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Figure 1: Worldwide Google Search for the term AI
Source : Factset
It is also hard for people not to draw comparisons to the tech bubble of the late 1990’s and early 2000’s. This recent and not too dissimilar example of a tech bubble is certainly a driving force behind the scepticism of many. During the tech bubble, massive investments were funnelled into internet startups with the expectation of rapid and transformative returns. As many of these companies failed to deliver, the bubble eventually burst, leading to approx. $5 trillion in loss and economic disruption. Figures place the number of dot-com companies that survived to see 2004 at just 48%. Sceptics have good reason to worry that AI might follow a similar trajectory if the exceedingly high expectations are not met, potentially leading to an economic downturn if a significant portion of AI ventures fail to achieve profitability.
Generative AI also faces its fair share of criticisms and limitations and understanding these challenges is crucial to developing a balanced perspective on the technology’s potential and the prudence required to make these significant investments. Despite its advancements, Gen AI faces several technological hurdles that impede its reliability and effectiveness, such as data quality and availability. These models require vast amounts of high-quality data to function effectively but sourcing this data can be challenging, particularly in fields where data privacy and security are crucial.
Additionally, poor quality or biased data can lead to inaccurate outputs, such as Google’s Gemini model creating images of racially diverse Nazis or the Founding Father’s.
Furthermore, training these ever more advanced AI models demands significant computational resources, which are costly and also require massive energy consumption, which raises questions about the sustainability of continued AI development. Current estimates show ChatGPT consumes up to 25 times more energy than a Google search would. This energy hungry technology is predicted to consume twice as much energy as the whole of France by just 2030. There are also several ethical concerns regarding the deployment of Gen AI, with privacy being at the forefront of these. Generative AI systems often require access to vast amounts of personal data and with that, comes the risk of misuse or unauthorised access to sensitive information, which can lead to breaches of privacy and trust. Additionally, AI systems can be opaque, making it difficult to understand how they arrive at specific decisions. This lack of transparency, often referred to as the "black box" problem, complicates efforts to hold AI accountable for its actions. Ensuring that AI systems are explainable, and their decision-making processes are transparent is a significant challenge.
One such application, that is already very much available, is to use AI to generate novel ideas and provide suggestions, making the model a “collaborator” of some sort. The productivity gains also stretch into innovation in product development, in which companies are using Gen AI to analyse consumer preferences, market trends, and competitive products to generate ideas for new products and features.
As impressive as some of the language models, like ChatGPT and the image generating models, like Midjourney, are, these technologies are still very much in their infancy and expected by some experts to improve at an exponential rate. The billions being invested into AI research and development are also sure to accelerate the rate of improvement, allowing AI to tackle increasingly complex tasks, as it also augments human creativity.