Databricks Acquired Row Zero for Work That Still Ends in Cells

AI-generated editorial illustration.

On September 24, Databricks announced that it had acquired Row Zero, a Seattle cloud spreadsheet company whose product works on live warehouse data. The terms and team size were not disclosed. Row Zero’s team will join Databricks, while the spreadsheet will remain available across major clouds and continue supporting data sources beyond Databricks.

Databricks’ own finance department supplied the path to the deal. According to TechCrunch’s account of the acquisition, finance employees were using Row Zero with Genie, Databricks’ AI coworker, because they could work beyond Excel’s 1,048,576-row ceiling without copying warehouse data into another desktop file. Executives noticed the combination. Databricks bought the smaller company.

The sequence reveals more than the undisclosed price. Rows and columns were not new to Databricks; the internal combination put a conversational agent beside the formulas, assumptions, scenarios and handoffs that analysts already use.

Databricks describes the result as a governed spreadsheet for humans and agents. Its September 24 announcement says finance, operations, sales and marketing teams will be able to move from chat into pivoting, modeling and visualization, then write results back while retaining permissions and auditability. Those are product commitments, not observed post-acquisition outcomes. The company has not published an integration date, a customer migration plan, a separate price or an error record from the combined product.

That persistence changes the AI coworker contest. A chat answer can disappear into a conversation. A workbook carries formulas, hard-coded assumptions, hidden sheets, comments, version history and the possibility of changing a system of record. Reviewers can inspect it. They can also reuse an error long after the original chat has closed.

So the rules have to reach the cell, not stop at the prompt: what an agent may change, what a person must verify and which version can move money or alter an operating plan.

September 24 made a finance workaround a product plan

Row Zero began from a familiar complaint. Business teams want the flexibility of Excel or Google Sheets, while data and security teams want queries to run against governed warehouse data without uncontrolled exports. The startup built an Excel-like interface with formulas, pivots, charts, Python and connectors to systems including Databricks, Snowflake, BigQuery, Redshift, Postgres, Oracle and Amazon S3.

Scale became its bluntest point of comparison. Row Zero says its free product handles tens of millions of rows and enterprise deployments can reach billions. In the company’s comparison, Excel stops at 1,048,576 rows per worksheet and Google Sheets at 10 million cells per spreadsheet. Those are vendor-presented limits, but the operating problem is ordinary: a finance team working with a detailed cloud-cost file, transaction history or usage log can hit them before it has built the first useful pivot.

Row Zero illustrates its scale claim with a 10-million-row cloud-cost example. It combines two years of hourly usage across 30 linked AWS accounts, 36 services and 15 regions. The example uses sample data rather than a disclosed customer account, so its $45 million on-demand-spend figure is illustrative. A detailed cost and usage report can still be too large for an ordinary worksheet, forcing finance or FinOps teams to sample, aggregate or ask a data team for another extract.

Databricks already had part of the route into Excel. In June, its public-preview add-in gained the ability to write Excel data back to a Unity Catalog table. Row Zero’s own comparison said the add-in made sense for teams committed to Excel, while Row Zero offered a browser-based, connected workbook and larger datasets. Databricks’ sales finance team was identified as a Row Zero user before the acquisition.

Buying Row Zero turns that partner comparison into a portfolio decision. Databricks can continue meeting analysts in Excel while building a native spreadsheet into Genie. The two routes may serve different customers, but they also create overlap in product ownership, support, security review and budget attribution. No public document yet says which route Databricks will recommend for a small workbook, a billion-row analysis, an offline model or a file that must circulate outside its platform.

Existing Row Zero customers have a separate concern. Some chose the product to work with Snowflake, BigQuery, Redshift or a mixed data estate rather than Databricks. The acquisition announcement promises continued support for those sources, but no published roadmap shows which features will remain platform-neutral once Genie becomes the primary distribution channel. Compatibility at launch and equal investment over time are different commitments.

Capital was not the obvious constraint. TechCrunch reported that Databricks had closed another $5 billion funding round in August and reached a $7 billion annualized revenue run rate. Row Zero had raised $10 million in May 2025 at an estimated $40 million valuation. Databricks co-founder Ali Ghodsi said the company intended to make more acquisitions. None of those figures reveals what Databricks paid for Row Zero or how much revenue the startup contributed.

Product craft and a team came with the deal. Databricks says Row Zero’s staff spent five years on the spreadsheet and that its founders and engineers came from AWS and Tableau. The public release does not disclose the number of employees joining, retention terms or whether every function will transfer.

Calling this an acquihire would require evidence that the price and strategic value mainly reflect the team. Calling it a completed product integration would run ahead of customers receiving one. For now, the signed direction is narrower: Genie needs a surface where a business user can inspect and modify the work.

June’s Genie One promise now has a different shape. At launch, Genie One was presented as an AI coworker that could answer questions, draft documents, orchestrate tasks and act through workplace systems. Its context came from Genie Ontology, while Unity Catalog and Unity Gateway governed data and tools. Row Zero adds a stateful artifact that can outlive the conversation and invite another person to continue the analysis.

Two audiences now sit inside the product plan. Executives may ask Genie why margins moved. Analysts need to see which revenue rows changed, which cost allocation remained hard-coded and whether a pivot still ties to the general ledger. A useful coworker has to satisfy both without letting the executive answer outrun the analyst’s evidence.

The people between those audiences inherit the hard part. A data engineer may receive fewer requests for extracts but more responsibility for certified tables and stable metric definitions. A controller has to decide which formula changes require sign-off. A junior analyst needs enough construction work to learn why a reconciliation fails, not only a queue of polished files to approve. A chief financial officer needs a faster answer without turning every workbook into a control-design project.

Chat can explain a margin; a workbook must survive edits

Ask a chatbot why margin fell and the interaction has a clean boundary. A person asks, the system finds data, and the response cites or summarizes its route. The answer may be wrong, but it is visibly an answer from the agent.

A spreadsheet blurs authorship. A formula can come from the analyst, an agent, a copied template or a workbook created years earlier. A person may overwrite one cell after the agent has changed 600 others. A later user sees the current value but may not see the chain of prompts, formulas, source versions and approvals that produced it.

Persistence helps reviewers and preserves mistakes. A workbook supports line-by-line inspection in a familiar syntax, then makes the result reusable whether it is correct or not.

Consider a gross-margin bridge. The agent can connect actual revenue, product cost, support labor and cloud usage, then build a monthly variance table. A finance partner can inspect the formula behind each subtotal, change an allocation and test a different volume assumption. That is more useful than a paragraph that says margin fell because cloud spending rose.

Quiet errors can survive in the same workbook. One formula may point to a fixed range that excludes a newly added product. A rate may be typed directly into 14 cells instead of referenced from one assumptions table. A currency conversion may use the current rate for a historical period. A pivot can refresh against new data while a narrative sheet keeps last week’s explanation.

No fluent chat response exposes all of those defects. Neither does a green check beside one output cell.

Databricks says Row Zero will keep agent work interpretable and auditable through spreadsheet syntax. The release also says queries will honor user permissions, authoritative data will refresh automatically, exports can be restricted and results can be written back. Each claim describes a desirable capability. The public material does not yet specify the final audit event schema for the integrated product: whether it records a full cell diff, formula lineage, prompt and model version, source snapshot, service identity, reviewer decision and downstream write.

Auditability has levels. A timestamped log that says “Genie edited workbook” is weaker than a diff showing every changed formula. A cell diff without the source-query version can reproduce the edit but not the input. A complete input and edit record still does not show that a qualified person accepted the result before it reached a planning table.

Microsoft has started to expose that distinction in Excel. Its June finance release says users can ask Copilot to plan before acting, naming the ranges, sheets, formulas and assumptions it intends to update. After the change, the Show Changes pane attributes edits to Copilot alongside human collaborators and links them to affected cells. That does not prove a formula is correct. It gives a reviewer a more useful starting point.

Google takes a different path in one part of Sheets. Its documentation for the AI function says generated content is inserted only after the user clicks, and version history attributes the edit to that user. The person remains the recorded actor even when the model supplied the cell content. That may fit Google’s interaction model, but it compresses generation and approval into one identity unless another record preserves the distinction.

Row Zero currently describes version history, enterprise controls and AI-assisted analysis. Its acquisition statement promises auditable agent interactions. The integration gives Databricks an opportunity to make machine authorship, human modification and final approval separate events rather than three labels on the same saved version.

Finance is only one consequence. Sales operations may write territory assignments back to a planning table. Marketing may change campaign budgets. Supply-chain teams may update reorder quantities. Workforce planners may move headcount assumptions between locations. The spreadsheet surface feels informal because its controls are familiar. The downstream decisions are not.

Analysts’ work changes with the artifact. When an agent handles data retrieval, formula assembly and formatting, less time goes into making the first version. More moves into defining the question, examining exceptions, testing assumptions and explaining why one version is acceptable. The output looks like the old deliverable, but the labor inside it has shifted.

Review can help junior employees if it is treated as trained work. They can compare an agent’s formula to an approved model, trace an unexpected value and learn why a hard-coded rate is dangerous. Learning thins out when junior staff only click approve on work they did not construct and cannot repair.

Teams will have to choose which parts of workbook production to automate and which parts still build the judgment required to challenge the next model. The old deliverable may stay while its division of labor changes.

Excel’s million-row ceiling created the opening

Row Zero’s strongest product case is not AI. It is a data path.

Traditional spreadsheet work often begins with an export. An analyst downloads a CSV, copies it into Excel, adds lookups and emails the result or uploads it to a shared drive. Each step creates another copy with its own timestamp, permissions and risk of becoming stale. Large files force the analyst to split, sample or aggregate the data before the business question is settled.

Row Zero says it queries the warehouse directly, processes results in memory and lets customers keep workbook content in their own object storage. Its zero-data-retention description says cell contents, formulas, query text, results, Python output and AI prompts need not be written to Row Zero’s persistent storage. It also says customers can restrict exports, clipboard use and downloads.

Those are vendor statements about architecture and configuration. They are not an independent audit of every deployment. A customer still has to verify which plan enables each control, where metadata and logs remain, how backups work, which subprocessors receive prompts, and what happens when a user exports an allowed result into another tool.

Identity deserves the same scrutiny. Row Zero’s shared-data-source documentation distinguishes two connection modes. With OAuth, a query runs as the end user and warehouse row-level security applies to that person’s access. With a service account, anyone in the organization who receives the shared data source can run it through the creator’s connection.

Service accounts simplify distribution and can widen effective access if a shared query or workbook reaches people who would not have direct warehouse permission. Row Zero does not describe that option as unsafe. Its documentation makes the chosen identity model part of the control design.

Agents add another identity. Databricks’ September release notes say service-principal use of Genie moved to standard pay-as-you-go pricing on September 24, while individual users receive a different allowance. A service principal may be the right actor for a scheduled report or controlled workflow. It also means the organization must distinguish an employee asking Genie a question from an automated process changing data overnight.

Writeback raises the consequence. Reading governed data into a workbook can produce a wrong analysis. Writing a transformed table back can turn the wrong analysis into a source for the next system. Permissions can stop an unauthorized user; they do not tell an authorized user or agent whether the calculation is sensible.

Row Zero currently says it can write transformed data to warehouses. Databricks’ announcement repeats that the combined product will support writeback. Neither public page defines which writes will require approval, whether append and overwrite receive different controls, or how a customer can make a write reversible. Those details may depend on Unity Catalog, warehouse policies and the eventual integration.

A safe design cannot be reduced to read-only humans and writing agents, or the reverse. An agent might append a draft forecast to a staging table while a controller approves promotion into the official plan. A human analyst might have broad write access and still make a formula error. Exploration, proposed change and accepted write need separate states.

Data scale does not resolve that workflow. Processing a billion rows quickly increases the number of cells that can influence a decision. It can reduce sampling error and export labor. It can also make manual spot checks less representative.

Row Zero’s familiar interface is useful because experienced spreadsheet users know where mistakes hide. They inspect totals, trace precedents, compare periods and test edge cases. The interface becomes dangerous if familiarity encourages the organization to treat every agent-built sheet as ordinary manual work.

Success depends on combining warehouse controls with workbook review. Unity Catalog can enforce access and lineage at the data layer. A workbook record can show the formulas, assumptions and changes that convert governed data into a decision. One does not substitute for the other.

Microsoft and Google already own the analyst’s muscle memory

An analyst does not arrive as a blank user. Microsoft and Google are putting models inside spreadsheet tools, file formats and collaboration habits that employees already use every day.

Microsoft made Agent Mode in Excel generally available on Windows in January, with Mac access following. The product can create workbooks, repair formulas, build charts and generate PivotTables. It now supports a multi-model system that lets eligible customers choose OpenAI or Anthropic models. In June, Microsoft added finance-oriented skills, connectors and more explicit change traceability.

Its separate Finance Agent links Excel and Outlook to ERP and financial-planning systems. Current documentation says the Excel workflow can compare financial structures, create reconciliation reports, suggest explanations for discrepancies and prepare action items for later review. The interface remains Excel, and the upstream system can be Dynamics 365, SAP or another supported finance platform.

Google has expanded Gemini from formula help into workbook creation and editing. In March, it reported a 70.48% success rate on the full SpreadsheetBench benchmark for a new Sheets experience. In August, it introduced Sheets canvas, which turns spreadsheet data into interactive mini-apps. Gemini can create tables, formulas, charts, pivots, filters and formatted ranges, then draw context from Drive and Gmail.

Vendor benchmarks require context. Google’s 70.48% figure came from its own product announcement and a public benchmark. It was not a claim of 70.48% correctness on a finance department’s models. The test tasks, evaluation rubric, product version and use of human review matter. Even the company’s phrasing said performance was nearing human experts rather than matching a professional control standard.

Databricks’ entry differs in two ways. First, its center of gravity is the data platform rather than the office suite. Second, Row Zero is designed to work directly with large governed datasets instead of beginning from a local workbook. Those advantages matter to organizations that already keep business data in the lakehouse.

Distribution favors the incumbents. Excel and Sheets come with habits, templates, keyboard shortcuts, file formats, add-ins, training and colleagues who know how to repair them. Databricks explicitly says Row Zero will complement those products and remain compatible with familiar formulas and pivots. An acquisition does not erase the cost of moving a team away from its existing workbook estate.

Some customers will not move. They may use Databricks’ Excel add-in, preserve .xlsx files and let Copilot handle the agent layer. Others may use Google Sheets for collaboration and query Databricks through connectors. A third group may put high-volume or tightly governed analyses in Row Zero while leaving small models in the old tools.

Most buyers will end up with a portfolio, not one winner. Procurement has to count the data platform, spreadsheet licenses, AI add-ons, model consumption, connectors, support and review labor. An employee who uses Genie to generate a result, Row Zero to model it and Excel to circulate it may improve the work while keeping three product layers.

Promotional pricing can hide that stack during a pilot. Databricks’ current Genie consumption guide says user activity in Genie One and Genie Agents is free through January 31, 2027. The same document warns that agentic consumption varies by task complexity, usage pattern and available context. Free usage appears under a promotional SKU rather than the billed usage a company will later use to size budgets.

A pilot can therefore prove utility without revealing steady-state cost. The organization needs to estimate the equivalent paid consumption, warehouse queries and review time before renewal. Service-principal activity already follows standard pricing, which makes scheduled and automated work a separate budget line.

Muscle memory is also a workforce asset. A company has spent years teaching employees formulas, pivots and workbook review. Row Zero’s strategy is to preserve those skills while changing the data foundation. Microsoft’s strategy is to preserve the application and add an agent. Google’s strategy is to expand the collaborative sheet. The useful comparison is not which interface looks most intelligent in a demo. It is which path lets a team produce an accepted workbook with the least untracked cost and the clearest responsibility.

Benchmarks leave too many cells unverified

The benchmark curve is moving. It has not yet reached unsupervised professional finance work.

SpreadsheetBench 2, a June preprint built from financial reports and corporate filings, contains 321 tasks. The average task spans 11.8 worksheets and requires 593.5 cell changes. Across eight frontier models in one shared agent scaffold, the best model reached 34.89% overall task accuracy. Debugging accuracy fell as low as 12%. The authors identified insufficient workbook inspection and wrong target-cell selection as leading failure modes.

SpreadsheetBench 2 does not audit Row Zero, Genie, Excel Agent Mode or Gemini in Sheets. It evaluates models and a specific scaffold against a research set. Product teams can add tools, constraints, retrieval, deterministic calculation and human review that change the result. The low scores still show why a model that handles a few edits cannot be assumed to manage a multi-sheet operating file.

Finch provides a different kind of difficulty. Its researchers reconstructed 172 workflows and 384 tasks from enterprise workspaces, using 1,710 spreadsheets with 27 million cells plus emails, PDFs and other artifacts. Domain experts spent more than 700 hours annotating the set. GPT-5.1 Pro passed 38.4% of workflows after 48 hours of total execution, while Claude Sonnet 4.5 passed 25% in the reported setup.

Those model versions are not September 2026 products. The paper’s value is the mess. Real work joins data entry, retrieval, modeling, validation, visualization and reporting across several files. A clean prompt and a single worksheet remove much of the context that makes finance work hard.

FinSheet-Bench narrows the task to question answering over 24 synthetic private-equity workbooks. Its best model, Gemini 3.1 Pro, reached 82.4%, or roughly one error for every six questions. The authors concluded that none of the tested standalone models was accurate enough for unsupervised professional finance use. Synthetic files and text-serialized inputs limit direct comparison with an interactive spreadsheet agent, but the remaining error rate is material when one answer changes a valuation or allocation.

BlueFin pushes toward expert criteria. Its 131 tasks contain 3,225 rubric items reviewed by human annotators. The strongest systems averaged below 50%, with particular weakness in dynamic correctness. WorkstreamBench similarly evaluates whether an agent creates a usable end-to-end workbook, including formula readability and ease of later modification, rather than merely returning the right number once.

No universal accuracy rate emerges from these studies. Their tasks, models, harnesses and graders differ. Some rely on model judges checked against expert ratings. Several are preprints. None measures Databricks’ future integrated product in a customer’s environment.

Together, they identify recurring review work:

• The agent may change the wrong range because it has not inspected the whole workbook.
• A formula can return today’s correct number while hiding a hard-coded assumption that breaks next month.
• Fixed ranges may exclude new rows.
• A workbook can be numerically correct but too opaque for another analyst to modify safely.
• One bad input can cascade through several sheets without a visible error state.
• Formatting and labels can look polished while the model points to the wrong period, unit or source.

These are familiar spreadsheet failures. Agents accelerate them as well as the repairs.

Manual work gives agents a fair counterargument. Spreadsheets already contain hard-coded values, stale links and copied formulas. An agent can trace dependencies, compare totals, generate test cases and expose inconsistent logic faster than a person working cell by cell. TechCrunch’s report that Databricks finance adopted Row Zero is evidence of internal product pull, even though Databricks has not published measured time savings or error reductions.

The governed-data route can also improve on a weak status quo. A live query removes at least one stale export. A recorded cell diff can reveal a model edit that would otherwise disappear inside a copied workbook. Automated cross-footing can run on every draft instead of only the version a reviewer happens to inspect. Human review is not automatically safer merely because a person made the first formula; the relevant comparison is which workflow exposes consequential mistakes before use.

A fair pilot should compare the joint system with the real baseline, not an imaginary flawless analyst. Measure the current error rate, cycle time, export count and review effort for manual work. Then measure the agent-assisted version. A model can be useful well below 100% autonomous completion if it reduces first-pass labor and makes errors easier to find. It can also be harmful above a benchmark threshold if staff trust polished work and skip the relevant checks.

Reliability therefore depends on task design and acceptance. Generating a draft variance table is not the same as posting an adjustment. Repairing a broken formula is not the same as choosing an accounting treatment. Building a scenario is not the same as approving the operating plan.

Those boundaries remain visible only when the product and the team preserve them.

A workbook acceptance file for humans and agents

AI-generated editorial illustration. The image maps the review path between a live data source, agent edits, human approval and controlled writeback.

An agent-built workbook needs a state between generated and official. The acceptance file below attaches evidence and an owner to that state.

Record	Minimum evidence	Primary owner	Failure it catches
Purpose and consequence	Question, intended decision, audience, deadline and whether the file is exploratory or can change a financial or operating record	Business owner	A draft model circulating as an approved decision
Source snapshot	Dataset, query or table version, refresh time, filters, currency, period and source permissions	Data owner	A correct formula running on stale or incomplete data
Agent identity	Product, model, skill or tool version, user or service identity, prompt and execution time	Platform owner	An edit that cannot be reproduced or attributed
Workbook scope	Sheets, named ranges, cells, formulas, charts and comments the agent may read or change	Workbook owner	The agent editing the wrong range or hidden dependency
Assumptions	Hard-coded values, scenario inputs, policy choices, confidence limits and person authorized to change them	Finance or domain owner	A guessed rate becoming an invisible fact
Mechanical tests	Recalculation, formula errors, range expansion, unit checks, cross-footing, duplicate detection and known-answer samples	Analyst	A workbook that looks complete but breaks when data changes
Business reconciliation	Tie-out to the ledger, certified metric, approved forecast or another independent reference	Controller or decision owner	A coherent model that disagrees with the official record
Human disposition	Reviewer, material corrections, unresolved exceptions, accepted use and time spent	Qualified reviewer	Approval theater and invisible rework
Writeback control	Staging or production target, append or overwrite, permission, approval, idempotency and maximum affected rows	System owner	A useful analysis causing an irreversible data change
Recovery and follow-up	Prior version, rollback test, downstream consumers, outcome window, correction notice and retirement date	Process owner	A wrong version surviving after the team discovers the error

No reviewer should inspect every cell. Review depth should match consequence. A marketing exploration can use samples and visible caveats. A month-end reconciliation needs deterministic tie-outs and a qualified finance reviewer. A write to an official planning table needs a staging step, an affected-row count and a tested rollback.

An illustrative month-end workflow shows the handoff. This is not a reported Databricks or Row Zero customer case.

First, the finance team freezes the source period and records the query or table versions. The agent receives permission to build a draft margin bridge in a new workbook, not to overwrite the official plan. It lists the sheets and ranges it intends to change.

Second, the agent creates formulas and labels its assumptions. Automated checks compare totals with the ledger, flag fixed ranges, search for formula errors and test whether a newly added product flows through the model. An analyst reviews exceptions rather than reconstructing the whole workbook from scratch.

Third, a controller reconciles the accepted version to the official record and approves a specific use. If the workbook will write a derived table back to Databricks, the write lands in staging with a row count and version identifier. Promotion into production requires a separate action.

Finally, the team records what happened after the workbook was used. It may have shortened close by two days, revealed an allocation error or produced no action. The outcome belongs beside the edit record so workbook volume is not mistaken for business value.

Three operating measures keep the pilot honest:

accepted workbook rate = workbooks used without material correction / agent-submitted workbooks

review cost per accepted workbook = model + warehouse + platform + human review + rework cost / accepted workbooks

reversed write rate = agent-originated production writes reversed or materially corrected / agent-originated production writes

Accepted-workbook rate prevents draft volume from looking like finished work. Review cost captures the labor and infrastructure hidden behind a quick first answer. Reversed-write rate separates harmless exploration from actions that change a shared record.

Teams should also track time to correction. A low error rate can still be expensive if wrong files circulate for weeks. A higher early correction rate may be healthy during a pilot because reviewers are finding defects before the system reaches production.

Junior analysts can use the acceptance file as training. They can review why a formula failed, compare the model with the approved version and take responsibility for a bounded section. Senior employees can focus on material assumptions and policy choices. Platform teams can improve tools from recurring error patterns instead of collecting thumbs-up ratings with no diagnosis.

Data teams need the same feedback loop. If reviewers repeatedly correct the meaning of net revenue or active customer, the fix may belong in a certified metric or source model rather than another prompt. If errors cluster around workbook structure, the agent needs better inspection and targeting. The acceptance record turns rework into a product and data backlog instead of hiding it in a finance employee’s evening.

It also gives product vendors a fairer test. Row Zero should not be judged by whether it eliminates spreadsheet work. Databricks should be judged by whether Genie and Row Zero reduce export sprawl, shorten accepted analysis, preserve permissions, expose agent changes and keep writeback reversible. Excel and Sheets should face the same evidence.

The renewal test sits in the change history

One transaction now places a large distribution system behind a specialized spreadsheet team. Databricks says more than 20,000 organizations, including 70% of the Fortune 500, use its platform. Row Zero’s site says more than 15,000 companies trust its product. Both are company-provided counts. Neither tells buyers how many customers use the products together, how frequently they use them or what work moved.

Product facts should appear first after the deal: native access in Genie, integration across web, desktop and mobile, continued support for other data sources, and a documented control model. Customers should look for cell-level change history, model and tool identity, source snapshots, approval states, staged writeback and export controls that survive the integration.

Operations evidence comes next. Did finance produce an accepted model faster? Did fewer CSV files reach laptops? Did a data team receive fewer extract requests? Did analysts spend less time repairing formulas, or did review work merely move to a more senior and expensive group? Did the company preserve an apprenticeship path for people learning how the model works?

Budget arrives after the promotion ends. Genie One and Genie Agents may appear free for user activity through January 31, 2027, but the company already advises administrators to estimate equivalent paid use. Warehouse compute, Row Zero’s eventual packaging, service-principal activity, Microsoft or Google licenses, connectors and human review remain separate lines.

Coexistence may be the most useful outcome. A team can keep Excel for portable models, use Sheets for broad collaboration and move large or sensitive workflows into Row Zero. That arrangement is rational if each file has an owner and the handoffs are visible. It is wasteful if three agents regenerate the same analysis in three formats and nobody can identify the approved version.

Databricks bought Row Zero before it had published that answer. The deal supplies a familiar place to work it out.

Picture the next forecast review. The chat says margin changed; that is the start, not the evidence. The useful screen is the workbook’s change history: the source snapshot, the cells an agent touched, the formulas a person corrected, the version a controller approved and the write the team can still reverse.