The 'AI Valuation Paradox' refers to the persistent gap between how much companies invest in artificial intelligence and how those investments are reflected in market valuations for S&P 500 firms. This study helps explain this paradox by showing that AI is not valuable in isolation; rather, value emerges when AI is combined with firm-specific complementary assets. When you invest in AI, you get negative marginal gains that are not important. The main new idea is to formalize and evaluate an integrated valuation method that divides firm value into three parts: basic value, real-option premium, and behavior-happiness adjustment. This model can explain 53% of the changes in Tobin's q. A study of dynamic events talks about a predicted "integration dip," which shows that the initial mood, which is lifted by story-driven hype, sharply corrects itself during operational implementation, which causes systemic instability. The results show that this difference exists because we are using tools from the Industrial Age to value assets from the Information Age, and the market constantly undervalues organizational change that is difficult and depends on the path. The results can be used and are important for two things: planning plans and looking at money.
Gabriel Silva Atencio· Computing&AI Connect· 0 citations
The expansion of the Metaverse presents security risks that conventional perimeter-based protections are unable to mitigate. This research introduces and experimentally substantiates a Zero-Trust Architecture (ZTA) that incorporates a federated ResNet-50 model for anomaly detection, a decentralized identification system based on Hyperledger Fabric using zk-SNARKs, and CRYSTALS-Kyber for quantum-resistant key exchange. The architecture attains a 42.7% decrease in False Acceptance Rate (5.2% compared to a 9.1% baseline, p < 0.01), 99.1% resilience to Sybil attacks, and partial compliance with GDPR using cryptographic erasure proofs. Quantum security is based on the Module-LWE issue, requiring 2187 operations, which surpasses NIST Level 3 standards, and incurs a latency overhead of 1.2 times and an energy consumption increase of 28.9% compared to AES-256. AI inference needs 3.1 times more GPU resources. Four innovative contributions are introduced: Integrated ZTA empirical validation, FAR reduction benchmark, zk-SNARKs for GDPR compliance, and human-centric validation indicating that usability predicts adoption (β = 0.47, p < 0.001). The architecture offers a scalable, experimentally substantiated basis for protecting decentralized virtual environments.
Gabriel Silva Atencio· Computing&AI Connect· 0 citations