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From SaaS to Systems of Work: The Vertical AI Opportunity with Nick Tippmann

  • Jun 26
  • 2 min read


Nick Tippmann is the Founder of TipTop Ventures where he invests in vertical AI and applied AI companies at the earliest stages. In this conversation, we talked about vertical AI, systems of work, and why distribution may matter more than ever in a world where software is getting easier to build.


The episode is now available on Apple Podcasts, Spotify, Amazon, and YouTube Music.


Chapters from SaaS to Systems of Work: The Vertical AI Opportunity with Nick Tippmann


00:54 — Joe introduces Nick Tippmann and the vertical AI opportunity

01:30 — Defining vertical AI and how it differs from traditional B2B SaaS

03:25 — How vertical AI expands TAM beyond software budgets

07:09 — Greenfield opportunities, incumbents, and the rise of “systems of work”

10:33 — What changes — and what does not — in AI go-to-market

16:42 — Defensibility against OpenAI, Anthropic, and the frontier labs

19:47 — Evaluating AI startups in the age of vibe-coded demos

24:30 — What Nick saw early in GC AI

28:22 — What VCs misunderstand about founders, and vice versa

32:39 — Nick’s contrarian take: vertical AI is not dead because labs move up the stack


Takeaways from SaaS to Systems of Work: The Vertical AI Opportunity with Nick Tippmann


  • Vertical AI shifts the unit of value from “time saved” to “work delivered.” Nick frames vertical AI as the next evolution of vertical software: workflows first, then workflows plus fintech rails, and now workflows plus agents that perform actual work.

  • The TAM expansion is the core reason vertical AI is compelling. Traditional vertical SaaS was priced against software budgets. Vertical AI can price against services, outsourcing, and labor budgets, opening up a much larger market opportunity.

  • Nick does not think vertical AI simply replaces SaaS. The best vertical AI companies, in his view, will still be software companies underneath, but with an agentic outcome layer on top.

  • Incumbents are most vulnerable when they are old systems of record that have stopped innovating. Nick is more cautious when the incumbent is still nimble, but more excited when the market is controlled by legacy players relying on lock-in and pricing power.

  • AI has not eliminated classic go-to-market fundamentals. Trust, content, community, brand, domain expertise, and a tight ICP still matter. In fact, as software gets easier to build, distribution and credibility may matter even more.

  • The foundation labs moving up the stack does not necessarily kill vertical AI. Nick argues that OpenAI and Anthropic investing in forward-deployed engineering and services suggests intelligence alone is not enough; workflow context and implementation still matter.

  • At pre-seed and seed, a slick AI demo is not enough. Nick looks for proprietary data, workflow complexity, reinforcement loops, enterprise-grade features, and a broader vision that compounds beyond the initial product.

  • GC AI fit Nick’s thesis because of founder-market fit, distribution, and a clear wedge-to-platform path. He was drawn to Cecilia Ziniti’s credibility in legal, the early community-building, and the company’s focus on in-house legal teams rather than law firms.



The content here is for informational purposes only and should not be construed as investment, legal or tax advice. The opinions expressed by guests are their own and do not reflect the views of Seaplane Ventures. Our host, guests and clients may hold investments discussed in this podcast. Please invest responsibly.

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