This AI Builds Your Pitch Deck in Seconds

The 60-Second Pitch Deck Is Here

Sixty seconds of typing, twelve slides of investor polish. That’s the pitch in a viral clip from creator Ethan Nelson, who shows an AI tool that spits out a full Investor Pitch Deck from a single text prompt describing your startup. No Keynote templates, no wrestling with fonts, just a short paragraph and a download link.

At the center is a Claude Skill wired specifically for fundraising. Feed it a plain‑language description—“AI tool for restaurant inventory,” for example—and it auto‑assembles a 12‑slide deck that mirrors what VC blogs and accelerators have been preaching for years.

The generated deck covers the standard Investor Pitch Decks canon end to end. Nelson’s demo calls out:

• Problem–solution narrative
• Market sizing
• Financial projections
• Competitive analysis
• A clear “ask” slide

Rather than dumping raw bullet points, the Claude Skill arranges them into a story arc investors instantly recognize. The flow tracks the familiar VC sequence: problem, solution/product, market, traction and roadmap, competition, business model and financials, team, then the funding ask.

That structure matters because most first‑time founders do not know what partners at a seed fund expect on slide 7 versus slide 10. By encoding those expectations, the tool acts like a rails‑based pitch coach, nudging every deck toward a de facto Sequoia‑style outline without users touching a how‑to guide.

Speed is the other hook. Nelson says the system goes from prompt to finished 12‑slide deck “in seconds,” collapsing what can be a week of procrastination and revision into a single pass. For founders staring at a blank Figma canvas, that instantly kills the “blank page problem”.

Design anxiety disappears too. The pitch here is that you do not need layout instincts or financial‑model screenshots ready to go; the Skill handles structure and presentation so you can iterate on substance. Founders can then tweak language, swap charts, and localize numbers, instead of guessing what slides to build in the first place.

From Prompt to Presentation: How It Works

Forget juggling Keynote templates and YC blog posts. This workflow starts with a single text box where a founder types a plain‑English description of their startup—no jargon required. “AI assistant for dentists that automates scheduling and insurance claims” is enough to light the fuse.

Behind that prompt, the Claude‑based skill parses the description into classic Investor Pitch Decks components. It identifies who has the problem, what hurts, and how the product fixes it, then maps those pieces onto a slide sequence that mirrors what most VCs expect to see.

First comes the problem–solution narrative. The tool drafts a problem slide that frames urgency and stakes, followed by a solution slide that spells out the product, key features, and why this approach beats the status quo. The tone reads like a seasoned founder who has already pitched a dozen partners’ meetings.

Next, the AI auto‑assembles market sizing. It leans on the familiar TAM/SAM/SOM breakdown: - Total Addressable Market (TAM) - Serviceable Available Market (SAM) - Serviceable Obtainable Market (SOM)

Numbers plug in as rough estimates based on the business description and public benchmarks. Founders get a structured market story instead of a blank chart.

Competitive analysis arrives as a dedicated slide, not an afterthought. The system lists incumbent players, recent startups, and adjacent tools, then positions the new company on a grid or table with differentiators like price, feature depth, or automation level.

Financial projections follow the same pattern. The AI sketches out 3–5 year revenue forecasts, key cost buckets, and simple unit economics, often enough for an early seed or pre‑seed conversation. Founders can swap in real numbers later, but they no longer start from zero.

Finally, the deck ends with a clear Ask slide: how much the startup wants to raise, broad use‑of‑funds categories, and target runway. All of this appears in roughly 12 slides, generated in seconds.

Speed is only half the pitch. Accessibility matters just as much: no slide‑design skills, no PowerPoint gymnastics, just a structured, VC‑style story from a paragraph of text.

Decoding the 'VC-Approved' Structure

“Structures your story the way VCs want” is the real swing here, not the auto‑generated charts. Anyone can spit out a dozen slides; encoding VC-approved sequencing is harder. That claim turns Claude from a slide robot into a fundraising co-pilot.

Most early founders don’t know the unwritten rules of Investor Pitch Decks. Top accelerators quietly enforce a canonical flow: problem, solution, market, traction, business model, competition, team, financials, ask. Miss one, and partners start mentally filling gaps instead of listening.

What Ethan Nelson’s skill hints at is an AI that has internalized those playbooks. Y Combinator’s public templates and Sequoia’s classic pitch outline all converge on nearly the same backbone. Claude appears to map a one-paragraph startup description onto that backbone automatically.

Look at the components Nelson calls out: problem–solution narrative, market sizing, financial projections, competitive analysis, clear ask. That’s almost a one‑to‑one with Sequoia’s guidance: company purpose, problem, solution, why now, market size, competition, product, business model, team, financials, vision. The AI simply compresses years of blog posts and office hours into a default.

Instead of magic, think codified best practice. The model enforces a checklist founders routinely miss: - Crisp problem statement and user pain - Concrete solution and product snapshot - TAM/SAM/SOM‑style market sizing - Revenue model and basic unit economics - Competitive landscape and differentiation - Team credibility and roadmap - Funding amount and use of proceeds

That’s what a human digital fundraising coach would do in a seed-stage bootcamp. Tools like Slidebean: AI Pitch Deck Creator + Pitch Deck Design Agency already sell this as a service: structure first, polish second. Claude just collapses that coaching into a single prompt.

For founders, the real unlock is constraint. When the AI locks you into a YC‑ and Sequoia‑inspired spine, you spend time sharpening arguments, not guessing slide order. You still need real traction and believable numbers, but you’re now speaking fluent VC by default.

The Good: Your New Fundraising Co-Pilot

Call it what it is: a massive accelerator for founders stuck at the blank slide. Instead of wrestling with Keynote for days, you paste a 3–5 sentence description of your startup and, in under a minute, Claude spits out a 10–12 slide deck that at least looks like something you could send to a seed fund associate without embarrassment.

Speed matters because fundraising is an iteration game. Founders routinely burn 20–40 hours per deck version; offloading the first draft means they can spend that time on customer calls, refining pricing, or fixing the product instead of nudging text boxes by two pixels.

Where this gets interesting is narrative A/B testing. A tool like this lets you generate multiple versions of the same story for different investor profiles: a deep technical cut for AI‑native funds, a go‑to‑market heavy version for growth‑oriented VCs, a capital‑efficiency angle for family offices.

You can quickly spin up variants that emphasize: - Market size and category creation - Traction and unit economics - Team and defensibility

Founders can then run real‑world experiments: send version A to 10 funds, version B to another 10, and track which one converts into partner meetings. That kind of structured testing used to require an expensive pitch coach or a very patient advisor network.

Knowledge that used to live in scattered blog posts and expensive accelerators now hides inside the prompt. The Claude skill effectively bakes in a composite of standard Investor Pitch Decks advice from places like Sequoia and YC: problem, solution, market, competition, business model, financials, team, ask.

For a first‑time founder in Lagos or Łódź, that embedded playbook collapses a brutal learning curve. Instead of reverse‑engineering what “good” looks like from random decks online, they get a baseline that already speaks in VC shorthand: TAM/SAM/SOM, CAC vs LTV, milestones to next round.

Language becomes less of a gatekeeper too. Non‑native English speakers can describe their company in imperfect English and get back clean, investor‑grade copy, then iterate by tweaking a few sentences rather than rewriting 30 pages of slides.

Democratization here doesn’t mean every deck suddenly wins funding. It means more founders at least start from a structurally competent, visually coherent, VC‑legible artifact—something that used to require either money, connections, or a crash course in fundraising dogma.

The Bad: Where AI-Generated Decks Fail

Speed comes with a catch: sameness. Feed a Claude skill a one‑paragraph description of a B2B SaaS startup and you usually get the same boilerplate language every other founder is generating: “frictionless onboarding,” “AI‑powered insights,” “unlocking unprecedented efficiency.” Experienced investors, who skim hundreds of decks a month, spot that generic tone in seconds and mentally downgrade it to templateware.

AI also tends to converge on safe, middle‑of‑the‑road narratives. Market slides default to “TAM/SAM/SOM” framings with vague top‑down numbers, while product slides lean on buzzwords instead of specific workflows, integrations, or customer anecdotes. That sameness erases the sharp edges that often make an early‑stage pitch memorable.

Hallucinated numbers pose a more serious problem. When a model confidently fabricates financial projections and market sizes, those figures are not research; they are scaffolding. Founders who copy them straight into a deck walk into partner meetings with revenue curves and CAC/LTV ratios they cannot defend under basic questioning.

Investors routinely pressure test numbers with simple follow‑ups: “What assumptions drive year‑three revenue?” “How did you get to a $4.2 billion TAM?” If those answers boil down to “the AI said so,” credibility evaporates. At seed and pre‑seed, trust in a founder’s grasp of their own economics matters more than a pretty chart.

AI‑generated competitive analysis often looks polished but shallow. Models infer a landscape from public text, then output feature matrices and positioning statements that may miss stealth players, shifting incumbents, or niche wedge strategies. VCs who know a space well will immediately see gaps or outdated references.

Authentic founder voice still resists automation. Strong decks encode how a specific team discovered a problem, what they learned from early customers, and why their timing is non‑obvious. A generic “problem/solution” paragraph rarely captures the weird story about a broken workflow, a hacked‑together prototype, or a contrarian bet that actually differentiates the company.

Customer insight also lives in details AI cannot invent responsibly: the exact sales objection that keeps coming up, the feature that unexpectedly drives retention, the procurement step that kills deals. Those nuances shape a strategic vision slide far more than another diagram of “land and expand” or “PLG funnel” that a model can spit out on command.

Used naively, AI pitch tools risk producing decks that look right but feel hollow. Investors increasingly know how to tell the difference.

The AI Pitch Deck Arms Race

AI pitch decks no longer belong to a niche. Tools like Tome, Gamma, and Decktopus already promise “idea to slides” in under a minute, auto‑generating layouts, imagery, and copy from a short prompt. They sit in the same wave as Canva’s Magic Design and PowerPoint’s Copilot, turning slide creation into a commodity feature.

Most of these platforms treat your startup like a fill‑in‑the‑blanks exercise. You choose a template, answer a series of questions, and the system drops text into pre‑baked slide types: problem, solution, market, team. The result looks polished, but the underlying story often feels like a Mad Libs version of a Sequoia pitch outline.

Ethan Nelson’s Claude Skill attacks a different layer of the stack: narrative coherence. Framed as an AI workflow rather than a standalone app, it leans on Claude’s long‑context reasoning to generate a full problem–solution arc, competitive positioning, and financial logic from a single paragraph. Instead of “fill this slide,” the Skill asks, “what is this business, and how would a VC expect to hear about it?”

That distinction matters in a market already crowded with AI slide factories. Tome and Gamma optimize visual storytelling with dynamic layouts and web‑style pages; Decktopus focuses on quick, semi‑guided slide generation with presets for sales, education, and fundraising. Claude’s Skill uses the same raw model that writes code and legal memos to infer a VC‑style narrative, then outputs a 12‑slide deck as a byproduct.

Legacy pitch‑deck players feel this pressure too. Slidebean, which built a business on expert‑designed Investor Pitch Decks and consulting, now pushes its own AI generator that assembles slides from a short brief. Startups like Beautiful.ai, Pitch, and Plus AI layer similar automation on top of classic presentation workflows.

Founders now face an arms race of “smart” decks, many claiming VC‑approved structures and data‑driven storytelling. Comparison guides such as 10 Best AI Pitch Deck Tools: Free and Paid Options already break the space into template‑driven generators, design‑first tools, and model‑centric approaches like Claude. The competitive question is no longer who can make slides fastest, but who can best encode how investors actually think.

Your New Workflow: AI Draft, Human Polish

Speed run comes first. Open the Claude Skill, paste a tight, 3–5 sentence description of your startup—problem, product, target user, business model—and let it generate the 10–15 slide outline. You want structure, not perfection: problem, solution, market, traction, competition, business model, roadmap, team, financials, and a clear ask.

Treat that output like a scaffold. Duplicate the deck into your preferred editor—Google Slides, Keynote, or PowerPoint—and keep slide titles and sequence mostly intact. Resist the urge to wordsmith yet; you will delete half this copy anyway.

Numbers come next. Every AI‑generated metric—TAM, conversion rate, CAC, LTV, revenue forecast—should trigger a red pen. Replace them with figures sourced from:

• Bottom‑up market sizing (customers × price)
• Your own funnel data or beta metrics
• Explicit financial models in Excel or Sheets

Assume any unedited AI number will be the first thing a partner challenges. If Claude says your TAM is $10 billion, document exactly how you get to $187 million SOM over 5 years instead. Add footnotes or speaker notes with links to reports, surveys, and internal dashboards.

Language then needs your fingerprints. Rewrite every slide in your own voice, even if you keep Claude’s structure. Swap generic lines like “We leverage AI to optimize workflows” with specific claims: “We cut invoice processing time from 3 days to 3 hours for 27 pilot customers.”

Anchor the deck in real stories. Add 1–2 short customer anecdotes, direct quotes, and screenshots of emails, dashboards, or product UI. Replace stock competitive grids with the 3–5 rivals your prospects actually mention on calls.

Design deserves a separate pass. Export the content into Figma, Keynote, or Pitch and standardize typography, color, and layout. Use consistent iconography, real product screenshots, and simple charts instead of AI‑generated clip art or default templates.

Beyond Decks: AI as Fundraising Infrastructure

Investor Pitch Decks are quickly becoming just one node in a much larger AI-assisted fundraising stack. Instead of treating deck creation as a one-off magic trick, founders are wiring tools like Claude into every repetitive, high-friction part of raising capital.

Outreach sits next in line. Founders already use AI to draft cold emails that adapt tone and content for a specific partner, fund thesis, or stage, pulling in details from LinkedIn, Crunchbase, and past correspondence. Models can generate 10 tailored variations in seconds, then A/B test subject lines and calls to action across a 200-contact target list.

Once conversations start, AI turns into a data-room traffic cop. Tools ingest financial statements, product specs, customer contracts, and security policies, then generate instant Q&A, redline summaries, and risk overviews for investors. Instead of manually stitching together responses, founders can query, “What are our top 5 churn risks?” and get a sourced answer pointing at specific PDFs and spreadsheets.

Scenario modeling is quietly becoming the most powerful upgrade. Founders can ask, “What happens to runway if we cut burn 20% and convert 10% of our waitlist?” and get dynamic projections, hiring plans, and sensitivity analyses. Modern models can generate multiple cases: - Base plan with current burn and growth - Aggressive plan with faster hiring - Survival plan extending runway to 24 months

Taken together, the pitch-deck skill Ethan Nelson showcases becomes just one surface area in a continuous, AI-driven fundraising workflow. Decks, investor briefs, follow-up notes, and board updates all pull from the same underlying model of the business. Fundraising stops being a series of handcrafted artifacts and starts looking more like an always-on system founders can query and reconfigure on demand.

The Investor's Perspective: Signal or Noise?

Investors have already started playing “spot the template.” After scanning hundreds of decks per month, most VCs can tell when AI has stitched together a Sequoia-style outline with generic copy and suspiciously round numbers. Pattern recognition is their job, and AI-generated Investor Pitch Decks create very loud patterns.

On the upside, many investors quietly welcome these tools. A Claude-generated deck that cleanly walks through problem, solution, market size, traction, and ask lets them skip the “what do you even do?” phase and jump straight to diligence.

Clear structure especially helps first-time founders and non-native English speakers. When Claude forces you into a standard problem–solution–market narrative, partners can benchmark you against the hundreds of similar pitches they already know. That speeds up “no” decisions but also accelerates the occasional “yes.”

The downside shows up in volume and sameness. Partners already complain about inboxes flooded with decks that all claim a “$50B TAM,” “10x better” product, and “AI-powered” everything, with no evidence attached.

AI pitch tools risk turning that trickle into a firehose of low-effort outreach. A founder can now spin up a dozen half-baked ideas in an afternoon, each wrapped in the same slick 12-slide format, forcing investors to spend more time filtering noise.

Veteran VCs respond by turning the screws on proof. They look harder at: - Actual revenue or usage - Cohort retention and unit economics - Unique distribution or defensibility

They also probe for depth behind the slides. If you cannot explain your own financial model without reading, or you fumble when they tweak assumptions, that polished deck becomes a liability.

Used well, AI becomes a thinking aid, not a mask. Investors consistently say they fund sharp, original insight into a market, not whoever has the prettiest Gamma or Tome export from a list like 6 Best AI Pitch Deck Generators in 2025.

Founders should treat Claude as a ruthless editor that forces clarity and order. The real signal still comes from the messy work underneath: hard data, customer conversations, and a plan you can defend under cross-examination.

Will Human-Made Pitches Go Extinct?

Human-made decks will not vanish; they will just stop being the scarce resource. When a Claude Skill can spin up a 12-slide Investor Pitch Decks outline in seconds, the bottleneck moves from “Can you build a deck?” to “Do you have anything compelling to put in it?” Structure becomes cheap; substance does not.

Founders will treat these tools like spreadsheets or Google Docs: default infrastructure. Just as no one brags about hand-coding a cap table in C, no one will brag about hand-kerning slide titles. You will plug your startup description into Claude, get a VC-style flow, then spend time on what actually moves a term sheet.

As decks converge on similar problem–solution–market–financials arcs, investors will lean harder on signals that AI cannot fake at scale:

• Actual traction: revenue, retention, DAU/MAU, cohort curves
• Team quality: founder–market fit, shipping velocity, prior outcomes
• Proprietary insight: data moats, hard-won domain knowledge, unique distribution

A pre-seed investor might still accept a mostly AI-written narrative if the product is live and growing 20% month-over-month. A Series B investor will care far more about pipeline coverage, gross margin, and churn than about your beautifully auto-generated competitive matrix.

Founders who refuse these tools will not look more “authentic”; they will just move slower. The edge will belong to teams that automate the drudgery—deck drafts, model scaffolding, FAQ prep—so they can run more experiments, talk to more users, and refine sharper hypotheses before every meeting.

Use AI to remove excuses. If a full deck is now a 60-second task, the hard question becomes unavoidable: is there a business here that deserves funding, or just a well-formatted story?

Frequently Asked Questions

What is the AI pitch deck tool from the video?

It's a custom 'Skill' for Anthropic's Claude AI that generates a multi-slide investor pitch deck from a simple business description, including narrative, financials, and market sizing.

Can this AI tool replace a human pitch deck designer?

It's best used as a first-draft generator and structural guide. For a final, polished deck, human refinement for brand consistency, data accuracy, and strategic nuance is still essential.

How does this tool know what VCs want to see?

The tool is pre-programmed with a standard, best-practice VC pitch deck structure (e.g., Problem, Solution, Market, Competition, Ask), encoding common investor expectations into its output.

Are the financial projections from the AI tool reliable?

No. Without real data input, the AI generates placeholder or estimated financials. Founders must replace these with well-researched, defensible models based on their actual business.