Contrary to the feeling of rapid technological change, economic data shows productivity growth has been extremely low for 50 years. AI is not just another incremental improvement; it's a potential shock to a long-stagnant system, which is crucial context for its impact.

Unlike previous top-down technology waves (e.g., mainframes), AI is being adopted bottom-up. Individuals and small businesses are the first adopters, while large companies and governments lag due to bureaucracy. This gives a massive speed advantage to smaller, more agile players.

Despite the power of new AI agents, the primary barrier to adoption is human resistance to changing established workflows. People are comfortable with existing processes, even inefficient ones, making it incredibly difficult for even technologically superior systems to gain traction.

Software engineering is a prime target for AI because code provides instant feedback (it works or it doesn't). In contrast, fields like medicine have slow, expensive feedback loops (e.g., clinical trials), which throttles the pace of AI-driven iteration and adoption. This heuristic predicts where AI will make the fastest inroads.

Andreessen now largely agrees with Peter Thiel's thesis: technological progress has been confined to "bits" (software) while the world of "atoms" (physical infrastructure, manufacturing) has stagnated for 50 years. This real-world inertia will significantly slow AI's broader economic impact.

Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.

Economist Tyler Cowen argues AI's productivity boost will be limited because half the US economy—government, nonprofits, higher education, parts of healthcare—is structurally inefficient and slow to adopt new tech. Gains in dynamic sectors are diluted by the sheer weight of these perpetually sluggish parts of the economy.

AI's "capability overhang" is massive. Models are already powerful enough for huge productivity gains, but enterprises will take 3-5 years to adopt them widely. The bottleneck is the immense difficulty of integrating AI into complex workflows that span dozens of legacy systems.

The widespread use of paper forms in healthcare and the persistence of billion-dollar fax and receipt industries signal that real-world AI penetration will be slow. If businesses haven't adopted basic digital tools, the leap to complex AI systems will likely take 20+ years, not a few.