Digital Infrastructure Investment is Crucial if Generative AI is to Seriously Boost Economic Growth

Generative AI will also yield complementary innovation, as it will be used in fields one might not typically associate with AI – and because it can easily be used by organisations not yet using the technology. For those organisations or those that have trouble with recruiting developers, it has the potential to democratise coding and software writing. Generative AI allows you to write down the code you want to write in plain English and delivers a coding output, even though you might not be able to write code yourself. It also has the potential to yield breakthroughs in fields like pharmaceuticals or food. For example, some companies work on developing and discovering new proteins for medicines and food with generative AI. It's used to categorise existing proteins but also to give suggestions for new ones. Even in sectors like mining, AI is already transforming the way daily operations are handled.

Another area where we might see rapid change resulting from AI is in industry and manufacturing, as AI exists as one of the key technologies in enabling so-called Industry 4.0. Industry 4.0 is essentially the fourth industrial revolution and is seen as the next phase in the digitisation of the manufacturing sector that should result in autonomous decision-making of computer systems. Last year, the European manufacturing sector was in phase two of six; isolated IT systems are now interconnected. Generative AI can aid manufacturers in making steps in this process.

The next phases for industry 4.0 are more insight, a better understanding of the interdependency between processes, the simulation of future scenarios to develop a predictive capacity, and eventually fully automated decision-making on topics ranging from maintenance to order processing. Generative AI delivers the computing power and automation potential that would enable the replanning of orders with internal and external facilities through predictive analytics, and will be able to make decisions on the quality of output through machine vision.

In essence, generative AI could extend to sectors that one might not immediately be associated with it.

In addition to generative AI becoming a general-purpose technology, it also has the potential to be an upskilling technology – and may change the way we interact with technology altogether.

Upskilling technology

Customer service workers, for example, no longer need to memorise manuals or information about the products their companies sell, but can learn by doing so with the help of generative AI. This could be an important new step in helping workers to learn faster.

Moreover, workers that use generative AI will likely finish tasks faster. This delivers both enhanced efficiency as well as the ability to try more solutions to potential problems. Developers that work with generative AI are found to finish tasks faster, and this gives them the opportunity to try a greater number of solutions to various issues. This could bring about better solutions, enhance quality and improve efficiency – or in the best case scenario, all three. This means that there is potential for automation. The extent of automation depends almost entirely on the capacity of future generative AI models. Predictions based on the economic effects of generative AI therefore hinge on expectations of its future capacity. We are sure that generative AI is a very potent technology that will certainly have some economic effects, but we firmly believe that this process will take time. Technological inventions tend to go much faster than technological adaptation.

Generative AI might impact the way we interact with technology, like the way the graphical user interface (GUI) did. Generative AI has similar potential and might enable us to interact with machines in a more natural way. Through multimodality (i.e., the ability for generative AI to interpret one input, such as text, and generate graphics with it – or vice versa) we might soon be able to have actual conversations with devices – as opposed to the short form commands that are currently used – to generate output rather than typing in commands on a keyboard. In addition, devices might soon understand human-made drawings and be able to convert those to code, text, or pictures. This will not only affect the efficiency or quality with which our jobs are carried out, but also the way we go about doing them. We will discuss this in more detail later in another article.

In short, generative AI is pervasive, improving quickly and may yield complementary innovation, therefore holding all of the characteristics of a general purpose technology. Moreover, it will change the way we interact with technology, which in turn is sure to impact all of our daily lives.

However, there are several hurdles to a more widescale deployment of generative AI. The first revolves around data in a broader sense. Generative AI applications were trained using publicly available data, by a plethora of different ‘authors’. In short, in its training runs, generative AI might have used copyrighted work to train, and its current output might be subject to copyright infringement. In addition, data found online can recreate existing biases. As an example, this could mean that generative AI’s output may discriminate against ethnic minorities. Regulation of high-risk AI applications (like the European AI Act) might also hinder future investment if data usage requirements are very strict. Regulations could include requirements on (model) transparency, but also on a fair and transparent application of algorithms. Lastly, generative AI requires good data, which means companies need to invest in having this available to train their own models.This might require considerable investment if the existing data quality is poor.

Three other elements are important from a cost perspective. The first is investment in human capital. Workers may need to be reskilled to learn to work with generative AI, which will prove costly. In addition, enterprise software packages with generative AI may are very expensive, and the question is whether companies can foot the bill and deem it relevant for all departments within their firm. Furthermore, if companies decide to purchase generative AI software for their own use, then it must be proved that the technology that it is safe to use. Currently, generative AI applications tend to ‘hallucinate’ every once in a while. This means that they output incorrect information, or even information that might sound plausible but is entirely made up. This would entail reputational as well as organisational risks, and it is therefore likely that the implementation of generative AI across organisations will be very gradual.

Lastly, and as mentioned previously, current generative AI models use a lot of computing power. This means that they require a lot of data centre capacity to run. Data centres, in turn, need electricity to run and fresh water to cool their servers. Yet, these resources are scarce in many countries and data centres already require large investments to keep up with growing demand. In short, the significant current investment levels in digital infrastructure and electricity grids probably need to increase to meet future demand. So, the question is whether data centre capacity will manage to keep up with the pace of current development, and if they will they be permitted to do so by governments.

In sum, generative AI has a lot of potential, and will likely cause a significant economic impact. However, there are still a number of practical hurdles for generative AI to overcome before it can be deployed on a wider scale and its tremendous potential is able to come to fruition.