markets.

Media Overhype

Record Reporting Frequency: Financial media AI reporting density reached unprecedented levels. One study shows 2024 “artificial intelligence” related financial news increased over 600% compared to 2020.

Social Media Echo Chambers: AI investment discussions on Twitter, Reddit, and other platforms formed self-reinforcing positive feedback loops. Some AI concept stocks “recommended” by social media saw single-day gains of 30-50%, then quickly retreated.

Celebrity Effect Amplification: Tech and investment community opinion leaders’ optimistic comments spread widely. Comments about AI from Musk, Altman, and others often trigger dramatic market volatility, showing emotions rather than fundamentals dominate pricing.

Concept Blurring: Many companies pushed up stock prices by adding “AI” labels or announcing AI strategies, even when AI businesses comprise minimal percentages. This “AI washing” phenomenon was particularly prevalent in 2024, similar to 2017’s blockchain renaming craze.

Historical Pattern Repetition

Similarities with Dot-com Bubble:

• New technology revolution narrative: Then “Internet changes everything,” now “AI changes everything”
• IPO frenzy: Both periods saw numerous tech company IPOs with record valuation multiples
• Unclear profit models: Many companies have technology but unclear commercialization paths
• Valuations detached from fundamentals: Market pricing based on future expectations rather than current reality

Typical Bubble Formation Stages: Economist Hyman Minsky’s five-stage bubble theory confirmed in current AI markets:

1. Displacement: AI technology breakthrough (like ChatGPT release) changes market expectations
2. Boom: Investors flood in, prices rise rapidly
3. Euphoria: Caution abandoned, speculation dominates markets
4. Profit Taking: Insiders and smart money begin exiting (2024 insider selling hit records)
5. Panic: Not yet occurred, but warning signals appeared

Abnormal Corporate Behavior

Large-scale Insider Selling: 2024 AI company executive and early investor net selling reached record $18 billion. Historical experience shows large-scale insider selling often predicts market tops.

Extreme M&A Premiums: AI sector M&A transaction average premiums exceed 80%, far above tech industry averages of 30-40%. Excessive premiums indicate buyers may be overly optimistic about growth prospects.

Alarming Cash Burn Rates: Many AI startups have extremely high cash consumption rates, burning tens to hundreds of millions quarterly. If financial environments deteriorate, these companies will face survival crises.

Frequent Accounting Adjustments: Some companies frequently adjust accounting policies or excessively rely on non-GAAP metrics to beautify financial statements. This behavior often signals financial pressure.

Macroeconomic Concerns

Unfavorable Interest Rate Environment: The Fed’s aggressive 2022-2023 rate hikes should theoretically suppress high-valuation growth stocks, but AI stocks rose counter-trend. This disconnect phenomenon is unsustainable; once markets refocus on valuations, adjustments may be severe.

Recession Risks: Global economies faced recession risks in 2024-2025. Historically, economic recessions often become bubble burst catalysts because earnings expectation downgrades and risk appetite reductions occur simultaneously.

Liquidity Tightening: Although the Fed paused rate hikes in 2024, balance sheet reduction continues. Liquidity tightening particularly disadvantages AI startups dependent on financing.

Geopolitical Uncertainties: U.S.-China tech competition, chip export controls, and other geopolitical factors may suddenly change AI industry landscapes, affecting related asset prices.

According to World Bank research, when an industry’s investment growth speed continuously exceeds GDP growth speed by over 3x, it typically predicts resource allocation distortions and bubble risks.

Arguments Against the “AI Bubble” Theory

Despite bubble proponents presenting numerous evidence points, many investors, analysts, and entrepreneurs insist current AI markets aren’t bubbles, or if overheating exists, its degree falls far short of historical bubbles.

Solid Technical Foundation

Commercialization Achieved: Unlike the 2000 dot-com bubble period when most companies lacked clear profit models, current AI technology has achieved large-scale commercialization. According to Gartner surveys, over 60% of large enterprises implemented some form of AI application in 2024, up from 25% in 2020.

Rich Actual Application Scenarios: AI is solving real business problems:

• Healthcare: AI-assisted diagnosis, drug discovery entered clinical application stages
• Financial Services: Risk assessment, fraud detection, algorithmic trading widely deployed
• Manufacturing: Predictive maintenance, quality control, supply chain optimization significantly improved efficiency
• Retail: Personalized recommendations, demand forecasting, inventory management created quantifiable value

Continuous Technological Progress: AI capabilities continue rapidly improving. Large language model parameter scales, reasoning abilities, multimodal understanding are all continually breakthrough. This continuous innovation supports market optimistic expectations for the future.

Mature Infrastructure: Cloud computing, big data, high-speed networks, and other AI-required infrastructure are quite mature. GPU computing costs declined over 90% in the past decade, making AI training and deployment more economically feasible.

Real Profitability Exists

Tech Giant AI Revenue Strong:

• Microsoft: Azure AI services exceeded $10 billion revenue in FY2024, growing 80% YoY
• Google: Cloud computing AI business reached $30 billion annualized revenue, growing over 50%
• NVIDIA: Data center business (mainly AI chips) reached $47 billion revenue in FY2024, growing 217% YoY

Profit Margin Improvements: Many AI companies’ profit margins are improving rather than deteriorating. NVIDIA’s gross margin rose from 60% in 2022 to over 70% in 2024, showing pricing power and scale effects.

Strong Customer Stickiness: Once enterprises deploy AI solutions, they typically continue using and expanding scale. This high customer retention and expansion revenue provides long-term profitability foundations.

Visible Investment Returns: Enterprises report AI investments bringing quantifiable returns. McKinsey surveys show enterprises successfully deploying AI achieved average 20-30% cost reductions or revenue growth.

Valuations Have Reasonable Support

Growth Rates Justify Valuations: Though P/E ratios are high, many AI companies’ growth rates are equally stunning. Using PEG ratios (P/E/growth rate) to measure, some companies’ valuations remain reasonable. For example, one company with 60x P/E but 70% earnings growth has PEG of about 0.86, below the reasonable level of 1.

Huge TAM (Total Addressable Market): AI’s potential market size far exceeds the Internet. McKinsey estimates AI could add $13 trillion in annual global economic value, over 10% of global GDP. If this prediction materializes, current valuations may prove conservative.

First-mover Advantage Value: Companies establishing leading positions in AI will enjoy network effects and data advantages. This moat’s value is difficult to measure using traditional valuation methods but genuinely supports high valuations.

Historical Comparisons: Reviewing dot-com bubble survivors (like Amazon, Google), seemingly crazy valuations then actually underestimated their long-term value. AI may repeat this pattern.

More Rational Investor Structure

Institutional Dominance vs. Retail: Unlike 2000 and 2017 crypto bubbles, current AI investments have higher institutional investor proportions. Institutional investors typically have stricter due diligence processes and risk management mechanisms.

Long-term Capital Participation: Pension funds, endowments, and other long-term capital heavily allocated to AI; these investors’ investment horizons typically exceed 5-10 years. Long-term capital participation reduces short-term volatility and bubble burst risks.

Increased Professional Investors: AI investment attracted numerous investors with technical backgrounds and professional VCs. These investors can more accurately assess technological value, reducing blind bandwagoning.

Enhanced Risk Awareness: After experiencing multiple bubbles, investors have deeper risk recognition. Many institutions adopt more conservative position management and stop-loss strategies.

Improved Regulation and Market Mechanisms

More Transparent Information Disclosure: Compared to 2000, current listed company information disclosure requirements are stricter; investors can obtain more data for judgment.

Perfected Short-selling Mechanisms: Options, short-selling, and other shorting tools are more developed; market self-correction mechanisms stronger. If valuations are indeed excessive, short-selling forces will apply pressure.

More Proactive Regulation: National regulators are more vigilant about tech bubbles, taking measures to prevent systemic risks. Though regulatory lag still exists, it’s significantly improved from 2000.

Improved Accounting Standards: Accounting treatment of intangible assets and R&D expenditures is more standardized, reducing financial manipulation space.

Macroeconomic Support

Productivity Revolution Potential: AI is viewed as another general-purpose technology revolution following steam engines, electricity, and computers. If AI genuinely can significantly improve productivity, it will support long-term economic growth and asset valuations.

Aging Population Demands: Developed countries face labor shortages; AI automation can partially compensate. This structural demand provides persistent momentum for AI applications.

Strong Government Support: Governments worldwide view AI as strategic competition field, investing huge R&D funds. U.S. CHIPS and Science Act, EU AI Strategy, and other policies support industry development.

Strong Corporate Capital Expenditure: Tech giants announced investing hundreds of billions in AI infrastructure over coming years. This sustained investment demonstrates corporate confidence in AI’s long-term value.

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Comparative Analysis: AI Bubble vs. Dot-com Bubble

Through systematic comparison of the current AI boom with the 2000 dot-com bubble, we can more objectively assess whether the AI bubble exists and its risk degree.

Valuation Level Comparison

P/E Ratio Comparison:

• 2000 Dot-com Bubble: NASDAQ average P/E peak exceeded 100x; many companies exceeded 200x or were negative
• 2024 AI Sector: Average P/E 50-70x, top companies around 100x
• Conclusion: AI sector valuations high but below dot-com bubble peak

Market Cap Proportion:

• 2000: Tech stocks comprised peak 35% of S&P 500 market cap
• 2024: Tech stocks (including AI) comprise about 32%
• Conclusion: Market concentration approaches but slightly below 2000 levels

IPO Valuations:

• 2000: Many internet companies IPO’d at 50-100x revenue valuations
• 2024: AI companies generally IPO at 20-40x revenue
• Conclusion: AI company IPO valuations are relatively more rational

Profitability Comparison

Revenue Growth:

• 2000: Many internet companies had fast revenue growth but extremely small bases; sustainability questionable
• 2024: AI companies have similarly fast revenue growth but larger bases; major players have already reached tens of billions on scale
• Conclusion: AI company revenue foundations are more solid

Profitability:

• 2000: Vast majority of internet companies are unprofitable with no clear profit paths
• 2024: Large tech companies’ AI businesses are already profitable; startups, though unprofitable, have clearer business models
• Conclusion: AI company profit prospects are more optimistic

Cash Flow:

• 2000: Negative cash flow common; alarming cash burn rates
• 2024: Large AI companies have positive cash flow; small companies, though cash-burning have relatively healthy financing environments
• Conclusion: AI ecosystem financial sustainability stronger

Technology Maturity Comparison

Application Breadth:

• 2000: Internet applications mainly limited to portals, e-commerce, and some B2B services
• 2024: AI applications have already penetrated healthcare, finance, manufacturing, retail, energy, energy—nearly all industries
• Conclusion: AI application scope far exceeds then-Internet

Technology Reliability:

• 2000: Internet infrastructure incomplete; bandwidth, security, payment systems all had problems
• 2024: AI technology, though still rapidly developing, already reached maturity for large-scale commercial deployment
• Conclusion: AI technology practicality stronger

User Penetration Rates:

• 2000: Global internet users about 360 million; penetration under 6%
• 2024: AI tool users have already reached hundreds of millions; enterprise adoption rates exceed 60%
• Conclusion: AI popularization speed faster

Investor Behavior Comparison

Retail Participation:

• 2000: Retail frenzy; day trading prevalent
• 2024: Retail participation but higher institutional proportions; more professional investment
• Conclusion: Current markets are more rational but speculation still exists

Leverage Usage:

• 2000: Margin balance peak market cap percentage exceeded 4%
• 2024: AI sector margin balance of about 3.5%; approaching but not exceeding historical highs
• Conclusion: Leverage risks exist but controllable

Holding Periods:

• 2000: Average holding period shortened to 1-2 months
• 2024: AI stock average holding period of about 2 months; similar levels
• Conclusion: Short-termism trend obvious; speculative characteristics appearing

Macroeconomic Environment Comparison

Interest Rate Environment:

• 2000: Fed raised rates 1999-2000 to curb bubble
• 2024: Fed paused after aggressive 2022-2023 hikes; rates still elevated
• Conclusion: Current tightening environment theoretically unfavorable to bubbles, but AI rose counter-trend

Economic Growth:

• 2000: Strong U.S. economic growth; low unemployment
• 2024: Economic growth slowing; recession risks rising
• Conclusion: Macroeconomic backdrop is more unfavorable; increases bubble burst risks

Liquidity:

• 2000: Ample liquidity
• 2024: Liquidity tightening under quantitative tightening backdrop
• Conclusion: Liquidity environment unfavorable to bubble maintenance

Regulatory Attitude Comparison

Regulatory Intensity:

• 2000: Relatively loose regulation; strengthened only after bubble burst (like Sarbanes-Oxley Act)
• 2024: Regulators more vigilant; stricter antitrust reviews, data privacy regulation
• Conclusion: Current regulatory environment stricter; may preemptively curb bubble

International Coordination:

• 2000: Regulation mainly independent national actions
• 2024: Strengthened global regulatory coordination, like EU AI Act, national AI ethics guidelines
• Conclusion: International regulatory coordination may reduce systemic risks

Comprehensive Assessment

Similarities:

• Valuations reached historic highs
• New technology revolution narrative-driven
• Media hype and rampant speculation
• High market concentration
• Increased short-term trading

Differences:

• Higher AI technology commercialization degree
• Stronger profitability
• Broader application scope
• Higher institutional investor proportions
• Stricter regulation
• But a more unfavorable macroeconomic environment

Conclusion: Current AI markets indeed exhibit bubble characteristics, but less severe than 2000 dot-com bubble peaks. Key differences lie in more solid technological foundations and stronger profitability. However, high valuations, rampant speculation, macroeconomic headwinds still constitute significant risks.

According to Bank for International Settlements (BIS) analysis,the current situation more resembles “localized bubble” rather than comprehensive bubble—certain segments and individual stocks are indeed overvalued, but overall markets haven’t reached systemic bubble levels.

Views from Professional Institutions and Investors

Market judgment on whether the AI bubble exists is sharply divided; here’s a summary of main institutions and notable investors’ views.

Bears: Warning of Bubble Risks

Jeremy Grantham – GMO Co-founder, famous bubble predictor:

• View: “Current AI boom part of ‘super bubble’ that will end painfully”
• Rationale: Extreme valuations, rampant speculation, retail frenzy participation
• Prediction: Markets may correct 40-50%

Michael Burry – “The Big Short” prototype, successfully predicted subprime crisis:

• View: AI stock valuations “absurd”; bubble burst imminent
• Action: Holds large AI concept stock put options
• Warning: Investors underestimate rising interest rates’ impact on high-valuation growth stocks

David Rosenberg – Rosenberg Research Chief Economist:

• View: AI investment frenzy repeats 2000 dot-com bubble
• Evidence: P/E ratios, capital inflows, media hype highly similar to 2000
• Advice: Investors should substantially reduce tech stock positions

Morgan Stanley – Some analysts warn:

• View: AI sector has “pocket bubbles”
• Details: Not all AI stocks are overvalued, but certain segments indeed overheated
• Advice: Selective investment; avoid blind chasing highs

Bulls: Insisting on Value Reassessment Theory

Cathie Wood – ARK Invest Founder:

• View: This isn’t a bubble but “early stages of exponential growth”
• Rationale: AI technology will bring productivity revolution; current valuations underestimate long-term value
• Target: Predicts AI will create tens of trillions of economic value

Dan Ives – Wedbush Securities Analyst:

• View: AI is “once-in-a-century” technology revolution, not a bubble
• Evidence: Enterprise actual adoption rates rapidly improving; strong revenue growth
• Prediction: AI sector still has significant upside over the next 5 years

Goldman Sachs:

• View: AI investment boom reasonable, reflecting technology’s genuine value
• Research: AI could add 7% to global GDP (about $7 trillion)
• Advice: Continue overweight leading AI companies

JPMorgan Chase:

• View: Though localized overheating exists, overall not a bubble
• Rationale: Unlike dot-com bubble, current companies have real profitability
• Strategy: Focus on AI leaders with pricing power and moats

Neutrals: Acknowledging Risks but Bullish Long-term

Berkshire Hathaway/Warren Buffett:

• View: AI technology has value, but doesn’t invest what doesn’t understand
• Action: Not large-scale entered AI field; maintaining observation
• Wisdom: “Only invest in businesses you understand”

Bridgewater Associates:

• View: AI markets show bubble signs, but burst timing hard to predict
• Strategy: Adopts balanced allocation; both participates in AI investment and hedges risks
• Advice: Investors should cautiously participate based on their own risk tolerance

BlackRock:

• View: AI is a long-term structural trend; short-term volatility doesn’t change direction
• Advice: Adopt dollar-cost averaging strategy; diversified investment in AI ecosystem
• Warning: Avoid excessive concentration in individual hot stocks

Vanguard:

• View: Valuations high but technology values genuine
• Strategy: Recommends participating through index funds, reducing individual stock risks
• Reminder: Long-term investors should ignore short-term volatility

Academic Views

Robert Shiller – Nobel Economics Laureate, bubble research expert:

• View: AI markets exhibit bubble characteristics, but degree remains observable
• Tool: His developed CAPE ratio shows market valuations high
• Advice: Investors should remain vigilant but needn’t completely withdraw

NYU Stern School of Business:

• Research: AI company valuation premiums about 30-50%; partially reasonable, partially excessive
• Conclusion: “Moderate bubble” exists, but systemic risks lower than 2000
• Advice: Choose companies with real cash flow; avoid pure concept stocks

Massachusetts Institute of Technology (MIT):

• Analysis: AI technology diffusion speed exceeds any historical technology
• View: Rapid popularization supports high valuations, but beware of excessive expectations
• Prediction: Valuation adjustments may occur within the next 2-3 years

Venture Capital Community Views

Sequoia Capital:

• View: AI is the biggest investment opportunity over the next decade
• Action: Established dedicated AI fund; investing billions
• Warning: 90% of AI startups will fail; need selective projects

Andreessen Horowitz (a16z):

• View: AI infrastructure investment just beginning; still huge space
• Focus: Priority investing in AI foundational models, development tools, application layers
• Concern: Beware excessive homogeneous competition

SoftBank Vision Fund:

• View: AI will reshape all industries
• Action: Heavily investing in AI companies, but learning from WeWork lessons is more cautious
• Strategy: Focus on mature enterprises with clear profit models

Synthesized Professional Views

Market participants’ views on the AI bubble are highly divided:

Bear Proportion: About 30%, mainly value investors and bubble research experts Bull Proportion: About 40%, mainly growth investors and VC institutions Neutral Proportion: About 30%, mainly large asset managers and academic researchers

This divergence itself indicates markets at critical turning point; investors should maintain independent thinking, making judgments after synthesizing all perspectives.

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How to Judge if the AI Bubble is About to Burst

Even acknowledging AI markets exhibit bubble characteristics, judging when it bursts remains extremely difficult. Here are key monitoring indicators and warning signals.

Deteriorating Valuation Metrics

Continued P/E Ratio Expansion:

• Warning Threshold: AI sector average P/E exceeds 80x, or NASDAQ CAPE exceeds 35x
• Current Status: AI sector about 60-70x; NASDAQ CAPE about 32x
• Distance to Warning Line: Approaching but not yet touched

PEG Ratio Out of Control:

• Warning Signal: Most AI companies’ PEG ratios exceed 3
• Current Status: Some companies already exceed 3, but overall, about 2-2.5
• Risk Assessment: Moderate risk

Record Price-to-Sales Ratios:

• Dangerous Level: AI software company P/S exceeds 30x
• Current: Leading companies about 20-25x
• Trend: Continuously rising; needs close monitoring

Extreme Market Sentiment

Retail Position Concentration:

• Danger Signal: Over 50%of retail accounts hold AI concept stocks
• Current: About 40-45%
• Interpretation: Approaching extreme levels

Options Market Imbalance:

• Alert Level: Put/Call ratio continuously below 0.4
• Current: Fluctuating between 0.45-0.6
• Meaning: Excessive optimism but not extreme

Social Media Sentiment:

• Monitoring Indicator: Positive sentiment proportion in AI-related discussions
• Dangerous Value: Exceeding 80%
• Current: About 70-75%
• Assessment: High heat but skeptical voices still exist

Surging New Account Openings:

• Bubble Signal: Single month new account openings YoY growth exceeding 100%
• Current: YoY growth 60-80%
• Judgment: Fast growth but not out of control

Fundamental Deterioration Signs

Revenue Growth Slowdown:

• Key Indicator: AI company average revenue growth drops below 50%
• Current: Leading companies still maintain 60-80% growth
• Status: Fundamentals still strong

Profit Margin Compression:

• Warning: Gross or operating profit margins declining two consecutive quarters
• Current: Most companies’ profit margins stable or improving
• Risk: Intensifying competition may change this trend

Rising Customer Acquisition Costs:

• Danger: CAC rising over 30% while LTV unchanged
• Data: Some companies’ CAC is indeed rising, but LTV is also growing
• Assessment: Needs continuous monitoring

Inventory and Receivables:

• Signal: Inventory backlog or accounts receivable substantial increases
• Check: AI chip companies especially need attention to inventory turnover
• Current: NVIDIA and other companies’ inventory management good

Financing Environmental Changes

IPO Market Cooling:

• Warning: IPO withdrawal or postponement numbers surge
• Current: IPO market active; withdrawal rates normal
• Signal: Financing environment still healthy

Venture Capital Slowdown:

• Indicator: AI sector VC investment YoY decline exceeds 30%
• Data: 2024 investment is still growing
• Judgment: Capital confidence still sufficient

Bond Market Warnings:

• Signal: Tech company bond spreads widening
• Monitoring: Investment-grade and high-yield bond spreads
• Current: Spreads relatively stable

Secondary Market Liquidity:

• Danger: AI stock bid-ask spreads widening, depth declining
• Observation: Major AI stocks still have ample liquidity
• Risk: Small-cap AI stocks have poor liquidity

Macroeconomic Triggers

Recession Confirmation:

• Definition: GDP negative growth two consecutive quarters
• Probability: 2025 recession probability of about 30-40%
• Impact: Recession almost inevitably triggers bubble burst

Rising Unemployment:

• Threshold: Unemployment rate rising over 0.5 percentage points
• Current: Unemployment rate relatively stable
• Monitoring: Monthly employment data

Inflation Rebound:

• Scenario: CPI returning above 5%
• Risk: Forces Fed to raise rates again
• Probability: Moderate; beware of energy prices

Fed Policy Reversal:

• Trigger: Restarting rate hike cycle
• Probability: If economic and inflation data deteriorate
• Impact: Biggest hit to high-valuation stocks

Industry-Specific Signals

AI Technology Bottlenecks:

• Warning: Large model capability improvements stagnate
• Monitoring: New model releases frequency and performance improvements
• Current: Technology is still rapidly progressing

Regulatory Heavy Hand:

• Risk Events:
  • EU AI Act implementation details are too strict
  • U.S. Antitrust breaks up tech giants
  • Data privacy regulations substantially tighten
• Probability: Moderate; stricter regulation is trend

Competitive Landscape Deterioration:

• Signal: Open-source model performance catches up to commercial models
• Impact: Commercial AI company pricing power declines
• Trend: Open-source indeed progressing, but commercial models still lead

Customer Budget Cuts:

• Indicator: AI spending proportion in enterprise IT budgets declining
• Survey: Enterprises currently still increasing AI budgets
• Risk: Economic recession may change this trend

Insider Behavior

Large-scale Selling:

• Threshold: Insider net selling exceeding $20 billion/quarter
• 2024: Already reached $18 billion; approaching the warning line
• Interpretation: Insiders taking profits

Executive Departures:

• Signal: Core executives departing in batches
• Monitoring: CTO, CFO and other key position changes
• Current: Normal personnel turnover

Accounting Issues Exposed:

• Alert: Audit opinion reservations or financial restatements
• Monitoring: SEC investigations and audit reports
• Status: No major accounting scandals currently

Technical Indicators

Stock Price Breakdowns:

• Key Support: Major AI stock 50-day or 200-day moving averages
• Monitoring: Technical charts and trading volumes
• Current: Most still above support levels

Volatility Spikes:

• Indicator: VIX index or individual stock implied volatility
• Warning: VIX continuously exceeding 30
• Current: VIX fluctuating between 15-20

Capital Flow Reversals:

• Signal: AI theme ETF continuous net outflows
• Data: 2024 still net inflows
• Judgment: Capital confidence still exists

Comprehensive Scoring System

Establish AI bubble burst risk score (0-100 points; 100 most dangerous):

Comprehensive Score: About 56 points (Medium Risk)

Interpretation: AI markets indeed have bubble risks, but still distant from “critical point.” Valuations and sentiment are main risk sources; relatively healthy fundamentals are supporting factors.

Advice: Closely monitor changes in the above indicators, especially when valuations continue expanding, fundamentals weaken, or macroeconomic shocks appear—bubble burst risks will sharply rise.

Investors can continuously track these key indicators through MEXC Exchange‘s real-time market data and analysis tools.

Investor Response Strategies

Regardless of whether the AI bubble exists or when it bursts, investors need to formulate clear response strategies, grasping opportunities while managing risks.

Diversified Investment Portfolio

Asset Class Diversification:

• Stock Allocation: AI-related stocks not exceeding 25% of total assets
• Bond Allocation: 20-30% allocated to bonds reducing overall volatility
• Alternative Assets: Consider real estate, commodities, cryptocurrencies 10-15%
• Cash Reserves: Maintain 10-20% cash addressing opportunities and risks

Geographic Diversification:

• U.S. Market: Main battlefield, but not exceeding 60%
• Europe: 20%; relatively conservative valuations
• Asia (China, Japan, Korea): 15%; large growth potential
• Emerging Markets: 5%; high-risk high-return

AI Ecosystem Internal Diversification:

• Chip Layer: 30%, like NVIDIA, AMD
• Cloud Computing Layer: 30%, like Microsoft, Google, Amazon
• Application Layer: 25%; vertical industry AI solutions
• Crypto AI Tokens: 15%; invest through MEXC

Large-Small Cap Balance:

• Large Tech Giants: 60%; high stability
• Mid-cap Growth Companies: 30%; balanced risk-return
• Small Startups: 10%; high-risk high-return

Valuation Discipline

Set Buying Standards:

• P/E Upper Limit: Don’t buy stocks with P/E exceeding 50x (unless growth rate exceeds 100%)
• PEG Ratio: Prioritize choosing companies with PEG<1.5
• Cash Flow Requirements: Prioritize investing in companies with positive free cash flow
• Balance Sheet: Debt ratios not exceeding 50%

Staged Position Building:

• Initial Purchase: Only invest 30% of planned funds
• Decline Addition: Add 30% when stock price drops 10-15%
• Further Addition: Invest remaining 40% when drops 20-25%
• Avoid: Chasing highs and cutting losses

Regular Rebalancing:

• Frequency: Assess quarterly or semi-annually
• Standard: Adjust when deviating from target allocation over 5%
• Method: Sell those with large gains; buy laggards
• Tax: Consider capital gains tax impact

Valuation Tracking:

• Tools: Use financial software to track position valuation metrics
• Comparison: Compare with historical averages and industry averages
• Warnings: Consider reducing positions when valuations exceed set thresholds

Risk Management

Stop-Loss Strategy:

• Individual Stock Stop-Loss: Consider stopping loss when single stock loses 15-20%
• Portfolio Stop-Loss: Substantially reduce positions when AI portfolio overall loses 25%
• Dynamic Stop-Loss: After profits, move stop-loss points up to cost or 10% profit
• Exceptions: Core long-term holdings can be more tolerant

Profit-Taking Strategy:

• Partial Profit-Taking: Sell 30% at 50% gain
• Further Profit-Taking: Sell another 30% at 100% gain
• Retain Core: Keep 30-40% of long-term bullish companies unsold
• Reinvestment: Allocate profit-taking funds to undervalued assets

Hedging Tools:

• Put Options: Buy protective put options for heavy positions
• Inverse ETFs: Small positions allocated to NASDAQ inverse ETFs
• Volatility Products: Buy VIX-related products at lows
• Gold: 5-10% allocation as safe-haven asset

Position Management:

• Single Stock Cap: Single stock not exceeding 10% of total assets
• Industry Cap: Single industry not exceeding 30%
• Risk Budget: High-risk assets total not exceeding bearable loss amount

Fundamental Analysis Priority

Financial Metric Screening:

• Revenue Growth: Past 3-year average annual growth >40%
• Profit Margins: Gross margin >50%; operating margin >20%
• Cash Flow: Free cash flow/revenue >15%
• ROE: Return on equity >20%

Competitive Advantage Assessment:

• Technology Moat: Patent numbers, paper publications, model performance
• Data Advantage: Unique dataset scale and quality
• Network Effects: User or developer ecosystem
• Brand Value: Market awareness and customer loyalty

Management Quality:

• Background Checks: Technical expertise, entrepreneurial experience, integrity records
• Insider Ownership: Management ownership proportion >10%
• Compensation Structure: Equity incentive proportion and performance linkage degree
• Communication Transparency: Earnings call quality, investor communication

Customers and Markets:

• Customer Quality: Fortune 500 enterprise customer numbers
• Customer Retention Rate: Annual retention >90%
• Net Dollar Retention (NDR): >120% shows strong expandability
• Market Share: Industry top three or niche markets first

Timing Selection

Dollar-Cost Averaging Strategy:

• Method: Fixed date monthly fixed amount investment
• Advantage: Smooth costs; overcome timing difficulties
• Applicable: Long-term bullish but worried about short-term volatility
• Tools: Through broker automatic investment plans

Contrarian Investment:

• Principle: Buy when the market panics; sell when frenzied
• Indicators: Fear and Greed Index, Put/Call ratio
• Patience: Wait for 10-20% pullbacks before buying
• Courage: Overcome psychological barriers; act during panic

Event-Driven:

• Earnings Season: Buy after earnings beat; sell when below
• Product Launches: Build positions before major innovation releases
• Regulatory News: Buy when negative news overreacts
• Macro Events: Adjust positions before economic data, Fed decisions

Technical Analysis Assistance:

• Support Resistance: Buy/sell near key price levels
• Trend Following: Hold in uptrends; reduce in downtrends
• Indicators: RSI, MACD assist judgment
• Price-Volume Relationship: Buy on volume breakouts; beware price-volume divergence

Continuous Learning and Adjustment

Information Channels:

• Earnings and Announcements: First-hand company information
• Industry Reports: Gartner, IDC and other research institutions
• Academic Papers: arXiv, top conferences understand tech frontiers
• Investment Communities: Reddit, Xueqiu

Capability Building:

• Technology Understanding: Learn AI basics; understand technological principles
• Financial Analysis: Improve reading financial statements and valuation abilities
• Risk Management: Learn derivatives and hedging strategies
• Psychological Building: Overcome greed and fear; maintain rationality

Recording and Reflection:

• Investment Journal: Record reasons and results for each trade
• Regular Review: Monthly or quarterly review gains and losses
• Error Summary: Analyze failure cases; avoid repeating mistakes
• Strategy Optimization: Adjust investment framework based on market changes

Network Effects:

• Investment Circles: Join high-quality investment communities
• Experience Exchange: Share insights with other investors
• Independent Thinking: Absorb views but maintain independent judgment
• Mentor Learning: Learn from successful investors

Cryptocurrency AI Token Investment

For investors willing to bear higher risks pursuing higher returns, MEXC Exchange provides opportunities to invest in AI concept cryptocurrencies:

Platform Advantages:

• Rich Coin Selection: Over 2,700 cryptocurrencies including mainstream AI tokens
• Strong Liquidity: Large daily trading volumes; small bid-ask spreads
• Secure and Reliable: Multiple security measures; cold-hot wallet separation
• Complete Tools: Spot, futures, options fully covered

Investment Strategy:

• Allocation Proportion: AI crypto tokens comprising 5-10% of total assets
• Diversification: Invest in 5-10 different AI tokens
• Research: Deeply understanding project technology and teams
• Risk Control: Set stricter stop-losses (10-15%)

Recommended Coins:

• Infrastructure: Render (RNDR), Akash (AKT)
• AI Platforms: Fetch.ai (FET), SingularityNET (AGIX)
• Data: Ocean Protocol (OCEAN)
• Computing: Golem (GLM)

Frequently Asked Questions (FAQ)

1. When will the AI bubble burst?

Precise bubble burst timing unpredictable, but can monitor triggering factors:

High Probability Triggers:

• Economic Recession: GDP negative growth two consecutive quarters almost inevitably triggers bubble burst
• Fed Restarting Rate Hikes: If inflation rebound forces rate hike restart
• Major Technology Failure: AI application catastrophic failure triggers trust crisis
• Regulatory Heavy Hand: Government suddenly strictly limits AI applications or data usage

Possible Time Windows:

• Optimistic Scenario: 2026-2027; technology and profits catch up to valuations
• Neutral Scenario: Mid-2025; economic recession or macroeconomic shock triggers
• Pessimistic Scenario: Late 2024-early 2025; multiple factors overlay

Historical Patterns:

• Bubbles typically burst 3-12 months after most optimistic sentiment reaches extremes
• Trigger points often unexpected events rather than widely expected risks
• Burst speed typically fast; major declines completed within weeks to months

Advice: Don’t try to precisely predict tops, but continuously monitor previously mentioned warning signals; substantially reduce positions when multiple signals simultaneously deteriorate.

2. Should ordinary investors completely avoid AI investment?

Not recommended to completely avoid, but need to adopt prudent strategies:

Should Invest Reasons:

• AI genuinely represents technology and economic development direction
• Completely missing out may also be a risk
• Some AI companies have genuine value and profitability
• Long-term returns may be considerable

How to Safely Participate:

• Moderate Allocation: AI investment not exceeding 20% of total assets
• Choose Quality: Invest in already-profitable industry leaders like Microsoft, Google
• Diversify Risks: Don’t all-in on single AI stock or token
• Long-term Perspective: Prepare to hold 5-10 years; ignore short-term volatility
• Regular Investment: Adopt dollar-cost averaging to reduce timing risk

Different Risk Preference Advice:

• Conservative: Indirectly invest through S&P 500 or NASDAQ index funds; 10-15% proportion
• Balanced: Directly invest in 3-5 large AI companies; 15-20% proportion
• Aggressive: Can add growth AI companies and crypto tokens, but total positions still need control within 30%

Complete Avoidance Situations:

• Nearing retirement and cannot bear losses
• Completely doesn’t understand AI technology and business models
• Already have large tech stock exposure needing balance
• Weak psychological tolerance; cannot endure large volatility

3. Which has greater risk: AI bubble or crypto bubble?

Both have different risk characteristics; direct comparison is difficult:

AI Stock Bubble Characteristics:

• Volatility: Relatively low; annualized volatility 40-60%
• Fundamentals: Most companies have actual revenue and partial profitability
• Regulation: Regulated by traditional securities law; better investor protection
• Liquidity: Major companies have ample liquidity
• Systemic Impact: If bursts may trigger economic recession

AI Crypto Token Bubble Characteristics:

• Volatility: Extremely high; annualized volatility 80-150%
• Fundamentals: Most projects have low commercialization degrees
• Regulation: High regulatory uncertainty
• Liquidity: Small-cap tokens have poor liquidity
• Systemic Impact: Relatively limited; unlikely to trigger comprehensive financial crisis

Risk Comparison:

Conclusion: AI crypto tokens have higher individual asset risk but smaller systemic impact. AI stock bubble burst may trigger broader economic effects.

Advice:

• Most funds invest in AI stocks
• A small portion (5-10%) invest in AI crypto tokens through MEXC
• Both asset classes have low correlation; can hedge each other

4. How to distinguish genuine AI companies from those riding the hype?

Identifying genuine AI companies requires in-depth research:

Revenue Structure Analysis:

• Genuine AI Companies: AI-related revenue proportion >50% and continuously growing
• Riding Hype: AI revenue <10%, or no separate disclosure at all
• Check Method: Carefully read financial statement segment revenue explanations

Technology Strength Verification:

• Patents and Papers: Top conference paper publications; high-quality patents
• Open-source Contributions: GitHub activity; open-source project influence
• Team Background: Whether the core team has top AI scientists
• R&D Investment: R&D expenditure as revenue percentage >15%

Customers and Cases:

• Genuine Companies: Specific enterprise customer lists and success cases
• Riding Hype: Only vague promotions; no verifiable cases
• Verification: Contact customers for confirmation or review third-party evaluations

Product Depth:

• Genuine Companies: Independently developed core algorithms and models
• Riding Hype: Only calling OpenAI and other third-party APIs
• Testing: Trial products; assess technology depth

Historical Consistency:

• Genuine Companies: Multi-year continuous investment in AI field
• Riding Hype: Suddenly announce AI transformation; history unrelated to AI
• Tracing: Review company business focus 5-10 years ago

Red Flag Warnings:

• Frequent renaming adding “AI” words
• Publishing many press releases but limited business progress
• Management lacks technical background
• Financial data opaque or frequently adjusted

5. How to hedge risks while investing in AI?

Multi-level hedging strategies can effectively manage AI investment risks:

Asset Allocation Hedging:

• Bonds: 20-30% allocated to high-quality bonds
• Gold: 5-10% allocated to gold or gold ETFs
• Value Stocks: Invest in low-valuation, high-dividend traditional industries
• Real Estate: REITs or physical real estate

Derivatives Hedging:

• Put Options: Buy protective put options for heavy AI stock positions
• Collar Strategy: Buy puts while selling calls; reduce costs
• Inverse ETFs: Small positions allocated to NASDAQ or tech stock inverse ETFs
• VIX Products: Buy when VIX is low; sell when high

Geographic Hedging:

• Europe: Invest in European AI companies; relatively conservative valuations
• China: Chinese AI companies have lower valuations but need bear policy risks
• Emerging Markets: Diversify geopolitical risks

Intra-Industry Hedging:

• Defensive Tech: Invest in diversified companies like Microsoft, Oracle
• AI Beneficiary Industries: Invest in traditional industries using AI to improve efficiency
• Competitor Portfolios: Simultaneously invest in multiple competitors dispersing single company risk

Time Hedging:

• Staged Investment: Not fully invested at once; retain cash addressing pullbacks
• Regular Rebalancing: Reduce when rises much; add when falls much
• Long-Short Combination: Some funds held long-term; some traded short-term

Cryptocurrency Hedging:

• Uncorrelated Assets: Bitcoin has low correlation with AI stocks; can hedge each other
• Stablecoins: Hold partial USDT through MEXC awaiting opportunities
• DeFi Yield: Stablecoin staking earning yields

Stop-Loss Discipline:

• Hard Stop-Loss: Mandatory sale when single stock loses over 20%
• Portfolio Stop-Loss: Substantially reduced when AI portfolio overall loses over 25%
• Dynamic Adjustment: Adjust stop-loss points based on volatility

6. What will happen after the AI bubble bursts?

Based on historical bubble experience, you can expect the following scenarios:

Short-term (0-6 months):

• Price Plunges: AI stocks may decline 40-60%
• Liquidity Exhaustion: Trading volumes shrink; bid-ask spreads widen
• Panic Selling: Retail and leveraged investors forced to close positions
• Chain Reactions: AI companies face financial difficulties; some go bankrupt

Medium-term (6-24 months):

• Industry Consolidation: Weak companies close or get acquired
• Valuation Reset: P/E ratios return to industry average levels
• Investor Confidence Damaged: Capital withdraws from tech sectors
• Economic Impact: May trigger or deepen economic recession

Long-term (2-5 years):

• Genuine Value Appears: Competitive companies stand out
• Technology Continues Progressing: AI development won’t stop; just pace slows
• New Growth Round: Survivors begin new growth cycles with healthier valuations
• Lessons Summarized: Markets and regulations are more mature

Historical Analogies:

• After Dot-com Bubble: NASDAQ declined 78%, but Amazon, Google eventually became giants
• Crypto 2018: Bitcoin declined 84%, but hit new highs 2020-2021
• Japan 1990: Stock market declined then long-term malaise, but some companies successfully transformed

Investor Response:

• Don’t Panic: Stay calm; avoid cutting losses at bottoms
• Distinguish Quality: Stick with quality companies; clear garbage stocks
• Prepare for Bottom-Fish: Bubble bursts create long-term buying opportunities
• Learn and Reflect: Summarize lessons; improve investment capabilities

Opportunities:

• Valuation Rationalization: Buy quality companies at reasonable prices
• M&A Opportunities: Acquire undervalued technology and teams
• Talent Mobility: Outstanding talent flows from failed to successful companies
• Accelerated Innovation: Resources shift from speculation to genuine innovation

Conclusion

There is no simple answer to the question of whether an AI bubble exists. Based on indicators such as valuations, market sentiment, and speculative behavior, the AI market does exhibit bubble-like characteristics. However, compared to historical bubbles, the current AI market has a stronger technological foundation, higher levels of commercialization, and greater profitability, which partially justify the high valuations.

Key Judgments:

1. Localized Bubble: Certain sub-sectors and individual stocks are indeed overvalued.
2. Not a Full-Scale Bubble: The overall market has not reached the extreme levels seen during the 2000 internet bubble.
3. Rising Risks: Multiple warning signals indicate an increasing risk of a bubble.
4. Timing Uncertainty: The timing of a potential burst depends on various factors and cannot be precisely predicted.

Investor Action Plan:

• Acknowledging the Value of AI: AI is truly transformative, with real long-term value.
• Beware of Valuation Risks: Avoid chasing overvalued assets and adhere to valuation discipline.
• Diversify Investments: Reduce exposure to individual risks through diversification.
• Monitor Continuously: Track key indicators and adjust strategies as needed.
• Stay Rational: Overcome greed and fear, and stick to long-term investment principles.

Final Recommendations:

Rather than focusing on whether AI is a bubble, concentrate on:

• Investing in companies with genuine value.
• Controlling risk exposure within manageable limits.
• Adopting an investment strategy that aligns with your own goals.
• Continuously learning and adapting to market changes.

Whether investing in U.S.-listed AI concept stocks or AI-related crypto tokens through MEXC Exchange, the key is rational analysis, prudent decision-making, and strict risk management.

Disclaimer: This article is reposted content and reflects the opinions of the original author. This content is for educational and reference purposes only and does not constitute any investment advice. Digital asset investments carry high risk. Please evaluate carefully and assume full responsibility for your own decisions.