AI & Machine Learning Pitch Deck Examples

AI and machine-learning pitch decks — teardowns on data advantage, model cost, and the difference between a product and a feature.

AI & Machine Learning Pitch Deck Examples

AI decks have to answer two questions they rarely pose to themselves: what is defensible beyond the model, and what inference actually costs.

200 teardowns in this category

200 teardowns in this category, spanning decks from 2005–2026. Of the 200 listed below, 183 list a funding stage and 177 disclose an amount raised. Every teardown includes the original deck, slide by slide.

What this collection contains

Measured across the 200 decks on this page. Decks that never disclosed a fact are left out of that figure rather than estimated.

More AI & Machine Learning teardowns in this category

5 further teardowns matching this category, listed so every one of them sits one click from this hub.

Browse this category by year

Other categories

Browse by round size

Decks that raised under $1M · Decks that raised $1M to $10M · Decks that raised $10M to $50M · Decks that raised $50M to $250M · Decks that raised $250M or more

Browse companies A–Z

A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z · 0–9

Browse by region

US pitch decks · UK pitch decks · European pitch decks · Canadian pitch decks · Asia-Pacific pitch decks · Latin America pitch decks · Middle East & Africa pitch decks

All pitch deck examples · All collections · Fundraising articles A–Z · Library

What 200 AI pitch decks actually look like

  1. The median AI deck in this cohort is 14 slides; half sit between 11 and 19, and the range runs from 5 to 57.

    There is no single correct length. The middle of the distribution is tight, and the long tail is almost entirely data rooms and SPAC decks rather than first-meeting decks.

    What to do with it: Aim for the 11–19 band for a deck you send cold. If yours is over 25 slides, the surplus is usually appendix material that should move behind the ask.

  2. Product is the most frequently labelled slide, found in 77 of the 196 labelled decks, and it typically lands around slide 5 — after problem (slide 4) and alongside solution and market.

    AI founders reach the product demonstration early, because the product is the claim. The abstract capability argument tends to come after, not before.

    What to do with it: Show the working thing by slide five. If a reader has to wait until slide nine to see what you built, the earlier slides are doing market-education work your investor probably does not need.

    Limitation: Slide labelling is partial, so these are counts of where a labelled slide was found, not evidence that the other decks lacked it.

  3. 156 of the 200 decks have a verifiable round size, and the median is $14.25M — but the band spread is wide: 19 under $1M, 47 between $1M and $10M, 39 between $10M and $50M, 41 between $50M and $250M.

    "AI deck" is not one fundraising context. The corpus mixes pre-product seed decks with growth-stage and listed-company material.

    What to do with it: Benchmark against your own stage, not against the industry median. The stage pages linked below are the honest comparison.

What investors say they want from an AI pitch

The points below come from the investors and accelerators themselves, each linked to the page it appears on. They are guidance, not measurements — treat them as the reader's expectations, and use the cohort numbers above for what decks actually do.

A slide order that matches how these decks are read

This ordering follows where labelled slides actually land in the cohort, tightened by the concision guidance investors publish themselves.

  1. One-line definition. What the company does, in a sentence a reader can repeat to a partner who was not in the room. (Sequoia Capital)
  2. Problem, with the person who has it. In the labelled decks, problem sits around slide 4 — early, and named concretely rather than as an industry trend.
  3. Product, shown. Product is the most common labelled slide here and lands around slide 5. A screenshot of the real output beats an architecture diagram.
  4. Why now. The specific capability, cost or data shift that made the product possible in the last 18 months. (Sequoia Capital)
  5. Evidence, in numbers a reader can check. Recurring revenue, usage, retention or design partners — a16z's metrics guide is explicit that recurring product revenue is valued differently from services revenue, so label which you have. (Andreessen Horowitz 2015)
  6. Team, and why this team. Team lands around slide 4 in the labelled decks — earlier than founders expect. Say what each person did before that makes them the right builder here.
  7. The ask, and what it buys. Y Combinator's guidance is to tell the investor how much money you need and what it gets you — a number without a milestone is an unanswered question. (Y Combinator 2018)

Material that usually belongs in an appendix rather than the main deck: model architecture and evaluation detail; full customer list and logo wall; detailed cohort and unit-economics tables; hiring plan by function.

Decks in this cohort worth reading in full

Each links to our teardown of the original deck. We show covers and commentary only; the slides remain the company's.

Patterns that separate the strongest decks here

Mistakes this cohort makes repeatedly

Checklist before you send an AI deck

See where your AI deck sits in this cohort

Pitch Score reads your deck and places its length, slide order and evidence against the same 200 decks measured on this page.

Score your deck against this cohort

AI pitch decks: common questions

How long should an AI pitch deck be?

In this cohort of 200 AI decks the median is 14 slides and the middle half sits between 11 and 19. Y Combinator's own advice is tighter still for a live pitch — around 5 to 7 ideas, legible from the back of a room. (Y Combinator 2023)

What goes on the first slide of an AI deck?

A single declarative sentence defining the company. Sequoia's business-plan guide opens with exactly that instruction, and it is the slide most AI decks spend on architecture instead. (Sequoia Capital)

How much do AI companies raise on these decks?

Of the 200 decks here, 156 have a verifiable round size, and the median is $14.25M. The spread is very wide — 19 rounds under $1M and 10 above $250M — so the stage-specific pages are the more useful comparison.

Do I need to mention regulation?

If you sell into the EU, yes. The European Commission's AI Act sets four risk levels and imposes obligations on high-risk systems before they go to market, so a reader will want to know which tier you are in. (European Commission 2024)

Should the team slide come early or late?

In the labelled decks in this cohort the team slide typically lands around slide 4, earlier than most founders place it.

Methodology and limitations

Where to go next

Sources

External facts on this page were checked against the pages below on 9 September 2026. Everything measured from our own deck corpus is marked as such.