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Biotech Just Had Its Best Six Months Since 2003 — And AI Is The Secret Ingredient

MarketDash Editorial Team
12 days ago
While everyone obsesses over AI chips and tech stocks, biotech has quietly posted its strongest rally in over 20 years. The reason? Artificial intelligence is fundamentally changing how drugs get discovered.

Here's something wild: while the entire investment world has been fixated on AI semiconductors and tech giants, biotech stocks have been quietly putting together their best performance in more than two decades. And nobody seems to have noticed.

The iShares Biotechnology ETF (IBB) just wrapped up six consecutive months of gains—its longest winning streak since 2012. But the real story isn't just consistency. It's the magnitude. Biotech is up 40% over that stretch, delivering the sector's best six-month rolling return since September 2003.

That's not a typo. We're talking about the kind of rally biotech hasn't seen since the early 2000s.

So what changed? After years of underperformance, biotech finally has a structural narrative that investors can get excited about: artificial intelligence is starting to crack the cost and efficiency problems that have made pharmaceutical R&D so punishingly difficult for decades.

The Old Model Was Brutally Expensive

Drug discovery has always been defined by brutal economics. The average drug costs $2.23 billion to develop, takes up to 15 years to bring to market, and has just a 7.9% chance of making it through human testing successfully. Those numbers are why pharma stocks have historically traded like boring utilities—slow, defensive, and rarely the stuff of growth portfolios.

But AI is starting to rewrite that script.

"We are no longer betting on science fiction; we are investing in the industrialization of biology," said Jordi Visser, head of AI Macro Nexus Research at 22V Research, in his latest report.

Visser argues we're witnessing a foundational shift from labor-driven science to compute-driven discovery. In this new model, biology gets treated more like information. R&D spending becomes a platform cost, marginal costs drop, and you start seeing software-like scalability emerge in drug development.

The payoff? Pipelines that move faster, cost less, and become more predictable—exactly the three variables that have historically kept growth investors away from the sector.

"Of all the industries where AI will benefit mankind and bring efficiency, none will be more impactful than health. This month may be the beginning of investors finally understanding its importance," Visser said.

From Hype to Implementation

Visser acknowledges that plenty of investors remain skeptical about AI, particularly those focused on valuation multiples and historical market comparisons. But he sees that skepticism as evidence of how early we still are in this transformation.

"Yet I continue to hear the same chorus from the cheap seats: investors who spend their days with financial history charts and comparisons, not scientific reality, insist that AI is a bubble," he wrote.

He challenges doubters to consider what AI might actually unlock: longer lifespans, cheaper treatments, higher productivity, and potentially cures for diseases once considered unreachable.

In his report, Visser shares an interesting thought experiment. He asked ChatGPT what would happen to markets if a cure for cancer were discovered and widely available. The AI's response? An immediate 20–30% surge in global equities, followed by a decade of 50–100% gains as markets reprice life expectancy, healthcare costs, and long-term consumption patterns.

That might sound like science fiction, but the thesis is backed by tangible progress happening right now. Companies like Moderna Inc. (MRNA), Insilico Medicine, and Eli Lilly Co. (LLY) are already deploying AI in ways that are actively changing drug development timelines, cost structures, and success rates.

This isn't theoretical research anymore. It's implementation at scale.

Visser's conclusion is straightforward: "The [AI] capital expenditure phase is over. The implementation phase has begun."

Translation? The money has been spent building AI infrastructure. Now we're watching it get put to work—and biotech might be one of the first places where the payoff becomes impossible to ignore.

Back to News

Biotech Just Had Its Best Six Months Since 2003 — And AI Is The Secret Ingredient

MarketDash Editorial Team
12 days ago
While everyone obsesses over AI chips and tech stocks, biotech has quietly posted its strongest rally in over 20 years. The reason? Artificial intelligence is fundamentally changing how drugs get discovered.

Here's something wild: while the entire investment world has been fixated on AI semiconductors and tech giants, biotech stocks have been quietly putting together their best performance in more than two decades. And nobody seems to have noticed.

The iShares Biotechnology ETF (IBB) just wrapped up six consecutive months of gains—its longest winning streak since 2012. But the real story isn't just consistency. It's the magnitude. Biotech is up 40% over that stretch, delivering the sector's best six-month rolling return since September 2003.

That's not a typo. We're talking about the kind of rally biotech hasn't seen since the early 2000s.

So what changed? After years of underperformance, biotech finally has a structural narrative that investors can get excited about: artificial intelligence is starting to crack the cost and efficiency problems that have made pharmaceutical R&D so punishingly difficult for decades.

The Old Model Was Brutally Expensive

Drug discovery has always been defined by brutal economics. The average drug costs $2.23 billion to develop, takes up to 15 years to bring to market, and has just a 7.9% chance of making it through human testing successfully. Those numbers are why pharma stocks have historically traded like boring utilities—slow, defensive, and rarely the stuff of growth portfolios.

But AI is starting to rewrite that script.

"We are no longer betting on science fiction; we are investing in the industrialization of biology," said Jordi Visser, head of AI Macro Nexus Research at 22V Research, in his latest report.

Visser argues we're witnessing a foundational shift from labor-driven science to compute-driven discovery. In this new model, biology gets treated more like information. R&D spending becomes a platform cost, marginal costs drop, and you start seeing software-like scalability emerge in drug development.

The payoff? Pipelines that move faster, cost less, and become more predictable—exactly the three variables that have historically kept growth investors away from the sector.

"Of all the industries where AI will benefit mankind and bring efficiency, none will be more impactful than health. This month may be the beginning of investors finally understanding its importance," Visser said.

From Hype to Implementation

Visser acknowledges that plenty of investors remain skeptical about AI, particularly those focused on valuation multiples and historical market comparisons. But he sees that skepticism as evidence of how early we still are in this transformation.

"Yet I continue to hear the same chorus from the cheap seats: investors who spend their days with financial history charts and comparisons, not scientific reality, insist that AI is a bubble," he wrote.

He challenges doubters to consider what AI might actually unlock: longer lifespans, cheaper treatments, higher productivity, and potentially cures for diseases once considered unreachable.

In his report, Visser shares an interesting thought experiment. He asked ChatGPT what would happen to markets if a cure for cancer were discovered and widely available. The AI's response? An immediate 20–30% surge in global equities, followed by a decade of 50–100% gains as markets reprice life expectancy, healthcare costs, and long-term consumption patterns.

That might sound like science fiction, but the thesis is backed by tangible progress happening right now. Companies like Moderna Inc. (MRNA), Insilico Medicine, and Eli Lilly Co. (LLY) are already deploying AI in ways that are actively changing drug development timelines, cost structures, and success rates.

This isn't theoretical research anymore. It's implementation at scale.

Visser's conclusion is straightforward: "The [AI] capital expenditure phase is over. The implementation phase has begun."

Translation? The money has been spent building AI infrastructure. Now we're watching it get put to work—and biotech might be one of the first places where the payoff becomes impossible to ignore.