The emergence of generative AI has technological, macroeconomic, societal (social, spatial and generational inequalities), managerial, demographic (life expectancy), political and geopolitical dimensions.
Of course, we don’t have the time in this column to go into precise and extensive detail on generative AI, and we will obviously return to the subject in greater detail in subsequent publications.
While a primitive concept of artificial intelligence (AI) can be traced back to the Bell Laboratories in the 1950s, it was not until this year that many of us experienced its potential for the first time, with the launch of OpenAI’s ChatGPT and other large language models. In simple terms, the simulation of human intelligence by machines requires computer programs that are trained on large amounts of data to infer or problem solve with minimal human intervention. As we embark upon the next wave of technological innovation, we reflect upon the balance of ESG risks and opportunities on the horizon.
Artificial intelligence may be the next frontier for fighting climate change. A recent study from the World Economic Forum[1] concludes that digital technologies, such as AI, can reduce greenhouse gas emissions by up to 20% by 2050 in the three highest-emitting sectors: energy, mobility and materials. In brief, AI can be used to better track and report greenhouse gas emissions, improve circularity and reduce emissions. A tangible example of this would be Johnson Controls International (JCI), which can deploy AI across its heating, ventilation, and air conditioning (HVAC) control systems to deliver significant improvement in building energy efficiency. The company’s suite of OpenBlue digital solutions allows customers to reduce operating costs and carbon emissions and improve indoor air quality. Using AI, HVAC systems can respond to data from internal sensors monitoring temperature and humidity and combine that with additional data, such as weather forecasts, occupancy levels, energy source and energy cost to optimize equipment efficiency.
Energy efficiency is especially relevant when considering that data centers running AI training and inference models are expected to consume an increasing amount of energy, not just for processing data but also for cooling equipment. Indeed, a peer-reviewed analysis[2] published last month lays out a range of scenarios around the growing energy footprint of artificial intelligence. In a base-case scenario, AI servers could use between 85 to 134 terawatt hours of energy annually by 2027, which represents around 0.5% of the world’s current electricity use and approximates what Argentina, the Netherlands and Sweden each use in a year.
While initially disruptive, industrial or technology revolutions have historically translated into an overall growth in employment opportunities. As evidence, a recent study by MIT economist David Autor indicates that 60% of today’s workers are employed in occupations that did not exist in 1940 – implying that 85% of employment growth over the last 80 years can be explained by technology-led creation of new jobs. Viewed differently, AI could be the new demographic that supplies the labor shortage caused by slower population growth and aging populations around the world. Unlike prior technological innovations, which have historically disrupted blue-collar jobs, AI is likely to impact white-collar jobs – several studies have identified knowledge roles in the administrative, computer, mathematical, business, design and media domains as most likely to be impacted. As noted earlier, history would suggest that AI is unlikely to result in a reduction of aggregate employment, but rather lead to the creation of new roles. As such, what we are focused on is the corporate response to these workforce changes with respect to upskilling resources for displaced workers.
The responsible phasing out of human judgment in favor of AI models carries significant risks associated with biased inputs, inaccurate results (model hallucinations), accessibility, data security and privacy and cybersecurity. Since AI models are trained on human-generated data, we run the risk of perpetuating biases that could have meaningful ramifications for a range of end-use applications, such as financial (i.e. biasing lending practices) or legal (i.e. biasing legal outcomes). Furthermore, the costs associated with training AI models today are prohibitively expensive, which concentrates access to this powerful resource into the hands of large and resourceful developers that may broaden access to these tools in limited ways. Looking ahead, we await clarity on the regulatory framework in the United States, where several government agencies are working on finding a balance between encouraging innovation and mitigating the potential harms of the use of AI without guardrails, including political polarization, privacy violations and social inequity.
[1] https://www.weforum.org/press/2022/05/digital-tech-can-reduce-emissions-by-up-to-20-in-high-emitting-industries/
[2] Joule, The Growing Energy Footprint of Artificial Intelligence, Alex de Vries, October 10, 2023
With their traditional sense of proportion, the markets have taken note of the gigantic potential of AI (Artificial Intelligence) and ML (Machine Learning) technologies. Even their most enthusiastic critics are struggling to establish the limits of what it will be possible to do with these tools.
Every economic agent is now called upon to consider the impact that AI will have on their business. But the question also arises in the area of climate change, especially as there are real differences of opinion at the moment. At this stage, some see AI as more of a threat than an opportunity.
Above all, we need to distinguish between geopolitics and geopolitical crises. By its very nature, a geopolitical crisis is a factor that cannot be quantified, modelled or controlled. We must therefore consider it as an exogenous factor in asset allocation. In fact, it has always been at the top of the list of “conceivable” risks for all strategists and economists, every quarter and every year. In other words, we need to bear in mind that geopolitical risk can interfere with our assumptions and scenarios.
Interview with Eric Daniel, Convertible Bond Strategy Manager, by Thierry Callault, Head of Business Development.
In the space of three years, the world has seen a global pandemic, a war between Russia and Ukraine and now an armed conflict in the Middle East between Israel and Hamas. Against this uncertain and frightening geopolitical backdrop, the central banks (FED and ECB) have embarked on a cycle of rapid and significant interest rate rises in order to combat inflation and put an end to years of accommodative monetary policy. While geopolitical events have had an impact on equity market performance and volatility, the central banks’ paradigm shift created very unfavourable conditions for convertible bonds in 2022 in particular. Indeed, the rise in interest rates, the widening of credit spreads, the fall in equity markets and a slight increase in equity volatility all worked against the asset class, and this lack of decorrelation made 2022 one of the worst years for convertible bonds.
“Geography is the only art in which the latest works are always the best“.
It is not certain that Voltaire would have said the same thing about geopolitics and, above all, about all the more or less enlightened considerations it is currently generating in the financial markets. For although the geopolitical dimension has never been so overwhelming, the reality of its impact, particularly on the bond markets, is paradoxically limited. This is clearly less the case for equity and currency markets. Jean-Pierre Petit discusses this at length in his analysis.
Whether we like it or not, geopolitics has and will continue to play a decisive role in the economy and financial markets.
After the illusions of the post-Berlin Wall era (1989) and the end of the USSR (1991) (‘peace dividend’) in the 1990s, the world went through a period of growing international tensions, symbolised at the start of the century by Vladimir Putin’s rise to power in Russia in 2000 and the attacks of 11 September 2001. Symbols of this development are the rise in military spending around the world and the increase in the number of conflicts (see graphs below).
With rising interest rates, slowing growth and tightening liquidity conditions, equities have their challenges. And yet we see pockets of the market that are in good health.
In this webinar held on 18 October 2023, Adrian Bignell and Marc Saint John Webb, Fund Managers of Quaero Capital Funds (Lux) – Quaeronaut Small & Mid Cap, look at some of the bright spots and scrutinise valuations.
Fragmented industries, entrenched managements and companies’ defence tactics hampering takeovers and weighing on competitiveness have long been the distinctive features of Japan’s economic landscape. Domestic government pressure such as new guidelines to promote M&A are starting to reshape the corporate environment and spurring consolidation.
In this webinar held 17 October 2023, Rupert Kimber, coming recently from a field visit in Japan, Fund Manager of Quaero Capital Funds (Lux) – Taiko Japan explained to us how the consolidation is now working. To illustrate the industry consolidation, he will use Yoshimura Food and Scallop as a case study; as well as other portfolio holdings in the consolidation space.
Link to Rupert Kimber’s trip observations in Japan September 2023
Link to webinar with Mr Motohisa Yoshimura, CEO of Yoshimura Food
During this webinar held on 16 June 2021, Rupert Kimber, Fund Manager of the Quaero Capital Funds (Lux) – Taiko Japan, had the pleasure to host Mr Motohisa Yoshimura. The CEO of Yoshimura Food, presented his development strategy and gave us an insight about how his company is taking advantage of the changes in Japan’s corporate landscape.
While water seemed inexhaustible almost everywhere in Europe, climate change and the increase in periods of drought have made a scarce commodity a critical resource. To tackle the crisis, major investments are needed, as well as a profound change in our consumption habits.