Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing

Artificial Intelligence now underpins investment workflows from data and prediction to execution and tool use, yet its technical prowess does not automatically translate into profitability. A comprehensive review of public research up to 31 August 2026 across equities, ETFs, crypto spot, perpetual futures, and on‑chain markets shows real progress in prediction, text processing, portfolio design, and workflow integration, but evidence for durable net performance remains thin. The study highlights that factors such as temporal contamination, survivorship bias, weak benchmarks, implementation costs, and venue mechanics can erode alpha, and no single AI architecture has proven to deliver persistent, cross‑regime, capacity‑aware net alpha. "whyItMatters":"The findings underscore that while AI advances are evident, investors must rigorously test and govern AI systems to avoid overestimating their profitability potential."

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• llms
• agents
• reinforcement-learning
• benchmarks