If you’re investing in AI today, here’s a simple but uncomfortable truth:
If a tool is useful to everyone, it will eventually belong to Big Tech.
We’ve seen this play out repeatedly. Teams that once paid hundreds of dollars per month for standalone AI tools: note-taking, transcription, summaries, task extraction are quietly cancelling those subscriptions. Not because the tools failed, but because Google, Microsoft, and others hyper-scalers have integrated similar (and often better) capabilities directly into their platforms.
This pattern will repeat across any generic knowledge-work workflow.
Which is why horizontal AI is a dangerous place for early-stage capital.
The real opportunity is Vertical AI: products designed around the exact workflows of a specific role, industry, or regulatory context.
Concrete Examples
Insurance: AI systems that understand claims logic, policy language, regulatory constraints, and fraud patterns not just document summarization.
Biotech & life sciences: Tools that assist with experimental design, protocol optimization, and regulatory documentation—not generic copilots.
Infrastructure & energy: AI that integrates field data, sensor outputs, maintenance logs, and compliance requirements into decision-ready insights.
Professional services (M&A, legal, audit): Workflow-specific intelligence that understands sequencing, risk thresholds, and industry norms.
These companies don’t look flashy. Their demos are often boring to outsiders. But inside the workflow, they’re indispensable.
Why Vertical AI Creates Defensibility
Vertical AI companies build moats through:
Process dependency: Users rely on them to complete work, not just enhance it.
Domain-specific tuning: Models, prompts, and logic reflect real-world constraints.
Embedded adoption: They sit inside systems of record, not on top of them.
Big Tech typically avoids these markets because:
They require deep domain expertise.
They involve regulatory and liability risk.
They don’t scale horizontally fast enough.
That’s precisely why they’re attractive to angels.
Investment Heuristics for Vertical AI
Look for companies where:
Founders have lived the workflow (former operators > career technologists).
The buyer is the user (or directly accountable for outcomes).
ROI is operational, not aspirational (time saved, errors reduced, throughput increased).
The product replaces steps, not just adds insight.
Expansion happens within the workflow, not across unrelated use cases.
Vertical AI won’t produce the next mass-market app.
It will produce the next generation of quietly dominant, cash-efficient companies.
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Excellent analysis! You've consistently highlighted Big Tech's gravitational pull. I just wonder how 'boring' vertical AI will attract early funding, but the logic is solld.