The AI Native Dev - from Copilot today to AI Native Software Development tomorrow

Interview with Ran Aroussi

Ran Aroussi · September 8, 2026

Entrepreneur · Artificial Intelligence · Finance · Author · Leadershipin

Interview with Ran Aroussi

The AI Native Dev - from Copilot today to AI Native Software Development tomorrow cover artwork

Podcast: The AI Native Dev - from Copilot today to AI Native Software Development tomorrow

September 8, 2026

Listen to Ran Aroussi on The AI Native Dev - from Copilot today to AI Native Software Development tomorrow.

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Key topicsAgent infrastructure is the next platform layerEvery major computing shift has needed a new layer: operating systems, the web, cloud, DevOps - and now, AgentOps. Ran explains how the next generation of value will come from tools that manage agent orchestration, communication, and observability. MUXI sits at this inflection point, showing how open, production-grade infrastructure will underpin the agentic AI ecosystem. Load Bearing — Why Production AI Agents Need a Layer That Takes the WeightRan Aroussi has built the layers other people's software runs on for 30 years, from ad engines serving 3 billion ads a day to yfinance, now installed more than 30 million times a month. Most teams treat agents as a model problem and reach for another framework, when the thing that breaks in production is everything underneath: memory, orchestration, audit trails, failure handling. He walks through what an agent-native layer has to do before autonomy is safe to ship, and why he wrote Production-Ready Agentic AI and built MUXI around declarative, inspectable configs. Listeners leave able to tell which parts of their agent stack survive real traffic, and which fail quietly.Plumbing — Why AI Money Ends Up With Whoever Owns the PipesRan Aroussi has spent 30 years building the parts of software nobody sees, from ad engines serving 3 billion ads a day to yfinance, downloaded more than 30 million times a month. Most of the attention in AI goes to the models and the apps sitting on top of them, and he argues the durable returns land a level below, the way operating systems, cloud, and DevOps each pulled the value down to themselves. He walks through what that means for agents in practice: who controls memory, orchestration, observability, and the audit trail when a run goes wrong, and why he wrote Production-Ready Agentic AI and built MUXI on open, declarative configs rather than closed tooling. Listeners leave able to work out which part of their own agent stack they actually own, and which part someone else will end up charging them for.View all topics →

About Ran Aroussi

Founder of Varops & AI Systems Architect Helping Organisations Make AI Truly Resident

35+ years Production coding experience30M+ Open-source downloads per month50K+ GitHub stars3B+ Ads delivered daily by systems he built

Ran Aroussi is a technology entrepreneur, engineer and founder of Varops, a new AI company focused on helping organisations integrate AI into the way they already work, rather than forcing people to adapt to yet another set of tools.

With more than 30 years of engineering experience across AdTech, finance, fintech and AI, Ran has built and scaled systems used by millions of people. He is also the creator of yfinance, one of the world's most widely adopted open-source financial data libraries, and has spent his career turning complex technology into practical infrastructure.

Today, Ran's focus has evolved beyond individual AI agents and developer infrastructure towards a bigger question: "What happens when AI becomes a resident member of an organisation rather than an outside tool people have to interact with?"

Through Varops, Ran is developing this concept of "resident AI" - AI that works within an organisation's existing workflows, processes and systems, quietly moving work forward without requiring employees to constantly learn new tools or change how they operate. Instead of asking people to adapt to AI, his philosophy is to mould AI around the organisation.

His work also explores organisational-level AI memory: systems that enable AI agents to understand not just isolated interactions, but the context, knowledge and relationships that exist across an entire organisation.