AI’s Trillions

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AI is extraordinary. The economics are not.
Artificial intelligence has triggered the largest coordinated capital commitment of the modern technology era. The builders will spend close to $800 billion this year and approach $1 trillion next year on chips, data centers, networks, and power. Total AI revenue this year will be roughly $150 to $200 billion.
AI will reshape the global economy, and much of the capital financing that transformation will be impaired. Revenue, productivity, and cash flow will all arrive, but on the wrong schedule, after much of what was built to capture them has lost its economic value.
We have seen this movie. Railroads. Telephone and wireless networks. Fiber. The commercial internet. Each transformed the economy. Each destroyed the capital of those who built it first. The infrastructure survived. The investors did not.
The question is not whether the AI opportunity is real. The question is who pays for the gap between when the capital is spent and when the value arrives. AI may define this era. The uncertainty is whether today’s owners, creditors, communities, and ratepayers capture enough of its value to justify what they’re building with their capital.

Doomsday, AI, and a Response to Dario Amodei

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Must the AI frontier slow down? Perhaps. That’s not the right question. We should ask what, precisely, must slow; what risk that restraint would reduce; who could enforce it; and what would happen if careful firms paused while less careful actors did not.
Dario Amodei has published one of the most consequential essays yet written by a frontier AI company leader. He is candid, and he is reconsidering an earlier, more benign, position. But his argument is flawed: a model’s capability is not an application or an outcome.
He says the latest models can offer dramatically good or dramatically bad outcomes. On the one hand, AI may cure most major diseases within five to ten years. But it may also become an agent swarm capable of seizing the internet within six to twelve months.
Both are wrong. These extremes are oversimplifications and can undermine both AI’s benefits in many areas and our ability to regulate and monitor it appropriately. Simply saying “boo” and hoping everyone else is frightened isn’t an effective, comprehensive strategy for addressing a serious issue.
Any good or bad from an AI model only appears when that model is part of a system that enters the world through software, capital, organizations, machines, biological systems, and public institutions. Understanding this lets us unleash AI’s potential for good while also building the systems and processes to protect society.

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.