<p>This study investigates the connectedness between AI- and big data–related stocks and tokens using Quantile-based Vector Autoregression (QVAR) with an Extended Joint normalization and a frequency-domain framework over the period December 17, 2020, to February 11, 2025. To the best of our knowledge, this study is among the first to integrate both AI/Big Data equities and tokens within a unified tail-risk framework using an Extended Joint QVAR approach, offering three primary contributions: (i) it integrates both asset classes within a unified tail-risk framework that captures extreme dependence asymmetries; (ii) it introduces the Extended Joint normalization to QVAR, thereby addressing biases associated with standard row-sum normalization procedures; and (iii) it provides a comparative hedging effectiveness analysis across normal and extreme market conditions. We find that major technology stocks, notably Microsoft (MSFT) and Amazon (AMZN), act as dominant shock transmitters, while AI-focused tokens such as SingularityNET (AGIX) and Numeraire (NMR) serve mainly as net receivers. While moderate connectedness exists during normal times, we find strong evidence of contagion during tail events, defined as a statistically and economically significant increase in cross-asset spillovers beyond what baseline relationships would predict. From a portfolio perspective, AGIX emerges as the most effective hedging instrument, followed by NMR and CTxc. The results offer practical implications for portfolio allocation and risk management in technology-intensive markets.</p>