Glenn Gow, The Scaling Executive Coach and host of The Scaling Executive Podcast, spoke with Dr. Brandon Colby, founder and CEO of Sequencing.com — the world’s largest direct-to-consumer platform for whole genome sequencing. The core insight from the conversation is specific and transferable: when a company’s product is built on complex scientific data, technical depth at the CEO level is not a differentiator. It is a requirement.
This episode is for CEOs scaling companies where the core product is built on complex, scientific, or proprietary data — and where a generic management playbook will steer you off a cliff.
Key Takeaways
- In companies where the product is built on complex or scientific data, Dr. Brandon Colby of Sequencing.com holds that a CEO without deep technical knowledge will make the wrong product bets, hire the wrong people, and miss what not to build — all at the same time.
- The gap between genetic research and clinical practice is not a scientific problem — Dr. Colby, a physician-turned-CEO, identified it as a leadership and translation problem and built Sequencing.com into the world’s largest direct-to-consumer whole genome sequencing platform by solving it.
- Media coverage claiming AI enables “90% fewer engineers” is, by Dr. Colby’s direct experience at Sequencing.com, overblown hype — but AI agents have allowed the customer success team to scale output while remaining entirely onshore and U.S.-based.
- AI cannot perform genomic analysis reliably at Sequencing.com because it has not been trained on the human genome at the depth required — it misapplies historical identification systems to current data without recognizing the error.
- The fastest path to scaling a technical company, per Colby’s practice at Sequencing.com, is hiring people smarter than the CEO in each domain and then raising the whole team’s capability by learning from each handoff.
Why Technical CEOs Build Better Deep-Tech Companies
Dr. Colby built Sequencing.com on a foundation that most consumer platforms do not need — three billion data points per person, roughly 100 gigabytes of raw data per genome, and a scientific landscape where the research identification systems that name specific genetic mutations change year over year. No CEO importing a playbook from retail, SaaS, or even conventional healthcare can navigate that product environment. The decisions that determine whether the company survives — what to build, what to skip, where to focus engineering — require someone who understands the technology’s limitations as precisely as they understand its possibilities.
“It’s not where you can just go and bring in a CEO that has a playbook from another industry that has worked and say, well, they were very successful in this industry. Let’s bring them in as CEO. Instead, when there’s really deep, complex technology and especially data, loads of data — from my perspective, it takes a CEO that has a very intricate knowledge of that technology.”
That is not a preference. It is how Colby describes the mechanism by which all company decisions flow: product development, hiring, strategic focus, and the equally important decision of what not to pursue. Without technical mastery at the top, those decisions default to convention. And in a field like genomics, convention is usually wrong.
How a Physician Saw the Gap No One Else Was Solving
Dr. Colby left clinical medicine not because he stopped caring about patients — he left because he realized that practicing medicine one-on-one would never produce the impact he was looking for. The trigger was a specific observation: genetic research was accumulating at scale, but none of it was reaching practicing physicians or patients.
“As a practicing physician, I saw all this research, but it was not being put into the hands of myself and other practicing doctors. And that was a greatly limiting factor. And I saw that that was an immense opportunity also to go and solve that problem.”
The gap Colby identified is structural. Medical school genetics education averages two to three weeks across four years, unless students specifically elect additional coursework — which most do not. Practicing physicians were not expected to translate research into clinical protocols. They expected someone else to do that work, and then they would adopt it. No one was doing that work. Colby’s decision to leave clinical practice was a business decision: the translation problem was solvable, and it required the kind of reach that only a company could provide.
His training at the Icahn School of Medicine at Mount Sinai reinforced that orientation. As he describes it, Mount Sinai trained physicians not just to apply existing knowledge, but to advance it — to treat the current state of medicine as a starting point, not a ceiling. That mindset became the lens through which he built Sequencing.com: understand the science deeply, then move it forward.
How Technical CEOs Scale Without Losing Their Edge
The tension every technical founder eventually faces: the deeper your technical roots, the harder it becomes to delegate when the company grows large enough that delegation is no longer optional. Dr. Colby’s answer is not to cling to technical ownership — it is to hire away from it deliberately.
“The most important thing that I’ve learned really in terms of how to grow a company successfully is to hire people that are smarter than you, to hire people that have more intricate understanding of different areas of that technology, of different understandings of engineering, software engineering, different understandings of the customer experience, and I start to learn from them.”
The mechanism matters here. Colby does not describe hiring smart people and then managing them at arm’s length. He describes a process of handing off a domain to someone with deeper knowledge in that area — and then leveling up by learning from them. The company grows because each hire raises the floor of the whole team, not just the ceiling of their function.
This principle is embedded as a cultural value at Sequencing.com: “No matter how good we think we are, there are other people out there that are smarter, that are innovative, and we want to work with those people. We want to go and bring them onto the team. We want to be humbled by them.”
That humility has a practical output. It is what allows a highly technical founder to scale past the point where they can personally hold all the technical context — because the team is collectively holding it, and the CEO remains the integrating intelligence rather than the limiting one.
Where AI Delivers at Sequencing.com — and Where It Does Not
Dr. Colby is direct about the gap between how AI is portrayed in the media and what it actually produces inside a complex data operation. The narrative of a five-person engineering team outproducing a full department is, by his assessment, hype — at least at this stage.
“You read articles how engineering team was able to go and let go 90%. And then there’s just about like five people on the engineering team that are accomplishing five years of work in one day. And that makes AI sound like it’s like amazing. And that all you have to do is you click a few buttons and then you get to that place. And what we have found internally is that it’s not there yet.”
What AI does deliver at Sequencing.com is in customer success operations. The support team now runs AI agents that handle prioritization and routing of customer inquiries — each team member effectively multiplying their own output by managing multiple agents rather than handling tickets one at a time. That has allowed Sequencing.com to keep its entire customer success team onshore and U.S.-based while scaling to meet significantly higher support volume, without adding headcount proportional to the volume increase. That is a real, measurable operational gain.
On the product side, the boundary is equally clear. AI does not perform genomic analysis at Sequencing.com — full stop. The reason is specific: AI has not been trained on the human genome at the depth required for accurate interpretation. The identification systems used to name specific mutations within three billion data points change year over year, sometimes decade over decade. An AI reading a 2010 research paper will misapply the ID system from that era to current data without flagging the error. “The AI is not capable of genetic analysis… it’s primarily incorrect. And it takes a lot of assumptions, a lot of guessing.” Sequencing.com’s algorithms handle that translation explicitly. AI cannot, yet.
What AI does exceptionally well in the product is the step after analysis: translating results into personalized reports and conversations tailored to the specific audience — a physician receives output formatted the way clinicians read data; a patient focused on brain health receives a discussion of their results anchored to their specific concern. As foundation models improve, that translation capability only gets stronger. Sequencing.com is positioned to capture each iteration of that improvement.
| Principle | What it means in practice | Named evidence from this interview |
| Technical mastery at the CEO level is non-negotiable in complex-data companies | Every product, hiring, and strategy decision in a data-intensive company requires a CEO who understands the technology’s limitations, not just its potential | Colby’s genomic knowledge — of shifting mutation ID systems, research-to-clinic translation gaps, and what AI cannot yet do in analysis — determined which products Sequencing.com built and which it did not. Those decisions produced the world’s largest direct-to-consumer whole genome sequencing platform. |
| Hire smarter than yourself and learn from the handoff | Scaling a technical company means finding people who know more than you in specific domains and then raising the whole team’s capability by learning from each new hire | Sequencing.com expanded its engineering depth and product scope by continuously bringing in people with deeper domain knowledge than existing leadership held — each hire raising the floor of the team, not just the ceiling of their function |
| Separate what AI does well from what it cannot yet do | In genomics, AI is unreliable for analysis but is outstanding at translating results into personalized, audience-specific reports | Sequencing.com deployed AI in customer success routing and post-analysis reporting — keeping the entire customer success team onshore and U.S.-based while handling higher support volume without proportional headcount growth |
| Identify the translation gap and make it your business | Between research and clinical practice, between scientific output and consumer understanding, there is always a translation gap that practicing experts can see and business founders can solve | Colby saw that no one was moving genetic research from bench to bedside — not because it was impossible, but because no one was funded or incentivized to do it. That gap became Sequencing.com, now the world’s largest direct-to-consumer whole genome sequencing platform. |
Quotes from This Episode
- “As a practicing physician, I saw all this research, but it was not being put into the hands of myself and other practicing doctors. And that was a greatly limiting factor. And I saw that that was an immense opportunity also to go and solve that problem.” — Dr. Brandon Colby, Founder and CEO, Sequencing.com
- “No matter how good we think we are, there are other people out there that are smarter, that are innovative, and we want to work with those people. We want to go and bring them onto the team. We want to be humbled by them.” — Dr. Brandon Colby, Founder and CEO, Sequencing.com
- “You read articles how engineering team was able to go and let go 90%. And then there’s just about like five people on the engineering team that are accomplishing five years of work in one day. And that makes AI sound like it’s like amazing. And that all you have to do is you click a few buttons and then you get to that place. And what we have found internally is that it’s not there yet.” — Dr. Brandon Colby, Founder and CEO, Sequencing.com
- “The AI is not capable of genetic analysis… it’s primarily incorrect. And it takes a lot of assumptions, a lot of guessing.” — Dr. Brandon Colby, Founder and CEO, Sequencing.com
- “It’s not where you can just go and bring in a CEO that has a playbook from another industry that has worked and say, well, they were very successful in this industry. Let’s bring them in as CEO. Instead, when there’s really deep, complex technology and especially data, loads of data — from my perspective, it takes a CEO that has a very intricate knowledge of that technology.” — Dr. Brandon Colby, Founder and CEO, Sequencing.com
Frequently Asked Questions
Does a CEO of a deep-tech or data science company need to understand the technology personally?
Yes — according to Dr. Brandon Colby, founder and CEO of Sequencing.com, technical depth at the CEO level is not optional in complex-data companies. When a company’s products are built on scientific or proprietary data, every strategic, product, and hiring decision flows from the CEO’s understanding of the technology’s limitations and possibilities. A CEO without that knowledge will build the wrong things, skip the right ones, and miss the gaps that make the business defensible. Colby credits his background as a practicing physician and medical geneticist with every early directional decision at Sequencing.com — decisions that produced the world’s largest direct-to-consumer whole genome sequencing platform.
How should a CEO in a technical company scale without losing decision-making quality as the team grows?
The approach Colby uses at Sequencing.com is deliberate: hire people who are smarter than the CEO in each specific domain, hand off that domain to them, and then learn from the handoff. The goal is not to maintain personal technical control but to raise the entire team’s capability with each hire. This requires the CEO to hold humility as an operating value — not just a personal trait. At Sequencing.com, that principle is embedded in company culture: the assumption is always that someone outside the team knows more, and the priority is to find them and bring them in.
Where is AI genuinely useful in a complex genomics product, and where does it fall short?
AI is not reliable for genomic analysis itself — it has not been trained on the human genome at the depth required for accurate interpretation, and it misapplies historical identification systems to current data without recognizing the error. At Sequencing.com, no genetic analysis is AI-driven. What AI does well is the translation step after analysis: turning complex genomic results into personalized reports and discussions tailored to the specific audience, whether a physician or a patient with a specific health concern. In customer success operations, AI agents now allow the team to scale output without proportional headcount growth, keeping Sequencing.com’s entire support team onshore and U.S.-based. As foundation models improve, that translation function gets stronger, and Sequencing.com’s product is built to benefit from each generation of model improvement.
CEOs Work with Glenn Gow to Scale Their Companies and Careers
Glenn Gow is The Scaling Executive Coach — he coaches ambitious executives into the CEO seat and CEOs into successful exits. With 25 years as a CEO and 5 years in venture capital, Glenn helps leaders scale their companies by scaling themselves first. If this conversation was useful, you can apply for executive coaching with Glenn Gow or apply to be a guest on The Scaling Executive Podcast.
