She scaled one company into a unicorn by watching where the durable value settled. Now, as CEO of AxonIQ, she is betting the next one is built on the part of AI everyone else overlooks - its memory.
Jessica Reeves runs AxonIQ, the company behind a widely used framework for event-driven software and, more recently, a platform built for AI applications that can account for themselves.
Her pitch is contrarian in a market obsessed with models. Most of the industry spends its attention on which large language model is smartest this quarter. Reeves spends hers a layer down, on the infrastructure that records what a system did, in what order, and under what conditions. She calls that property explainability, and she thinks it is where the enterprise money will eventually land.
The conviction is not academic. Reeves spent roughly eight years at Anaconda, the open-source data-science company, rising to chief operating officer and, for a stretch, interim chief revenue officer. She was on the deal team that closed a Series C north of $150 million, a round that valued the company at around $1.5 billion and pushed it to unicorn status with more than $150 million in annual recurring revenue. She watched a loose community of data scientists become the substrate of modern AI, and she watched the infrastructure quietly outlast the applications built on top of it.
That pattern is the whole thesis. "Every major technology shift in my career has followed the same pattern," she has written. "The applications get the attention, but the infrastructure gets the lasting value." AxonIQ is her attempt to be early to the next one.
My responsibility is to convert our architectural advantage into category-defining growth.
The biggest challenge isn't technical. It's educational. Most enterprises still focus on models.Jessica Reeves, CEO of AxonIQ
Reeves describes her path as non-traditional, and she means it in more than one sense. She was adopted from South Korea, raised in rural Ohio, and marked by early family losses. She started her working life in human resources, not engineering - a fact she now treats as an asset rather than a detour.
"Understanding people, incentives, and organizational dynamics became the foundation for everything that followed," she has said. That human-centered read on how organizations actually behave is, in her telling, the same instinct that lets her judge which technologies will stick and which will fade.
She is direct about identity, too. "As a woman and Asian-American CEO in AI infrastructure, I talk openly about who builds this technology." For Reeves, the question of who builds the systems is not a side conversation. It is bound up with whether those systems can be trusted at all.
Her four-word operating philosophy
My non-traditional background and outsider perspective contributed to that success, not despite it but because of it.Jessica Reeves
Begins an eight-year run scaling the open-source and enterprise data-science company that became foundational to modern AI.
Rises into the operating and revenue seats, and joins the core deal team that closes Anaconda's $150M+ Series C at a $1.5B valuation.
Publishes "Building My Next AI Unicorn" and reunites with former Anaconda leader Barry Libert, now AxonIQ's chairman.
Leads AxonIQ's launch of a unified platform with a native explainability layer, and writes a series of essays on AI and event sourcing.
Reeves' argument runs on a simple metaphor. Event sourcing - recording every change to a system as an ordered sequence of events - is, she says, "the correct mental model for how complex systems actually behave: as a sequence of things that happened, in order, with causes and consequences."
Apply that to AI agents and the case sharpens. An agent that can only tell you its final answer is a black box. An agent built on a durable event log can be asked why it did what it did - "in what order, under what conditions" - and answer. In regulated industries, that traceability is not a nicety. It is the difference between shipping and not shipping.
Her market bet follows: by 2028, she wants enterprises to budget for AI infrastructure the way they already budget for databases and security. Not a nice-to-have. A line item.
WHERE THE ATTENTION GOES vs. WHERE THE VALUE SETTLES (Reeves' framing, illustrative)
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Every major technology shift in my career has followed the same pattern: the applications get the attention, but the infrastructure gets the lasting value.