What 50 Years Of Bubbles Say About The Next One

Market data since 1976 shows a consistent pattern across asset bubbles:

Prices tend to peak well before sentiment deteriorates.

Historically, bubbles end when valuation levels become unsustainable relative to cash flows, liquidity, or growth expectations.

Today’s market presents a similar setup in large-cap technology.

The Magnificent 7 trade at an average P/E of ~73, but this figure is heavily skewed by Tesla.

When TSLA is excluded, the average P/E falls to ~35–36, which remains elevated but far below headline figures.

Current trailing P/E ratios:

• AAPL: 36.6
• MSFT: 33.6
• META: 28.7
• AMZN: 32.0
• GOOG: 31.1
• NVDA: 46.7
• TSLA: 302.3

For comparison:

• S&P 500 P/E: 31.16
• Long-term S&P 500 average: 16.19

Historical cycles suggest reversals tend to occur when one or more constraints emerge:

• earnings growth fails to justify forward expectations
• liquidity tightens
• marginal buyers disappear
• capital costs rise
• or concentration risk increases volatility

Importantly, sentiment usually turns after prices begin to fall, not before.

Past bubbles across equities, real estate, commodities, and crypto show that valuation compression often precedes narrative shifts — not the other way around.

The common thread across Morgan Stanley’s 2026 ideas is not sector selection — it’s regime dependence.

All highlighted equities and themes assume a continued growth-friendly environment, characterized by:

• stable financial conditions
• sufficient market liquidity
• no major macroeconomic shock

These ideas are not valuation-driven.

They rely on future earnings expansion, supported by:

• AI-related capital expenditure
• infrastructure build-outs
• cyclical economic recovery

While the labels differ — AI infrastructure, cyclical recovery, healthcare revival — the risk drivers are closely aligned.

Many of these equities share:

• sensitivity to liquidity conditions
• exposure to corporate spending cycles
• dependence on forward growth expectations rather than current cash flows

As a result, the portfolio behaves less like a diversified set of ideas and more like a concentrated expression of the current market regime extending into 2026.

If growth slows, capital spending tightens, or liquidity conditions change, outcomes across these positions are likely to move together.

Reading list for 2026.

Markets change. Cycles repeat. Human behavior doesn’t.

This list covers value investing, cycles, risk, behavior, and long-term wealth—drawing lessons from past dislocations and timeless principles. Its purpose isn’t prediction, but clearer thinking across full market cycles.

We’re seeing a noticeable rotation into precious metals.

Since 1997, gold and silver have both outperformed the S&P 500, despite long periods of underperformance in between.

The recent move stands out not because metals are rising—but because gold, silver, and copper are advancing together.

Historically, this pattern tends to appear late in cycles, when capital shifts toward assets perceived as stores of value rather than future growth.

It’s less about short-term inflation spikes and more about confidence in financial and political systems.

When investors rotate into multiple hard assets simultaneously:

Recent developments in global supply chains—especially around critical minerals and export controls—add another structural layer.

Reduced availability can amplify price moves even without a surge in end demand.

This doesn’t signal an immediate market outcome.

But it does suggest that portfolio behavior is changing.

AI inference is the execution phase of artificial intelligence.

It’s the moment a trained model stops learning and starts producing real-world outputs.

When an AI system classifies an image, answers a question, recommends a product, or detects fraud, it is performing inference.

The AI industry today resembles:

The underlying technology proved transformative in both cases.

Yet, most companies failed, despite the future long-term success of the technology itself.

AI inference sits at a similar inflection point.

The demand is real, but the structure of the industry is still forming.

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