Crypto bubble vs AI bubble: two manias compared
The essentials in 30 seconds
- The EQOS Asia crypto study scores crypto at 0.340 and the AI study scores enterprise AI at 0.337, both in the fragile zone.
- Crypto assets lack a revenue stream, while AI products sell to paying customers, so the foundations differ.
- The FTX collapse of November 2022 wiped out $8 billion of customer funds.
- Bitcoin moves about 30% per month, and 3 exchanges hold 70% of crypto volume (EQOS Asia crypto study, 2026).
- Two detailed pages cover the crypto crash and Bitcoin against the AI bubble.
The crypto bubble and the AI bubble are the two manias of this decade, and readers often ask whether they are the same thing. This page belongs to the comparison in AI bubble vs dot-com bubble. It sets crypto next to AI on four points, as of October 2026: what each one values, how each is funded, how concentrated each market is, and who bears the risk.
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What do the crypto bubble and the AI bubble have in common?
Both manias rest on a story about the future, both attracted fast inflows of money, and both drew a crowd that buys because others buy. The EQOS Asia scores place them close together: 0.340 for crypto (EQOS Asia crypto study, 2026) and 0.337 for enterprise AI (EQOS Asia AI study, April 2026). Both sit in the fragile zone, where the system works and a shock can tip it.
Close scores do not mean identical risks. The score summarises the structure of nine dimensions, and two systems can reach the same total through different weak points. Crypto scores lowest on stability and care. AI scores lowest on communication, which means the ability to explain what a model does (0.305). Different weak points call for different protections.
Does the crypto bubble or the AI bubble rest on revenue?
An AI product sells a service to customers who pay for it, so AI valuations can be tested against revenue and earnings. The EQOS Asia AI study records valuations up ×4 in 18 months and 95% of AI projects failing in production (MIT 2025, cited by the study). The gap between the two numbers is the AI question.
Crypto assets such as Bitcoin carry no revenue stream of their own, so their price rests on demand and belief. That structure makes the price harder to anchor and the swings larger. A reader comparing the two manias should ask, for each, what a buyer receives in return for the money, and whether that return can be measured.
How are the crypto bubble and the AI bubble funded?
AI draws money from large companies, investors, and lenders, with about $200B a year invested in AI research and deployment (EQOS Asia AI study, April 2026). Much of this spending is made by firms with earnings, which gives the system a base of cash flow. The share funded by outside investors still needs checking each quarter.
Crypto draws money from retail buyers, trading venues, and since January 2024 from exchange traded funds approved by the SEC (EQOS Asia crypto study). Institutional adoption has grown, and the trading base remains broad and fast. The difference in funding sources explains why crypto prices react within hours while AI spending plans change over quarters.
What did the crypto crash show about how an AI bubble could end?
The collapse of FTX in November 2022 wiped out $8 billion of customer funds and exposed the absence of proof of reserves (EQOS Asia crypto study, 2026). The event shows how trust breaks at the point where outsiders cannot see the books, and how a single failure reaches the whole market.
The page on the crypto crash and an AI bubble burst follows that chain step by step and asks which of its links exist in AI. It turns the FTX story into a list of warning signs that decision makers can check in AI contracts, such as reserves, audit rights, and dependence on one counterparty.
Is Bitcoin more speculative than the AI bubble?
Bitcoin moves about 30% per month in the EQOS Asia crypto study, which scores its stability at 0.300, at the critical line. AI valuations also rose fast, but the businesses behind them report revenue and customers. Speculation is a matter of degree: it grows with the share of price that rests on belief alone.
The page on Bitcoin against the AI bubble measures that share with a few tests and ranks the two on each. It reaches a clear reading for each test, and it avoids a single winner, because AI includes speculative corners and crypto includes uses with real activity. The tests let you place any asset on the scale.
How concentrated are the crypto and AI bubble markets?
The crypto study finds that 3 exchanges, Binance, Coinbase, and Kraken, handle 70% of volume, which makes the claim of decentralisation partly a marketing story. A problem at one venue reaches most of the market. The study scores crypto coherence at 0.380 for that reason.
AI concentrates differently: a few chip makers, cloud providers, and model builders sit at the centre of the chain, and many companies depend on them. In both markets the useful question is the same. If the largest participant failed, how many others would feel it within a month? A short list of dependent firms signals fragility.
Who bears the risk in the crypto bubble and the AI bubble?
The crypto study cites the UK FCA on retail results: 80% of retail investors lose money. Retail buyers carry the risk directly, with limited information. In AI, much of the risk sits with companies that sign multi-year commitments and with investors in listed technology names. The AI study records 95% of projects failing, so project owners also bear a share.
Whoever you are, size exposure to what you can carry if prices halve. The EQOS Asia method page explains the scoring, and the Λ simulator gives a φ(S) estimate in 10 minutes. A 30-minute pre-diagnostic applies it to your case in Singapore, ASEAN, or Hong Kong. This page is general information, not investment advice.
See also in this guide
Frequently asked questions
Is the crypto bubble the same as the AI bubble?
No, they share a fragile structure but differ in foundations. The EQOS Asia crypto study scores crypto at 0.340 and the AI study scores enterprise AI at 0.337, both fragile. AI products sell to paying customers, while crypto assets carry no revenue stream of their own.
Which bubble is riskier for retail investors?
The crypto study cites the UK FCA on retail results: 80% of retail investors lose money. Bitcoin moves about 30% per month. Retail exposure to AI often runs through listed technology shares. This page is general information, not investment advice.
What did FTX show about bubbles?
The FTX collapse of November 2022 wiped out $8 billion of customer funds and showed what happens when outsiders cannot check reserves. The warning sign carries over to AI contracts: ask for audit rights and clarity on counterparties.
Where do I read more on each comparison?
Two linked pages go deeper: the crypto crash and what it teaches about an AI bubble burst, and Bitcoin against the AI bubble on speculation. Each ends with tests that you can apply to your own exposure.
Test your knowledge
1. What score does the EQOS Asia crypto study give crypto?
- 0.340
- 0.618
- 0.095
Show the answer
0.340.
2. How much customer money did the FTX collapse wipe out?
- $80 million
- $800 million
- $8 billion
Show the answer
$8 billion.
3. Which three exchanges handle 70% of crypto volume in the study?
- Binance, Coinbase, and Kraken
- Nasdaq, NYSE, and Tokyo
- Visa, Mastercard, and PayPal
Show the answer
Binance, Coinbase, and Kraken.
4. What does an AI product have that Bitcoin lacks?
- A monthly volatility of 30%
- Three main exchanges
- A paying customer base
Show the answer
A paying customer base.
5. What does the page advise on sizing exposure?
- Match it to the best case
- Size it to what you can carry if prices halve
- Ignore it
Show the answer
Size it to what you can carry if prices halve.
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.