ARTIFICIAL INTELLIGENCE

Artificial intelligence is not a single technology. It is a field — a collection of approaches, architectures, and techniques unified by one goal: making machines capable of behavior that would require intelligence if a human did it.

The current generation of AI systems — large language models, diffusion models, reinforcement learning agents — represents a qualitative shift from earlier AI approaches. Previous systems were largely rule-based or relied on hand-crafted features. Modern AI systems learn representations directly from data at scale, which is why their capabilities have improved so dramatically as compute and data have expanded.

Why it matters for technology: AI is becoming infrastructure. The same way cloud computing became a default substrate for software, AI capabilities are becoming embedded in development tools, business software, and consumer applications. Understanding AI is now a prerequisite for making informed technology decisions.

Why it matters for economics: The economics of AI are unusual. Training costs are enormous and front-loaded; inference costs fall rapidly. This creates a winner-take-most dynamic in foundation model development while enabling a long tail of application-layer businesses built on top of commodity APIs. The capital allocation implications are significant for investors and operators alike.

What most coverage gets wrong: AI coverage tends to oscillate between hype and panic. The more useful frame is industrial: AI is a general-purpose technology in the early adoption phase, similar to electricity in the 1900s or the internet in the 1990s. The transformative effects are real but the timeline and distribution of those effects is uncertain and uneven.

The capability-deployment gap: AI capabilities are advancing faster than deployment. The gap is not technical — it's organizational, regulatory, and economic. Changing a hospital's clinical workflows requires FDA clearance, physician retraining, liability restructuring, and EHR integration; none of those are solved by a better model. The industries where AI deployment is moving fastest are those with the fewest regulatory constraints, the most digitized data, and the clearest ROI on reduced headcount. The industries moving slowest are not technically resistant — they're structurally resistant, and the constraint is institutional rather than computational. This gap is why AI investment and AI productivity gains diverge so sharply in the near term: the capital is deployed years before the workflow and institutional changes catch up.