The new features combine AI-driven analysis with what Chronosphere calls a Temporal Knowledge Graph, a continuously updated map of an organization's services, infrastructure dependencies, and system changes over time. The technology aims to address a mounting challenge in enterprise software: developers are writing code faster than ever with AI assistance, but troubleshooting remains largely manual, creating bottlenecks when applications fail.

Presented by Celonis

Across industries, rising compute expenses are often cited as a barrier to AI adoption — but leading companies are finding that cost is no longer the real constraint. The tougher challenges (and the ones top of mind for many tech leaders)? Latency, flexibility and capacity. At Wonder, for instance, AI adds a mere few cents per order; the food delivery and takeout company is much more concerned with cloud capacity with skyrocketing demands. Recursion, for its part, has been focused on balancing small and larger-scale training and deployment via on-premises clusters and the cloud; this has afforded the biotech company flexibility for rapid experimentation. The companies’ true in-the-wild experiences highlight a broader industry trend: For enterprises operating AI at scale, economics aren't the key decisive factor — the conversation has shifted from how to pay for AI to how fast it can be deployed and sustained. AI leaders from the two companies recently sat down with Venturebeat’s CEO and editor-in-chief Matt Marshall as part of VB’s traveling AI Impact Series. Here’s what they shared.

Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with Representation Autoencoders” (RAE) challenges some of the accepted norms of building diffusion models. The NYU researcher's model is more efficient and accurate than standard diffusion models, takes advantage of the latest research in representation learning and could pave the way for new applications that were previously too difficult or expensive.

Across industries, rising compute expenses are often cited as a barrier to AI adoption — but leading companies are finding that cost is no longer the real constraint. The tougher challenges (and the ones top of mind for many tech leaders)? Latency, flexibility and capacity. At Wonder, for instance, AI adds a mere few cents per order; the food delivery and takeout company is much more concerned with cloud capacity with skyrocketing demands. Recursion, for its part, has been focused on balancing small and larger-scale training and deployment via on-premises clusters and the cloud; this has afforded the biotech company flexibility for rapid experimentation. The companies’ true in-the-wild experiences highlight a broader industry trend: For enterprises operating AI at scale, economics aren't the key decisive factor — the conversation has shifted from how to pay for AI to how fast it can be deployed and sustained. AI leaders from the two companies recently sat down with Venturebeat’s CEO and editor-in-chief Matt Marshall as part of VB’s traveling AI Impact Series. Here’s what they shared.

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.