^{Building Wealth} Unpacking the real-world impact of generative intelligence in financial services

Generative Artificial Intelligence – hype or hope?

• AI is an overnight success seventy years in the making; now it is a game changer – ignore it at your peril.
• AI can give the much-needed productivity boost to the global economy, even if it causes some disruption to the job markets.
• Generative Artificial Intelligence (GAI) has huge growth potential with addressable markets spanning a range of industries, but risks do exist.

What is Artificial Intelligence (AI)?

In a linguistic sense, according to the Cambridge dictionary, AI is the study of how to produce machines that have some of the qualities that the human mind has, such as the ability to understand language, recognize pictures, solve problems and learn. In a technical sense, AI is a field that combines computer science and robust datasets to enable problem-solving.

What is Generative AI (GAI)?

GAI is a type of AI technology that can generate new content (such as new text, images, audio, software code, etc.) on its own after being trained by a machine learning model. Traditional AI, on the other hand, focuses on pattern recognition but cannot generate new data.

History of Artificial Intelligence

Primitive concepts of AI date back to 1950s. However, the most prominent development in GAI occurred in 2017, when Google published its research paper titled “Attention is all you need”. More recently, on November 30, 2022, OpenAI released ChatGPT which amassed over one million users within a week. On March 14, 2023, OpenAI released GPT-4, the successor to ChatGPT. Google subsequently released its chatbot, Bard.

Financial markets truly caught the AI fever when NVIDIA’s blowout revenue guidance in late May 2023, driven by demand for its AI chips, sparked a rally among stocks exposed to the technology. From a model evolution perspective, over the past 70 years, AI has evolved from rule-based data processing (1950s-1980s), to traditional machine learning (1980s-1990s), to neural networks (1990s-2017) and finally to transformer models (since 2017).

Why GAI is a game changer?

GAI is a game changer given its ability to generate new content on its own. The reason GAI is so transformative is its applicability to virtually all industry sectors and its potential to fuel a new wave of productivity and innovation in the global economy. Beyond search, use cases today include content creation, software development, marketing and sales, customer service, data analysis, healthcare, drug discovery and education; and the list is growing.

Value chain of Generative AI (GAI)

Understanding the ecosystem of GAI helps identify the key beneficiaries. Very broadly, the GAI value chain comprises of the following four groups:

AI could boost productivity

Between 2005 and 2023, corporate profit margins were elevated thanks to a global supply glut of labour, globalization and trade deals, lack of unionization of labour and the powerful network effects of the new economy corporations. As a result, productivity growth remained low as corporations did not need to innovate. However, post-pandemic, this has changed and we are increasingly seeing corporations investing in capital expenditure (CAPEX) and automation. AI has a lot to contribute here. It is reasonable to expect the cost savings will be realized by larger companies first and then move down the size ladder as implementation becomes more affordable and benefits become more apparent. Furthermore, AI can automate routine and increasingly non-routine tasks. This allows workers and consumers to focus on more complex and creative tasks, thereby boosting productivity and economic growth.

AI and the future of jobs

While technological advances are accelerating, the deployment of GAI could be constrained by “softer” considerations such as privacy, security and legal and/or ethical considerations.

Another research paper published by OpenAI points out that “approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of [Generative Pre-trained Transformer] GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure.” However, the real question remains, how quickly will corporations adopt AI and disrupt the job markets?

What could halt the rise of GAI?

Taking a broader macro view, we expect the impact of technologies on the overall job market to be positive over the next five years. However, one should not underestimate the potential disruption and churn that are likely to occur in certain sectors and specific jobs. For instance, a recent report from the World Economic Forum highlighted that employers anticipate a structural labour market churn of 23% of jobs over the next five years.

Reflections we should be making now include: How can sensitive data be processed without compromising privacy? How can proprietary data be safeguarded? And is it legal and ethical to scrape copyrighted content from the web?

AI and the future of the human race

Admittedly, this is a sensitive topic and each of us has our own opinion. However, it is interesting to read what great thinkers have to say on the matter. We recently came across the book “Will Artificial Intelligence outsmart us?” which contains essays by Stephen Hawking. We were particularly impressed by the following argument:

“When we invented fire, we messed up repeatedly then invented the fire extinguisher. With more powerful technologies such as nuclear weapons, synthetic biology and strong AI, we should instead plan ahead and aim to get things right the first time because it may be the only chance we will get. Our future is a race between the growing power of our technology and the wisdom with which we use it. Let’s make sure that wisdom wins.”

Industry size and expectations

The potential for applications of GAI across a wide range of industries makes it difficult to quantify the full potential market opportunity for the technology. Therefore, one should keep in mind that these initial estimates are ‘speculative’ given the rapid development in the AI space.

1. In terms of additional outlays, based on estimates from research and analytics firm International Data Corporation (Worldwide Artificial Intelligence Spending Guide, August 2022), global spending on AI is set to rise to USD301 billion by 2026, implying a 5 year compound annual growth rate (CAGR) of 26.5%. Banking, retail, professional services and manufacturing will account for more than half of global IT spending on AI in 2026.
2. In terms of revenues, IDC (Worldwide Artificial Intelligence Software Forecast, 2022–2026, August 2022) estimates AI software market to reach USD792 billion in 2025 at a CAGR of 18.4%, with the AI-centric segment (where core AI technology is essential for operations) approaching USD193 billion at a CAGR of 31.2% over the forecast period.
3. On the AI-related hardware, according to estimates from Precedence Research, the AI chip market will grow from USD17 billion in 2022 to more than USD227 billion in 2032.

Risks from an investor’s point of view

Business model-related risks include involvement of sensitive data in training models, legal and ethical concerns around the use of copyrighted information, data accuracy and model biases that could impact the quality of responses and unintended consequences arising from the use of GAI where the model outcome is inaccurate. As GAI adoption takes off, potential losers in the race are likely to emerge fully disrupted.

Underpinning the risks around data are the high environmental costs associated with training and running GAI models. Philosophically, oversimplification of what AI can achieve is a key risk at this stage. Given the recent explosion of interest in AI, investors should also be mindful of the prices they pay to acquire a share of these businesses – both in public and private markets.