AI coding, vibe coding and agentic swarm have made a dramatic and astonishing recent market entrance, with the AI Code Tools market valued at $4.8 billion and expected to grow at a 23% annual rate. Enterprises are grappling with AI coding agents and what do about expensive human coders.
Deep insights for enterprise AI, data, and security leaders
The announcement, made Thursday, centers on Ironwood, Google's latest custom AI accelerator chip, which will become generally available in the coming weeks. In a striking validation of the technology, Anthropic, the AI safety company behind the Claude family of models, disclosed plans to access up to one million of these TPU chips — a commitment worth tens of billions of dollars and among the largest known AI infrastructure deals to date.
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By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an engineering challenge and also exhibit undesirable traits.
The announcement, made Thursday, centers on Ironwood, Google's latest custom AI accelerator chip, which will become generally available in the coming weeks. In a striking validation of the technology, Anthropic, the AI safety company behind the Claude family of models, disclosed plans to access up to one million of these TPU chips — a commitment worth tens of billions of dollars and among the largest known AI infrastructure deals to date.
Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document repositories. The culprit isn't model quality but architecture: Traditional retrieval augmented generation (RAG) systems were designed to retrieve and summarize, not analyze and aggregate across large document sets.
A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in minutes using tools that would have required entire specialized teams just months ago.
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and superscalar execution had been in earlier decades. Each marked a generational leap in microarchitecture. By predicting the outcomes of branches and memory loads, processors could avoid stalls and keep execution units busy.
Integrating AI models directly into extended detection and response (XDR) platforms is delivering breakthrough improvements in SOC investigation speed and accuracy.
Every SOC leader knows the feeling: drowning in alerts, blind to the real threat, stuck playing defense in a war waged at the speed of AI.
OpenAI has introduced Aardvark, a GPT-5-powered autonomous security researcher agent now available in private beta.
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The new features, announced today, include additional governance tools for enterprises and expanding the capabilities for creating agents with just a few lines of code, moving faster with state-of-the-art context management layers and one-click deployment, as well as managed services for scaling production and evaluation, and support for identifying agents.
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The latest big headline in AI isn’t model size or multimodality — it’s the capacity crunch. At VentureBeat’s latest AI Impact stop in NYC, Val Bercovici, chief AI officer at WEKA, joined Matt Marshall, VentureBeat CEO, to discuss what it really takes to scale AI amid rising latency, cloud lock-in, and runaway costs.
The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place.
When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence.
Market researchers have embraced artificial intelligence at a staggering pace, with 98% of professionals now incorporating AI tools into their work and 72% using them daily or more frequently, according to a new industry survey that reveals both the technology's transformative promise and its persistent reliability problems.
SAP aims to displace more general large language models with the release of its own foundational “tabular” model, which the company claims will reduce training requirements for enterprises.
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