For the last few years, AI has had the venture market in a frenzy. Everyone, especially venture capitalists (VCs) bought into the hype, seemingly writing checks whenever someone mentioned AI in a pitch. Valuations soared, and even the most basic chatbots and AI applications won funding.
But now, VCs’ attitudes have undergone a fundamental shift. No longer are they impressed with the fact that you have a GPT wrapper. Where’s the unique insight? Where's the defensibility? VCs are learning hard lessons from lofty AI investments that overpromised and underdelivered. This new attitude has driven the criteria and standard expectations for securing funding for AI startups to evolve rapidly.
If you’re a tech founder of an AI startup and you’re looking for venture funding in 2026, here are four things to consider before pitching your company.
What Do VCs Want to See From AI Startups?
- Proof of customer traction and defensibility, i.e., a moat.
- Superior execution.
- Hiring and retaining the best AI talent.
- Ensuring the business is ethical, transparent, and regulation-ready.
More Proof of Substance
The barrier to putting a product in the market is the lowest it has ever been. In most cases, you need to show proof of customer interest; this is even more true if you’re a first-time founder. A year ago, businesses were still trying to understand if AI would be useful. Today, they’ve realized they can’t afford to sit on the sidelines.
But many questions remain around retention, renewal and defensibility. Unlike the past, a startup landing pilots and generating $1M in early revenue is no longer a strong indicator of product-market fit or a sustainable business. Investors now want to know what happens after you’ve tested your product. Did you learn something from clients that churned? For customers who churned, what alternative did they go with and why? Do you have a unique distribution that gives you an unfair advantage to get in the hands of more customers?
Expect VCs to push back on $20 to $30 million valuations with only a few hundred thousand in revenue and no clear moat. Of course, top tier accelerator graduates will get exceptions because many VCs still chase the same pipelines for quality deal flow, but that won’t be common to founders in general.
The bar is rising: Startups need to show not just traction, but repeatability and defensibility. VCs no longer care about who’s first to market with a flashy AI demo. They want to know who’s building something that can last, earn trust and actually reshape how work gets done.
Explain Why Someone Else Can’t Do This
Founders who moved quickly on generative AI have established several new, generational companies. These founders raised a lot of capital from VCs in a frothy market and delivered results. They can defend their niches because they capitalized and delivered before the rest of the world.
I speak to hundreds, if not thousands, of tech founders a year who pitch my fund as they raise their seed round. The one thing VCs, including myself, care most about are the founders who effectively explain their competitive edge by focusing on a few key areas.
1. Unique Value Proposition
VCs want to see proof of the specific benefits your AI product offers that competitors do not. This needs to go beyond features, as those can be easily mimicked, but focus more on the tangible outcomes for the customers.
2. Differentiation
Highlight specific differences that make your AI offering superior to others. Is your data proprietary? Is it deeply differentiated from large general purpose LLMs, etc? Do you have exclusive partnerships that others can’t secure? Double down on this.
3. Moat
Is your business sustainable? Explaining what will prevent other competitors from taking over your turf is crucial. This again goes back to intellectual properties or deep domain expertise within the team.
VCs want to back companies that can’t easily be replicated by a competitor. So many tech founders believe that their product is one of a kind, but in reality there are a vast number of other companies doing the exact same thing, but better. Whomever can execute the best will get VC money.
Hire the Best AI Talent
You have to hire and retain the best AI talent if you want VC money. Hiring top AI talent is crucial for business success because it enables innovation, drives efficiency and provides a competitive edge in an increasingly AI-driven market. VCs want to know what you are doing to attract and retain the best AI candidates. Especially as the demand for the top AI talent is growing faster than ever, this needs to be a founder’s number one priority.
As a VC, I can tell you that the field is paying more attention to who is on the team. AI has changed the way we analyze talent. Demonstrate what your team’s strengths are. What unique backgrounds and expertise sets them apart from the sea of talent out there? Founders need to know that it’s not enough to offer AI talent a high salary and cool benefits. The top AI talent are only interested in opportunities where they can truly innovate and make a real impact.
Whether you’re looking for ML engineers or data scientists, make sure you’re finding talent that is eager to uphold responsible AI practices. Sure, it’s important to find candidates who can design and deploy scalable ML models across various data sets or those that understand model integration. But it’s becoming painfully obvious that the top AI talent are looking for forward-thinking organizations that operate ethically. AI biases are real. The workplaces and their teams that honor the importance of AI guardrails will attract the best AI talent organically.
Be Ethical, Regulatory-Ready and Transparent
Building thoughtful guardrails that allow space for forthcoming regulation has never been more important for AI. VCs are actively looking for founders who understand the regulatory landscape, are proactively engaging with emerging standards and are building with ethical foresight. In many ways, being “regulation-ready” is becoming as important as product-market fit because the companies that anticipate compliance challenges will be the ones who scale with stability and trust. We want to know whether or not you’re violating any IPs or trademarks and if your solution will be accepted by your customer set based on current and forthcoming policies.
For example, California (where a huge portion of AI companies are based) recently passed the Transparency in Frontier Artificial Intelligence Act, the first state law to require large AI developers to disclose publicly a safety framework that incorporates widely accepted safety standards and explains a model’s capacity to pose and mitigate “catastrophic risks.” More rules and regulations like this will be forthcoming and VCs want to know if your business is ready.
Ethical AI practices are a necessity. Investors now frequently ask harder questions: How is the model trained? Can your outputs be audited? What is your model’s mental framework? Companies that can confidently answer these questions are not only better positioned to raise venture funds, but also to secure the trust of larger enterprise customers. In today’s market, founders need to build AI that is both ethical and regulation compliant, giving them new potential moats as the landscape evolves.
As AI startups go out to pitch VCs, it’s important to demonstrate product-market fit and traction. That’s rule number one. We also want to know that you have the best talent working on the product, your regulatory readiness and ethics are intact, and that you’re building a tech company that can’t be easily replicated by competitors. If you possess all of this, chances are you’ll close your next funding round.
