Morgan Livermore at SuperCruise Capital: Software Defensibility 2.0
Software valuations and the two new nodes of defensibility
For two decades, the defensibility playbook in software was relatively stable: own the workflow, lock in the data, compound switching costs. That playbook isn’t wrong — especially in vertical markets — but it’s now incomplete. The AI stack is commoditizing the build layer so fast that the old question of can you build it? has been fully replaced by why should anyone stick with you once they can? Defensibility isn’t disappearing. It’s shifting. Away from product features and toward the harder-to-replicate assets underneath them: technical and domain-specific moats.
Recent seismic shifts in the public markets backdrop adds urgency to this consideration of software (and AI) defensibility. Forward PE multiples for public SaaS companies compressed below the S&P 500 for the first time in the modern era. What’s happened? The market isn’t just repricing growth expectations — it’s asking a structural question about which software businesses have earned the right to compound for another decade. Whether via real math or just vibes, the terminal values of software titans are being questioned with the incredible emergent growth of AI-native disruptors. Some legacy SaaS have earned the right to continue existing and are oversold. Others — as Chegg demonstrated — can see their core value proposition open-sourced overnight by an LLM.
Morgan Livermore — a 15-year enterprise and infrastructure investor with a background at Accel, Geodesic, and Quiet Capital before founding SuperCruise Capital — is watching this play out from the growth stage. In today’s episode of Verticals, he shares his view on what’s going on in software, what that means for defensibility in the era of AI, and why that led him to launch a new firm.
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Morgan’s view: “I would much rather own a 20 to 30% compounding software company for the next decade than certain other more legacy businesses.” So would we — but only if the compounding assumption is backed by something more durable than a feature lead. For founders, the takeaway is concrete: know where your defensibility lives, and build toward it from day one. Speed still matters at inception, but the window in which growth alone was enough has closed.
His framing is blunt: if five competitors can build something quickly and cheaply, “the chance that they figure out those workflows is pretty high.” The implication isn’t that defensibility is dead. It’s that the altitude at which it operates is changing. Feature-level differentiation degrades faster than ever. What endures sits either deeper in the technical stack or deeper in the domain.
As an investor in both vertical and horizontal markets — with a deep background in infra investing — Morgan highlights the growing importance of technical differentiation. For example, he shares a portfolio company of his that’s winning thousands of “customers” that aren’t human — they’re AI agents. Claude, GPT, and other coding agents are autonomously discovering and adopting the product because its documentation and API surface are the most machine-readable in the category. Naturally, that requires a completely new stack and a reimagination of what scalability means.
“We have the best documentation, agents can know what we do, understand it,” the founder told him. “And because of that it’s actually started compounding.” The company reportedly has tens of thousands of agents on its platform, none acquired through a sales call. This is infrastructure defensibility rewritten for the agentic era — discoverability, developer experience, and docs as distribution. It echoes the open-source playbooks of Vercel, Confluent, and HashiCorp, but with a twist: the “developer” evaluating your product may not be human anymore.
That said, Morgan agrees with the second note of defensibility: domain expertise. As the AI stack stabilizes — more reliable models, better tooling, clearer architecture — understanding how to apply commoditized stacks to take advantage of unique market surface areas is increasingly valuable. Consider voice AI intake: hundreds of startups can answer calls for dentists and dealerships using commodity models. But as we’ve explored in Voice-First Playbooks in Vertical AI, the wedge is not the business. The founders who understand which scheduling edge cases cause no-shows, or how a specific ERP integration unlocks procurement data, are the ones building platforms behind the commodity wedge. Domain expertise lets you anticipate the next product, not just replicate the current one — and that’s where durable vertical moats get built.
As we catalogued in Dude, Where’s My Moat?, the immutable primitives of workflow and data take many forms — but technical and domain edges are becoming ever more important. Because the speed at which surface-level advantages erode is increasing rapidly with AI, those that don’t find a truly defensible one — whether disruptor or incumbent — may fall into another class of multiple premium altogether. Veeva doesn’t charge what it does because its interface is irreplaceable. It charges what it does because its understanding of life sciences workflows and regulatory data is — and its product demonstrates that. Depth — whether technical or domain — is what separates compounders from commodities.
Hear Morgan walk through the full case for efficient growth and shifting defensibility in software — catch the full episode on YouTube and wherever you listen to podcasts.
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