AI startups in the US won’t win on the best model. Learn why defensibility, not ‘we use AI,’ will decide who raises seed funding and survives.
Every AI Pitch Deck Sounds the Same
A partner at a mid-size fund told me last month he’s seen over 200 AI decks this year. At least half open with some version of the same line: “We use AI to help [industry] do [task] faster.” He doesn’t remember most of them by Tuesday.
That’s not a knock on the founders. It’s a signal about where the market actually is. The future of AI startups in the US won’t come down to who adopted AI first. Everyone already has. What matters is who can survive a market where “we use AI” is no longer a pitch. It’s now the price of entry.
Why Thin AI Products Are Easy to Copy
Two years ago, building a thin interface on top of a foundation model was enough to raise a seed round. Investors were still figuring out what “AI-native” meant, so speed to market covered for a lot of thin positioning.
That window is closing. Foundation model providers keep absorbing the exact features startups built businesses around: a summarization tool, a chatbot wrapper, a document generator. They fold them into the base product for free. The startups feeling the squeeze hardest right now don’t have bad products. Their whole value sits one API call away from the company they’re building on top of turning it into a commodity. The future of AI startups in the US market belongs to the founders who can answer one question. What happens to their business the day OpenAI or Anthropic ships a feature just like theirs?
How AI Startups in the US Can Stand Out
The founders still raising well aren’t necessarily the ones with the most technically impressive model. They’re the ones who own something a foundation model can’t easily replicate. Maybe it’s a proprietary dataset from years in one industry. Maybe it’s a workflow so specific to one type of customer that a general tool can’t touch it. Or it’s distribution into a market that doesn’t trust a generic AI vendor.
An operations lead at a 60-person logistics company doesn’t want “AI-powered routing.” She wants a tool that already knows her carriers, her delivery windows, and her specific failure points. A generic model with no context on her business is just a worse version of what she already does manually. That specificity is where real defensibility shows up right now. It lives in everything around the model, the parts a bigger company has no reason to build.
Why the Next 18 Months Will Be Harder
Capital hasn’t dried up, but it’s gotten a lot more selective about where it goes. Wrapper startups burned a lot of investors in 2023 and 2024, so now they ask sharper questions earlier. What’s the moat? What does retention look like without a sales team hand-holding every account? What happens when the model itself gets cheaper and better next quarter? The market is going to reward founders who treated AI as a tool for solving a real problem. It won’t reward the ones who treated the problem as an excuse to build an AI product.
That’s a harder standard than the one the market applied eighteen months ago. That standard will separate the companies still standing at Series B from the ones that quietly ran out of runway.
Here’s a simple test. Write down what your best customer would lose if a foundation model shipped your feature tomorrow. If the honest answer is “not much,” fix that first.
Not sure if what you’ve built is a real business or a feature a bigger company will absorb? Start with finding real product-market fit. When you want a clear-eyed read on where your positioning actually stands, book a Scale call.
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