From Domestication to AI: What Each General Purpose Technology GPT Unlocked—and How to Use the Lessons
Lessons from past waves for the age of AI
To navigate the realm of Artificial Intelligence, examining history can be helpful. It is not the first instance of developing transformative technology in human history. According to Richard G. Lipsey, Kenneth I. Carlaw, and Clifford T. Bekar in Economic Transformations: General Purpose Technologies and Long-Term Economic Growth, a General Purpose Technology (GPT) is defined by its specific characteristics.
Scope of improvements
Multiple uses
Massive spillover effect
Timeline of GPTs and what each wave unlocked
10,000–4,000 BC — Domestication (plants & animals). Surplus food enabled permanent settlements, urbanization, and occupational specialization. The plough, wheel, and animal traction amplified farm and transport scale. Close contact with animals created new disease regimes that reshaped population dynamics.
c. 3200 BC — Writing and numeracy. Ledgers, contracts, and taxation became scalable because information could be stored and transmitted reliably. Writing turned information into infrastructure, expanding long-distance trade and state capacity.
3300–1200 BC — Bronze metallurgy. Strong, standardized tools and weapons professionalized craft and warfare. Long-distance tin–copper trade formed early strategic supply chains and supported money use, codified law, and imperial expansion.
1200 BC onward — Iron and steel. Cheaper, tougher iron tools diffused widely across farms and workshops while iron weapons broadened military capability. A long learning curve culminated in steel, transforming agriculture, construction, and the balance of state power.
AD 800–1200 — Medieval agriculture bundle. The heavy plough, three-field rotation, and the horse-collar lifted yields and labor productivity on heavy soils. Village institutions coordinated teams and managed risk, fueling sustained population growth.
AD 900–1300 — Water and wind mechanization. Mills converted inanimate power into industry-grade work in activities like fulling, sawing, and papermaking. Capital pooling and riparian law matured around these installations, while mechanization generated visible social frictions.
1450s — Printing press. Movable type collapsed the cost of exact copying, accelerating literacy and the recombination of ideas. Scientific communication, standardized doctrine, and the Reformation spread on the back of cheap replication.
1700s–early 1800s — Steam and the factory system. Inanimate power combined with precision tooling to enable continuous, paced production under one roof. Urban industry, shift work, and standardized parts reorganized labor and capital formation.
1830s–1870s — Railways. High-speed, high-capacity transport integrated regional markets and slashed time–distance frictions. Time zones, telegraph dispatch, and large-scale bond finance emerged to coordinate and fund the network.
Late 1800s–early 1900s — Electricity, chemicals, telegraph/telephone. Flexible, point-of-use electric power and laboratory science created versatile factories and new materials. Real-time communication synchronized services and management across distance.
Early–mid 1900s — Internal combustion, oil, aviation; mass production and quality control. Cheap mobility and interchangeable parts enabled global supply chains and affordable consumer durables. Statistical quality control and disciplined workflows made scale efficient rather than sloppy.
1950s–1970s — Computers and semiconductors. Programmable logic automated calculation and control while software ecosystems took shape. Data storage and processing reorganized offices and industry as compatibility standards spread.
1980s–2000s — Networks and the internet → mobile/cloud. Universal protocols and browsers drove the near–zero marginal cost distribution of digital goods and services. Platform markets globalized services, shifting value toward identity, trust, and governance.
2000s–present — Biotech/genomics; advanced materials; AI (candidate GPT). Design-rather-than-discover paradigms and data/compute-driven automation broaden what can be built across science and industry. The new bottlenecks center on energy availability, high-quality data, rigorous evaluation, and credible safety/standards.
The GPT Playbook
GPTs are bundles, not widgets. Value arrives only when devices, skills, organizational design, and infrastructure click together. Allocate as much time and capital to these complements as to the core technology.
Diffusion beats invention. Most gains come from many sectors retooling rather than from the first demo. Track adoption along supply chains and ecosystems, not just showcase pilots.
Institutions co-evolve. Law, finance, and standards lower coordination costs as technology scales. Push standard-setting, liability clarity, and financing mechanisms early.
Bottlenecks migrate. Each wave shifts the limiting factor—from power to transport to data and now to energy again. Re-scan constraints regularly and redirect investment to the new chokepoint.
Learning curves win. Experience reliably drives costs down and quality up, favoring scalable “good-enough” solutions. Choose architectures and processes that iterate quickly and learn cheaply.
Systems beat point solutions. Integrated packages like the factory system outperform isolated tools. Build end-to-end stacks and operating routines rather than feature islands.
Adoption is uneven. Geography, vested interests, and path dependence stagger uptake across places and sectors. Time your bets for the S-curve middle and tailor rollouts to local constraints.
Friction is normal. Skill churn and displacement accompany every productivity jump. Pair deployments with retraining, role redesign, and humane safety nets.
Intangibles lead early. Software, data, and process changes appear before clean P&L gains. Measure capability KPIs—cycle time, error rate, and throughput—alongside ROI.
Expect the J-curve. Costs often rise before complements mature enough to pay off. Stage milestones and avoid canceling just before compounding starts.