The first wave of the artificial intelligence boom was a boon for chipmakers, with companies like Nvidia riding surging demand for the processors that power AI models. But according to BlackRock's Becci McKinley-Rowe, the next set of winners may not be the companies building the technology—but those using it to transform their own businesses. And she's looking hardest at healthcare.
In a Reuters column, McKinley-Rowe, who leads BlackRock Fundamental Equities, points to the firm's review of more than 1,000 earnings reports suggesting AI is starting to show measurable benefits across sectors, even if adoption is still early. The key, she argues, is that healthcare has two things many industries lack: decades of proprietary data and a clear payoff from getting faster.
Why healthcare's data is a goldmine
Developing a new medicine is a notoriously long and expensive slog. It typically takes about 10 years and $2 billion to bring a drug to market, and roughly 90% of drug candidates never make it. That leaves behind a vast graveyard of "failed" datasets inside companies—information about compounds that didn't work, trials that missed their endpoints, and biological pathways that proved dead ends.
Training AI on that history could help researchers pick better targets, run trials more efficiently, and automate documentation. The potential payoff is significant: lower costs, higher success rates, and faster timelines. Because patents and competition give drugs a limited window to earn most of their money, months saved can mean earlier revenue and a longer effective period of market exclusivity, improving the economics of a program.
This isn't just theoretical. Recent earnings from drugmakers like Eli Lilly and Merck have already shown how operational efficiency can move stock prices. And the broader trend of AI adoption in healthcare is part of a larger shift where even AI infrastructure players are diversifying beyond the usual suspects.
What it means for investors
For markets, the implications are nuanced. The Russell 3000 Healthcare Index already had a 122% winner-loser gap from 2005 to 2025, according to FactSet data. That means the gap between the best-performing and worst-performing healthcare stocks was far wider than in the broader Russell 3000. If AI really shortens timelines or nudges success rates higher, the payoff won't land evenly.
Small process gains can lift a drug's net present value—basically what future profits are worth in today's dollars—by pulling cash flows forward and reducing years of spending before a product sells. That favors companies with deep, well-organized data and the ability to plug AI into research, clinical trials, and regulatory work. In other words, any healthcare re-rating may show up more as stock-by-stock winners and losers than as a smooth, sector-wide move.
This is a shift from the first phase of the AI trade, where chipmakers like Intel and others were the clear beneficiaries. Now, the focus is on "AI adopters"—companies that use the technology to improve their own operations. Healthcare, with its data-rich environment and high-stakes economics, is a natural candidate.
But investors should be cautious. Not every drugmaker will benefit equally. The winners are likely to be those with the most comprehensive data, the technical expertise to deploy AI effectively, and the organizational will to change long-standing processes. The losers could be those that lag in adoption or fail to integrate AI into their workflows.
For everyday investors, the takeaway is that the AI story is broadening. It's no longer just about buying the picks-and-shovels companies. It's about identifying which businesses can turn AI into a competitive advantage. In healthcare, that could mean a new era of drug development—and a new set of market winners.
