The IT Nerd

Databricks today announced it has acquired Row Zero, the spreadsheet built for humans and agents to work together with data. The acquisition will expand the capabilities of Genie, Databricks’ AI coworker, which helps business teams turn data into trusted answers and actions. Genie can analyze why margins changed or produce a document on sales pipeline opportunities. With Row Zero, Genie will add a familiar spreadsheet interface that business teams can use to explore, model, and collaborate, all on a governed foundation powered by Genie Ontology, Unity Catalog, and Unity Gateway.

Why Bring Secure Governed Spreadsheets to Genie?
After four decades, spreadsheets remain the most broadly used analytics tool in the business world. Their flexibility lets users represent the most sophisticated business models through a set of familiar, well-proven formulas. For this reason, spreadsheets remain the primary engine of business decision-making despite continuous advances in the data and analytics market.

The prevalence of spreadsheets also creates major security and governance challenges for the modern enterprise. Teams have long relied on ungoverned spreadsheet files (“spreadmarts”) to store data or to share between humans and agents. Every data export adds to what is arguably the largest set of ungoverned data pipelines in the enterprise. As companies deploy agents that analyze, model, and act on business data, they can’t afford to have those agents operating on sensitive data of unknown lineage and quality.

Row Zero closes this gap by offering a scalable spreadsheet that connects directly to live, governed data. This means teams can work the way they always have, backed by built-in security and governance. And because they can work directly with the spreadsheet via Row Zero’s agent tools, every agent action remains fully interpretable and auditable by the business teams using the spreadsheet’s syntax and processing model. Row Zero will natively integrate with the Databricks Data + AI Platform, including Genie on web as well as desktop and mobile apps, so teams can easily move from chat to hands-on pivoting, modeling, and visualization.

Flexibility Meets Governance with Row Zero
Finance, operations, sales, and marketing teams have already picked spreadsheets as their tool of choice. Now, Row Zero extends Genie and the Databricks Data + AI Platform within the familiar spreadsheet interface. Key capabilities include:

• Full compatibility with leading spreadsheets: Designed to complement popular tools such as Microsoft Excel and Google Sheets, Row Zero features the familiar formulas, pivots, and keyboard shortcuts teams already know and love, so users can leverage their skills and assets frictionlessly. 
• Governed integration into leading data platforms: Row Zero features direct integration into leading data sources. Queries honor each user’s permissions, data auto-refreshes from authoritative live sources, exports can be locked down, and users can write results back, all without stale copies or manual refreshes. Every interaction is auditable, and security controls travel with the data wherever it goes.
• Lightning-fast performance at scale: Row Zero features a data processing engine that allows business users to work with billions of rows at interactive speeds.

The team behind Row Zero was founded by former AWS and Tableau engineers. They are joining Databricks to continue expanding Genie’s capabilities. Row Zero will be available to all Databricks customers across all major clouds and will continue to feature support for data sources beyond Databricks.

There’s a new technical post from Databricks on Adaptive Instructed-Retriever, a retrieval model that adjusts how much search an AI agent performs based on the complexity of a query.

The model combines parallel retrieval with adaptive, multi-step search. It can stop once it has gathered sufficient evidence for a straightforward query, while more complex requests can trigger additional search steps.

Across seven held-out internal and external retrieval benchmarks, Databricks says the model performed comparably to Claude Sonnet 5, GPT-5.6 Luna and DeepSeek-V4-Flash, with average end-to-end latency of 5.8 seconds – more than twice as fast as the comparison models.

Databricks trained the model using reinforcement learning to balance retrieval performance against the cost of additional search. The approach is aimed at AI agents that need to find information across enterprise data, including tables, notebooks, dashboards and documents.

The blog can be found here: https://www.databricks.com/blog/adaptive-instructed-retriever-frontier-quality-search-2x-lower-latency

Databricks today announced it crossed a $7 billion revenue run-rate, delivering >80% year-over-year growth during its Q2. Building on this momentum, the company closed a $5 billion strategic funding round at a $190 billion valuation. The investment will drive continued innovation across Lakebase, its serverless Postgres database built for AI agents, Genie, Databricks’ AI coworker that turns business data into trusted answers and actions, and Unity AI Gateway, for multi-AI governance and cost controls.

The round was led by Coatue, along with Blackstone, MGX, accounts advised by T. Rowe Price Associates, Inc. and T. Rowe Price Investment Management, Inc., and new investor Sixth Street Growth. Other new investors included BOND, Clearlake Capital, Point72, Premji Invest, and TPG alongside existing investors Andreessen Horowitz, Dragoneer, Fidelity Management & Research Company, Franklin Templeton, GIC, Growth Equity at Goldman Sachs Alternatives, Insight Partners, J.P. Morgan Private Capital, Kinetic, Morgan Stanley Investment Management, NEA, Ontario Teachers’ Pension Plan, Temasek, Thrive Capital, and WCM Investment Management.

The Future of Data + AI 
Today, companies have a new set of employees to support: AI agents. To do their jobs, agents need a reliable, scalable foundation, clear, accurate answers from enterprise data, and the ability to easily forecast budgets and switch to more cost-effective models to avoid burning through expensive tokens. The Databricks Data + AI Platform delivers the foundation with Lakebase, enterprise context with Genie, and smart routing and cost controls with Unity AI Gateway, giving teams a simple way to build AI that actually works.

Databricks’ Financial Momentum
This funding follows Databricks’ continued business momentum, including:

• Growing >80% year over year, surpassing $7B revenue run-rate
• Continuing to deliver positive adjusted free cash flow over the last 12 months
• Surpassing $1.5B revenue run-rate for Lakehouse, its data warehousing product, growing over 100% year over year 
• Exceeding $100M revenue run-rate for Lakebase
• >1,000 customers consuming at over $1 million revenue run-rate
• >100 customers consuming at over $10 million revenue run-rate

Databricks has just published “The State of AI Agents” summarizing its telemetry revealing that enterprise adoption of AI has spread well beyond copilots, isolated pilot projects, dashboards, and analysis functions, and is now widely entrusted with core systems.

“The State of AI Agents” specifies four key findings:

• Multi-agent systems are becoming the new enterprise operating model. Enterprises are transitioning from single chatbots to multiagent systems built on domain intelligence. Use of these systems grew by 327% in just four months.
• AI agents are driving core database activities. 80% of databases are built by AI agents. 97% of database testing and dev environments are now built by AI agents. This shift is driving the need for a new kind of database called Lakebase.AI is now part of critical workflows across industries. Most GenAI use cases are focused on automating routine necessary tasks, with 40% related to customer experiences. Model flexibility is the new AI strategy, with 78% of companies are using two or more LLM model families.
• AI evaluations and governance are the building blocks of production. Companies that use evaluation tools get nearly 6x more AI projects into production. Companies using AI governance put over 12x more AI projects into production. AI governance is a top investment priority, and grew 7x in nine months.

You can get the Databricks paper here: https://www.databricks.com/resources/ebook/state-of-ai-agents#:~:text=Key%20findings%3A&text=Enterprises%20are%20transitioning%20from%20single,more%20AI%20projects%20into%20production.

Sunil Gottumukkala, CEO, Averlon:

   “When AI agents create databases at machine speed, ‘Secure by default’ becomes critical. Agents today optimize for the fastest path to completion, not safe configurations, so insecure defaults get replicated at scale. We saw this with row-level security gaps like the Moltbook incident. Teams need guardrails that catch risky configurations as they’re introduced and an operating model that prioritizes remediation when insecure defaults slip through.” 

Ryan McCurdy, VP, Liquibase:

   “When AI agents can create and modify database environments on demand, the database becomes a high frequency software event. The risk is uncontrolled change. Policy enforced in the workflow, automatic audit evidence, drift detection, and trusted rollback are essential to keep velocity without sacrificing control.

    “Moreover, agentic development will multiply database changes. If governance stays manual, you get drift, surprise outages, and you can’t explain what changed when it matters. Database Change Governance is how enterprises keep the data layer fast, trusted, and auditable as it goes agentic.

   “The answer isn’t more humans reviewing more changes. It’s policy enforced in the workflow, automatic evidence capture, and trustworthy rollback.”

John Carberry, Solution Sleuth, Xcape, Inc.

   “The discovery that 80% of new enterprise databases are currently created by AI agents signifies a historic transition from human-centric administration to “vibe coding” on an industrial scale. Although this increase in autonomous infrastructure speeds up development, it also adds a significant “governance debt” by directly incorporating security logic into AI-generated code that is rarely submitted to human peer review.

   “The main risk is “excessive agency,” whereby these agents might unintentionally produce vulnerable endpoints, excessively lenient access rules, or unsafe schemas that get beyond conventional perimeter defenses. Moreover, these databases produce a vast, undetectable attack surface called Shadow Data, which is usually left out of centralized logging and auditing because they are routinely spun up in real-time “branches” for testing and development. In response, SOC teams must switch from post-deployment scanning to infrastructure-level enforcement, in which the security border is located outside of the code that is generated and checks each database operation against a policy that is hardcoded at runtime. The function of the DBA is changing from being a builder to a high-level auditor of autonomous systems as AI progresses beyond creating chatbots to designing the enterprise’s basic foundations.

    “The ‘human in the loop’ becomes a myth when 80% of your data infrastructure is built by AI.”