From Chinese innovation to AI, biotech CEOs are being hit with challenges at a breakneck pace. Three leaders from BioSpace’s NextGen Class of 2026 told us about the issues keeping them up at night.
For American biotech to compete with China, the industry and supporting government agencies need to go on the offensive, a trio of biotech leaders say. But that doesn’t mean the therapies emerging from the U.S. can’t hold their own in the market.
“I’m here in California, and the sun is shining, and it’s a beautiful day, and I say competition, and what’s happening in China—bring it on,” said Mo Trikha, CEO of Kivu Bioscience, during a BioSpace webinar on May 21. Kivu is one of BioSpace’s 2026 NextGen class of up-and-coming young biotechs.
Trikha was joined by fellow NextGen leaders to talk about the state of biotech and the key issues they are battling at the moment, from Chinese innovation to the rise and infiltration of AI.
City Therapeutics CEO Andy Orth similarly welcomed the competition but highlighted ways that the federal government can help—particularly after last year’s slashing of National Institutes of Health grants that challenged early-stage biotech startups.
“My firm belief remains that competition is good for everybody, and . . . it’s going to be good for patients,” Orth said. He noted that his company, with its sRNA and RNAi focus, directly competes with emerging Chinese biotechs and advocated for more government support for basic science, among other policy shifts. “We need to behave in an offense manner.”
In addition to restoring NIH funding, Orth said regulatory flexibility at the FDA could help make biotech better, faster and easier in the U.S. China’s government has actively worked to ease regulatory hurdles, speed drug reviews and fund science, while the opposite has been happening in the U.S., Orth noted.
“It is not necessarily an equal playing field,” he said, describing China as “a purely organized effort to lead in biotech. . . . So we need to fix that.”
Trace Neuroscience, which is tackling a new target in amyotrophic lateral sclerosis (ALS), is not directly competing with China like City or Trikha’s ADC-focused Kivu. But Trace CEO Eric Green said he’s been impressed by some of the early neuroscience work coming out of China, and his company is actively thinking about how they may respond if Chinese innovation crosses into neuroscience.
“The litmus test that we always apply is, how can we do this in a way that is going to be beneficial to people with ALS around the world,” Green said. “If that means working with groups in China, well, then that’s the right thing for us to do.”
Trikha also believes in exploring partnerships with China where they make sense. “We have to embrace it. We have to build a Golden Gate Bridge.”
That goes beyond signing licensing deals to bring China-discovered products to the U.S. Trikha said partnerships should be a two-way street, with promising American products eventually being offered to China or India or anywhere they need to be to reach patients.
But Trikha stressed that scientists and companies need to be thinking about their intellectual property. “How do you find the right balance between presenting at a scientific conference versus protecting your IP?” he said. There needs to be more education for scientists on IP protections, he added.
“When I was in graduate school, I wasn’t taught a single business course in my PhD [in] biochemistry, molecular biology. There were some hard lessons I had to learn when I switched from academics to industry.”
Green also noted that the rise of China has shown the power of clinical data, even from smaller groups of patients, compared to decades of animal studies. That’s a lesson that the U.S. should be taking away, he said.
Trikha seconded that idea, adding that more could be done to build and ease relationships between academic institutions, biotechs and pharmas. For instance, academic institutions need to be able to more easily launch trials for small patient groups—the kind of data generation Green is talking about.
Even ahead of human trials, academic research is critical for the future of biotech, Green argued. Trace’s lead asset TRCN-1023 is rooted in studies from Stanford and University College London, with scientists on both sides of the pond uniting over the common goal of advancing treatment for ALS.
“It’s something I worry a lot about, as we think about this point of, how do we continue to innovate on first-in-class, really transformative medicines?” Green said on the BioSpace webinar. “It requires a healthy biotech ecosystem, but the foundation of it continues to be investment in academic science, and my hope is that, although this has been a tumultuous time for that investment, that there continues to be support from across the political spectrum, and recognizing that that has been a huge part of the engine of our success and medical innovation in this country.”
On AI
With more competition comes pressure to innovate—and quickly. Much of the biopharma industry has turned to AI for myriad ways to speed operations. The NextGen leaders said they are picking AI innovations carefully.
“I’m not looking for artificial intelligence, I’m looking for real intelligence,” Trikha said. He wants AI applications that can speed decision making, but this has to come from the right dataset. “Ideas are cheap. Time is something that patients don’t have. . . . Money I can always find. So give me the right data package that allows us to make the decision.”
I’m not looking for artificial intelligence. I’m looking for real intelligence.
City’s higher-end genetics and data analytics teams have been integrating AI in an opportunistic way, which has proven to speed some operations, Orth said. But the technology does not change the biotech’s mission: find new RNAi drugs. AI can’t do that—not yet, anyway.
“There are things coming that are going to disrupt how we develop and discover drugs, so we can’t have our head in the sand, but I’m also cautious of being distracted on this front,” Orth said. “I have a couple people walking around these halls who are exceptionally excited about it, and I can be too, but we’re also just trying to keep ourselves focused.”
Trace, which is a small, lean organization, is looking at ways to analyze large natural history datasets and develop efficient clinical trials using AI. The biotech also did a collaboration with Unlearn AI using digital twins. The main goal is to “do more with less,” Green said.
The NextGen leaders aren’t cowed by the major funding that dedicated AI biotechs have been receiving. The week of the webinar, the biotech world was atwitter about Isomorphic Labs securing the second-largest biotech funding round ever at $2.1 billion. The company did so without revealing a single molecule or clinical plan. But Orth argued that this wasn’t stealing money from more traditional biotechs—if you can call them that—as AI is attracting a tech-heavy base.
“I don’t think it’s that much of a fixed pie that it’s literally siphoning it off from one in order to give to another,” Orth said.
Trikha also warned other biotechs to not follow the hype immediately, and instead to stick to what you know when you know it. He also recommended strong basic research and challenging the data.
“When you embark on a journey of whatever medicine you’re working on, you need to be aware of the competition, but be a palm tree and not a banyan tree, because the competitive world is changing, right? So you have to be flexible, but you can’t always keep changing.”
