Your Startup Deserves Better Than AI’s Pattern-Matching Pitches

The fever around AI has many founders asking whether they should let an LLM like ChatGPT take a crack at their pitch deck. After all, if AI can write code and create marketing copy, why not trust it with your startup's story?

In Short: No

LLMs are essentially sophisticated pattern matchers, but I would argue unsuitable for breakthrough ideas. These models work by predicting what words should come next based on the statistical patterns in their training data – they're essentially playing a complex game of "finish this sentence." And while they might seem magical, they're ultimately bound by the patterns they've seen before, creating legible remixes rather than truly novel thought.

Most models try to fake insight with a dash of randomness in their output (what AI researchers call "temperature"), but even this randomness is still just pulling from their statistical set. Your startup’s idea is the opposite of pattern matching. You're building something new, something based on your real-world insight that will break patterns. Something that will disrupt.

Think about how a founder identifies a real opportunity: they notice friction in daily life, spot inefficiencies in their industry, or experience a problem firsthand that no one else has solved. This kind of pattern breaking – seeing what's missing rather than what's already there – is precisely what LLMs can't do. They can tell you all about successful companies that exist, but they can't identify the gaps that will lead to the next breakthrough.

Regardless of the effectiveness of AI, creating a pitch deck isn't just about the end product. It's a mental exercise for founders to crystallize their thinking about their market, users, and competitive landscape. Skipping this process by outsourcing it to AI is like trying to get in shape by having someone else do your pushups. The real value isn't in the deck itself – it's in the mental model you build while creating it.

After working with countless founders on their decks, time and time again I hear that the shiny slides were just half the value, and an often better takeaway from fundraising is the deeper understanding of their venture and how it fits into a changing world. Even if AI gets good enough in the next few years to truly create great decks, I'd still recommend founders go manual so they can experience the deep dive.

Where AI Actually Makes Sense

That said, AI isn't entirely useless in the pitch process. Consider using it as an occasional tool, not a crutch.

Some founders have the opposite problem of having too little to say – they have too much. When you're deep in the trenches of building, it's easy to write paragraphs where sentences or even half sentences would do. This is where AI shines: as an efficient editor. Feed it any verbose slide, and it can help distill your message to its essential elements while preserving your core insights. Founder bios are an obvious candidate for this treatment – AI can help transform a meandering career history into a sharp narrative about why they’re the right person to solve this specific problem. The same approach works for market analysis slides that read like research papers, or product descriptions that get lost in technical details.

Again, the mental exercise of cutting down text yourself is probably more useful in helping you understand what really matters for your business. But using AI in this way at least avoids the issues of hallucination or lacking your novel insights.

Once you feel your deck is ready for primetime, AI can be used to review and practice your pitch. Tools like Fornax.ai can analyze your deck against common pitch deck tactics, highlighting where you might be breaking conventional wisdom (in good or bad ways). Additionally, platforms like Yoodli can help you with verbal pitch practice by providing AI feedback on everything from your pace to your handling of questions.

The key is remembering that AI can help you refine your pitch, not create it. Your unique insights, experience, and deep understanding of the problem you're solving – that's still on you.