The biggest problem in AI isn't building the product.

It's staying relevant six months later.

Models improve every month. New competitors appear every week. And switching costs are lower than most founders want to admit.

So what actually creates a moat?

In this episode of Verticals, Luke and Nic sit down with Kyle Lui from Bling Capital to break down the new rules for product-market fit, market sizing, and defensibility in the AI era.

They also cover:

-Why AI-native companies are growing faster than SaaS ever did

-Why some of the best opportunities now look “too niche” for traditional VC

-How AI is changing org charts, hiring, and management itself

If you are building, investing, or trying to understand where vertical AI is actually headed, this is a sharp tactical breakdown of what matters now(and what probably won’t matter anymore).

👉 FOLLOW KYLE LUI

LINKEDIN ➡️ https://www.linkedin.com/in/kylelui

👉 FOLLOW LUKE SOPHINOS

X ➡️ https://x.com/lukesophinos

LINKEDIN ➡️ https://www.linkedin.com/in/lukesophinos/

NEWSLETTER ➡️ https://www.newsletter.lukesophinos.com/

👉 FOLLOW NIC POULOS

X ➡️ https://x.com/picnoulos

LINKEDIN ➡️ https://www.linkedin.com/in/npoulos/

NEWSLETTER ➡️ https://insights.euclid.vc/

🎬 TIMESTAMPS:

00:00 Intro

03:26 The frameworks top investors use to spot breakout startups

07:19 What product-market fit actually means now

13:12 Why “AI services” are changing software economics

18:05 The hardest question in AI

24:36 The hidden advantage of routing across multiple models

31:04 Are AI models becoming a commodity?

35:47 How AI companies tap into labor budgets

39:16 Why “too niche” markets might become massive in AI

40:23 Will vertical AI become winner-take-all like SaaS?

45:14 The biggest mistake investors are making right now

52:21 The niche AI market that surprised even their LPs

If you enjoyed this episode, drop a comment and hit subscribe so you don’t miss what’s next!