Key Takeaways

  • Mighty Capital mapped 550 companies across the 7 Powers framework to measure defensibility against total capital raised.
  • Enterprise switching costs and proprietary data moats are deteriorating rapidly as artificial intelligence removes workflow friction.
  • Capital-efficient winners rely on network effects and counter-positioning rather than massive equity rounds.
  • Mighty Capital tracks these architectural shifts across its network of 600,000 product executives and builders.

The Collapse of Legacy Software Moats

For two decades, software investing followed a predictable playbook: build proprietary databases, lock in enterprise workflows, and collect recurring revenue behind high switching costs. That formula is breaking.

SC Moatti and her team at Mighty Capital mapped 550 companies against Hamilton Helmer's 7 Powers framework to study how defensibility correlates with capital raised in the current cycle. The results split the market into two distinct groups.

“There's a cluster of companies that have raised a ton of money but aren't defensible because they rely on moats that no longer work in AI like switching costs or even cornered resources like a data moat,” Moatti explains.

In standard SaaS, exporting records, rebuilding logic, and training staff on replacement software created massive inertia. Generative tools change that equation. Code generation and automated schema mapping make migrations faster and cheaper. At the same time, proprietary internal data sets offer less protection when foundation models can approximate domain knowledge from public sources or synthetic data. High retention rates driven purely by customer exhaustion are no longer durable.

The Return of Counter-Positioning and Network Effects

While heavily funded startups struggle to defend basic wrapper products, a different cohort is compounding value on minimal capital. Moatti identified a second cluster in Mighty Capital's 550-company study: lean businesses building structural advantages from inception.

“And then there's also a cluster of companies that have raised very little because they're super capital efficient... and they're super valuable because they rely on two moats,” Moatti notes. “One being network effects, which is saying how do you make your product more valuable as people use it. And then the second one is a different kind of moat which is counter-positioning, which is the traditional moat you see when you have a platform shift and you say I'm going to do the same thing but differently.”

Counter-positioning punishes incumbents. A legacy vendor selling per-seat subscriptions cannot pivot to autonomous agent pricing without cannibalizing its core revenue model. New entrants design around outcomes and consumption from day one. When combined with direct network effects, where user participation directly improves the system for the next customer, the resulting feedback loop compounds faster than an incumbent can re-architect its stack.

As Moatti summarizes: “The moats that work in AI are completely different than the moats that worked in the SaaS era of switching costs and a data moat. It's really network effects and counter-positioning.”

Why It Matters

This dynamic signals a sharp valuation correction for mid-tier SaaS assets relying on historic retention metrics rather than structural defensibility. Private equity acquirers and growth investors must audit whether portfolio retention stems from genuine product stickiness or temporary friction that automated code conversion will erase. Capital intensity is decoupling from enterprise value creation, rewarding operators who exploit architectural platform shifts over those who simply outspend rivals.