The intersection of cryptocurrencies and artificial intelligence is quiet these days, according to two venture investors. Consensus Hong Kong 2026 It was reported Wednesday (February 11) by CoinDesk.
Anand Iyer of regular cipher and Kelvin Coe of spartan group The report depicts the current environment as a post-hype phase for decentralized AI protocols. Capital and talent are showing significant interest in more focused utility-driven applications.
“I think we’re in a trough right now,” said Iyer, whose company is investing in early-stage infrastructure and applications built on decentralized networks, according to the report. “We went through a period of bubbles. Now it’s about understanding where the real strength lies.”
Iyer and Ko were critical of their argument. they said Viewed as over-investment in the GPU market and efforts to build decentralized alternatives to large-scale AI models like them from OpenAI or humanaccording to the report.
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The necessary funds are “day and night”” versus Ko said that the money available in cryptocurrencies is: around Report. They instead see potential in AI tools. start by focusing about specific issues.
Their comments came two days later. reported that there were changes between crypto venture capital Put money into other opportunities such as stablecoin infrastructure, prediction markets, fintech, and AI.
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Driving change is falling prices of digital assets that Saw Bitcoin Altcoins have lost 70% of their value over the past year, losing nearly half of their October highs.
At the same time, crypto-native funds look Competition with traditional venture capital funds is intensifying in fields such as prediction market As Wall Street gains more attention, so do stablecoins. those region.
Meanwhile, PYMNTS looked into funding the AI space this week. meanwhile venture capital continued to flow As AI development begins in the first weeks of February, the targets are changing, with investors now focused on “technology durability and economics” rather than technology durability and economics. simple scale.
“Some of the largest increases centered on the technological foundations needed to make AI systems faster, cheaper, and more reliable at scale,” the report said.
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