The macro impact of Generative AI: Learning from previous tech revolutions
- There have been five technological revolutions since the 18th century’s Industrial Revolution. AI looks to be the next
- Previous waves emerged at a pace governed by broader societies and institutions, rather than the technical feasibility, something that looks similarly challenging today
- The economic impact of AI has the potential to boost productivity and growth and lower inflation. However, governments and regulation will have a bearing on eventual economic outcomes
Generative artificial intelligence (AI) promises to be the next in a wave of ground-breaking technologies given it boasts a wide range of applications across sectors and industries. Broadly, there have been five technological revolutions – and each significantly impacted economies, societies and culture.
The five great technological revolutions to date are:
- The industrial revolution (1770; UK)
- Steam and railways (1830; UK/US)
- Steel, electricity and heavy engineering (1875; US/Germany)
- Oil, cars and mass production (1910; US/Europe)
- Information Technology (IT) (1970; US/Europe/Japan)
There is a big question as to how much infrastructure will be required to facilitate AI’s implementation compared to previous innovations. AI’s physical nature appears small in comparison to the creation of industrialised cities, rail networks and steel plants. However, the production and advancement of semiconductor technology, data centres and (clean) energy production required to drive AI comes with a large cost, although efficiency gains may increasingly be driven by AI.
Generative AI could be implemented more quickly than previous tech revolutions, and could be further aided by recursive learning and AI itself providing insights for a more efficient roll-out. However, it will at least in part depend on the regulatory, governmental and societal reactions to its wider introduction.
Preliminary economic consequences
History teaches us that technological advances have boosted productivity, reducing the need for labour in certain sectors, but simultaneously created jobs, often in new areas. The movement from agriculture to manufacturing is the textbook example.
However, historic shifts saw large sections of the population move from abject poverty on the land to penury in newly industrialised cities and led to material changes in the structures of society. More recently, technological gains have led to deindustrialisation leaving heavily concentrated impacts in some areas.
Insofar as we consider AI to be a material positive supply-side boost, economic theory suggests it should create disinflationary pressure. The actual impact, however, is likely to reflect institutional frameworks. Productivity gains mean producers can produce more for less. But whether these gains are passed on to consumers through lower prices, or retained as profits, will depend on the scale of competition that producers face.
The degree of the eventual industry concentration may also be a function of the technology itself. If AI develops quickly, it may be easier for its developers to quickly expand ensuring a dominant position to exclude later competition - a more monopolistic outcome. However, if it develops slowly, it is likely progress would not be limited to one initial developer, creating a more competitive and likely disinflationary landscape.
Government, regulation and growth
Plausible largescale disruption to labour markets could have a major impact on governments. In previous technology waves, governments have played an active role in boosting the education of the workforce. They may have a further role to play this time by increasingly providing re-training opportunities for workers displaced by AI.
Given AI’s far-reaching potential, governments are already focusing on regulation. The urgency of such regulation would likely increase if AI were to develop quickly or in a network structure that suggested greater concentration of market power. The question would then be how effectively it addressed such AI complications and how much it might delay or divert AI implementation?
The ultimate impact on growth is perhaps most difficult to fathom. A material positive supply shock should lift the trend rate of growth for the global economy, all else being equal, quickening expansion across many sectors.
The most immediate beneficiaries are likely to be those economies most involved in the development of AI, including the US, Japan and South Korea – leaders in the semiconductor industry – but also those that would gain the most from implementing AI, including Europe and the UK.
The new wave
Generative AI offers the promise of a new technological revolution; one which could be as far-reaching as previous mass tech breakthroughs. That is a truly exciting prospect and suggests material change both socially and economically. But history suggests these changes roll out over relatively prolonged periods – typically over a half a century.
Previous waves also indicate that these transition periods deliver significant productivity and growth gains but also material disruption.
The way AI impacts over the coming decades will be a product of the institutional choices we make as societies and how we co-ordinate globally. AI appears to offer the possibility of a material boost to productivity, one that can help raise living standards, reduce inequality and benefit in the fight against climate change.
But such outcomes are not given – there are several challenges which will need to be managed to avoid alternate and less universally beneficial outcomes emerging.