Economics of knowledge
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Stakeholder Type

Economics of knowledge

4.2.2

Sub-Field

Economics of knowledge

The global economy is undergoing profound changes driven by digital knowledge and the transformational ways it is being used, including frameworks for privatising knowledge through intellectual property rights (IPRs) as well as the simultaneous push for knowledge commons and open-source philosophies. This has pushed the need for the development of new economic theories for understanding technology, innovation and the creation and dissemination of new knowledge.

Future Horizons:

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5-yearhorizon

A new economics of AI agents emerges

The widespread adoption of AI agents dramatically increases productivity in a few influential applications, such as automated discovery in science. This leads to the emergence of a new economics of AI agents and AI firms.

10-yearhorizon

Novel welfare systems fail to match rapid changes in employment

AI systems become able to perform any computer-based task as proficiently as humans, further disrupting labour markets and economic structures. The need for new welfare systems becomes urgent but the economics of public policy must evolve more rapidly to cope with changes in levels and patterns of employment.

25-yearhorizon

AI self-improvement drives new social contracts

AI takes over its own innovation process, leading to self-redesign and exponential growth in capabilities. In the absence of regulation and social control, this can drive a dramatic and unpredictable transformation of all aspects of society. This would create urgent demand for new social contracts that address the fundamental issues of resource distribution, purpose and governance in a vastly different world.

The deployment of AI is among the important shifts. AI is already affecting the economics of the workplace, with the potential to dramatically increase productivity in some sectors,3 while displacing various roles.4 Lower- and middle-income countries, already bypassed by industrialisation, face an even steeper path if AI takes over both manufacturing and service jobs, especially if the economic value it creates becomes concentrated in the hands of a few technology companies and their investors.

Another threat is the possibility of a dangerous AI race without adequate societal controls, driven by capitalist and geopolitical competition. AI development is resource-hungry, exacerbating global inequality and geopolitical tension, and such a race may prioritise rapid development over safety and environmental concerns like energy and water consumption for data centres. How governments and transnational organisations protect public interests in this scenario is an important open question, which will also require the integration of ecological economics and transnational economics in determining how the knowledge economy evolves.

All this is forcing economists and policy-makers to rethink traditional assumptions and ask difficult questions about values, well-being, control of technology and wealth distribution. As AI transforms the digital and real economy, the greatest challenge will be to ensure that society acts to ensure this process benefits all and not just the powerful few.5