AI innovation with human benefits

The biotech sector has staged a strong recovery, with the NBI up more than 50% from April 2025 lows to year-end, significantly outperforming the S&P and reaching levels last seen in 2021. Importantly, we view the sector as structurally healthier today, supported by several factors: valuations have reset and positive clinical data catalysts are once again being rewarded; large-cap biopharma remains active in M&A with over $100B in available capital; and macro headwinds such as interest rates and drug-pricing overhangs appear to be easing. While innovation from China and shifting global pharma spend could introduce competitive pressures, near-term sector fundamentals remain solid. We expect performance in 2026 to be highly idiosyncratic, driven by clinical execution and commercial launches, and we recommend selective exposure to SMID-cap names with late-stage readouts and best-in-class potential.  

Biotech’s improving momentum is supported by structural factors that position the sector for continued growth. While smaller-cap companies dominate the landscape (75% of companies are <$1B market cap), this diversity creates a fertile environment for innovation, with significant value concentrated among firms above $1B market cap. The IPO market, though below pandemic-era highs, shows early signs of recovery, and we expect activity to accelerate as investor confidence strengthens. M&A remains steady, complemented by an uptick in licensing and partnership deals that provide strategic flexibility and broaden access to capital. These dynamics suggest a healthier, more disciplined market environment, where continued innovation across the sector is again entering the forefront for investors. 

AI is emerging as one of the most transformative technologies in health care, driven by the sector’s inefficiencies and vast untapped data resources. As we highlight in our 8-part AI Basics in Health Care Series, health care represents a $10 trillion global market, yet more than 95% of its data remains unused, and processes such as drug development and claims management are slow and costly. AI offers a compelling solution by automating manual workflows, improving diagnostic accuracy and enabling personalized treatment strategies. Current adoption, however, is still in its early stages, with most implementations focused on narrow, efficiency-driven applications rather than high-value innovations like predictive treatment modeling or generative drug design. Despite these limitations, the long-term potential is significant: 

, supported by hyperscaler investments in compute and storage infrastructure and growing partnerships between big tech and health care incumbents.  

Two areas where AI is seeing the most traction today are administrative automation in hospitals and drug discovery in biotech. AI could double a physician’s productivity through reducing administrative tasks such as electronic health record (EHR) updates, billing and claims processing that are highly manual and time-consuming. Hospitals are increasingly deploying AI tools to automate chart summarization, patient intake and revenue cycle management, with even a 1–2% operating margin improvement potentially driving >$100M in cost savings to the system. In parallel, AI is accelerating drug discovery by leveraging generative models and high-performance computing to analyze biological sequences, predict molecular interactions and optimize candidate selection.

This exemplifies a “blurring of the lines” between traditional tech and biotech, with AI shortening development timelines and reducing costs in early-stage research, addressing one of the most expensive and failure-prone aspects of health care. Over time, as reasoning-based AI systems mature and integrate across workflows, applications will expand to more complex domains such as personalized medicine and predictive analytics, requiring significantly higher compute resources and deeper collaboration between technology providers and health care systems. We expect this wave of innovation will drive new company formation and M&A over the next decade as incumbents seek to scale exposure to AI.