Rick Beaton on Why He Sold His Company the Year He Learned It Couldn't Scale

What we talked about.

Rick Beaton, founder and CEO of Elevate Labs, joins Sunny Ray to trace nearly two decades spent asking why so few people truly flourish, even those with every resource available. Beaton describes his early years working in refugee camps in Asia, which first raised the question of individual human potential beyond scaled group services. That question later shaped MirrorGold Associates, a consulting firm he built with PhDs from multiple disciplines who worked together in real time to give wealthy clients a comprehensive, research based understanding of themselves. The conversation turns to the pivotal moment a senior Goldman Sachs executive told Beaton the model could never scale, a verdict that proved correct and eventually led to the company's sale in 2018. Beaton explains the years spent searching for a way to democratize that same depth of understanding, and how agentic AI finally offered a path forward through Elevate Labs and its tool Amber. He details Amber's layered approach, starting with validated personality science and building toward a longitudinal model that tracks narrative and biometric data over time.

Rick Beaton on building MirrorGold, hearing you can't scale this from Goldman Sachs, and using AI to democratize deep self understanding.

The questions, and the answers.

What first put the question of why so few people flourish in front of you, and why did it stick for two decades?

I was young, working in Asia for an international nonprofit, building refugee camps and working with the poor. I kept asking, when you take one person out of a scaled program, what's their actual capacity and how do we help them reach it? Years later, wealthy board members told me nobody in their life saw them as a whole. We spent a year building a research based model covering psychology, sociology, and even theology, and it transformed their lives.

There's a specific moment where a Goldman Sachs leader told you essentially you can't scale this. What did that conversation feel like?

We were young and thought we had something ready to scale. The number two guy at Goldman heard the whole pitch, loved it, then said he wasn't giving us a penny. He explained that even five hundred PhDs like ours wouldn't be a big business, just a midsize consulting firm. It stung at first, but he was completely right. Everything we built was expensive, relational, and designed for elites, not for scale.

Why did the model require two PhDs synthesizing live in the room instead of one great practitioner with a good framework?

There are three problems: the client doesn't see themselves clearly, the advisor brings their own disciplinary bias, and everyone is shaped by a sociological context they don't fully understand. Two older PhDs each carrying a deep discipline plus broad life experience effectively brought ten disciplines into the room. That combination let us see and hear things about a person that would never have been obvious to either of us alone.

You sold MirrorGold in 2018. What made that the right moment to sell rather than continuing to push on the scaling problem yourself?

We were working with a fast growing firm that had funding for acquisitions, and they realized it would cost less to just buy us than to hire us. We joined them, helped with acquisitions and reading leadership internationally, and it worked well. Once I accepted we couldn't scale the way we wanted, I could already see consulting as an industry heading toward disruption from technology, so I wanted to move that direction myself.

After the sale you sat with the how do we scale it question for years before Elevate even existed. What did that period actually involve?

We did keep pushing, but the space kept getting more crowded with consultants and coaches, and I started questioning why our subjective viewpoint should carry more authority than anyone else's. The real turning point came in DC, when a friend introduced me to a Microsoft AI person who laughed and said agentic AI was coming and could scale exactly what we did. He was completely right, and we spent nine months building on that idea.

Amber starts with a personality assessment as the entry point. Why start there instead of a conversation or goal setting exercise?

Personality assessments aren't meant to be an overarching data point, but they measure something real and are tied to neurobiological functioning, about sixty to seventy percent nature. Once your brain stabilizes in your mid twenties, personality becomes stable, so it gives us an accurate, research backed starting point for understanding why someone sees and engages the world the way they do, before we layer in anything else.

You describe Amber as a longitudinal model rather than a snapshot. What breaks when you try to make something continuous instead?

Personality is fairly stable once your brain matures, but narrative constantly evolves, and eventually we'll add biometrics too. Nobody has ever tried building this kind of long running model of a person before. With years of data, an agent can spot a wobble, the way a cardiologist spots one in heart data, and tell you that you've been here before so you can actually see your own patterns.