Four leading cryptocurrency analysts have reached a consensus that artificial intelligence is transforming trading research rather than replacing human expertise. According to reports from BeInCrypto, Charles Edwards of Capriole Investments and Julio Moreno of CryptoQuant describe AI as an accelerant for serious research that shifts opportunities toward those who invest time and effort. Edwards noted that AI shifts the playing field more opportunistic for certain people, while Moreno emphasized that institutions trust the data but verify it a lot and continuously monitor if the data remains relevant. The latest consensus among analysts Charles Edwards, Julio Moreno, Benjamin Cowen, and Michael van de Poppe agrees that AI accelerates research but requires clean data and human judgment for effective use. This consensus aligns with broader industry findings that 88% of firms are using AI in at least one business function, fewer than 40% report any meaningful bottom-line impact.
The most tangible benefits of AI in crypto trading emerge from compressed research processes that once required hours. As reported by BeInCrypto, Edwards highlighted that tool sets have become much more powerful and can be done more quickly today with AI. Van de Poppe demonstrated this accessibility by building a sample crypto portfolio using a chatbot and free data feeds within five minutes, noting that tools like AI agents now pull live market data on demand. However, he warned that AI doesn't create a basket of uncorrelated cryptos or include any macros, requiring human judgment to fill these gaps. The latest expert consensus confirms that AI tools have become mainstream in crypto research, compressing tasks that once took hours.
Institutional funds are treating AI as infrastructure rather than a crystal ball, building comprehensive models from multiple data sources. According to BeInCrypto reports, Edwards explained that Capriole Investments builds hundreds of metrics and uses hundreds of other data sources to build out comprehensive models combining on-chain technicals and macro data. The firm's Macro Index reflects this approach, combining more than 60 on-chain, macro, and equities metrics into one machine-learning model. Similarly, Cowen is building his own bot from the ground up, ensuring it regurgitates only things he says and avoids training on low-quality AI output to prevent model decay. The latest analysis emphasizes that professional funds use AI as part of a broader infrastructure, combining it with comprehensive models and high-quality data to guide trading strategies.
Professional traders are leveraging AI for real-time monitoring of critical market signals. As reported by BeInCrypto, Moreno provided the example of monitoring mining stocks in real time instead of waiting for quarterly reports, using network hashrate as a real-time signal tracking how much computing power miners commit to Bitcoin each day. The same approach applies to equity exchanges, with some crypto exchanges starting to trade on stock exchanges, allowing monitoring of trading volume to assess revenues. Cowen values historical data, noting that data before 2022 is actually really valuable because it was data before all the AI stuff was even here. The latest expert consensus notes that as AI adoption grows, the ability to effectively interpret and apply AI-driven insights remains a key differentiator for successful traders.
