AI driving efficiency in trillion-dollar industries

When ChatGPT captured global attention at the end of 2022, it quickly demonstrated the immense potential of AI. For the first time, humans and machines could communicate using everyday language, instead of code. Initially, this led to widespread curiosity, and within months, ChatGPT amassed over 100 million users. Despite this rapid “consumer” adoption, significant monetisable enterprise use cases seemed elusive, prompting scepticism about whether AI was merely hype.

In the following months, leading AI companies such as OpenAI, Anthropic, and Google intensified their efforts to enhance their models, striving for dominance in the AI landscape. Although early AI demos were impressive, practical applications initially appeared cumbersome.

Fast forward one and a half years, and we believe a plethora of use cases have emerged, supported by substantially large markets, that have remained somewhat below the limelight. We strongly believe the following use cases present amazing revenue opportunities for companies or an excellent way to optimise costs, or a combination of both.

The developer revolution: AI in code development

Developers were among the first to recognise AI's potential. Given ChatGPT's proficiency in various languages, it was a logical step to apply it to code development, one of the world's best-structured and most documented languages. Microsoft's GitHub pioneered with its Copilot, boasting up to 55% faster coding, resulting in more and higher quality code. And multimodal takes it one step further: a picture of a website is enough for ChatGPT to write the code to exactly copy the layout and functionalities.

Source: Github Blog, 2023

Why is this important? The code development market is enormous, valued in the trillions. The global shortage of developers is severe. Thanks to even a small productivity boost, software companies can make large savings on the expense line, or chase new revenue opportunities through increased R&D. Because of AI, we have a strong belief that there will be much more software in the future.

Customer service automation

AI's impact extends beyond development into customer service. Take fintech company Klarna, for instance. In February 2024, Klarna announced that its AI assistant, powered by OpenAI, handled 2.3 million conversations in its first month, equivalent to the work of 700 full-time agents.

Source: Klarna, 2024

The AI managed two-thirds of all customer service interactions, achieving customer satisfaction scores on par with human agents while resolving issues in under two minutes compared to the previous 11 minutes. Operating in 35 languages around the clock, Klarna anticipates USD 40 million in yearly profits from these improvements. With the customer service market valued at USD 300 billion, AI is expected to drive substantial disruption and efficiency.

Content creation and personalisation

Image creation was one of the earliest generative AI applications but is still evolving rapidly, as evidenced by the picture below. Creating, modifying, and enhancing digital content has become much more accessible and efficient, significantly boosting the productivity of creative designers, a market worth hundreds of billions.

Source: Stanford University AI Index report, 2024

Facebook and Amazon, for instance, allow their advertisers to generate multiple versions of ads or product listings through an automated generative process. This improved personalisation has led to higher click-through rates, enhancing monetisation and boosting growth for these tech giants while saving operational costs for their clients.

Beyond generation: recommendation engines

AI's influence isn't limited to generative models. Recommendation engines, like those used by Facebook or Netflix, have significantly enhanced user engagement on social platforms. Today, nearly 50% of social network content presented to users is AI-curated, offering highly individualised content – outside of the social circle – based on user profile and behaviour. Personalised content leads to increased user satisfaction, longer time spent on platforms, and more monetisation opportunities.

Pioneering new markets: autonomous driving

Generative AI's potential extends beyond already established markets to creating entirely new ones. Tesla, impressed by the early results of using a Large Language Model (LLM) for autonomous driving, replaced its decade-old codebase with this new technology. The result, FSD v12, represents a monumental leap forward, with Tesla claiming a 100x reduction in interventions. This innovation points to a significant transformation in the large mobility market, estimated to be more than USD 200 billion.

Conclusion

Already today, several impressive use cases exist, without the above list being exhaustive. We haven’t even mentioned the proof of concepts many companies with large (proprietary) datasets are building and should come to market soon. It’s fair to say that these diverse AI use cases underscore the substantial productivity gains that are ready to be realised across various industries.

This progress justifies the current investments in AI infrastructure, a critical focus of our thematic strategies, particularly in our AI investment strategy. By leveraging these advancements, we are positioned to capitalise on the structural trends underpinning AI's transformative potential.

More information on our AI strategy: https://www.dpaminvestments.com/professional-end-investor/be/en/A-focus-on-AI

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