Here's a hard truth about AI in SaaS: the capabilities you're differentiating on today will be table stakes in 18 months. Not because your engineering isn't good — because the underlying model APIs keep improving, and they're improving faster than you can maintain a gap.
In 2024, "we have AI summaries" was a differentiator. In 2026, the question is which SaaS products don't have them.
This is the commoditization cycle. It's happened before — with cloud hosting, with search, with maps. When a core capability commoditizes, the winners are the companies that used the window to build distribution and data advantages, not model advantages.
What doesn't commoditize:
Customer relationships and distribution. The ability to reach, sell to, and retain customers in a specific market doesn't get disrupted by a new API. If you have 500 enterprise customers who trust you, that's a distribution moat. A new entrant with better AI still needs to sell to those same customers.
Domain-specific training data. Models trained on your customers' proprietary data, their historical patterns, their specific terminology and workflow context — that doesn't exist in any general model. Collect it deliberately.
Workflow integration depth. The more integrated your product is in your customer's stack, the higher the switching cost, regardless of AI capability gaps. Every integration you add is a vote for you staying in the stack.
Brand and category ownership. Being the recognized leader in a specific category — even a small one — is worth more than any technical advantage. Category ownership is durable. Feature advantages aren't.
The strategy for this era: treat AI capabilities as necessary but not sufficient. Use them to compete. Build distribution and data to win.
Stop differentiating on model capabilities. Start differentiating on what models can't replicate.