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Mega-caps spend billions like they know where the next wave is. They don't. Here's what buying drilling rights in pharma actually looks like — and why even the best-positioned players still miss almost every time.

A known disease. A known market. So why does pharma investing still feel like guessing? The answer splits cleanly into what you can actually predict — and what stays a coin flip no matter how good the science looks.

Roughly 97.5% of drug candidates die before a patent is ever filed on them — invisible to competitors, regulators, and the public. This piece breaks down how AI is starting to mine that hidden graveyard, and why no single unpatented failure has turned into a headline win yet.

Warren Buffett says he could still compound money at 50% a year. The data says the market that let him do that in the 1950s doesn't exist anymore — and here's the specific evidence why.

The medtech ETF beat the pharma ETF over the last decade — not because medtech won, but because two stocks inside it did. Here's what you're actually buying, and why the S&P 500 beat both anyway.

The FDA has cleared 1,451 AI medical devices since 1995. Only two have ever proven they actually change what happens to a patient — everything else has proven it's accurate, not that it matters.

AI is changing medical technology in two opposite ways. Using Abbott's cardiovascular devices and Intuitive Surgical's robotic platform as case studies, this article shows how AI strengthens the core switching-cost moat while gradually eroding the vendor-specific software layer surrounding it.

Artificial intelligence is transforming both pharma and medtech—but not in the same way. This article explains why AI reinforces pharma's patent-based competitive advantage while offering only limited moat expansion for most medical device companies, and what that means for long-term investors.

Casgevy's climb from $10 million to a projected half-billion in revenue looks like one decision. It's actually six — and the same stack explains almost any pharma company's next move.

Rare earth materials show up across the AI infrastructure stack — in chips, chipmaking equipment, and power/cooling systems — but exposure varies sharply by company. Here's a full-stack look at where dependency is highest (TSMC, Micron, ASML), where it's lowest (Vertiv, GE Vernova, Eaton), and how to think about diversifying an AI infrastructure allocation accordingly