The build vs buy calculus has flipped.
Over the past year, we've worked with companies building AI into their products, operations, and internal tools.
Through the Builders Collective, a community we created for service company founders, we recently hosted a roundtable where founders compared notes across hundreds of AI projects.
Three things kept coming up:
The build vs buy calculus has flipped.
As build times compress, scoping what to build is now the highest-leverage step.
Execution speed is now the moat.
The build vs buy calculus has flipped.
Proof of concepts and MVPs that used to take quarters can now take weeks. That changes what's worth building for in the first place.
Problems that were previously too niche to justify may now be practical to build. Things like highly specific internal tools, system-to-system workflows, and nuanced processes. The cost of building something custom has dropped. The value of a precise fit has gone up.
The market data tracks with what we're seeing on the ground. In Retool's 2026 Build vs. Buy report, 35% of teams said they'd already replaced at least one SaaS tool with something custom, and 78% expect to build more of their own tools next year.
We saw this firsthand. We built an internal CRM connected directly to our staffing allocation tool, so resourcing updates automatically as deals move through the pipeline. No off-the-shelf product supported the exact workflow we needed. A few years ago it would have been a major project. Instead, one engineer built an MVP in just over two weeks.
Others in the room had similar stories:
The build vs buy calculus has flipped.
As build times compress, scoping what to build is now the highest-leverage step.
Execution speed is now the moat.
125k
Of users across Google
1230m2
Of square feet analyzed
43
Of buildings across Google
Knowing what to build is now the highest-leverage step.
Proof of concepts and MVPs that used to take quarters can now take weeks. That changes what's worth building for in the first place.
Problems that were previously too niche to justify may now be practical to build. Things like highly specific internal tools, system-to-system workflows, and nuanced processes. The cost of building something custom has dropped. The value of a precise fit has gone up.
The market data tracks with what we're seeing on the ground. In Retool's 2026 Build vs. Buy report, 35% of teams said they'd already replaced at least one SaaS tool with something custom, and 78% expect to build more of their own tools next year.
We saw this firsthand. We built an internal CRM connected directly to our staffing allocation tool, so resourcing updates automatically as deals move through the pipeline. No off-the-shelf product supported the exact workflow we needed. A few years ago it would have been a major project. Instead, one engineer built an MVP in just over two weeks.
"Fluxon took a vision and turned it into something we could showcase with real pride in just about 8 weeks. If you're building AI on a complex platform and need a partner that can make the right calls under pressure, Fluxon”
The bottleneck has moved upstream.
The winners are no longer just those with the best ideas. They're the fastest learners and executors. And speed without scoping discipline is just churn at a faster clip. Across the projects we've been part of, the companies that moved fastest were almost always those that took time to scope carefully at the start and prioritize the right features. That's the quiet cost of the new economics: the bottleneck moves upstream, to whether you scoped the right problem in the first place.
AI hasn't just changed what's possible to build. It's changed what it takes to build well.
Thanks to those who joined the first Builders Collective roundtable:
Philip Clements Samuelraj (Techjays), Harit Patel (Lodestone), Jasmeet Kanwar (Matchpoint), Jimmy Bijlani (AI Momentum Partners), Ajay, John Koshy (PeakIT), and Thanujan Ratnarajah (Verdant Labs).
