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House Democratic Caucus Vice-Chair Ted Lieu (D-CA) speaks at a press conference with Chair Pete Aguilar (D-CA), following a caucus meeting, at the U.S. Capitol in Washington, DC on May 13, 2026. Nathan Posner/Anadolu via Getty Images ByAlexandra Kelley, Staff Correspondent, Nextgov/FCW July 23, 2026 01:38
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Sponsor Posts Subquadratic: the LLM built for 12M-token reasoning — SubQ can reason across entire codebases and document sets in one pass with no RAG workarounds. Read how SubQ 1.1 Small holds near-perfect retrieval out to 12M tokens. Most carriers track everything. Cape doesn't. — Unlimited talk, text &
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Sponsor Posts Subquadratic: the LLM built for 12M-token reasoning — SubQ can reason across entire codebases and document sets in one pass with no RAG workarounds. Read how SubQ 1.1 Small holds near-perfect retrieval out to 12M tokens. Most carriers track everything. Cape doesn't. — Unlimited talk, text &
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Could Nvidia's days of datacenter dominance be threatened? With the launch of Helios, AMD’s first true rack-scale AI platform, the House of Zen is done playing catch up. The company claims the rack system is powered by the fastest AI accelerators on the market. And, at least
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The math behind reinforcement learning (RL) post-training for large language models (LLMs) is notoriously unforgiving. As frontier AI labs push the boundaries of reasoning and coding models using RL post-training algorithms like Group Relative Policy Optimization (GRPO), they routinely hit hard architectural and infrastructure constraints. While much of the industry&
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At a CISO roundtable organized by Anthropic's chief information security officer, Dev Rishi asked a simple question: Did everyone in the room have their AI governance and security policies written down? Every hand went up — about 14 people, by his count. His follow-up, about how anyone actually enforces
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Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control
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Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited
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Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already
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Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most
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Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority
