Ran Aroussi

Ran Aroussi has spent 35 years building software infrastructure. Now he thinks we’re building AI for businesses backwards.

Most companies are adding copilots, chatbots, and agents, then asking employees to learn how to use them. Ran’s argument is almost the opposite: people shouldn’t have to adapt to AI. AI should adapt to the company.

He’s the founder of VarOps, where he’s building what he calls "Resident AI": AI that lives inside an organization, learns how it actually operates, and works through the tools and workflows people already use. No new destination. No constant prompting. Ideally, employees barely notice it’s there.

The idea grew out of a problem Ran believes the AI industry has underestimated: AI doesn’t understand organizations.

A chatbot might have access to every document, meeting, and message in a company and still not understand why a decision was made, which unwritten rule matters, who actually knows how something works, or that the official process hasn’t been followed in three years.

Retrieval gives AI information. It doesn’t necessarily give it understanding.

Ran’s work focuses on building that missing layer: a living model of the organization itself – its knowledge, decisions, relationships, processes, and unwritten operating context. He describes it as an "Organizational Language Model" (OLM) rather than another LLM with access to company data.

That distinction leads to a provocative conclusion: the smartest AI model in the world doesn’t need to know your company. The system does. VarOps’ research suggests that much of this institutional understanding can be built using small, locally operated models, while frontier AI can be brought in only when its reasoning ability is actually needed.

Before VarOps, Ran spent more than three decades engineering systems across AdTech, finance, fintech, and AI. He created yfinance, the world’s most widely used open-source financial data library, and MUXI, an open-source infrastructure framework for building production AI agents.

He also runs Automaze, a CTO and technical co-founder service that works hands-on with companies building and scaling technology. That gives him a view of AI adoption from both sides: building the infrastructure and watching what actually happens when businesses try to use it.

Ran is the author of two books on the subject: Production-Grade Agentic AI, a technical guide to building reliable AI agent systems, and Company-Scale Agentic AI, written for leaders trying to make AI work across an entire organization.

On podcasts, Ran is less interested in predicting what AI might do in 2035 than discussing what businesses are getting wrong today – and what happens when AI stops being another tool employees have to use and becomes part of the organization itself.