Execution Economics.
In 4 Parts.
During RSA week, I spoke with a few CISOs about how they’re evaluating new AI vendors. The reaction was consistent:
“A lot of this feels like bespoke consulting…
but it’s also how we think we’ll buy software going forward.”
I’ve found myself having the same conversation repeatedly in every category the last few months - from department heads of enterprises like finance to specific verticals like healthcare. AI products look like services early on as they lead with FDE. The question is whether they stay that way.
Revenue growth doesn’t necessarily mean leverage and unwillingness to switch to a new vendor. Large contracts don’t necessarily mean scale. Everyone is testing with real spend and building, and as processes collapse as AI agents do more, it’s not clear whether things will stay when you can rewrite the whole process abstraction in days instead of years. Change management is evolving so fast, and people are adapting in real time.
So the real question is simpler - what actually compounds?
This series is an attempt to answer that from an operator’s perspective.
When does a services-heavy model become a software business?
What signals show a company is actually compounding?
Where does defensibility come from in AI?
As we’ve seen hundreds of pitches of AI services turning into software pitches for every category, I’ll share some observations I’ve found common when working with companies going from early traction to real scale. Hitting these markers isn’t everything, but it does provide a clearer way to evaluate what’s actually happening underneath the surface as people ask whether this is tech-enabled services or a leap toward becoming a software platform.
→ Part I: Customer Two Is the Only Metric That Matters
→ Part II: Services Aren’t the Sin. Linear Scaling Is.