AI Boom: Valuation Bubbles, Investment Risks, and Strategies

The following is a comprehensive analysis and evaluation based on the latest data (Jinling API and web search/news):

1. Wealth Effect and Valuation Inflation Phenomena

• The AI sector spawned over 50 new billionaires in 2025; among them, a 22-year-old co-founder entered the billionaire ranks via an AI company [1].
• AI startup funding scale surged: market reports show investors poured over $2023 billion into the AI sector, accounting for nearly half of the global venture capital market [1].
• Benchmark cases:
  • Anthropic’s valuation rose significantly (public reports indicate it increased from approximately $61.5 billion to $183 billion, according to statistics from Forbes and other media [1]).
  • Mercor (AI recruitment platform) completed a funding round at a valuation of approximately $10 billion in October 2025 [1].
  • Scale AI and related projects are valued in the billions of dollars range (based on media statistics and estimates [1]).

2. Capital Expenditure and Debt Expansion

• Some tech companies sharply increased capital expenditure on AI data center construction and infrastructure investment; at the same time, bond issuance scale rose.
• Oracle (ORCL)'s credit risk indicator CDS rose to its highest level since 2009, reflecting market concerns over potential risks from AI-related spending and debt expansion [2].
• Market analysis points out that if AI investment return rates cannot cover the speed of debt growth, credit pressure may further accumulate [2].

3. Valuation Pressure Signals

• Public company valuation divergence is obvious:
  • NVIDIA (NVDA) has a market capitalization of approximately $4.64 trillion; latest P/E ratio is about 46.72x, higher than the median level; P/B ratio is about 38.98x; 2026 fiscal year consensus target price is approximately $257.50 [0].
  • Microsoft (MSFT) has a market capitalization of approximately $3.63 trillion; latest P/E ratio is about 34.55x; 2026 fiscal year consensus target price is approximately $640.00 [0].
  • Alphabet (GOOGL) has a market capitalization of approximately $3.78 trillion; latest P/E ratio is about 30.50x; consensus target price is approximately $305.00 (slightly below current price) [0].
• DCF scenarios show: NVDA’s fair value midpoint across three scenarios is significantly lower than current price (base scenario ~$86.33); MSFT’s base scenario is ~$375.23, optimistic scenario is ~$537.97 [0].

Compared with the 2000 internet bubble, the current AI boom has differences in fundamentals and risk structures:

• Demand authenticity: AI chips and data centers have high utilization rates and ongoing shortages, reflecting real demand (web search and industry reports [1]).
• Financial health: Large tech companies have ample cash flow to support capital expenditures, and overall financial health is better than internet companies of that era [1].
• Profitability and cash flow: Leading companies have considerable profits and free cash flow, with the ability to sustain investment (financial reports and industry research [0][1]).

However, vulnerabilities and valuation bubble risks in some areas cannot be ignored:

• Some niche segments have high valuations and unclear profit models (some high-valued startups have not yet formed stable cash flow, and their valuations are based on high growth expectations [1][2]).
• Rising credit risk from debt expansion (e.g., CDS widening for some companies [2]).
• After high funding enthusiasm, if industry growth slows or competition intensifies, valuation corrections and capital reallocation pressure may occur.

The following is a multi-dimensional risk assessment and verification framework:

1. Valuation Indicator Verification

• P/E and P/S: Combine historical ranges and industry comparisons to distinguish whether growth has been priced in.
• DCF stress test: Use multi-scenario (low, medium, high) calibration, focusing on the sensitivity of discount rate and perpetual growth rate assumptions.
• Relative valuation: Horizontal comparison with peer companies and historical ranges (e.g., AI infrastructure, cloud services, SaaS, etc.).

2. Financial Health

• Cash flow: Whether operating cash flow and free cash flow are sufficient to cover capital expenditures and debt repayment.
• Leverage and liquidity: Debt ratio, interest coverage ratio, short-term debt pressure; beware of excessive leverage.
• Profit quality: Whether revenue growth is sustainable, and whether gross profit margin and net profit margin are stable.

3. Business Moat and Competitive Landscape

• Technical barriers: Chip computing power, model capability, data and algorithm network effects.
• Ecological niche: Infrastructure (GPU/TPU/cloud), model layer (MaaS), application layer (industry solutions).
• Customer stickiness and switching costs.

4. Technical Route and Policy Risks

• Model iteration speed and open-source competition: Impact of open-source models on closed-source business models.
• Regulation and compliance: Data privacy, model interpretability, and compliance costs.
• Geopolitics: Supply chain, export controls, and market access restrictions.

5. Market Sentiment and Liquidity

• Cyclicality of funding enthusiasm and valuation multiples.
• Transmission mechanism between primary and secondary markets (e.g., the impact of valuation reset of high-valued unicorns on market sentiment).

1. Return Source Decomposition

• Leading platform companies: Cloud revenue, AI service subscriptions, enterprise product implementation (MSFT, GOOGL, etc. [0]).
• Infrastructure and chips: Data center spending, GPU/TPU demand, supply chain premium (NVDA, etc. [0]).
• Applications and vertical segments: Companies with clear efficiency improvement and user monetization paths (e.g., finance, healthcare, marketing AI applications).

2. Investment Recommendations (Stratified by Risk Preference)

• Conservative: Allocate to profitable, cash-flow positive platform and infrastructure companies, controlling valuation and leverage levels.
• Growth-oriented: Seek reasonably valued targets among tech-leading companies with strong data and network effects, focusing on the pace of profit realization.
• Opportunistic: Focus on high-growth but volatile niche application and tool companies, with good position management and stop-loss discipline.

3. Portfolio Recommendations

• Long-term allocation: Platform and infrastructure leaders (balancing valuation and cash flow).
• Satellite allocation: Vertical applications and tools with clear business models and implementation paths.
• Hedging and diversification: Moderately add defensive assets to reduce industry concentration.

• Valuation divergence and partial overheating: The AI sector is generally booming, but the matching degree between valuation and fundamentals varies significantly across different fields and companies, and some links have partial bubble risks [0][1][2].
• Fundamental support and long-term value: Leading companies’ profitability, cash flow, and real demand provide support for long-term value; AI’s potential in productivity improvement is still being gradually realized [1][2].
• Risk management is key: It is recommended to enjoy the long-term growth dividends of AI while controlling drawdowns and volatility through valuation verification, cash flow tracking, position management, and portfolio hedging.
• Time dimension: AI investment is a long-cycle theme; short-term valuation fluctuations and sentiment disturbances will bring rhythmic opportunities to increase or decrease positions; the core is to identify which companies can deliver technological dividends and form sustainable profitability.

[0] Jinling API Data (stock prices, financial indicators, valuation and market data, etc.)
[1] Forbes - AI Minted More Than 50 New Billionaires In 2025 (https://www.forbes.com/sites/aliciapark/2025/12/25/ai-minted-more-than-50-new-billionaires-in-2025/)
[2] Yahoo Finance/WSJ/Bloomberg (AI investment, credit risk and market risk signal related reports, such as Oracle CDS rise and AI investment return concerns) (https://hk.finance.yahoo.com/news/ai泡沫要爆-甲骨文风险亮红灯-cds飙破16年高点-180003427.html)
[3] Deloitte Insights Industry Report (2025 AI and Semiconductor Outlook, Demand and Capital Expenditure Trends) (https://www2.deloitte.com)
[4] Reuters/Bloomberg (Industry spending, funding, and competitive landscape related reports)
[5] Morningstar/Brokerage Research (AI and tech sector valuation comparisons and risk warnings)

The above content is for professional analysis and evaluation only and does not constitute investment advice.