Unlocking the Potential of Generative AI

Interest in artificial intelligence (AI) has exploded over the past year in tandem with hopes and fears about the technology’s potential – what it could mean for jobs, businesses and even the future of humanity. We are optimistic about the way forward, believing that Generative AI (GenAI) could increase knowledge, help improve productivity and open new avenues of growth. GenAI models can be used to generate text, images and music, hence the term “generative”. After a year of fanfare, we believe AI’s commercial promise can now gain traction. We see two areas that are particularly well positioned to benefit: firstly health care firms, and secondly tech companies that are able to monetise proprietary – as opposed to open-use – intellectual property.

The past five years have seen considerable leaps in machine-learning AI models that can train computers to carry out complex tasks. Interest and investments are growing fast among data-intensive firms across the spectrum. Indeed, data-intensive firms like insurers, financial-service providers and parts of the manufacturing sector have been among the earliest adopters and beneficiaries of generative AI. 

We believe Medtech and Life Science companies among are those most likely to benefit from advances in AI in the near term. GenAI will also contribute to the innovation that biopharma and managed-care companies need to show to stay competitive. In the tech sector itself, some firms companies will be able to monetise AI better than others. Following are our views on the prospects for AI in both the health care and tech sectors:

Health care

There are already reports that drugs conceived by GenAI have moved to the human-trial stage in China. Health care firms in the West are also already using GenAI to discover new drugs and to create an increasing array of AI-powered medical devices. Nonetheless, we see GenAI as an evolution rather than a revolution in the immediate future, with the most probing applications perhaps in three precise areas.

First, it could boost market growth for early adopters in Medtech and Life Sciences as AI  enhances the ability to leverage data. GenAI is already helping to improve diagnostics, surgical outcomes and medical imaging. AI is also today being applied in robotic surgical procedures, with further innovation on the horizon, while imaging/radiology driven by generative AI can be expected to expand given that demand for imaging is far outstripping growth in the number of human radiologists. Medical imaging rooted in AI is already enhancing the speed and accuracy of procedures and broadening the range of information collected.

Second, AI could improve pricing models and boost digital health in managed care. Greater administration efficiency, greater personalisation of the services provided and greater automation could all improve the profitability of managed care as AI expands its role. Third, while in-clinic validation is set to remain the most critical and lengthiest part of drug development, GenAI could improve drug discovery in biopharma; already this year, we observe 70% reduction of discovery time in biopharma with the use of AI compared to conventional processes*. While still relatively limited, GenAI investments in this segment of healthcare can be expected to grow.

In short, we believe Medtech together with Life Science tools & Diagnostics look like the sectors most likely to benefit from AI’s application in the near term. GenAI will also contribute to the innovation that biopharma and managed care companies need to show to stay competitive.

Technology

The recent growth and upsurge of interest in GenAI is related to its potential to disrupt business models, lift productivity and create strategic advantages for well-placed technology companies. According to the International Data Corporation, the global AI market, including hardware, software and services, is set to grow by nearly 19% per year, reaching USD900 bn by 2026. But with valuations high, it makes sense to look carefully at how one approaches the AI theme.

We believe that well-known, established companies active in cloud infrastructure (data-centric generative AI needs large cloud platforms known as hyperscalers) could be a major beneficiary of GenAI’s growth. Semiconductor companies have an obvious role in GenAI’s development as have providers of AI-powered software and applications, with established platform vendors standing to benefit. While uptake of AI-enabled semiconductors (ie. GPU accelerator cards) remains low for the moment, we believe demand could grow by USD30 bn over the next five years, driven by model training. Finally, we believe generative AI will up the requirement for cybersecurity solutions.

In terms of where the greatest potential lies within AI, we would argue that companies that are able to monetise proprietary (as opposed to open-use) intellectual property in areas such as semiconductors, vertical software (designed for niche industries and applications) and the vertical cloud stand to advance the most.

Overall, the advances in GenAI are set to spread into more and more areas of the economy, affecting a swath of industries and the people that work in them. We are optimistic about the way forward, believing that GenAI could increase knowledge, help improve productivity and open new avenues of growth.