When may AI start impacting the health care sector?
How long it may take for these potential advances to come to life may depend on how smoothly the present AI infrastructure build-out unfolds—and how long it takes for AI computing power to become more broadly available (and to fall in price).
While the timing of any breakthroughs remains uncertain, Yoon has been leaning into areas he believes could ultimately have exposure to these AI-related themes—but that could also benefit from more near-term tailwinds. For example, the fund has recently held overweights in subsectors such as life sciences tools and biotechnology, areas he perceives to be particularly dynamic.
Life sciences tools
Yoon believes that life sciences tools—the equipment, testing technologies, and analytics that underpin modern biomedical research—could benefit from the trend of manufacturing reshoring, which has been encouraged by certain federal policies. Yoon expects a capital investment wave as these facilities open over the next few years, which could stimulate rising demand for analytical tools.
“I believe that AI could drive efficiency in the clinical development part of R&D,” he says (referring to research and development). Clinical development accounts for the bulk of spending in pharmaceutical companies’ R&D budgets. So improvements in clinical-development efficiency could imply that “dollars may be reallocated to the ‘R’ side of things, which could be great for some life-science tool companies.”
Two large life science and diagnostics players in the industry that Yoon’s fund has held exposure to are Danaher () and Thermo Fisher Scientific ().
Biotechnology
Yoon is also constructive on biotechnology for several reasons. For one thing, he says, the “hit rate” for clinical trials—meaning, the percentage of treatment candidates that are successfully able to move from one phase of trials to the next—has jumped dramatically in the past year or so.
“The quality of clinical data that I’m seeing out of the biotechnology industry has been unbelievable over the last 12 to 18 months,” Yoon says. “Biotech has been seeing a positive rate of change in innovation even though the AI tailwind hasn’t hit the industry yet.”
The industry has really come of age, he notes, with dozens of biotech companies generating $500 million or more a year of revenue. The industry’s economics are unusual: A biotech company can be unprofitable for a decade while it’s investing in new drug research, but once treatments are commercialized, these firms can suddenly become highly profitable. “The market consistently underestimates how once these companies turn profitable, they can become massively profitable,” Yoon says.
He’s held some biotech names for more than a decade, such as Alnylam Pharmaceutical () and Argenx SE ADR (). One factor supporting valuations in the biotech segment is lively dealmaking activity as pharmaceutical giants—facing patent cliffs or gaps in their drug pipelines—look to acquire smaller biotech firms with promising treatments.
Some of the treatments, improved life science tools and diagnostics, and research breakthroughs that Yoon anticipates being enabled by AI in the coming years may sound like science fiction today. That’s nothing new in the industry. Notes Yoon, “Within health care, innovation has been a constant and always underappreciated over my 25 years in the industry.”