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Science in Real Time (ScienceIRT) podcast serves as a digital lab notebook—an open-access, conversational platform that brings the stories behind cutting-edge life science tools and techniques into focus. From biologics to predictive analytics and AI-powered innovation, our guests are shaping the future of therapeutic discovery in real time.
Transcript
Carli: Hey everyone—welcome to Science in Real Time. This is the show where we sit down with the people pushing science forward and talk about what’s really happening behind the breakthroughs. Before we dive in, subscribe so you never miss an episode. We’ve got some incredible conversations coming your way. Thank you so much for being here. It’s a pleasure to have you on Science in Real Time. To start, tell us who you are and what drew you into the life sciences.
Kirk: Thank you for having me—I’m excited for this conversation. I started my career in academia as a professor, with a PhD in biology. Around the time the Human Genome Project was ramping up, I got an opportunity to move into industry—and I loved it right away. I was drawn to the complexity of the science, the way scientific decisions connect to business questions, and the fact that it’s deeply multidisciplinary and people-driven. You’re solving meaningful problems—infectious disease, cancer, discovery—and it combines complex problem-solving, collaboration, and real impact on human health. That mix is what pulled me in.
Carli: You’ve held a lot of leadership roles in life sciences, including helping transform companies like Illumina and Verogen. Is there a thread or philosophy that’s guided you throughout your career?
Kirk: I’ve always been drawn to leadership, organizational design, and commercial strategy—especially in companies going through rapid growth. I’ve been fortunate to work at several organizations in that phase, and those challenges really energized me.
Carli: What are some of the most defining decisions you’ve seen in companies that enable sustainable growth?
Kirk: Sustainable growth in this industry is hard—especially for capital equipment companies—because you have to anticipate where market needs are heading. In high-growth companies, success often comes from creating a paradigm shift in the scientific process. But then you also have to educate and develop the market so people understand the new paradigm. PCR was paradigm-shifting. High-throughput genotyping was paradigm-shifting. Next-generation sequencing was paradigm-shifting. And I think what Araceli is doing for drug discovery is paradigm-shifting too. That’s foundational. Another key lesson: when the opportunity is real, you have to be willing to take big swings. For example, Illumina acquired a sequencing company when we were barely profitable—and we paid more for it than our own company was worth. We did it because we believed next-generation sequencing would displace legacy technologies and change the field. Araceli’s investment in AI is similar. Araceli began as an instrument company with strengths in imaging and software, but leaning into AI opens new ways to do drug discovery—ways that can change pharma business models. You can be an excellent instrument company, but sustained global competitiveness often requires creative thinking and meaningful risk-taking.
Carli: We hear a lot about balancing scientific excellence with strategic clarity. What does that alignment look like in practice?
Kirk: There are established ways to do this, especially in larger companies. Typically you have a large set of ideas—maybe 100 projects. Ahead of strategic planning, the commercial team does an initial assessment: where it fits in the roadmap, market fit, revenue potential, and competitive landscape. It’s not an exhaustive consulting-style analysis—more of a feasibility-level pass. Then leadership and commercial partners prioritize those ideas—often literally ranking them and drawing a line based on budget. Each project is evaluated on expected investment, timeline to launch, revenue potential, and ROI. It becomes spreadsheet-driven, but the real value is in the discussion: what’s strategically differentiating five to ten years out, and why choose one path over another? After that, you move into stage gates—feasibility studies to validate technical reality and refine cost. Many ideas drop out there. The remainder move into development and eventually launch. Ideally, it’s a disciplined narrowing process from many creative ideas to a focused portfolio.
Carli: Can you share an example where aligning vision and strategy made all the difference?
Kirk: A good example is Verogen. It’s a next-generation sequencing company I spun out of Illumina to focus on forensic analysis and human identification. Many cold cases you’ve heard about—including the Golden State Killer—were solved using next-generation sequencing and Verogen’s technology. The vision was to replace a decades-old technology—STRs (short tandem repeats)—which is difficult with degraded samples, not very informative, and time-consuming. The opportunity was clear: huge backlogs like rape kits sitting untested for months or years, identifying remains from war zones, and cases where there’s no match in existing databases.With sequencing, you can analyze degraded samples that weren’t usable before, and you can extract additional information—like ancestry, sex, and other traits. Combined with genealogy, it can even help narrow to a surname. That was the strategic rationale behind building Verogen as a standalone business outside Illumina.
Carli: Now every time I watch a documentary about identifying remains or solving cold cases, I’ll be thinking about you—because this work is incredibly needed. It also sounds like a very natural place for your team to grow.You’ve watched life sciences evolve dramatically over the last 25 years—and it’s accelerating even faster now. What shifts are exciting you most?
Kirk: AI, without question. Many earlier paradigm shifts were chemistry-driven, and computing tried to keep pace with the data explosion from genomics and sequencing. But AI changes the game because it can interpret data in real time. I saw this recently during an Araceli demonstration: image analysis in real time; phenotypic, morphological, and genomic data analyzed in real time. That enables a much faster experimental loop. The instrument can already scan dramatically faster, but speed only matters if you can analyze at the same pace. What’s exciting is the combination: scanning more cells faster, while AI does quality control and begins answering biological questions in real time—on the same plate. Early wells can train or guide the model, and later wells can be interrogated based on what the system is learning. That accelerates the search for the right drug, indication, and combinations—far beyond the traditional approach of teams manually reviewing images for months and then redesigning experiments. In principle, you can run multiple plates in sequence and adapt each round based on what the AI learned from the previous ones—getting answers in days that used to take months. It’s not just more data; it’s real-time interpretation and real-time iteration of experimental design at the bench.
Carli: How do you see companies like Araceli changing the game? Speed comes to mind, but I’d love your take.
Kirk: Everyone talks about AI, but fewer people talk about the teaching problem: AI needs data at the rate it needs to learn. The most advanced AI is limited if it doesn’t have enough high-quality data to train on. To me, Araceli helps unlock that bottleneck in wet-lab biology by generating biological data at the pace AI requires. Otherwise, even if AI is fast, you can still spend years generating the data to teach it—so the system doesn’t truly accelerate. We’re already seeing AI used in areas like radiology and medical devices—often with a clinician in the loop. Like any paradigm shift, there will be iterations, and there will be moments where we learn we relied too much on automation or didn’t have the right data. But long term, it should make processes more efficient across product development, R&D, technology, and healthcare. There’s also a generational aspect: younger people will approach problem-solving differently because AI tools will be normal. Ideally, that frees human creativity by reducing repetitive work.
Carli: We share that aspiration. AI has huge potential to improve efficiency. I’d love to switch gears now and go behind the scenes—your journey and philosophy. Looking back, what was one defining moment that shaped your approach to leadership?
Kirk: It happened at Illumina. Early on, I led customer support—field applications scientists, tech support, customer service, field service engineers, and training. We built a strong global team. One of my responsibilities was updating the executive staff on product quality issues and customer complaints. For a while, I became “the complaint guy.” Others would report wins—revenue, marketing visibility—and I would report what wasn’t working. The turning point was learning to frame the same information in a solution- and business-oriented way. Instead of listing the top complaints and failure modes, I reframed it as: if we invest X dollars to improve this product, we save Y dollars in service and support, and we increase instrument utilization. That shift—packaging technical realities with economic context and a clear solution—changed how leadership viewed my work and changed the trajectory of my career from functional leader to business leader.
Carli: After such an accomplished career, what continues to motivate you to stay involved in life sciences?
Kirk: It’s fun to work with companies like Araceli, see new technologies, and stay close to how quickly the field is evolving. As an investor, advisor, and board member, I can view the business from different angles. When you’re running a company, you’re focused on execution and near-term goals. As an advisor, you can step back and focus on the bigger questions: what problem are we solving, how does the technology match market need, how do you go to market, and how do you stay ahead. There are so many exciting companies right now, and I enjoy being part of those conversations.
Carli: Where do you think we’ll see the most meaningful differentiation in life sciences—data acquisition, technologies, or leadership mindset?
Kirk: The leadership mindset should stay grounded—treating people well and keeping the human element strong—while adapting quickly to business realities. The biggest change will be how we use data and make decisions. We’ll see a shift from generating more data, to using AI to aggregate and interpret it, to AI increasingly making decisions from it—and acting on those decisions, whether that’s selecting the next experiment or informing product design. How far that goes will be fascinating.
Carli: That will definitely change how we interact with data, what conclusions we draw, and where we go next. For listeners who want to learn more about you or reach out—where should they go?
Kirk: LinkedIn: Kirk Malloy. Or email me at Kirk at bioadvisors.com. I always like hearing about new companies and new technologies, and if I can help something grow or become more successful, I’m happy to.
Carli: You heard it here—find Kirk Malloy on LinkedIn. We’ll include those links in the description as well. Last question: what advice would you give emerging leaders in biotech about staying visionary in a data-driven world that’s changing so fast?
Kirk: Stay current on the range of business models out there. There are so many new models, and if you’re not paying attention, your approach can become obsolete before you even deploy it. Read broadly—books, podcasts, investor reports—and keep challenging how you think about building a business. The landscape is very different than it was even five years ago. And don’t lose the human element. In a world of AI and fast-moving competition, people are still the ones doing the work. It’s easy to focus on technology and forget that the most important work gets done by teams.
Carli: That really resonates—and a quick plug: at Science in Real Time, we’re always trying to stay ahead of the curve and highlight the people behind the progress. This has been wonderful. As mentioned, you can find Kirk’s info in the show notes. I hope this isn’t the last time we connect—Araceli will certainly cross paths again. Thank you.
Kirk: Thank you. I really enjoyed it.
Carli: Thanks for tuning into Science in Real Time. We’re glad you spent time with us. If today’s conversation sparked questions or ideas, keep it going—connect with our guest and the Science in Real Time team using the links in the description. If you enjoyed this episode, like, subscribe, and share it with someone who loves science as much as you do. Until next time—thanks for watching.