Nvidia’s death cross sparks AI crypto sector questions

Nvidia’s stock recently formed a “death cross”—a bearish technical pattern where the 50-day moving average falls below the 200-day average—raising questions about potential ripple effects on AI-focused crypto tokens.

The last such occurrence in April 2022 preceded a 47% stock decline, according to Barchart.

The death cross emerged as Nvidia’s stock price dropped 9.66% over the past month, despite its role as a bellwether for AI innovation.

While some crypto traders historically linked AI token performance to Nvidia’s movements—such as a 70% rally ahead of its Q2 2024 earnings—recent data shows diverging trends.

AI crypto tokens like Render (CRYPTO:RNDR), Bittensor (CRYPTO:TAO), and Artificial Superintelligence Alliance (CRYPTO:FET) rose 2–4% in late March, even as Nvidia’s stock signaled bearishness.

The AI crypto market has faced skepticism, with the top AI and big data tokens losing 23.70% in market cap over the past month.

Near Protocol (NEAR), the sector’s largest token, has declined nearly 59% over the past year.

“The whole AI hype has already died down, now it’s time for those who provide market solutions and have revenue,” crypto trader CryptoCosta argued. 

A CoinGecko survey of 2,632 respondents found divided views: 44.3% were bullish on AI crypto tokens in 2025, while 26.3% leaned bearish.

“Agents can take fees in an existing crypto for providing a service. Launch a coin only if you have scale. Focus on utility, not tokens,” former Binance CEO Changpeng “CZ” Zhao cautioned, warning against token proliferation.

Sygnum’s February report noted AI agents’ struggle to prove value beyond speculation, highlighting the sector’s reliance on real-world applications.

While Nvidia’s death cross may signal broader tech-sector caution, AI crypto’s trajectory hinges on utility and investor confidence in tangible solutions.

Nvidia’s technical signal underscores market volatility but does not guarantee a direct impact on AI crypto tokens.

Sector performance will likely depend on project-specific fundamentals, regulatory clarity, and adoption of utility-driven models.