We reported strong Q2 earnings as companies accelerate efforts to give AI agents access to their critical corporate information and unstructured data. @levie spoke with @CNBC about what we're seeing across every major market and industry. From financial services, life sciences, government, media and technology. "Every enterprise on the planet is sitting on an incredible wealth of information."

Databricks Genie One MCP is now generally available! Teams working in ChatGPT, Claude, Cursor, or custom agents can now use one interface to access structured and unstructured data, insights, and answers from Genie One, grounded in governed business context from Genie Ontology. Key highlights: • 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 across agents, with shared definitions and domain semantics • 𝐑𝐢𝐜𝐡, 𝐝𝐚𝐭𝐚-𝐬𝐦𝐚𝐫𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 including questions, results, visualizations, and Genie Ontology citations • 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 through Unity Gateway, with fine-grained policies and audit logging Genie One MCP brings trusted business context into the tools teams already use.

gm. I've been hands down building together with the team. TL;DR @CournotProtocol is expanding beyond settlement via its Resolution Oracle. We call it 'Intelligence Oracle'. It is designed to continuously monitor structured and unstructured data, detect material events, cross-check evidence across sources, interpret what those events mean, and identify which markets, assets, or applications may be affected. The result is a progression from: What is the data? to What does it mean? to What is happening? to What would happen next? Starting with prediction-market resolution, We are building toward persistent intelligence for trading signals, risk monitoring, RWA verification, credit intelligence, and autonomous agents that need a trusted view of the real world.

Box CEO @levie says AI has been “unequivocally” a net positive for software. “You look at infrastructure providers—the Cloudflares of the world—totally on fire, because agents need sandboxes, they need compute, they need network, they need gateways. Great business.” “For us, we have reaccelerated growth far past our internal plans because it turns out that enterprises need core systems to be able to manage their unstructured data.” “Agents need to be able to work with that unstructured data to make decisions or move information through a workflow—whether it’s all of your contracts, your research materials, marketing assets, or financial documents.” “Now imagine an enterprise with 10,000 employees. How do you ensure that those agents continue to go after the actual canonical records, the actual documents that are the real sources of truth and the authoritative versions of that data?” “That means you need platforms.” “And all of that is creating more value for these platforms.”

The Box connector, powered by the Box MCP server, is now live in @GeminiApp Enterprise. In our demo, Gemini reads six vendor contract PDFs, resolves a quiet amendment, and writes structured metadata back onto every file using Box Extract. A second prompt builds an interactive HTML dashboard - to not only see all of that metadata in one consolidated view, but also highlight key details - such as $760K in contract value auto-renewing in the next 90 days or another contract that is still cancellable for five more days - so that humans can take action on those insights. Unstructured PDFs to structured metadata to an actionable dashboard. This is what it means to turn unstructured data into actionable business insights.

Run inference over millions of records — free of SQL, and without your data ever leaving Snowflake. Here's distributed batch inference at scale ⚙️ Title: Batch Inference at Scale URL: ⚙️ Overview A capability that runs distributed inference workloads on Snowpark Container Services (SPCS) with Ray as the execution framework. Inference runs as a dedicated distributed workload, supporting both traditional models and LLMs, consolidating complex operations into a single API call. ❓ Challenges Solved Many customers, especially those migrating from non-SQL systems, need batch inference decoupled from SQL. ・This is especially true for files and unstructured data at large scale ・Rearchitecting workflows around SQL-first patterns is a heavy burden 💡 Methodology & How It Works ・The input DataFrame is materialized and written to a stage as Parquet files ・A job is provisioned on SPCS; the primary node initializes as the Ray head and replicas join as workers ・Each worker reads staged data, performs inference independently, and writes results to an output stage ・Unified API: a single run_batch() call handles both structured and unstructured data ・Multimodal support (images, audio, video); workers load weights once and reuse across batches; JobSpec controls workers and GPU allocation 🌍 Use Cases ・Nightly summarization of millions of support tickets ・Product catalog enrichment via image-to-text generation ・Information extraction from scanned PDFs, audio transcription and labeling, video classification and description BatchInferenceTask integrates with Snowflake Tasks for DAG automation, and all processing stays inside Snowflake — running large-scale inference while preserving data governance. #Snowflake# #BatchInference#

Dell earnings call. Best hand picked excerpts: "Revenue was $47 billion, up 58%, and earnings per share was $7.04, up 203%.” “Over the past 12 months, we have booked more than $130 billion in AI server orders.” “Our AI server momentum continues to accelerate. We booked $60.9 billion of AI orders in this quarter, the most in our history. We are also seeing AI-related tailwinds in traditional servers and networking, along with early signs of increased storage demand as customers prepare, manage, and protect growing volumes of data.” “Demand is broadening across Neoclouds, sovereigns, and enterprise customers, and our customer count has surpassed 6,500.” “We are seeing a growing trend of customers that require meaningful CPU compute capacity to support AI and agentic workflows.” “If I look at the longer-term trends, I know you're a believer of this, but as we see it, agentic demand is reshaping the data center and the underlying infrastructure. Inference is past training and is pure demand on our industry. We think the tokens that inference drives is going to grow 87x to 3,600 quadrillion tokens by 2030. Training demand grows 5x to 850 zettaflops by 2030. Enterprise agentic is expected to be the single largest workload by 2028." "We're expecting AI to be 75% of all data center demand by 2030, adding 200 GW of power over that same timeframe, and half of that, we believe, is right in our sweet spot with our customers, the Neocloud sovereigns and enterprises. If you look at that math, we think the opportunity in front of us is more than a trillion dollars over that time frame.” “If we think about this across agentic workloads as we head towards physical AI and what's going to happen in manufacturing and IoT sensors and robotics, which drive tremendous amounts of multimodal unstructured data, Arthur likes to call it unstructured repositories. There's a lot of structured data in databases. The growth of that is immense, and we actually see it accelerating, not slowing down.” “The constraints remain the same. DRAM, DRAM, DRAM followed by NAND, NAND, NAND. We have spotty CPU shortages. There are shortages with disk drives. If you go further down in the supply chain, just about every product going through a leading node is constrained. Mature nodes that are building MOSFETs, power ICs, microcontrollers, drivers are constrained. There's shortages of ABF substrate, T-glass, all of which we monitor. There's shortages in optical. The AI supply chain is working red line all out to build CDUs, power racks. Welcome to the life of a supply chain person at Dell. This is what we do, chasing parts. We love it.” “But the underlying demand for the technology is significant. I think about the new use cases, that's all new use cases, all new growth, which is being driven by agentic AI, essentially running the harness, if that makes sense. We continue to be optimistic about the prospects. Again, demand outran supply last quarter. Demand outran supply this quarter. The pipeline remains robust.”