Even if AI saves time on tasks like curriculum planning, a teacher's overall productivity is constrained by the need to be in a classroom. This illustrates how job-level productivity gains can be limited by non-automatable "bottlenecks," potentially reducing AI's aggregate economic impact.

While AI can make individuals 10x more productive, this doesn't automatically create a 10x more valuable company. An 'institutional AI' layer is needed to coordinate efforts and align individual output toward shared business goals like scaling revenue.

Even if AI perfects software engineering, automating AI R&D will be limited by non-coding tasks, as AI companies aren't just software engineers. Furthermore, AI assistance might only be enough to maintain the current rate of progress as 'low-hanging fruit' disappears, rather than accelerate it.

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.

The proliferation of AI has dramatically reduced development time, shifting the primary constraint in product delivery from engineering capacity to the customer's ability to learn and integrate new features into their workflow. More output no longer guarantees more value.

Even if AI accelerates parts of a workflow like coding, overall progress might stall due to Amdahl's Law. The system's speed is limited by its slowest component, meaning human-dependent tasks like strategic thinking could become the new rate-limiting step.

The AI productivity boom is confined to tech because developers have fewer adoption hurdles. Coding is a text-only medium with self-contained context in a codebase. In contrast, roles like marketing or law require complex data setup and workflow re-engineering, slowing down the productivity gains seen in macro-economic data.

When one team member uses AI to achieve 10x capacity, it creates a "train wreck" if their work is handed off to someone operating at 1x capacity. Leaders must analyze and redesign the entire workflow, not just empower individuals, to realize true organizational gains.

Just as electricity's impact was muted until factory floors were redesigned, AI's productivity gains will be modest if we only use it to replace old tools (e.g., as a better Google). Significant economic impact will only occur when companies fundamentally restructure their operations and workflows to leverage AI's unique capabilities.