AI will never master PowerPoint

Why visual editors are the wrong interface for LLMs

PowerPoint’s modern XML-based format was introduced in 2007, long before anyone imagined computers could reason about layout and make presentations for us. The core assumption was simple: a human will drag shapes/text around a canvas and visually confirm that things look right.

That assumption no longer holds.

Progress has been made, but AI will never truly master PowerPoint because PowerPoint was built to be manipulated by hand, not generated programmatically. This is just one example of a broader trend: formats that are purpose-built for AI to create and edit will eventually displace those designed for humans alone.

Why powerpoint is hard for AI

Consider a task that takes two lines of CSS but requires AI to perform five separate calculations in PowerPoint: making items equally spaced.

HTML/CSS: Represents Intent

.container {
  display: flex;
  justify-content: space-evenly;
}

This code says: “arrange the items inside this container with equal spacing between them.” The browser figures out all the pixel math. If you resize the container, the spacing recalculates automatically. The AI doesn’t need to know the container width, the number of items, or do any arithmetic. It just states the intent.

PowerPoint: Represents Outcomes

Under the hood, a .pptx file is just a ZIP archive containing XML files—specifically, a format called Office Open XML (OOXML). Every shape, every text box, every image is defined in XML with absolute positioning:

<p:sp>
  <p:spPr>
    <a:xfrm>
      <a:off x="1676400" y="914400"/>    <!-- Position in EMUs -->
      <a:ext cx="1219200" cy="1219200"/> <!-- Size in EMUs -->
    </a:xfrm>
  </p:spPr>
</p:sp>

To create “equally spaced” items, the AI must:

1. Read the slide width from the slide size XML element

2. Read each shape’s width from the <a:ext> XML element shown above

3. Calculate spacing: (slide_width - sum(shape_widths)) / (n + 1)

4. Calculate each shape’s x position: spacing + sum(prev_shape_widths) + (i * spacing)

5. Write each x position to the <a:off> XML element shown above

“Equally spaced” and “arbitrarily placed” require the exact same amount of work; they’re just different numbers. There’s no semantic distinction in the data model.

You could create friendlier wrappers and APIs around the XML content, but that’s a clunky workaround rather than a proper solution to the problem.

What’s the alternative?

1. AI-native presentation software

Products that are built with AI in mind can design their internal object model in a way that makes it easy for AI to create and modify.

Gamma is the best example of this approach. Recently valued at $2.1B with $100M in ARR, they’ve grown rapidly and have a very different product—instead of absolutely-positioned shapes, they use scrollable, block-based ‘cards’ that feel more like web documents.

They don’t provide a ton of visibility into their internal representation, but the focus on structured content + strong AI features make me confident that it’s significantly easier for AI to work with than PowerPoint’s OOXML.

2. PowerPoint as an export format

Even if you’re set on creating a .pptx file, using PowerPoint isn’t necessarily the best way for AI to get there.

Anthropic’s Claude Code skill has the best output of any general-purpose AI I’ve seen, and it creates slides by writing HTML that gets converted into a PowerPoint file using a ~1000 line conversion script built on PptxGenJS, Playwright, and Sharp. The data model that the AI actually works with is HTML, not PowerPoint¹.

If this pattern grows, PowerPoint could end up as a legacy export format rather than the primary place people actually do work.

3. Code-native presentations

If LLMs are great at writing HTML, why convert to .pptx at all? Reveal.js is an open-source HTML presentation framework that makes slides first-class web documents. The advantages:

• Makes AI creation and iteration easy with plain HTML+CSS and DeckTape for quick slide screenshots

• Includes the core features presenters expect: speaker notes, slide transitions, progressive content reveal, PDF export

• Offers new features that traditional software can’t match: syntax-highlighted code with line-by-line reveal, vertical slide stacks for drilling into subtopics, and embedded interactive content

The one thing Reveal.js lacks out of the box is an interactive visual editor, but Slides.com attempts to fills that gap² —it’s a GUI built on top of Reveal.js. This inverts PowerPoint’s approach: instead of building a visual editor first and treating the data model as an afterthought, you start with an AI-friendly format and layer human editing on top.

I put together a quick Claude Code skill for Reveal.js (revealjs-skill) and put it head to head vs. Anthropic’s pptx skill to generate a pitch deck for a fictional legal AI startup. Here’s the prompt I used, pptx skill output, revealjs skill output.

I’m a little biased but would argue that the Reveal.js output looks better, and it took less time to generate; see the footnotes for a full breakdown³.

Beyond PowerPoint

Presentations aren’t the only category where legacy formats aren’t ideal for AI; this pattern appears across productivity software.

Documents

Word’s .docx format has a similar structure as PowerPoint: complex XML with formatting specified in separate blocks per sentence/word/character. Word focuses on storing the visual rendering details of the content, which aligns with a human-designed editor, while Markdown has been used in codebases for decades and much more concisely stores the content + structure.

Markdown-like editors (Notion, Obsidian, GitHub) are much more common than presentation editors and it’s generally an easier format to convert to, so there’s less friction—Markdown is already a go-to for AI-generated reports or documents, with exports to Word becoming less common.

Spreadsheets

Continuing down the Microsoft Office suite, the same pattern is emerging for Excel.

Univer is an open-source framework for building “AI-native spreadsheets” and currently leads the SpreadsheetBench leaderboard at 68.9%, beating Microsoft Copilot’s Agent Mode (57.2%) and Claude (42.9%) by a significant margin.

Unlike add-ons that layer AI directly on top of Excel, Univer is a totally independent spreadsheet engine built from the ground up for natural language interaction. It can import and export .xlsx files, but that’s legacy compatibility rather than a primary format.

Business Intelligence

Tableau and Power BI have historically dominated the dashboarding market, but their formats have limitations similar to PowerPoint. Tableau’s .twb files are XML documents encoding visual layouts with absolute coordinates, and Power BI’s .pbix files bundle similar positional data with a proprietary query language. Both were designed around drag-and-drop interfaces where humans visually arrange charts on a canvas.

A growing category of “BI-as-code” tools take a different approach. Evidence.dev uses SQL for queries and Markdown for layout; Lightdash defines metrics in YAML and integrates with dbt. Instead of encoding “place this bar chart at coordinates (1200, 400),” these tools let AI write SQL queries and declare charts in Markdown or YAML—formats it already understands.

Conclusion

In the short term, building to extend and automate legacy human tools makes a lot of sense; it’s where everyone is already doing work. In the long term, though, if AI is really going to transform and potentially automate knowledge work as we know it—why in the world would it be using PowerPoint?

1. To edit existing files, Claude does modify the OOXML directly, but results have been mixed.

2. Slides.com has some limitations when trying to edit AI-generated Reveal.js, but it still proves the concept and is a step in the right direction.

3. The Reveal.js skill took 6m 23s, whereas the pptx skill took 9m 52s. That’s the happy path for pptx; another time where I tried it (from the web UI) it took over 15 minutes because it was having issues with footnotes getting cut off, and the cycle of html → pptx → screenshot is quite slow.

Stylistic choices aside, there are some objective issues with the pptx skill’s output:

• I had to manually open it in PowerPoint and print to pdf for the charts to work

• The chart on page 7 is awkwardly small

• The box on page 8 is completely empty

• There’s no margin between the bottom of the content and bottom of the slide on page 8

• The spacing/kerning on the text is a bit off throughout (e.g. “ae” and “ez” in “Michael Rodriguez” on page 10)

• Some of the text in the bar chart on page 11 (bottom left) has poor visibility/contrast