<\/p>\n
The shift from on-premises computing to software as a service modified the technology model and compelled information technology organizatios to modernize the way it builds, buys and operates software.<\/p>\n
It also reshaped how software vendors price, deliver, and add value. But for many buyers, SaaS didn\u2019t fundamentally change how the corporate made money or how work got done day after day. The business became more agile and IT became less of a friction point, however the operating model of the enterprise largely stayed intact. SaaS transformed software corporations and IT departments first \u2013 and mostly stopped there.<\/p>\n
Artificial intelligence is different. In our view, this wave reaches past the IT function and into the core mechanics of the enterprise \u2013 how decisions get made, how work gets executed, how risk gets governed and the way capital gets allocated. The rationale is most corporations still run as federations of departments with their very own application stacks, their very own data models and business logic trapped inside systems that were never designed to coordinate as one. Those systems might be \u201cdeterministic\u201d inside a silo, but across silos the enterprise runs on reconciliation \u2013 spreadsheets, meetings, approvals and tribal knowledge. That\u2019s not an IT problem. That’s an organizational tax.<\/p>\n
The promise of AI is to eliminate much of that tax. Not by sprinkling copilots onto yesterday\u2019s apps, but by bringing probabilistic intelligence into the enterprise in a way that’s governed by deterministic constraints. Frontier models are the core engine of this shift \u2013 they’re getting more capable and more functional, and they’re going to remain a linchpin of the longer term software stack.<\/p>\n
But model power alone doesn\u2019t solve the enterprise problem. The winners will likely be the organizations that construct an entire system around those models \u2013 one which connects to existing deterministic applications, creates a shared truth layer, controls agents as they take motion, and uses human feedback to repeatedly improve.<\/p>\n
For this reason we consider the upside is so large. Enterprises that get this right won\u2019t just run more cheaply \u2013 they may run in a different way. They are going to scale with less proportional labor growth, compress cycle times from insight to motion, and begin to behave more like platform corporations, with compounding advantage that’s difficult for competitors to repeat.<\/p>\n
On this Breaking Evaluation, we use George Gilbert\u2019s model to explain how that transformation unfolds \u2013 and why it requires an entire stack revolution to supply:<\/p>\n