INVESTOR BRIEFING: AI EQUITY BUBBLE BURST & SYSTEMIC CRYPTO MARKET SHOCK

1. Overview: Regulators Now Frame AI as a Bubble Risk

Senior officials at the ECB, Bank of England, IMF and WEF are no longer speaking about artificial intelligence valuations in cautious terms – they explicitly describe AI-related equities as bubble-like and exposed to a sudden, disorderly correction.

The current AI boom is increasingly viewed as a financial stability threat that may require investors to rebalance portfolios without delay. Policymakers are openly drawing comparisons with the dot-com bust of 2000 and the global financial crisis of 2008, arguing that today’s AI euphoria combines stretched valuations with complex leverage and opaque funding structures.

2. Extreme “Doomsday” Scenario: How the AI Bubble Could Unwind

2.1 Circular Funding and Hidden Leverage

The core fragility lies in circular financing loops within the AI ecosystem:

• AI businesses purchase and “invest” heavily in each other’s services,

• this inflates reported revenues and growth trajectories,

• while real end-user demand and sustainable cash flows lag far behind.

At the same time, non-bank private credit has become the dominant funding source for AI infrastructure:

• Around 50% of large-scale AI infrastructure (data centres, GPU clusters, chip manufacturing capacity) is now financed by private credit vehicles rather than traditional banks.

• These exposures amount to trillions in leveraged positions, often with complex, lightly regulated structures.

If this circular system unravels, debt repayment chains could transmit stress through global markets faster than a conventional equity sell-off.

2.2 Scale vs. the 1990s Internet Bubble

Aggregate AI-linked market capitalization is estimated to be roughly one-third larger than the total size of the 1990s internet bubble, magnifying potential downside.

If promised productivity gains and monetisation do not materialise:

• Institutional investors may dump AI holdings to meet risk limits,

• margin calls could hit portfolios loaded with leveraged treasuries and venture debt,

• forced liquidations would exacerbate price declines across multiple asset classes.

Source: nytimes.com.

3. Potential Trigger Sequence

A severe correction is most likely if several shocks arrive in close succession:

1. Earnings disappointment from AI megacaps

• Leading AI-focused giants miss quarterly earnings targets.

• Management teams revise down AI monetisation expectations.

• Confidence in long-term growth narratives erodes.
  (Source: goldmansachs.com)

2. Bond market repricing and rate shock

• US yields jump as investors reassess debt sustainability.

• Central banks stay restrictive to contain a second wave of inflation.
  (Source: Reuters)

3. Global growth slowdown

• Data points confirm a downturn in global activity.

• Overcapacity in data centres and AI infrastructure becomes visible.
  (Source: The Times of India)

4. Equity Market Impact: From AI Leaders to Global Benchmarks

Once confidence cracks, the initial damage concentrates in AI leaders but quickly spreads:

• The dominant AI champions (often dubbed the “Magnificent 7”) could drop 60–75%.

• Broad global tech indices might fall 50–65%.

• Wider US and European equity benchmarks could decline 30–40%.
  (Source: Le Monde.fr)

Mechanically driven strategies would amplify the move:

• Risk-parity and volatility-targeting funds cut exposure as volatility spikes.

• Margin calls proliferate as collateral values plunge.

• ETFs and passive flows magnify outflows and accelerate forced selling.

5. Crypto Contagion: BTC, ETH and Altcoins in a Full AI Bust

5.1 Tight Correlation With Risk Assets

In recent years, major cryptocurrencies have traded increasingly in tandem with US equities, especially during Fed-induced risk-off phases. (Source: bloomberg.com)

This means a deep AI-led equity crash would likely drag crypto down, rather than offer diversification.

5.2 Bitcoin (BTC)

In an AI bubble burst:

• The narrative of Bitcoin as “digital gold plus AI infrastructure proxy” collapses.

• BTC trades primarily as a high-beta risk asset.

• A 60–80% decline from pre-crash levels becomes plausible as:

• leverage is flushed out of perpetual futures markets,

• major lenders raise collateral haircuts and restrict credit.

• Price levels in the $94,000–$95,000 range could fall back towards $19,000–$38,000 support zones.

5.3 Ethereum (ETH)

Ethereum carries additional “earnings-style” risk because its ecosystem depends on:

• NFT markets,

• DeFi activity,

• rollups and L2 transaction volume.

In a severe liquidity crunch:

• ETH might experience 70–90% drawdowns,

• extensive DeFi liquidations could occur,

• protocol treasuries would suffer losses,

• rollup and L2 projects could face funding and runway stress.

5.4 Altcoins, AI Tokens and Stablecoins

For the long tail of crypto assets, the picture is even darker:

• Small-cap and illiquid altcoins, including AI-themed tokens and many DeFi projects, could fall 90–100%, with a large share never regaining previous highs.

• Stablecoin redemptions may spike as investors flee risk, putting pressure on:

• issuers’ reserves,

• liquidity on centralized and decentralized exchanges.

The combination of forced selling, shallow order books, and stablecoin stress would severely drain on-chain liquidity and magnify volatility.

6. Why This Could Be Worse Than a “Normal” Recession

This scenario goes beyond typical recession-era correlations:

• Elevated leverage and algorithmic trading mean that:

• overextended positions trigger automatic liquidations,

• orders execute at market prices into falling bids,

• volatility becomes self-reinforcing.

The consequences could include:

• A multi-year crypto winter,

• widespread project and protocol failures,

• tougher regulatory responses targeting leverage, stablecoin frameworks, and aggressive retail marketing practices.

7. Scenario Probabilities & Macro Conditions

7.1 Qualitative Scenario Assessment

Scam-Or Project’s qualitative assessment of medium-term outcomes:

7.2 Conditions for the Tail-Risk Scenario

The most severe “doomsday” pathway likely requires several macro ingredients:

• Resurgent inflation forcing central banks into additional rate hikes,

• Recession in the US and EU combined with significant earnings compression,

• Sovereign-debt or private-credit stress interacting with:

• inflated AI asset prices,

• speculative crypto valuations.

(Sources: ECB, BOE)

8. Practical Risk Management Considerations

(Not Investment Advice)

Given the outlined risks, investors may consider the following principles:

1. Reframe AI and Crypto in Risk Terms

• Treat AI-heavy tech stocks and cryptocurrencies as correlated, high-beta risk assets, not as independent diversifiers.

2. Stress-Test Portfolios

• Model scenarios where:

• crypto holdings fall 60–80%,

• major equity indices decline 30–40%.

• Assess whether margin, liquidity and psychological tolerance can withstand such shocks.

3. Prioritise Liquidity, Discipline and Low Leverage

• Maintain ample, high-quality liquidity buffers,

• keep leverage conservative or minimal,

• define clear exit rules ahead of time instead of chasing AI or crypto FOMO trades.

These measures cannot eliminate market risk but can improve resilience if the AI bubble deflates in a disorderly and contagion-prone fashion.