Generative AI unlocking a US$4trillion opportunity – Part I

Generative AI will transform our lives in countless ways and its revolutionary ability to generate content from prompts will redefine the global economy

• Customer interacts with a humanlike chatbot that delivers immediate, personalised responses to complex inquiries, ensuring a consistent brand voice regardless of customer language or location.
• Human-agent uses AI-developed call scripts and receives real-time assistance and suggestions for responses during phone conversations, instantly accessing relevant customer data for tailored and real-time information delivery.
• Sales and marketing professionals efficiently gather market trends and customer information from unstructured data sources (for example, social media, news, research, product information, and customer feedback) and draft effective marketing and sales communications.
• Software engineers and product managers use generative AI to assist in analysing, cleaning, and labelling large volumes of data, such as user feedback, market trends, and existing system logs.
• Engineers use generative AI to create multiple IT architecture designs and iterate on the potential configurations, accelerating system design, and allowing faster time to market.
• Coding Engineers are assisted by AI tools that can code, reducing development time by assisting with drafts, rapidly finding prompts, and serving as an easily navigable knowledge base.

These are just some of the countless ways Generative AI will transform our lives, occupations and society. Its revolutionary ability to generate content from prompts will redefine the global economy, unlocking profound economic opportunities but at the same time widening the economic inequalities between countries that possess these technologies and others who play catch up.

This transformative technology is expected to add trillions of dollars in value, potentially increasing the impact of all artificial intelligence by 15 to 40 percent. According to a McKinsey study on the economic opportunity of Generative AI’s impact on productivity, it could easily add an equivalent of $2.6 trillion to $4.4 trillion annually across diverse use cases. To put this into perspective, the United Kingdom’s GDP in 2021 was $3.1 trillion. This estimate could double if we consider the impact of integrating generative AI into software currently used for tasks beyond these use cases.

Few countries have an edge

Concurrently, it raises the multifaceted issue of widening the economic gulf between nations’ that possess this technology and those who don’t. The impact of generative AI innovations, patents, and applications on the economic divide between rich and emerging nations is a complex issue. On one hand, these technologies have the potential to significantly boost productivity and economic growth, which could benefit all nations. On the other hand, if these technologies are primarily developed and owned by rich nations, they could potentially exacerbate existing economic inequalities.

If one explores the patent landscape in Generative AI or in AI per so, the US. China, South Korea and some EU countries leads the world by the huge margin. Some of the world’s most advanced generative AI models, such as OpenAI’s GPT-3, were developed in the US. The US also has a strong ecosystem of generative AI start-ups and companies, such as DeepMind, Google AI, and Nvidia. China is now home to some of the world’s leading generative AI research labs, such as the Beijing Academy of Artificial Intelligence and the Shanghai Jiao Tong University Institute of Artificial Intelligence.

10X growth in VC deals

Deutsche Bank research finds that in absolute terms, venture capital activity and patents in AI have surged since 2012. The number of venture capital deals multiplied over ten times to 3,884 and the value of deals in 2022 was almost 50 times higher than in 2012, at $83bn. At the same time, the number of AI patents increased seven times to almost 37,000 in 2022. From 2012 to 2022, the number of VC deals has increased from 270 to 3,006, more than a tenfold increase. In 2022, the total deal value hit $62bn, up from $1.3bn in 2012.

And patents published within these sectors has also been booming. The number of patents being published under the sectoral applications umbrella has increased six-fold since 2012. Despite covering a broad range of applications, the companies holding the most patents are well-recognised technology incumbents, the likes of IBM, Samsung, Intel, LG Electronics and Qualcomm.

The surge in interest from early-stage VC companies alone is staggering, with a total of $2.2 billion raised in 2022. Language model developer Anthropic has secured a whopping $1.3 billion in VC funding. OpenAI has raised over $1 billion. Cohere, Inflection and Stability AI have all raised over $100 million, which are all very respectable sums.

The value generative AI could deliver is spread across four areas: Customer operations, marketing and sales, software engineering, and R&D. These areas account for about 75 percent of the potential value. Generative AI can address specific business challenges in ways that produce measurable outcomes, such as supporting interactions with customers, generating creative content for marketing and sales, and drafting computer code based on natural-language prompts.

Unlocking US$340bn value in banking

Industries like banking, high tech, and life sciences could see the most significant impact from generative AI. For instance, in the banking industry, the technology could deliver value equal to an additional $200 billion to $340 billion annually if fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.

Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. This acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time.

The pace of workforce transformation is likely to accelerate, given the increases in the potential for technical automation. Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.

[To be concluded]

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