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This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 3,600+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.
TL;DR:
- In early-stage AI, great founders and real revenue matter more than pitch decks and projections.
- General-purpose AI tools are fading; startups with vertical depth and real-world traction are standing out.
- AI due diligence is still human-led, but smart tools help filter, prioritize, and evaluate broader opportunity sets.
- Corporate-backed spinouts with AI IP and proven demand offer faster paths to market and stronger moats.
- Austin sees the biggest near-term wins going to vertical AI startups that move fast, solve real pain, and build with domain insight.
Before we dive into the key takeaways from this episode, be sure to catch the full episode here:
Meet Austin - Founder and Venture Investor
Austin Moss brings a unique dual perspective to venture: a founder who’s built multiple companies from scratch and an investor who’s reviewed thousands of businesses. With a background in alternative finance and a deep understanding of operational execution, Austin applies his entrepreneurial instincts to early-stage investing.
Today, he runs seven companies and a venture firm, with a focus on vertical AI, fintech, and healthcare. He looks for founders with proven grit, past exits, or at least post-revenue traction people who know how to go from zero to one and beyond.
Austin believes in hands-on support, fast feedback loops, and scalable intelligence across teams. Whether investing or incubating, he pushes for real products that solve real problems, not just hype-fueled concepts with short shelf lives.
The Founder Litmus Test Still Matters Most
For Austin, the team behind the startup is still the clearest signal of long-term success.
“The first litmus is the founder, right? Do they have the competency? Do they know what they’re doing?” he says.
He looks for operators who have either exited before or already have paying customers. Without that, it is often just too early. “If they have revenue and there’s some sort of IP or some sort of moat, then it’s something we’ll look at,” he adds.
Rather than getting distracted by hype, Austin’s approach centers on experience, grit, and evidence of execution. Founders who can move fast and are deeply immersed in their space stand out.
“You’re betting on people,” he explains, “not PowerPoints.” This is how business leaders should approach choosing an AI provider, too, especially if they have sensitive workflows. The team behind the products and solutions matter, a lot.
Why Generalist AI Startups Are Losing Steam
Austin believes the era of broad, general-purpose AI products is already waning.
“Just building something general is not the right way right now.” — Austin Moss
He sees more traction in startups that are vertically integrated with domain-specific insight. “It’s like the GPT version of TurboTax—okay, that’s cool, but is it solving a real pain?” Startups trying to boil the ocean or chase every use case often fail to find a wedge.
“There’s a lot of cool stuff out there, but not a lot that’s making money for VC,” Austin adds.
Investors now prioritize focused, problem-driven businesses that know their customer, industry, and buying cycle.
If the product cannot deliver value in a specific workflow, it will not last.
Spinouts and Vertically Integrated AI Offer a Faster Path
Some of Austin’s favorite models right now are spinouts from companies that already have traction and domain-specific data. “You’re the guinea pig for your own product,” he says.
“You’re not testing, you’re going to market.” These ventures often begin with internal demand, then scale externally once proven. Austin values this go-to-market clarity because it de-risks adoption and improves the feedback loop.
“That’s when you can actually move really fast,” he explains. These businesses often have distribution, data access, and a clear use case from day one.
“It is not just about the tech. It is about the use case and the distribution.” — Ankur Patel
The best part? They can avoid the typical startup cold-start problem. “You’re not just building with a theory. You already know what the business needs,” says Austin. That advantage matters now more than ever.
AI in Diligence: Screening Is In, Decision-Making Is Not
Austin and his team use AI tools to help with inbound filtering, but final decisions remain human-led.
“We use it to get to yes or no faster, but we still want to talk to the right founders.” — Austin Moss
He is skeptical of pitch decks and early-stage data that might be inflated or AI-generated. “Ninety percent of the time, the data is bullshit and created by AI,” Austin explains. While the tools can help identify patterns or surface hidden gems, they are not replacing judgment.
The nuance behind a good deal, founder psychology, GTM readiness, and customer feedback cannot be fully captured by a model. “It’s helpful for scanning, not investing,” he says. Good investors still do the hard work themselves.
So, when choosing an AI startup to partner with or invest in, business leaders and investors must consider whether the solutions allow them to keep humans in the loop — especially in regulated industries.
Building a Moat in a 12-Month Shelf-Life Market
With AI moving so fast, the average product shelf life is shrinking. “Once something is good, it gets copied. You have to be the best early,” says Austin.
He and Ankur both note that vertical depth and execution speed now define defensibility. “The product shelf life is under 12 months,” Ankur adds.
That means founders cannot rely on model quality alone. They need customer traction, integration into real workflows, and a feedback loop that compounds value. “We’re seeing moats built around speed and customer intimacy,” Austin says.
Startups that win are the ones that move quickly and go deep. “If you can own the process, own the data, and build trust fast, you’re hard to beat,” he explains.
