
AI companies have implemented strict guardrails on their models to prevent misuse, but these measures are now obstructing cybersecurity researchers. The restrictions are meant to prevent malicious activities but also hinder legitimate efforts to identify vulnerabilities. Researchers are increasingly turning to open-source models without such constraints, raising concerns about the balance between security and accessibility. This issue underscores the challenges in providing effective cybersecurity tools while preventing potential misuse.
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© TechCrunch AIMeta’s consumer agent Muse is rolling out voice calling to US businesses for paid subscribers, targeting the tedious task of customer service navigation. With over 10 million users, Meta is betting that AI’s infinite patience will replace human frustration with phone trees and hold times. The feature leverages improved voice mimicry to interact with receptionists, though many still hang up upon detecting non-human voices. This moves Muse from text-based assistance into active, autonomous execution of real-world tasks, marking a significant step toward mainstream consumer AI utility.
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Nebius is facing the harsh reality of scaling AI infrastructure: even with $40 billion in planned spending and massive prepayments from Microsoft and Meta, physical construction delays are derailing timelines. A key New Jersey site is months behind schedule due to regulatory fines and generator issues, forcing a shift in delivery dates to 2027. This isn't just a logistical hiccup; it signals that the 'neocloud' model of financing builds with customer cash is fragile when third-party operators fail. The market reacted swiftly, dropping shares by 17% after Nebius cut its 2026 revenue forecast, proving that hype cannot override concrete capacity.