AI is Not a Magic Bullet

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AI cures disease only if the cures already are within a set we have measured, waiting for a better approach. One problem: they do not. Most of human biology has never been observed with detail and understanding sufficient to target a therapy. No amount of inference recovers data that was never collected. The need for AI in life sciences and drug discovery is indisputable. Of roughly ten thousand known human diseases, the large majority have no approved therapy at all; among rare diseases, the figure approaches ninety-five percent. Most approved drugs slow a disease rather than stop it. For the bulk of human illness, medicine offers management or nothing. Humans are systems, and the hardest diseases are within human systems and unsolved. The constraint is not intelligence. It is understanding the system.

The New Fire: Artificial Intelligence, Geopolitics, and a Transformed World

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Technological, geopolitical, and capital transformation are interconnected, with unprecedented impacts on a globally connected, directly entangled economic and geopolitical world. Choices are simultaneously investment decisions, national security decisions, and civilizational bets on which technological architecture will define the next century.

Understanding them requires insightful economic, strategic, and institutional thinking. More than ever, it requires intellectual courage and patience with complexity.

The Real World and AI

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AI and real-world visual understanding remain unsolved. Machines can recognize a face, caption a photograph, describe a scene, and outperform radiologists on narrow diagnostic tasks. So, if machines can see what we see, they must be doing what we do. They don’t. Nature does not produce straight lines, perfect circles, or right angles. AI lacks comprehensive human visual reasoning.

The Computable Molecule

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Artificial intelligence is no longer a tool that the life sciences industry is adopting. It is a force that is relocating where value is created and who captures it. Three costs are collapsing at once: drug discovery, company independence, and the ability to reach the patient. Each of these costs was, for forty years, a moat protecting the incumbents who could afford to pay it. In addition, the capacity to discover and manufacture medicine has become a strategic infrastructure in the same category as energy, semiconductors, and compute. The molecule has become computable. The architecture of value creation in life sciences has changed.

The AI Molecule

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Artificial intelligence is reducing the time and cost required to discover new drug candidates. New forms of late-stage capital are allowing better companies to stay independent longer. Direct-to-patient distribution is weakening Pharma’s control of the commercial channel. Together, these changes alter the architecture of biotechnology. The molecule is being separated from the old machine that used to deliver it. This is a structural change in how drugs are discovered, financed, developed, negotiated, and delivered. Biotech companies will build discovery systems, develop clinical evidence, control proprietary data, preserve financing options, and reach patients more directly.

The Next Human Crisis

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Albert Camus’s warning from nearly 80 years ago, that humanity is subordinate to abstraction, people are replaced by calculations, and the willingness to accept suffering as an administrative variable persists. We have industrialized the human crisis. We are at an inflection point where the consequences of our choices, both good and bad, will arrive faster, hit harder, and spread more widely than any prior moment in history. We have the proven capacity to recover from previous crisis. The question is whether the next crises potentially makes recovery impossible.

The Price-to-Dream Ratio

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Autonomous shopping agents, co-working and research agents, and coding agents that write, test, and deploy software. The demos are impressive and the announcements relentless, but how much economic value is any of this generating? Almost all AI-related spending is capital expenditure. Companies are buying chips, building data centers, and scaling up cloud capacity. This is spending on AI infrastructure, not productivity from AI deployment. AI is in the infrastructure buildout phase, not the value capture phase. Mass spending is generating minimal returns, but the market has decided to price the dream rather than the earnings. Can any of this translate into economic reality before the capital runs out and political patience expires? Infrastructure, capability, and revenue growth are happening. Productivity is developing. But a significant gap still exists between capital investment and return on that investment. AI is risky, but these investments are not irrational. They are pricing the dream, and the long-term winners remain unclear.

The New Software Stack

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For the better part of three decades, enterprise software followed a remarkably stable economic logic. You built a product. You sold access to that product. You charged per seat. You expanded revenue by increasing the number of people required to operate the system.
It was elegant, scalable, and wildly profitable. Now, it is breaking. It is the decoupling of software revenue from human labor. The industry continues to frame this moment as a competition between AI and software. That framing is wrong. AI is not competing with software. It is becoming the operating system for work.

Reimagining Software

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Software Is the central nervous system of the global economy and its demise is greatly exaggerated. There’s a growing narrative thatsoftware is becoming commoditized. Large language models write code. Autonomous agents assemble applications. The barriers to building digital products appear to be collapsing. If software can be generated instantly, then software itself must be losing value.
This conclusion fundamentally misunderstands how technological disruptions develop and expand. Software is becoming the infrastructure layer of modern civilization. The economic, industrial, and geopolitical systems being constructed over the next three decades will not run on software. They will run as software.

Space – The New Version

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Space is no longer a frontier. For most of the modern era, space has been misunderstood—not technologically, but economically.
Space was a destination rather than a system and a heroic engineering challenge rather than an industrial platform with a continuous operational, and commercial potential. Many early “commercial space” narratives sought to impose venture logic on a domain that remained structurally dependent on government capital, prestige economics, and one-off missions. The result was predictable: excitement without durability, valuation without cash flow, and ambition without a stable market. Now, space is about economic persistence: building businesses that treat space not as a product but as a technological and economic stack – a physical layer supporting a stack of software services and networks.