Noor's Newsletter- Issue #6

Noor Shaker

— 4 min read

This isn’t a recap of the weeks—it’s an attempt to understand the forces reshaping how we live, govern, and evolve.

The Great Divergence: AI Healthtech Splits from the Pack

For the past year, a sense of caution has settled over the biotech sector. The exuberant post-pandemic funding environment has given way to a more sober reality, a trend reflected in the latest MassBio industry report which points to a challenging capital market, and a general slowness in large-scale M&A. It would be easy to assume this caution applies across the board. But looking at the news from the past two weeks, it's clear we're not seeing a uniform winter; we're seeing a great divergence. While the broader market remains cool, a specific subset of health technology—companies where AI is not just a feature but the core engine—is attracting significant capital and regulatory momentum.

The evidence for this split is compelling. The Wall Street Journal reports that healthtech venture funding is rebounding, driven almost entirely by investor appetite for AI-native companies. This isn't just a trend; it's translating into major deals. Chai Discovery, an AI-driven drug discovery company, just announced a $70 million Series A to advance its AI-driven drug discovery platform. Ambience Healthcare closed a $243 million round, and Qventus secured $105 million for its hospital operations AI. In a constrained market, these are not incremental plays; they are outsized bets on a new way of doing science and care delivery.

The reason for this divergence is maturation. AI in medicine is moving from a promising but speculative research tool to a tangible, productized, and regulated part of the healthcare stack. The most significant signal of this came on August 18th, when the FDA issued its final guidance on Predetermined Change Control Plans (PCCPs). This isn't a minor rule change; it's a foundational piece of the puzzle. The FDA has now created a formal pathway for AI models that learn and evolve over time. This is a clear acknowledgement that AI is not a static piece of software, but a dynamic system, and it provides the regulatory certainty needed for real-world deployment.

We're now seeing that deployment happen. Epic, a dominant EHR provider, is officially launching AI tools to help clinicians draft messages and patients understand their bills. In parallel, medical device companies like Esaote are embedding AI directly into clinical workflows, with new ultrasound systems that use AI to guide the clinician in real-time. These are no longer just press releases about potential; they are product launches.

This is the great divergence: while the traditional biotech market navigates a cautious financial climate, a new class of company is emerging. They are attracting capital and building products on a newly solid foundation of regulatory clarity. At the same time, the sheer scale of the winners in the traditional pharma space is becoming almost geological in its impact, creating new forms of national economic risk. In a stunning illustration of this, Denmark was forced to slash its national GDP forecast for 2025. This was prompted by an anticipated slowdown in growth for Novo Nordisk's blockbuster GLP-1 drugs. The move reveals the incredible extent to which the company's success has been powering the Danish economy. When a single company becomes so large that its specific commercial challenges—such as manufacturing constraints or emerging competition for its key products—can materially affect the economic outlook of its home country, it highlights a profound concentration of risk. This is the other side of the winner-take-all dynamic: the success is immense, but it makes the entire ecosystem vulnerable to the fortunes of a single player.

The divergence, then, is twofold: AI healthtech is diffusing risk through platforms, while pharma is concentrating it in molecules. Both paths create value—but the system-level consequences couldn’t be more different.

  • Noor

Interesting Things in Research and Beyond

Sometimes the most profound insights come from being able to truly see a problem. Researchers at Tufts University have developed a novel AI method to visualize the precise physical damage that different tuberculosis (TB) drugs inflict on bacterial cells. By training a model on thousands of images, they can create detailed "death portraits" that classify the exact mechanism of action—whether a drug is damaging the cell wall, DNA, or ribosomes. This moves beyond simply knowing if a drug works to understanding how it works at a granular, visual level, which could be a powerful new tool for designing more effective combination therapies.

Signals to Watch

Lately I’ve been diving into the story of Epic Systems and its remarkable founder, Judy Faulkner. It’s hard not to be impressed: she bootstrapped the company from the ground up, steering it to a multibillion-dollar valuation without outside capital—a rarity in healthcare and in tech more broadly. We don’t often hear about billion-dollar companies led by female founders that are completely self-funded. Even more interesting is how she articulated, and stuck to, her “10 commandments” for running the company—principles that still guide Epic today.

On a different note, I’ve also started reading Empire of Pain, the story of the Sackler family and the origins of modern pharmaceutical marketing and regulation. It’s a fascinating, and at times disturbing, account of how medicine discovery, regulation, and advertising co-evolved in the U.S. The book traces how new drugs moved from the lab bench to the marketplace, and how the rules governing safety, approval, and promotion were shaped—often in response to excesses and scandals. I’m only halfway through, but already finding it both gripping and illuminating.

Read more