Historically, the combination of crypto and AI has already shown early signs in various aspects: on-chain analysis companies use models to identify money laundering and fraud patterns, protocol parties use AI to monitor contract risks, and traders use agents for conditional trading and timing; Jason's straightforward yet clear statement elevates scattered practices to an "industry minimum consensus": in a high-noise, high-frequency market, not using AI means voluntarily reducing dimensionality in data processing and execution.

In terms of capital pathways, viewing "using AI" as a basic action for crypto practitioners effectively directs capital flows along two main lines: one towards AI tools and agent platforms for end traders (risk control, automated trading, investment research assistants), and the other towards security, compliance, and monitoring products for institutions and protocols; as more funds and users migrate to "AI-enhanced" strategies and infrastructures, the revenue share and bargaining power of model developers and data service providers in the crypto capital structure will increase.

In analogy and industry positioning, this assertion of "if in crypto, use AI" is similar to the historical turning point in traditional finance from "manual trading intuition" to "quantitative and algorithmic trading": once quantification becomes infrastructure, participants still relying on pure manual analysis will either retreat to niche strategies or be forced to embrace tools; at the current stage of the crypto track, AI is transitioning from "experimental plugins" to "default layers", making the overall crypto market closer to a data and model-driven environment, rather than just a battleground of emotions and narratives.

Structurally, this trend essentially belongs to "technological substitution + transfer of pricing power": technologically, AI replaces a large amount of repetitive labor by crypto practitioners in information filtering, pattern recognition, and execution; in terms of pricing, the value focus of strategies and tools shifts from "human intuition" to "the quality of models and data"; those who possess high-quality models and data—whether large platforms or specialized teams—will gain stronger pricing power in strategy returns, risk control capabilities, and tool fees, while human traders will participate more as "problem setters and risk supervisors".

ABAB News · Law of Cognition

Refusing to use AI in crypto is not conservative but a voluntary relinquishment of information advantage.
When trading shifts from intuition to models, what truly becomes valuable is no longer speed but data and computing power.
With humans making decisions and AI executing them, once this division of labor solidifies, the pricing power of strategies will lean towards algorithms.