The Power and Promise of Vertical AI

**Ray Rike** (0:08)
Welcome back to AI to ROI, the Big Story Edition. I'm Ray Rike, founder and CEO of Benchmarkit. And joining me as always is my co-host, Peter Buchanan.

**Peter Buchanan** (0:19)
Yup, I'm Peter Buchanan. I'm the founder of NewPlan, and Ray, it is great to be here. This week, we're diving into what I think is the most important AI success story that nobody talks about.

**Ray Rike** (0:32)
Okay, a little bit of a tease here, but that's exactly right, because if you read and just pay attention to all the headlines, you think the AI market is all about open AI, raising $110 billion or anthropic in the federal government, getting into a war, so to speak, a war of words and contracts, and then using clotting and combat operations hours later. Hyperscalers spending $700 billion funded by debt, the issue around private credit, it's just chaos, Peter.

**Peter Buchanan** (1:03)
It is chaos. Yeah, totally.

**Ray Rike** (1:06)
But spoiler alert, while everyone's focused on the infrastructure and foundation, there's a category of AI companies that are quietly delivering massive, measurable AI to ROI. So maybe today we break down vertical AI, what it is, why the business model is fundamentally different from traditional SaaS, and how the funding environment has exploded for vertical AI companies. And then we'll profile four companies that really do define what vertical AI and why it matters.

**Peter Buchanan** (1:41)
Sure. So let's start by defining vertical AI, because the distinction between different types of AI is critical. So there are two types of companies in the AI application layer, which rides on top of all these famous models from OpenAI and Anthropic and Meta and Deepsea.
And so the first category is horizontal AI and we all use those products. It's things like Copilot, Google Workspace AI, Notion AI. So these tools give you a capability and they lead the business application to how you're going to use them up to you. So they're useful, but they're general purpose and they're not vertical. They're not embedded deeply into functional vertical workflows. But vertical AI is different.

**Ray Rike** (2:31)
Yeah, and I want to talk about vertical AI, but I want to ask you a question because we've talked about this before here in the podcast is that these coding assistants, Clod Code, Cursor, Lovable, Replet, they've really exploded. Would you consider them a horizontal AI application or vertical, Peter?

**Peter Buchanan** (2:51)
I think they're a combination of the two. I think they're horizontal and that you can use them basically to do anything you want. That involves software code, but of course you're building something that's specific and vertical with them and you're getting deeply embedded into a workflow where if the product is really successful and your pipeline is much faster and your code quality is higher, then yeah, it's pretty much like a vertical AI product, so it looks kind of horizontal. But the top products, Replete, Cloud Code, Lovable, Cursor, they're incredibly sticky.

**Ray Rike** (3:30)
They are. Well, but let's talk about the classic definition of vertical AI applications that primarily are built and purpose-built for a specific vertical industry or functional workflow. These companies don't make the AI models, they typically use them. They're not raising a lot of capex to build data centers. They're renting space in data centers, and then they're leasing the foundational models by paying for the inference and tokens on typically a consumption basis. But they're delivering real value, Peter, because they often don't just augment, but they can replace expensive labor, they can compress process execution time, and even increase revenue. And I'm going to say this probably two or three times throughout today's podcast. An important part of these vertical AI applications is they're often not just assisting humans, they are actually replacing quite a bit of the labor that humans do. But here's a question I have for you. How is this different than what some incumbent, I'll say technologists, software companies like Thomson Reuters are adding to Westlaw? They're using AI too. What's the difference?

**Peter Buchanan** (4:47)
So it's a great question. So when Thomson Reuters adds AI features to Westlaw or Viva adds AI to CRM, it's a big improvement, users appreciate it. But without major investment in product architecture, the way they price, the way they organize their work, they operate in the pre-AI world. So they are helping a human do the job rather than actually taking over the process of doing the job like a typical vertical AI product would do.

**Ray Rike** (5:23)
Yeah, and I'll just harken back to a podcast episode we did about two weeks ago where we talked about three companies that really had made the progression from a legacy SaaS company to an AI-first company. We talked about Notion, Canva, and ServiceNow. So I highly recommend people to go listen to that if they want to see how some companies are actually trying to make the transition to be more AI-first or AI-native. But to your point, vertical AI companies are typically built on top of an AI foundation from day one. Everything about how they operate is designed around that reality, both a product architecture but also go to the market, and the results, they're growing faster than any enterprise software company or market ever has. So that's why I think the vertical AI business model is fundamentally different. An example is Bessemer Venture Partners has invested over a billion dollars in AI-native startups since 2023, and their vertical AI playbook that they recently published lays this out clearly. Traditional SaaS companies target IT budgets, or IT and software budgets, and vertical AI companies are targeting labor budgets. How big of a difference is this, Peter?