Beyond the AI Hype: How to Stand Out to Investors

Artificial Intelligence (AI) continues to be one of the most dominant forces in early-stage investing. The funding environment for AI startups remains strong, with 37% of investors backing an AI company in 2024, according to our latest State of the Pre-Seed Market report. However, as more AI startups flood the market, investors are becoming more selective, prioritizing differentiation and real-world applications over generic AI solutions.

At our recent State of Pre-Seed & Seed VC panel, investors echoed this sentiment, emphasizing that founders must stay disciplined and avoid chasing the AI hype without a clear, scalable business model.

1. AI is an Enabler—Not the Business Itself

Many founders mistakenly assume that because they use AI, they should position themselves as an "AI company." But for most investors, AI isn’t the product—it’s a tool.

📌 Jenny Fielding (Managing Partner, Everywhere Ventures) made this distinction clear:

💡 “AI is an enabling technology—it’s not the business itself.”

At Everywhere Ventures, they don’t invest in “AI-first” companies. Instead, they back startups solving meaningful problems in healthcare, fintech, climate, and other industries—whether or not they use AI.

Why This Matters

✅ AI is now table stakes – Most companies leverage AI in some way. It no longer differentiates you.

✅ Investors care about outcomes, not tech buzzwords: If you’re building an AI company, focus on an industry-specific problem where AI creates 10x better efficiency, cost savings, or user experience—not just AI for AI’s sake.

✅ The best AI startups are solving big, real-world problems – AI should be a means to an end, not the entire value proposition.

✅ Tailored AI solutions in healthcare, finance, and manufacturing are seeing increased investment, as these sectors require domain-specific AI models with deep industry expertise.

Key takeaway: Instead of branding your company as an "AI-first" startup, focus on the problem you’re solving and how AI enhances your ability to do it better.

2. The AI Gold Rush: How Investors Are Thinking About It

The surge in AI investment has led to both opportunities and challenges for founders. While capital is flowing into AI, investors are becoming more discerning.

🚀 According to Silicon Valley Bank’s H2 2024 report, the median time for a startup to reach unicorn ($1B+) valuation status is 6.8 years, but AI startups have accelerated this timeline to 5.4 years.

💰 30% of these unicorn AI companies are still considered early-stage—raising concerns that some valuations may be outpacing real business fundamentals.

💡 “We look for differentiated solutions that leverage AI to solve real problems.” -  Brian Hollins (Managing Partner, Collide Capital) 

How VCs Are Evaluating AI Startups

✅ Clear differentiation – Is your AI-driven solution truly unique, or is it just a thin wrapper around existing technology?
✅ Tangible business value – Are you using AI to reduce costs, increase efficiency, or create new revenue streams?
✅ Data & defensibility – Does your AI solution improve over time? Do you have proprietary data that strengthens your competitive moat?

💡 Brian also cautioned against chasing trends: “Don’t build an AI business just because it’s hot. Build something that stands out in a crowded space.”

Key takeaway: AI hype can help you get in front of investors, but it won’t guarantee funding. VCs are looking for sustainable business models and long-term competitive advantages, not just an AI label.

3. How to Differentiate Your AI Startup in an Overcrowded AI Market

With thousands of AI startups emerging, how can you stand out?

Tactics for Differentiation

✔ Specialization beats generalization – Instead of being a “broad AI company,” focus on a niche with high-value, underserved customers.
✔ Unique data sets – Proprietary data is one of the strongest moats in AI. The best AI startups own their data advantage.
✔ Clear ROI – Can you quantifiably show how AI improves efficiency, revenue, or customer experience? If not, investors won’t buy in.
✔ Strong technical & business leadership – Having an AI-savvy founding team that understands both technology and go-to-market execution is crucial.

💡 Jenny summed it up: “The most successful AI startups aren’t just riding the AI wave—they’re solving high-impact problems with AI as an advantage.”

4. For Non-AI Startups: Should You Incorporate AI?

If you’re not an AI startup, should you integrate AI into your product?

When It Makes Sense to Use AI

✅ To enhance efficiency – AI can automate workflows, improve personalization, and optimize decision-making.
✅ To create defensibility – If AI allows you to develop a stronger moat (e.g., proprietary recommendations, fraud detection), it’s worth exploring.
✅ If it’s core to the user experience – AI should be more than a feature—it should fundamentally improve how customers interact with your product.

When AI Isn’t Necessary

🚫 If it’s just a marketing gimmick – Slapping “AI-powered” on your website won’t fool investors.
🚫 If it doesn’t meaningfully improve your product – If your solution works fine without AI, adding it just for the sake of it won’t move the needle.

Key takeaway: If AI can genuinely enhance your product, use it. But don’t force AI into your business just because it’s trendy.

Final Takeaways: Winning in the AI Era

✔ AI is a tool, not the business – Focus on the problem you’re solving, not just the technology.
✔ Investors want differentiation – The most successful AI startups have a strong moat, clear ROI, and real business value.
✔ Avoid the AI hype trap – Don’t build an AI startup just because it’s hot—focus on long-term sustainability.
✔ If you’re not an AI company, use AI strategically – Leverage AI to enhance your solution, but don’t force it if it doesn’t add real value.

By staying disciplined and focusing on differentiation, defensibility, and tangible business impact, AI founders can rise above the noise and attract the right investors.

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FAQ

1. What is the focus of the article?
   The article examines how AI startups can attract early-stage investment by emphasizing real-world applications, differentiation, and sustainable business models.
2. Why shouldn’t startups simply label themselves as “AI-first”?
   Investors are looking for tangible business value and clear problem-solving; AI is seen as a tool, not the core business.
3. What factors do investors prioritize in AI startups?
   Investors look for clear differentiation, measurable ROI, proprietary data advantages, and a strong balance between technical innovation and market execution.
4. How can non-AI startups benefit from incorporating AI?
   Non-AI startups can enhance efficiency and create defensible products with targeted AI integration, provided it adds real value.
5. What are the key takeaways for founders in the AI era?
   Focus on solving specific problems, avoid riding the hype, and ensure that any AI integration provides a meaningful, scalable advantage.

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