I have been reviewing dozens of pitch decks that are submitted to the Vestbee platform. These startups operate in different verticals, come from different countries, and are at different stages of development, and yet somehow, some of them look strangely the same.
The writing style, grammar, typography, visuals, slide structure, and even the tone of voice feel interchangeable to me. Some decks are polished, competent, and perfectly aligned with the startup advice circulating across LinkedIn. You could easily mistake one for the other, even though one sells, let’s say, drones, and the other sells subscriptions for velvet sofa rentals.
It made me think that many of these decks had been created with significant help from AI, and I could even recognize the same Gamma templates being used across different companies. To be clear, I don’t think using AI is a bad thing. However, AI-generated content makes it harder and harder to actually spot good, disruptive solutions.
Investors review hundreds of startups every year. When numerous decks use the same visual language, the same market narratives, and the same polished framing, VC attention becomes harder to earn. Previously, we published an article featuring advice from European investors on the mistakes to avoid when creating pitch decks and pitching. This time, we share the observation on recurring AI patterns and our perspective on how founders can stand out from the crowd.
The trap of homogeneity
AI can produce good writing, but even skilled prompters can be surprised, as behind the generated flowery prose, there can be little original insight and real evidence.
Many AI-assisted decks follow the same formula. They open with a “massive” problem, present a “simple” solution, identify “perfect” market timing, describe a “huge” opportunity, claim “clear” differentiation, and conclude with a “compelling” vision of the future.
The structure is familiar, but it seems like the founders are no longer presenting evidence that they understand a market, but rather a simulation of what market understanding is supposed to look like. A deck becomes a performance of what a startup should look like rather than an explanation of what the startup actually is.
The missing “why”
At the early stages, investors often bet on the team and the founders, but it can be difficult when the unique founder’s perspective disappears behind layers of generated text and AI-suggested slide templates. Instead, we see buzzword density reaching its peak: "ecosystem intelligence," "AI-operated revenue channels," "multi-agent workflows," and similar phrases that sound as though they were generated from a blend of consulting reports and startup newsletters.
What is usually missing is the insight that could only come from direct exposure — actual data, rooted in the human experience, specific to the solution, to the described market, and the audience. AI tends to smooth out exactly the kind of sharp, unconventional insights that lead founders toward meaningful breakthroughs. If a deck sounds like everyone else's, investors may reasonably wonder whether the strategy does too.
The perfectly “de-risked” startup
Another recurring pattern in the AI-generated narration is the elimination of risk and the expressed conviction that lacks nuance. We often see this in completely abstract claims — a product is described as "100% de-risked", or “the R&D is supposedly assured”, or the market fit is “perfect”.
Some hardware startups say they have "finished products" and nothing more to prove that, or software startups describe their markets as if customer adoption is inevitable. These claims need to be supported by facts and numbers.
AI-generated narration is usually full of “empty” adjectives to mask the messiness that comes from being an early-stage startup in a chaotic world. Which is understandable, but it is debatable whether, for example, hardware investors are looking for a compelling narrative wrapped in a sleek, AI-generated imagery of a tool that looks like a cyberpunk prop.
So, the traction slides don’t have to be perfect, but really actually rooted in the reality of what the founder is building. They should signal competence and evidence of real-life validation, beyond just the “success story narration”— customer adoption retention curves, usage patterns, conversion rates, revenue growth, failed experiments, lessons learned, and so on.
A human pitch
Pitching is proof of the founder’s work. It’s a proxy for how they will expand their business, recruit talent, and sell to enterprise customers. Many of these tasks cannot be outsourced to a generative model. Founders, don’t be scared of putting in your pitch some weird, industry-specific words your customers use, not "optimized ecosystem synergy”.
