Building My Next AI Unicorn

From Startup to Unicorn to Next Chapter: The AI Open Source Journey

Eight years. That's how long I spent at Anaconda, watching it transform from a promising startup into the backbone of open source, modern day, data science and AI development. As COO and interim CRO during the hyper-growth stage (2023-2025), I had the privilege of being part of something extraordinary—not just building a company that reached unicorn status profitably, but nurturing an entire ecosystem that would reshape how the world approaches open source artificial intelligence.

Last week, as Anaconda announced its $150+ million Series C funding led by Insight Partners, achieving unicorn status at a $1.5 billion valuation with over $150M in annual recurring revenue, I wanted to reflect on the journey that brought me here. While this announcement comes after my voluntary departure, I was part of the core deal team that closed this transformative round—a milestone that validates everything we built together. I will forever be grateful for my time at Anaconda and can’t wait to see their success in their next chapter.

Now I'm applying these lessons as CEO of AxonIQ, reuniting with Barry Libert (former Anaconda CEO/Executive Chairman, now AxonIQ Chairman). Together, we navigated the complex journey from startup to unicorn, learning how to balance aggressive growth with sustainable business practices with a dedication to understanding customers and users to enable us to design products and services our customers want in the age of AI.

The Early Days: The Open Source Commercialization Challenge

When I joined Anaconda in 2017, I faced the same fundamental challenge that has defined every successful open source company: how do you build a sustainable business while staying true to the community that makes your technology and networks so powerful?

This wasn't a new problem. Red Hat faced it in the late 90s when they were figuring out how to monetize Linux distributions. Docker wrestled with it as containerization exploded. HashiCorp navigated it as infrastructure-as-code became mainstream. Each found their own path, but all shared common principles.

The pattern is clear across successful open source companies:

• Community first, commerce second: Red Hat spent years building Linux credibility before enterprise subscriptions took off
• Open core with enterprise differentiation: Docker's community edition drove adoption while enterprise features captured value
• Infrastructure timing: HashiCorp and the DevOps wave; their revenue grew from $0 to $100M+ as cloud infrastructure matured

At Anaconda, we started with millions of users and billions of downloads, but translating community love into commercial success required a different approach. The data science and AI wave was just beginning, and we were positioned at the epicenter.

In my opinion, great commercial products don't exploit open source communities—they empower them. Every enterprise feature you build should make the open source ecosystem stronger. Every dollar of revenue should be reinvested in community tools and infrastructure.

Building Traction: The Virtuous Cycle

I believe the breakthrough comes when companies realize that community and commerce aren't opposing forces, they're symbiotic.

The numbers tell the story:

• Red Hat's journey: 15+ years from founding to $1B revenue, proving enterprise open source viability
• Docker's trajectory: $0 to $50M ARR in 3 years during the container boom
• HashiCorp's path: $0 to $100M+ ARR riding infrastructure automation trends
• Anaconda's acceleration: Reached triple-digit ARR while simultaneously growing to 50M users

As AI moved from research labs to production systems, organizations needed more than just tools; they needed a complete ecosystem.

History shows that common learning loops create more value:

• Product learning: Rapid iteration cycles with community feedback
• Market learning: Deep engagement with enterprise customers understanding their evolving needs
• Technical learning: Staying ahead of AI/ML trends by working directly with practitioners
• Commercial learning: Understanding how open source adoption translates to enterprise value
• People learning: Aligning the right people in the right seats of the bus, at the right time.  Ensuring the culture encompasses “Day 1 Thinking”- customer obsession, relentless iteration, long-term vision
• Capital Markets learning: leveraging expertise from market leading experts. My experience with Lazard was instrumental in guiding insight in evolving markets.

By serving 95% of the Fortune 500 while maintaining one of the most vibrant developer communities in tech, we proved the model worked. The cycle of community adoption drives enterprise interest, enterprise revenue funded better community tools, which can drive more adoption.

The Unicorn Moment: Validation and Vision

The $150+ million funding led by Insight Partners, achieving unicorn status at a $1.5 billion valuation with over $150M in annual recurring revenue—and profitably—wasn't just about the money.

It was validation of a decade-long thesis about the future of AI Open Source.

The timing parallels other Open Source unicorns:

Red Hat IPO (1999): $14M revenue, proving enterprise Linux viability during the dot-com boom and $34 Billion acquisition by IBM

Docker's peak valuation (2019): $1.3B+ during the container orchestration wars

HashiCorp IPO (2021): $1.3B+ valuation as cloud infrastructure matured

But Anaconda's path was different. We achieved profitability while scaling—something many high-growth infrastructure companies struggle with.

Having built deep relationships with enterprise customers over eight years, we could speak with authority about where the market was heading. The Series C wasn't just funding growth—it was betting on the infrastructure and plumbing layers that would power the AI transformation.

The Next Chapter: From Pattern Recognition to AxonIQ

Why AxonIQ? Why now?

The AI boom has created a fundamental infrastructure and systems problem. As enterprises move from specialized data science to generalized AI systems, they need architectures that can handle the complexity of compound AI applications—systems that combine multiple models, agents, and data sources in real-time. We do this at AxonIQ, an extraordinarily talented team building an OS framework that is utilized by over 60,000 companies, 80% of fortune 100.

Event-driven architectures aren't just a technical pattern; they're the foundation for building AI systems that can learn, adapt, and scale. At AxonIQ, we're not just building tools—we're building the nervous system for intelligent applications.

The infrastructure and systems opportunity is massive:

• Docker showed: Container infrastructure could reshape development workflows
• Red Hat proved: Operating system infrastructure could be a $34B+ business
• HashiCorp demonstrated: Infrastructure-as-code could become a multi-billion market
• Anaconda validated: Data science infrastructure could power an AI revolution

The infrastructure and systems layer for AI is still being written. While everyone focuses on models and applications, the biggest opportunities lie in the plumbing—the event streams, data pipelines, and orchestration layers that will make AI applications reliable, scalable, and maintainable.

Building the Next Unicorn

History shows us a clear pattern: every transformative technology creates demand for new infrastructure and systems. Personal computers needed operating systems. The internet required web servers. Mobile demanded app platforms. Cloud computing called for orchestration tools. Now, artificial intelligence needs event-driven architectures.

AxonIQ: The Proven Foundation for Intelligent Systems

We've built the infrastructure and systems  that AI demands. AxonIQ provides the proven backend platform that global enterprises need to build reliable, intelligent systems and agents. While lightweight development tools leave organizations struggling with fragile AI implementations, we deliver the speed, reliability, and transparency that mission-critical applications require.

Our platform offers what others can't: durable application memory that preserves context, explainable outcomes that build trust, and built-in governance that ensures compliance. This isn't theoretical—leading companies like Barclays, Ford, H-E-B, and Wells Fargo rely on AxonIQ to safely transform their core systems, achieving remarkable results: 98% faster time to market and 6x faster deployment cycles.

Beyond AI Companies: The Infrastructure and Systems Play

The next unicorn won't just be another AI company building applications. It will be the infrastructure and systems company that makes all other AI companies possible—the foundation that enables the entire ecosystem to thrive.

We've already proven we can build Open Source unicorns. The experience is there. The technology is there. The market timing is perfect. Now we're building a small and mighty company—leveraging AI everywhere within our own operations to punch above our weight class. With such talented people who understand both the technical depth and market opportunity, an amazing product that users actually want, and an abundance of users providing real market pull, we have all the ingredients for something truly foundational.

This is my playbook, my map: People, Pipeline, Product, Purpose.

The right people who can execute at the highest level. The pipeline that connects market need to our capabilities and understanding of what they actually want and need. The product that becomes indispensable to your development. The purpose that drives everything we build toward enabling the entire ecosystem to thrive.

The journey continues. The partnership endures. The learning never stops.

And yes, Unicorn #2 is firmly in our sights. 

What challenges are you seeing in AI infrastructure and systems? What would you build if you had the chance to shape the next wave of technology? I'd love to hear your thoughts as we continue building the future together.

Disclaimer: The opinions, findings, and conclusions expressed in this blog are those of the author alone and do not represent the views, policies, or positions of Anaconda, Inc. or its affiliates or other companies mentioned. All data utilized herein consists of publicly accessible information, and no proprietary or confidential data has been disclosed.