AI Salon talks with two founders about real-world AI applications — and what policymakers need to understand

AI Salon: Tell us a little about yourself — who you are, what you do, and how AI helps you do it.

Crystal Brown: I’m Crystal Brown, co-founder of a Detroit-based biotechnology company called CircNova. We use AI to help design, develop, and analyze RNA-based therapies for diseases that have long been deemed untreatable.

Kevin Lang: I’m Kevin Lang, CEO of Agerpoint. We’re an agriculture-focused tech company based in Research Triangle Park, NC. We digitize information from farms and fields, then use AI to analyze the information — unlocking insights that support sustainable agriculture.

AI Salon: So does AI enhance your work, or make it possible?

Brown: In theory, scientists could develop these therapies without AI. But AI allows us to develop them in a fraction of the time and cost it would take scientists.

That efficiency allows us to explore vastly more potential treatment approaches and to identify earlier whether a particular approach is worth pursuing. In drug development, where time and capital are limited, scalability is a game-changer.

Lang: Crystal’s point about AI enabling scale is really important. In an agricultural setting, there are often only a few agronomists with deep expertise about things like when to harvest, how to identify diseases, and how to respond to challenging crop or weather conditions. Workers in the field generally don’t have that level of expertise. But our AI-powered tools can deliver it to them via a smartphone or other portable device.

At the same time, our tools collect, transmit, and centralize data from field workers so agronomists and decision-makers can understand what’s happening across many fields simultaneously. That aggregation allows them to make informed decisions, push smart guidance out to workers, and make more accurate predictions.

AI allows us to transmit expertise and data to and from the field — and to make better predictive decisions. Those are capabilities that simply wouldn’t have been possible prior to AI.

AI Salon: How did you get involved in AI-powered work?

Lang: I started as a mechanical engineer designing tractors at John Deere. Later, I moved into consulting and then worked with an NGO in Switzerland, where I was introduced to

remote-sensing drones and satellite technologies. I began to see how combining those tools with AI could solve real problems in agriculture.

Brown: My path was definitely unorthodox. I spent the first half of my career in automotive manufacturing, focused on finance and operations. Then, one day, a friend of mine asked me if I wanted to help out with bookkeeping at a biotech startup.

I knew nothing about life sciences, but by asking questions about key metrics and operational milestones, I began to understand drug development. Over time, I immersed myself in the science and merged my business background with biotech innovation, leading me to found CircNova.

AI Salon: What do you want lawmakers to understand about AI?

Lang: A lot of the public conversation about AI still feels theoretical — like AI might change things, or could make a difference. But we’re already using AI to help farmers educate workers, make better day-to-day decisions and forecast crop yields. Lawmakers need to understand that AI is already having a real impact.

They also need to understand that AI, like the internet, isn’t neatly fit into a single category and can’t be regulated by a single set of rules. If policymakers treat all AI systems the same way, without really understanding how they’re applied in different industries, they’ll create serious friction for businesses solving practical problems — and hamper innovation.

Brown: I agree. In biotech, AI is transforming drug development. But biotech already operates within a highly regulated environment. The challenge is to ensure that any new AI policies account for how the industry actually operates. If rules are written too broadly, without

industry-specific nuance, they could unintentionally slow or even prevent companies from advancing new therapies.

I think about the spread of AI like the industrial revolution. It affects every industry, but in very different ways. Kevin and I both use AI, but our regulatory needs differ.

Lang: Lawmakers have to think about AI policy from a long-term, strategic perspective. AI has huge implications not only for U.S. innovation, but also for competitiveness, leadership, and security. Lawmakers, industries, and regulatory bodies need to work together to get this right.

Brown: And getting it right won’t be easy. But if policymakers take the time to understand how AI is being put into practice — and strike the right balance between mitigating risk and fostering opportunity — we can keep unlocking innovation that makes a real difference in people’s lives.