AI-linked crypto tokens can fall even while enthusiasm for artificial intelligence remains strong. The recent downturn described here affected projects spanning decentralized AI networks, application platforms, distributed computing and meme-driven speculation. That contrast matters: a powerful technology narrative does not automatically support the price of every token associated with it.
What happened to AI crypto tokens?
The market snapshot showed broad weakness among several prominent AI-related cryptocurrencies. Bittensor, NEAR Protocol, Internet Computer, Render, Story Protocol and Virtuals Protocol all recorded negative weekly and monthly changes. The losses across projects with different purposes suggest that the pressure was not confined to a single type of AI token.
Bittensor (TAO) fell 6.8% over the week and 23.55% over the month, while NEAR Protocol (NEAR) declined 9.64% and 39.61% over the same periods. Internet Computer (ICP) registered the largest monthly loss in the supplied comparison, falling 14.86% for the week and 61.85% for the month.
Why the AI boom did not protect token prices
AI technology and AI-themed crypto assets are related narratives, but they are not the same market. Demand for model training, computing capacity and other AI infrastructure can grow without producing equivalent demand for a particular token. Token prices also reflect crypto-market sentiment, profit-taking, risk appetite and expectations about whether individual projects will deliver useful products.
The downturn therefore raises a practical question: are buyers valuing working networks and demonstrated demand, or are they mainly buying exposure to the AI label? The available evidence does not settle that question for every project. It does show that enthusiasm surrounding artificial intelligence did not prevent substantial token losses during the measured periods.
The outlier: Fartcoin
Not every AI-themed asset moved lower. Fartcoin (FARTCOIN) gained 13.73% over the week and 38.16% over the month. Its positive performance contrasted sharply with the declines elsewhere in the comparison.
That exception does not establish a broader recovery. Instead, it illustrates how differently individual crypto assets can behave, particularly when attention and meme appeal influence trading. One rising token cannot erase the wider weakness recorded across the other named projects.
Bitcoin’s rebound did not remove the risk
Bitcoin also moved sharply during the period covered by the source. After trading below $88,000, it reached an intraday high of $91,767, while more than $348 million in leveraged positions were liquidated within 24 hours. The move offered temporary relief, but it did not by itself reverse the negative weekly and monthly performance reported for the AI-token group.
The liquidation figure is a reminder that leverage can amplify rapid market moves. A rebound in Bitcoin may improve sentiment, but it does not prove that AI-related tokens have reached a durable bottom or that their individual fundamentals have improved.
Is the AI bubble bursting?
The token declines alone cannot prove that the wider AI industry is a bubble. They show a correction in a group of crypto assets associated with AI. The broader AI boom also involves data centres, computing hardware, model development and commercial services, which should not be treated as interchangeable with token performance.
For AI crypto projects, the more useful test is whether a network can demonstrate sustained use, transparent economics and a credible reason for its token to exist. Those questions require project-specific evidence. Price performance can reveal market sentiment, but it cannot answer them on its own.
How to assess an AI crypto project
Start by separating the project’s AI function from its marketing. Identify what the network provides, who uses it and why a blockchain or token is necessary. Then examine whether demand for the service creates demand for the token, rather than assuming that growth in artificial intelligence will automatically benefit every associated asset.
Also distinguish a temporary market correction from weaknesses specific to a project. Broad declines may reflect reduced risk appetite, while a deeper project-level assessment must consider delivery, adoption, transparency and regulatory uncertainty. The supplied evidence does not support naming a single “most promising” coin or forecasting an extreme return.
What the downturn means for investors
The central lesson is straightforward: exposure to a popular technology theme does not remove crypto volatility. Recent losses across several leading names show that narrative strength and token performance can diverge sharply. The contrasting rise in Fartcoin also shows why short-term price action should not be mistaken for a reliable measure of technological value.
Approach forecasts cautiously, evaluate each project separately and avoid treating the AI label as evidence of quality. Whether the downturn becomes a prolonged correction or a temporary setback will depend on future market conditions and whether individual projects turn their stated purposes into demonstrable use.
