For new digital technologies like artificial intelligence, the rate at which productivity growth will be boosted depends not just on the innovations themselves. How business processes are adapted to take advantage of them is also crucial – as are complementary investments by firms and governments.
Copyright: economicsobservatory.com – “Can Digital Innovation, Including AI, Improve Productivity Growth?”
Innovation in new products and processes is the engine of long-term growth in productivity – the increased efficiency of production of goods and services that ultimately underpins rising living standards. But there is a productivity puzzle: despite astonishing scientific progress in recent years – from biomedicine to advanced materials to artificial intelligence (AI) – the wave of innovations is not showing up in overall productivity growth in the UK.
Competing explanations for the digital paradox
One way to explain why today’s digitalisation isn’t translating into productivity gains is that these innovations are simply less valuable than older ones such as electricity.
Another is that it always takes time for businesses and consumers to adopt a new technology, and that diffusion and adoption are slower with today’s technologies because they involve complex software.
The balance of evidence is tilting toward the latter explanation as digital innovations and data are enabling a minority of already high-productivity businesses to pull further ahead of others in their industries.
But this in turn raises further questions about how adoption might be accelerated and what the barriers are to using digital tools to drive faster productivity growth.
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Why do digital technologies take so long to diffuse?
Digital technologies generally have a high upfront cost (such as developing code and collecting data) and low marginal cost (copying software or duplicating data are essentially free). As a result, it can take a long time to get to the critical mass.
Use then grows dramatically, especially if there are network effects benefiting existing users when more users are added, as in a telephone network. Other influences matter too – personal networks and face-to-face contact can help to spread the technology.
As it takes time to learn how to use new digital tools effectively, there may even be a reduction in firms’ productivity at first, followed by a later acceleration. This has been labelled the ‘productivity J-curve’.
If this is correct, the productivity dividend from recent digital innovations will eventually arrive. It might take the form of digitally discovered new drugs or materials (such as the AlphaFold protein structure prediction tool or the new DeepMind materials database). Or it might take the form of improved prediction and reduced inventories.[…]
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