Venture investors focused on cryptocurrency say enthusiasm around decentralized artificial intelligence projects has cooled, marking what they describe as a post hype phase for the sector. Speaking during Consensus Hong Kong 2026, investors from Canonical Crypto and Spartan Group suggested that capital is now shifting toward more practical and economically sustainable applications.
Anand Iyer of Canonical Crypto and Kelvin Koh of Spartan Group characterized the current market as a trough following a period of heavy speculation and rapid funding. According to their remarks, earlier excitement around decentralized AI protocols led to aggressive investment in infrastructure projects, including distributed GPU marketplaces and blockchain based alternatives to large language models.
However, both investors indicated that replicating or competing directly with established AI firms requires capital levels that far exceed what most crypto native funds can realistically deploy. Building decentralized versions of large scale AI systems involves significant hardware, data and research expenditures, making the funding gap substantial compared with traditional venture backed technology companies.
Instead, venture capital in the crypto AI space is increasingly concentrating on narrower use cases. Investors are looking for applications that solve specific problems and demonstrate clear revenue models rather than attempting to recreate full scale AI ecosystems. This shift aligns with broader venture trends emphasizing durability, cost efficiency and long term economic viability.
The cooling of decentralized AI enthusiasm also comes amid a wider recalibration in digital asset markets. Bitcoin has traded well below its previous record highs, while many alternative tokens have experienced deeper percentage declines. Reduced token valuations have constrained liquidity across crypto venture funds, prompting more selective capital allocation.
At the same time, traditional venture capital firms have intensified their focus on blockchain adjacent sectors such as stablecoin infrastructure, prediction markets and financial technology platforms. This growing overlap between crypto native and traditional investors has increased competition for high quality deals, particularly in segments perceived as having clearer regulatory paths and immediate commercial demand.
Broader artificial intelligence funding trends reflect a similar pivot. While investment in AI remains strong, recent fundraising rounds have prioritized foundational technologies that enhance speed, reduce operational costs and improve system reliability. Investors appear less interested in speculative scale plays and more focused on tangible technical improvements and enterprise integration.
The intersection of blockchain and AI continues to attract experimentation, particularly around agent based systems, decentralized data marketplaces and automated financial services. Yet venture leaders caution that hype cycles can distort capital allocation and obscure fundamental challenges. Sustainable growth, they argue, will depend on aligning token incentives with real product adoption and measurable user value.
As venture funding becomes more disciplined, crypto AI startups may need to demonstrate clearer pathways to revenue and defensible technical advantages. The transition from speculative enthusiasm to targeted investment could reshape how decentralized AI projects are built and financed in the years ahead.
