Global equity markets flourished in the first half of 2023, with the MSCI World Index returning +15% in U.S. dollars (USD), and now up over a quarter from the third quarter 2022 trough, say Morgan Stanley Investment Management's international equity team's Bruno Paulson, managing director, and Emma Broderick, analyst. 

The sector picture has been a reversal of 2022, as the market has been led by the growthier sectors that suffered last year: consumer discretionary, communication services and, in particular information technology, with great enthusiasm around the promise of generative artificial intelligence (AI) offering a new lease on life for tech mega-caps after a tough 2022. 

The generative AI phenomenon

AI has entered its next chapter, with algorithmic and processing power advancements, in addition to the explosion of data in recent years, ushering in a new era of generative AI. These large language models (LLMs), powered by advanced machine learning (ML) algorithms and trained on an enormous number of parameters, analyse and learn from the vast amounts of data they are fed to generate original, human-like content at warp speed.

Since the debut of ChatGPT by OpenAI, the market has been preoccupied with how to understand, implement and price the accessibility advancements offered by generative AI.

What is unusual about the AI frenzy is that this isn't a eureka moment. While the use of generative AI has surged in recent months, narrower AI technologies like ML and natural language processing (NLP) have already been in use for several years. Facial recognition, for example, uses ML algorithms to unlock your smartphone, and digital voice assistants use AI, NLP and ML to understand commands and carry out a range of tasks.

AI algorithms are used in e-commerce to make personalised shopping recommendations, in clinical trials to improve drug discovery and efficiency, and elsewhere across an array of industries to automate a host of back-office tasks. The incremental improvement of models from learned behaviour, along with the arrival of big data and computer processing power advancements, have all played their part in the release of generative AI.

Nonetheless, there have been two major surprises this year.

The first is the speed of consumer adoption. In the past, it has taken technology applications months if not years to reach one million users; for ChatGPT, it took just five days, with the service reaching 100 million users in a then groundbreaking two months - at the time, the fastest adoption of any technology in history.  

The second, and arguably more significant, surprise is the lack of barriers to entry to run AI code. The general assumption up until now, which we shared, was that large incumbents developing AI models would dominate given their economic moats: cloud expertise, computing power and huge stores of proprietary data - not to mention they have invested enormous amounts of capital to refine their AI capabilities. However, this doesn't appear to be the case.

New large-scale, open-source models based on readily available application programming interfaces (APIs) are public; anyone with a good level of coding knowledge can adapt and redistribute the data architecture to satisfy their own specifications without requiring the large computational power and storage space normally necessary to run these.

While this has advantages from a consumer perspective (including access to customisable AI models at far lower cost), for corporates, the barrier to entry for trialling code has reduced to one person with a laptop. The moat seems not to be the AI technology itself, but rather other elements of the business model - for instance, access to proprietary data, customer base or the ability to provide services at scale.

The shovelers

As in previous tech cycles, the early winners of the "AI gold rush" have been the pick and shovel sellers, notably the semiconductor providers and the "hyperscalers" - cloud computing service providers - who are responsible for the infrastructure necessary for generative AI deployment. Also benefitting are those offering AI services to customers; for instance, multinational technology conglomerates incorporating AI tools into search engines and wider product families.  
Identifying the opportunities…

While the full impact of AI remains unknown, looking at the current environment through a high-quality investment lens, we see opportunities for a broad range of companies, particularly in terms of cost reduction and value creation. 
●    Process improvement/cost reduction: AI presents clear opportunities for cost reduction as existing processes get automated, particularly rules-based functions. Much of this is not new. A professional services company we own talks of its outsourcing business having already gone from 90% labour and 10% automation to 60:40, with plenty of progress still to be made. Broadly, generative AI should help content creation in customer operations, sales and marketing, and software development by sharply reducing the quantity of human inputs. 

●    Value creation: AI should enable companies to improve the quality of their services and product offering. Companies with large proprietary datasets across an array of industries may be able to use AI to run more effective and efficient data analysis. In the healthcare industry, AI presents the possibility of improved capabilities in patient diagnostics and the optimisation/automation of several parameters in the drug discovery process. For example, a supplier of scientific instrumentation, consumables and software services has used AI to develop an electron microscope, helping researchers analyse the structures of molecules, proteins and cells and expediting the process by automating vital steps. Meanwhile, for consumer brands, AI can enhance the customer experience through virtual offerings and personalised advertising.

With an eye on the threats

In addition to where the opportunities lie, we also think it is important to think about how change might adversely affect companies. 

●    Customer automation risk: AI should be able to help corporates cut costs by automating a range of rules-based and back-office tasks. This is a potential downside if a company's model depends on supplying services that get automated, for instance operating call centres, or on supporting personnel that may be automation targets, such as offering a data product to junior lawyers. IT services companies will need to generate enough high-value expertise-based work to compensate for any losses for automation.
●    Disruption risk: AI will likely disrupt existing business models. AI's ability to accelerate the writing of code may provide extra competition for software providers, for instance. This threat requires constant vigilance as the technology evolves, and we are closely watching our holdings' proprietary data sources in case AI generates viable alternatives.
●    Legal and regulatory risks: It is still early days for generative AI adoption, and as such regulatory and legal frameworks are very underdeveloped. The issue of patent and copyright is a central one, as models are often trained on intellectual property without any compensation to the owners. In addition, there remains the risk of hallucinations, whereby input data is reconfigured or learned in a way that is factually inaccurate. More significantly, there is the worry about AI embedding discriminatory "black-box" algorithms into processes. In general, regulation may choke off innovation and the ability to create value, particularly if global companies are only able to progress at the speed of regulation in the slowest geography.

●    Disappointment risk: Aside from AI hurting companies' future earnings, there is potential risk to valuations if the current excitement dissipates. Gartner's hype cycle has five phases when it comes to emerging technologies: the upward curve of the Technology Trigger and the Peak of (Inflated) Expectations, followed by the slump into the Trough of Disillusionment before the gradual recovery into the Slope of Enlightenment and the Plateau of Productivity. The risk is that as we approach the peak of expectations, a trough may not be far behind…

It is still early days for generative AI and its full impact remains unclear. Which industries and companies will thrive and whose business models will be made redundant? What does employment, education, health care, finance, consumption and politics look like in an AI world?

Will regulation be fast and sensible enough to put guardrails in place without hindering progress? As the adoption of generative AI continues, investors ought to remain level-headed, and carefully consider the potential risks as much as the potential opportunities. After all, the world is an asymmetric place, with earnings downsides in bad times far higher than the upsides in good times… 

By Morgan Stanley Investment Management's international equity team's Bruno Paulson, managing director, and Emma Broderick, analyst.