For a long time, “scaling” in foundation models mostly meant one thing: spend more compute on pre-training and capabilities rise. That intuition was supported by empirical work such as Kaplan et al. (2020), which reported predictable power-law trends in loss as you scale model parameters, dataset size, and training compute. In practice, these trends justified sustained investment in large-scale accelerator capacity and the surrounding distributed infrastructure needed to keep it efficiently utilized. But the frontier has evolved—and scaling is no longer a single curve. NVIDIA’s “from one to three scaling laws” framing usefully emphasizes that, beyond pre-training, performance increasingly scales through post-training (e.g., supervised fine-tuning (SFT) and reinforcement learning (RL)-based methods) and through test-time compute (“long thinking,” search/verification, multi-sample strategies).
Taken together, these scaling regimes push the foundation-model lifecycle—pre-training, post-training, and inference—toward convergent infrastructure requirements: tightly coupled accelerator compute, a high-bandwidth low-latency network, and a distributed storage backend. They also raise the importance of orchestration for resource management, and of application- and hardware-level observability to maintain cluster health and diagnose performance pathologies at scale. — Read More
Daily Archives: May 12, 2026
AI Gateways vs. MCP Gateways: What Security Teams Need to Know
Many vendors in AI security are talking about gateways right now, but they don’t all mean the same thing. Between all of these, the word “gateway” is doing a lot of work, and not all of it is consistent.
Security teams are being asked to evaluate these technologies, and the terminology is genuinely confusing. In conversations with enterprises across financial services, insurance, pharma, and tech, we consistently find that teams conflate AI gateways with MCP gateways. They assume one covers what the other does. Some vendors actively blur the lines by combining both functions into a single product. Others treat them as entirely separate categories.
This post breaks down what each type does, where the real value is, and where the gaps are that neither fills. We will focus on functionality first, not vendor definitions. A note on terminology: the market uses “AI gateway,” “LLM gateway,” and “MCP gateway” loosely, and some vendors bundle multiple functions under a single label. Throughout this post, we use “AI gateway” to refer specifically to the LLM inference proxy layer (managing traffic between agents and model providers), distinct from “MCP gateway” (managing traffic between agents and their tools). Where vendors combine both, we will call that out. — Read More
Video: Figure’s humanoid robots organize room, hang clothes, and make bed without humans
US robotics firm Figure has showcased its humanoid robots performing household tasks in a coordinated bedroom-cleaning demonstration. In a newly released video, two robots enter a minimalist room and begin organizing items, including hanging up a coat, closing a laptop, and placing headphones away. — Read More
GTIG AI Threat Tracker: Adversaries Leverage AI for Vulnerability Exploitation, Augmented Operations, and Initial Access
Since our February 2026 report on AI-related threat activity, Google Threat Intelligence Group (GTIG) has continued to track a maturing transition from nascent AI-enabled operations to the industrial-scale application of generative models within adversarial workflows. This report, based on insights derived from Mandiant incident response engagements, Gemini, and GTIG’s proactive research, highlights the dual nature of the current threat environment where AI serves as both a sophisticated engine for adversary operations and a high-value target for attacks.
… For the first time, GTIG has identified a threat actor using a zero-day exploit that we believe was developed with AI. The criminal threat actor planned to use it in a mass exploitation event but our proactive counter discovery may have prevented its use. Threat actors associated with the People’s Republic of China (PRC) and the Democratic People’s Republic of Korea (DPRK) have also demonstrated significant interest in capitalizing on AI for vulnerability discovery. — Read More
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