The crypto crash and what it teaches about an AI bubble burst
The essentials in 30 seconds
- The FTX collapse of November 2022 wiped out $8 billion of customer funds and had no proof of reserves behind it.
- A drawdown of 30% in a month is normal for Bitcoin, and the EQOS Asia study asks if your treasury can absorb it.
- Five warning signs carry over to AI: reserves, concentration, opacity, regulation, and drawdown capacity.
- The EQOS Asia AI study scores enterprise AI at 0.337, and the crypto study scores crypto at 0.340.
- A rule change can move crypto value by 20% to 40% in the study.
The crypto crash is the nearest recent example of a market where trust broke in days, and it offers practical lessons for an AI bubble burst. This page belongs to the comparison in crypto bubble vs AI bubble. It follows the 2022 failure of FTX, the rebuilding that followed, and five warning signs you can check in AI exposures as of October 2026.
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What happened in the crypto crash of November 2022?
In November 2022 the exchange FTX collapsed, and $8 billion of customer funds were lost (EQOS Asia crypto study, 2026). Customers had believed their money was held safely. The EQOS Asia crypto study notes that FTX had no proof of reserves, so no outsider could confirm that the money existed in the amounts claimed.
The event matters because it hit the market’s trust, not only its prices. Customers rushed to withdraw, other venues faced questions, and the market learned that its largest names could fail. A price fall is a loss that you can measure, while a loss of trust changes how every participant behaves afterwards.
Why did one exchange failure become a market wide crypto crash?
Three links carried the failure across the market. Concentration put much of the volume on a few venues, opacity hid the state of their books, and leverage tied participants to each other. The study finds that 3 exchanges handle 70% of volume, so a problem at one venue reaches most holders quickly.
Each link can be checked in advance. Ask how many venues or suppliers carry the activity, how much of their books you can see, and who owes money to whom. Where the answers show few venues, hidden books, and tangled debts, a single failure can spread. This is the pattern to look for in an AI bubble burst.
How did the crypto market rebuild after the crash?
Rebuilding took two forms. In January 2024 the SEC approved Bitcoin exchange traded funds, which brought institutional money back under strict transparency rules, and the European Union put MiCA into force as a framework (EQOS Asia crypto study, 2026). Institutional adoption resumed, but with checks that were absent before.
The sequence suggests a pattern for AI. After a trust shock, regulation and disclosure arrive and capital returns to those who comply. The study still notes that trust rebuilds slowly and that the scar remains. Plan for a period in which funding is available only to firms that can show their reserves, contracts, and customers.
What is the AI bubble version of proof of reserves?
The study’s first remedy after FTX is audited proof of reserves, and a refusal to provide it counts as a risk signal. The AI equivalent is evidence that a supplier can deliver what it sells: audited accounts, the cash left, signed capacity for chips and power, and the share of revenue that comes from paying customers.
Ask for these items in every contract review. A supplier that shares them lets you judge its staying power. A supplier that holds them back gives you a signal of its own. The EQOS Asia AI study records about $200B a year invested in AI (EQOS Asia AI study, April 2026), so even a small share of unverified spending is large.
Can your treasury absorb a crypto style drawdown in an AI bubble?
The crypto study gives a simple test: if your treasury cannot absorb a 30% drawdown, the exposure is too large. Bitcoin moves about 30% per month, so the test is realistic there. Apply the same test to AI. Compute the loss on your AI holdings and commitments if their value fell by 30%, by 50%, and by 75%, and compare each loss with your cash.
The 75% case matters because AI valuations rose ×4 in 18 months, and a fall of 75% would reverse the whole rise. If the loss at that level threatens the business, reduce or stage the commitment. If the loss is bearable, you can hold the position knowing the worst case in advance.
Could a rule change hit an AI bubble as it hit crypto?
The crypto study states that a regulatory reclassification can move value by 20% to 40%, and that unclear rules between the SEC and the CFTC slow institutional adoption. Rules shape what buyers may hold and what suppliers may sell, so they act as a trigger as strong as any earnings report.
AI has its own calendar. The EQOS Asia AI study lists AI Act compliance on 2 December 2027 as a date to plan around. Mark such dates in your plan, estimate the cost of compliance for each supplier, and ask which suppliers have budgeted for it. Prepared suppliers gain share when rules arrive.
What should you do now with the crypto crash lessons for AI?
Run the five checks on one AI exposure: reserves, concentration, opacity, regulation, and drawdown capacity. Score each as supportive, mixed, or fragile, and record the date. The EQOS Asia method page shows how scores become a recomputable measurement, and the Λ simulator gives a φ(S) estimate in 10 minutes.
A 30-minute pre-diagnostic applies the scoring to a decision in Singapore, ASEAN, or Hong Kong. You bring the decision, and the session returns a score and a ranked set of options. This page is general information, not investment advice, and the crypto record describes the past, not a prediction for AI.
See also in this guide
Frequently asked questions
What caused the crypto crash of 2022?
The collapse of FTX in November 2022 wiped out $8 billion of customer funds. The exchange had no proof of reserves, so outsiders could not confirm the money existed. Concentration on a few venues and hidden books spread the shock across the market.
What does the crypto crash teach about an AI bubble burst?
It teaches five checks: reserves, concentration, opacity, regulation, and drawdown capacity. Each one can be scored from public reports and contracts. A supplier that shares evidence gives you confidence, and a supplier that withholds it gives you a warning.
How should I test my exposure to a fall?
Compute the loss if your AI holdings and commitments fell by 30%, 50%, and 75%, and compare each loss with your cash. A fall of 75% would reverse the ×4 rise in valuations recorded by the EQOS Asia AI study. This page is general information, not investment advice.
Did the crypto market recover after the crash?
The market rebuilt with new rules. The SEC approved Bitcoin exchange traded funds in January 2024 and MiCA came into force in the European Union. The study notes that trust rebuilds slowly and that the scar remains.
Test your knowledge
1. How much customer money was lost in the FTX collapse?
- $8 billion
- $80 billion
- $800 million
Show the answer
$8 billion.
2. What was missing at FTX according to the study?
- Proof of reserves
- A website
- Customers
Show the answer
Proof of reserves.
3. What share of crypto volume do 3 exchanges handle in the study?
- 20%
- 45%
- 70%
Show the answer
70%.
4. Which fall would reverse a rise of ×4 in AI valuations?
- 30%
- 75%
- 10%
Show the answer
75%.
5. What is the AI version of proof of reserves?
- A new logo
- Evidence that a supplier can deliver what it sells
- A longer sales pitch
Show the answer
Evidence that a supplier can deliver what it sells.
About the author
Stéphane Jambu
President, EQOS Forecast Asia
Stéphane Jambu leads EQOS Forecast Asia and works as a consultant in e-commerce, search visibility, and AI. LinkedIn profile
Sources: EQOS Asia crypto study (eqos-asia.com/case-studies/crypto-2026.html), EQOS Asia AI impact study (eqos-asia.com/case-studies/ia-2026.html), EQOS Asia method page (eqos-asia.com/method.html), contact page (eqos-asia.com/contact.html), consulted on 22 October 2026. Page updated on 22 October 2026.