Three use cases: How the crypto world applies AI

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How the crypto world uses AI

Case #1: Cryptocurrency Market Research

Before the rise of AI tools, users had to gather information manually using various search engines like Google, Yahoo, Bing, and others.Now, things have fundamentally changed. The market is filled with tools that deliver relevant data within seconds, including all the essential information about a cryptocurrency—its origin, structure, evolution, and more.

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However, responses from platforms depend on the initial data entered and the developers’ priorities. For instance, when asked “What is Bitcoin?”, different AI services offer different answers.

Grok, which Elon Musk successfully integrated into X (formerly Twitter), starts with:

“Bitcoin is a decentralized digital currency created in 2009 by an anonymous person or group under the pseudonym Satoshi Nakamoto.”

ChatGPT, on the other hand, emphasizes decentralization:

“Bitcoin is a decentralized digital currency that enables direct transactions between users over the Internet without the need for central authorities such as banks or governments.”Overall, AI tools provide information on price fluctuations and market dynamics. Using data from sources like CoinMarketCap and CoinGecko, services such as ChatGPT and Grok can quickly generate charts and share pricing updates for all major cryptocurrencies and beyond.

Case #2: Trading

Traders are increasingly relying on AI technologies to enhance their strategies, make informed decisions, and boost trading efficiency.

And it’s no surprise—AI can analyze vast amounts of data in real-time, recognize patterns, and predict price movements. This is especially crucial in the crypto world, where markets are more volatile than traditional ones. One key advantage of AI is its speed in processing massive datasets while also analyzing market sentiment through social media and news sources. Risk management tools help detect and minimize potential losses.

For trading, investors frequently use tools like trading bots. These bots automatically buy and sell assets under predefined conditions based on various data points. AI also utilizes predictive analytics, which forecasts future price movements using historical data to analyze trends. Sentiment analysis systems evaluate public opinion on social media platforms.

Despite their effectiveness, these tools are not without limitations. AI can struggle with assessing market conditions when faced with high volatility, sudden regulatory changes, the need for constant model updates, and other factors.

Case #3: NFTs and Digital Art

While non-fungible tokens (NFTs) have significantly declined in popularity, people still use AI for their creation. Many use AI art models like DALL·E, Midjourney, or GANs to generate images, music, and 3D objects for NFT collections.

Moreover, some NFT projects use AI to evolve or transform artwork based on user interaction or market trends. For example, “AI-generated avatars” that change depending on activity in your crypto wallet.

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Different platforms also allow for new creations by blending existing images. One such platform is Artbreeder. With its user-friendly interface, artists can mix and match artworks to create unique AI-generated visuals.

Artbreeder uses a genetic model that lets users “breed” new images by combining several pictures to generate entirely new pieces. Advanced AI algorithms ensure each image looks like a vivid digital entity.

What are the pitfalls?

Despite the obvious advantages, AI has its downsides. Training large models like GPT or DALL·E requires significant energy resources, raising environmental concerns. This is why lawmakers in countries like the U.S. are proposing special fines for AI data centers.

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AI often produces results that aren’t entirely accurate, potentially distorting real-world situations. At some point, people might start trusting AI outputs blindly without critical thinking. Additionally, over-automation could lead to the loss of basic skills like navigation, writing, and analysis.

In specialized areas like trading, AI needs improvement in several areas. These include more precise and reliable trading algorithms, collaborative AI systems that analyze data jointly, and continuous learning based on market data.

Major AI players are already addressing many of these issues. This is particularly evident in the U.S. and China, where giants like Google, Microsoft, Meta, and OpenAI are investing heavily in the development and refinement of AI systems.

China, in particular, has made AI a national priority. With strong government backing, massive data resources, and corporate efforts from companies like Baidu, Alibaba, and Tencent, China is quickly narrowing the gap—especially in applied AI and surveillance technologies.

There is no doubt that AI will continue to develop, though it remains uncertain how quickly and effectively its shortcomings can be resolved.

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