Futures
Hundreds of contracts settled in USDT or BTC
TradFi
Gold
Trade global traditional assets with USDT in one place
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Futures Kickoff
Get prepared for your futures trading
Futures Events
Participate in events to win generous rewards
Demo Trading
Use virtual funds to experience risk-free trading
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
Launchpad
Be early to the next big token project
Alpha Points
Trade on-chain assets and enjoy airdrop rewards!
Futures Points
Earn futures points and claim airdrop rewards
Investment
Simple Earn
Earn interests with idle tokens
Auto-Invest
Auto-invest on a regular basis
Dual Investment
Buy low and sell high to take profits from price fluctuations
Soft Staking
Earn rewards with flexible staking
Crypto Loan
0 Fees
Pledge one crypto to borrow another
Lending Center
One-stop lending hub
VIP Wealth Hub
Customized wealth management empowers your assets growth
Private Wealth Management
Customized asset management to grow your digital assets
Quant Fund
Top asset management team helps you profit without hassle
Staking
Stake cryptos to earn in PoS products
Smart Leverage
New
No forced liquidation before maturity, worry-free leveraged gains
GUSD Minting
Use USDT/USDC to mint GUSD for treasury-level yields
Steve Eisman sees the AI boom as the next financial bubble – Here are his arguments
The legendary investor Steve Eisman, known from the movie “The Big Short” for his successful prediction of the 2008 housing crisis collapse, now warns of a new financial bubble: the unprecedented investment boom in artificial intelligence. In his recent analyses, he paints a concerning picture of the current market development, reminiscent of past euphoria phases.
From Gold Rush to Overinvestment: Historical Parallels
Steve Eisman repeatedly emphasizes on his YouTube channel the historical parallels between today’s AI craze and previous market bubbles. He points to 1999 and the subsequent dot-com crash, when internet analysts enthusiastically promoted the potential of the World Wide Web — which proved its long-term value but initially led to massive overinvestments. According to Eisman, these excesses were mainly responsible for the 2001 recession.
The parallels are obvious to him: while experts back then “invested too much, too early” in internet infrastructure, we are now experiencing a similar gold rush in artificial intelligence. Tech stocks took years to recover after the 2001 crisis — a fate Eisman considers quite possible for today’s AI investors.
$300 Billion Annual Budget – Is This Sustainable?
Tech giants like Meta, Google, and Amazon collectively spend over $300 billion annually on AI-related capital expenditures (CapEx). This astronomical sum demonstrates how intense the competition for AI dominance has become. “Everyone is chasing AI,” Eisman notes dryly — but this chase could turn out to be a Pyrrhic victory.
Eisman’s main concern lies in the returns of these enormous investments: no one can reliably predict what concrete economic benefits will result from these sums. If the initial yields fall short, it could lead to a rapid turning point.
Slow Pace of Innovation as a Critical Warning Signal
The investor also identifies early alarming signs on the innovation front. He points out that the current business model of AI development relies on continuous scaling of large language models — an approach that some experts believe is gradually reaching its limits. As a concrete example, he cites ChatGPT 5.0, which, despite massive new investments, delivers only marginally better results than its predecessor ChatGPT 4.0.
This slowed pace of innovation — while investments continue to explode — is, for Eisman, a nightmare scenario: a situation where enormous capital is pumped into a system that no longer significantly improves.
The Sword of Damocles: A Consolidation Phase
Eisman envisions a scenario where the current rapid investment dynamics abruptly end. If it turns out that the returns on AI CapEx fall short of expectations, a drastic rethink would ensue. The subsequent consolidation phase could be as “painful” as the market correction of 2001, with long-term effects on stock prices and capital allocation.
He emphasizes that he does not claim to be an AI expert, but precisely that makes his warning valuable: it comes from someone observing externally with a historical perspective, recognizing patterns that specialized AI enthusiasts might overlook. His track record in predicting market crises adds weight to his concerns.
With his analysis, Steve Eisman hits a sore spot in the current tech industry — the unanswered question of whether the invested billions will truly generate sustainable value or simply vanish into a new, modern fairy-tale castle.