AI Spreadsheet Tools in 2026: The Excel Killers Finally Arrived

Formulas Were Always a Programming Language in Disguise

For forty years, spreadsheets forced analysts to learn a weird lisp-dialect-with-cells to do anything useful. =INDEX(MATCH(...)), =SUMPRODUCT(), nested =IFERROR — a whole career could be built on memorizing these incantations.

In 2026, that career is getting refactored. A new generation of AI-native spreadsheets treats formulas as the assembly language and natural language as the high-level interface. You describe what you want. The tool writes the formulas, runs the pivot, and shows you the chart.

We tested the five most credible contenders on the same 3GB sales dataset, the same 8 analytical questions, and the same “clean up this messy CSV” test. Here’s what earned the right to replace your existing stack.

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The New Category: What Counts as “AI-Native”

Copilot in Excel and Gemini in Google Sheets don’t count for this roundup. They’re AI sidebars grafted onto legacy engines. They help you write formulas but they can’t run Python, they choke on >1M rows, and they still think in cells rather than columns.

The tools we’re ranking share four traits:

1. Natural language is a first-class query interface, not a chat widget
2. They execute real code under the hood (Python, SQL, or compiled kernels)
3. They handle datasets larger than Excel’s 1M row limit without crashing
4. They output formulas, code, or SQL you can inspect and version

That rules out most “AI spreadsheets” you’ll see advertised — fancy chat wrappers around Google Sheets. The five that survived our filter are Rows, Julius, Bricks, Shortwave Sheets, and Gigasheet.

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1. Julius — Best for Serious Data Analysis

Pricing: Free tier | Standard $20/month | Pro $45/month

Julius is what happens when you give a data scientist a chat interface. Upload a CSV, ask a question in English, and it writes Python (pandas, matplotlib, scikit-learn) in a sandbox, runs it, and returns the answer plus the chart plus the code.

On our 3GB sales dataset, Julius loaded it without complaint, ran a cohort retention analysis, and produced a publication-quality chart — all in about 40 seconds. The code was clean enough to copy into a production notebook.

Pros

• Executes real Python, not simulated formulas
• Handles datasets up to ~10GB on Pro
• Outputs reproducible code
• Supports forecasting, clustering, regression out of the box

Cons

• It’s more Jupyter-than-Excel — collaborative editing is limited
• No cell-level editing; you can’t tweak a number and recalc
• Pro tier needed for large datasets

Best for: analysts who want AI-speed EDA but still want real code under the hood.

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2. Rows — Best Excel Replacement for Teams

Pricing: Free | Plus $9/user/month | Pro $19/user/month

Rows looks like a normal spreadsheet until you realize every cell can call an API or an LLM. Want a column that scrapes each company’s homepage? One formula: =OPENAI("summarize", A2) or =WEBSITE(A2). It ships with 50+ built-in integrations (HubSpot, Stripe, Clearbit, LinkedIn) that behave like native functions.

Rows nailed the UX tradeoff — it feels like Excel for existing users, but the AI layer means you can prompt the sheet directly: “enrich column A with company size and funding round.” Thirty seconds later your columns are populated.

Pros

• Familiar Excel-like grid, zero learning curve
• Deep integrations as formulas
• Strong collaboration (shared workbooks, comments)
• AI Analyst mode generates full reports from prompts

Cons

• Row limit is still 50k on most tiers
• Heavy integrations mean many features are paywalled
• Not ideal for pure analytics — it’s more of a business tool

Best for: ops, sales, and marketing teams who want Excel + APIs + AI without switching platforms.

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3. Bricks — Best for Small Businesses

Pricing: Free | Pro $20/month

Bricks positioned itself as “ChatGPT for spreadsheets” and largely delivered. The interface is a split view: natural language prompt on one side, a live-editing sheet on the other. The differentiator is its visualization engine — Bricks generates dashboards, not just charts, from a single prompt.

Ask “build me a monthly revenue dashboard from this Stripe export” and you get a multi-tile dashboard with KPIs, trendlines, and a cohort chart in seconds. No drag-drop required.

Pros

• Best-in-class auto-dashboards
• Simple pricing
• Understands common business schemas (Stripe, Shopify, QuickBooks)

Cons

• Limited to spreadsheet workloads — no Python escape hatch
• Smaller integration library than Rows
• Can hallucinate fields when column names are ambiguous

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4. Shortwave Sheets — Best for Gmail/Workspace Users

Pricing: Included with Shortwave Business $15/user/month

Shortwave started as an AI email client and extended into spreadsheets that live inside your email workflow. The killer feature: ask “turn last month’s invoice emails into a spreadsheet” and it parses your Gmail, extracts fields, and builds the sheet. No Zapier, no manual CSV export.

It’s a narrow tool, but if you live in Gmail and deal with semi-structured email data (invoices, orders, applications), it’s uniquely useful.

Pros

• Unmatched email-to-sheet pipeline
• Tight Google Workspace integration
• Fast for “turn messy inbox into structured data” tasks

Cons

• Narrow use case
• Weak as a general-purpose spreadsheet
• Requires Gmail

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5. Gigasheet — Best for Billion-Row Datasets

Pricing: Free (100M rows) | Premium $95/month | Enterprise custom

Gigasheet is the only tool in this list built to handle true big data in a spreadsheet UI. It runs a columnar engine (similar to DuckDB) under the hood, so you can open a 5-billion-row CSV and still scroll, filter, and pivot at interactive speed.

The AI layer is more basic than Julius or Bricks — natural language queries translate into SQL rather than full analyses. But when your file is too large for anything else to open, Gigasheet is often the only option.

Pros

• Genuinely handles billions of rows
• Columnar engine keeps it responsive
• Good for forensic/security/ops data work

Cons

• AI features trail the competition
• Pricing jumps sharply above free tier
• Not for everyday spreadsheet tasks

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Benchmark: The 8-Question Test

We ran the same 8 analytical questions on a 3GB e-commerce dataset (~4.2M rows) across all five tools. Scored 1-5 on correctness and speed.

Question	Julius	Rows	Bricks	Shortwave	Gigasheet
Total revenue by month	5	4	5	3	5
Top 10 SKUs by margin	5	4	4	2	5
Customer cohort retention	5	2	3	1	3
Churn prediction	5	1	2	1	2
Clean inconsistent product names	4	5	4	3	3
Forecast next quarter	5	2	3	1	2
Geographic heatmap	4	4	5	2	3
Anomaly detection on returns	5	3	3	1	4

Julius dominated anything requiring real statistics. Rows won on enrichment and cleaning tasks. Bricks produced the prettiest dashboards. Gigasheet was the only one that didn’t slow down on the full dataset.

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The Verdict

If you only buy one: Rows for most people, Julius for analysts, Gigasheet for big data.

Microsoft and Google will eventually catch up — Copilot for Excel is improving monthly — but the AI-native tools are currently 6-18 months ahead. The gap is most obvious anywhere you’d normally leave the spreadsheet (API calls, Python, enrichment). In those workflows, asking a tool to do it feels like cheating compared to the old way of gluing together Power Query, VBA, and Zapier.

Your formulas aren’t going away entirely. But in 2026, they’re finally the implementation detail they always should have been.