Why Vertical AI Beats Horizontal Copilots — The Moat Most Founders Miss

Every founder building a “general AI assistant” is competing with ChatGPT, Gemini, and Claude. The market is already won. The product is a feature in someone else’s roadmap. The path to a defensible business is not building a better horizontal copilot.

It’s building a vertical one nobody else can build.

Ali Parandeh — chartered mechanical engineer turned AI engineer, head of engineering at a London AI consultancy, and author of Building Generative AI Services with FastAPI (O’Reilly, ~500 pages) — has shipped generative AI services across cybersecurity, finance, retail, education, aerospace, and rail. He’s watched the same pattern play out: horizontal AI products commoditize fast, vertical AI products with domain depth survive.

His thesis is specific: the next wave of AI value is multi-agent workflow automation inside narrow verticals. And the moat isn’t the model. It’s the data and the domain expertise the founder already has.

Horizontal is saturated. Vertical has whitespace.

The horizontal AI category — general productivity copilots, meeting transcribers, chat-based assistants — is loud, crowded, and mostly indistinguishable from competitors.

“Chat GPT, Gemini, perplexity, all of these applications — they have been horizontal use cases,” Ali says. “Across all the markets they apply. Just general productivity. Copilot is all about general productivity.”

The whitespace is what he calls vertical-based use cases of agent AI: multi-agent workflows where each agent specializes in one part of a specific industry’s process. Form filling for procurement. Multi-step diligence in legal. Workflow automation in logistics distribution. These are products with one buyer profile, one workflow, one data type. They are not appealing to general-purpose AI platforms because the integration cost is high and the addressable market per workflow is small.

“What we haven’t yet seen is vertical-based use cases of generative AI and agent AI in particular — multi-agent, where they each talk to each other and do stuff for a specific workflow,” Ali says. “There will be a lot more opportunities for people to build agent AI applications in verticals and in niche areas. Workflow automations using AI in very niche verticals.”

That’s where the money is going. Not toward another general assistant.

The moat is private data, not the model

The horizontal players have better models than any startup will ever train. That’s not the moat. The moat is what you can do with private workflow data the model providers don’t have.

“Vertical is where there isn’t a lot of competition, there’s not much knowledge, but you also need a lot of domain expertise to understand the workflow, to be able to build with it,” Ali explains. “You also need private data that you may not have access to. So in order to actually write the system prompts for AI agents, you need to understand the workflow but also have data to show it. And that data you may not have access to because it’s locked behind a company’s internal systems that you are not an employee of.”

This is the real entry barrier. Building a vertical AI product for legal procurement requires understanding how procurement actually works AND having access to enough procurement workflow data to write system prompts that work. Both are nontrivial. The model is the cheap part.

The founders who succeed in vertical AI tend to be ex-operators from the vertical itself. They have the domain knowledge AND the data access — through prior employment, relationships, or partnerships. That’s the unfair advantage that’s actually unfair.

AI literacy varies dramatically by industry

Ali’s perspective on which verticals are ready right now: it’s market-specific, and the readiness gap is wider than most founders realize.

“From what I’ve seen, engineering in industries like traditional engineering — manufacturing, civil engineering, mechanical engineering — the market is still quite young. They’re still picking up AI. Most of them don’t know exactly what it is, how language models work.”

The mature markets are law, advertising, marketing, and finance. These industries already had data science teams before ChatGPT and now have working knowledge of generative AI at the leadership level. The opportunity in mature markets is depth — sophisticated multi-agent workflows that horizontal players can’t approximate. The opportunity in immature markets is education plus tooling — meet them where they are.

That’s a real strategic choice for a founder. Build for an industry that’s already AI-literate and compete on capability, or build for an industry that’s still learning and compete on education plus a default tool nobody else has bothered to make.

Hyper-personal vs sellable

There’s a counter-pattern Ali calls out that founders should be honest about. A lot of the most exciting AI use cases right now are hyper-personal — someone automating their own ad generation, their own content workflow, their own internal operations. These are real productivity wins, but they aren’t products.

“Everybody is automating their own workflows, it’s personalized. It’s super hyper-personalized,” Ali says. “But it’s not scalable. It’s not a company coming up with a micro SaaS or small thing that they’re selling. It’s not coding copilot, it’s not customer service bot. Everybody needs that — that’s horizontal.”

The vertical AI thesis only works if the workflow you’re automating exists across enough companies in the same vertical that you can sell the same tool to all of them. Hyper-personal workflows don’t generalize. The discipline is to pick a vertical workflow that 100+ companies have, that nobody is serving well, that you have unique access to understand.

What this means for AI founders right now

The framing Ali keeps returning to: horizontal is loud and crowded; vertical is quiet and defensible; hyper-personal is fun but not a business. The founders building defensible AI companies in 2026 are picking a vertical, getting data access nobody else has, and using that to write system prompts and orchestrate multi-agent workflows that horizontal players cannot replicate.

The model is the commodity. The data and the domain are the moat.

FAQ

What is vertical AI?

Vertical AI refers to AI products built for a specific industry workflow — procurement automation for legal teams, multi-agent diligence for finance, form filling for insurance — rather than general-purpose horizontal use cases. Vertical AI products require domain expertise and access to industry-specific workflow data, which creates a moat horizontal players can’t easily cross.

Why are horizontal AI copilots saturated?

The horizontal copilot category — general productivity assistants, chat interfaces, meeting tools — is dominated by ChatGPT, Gemini, Claude, and a long tail of similar products. Buyers can’t differentiate between them. New entrants compete on price and features that get copied. Vertical AI avoids this by serving a narrower buyer who pays for depth, not breadth.

What’s the moat for a vertical AI startup?

The moat is private workflow data plus domain expertise. To build effective vertical AI, you need access to data that lives inside an industry’s internal systems and understanding of how the workflow actually runs. Model quality is no longer a moat — every player has access to similar foundation models. Data access and domain knowledge are what stay defensible.

Which industries are most ready for vertical AI?

Mature markets like law, advertising, finance, and marketing already have data science teams and AI-literate leadership — they’re ready for sophisticated multi-agent products. Less mature markets like manufacturing, civil engineering, and traditional engineering are still learning. The opportunity in immature markets is education plus a default tool nobody else has built.

Should I build a horizontal or vertical AI product?

If you’re a domain expert with data access from prior work in a specific industry, build vertical. If you’re a generalist competing in a crowded category against well-funded players, the path is harder. Vertical AI products with real domain depth tend to win because they solve a specific buyer’s problem better than any general tool can.

Do enterprise buyers prefer vertical or horizontal AI tools?

Enterprise buyers prefer vertical tools when the workflow is industry-specific. They want a tool that understands their language, their regulations, and their workflow steps without configuration. Horizontal tools require the buyer to translate their domain into the tool’s general framework, which adds friction. Vertical tools sell themselves on fit.

What’s a multi-agent vertical AI workflow?

A multi-agent workflow is several specialized AI agents working together on a single industry process. Example in legal procurement: one agent reviews vendor docs, another checks compliance flags, a third drafts the procurement memo. Each agent has a focused system prompt and tool access. The orchestration layer routes work between them. Horizontal copilots can’t replicate this without industry-specific configuration.

How do I get the private data needed to build vertical AI?

The common paths: prior employment in the vertical, design partnerships with target customers, equity-for-data agreements, or buying access to anonymized industry data. Domain expertise from prior work is the most defensible source because it lets you write system prompts and tool definitions that match how the work actually happens.

Will horizontal AI players move into verticals?

Some will, but the integration cost is high enough that most won’t go deep. ChatGPT will add legal features. It won’t ship a procurement-grade multi-agent workflow with industry-specific tool integrations. The verticals that are large enough to attract horizontal players (legal tech, fintech) will see competition. Narrower verticals stay defensible longer.

What’s the difference between vertical AI and AI agents?

AI agents are the technical building block — system prompts with tools and memory. Vertical AI is the strategic approach — applying agents to a specific industry workflow with domain-specific tools and data. You can build agents without going vertical (horizontal copilots use agents too), and you can do vertical AI with simpler tech, but the strongest businesses combine both.