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    Home»Bitcoin»Hyperliquid’s Jeff Yan warns crypto is losing its brightest minds to AI
    Hyperliquid’s Jeff Yan warns crypto is losing its brightest minds to AI
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    Hyperliquid’s Jeff Yan warns crypto is losing its brightest minds to AI

    July 18, 2026
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    Hyperliquid’s Jeff Yan warns crypto is losing its brightest minds to AI

    Hyperliquid co-founder Jeff Yan has warned that crypto’s failure to attract enough top entrepreneurs has become one of the industry’s biggest obstacles as young talent moves toward artificial intelligence.

    Summary

    • Jeff Yan says crypto is struggling to attract top young entrepreneurs.
    • AI’s prestige and rapid growth are pulling talented founders away from on-chain finance.
    • George Noble warns heavy AI spending could create serious financial risks.

    The VALR podcast featured Yan’s comments on how the AI boom and the social status attached to the technology have influenced career choices among young founders. According to Yan, many talented people remain unsure which field would allow them to create the most value, leaving relatively few to pursue work in cryptocurrency and fintech.

    Yan argued that rebuilding the financial system from first principles offers young entrepreneurs a chance to solve difficult real-world problems. In his view, the work involves turning academic ideas into market designs that can operate reliably at scale.

    Rather than judging industries by their surface appeal, Yan urged prospective founders to study the problems each sector is trying to solve. He identified on-chain finance as an area where entrepreneurs can help develop new financial systems and market structures.

    AI is drawing young founders away from crypto

    Yan’s concern comes as Chinese AI developers gain attention for their progress in global model rankings. China’s Kimi K3 recently reached first place on the Frontend Code Arena, a result that prompted former White House crypto czar David Sacks to raise concerns about America’s position in the AI race.

    Sacks described Kimi K3’s performance as troubling because the model also ranked close to leading systems across several other evaluations. He argued that rules covering data centers, state-level requirements and proposed federal reviews could slow US developers while Chinese companies continue improving their models.

    “This is how you lose the AI race,” Sacks wrote.

    Drawing a comparison with the early internet, Sacks argued that the United States became a technology leader by allowing companies to build products without first seeking government permission. He called for Washington to take a similar approach to AI while using focused regulations to address specific safety concerns.

    The competition described by Sacks helps explain why AI has become attractive to ambitious young developers and founders. Yan, however, believes crypto still offers meaningful technical work because building on-chain financial markets requires both entrepreneurial judgment and knowledge of economic design.

    Heavy AI spending carries a separate market risk

    While AI companies compete for talent and capital, former Fidelity fund manager George Noble has warned that the investment boom could create severe financial risks. Noble estimated that an AI bubble collapse could cause 17 times more damage than the dot-com crash, which erased about $5 trillion from the Nasdaq.

    Noble linked that forecast to the large amount of money being directed toward AI infrastructure. If those investments fail to produce the returns expected by investors, he argued, the losses could spread beyond technology companies and affect other parts of the financial system.

    “The fallout from this could really be much more significant,” Noble said while discussing the rise in AI capital spending.

    Yan did not frame AI’s expansion only as a financial threat to crypto. His warning focused on the people entering the sector, with the Hyperliquid co-founder arguing that on-chain finance will need more capable entrepreneurs if it is to turn complex theories into financial markets that can serve users at scale.

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