Why AI’s Next Chapter Will Be Vertical, Not Viral
There’s a recurring myth in tech that if you build something powerful enough, it can serve everyone. But as we move into the next chapter of AI, the real leverage is showing up not in broad capabilities, but in narrow, deep application.
Mike and I broke down why verticalization isn’t just a trend but becoming a requirement. Whether you’re building in AI, SaaS, or services, general-purpose is being commoditized fast. The moat now? Deep context, tailored service, and real understanding of a specific market’s pain.
Here’s the breakdown:
1. Verticalization isn’t just a GTM move. It’s a product strategy.
You don’t pick a vertical because it's hot - you pick it because it has signal. The strongest indicators aren’t always in TAM slides or investor decks; they’re in the conversations you keep having, the workflows that keep breaking, and the end users who light up when they see a better way. That’s what happened with Improve and RIAs (Registered Investment Advisors). They weren’t the original target—but they became the clear answer.
2. AI doesn’t replace humans. It scales their capacity.
We’re entering a phase where “AI-powered” doesn’t mean removing people. Instead, it means enabling them to handle 2x or 3x the relationships with greater context and less cognitive overhead. Financial advisors, lawyers, consultants — their value comes from trust and judgment. AI can’t replicate that. But it can remember your last conversation, draft the right follow-up, and make the experience feel personal, even at scale.
3. Product-market fit is now persona-market fit.
It’s not enough to build for a job title; you need to build for a lived experience. Understanding how that person feels about their work, what slows them down, and where their energy goes matters more than ever. The most effective vertical AI products aren’t transactional tools; they’re operational sidekicks. You only get there by immersing yourself in the world of your user through conversations, not just data.
4. Trust is the differentiator. Not just functionality.
Founders love features. Users care about trust. In highly regulated industries like wealth management, that means respecting privacy, building for compliance from day one, and showing your hand in terms of how you handle data. General-purpose AI can feel like a black box. Purpose-built tools, especially those with service components, have an edge because they know what matters to this audience.
5. The future is small teams, high trust, AI-augmented.
As Mike said: “The future belongs to small, tight-knit teams augmented by AI.” That’s not just a punchy quote—it’s the new operating model. High-performing boutiques, vertical platforms, and service-plus-software hybrids are going to beat the bloated generalist players because they’re designed to care about the customer in front of them, not the hypothetical millions they might one day reach.
The bottom line?
AI isn’t about building smarter tools. It’s about building smarter companies—ones that know who they serve, why they matter, and how to scale service without losing soul.
Takeaways for Builders and Operators:
Look for signal, not scale. TAM can be misleading. Build where the urgency is felt.
Use AI to deepen relationships, not just automate tasks. Retention is still a human game.
Specialization is a moat. In a world of public LLMs, context becomes the differentiator.
Customers don’t care if it’s AI-powered. They care if it solves their problem with minimal friction.