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 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 Application Layer

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Artificial intelligence is a stack: energy, silicon, cloud, models, and applications. Each has its own economics, competitive dynamics, and challenges. Mistaking one layer for the whole industry causes confusion, misrepresentation, bad decisions, and misguided capital allocations. The infrastructure builders enable the platform; the application builders capture the value. The question is now, what value does all this deliver? Energy, silicon, cloud, and models only serve to deliver that product. There is a robust argument that we are at the beginning of an unprecedented value-creation curve. Built on the infrastructure and services provided by the other layers of the stack, the AI application layer will be globally transformative and disruptive. The constraints are imagination, execution, and the willingness to rebuild how work is done.

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.

The Wheel, the Cart, and AI Systems

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The wheel was a great invention. But not until it was combined with other wheels to create a usable cart was it an innovation. The wheel was a breakthrough; a moving, stable cart was a system. Systems create intelligent, scalable, and disruptive technology. Innovations are not new technologies. Breakthroughs are necessary, but it’s systems that are the solution. The value created by AI in the physical world is not scaling software. It is focus, discipline, and constraint within effective systems. The systems that endure will not be those that promise universality, but those that dominate specific economic niches, involve humans strategically, and survive year ten of operation.

Physical Intelligence

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Robotics and related technology are ready for deployment, but the industry hasn’t crossed the threshold into full-scale production. Computational breakthroughs in stunning demonstrations are attention-grabbing, but the realities of industry quickly take over. There is a gap between robotics and artificial intelligence (“physical intelligence”) as it transitions from potential to hardware delivery in a demanding industrial setting. Physical AI and its integration into robotics may become one of the largest markets in history. But it is an industrial problem whose solution is not on a software timeline. In other words, its commercial deployment requires much more systems integration and real-world constraints than a software slide deck contemplates.

The Failure of Simplicity

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Markets destroy the comfortable assumption that tomorrow behaves like yesterday. They reward those who can identify when the system’s structure changes and punish those who try to fit new realities into old frameworks.

That is why the conventional idea of “what something is worth” has become less relevant than how systems evolve. Investors who cling to formulas intended for stable conditions will always be surprised by nonlinear disruption.

Nowhere is this more obvious than in AI and energy, where the variables are not just changing; the equations themselves are being rewritten.

Bubbles, AI, and the Economics of Belief

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The selloff in technology stocks this week startled some investors. It shouldn’t have. The signals of an AI bubble have been flashing for some time: billion-dollar raises for companies with no product, multibillion-dollar valuations for companies with no revenue, and nine-figure offers made to individual researchers. The AI race is building products that are economic complements to one another—you need the turbines that power the grids, that power the chips, that run the models, that power the products. And you need firms to build their growth and hiring plans around the expectation that ever more of their work will be done by AI. AI is in a bubble, companies will fail, and capex is unsustainably high. The real question is whether the infrastructure being built now will unlock a technological era that outlasts the speculation that paid for it.
History suggests yes. The pattern repeats because the pattern works. The bubble is not the danger. Missing the moment is.