Nicholas Mitsakos

Investor, Entrepreneur, Writer, and Lecturer

Books
Articles

Articles

Current research and analysis on topics ranging from innovation, disruption, and opportunity, as well as hype, irrationality, and absurdity.

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Blog

Blog

Commentary about recent technological, market, economic, and geopolitical events

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Lectures

Lectures

Presentations about developments in technology, life sciences, digital assets, and other transformational businesses, as well as market, economic, and geopolitical developments

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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.

Capturing AI

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AI models produce raw intelligence. They generate tokens. But tokens are an intermediate good, not a finished product. What customers actually pay for is legal work completed, code shipped, claims processed, research synthesized, and decisions supported.

They pay for refined output.

Attention has focused on the infrastructure layer — the frontier labs, the compute stack, and the data centers. That attention is not misplaced, but it overlooks a structural shift already underway. Once you understand the model as an intermediate good rather than the end product, the center of gravity moves. The decisive question is no longer who can produce intelligence, but who can turn it into something usable, trusted, repeatable, and economically defensible.

In other words, who can refine it into a usable product?

At the base of the chain sit the token producers — OpenAI, Anthropic, Google DeepMind, Meta, DeepSeek, and Qwen. They produce raw capability. This layer is expensive to build, technically formidable, and still moving fast. But crude oil is not gasoline.

Enterprises and consumers pay for gasoline.

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.

Blame Prometheus

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As the era of artificial intelligence is here, it’s easy to fall into the trap of despair and fear over the loss of control and the worry that artificial intelligence is about to unleash killer robots and enslave humanity. Either that, or Artificial intelligence will improve lives, expand access to education, advance healthcare, and advance climate science, among many other improvements. Luckily, AI’s benefits greatly outweigh its costs. Nothing is free, and everything comes with a price (there are always both sides to the ledger), but the extraordinary benefits that artificial intelligence can unleash are worth the effort. It would be a mistake to slow down, pause, or restrict research, development, and AI applications. AI will not destroy the world — and it is more likely to save it.

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.