The AI-Native System of Record

With all the talk of AI agents and vibe coding new software, one issue that keeps coming up is the system of record.

Historically, the system of record was the main software-as-a-service application a company used. Think Salesforce.com or HubSpot in CRM, Workday and Rippling in HR, and Pardot and Marketo in marketing automation.

As part of the SaaSpocalypse beatdown, followed by the slight improvement, one of the big ideas was that companies charging based on users or seats would come under pressure. If an AI agent is one seat but can do the work of 10 or 50 humans, the number of seats a company needs could go down dramatically over time.

Combine that with much better tools for building one-off applications and moving data from system A to system B, and there may also be less lock-in from legacy systems of record.

All of that might be true.

But there’s another idea that hasn’t been discussed enough: the potential for an entirely new system of record to emerge.

Imagine a startup that provides the backend, database, data lake, and underlying infrastructure, but also includes roles, groups, responsibilities, governance rules, workflows, required approvals, and clear designation of who owns each piece of data, who can change it, and who can’t.

Right now, when you vibe code a new CRM, go-to-market tool, HR application, or almost any other piece of software, the AI typically creates a new database with relatively limited rules around it. That becomes more complicated once multiple products and systems are reading from and writing to the same data. There are second- and third-order effects around permissions, ownership, governance, consistency, and control.

This creates an opportunity for a new startup.

It wouldn’t be an incumbent with existing pricing power and an innovator’s dilemma problem. Instead, it could provide a data store with a governance engine on top at a much lower cost than interfacing with a headless Salesforce.com or another legacy application.

Then, through open source libraries, great APIs, and excellent engineering, it could position itself so that the agents deciding which products and modules to use naturally select it as the system of record.

The goal would be to create a new industry-standard system of record that is AI-agent native, easy to drop into existing systems, compatible with applications that previously wrote to legacy systems, and available through a self-service freemium model without an expensive go-to-market motion.

For enterprise-focused entrepreneurs looking for a potential business idea, being the system of record has historically been incredibly valuable. It’s also clear that many legacy incumbents face the traditional innovator’s dilemma.

That creates an opportunity to build the next-generation system of record: AI-agent native, optimized for vibe coding, neutral across applications, easy for developers and agents to adopt, and priced like modern infrastructure instead of legacy enterprise software.