saas.group

AI-generated code isn’t what slows down a SaaS acquisition. “We don’t really know” is. Every SaaS has tech debt. Acquirers price risk all the time. Uncertainty is much harder to price.

Female founders exit faster than average: 7.2 years vs 8.1. They still walk away with less. For every $1M a female founder gets at exit, a male founder gets about $1.5M for a similar business.

Most people building with AI today are tenants in someone else's ecosystem 🏚 Devansh's argument: you can't hand everyone a GPU cluster, but you can hand them the math. Open research plus consumer hardware is enough to build something real.

Clean code. Clean books. And a growth engine that stops working three months after close. Financial diligence shows you where a business has been. Marketing diligence shows you where the revenue is actually going.

"We've seen an equal or bigger transformation in how we go to market than how we develop product." That one stuck with us. Pete Hunt @ Dagster Labs, on the new saas.unbound 🎙

Acquirers audit your tool stack in due diligence. Redundant SaaS, shadow IT, vendor security gaps: each one slows the deal or dents the valuation. Clean it up before you're in a data room.

34 videos. Every stage of due diligence in a SaaS acquisition. Technical. Financial. Marketing. What actually kills a deal. Playlist in the reply.

AI-related risk in your codebase can cut your valuation multiple by 15 to 30%. Not because the code is bad. Because nobody can explain where the risk is.

Most SEO budgets are optimised for a search landscape that no longer exists. AI answers now sit between you and your buyers. The ROI math has to change with it.