The 1960s-70s mainframe era established a pattern; enterprises rejected commercial software offerings, choosing instead to build custom applications in-house. The willingness to accept substantially higher costs and longer development timelines reflected a single calculus - strategic control over technology tethered to competitive advantage outweighed efficiency gains from standardised platforms.
Fast forward to today. A systematic study of production AI agents (engaging 306 practitioners and 20 detailed case studies) documents that 85% of case study teams proceeded without third-party agent frameworks, building custom implementations from scratch. Human evaluation was relied on in 74% of cases and agent autonomy was constrained to fewer than 10 steps in 68% of cases.
External industry forecasts have projected the agentic AI market will grow from around $5b to circa $200B by 2034 and this is likely fueled not by autonomous platforms, but by custom, human-supervised approaches.
Both eras reveal that when organisations embed technology into competitive strategy, the build-vs-buy decision systematically favors building, despite higher costs. The precedent established in the mainframe era persists: control beats convenience when business continuity is at stake.
What if the real market opportunity isn't selling AI platforms to enterprises, but selling the tools, infrastructure, and services that enable enterprises to build competitive AI systems themselves?
Profiled research:
Measuring Agents In Production - https://arxiv.org/pdf/2512.04123.
#AI #EnterpriseAI #AIValue #FrontierAI #AppliedAI #TrustedAI #AIGovernance #AISafety #ResponsibleAI #AIStressTest #Learning #History #Technology #Innovation
