AI

The Last Problem For AI to Solve is Health

Martin Metodiev

— 2 min read

The article "The Last Problem For AI to Solve is Health" by Omri Drory (NFX) posits that as AI commoditizes intelligence and software, the final and most significant frontier for human innovation is the biological limitation of our own bodies.

It is a classic "Venture Capitalist Manifesto": it identifies a massive market gap and provides a moral and economic imperative to fill it. While its optimism about AI's ability to "solve" biology is inspiring, it perhaps underestimates the non-technical barriers—legal, ethical, and biological—that keep health from becoming a commoditized utility like software.

Summary

The core thesis is built on the concept of Technological Abundance. Drory argues that throughout history, only science and technology have sustainably raised human living standards by creating abundance where there was once scarcity.

  • AI as a Catalyst for Abundance: AI is currently doing for software and "intelligence" what the Industrial Revolution did for physical labor. It is driving the marginal cost of coding, content creation, and basic cognitive tasks toward zero.
  • The Shift to Biological Scarcity: As digital problems are "solved" and become abundant, the remaining areas of scarcity become more glaring. Drory identifies Health and Longevity as the ultimate scarcity. While we have "good enough" iPhones and TVs (hitting a point of diminishing returns or "asymptotes"), we are nowhere near an asymptote in health.
  • The "Premium" Gap: In many sectors, the difference between what the ultra-wealthy and the average person consume has narrowed (e.g., both use the same GPS and smartphones). However, in health, the gap remains massive. True abundance in health means making elite-level longevity and curative care available to everyone.
  • Moving from Theoretical to Scalable: Drory outlines a three-stage curve for progress: Theoretical → Practical → Scalable. He argues that many biotech breakthroughs (like monoclonal antibodies) took decades to move through these phases. AI’s role is to collapse this timeline, moving biological "cures" into the "scalable and affordable" phase.
  • The Investment Horizon: The article serves as a call to action for founders. As capital and talent flock to "easier" AI software problems, the most significant (and defensible) value will be created by those applying AI to the "hard" problems of TechBio—curing diseases and extending the human healthspan.

Critical View

While the article presents a compelling and optimistic vision of a "post-scarcity" world enabled by AI, several critical points merit consideration:

  1. The "Last Problem" Fallacy: Labeling health as the "last" problem is arguably reductive. It overlooks other systemic "hard" problems that AI has yet to solve, such as climate change, energy storage (fusion), or even the "alignment problem" of AI itself. By framing health as the final frontier, the author may be oversimplifying the complexity of global infrastructure and social stability.
  2. Technological Determinism: The piece leans heavily on the idea that technology alone drives living standards. It minimizes the role of policy, ethics, and economic distribution. Even if AI makes a cure "scalable," the history of the pharmaceutical industry suggests that market dynamics and patent laws often maintain scarcity to preserve high margins, regardless of the underlying technology's efficiency.
  3. The "Asymptote" Argument: Drory suggests we have reached a plateau in consumer tech (TVs, phones). However, history shows that "good enough" is often just a precursor to a paradigm shift (e.g., from "good enough" horse carriages to the car). We may not be at an asymptote, but rather in a lull before the next shift in how humans interact with the digital world (e.g., neural interfaces or AR).
  4. The Complexity of Biology vs. Code: The article assumes a level of "collapse" in the biotech timeline that may be over-optimistic. Unlike silicon, biology is "wetware"—noisy, unpredictable, and subject to intense regulatory oversight (FDA/EMA). AI can accelerate drug discovery, but it cannot yet bypass the years-long necessity of human clinical trials, which remain the primary bottleneck.

This video explores how AI is revolutionizing biotechnology and drug discovery, directly illustrating the "Theoretical to Scalable" shift discussed in the NFX article.

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