Taylorism involved studying expert workers to codify their craft into a science owned and controlled by management. Today's AI achieves a similar end by training on expert data, concentrating knowledge and power with capital owners while devaluing individual artisan skills.

Critical manufacturing expertise is not easily codified in manuals; it's tacit knowledge embedded in experienced teams. Offshoring production leads to an irreversible loss of this 'process capital,' hindering a nation's ability to innovate and scale complex industries, as demonstrated by the transfer of German rocket scientists after WWII.

History shows that transformative technologies—the industrial revolution, electricity, the internet—create massive long-term value. However, they also render the skills of one to two generations of workers obsolete, leading to widespread career and economic disruption for those individuals before their grandchildren reap the benefits.

The official focus on bamboo scaffolding after a deadly fire may be a political pretext to phase out a traditional industry with a strong, union-like guild. This would allow mainland-controlled firms to take over, despite evidence that much of the bamboo scaffolding survived the blaze intact.

In Hong Kong's dense urban landscape, ancient bamboo scaffolding is more practical than modern steel. Its light weight, adaptability, and ability to be assembled without a ground-up structure makes it faster, cheaper, and less disruptive for construction in tight, complex spaces.

The drive for AI efficiency is eliminating entry-level jobs, breaking the traditional apprenticeship model. This dynamic risks creating a future deficit of skilled experts ("verifiers") needed to manage complex AI systems, while simultaneously accumulating hidden systemic risks.

Foster attributes lagging construction productivity in the West to cultural shifts, such as the UK's dismantling of its industrial base. When a society ceases to value the craft of 'making things,' it loses the skills and status associated with that work, leading to productivity declines.

By replacing junior roles, AI eliminates the primary training ground for the next generation of experts. This creates a paradox: the very models that need expert data to improve are simultaneously destroying the mechanism that produces those experts, creating a future data bottleneck.

Technology isn't just patents and tools; it's the unwritten, hands-on "process knowledge" gained from doing. By offshoring manufacturing, the US lost this tacit knowledge, which cannot be easily documented or re-learned. This explains why industrial giants like Boeing and Intel now struggle with basic execution.