Every investor I talk to right now is asking some version of the same question: where do moats come from in the age of AI?

Traditional SaaS defensibility was built on data silos, switching costs, and learned interfaces. All three are eroding.

So what's left?

I've been thinking about this a lot, both as an investor evaluating software deals and as someone who has spent the past months rebuilding my own workflows from scratch with AI-native tools.

And I think the answer requires understanding an evolution that's playing out in three distinct stages, each one dissolving the moats of the stage before it.

Here's my framework.

The first generation of enterprise software created value by capturing and organizing data that was previously trapped in spreadsheets, emails, and people's heads. CRMs, ERPs, HRIS platforms, deal flow tools, portfolio monitoring systems.

Each one became a system of record for a specific domain.

The moat was straightforward: once your data lived inside Salesforce or HubSpot or Carta, switching was painful.

Not because the interface was hard to learn (it was, but that's a weak moat), but because the data itself was locked in. Your pipeline history, your customer interactions, your portfolio metrics, all structured in a proprietary format that didn't talk to anything else.

That’s what we call backward-looking lock-in effects.

But it also created a fundamental problem: every tool became an island. Your CRM didn't know what was happening in your calendar. Your deal flow tool didn't know what your partners were discussing on Slack. Your portfolio monitoring system couldn't see the LP emails that provided context for why certain metrics mattered more than others.

The data was captured, but the context was fragmented.

For years, this didn't matter much. Humans were the integration layer. You, the investor or operator, were the one who held the full picture in your head and moved between tools to synthesize information.

The value of each tool was measured by how well it handled its specific domain, not by how well it connected to everything else.

AI changed that equation entirely.

When vendors started integrating AI into their vertical solutions (and nearly every software company has by now), they hit a wall that most didn't anticipate: AI is only as good as the context it can see.

A CRM with an AI copilot can summarize your pipeline and draft outreach emails. But it can only work with what's inside the CRM.

It doesn't know that the founder you're about to email just posted something on LinkedIn that changes the conversation.

It doesn't know that your partner mentioned concerns about the space in yesterday's IC meeting notes.

It doesn't know that an LP asked about your exposure to this sector last week.

The AI lives in the silo. And a siloed AI, no matter how capable the underlying model, produces incomplete and often misleading outputs.