CrowdStrike is shifting frontier AI research toward security operations via a cyber superintelligence lab that turns threat-research knowledge into defense-focused models. In CrowdStrike’s evaluations, the models delivered a 29% higher detection rate and remediated threats six times faster than leading frontier and open-source alternatives. The lab will publish benchmarking and validation work and focus on training, reinforcement learning alignment, and building agentic systems to commoditize defense capabilities beyond a single platform.
Multiverse Computing launched Quasar 438B, its first large-scale reasoning model for coding and enterprise AI agents, and reported benchmark and speed results to support its agent-readiness claims. It said Artificial Analysis measures Quasar’s output at about 183 tokens per second. The company is relying on its CompactifAI compression approach to try to make repeated agent reasoning and tool-calling faster and cheaper, though key details like the compression savings and required hardware were not disclosed.
The article argues that organizations that prioritize AI access but not AI fluency see uneven adoption and declining performance in some teams. A Harvard Business School study found workers using AI completed tasks 25% faster but performance declined when they didn’t understand where the tools applied. It recommends shifting to an operating model with a central “hub” and AI engineers embedded in departments to provide guidance, re-engineer workflows, and build ongoing fluency.
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