While AI might be the topic du jour, Philippe de Weck, chief investment officer for equities at Pictet Asset Management, thinks there’s another AI that investors need to reckon with: the adaptation imperative. 

While much of the attention has been focused on mitigating climate change by investing in energy sources that reduce the carbon dioxide intensity of the energy mix, the reality is that “climate change is occurring and impacting assets”, and it’s necessary to make them resilient against it.

“Corporates do acknowledge that climate has an impact on their business, but they’re not sure how to respond,” de Weck told the Top1000funds.com Fiduciary Investors Symposium at Stanford. “But we believe that, as events happen, corporates will start investing more and that gap will close, and that’s what we seek to benefit from.”

Pictet has made investments in companies like Xylem, which is a holding in its water investment strategy, and offers solutions that can help to manage large volumes of water and enable its efficient recycling at venues like sports stadiums. Then there’s Bentley Systems, which provides software for digitally modelling and inspecting infrastructure assets like dams to determine where stresses are occurring.

“The most important thing is to avoid losses,” de Weck said. “To avoid losses, you need to make assets more resilient.

“I think there’s a perception that investments in climate mitigation or adaptation are charity, but that’s not the case. These things have drivers behind then, and they’re supposed to achieve market-beating returns because of megatrends that mean these businesses will be favoured.”

The main reason that the $556 billion CalPERS invests in climate is to “generate alpha”, Peter Cashion, its managing director for sustainable investments, told the same session.

“We see climate as one of the megatrends, alongside AI and deglobalisation, and we see strong parallels with the technology revolution started 30-plus years ago,” Cashion said.

“Even though Silicon Valley is right there, [California pensions] weren’t early investors in that. Unfortunately, we’d probably be closer to fully funded at this point if we were.

“So in this climate revolution we want to be taking a leadership position in terms of identifying opportunities that we might otherwise miss, and that’s why we have a dedicated focus on it.”

CalPERS wants to have $100 billion invested in climate solutions by 2030. It’s coming up on $60 billion after starting from a base of about $47 billion just two years ago and sees potential climate investments as fitting into three categories: renewable energy, adaptation and transition.

“We’ve invested recently in a fire mitigation company that uses drones and AI to detect wildfires, as well as heat resistant crops… I would say that about 10 per cent of our investments in our portfolio are adaptation, and we expect that to grow,” Cashion said.

“We’re actually looking at some public equity strategies where the main focus is companies that are doing adaptation, because we anticipate higher growth there.”

Elizabeth Steiner, Treasurer of Oregon, said that, because the $100 billion Oregon Public Employees Retirement System (OPERS) represents almost the entirety of the state’s employees – and that the state’s political leaning can be best described as “purple” – there’s lots of different views.

“When we were thinking about this issue around climate resilience and climate positive investing, we had to take an approach that addressed the concerns of all of those constituencies. And that means, first of all, broad stakeholder engagement in the conversations,” she said.

OPERS’s stakeholders include six major public employee unions that “run the gamut” in terms of their interest in the topic. Steiner said some members would prefer the fund divest immediately and completely from fossil fuels. Others think the fund “shouldn’t even be talking about climate positive investments”.

“So we have to navigate through that, and we have to navigate through the legislature, and we have to navigate through the investment council, many of whom are concerned that if they go too far in one direction or another, there’s going to be litigation. So that’s a lot of navigation that’s required,” she said.

“[But] our conviction in Oregon is that to talk about environmental, social and governance investing is no different from talking about almost any other constraints that’s going to affect a company’s bottom line, right?” she said.

“If corporations are not taking into consideration the impact of climate change, for example, on their business model, then they are failing in their fiduciary responsibility.”  

Making sense of an increasingly complex and chaotic world is testing the capabilities of even the most seasoned investors as they try to determine whether the established orthodoxy of causality in markets and economies still holds true.

Bridgewater Associates has found success employing a proprietary AI, which it calls AIA (short for Artificial Investment Associate, and pronounced “eye-a”) to help it make sense of this shifting landscape and to manage portfolios. The Fiduciary Investors Symposium at Stanford University heard that while there are strong use cases for AI, it also has limitations.

Alex Smith, partner and senior portfolio strategist at Bridgewater, said the firm’s ambition is to use AI to help it “understand the world, and importantly not in a black box way, [but] in a causal way, a fundamental way”.

“When we think about how to understand the world… predicting the future is really hard, and so our approach has always been to take fundamental understanding, and by that, I just mean we’re obsessed with causality,” he said.

“We want to know why something works. If we’re going to put capital behind it, we think the world works like a machine. If you understand that machine, measure conditions, you can better anticipate outcomes.”

Smith said that once the fundamentals of something are understood, they can be systemised. And that system can be tested “across every historical case you can find, across every country you trade, to test its reliability”.

If it proves to be reliable, it’s turned into an algorithm, which can be applied at scale.

Recent events – geopolitical, macro-economic and market-related – might appear chaotic or random, and have certainly tested investors’ mettle, but Smith says that “all the causal linkages that have always mattered”, including growth, inflation, monetary policy, commodity prices, portfolio allocation, still matter.

But two unique challenges stand out today. One is that understanding causality relies on being able to manage unstructured data “that doesn’t fit neatly into a numerical time series”.

“It’s always important, but it’s really important today, because in this mercantilist world, so much policy is government-driven,” he said.

“And related to that, many of these events [have] very few historical precedents. The challenge is that with unstructured data that’s hard to measure and systemise. You can do it, but it’s hard, and because there’s few historical precedents, there aren’t that many analogues that you can stress-test against, which underscores the need to be causal, the need to be fundamental.

“And up until recently, that had been a unique ability that we humans have.”

Generational technological progress

Nina Lozinski, co-head of artificial intelligence and machine learning investment strategy at Bridgewater, said these unprecedented geopolitical and economic events are “happening against the backdrop of generational technological progress”.

Lozinski said the pace of progress is accelerating: it took 15 to 20 years for technology to progress to the point of being able to understand handwriting – and even then the only organisation really excited about that development was the Post Office – but the advent of speech recognition, then visual reasoning and image recognition, happened much more quickly. Then, “all of a sudden, now we have another kind of intelligence that’s able to process language”, and that opens up vast new possibilities.

“The goal in creating AIA for us was to create something that would be good, that we could have confidence in, that still understood causal linkages, that was stress-tested, that was diversified, but that also would be different, that would lean into the kinds of things that machines are really good at, that humans may not be as good at,” Lozinski said.

“Things like, how do you think in many different dimensions at once? For us, we’ve got two dimensions, we’re pretty good at three dimensions, but past that is more difficult to achieve.”

Using AIA to make decisions on how to invest has resulted in portfolios that look different from portfolios constructed by human investors.

“It’s been good, performance has been solid; but it’s also been different. We’ve had different positions, we’ve had different periods of being up and down, and that’s been the experience so far,” she said.

Something that looks like reasoning

Lozinski said that it appears AI is capable of doing something that looks like reasoning, which leads to questions such as how to use that reasoning ability to help build portfolios.

“Of course, there’s very real weaknesses, and so you may have experienced some of these if you’ve tried to experiment with AI,” she said.

“Some of the ones I’d highlight are AI systems today are not yet very good at complex analytical tasks – they lack number sense. You shouldn’t use them to be a calculator. They can hallucinate. They can make up facts that aren’t in the source materials.”

AI systems also have embedded knowledge, Lozinski said, which raises the issue of working out if a system is genuinely reasoning or just relying on what it has, in essence, remembered.

“In many ways, these are just different ways of saying there’s really two problems. The first is that predicting market returns directly is still too hard, and that’s a very hard problem, and an AI is not ready to do that just as a magic off-the-shelf tool yet,” she said.

“And the second thing is that AI will answer any question you give it, but most of those answers are going to be bad. So how do you know if what you’re going to get back from AI is any good? And those are some of the things I think about a lot.”

Smith and Lozinski said that despite current limitations, there are real opportunities to use AI to address the increasingly complex issues facing investors.

A live, on-stage demonstration of how Bridgewater deploys AIA to process unstructured data and form views about how the world is likely to play out revealed the complexity of the processes it goes through in response to an inquiry.

“It went through a process just now of searching what’s going on in the internet, thinking about what it found, searching again if it needs new information, and coming up with its own independent forecast,” Lozinski said.  “We’ll actually have a panel of 10 forecasts here.

“The step that it’s doing now, which is the second phase of this process, is reconciling them. So now, we have another agent that is currently going through each of those 10 trying to understand, well, if they agreed, why did they agree; and if they disagreed, why did they disagree? And what can we do to suss out those questions and try and do a second step which is, on the areas of disagreement, should I be looking up anything? Should I be thinking?”

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Producing 50 grams of protein in the form of beef produces about the same amount of carbon dioxide – roughly 19 kilograms – as 617,000 Gemini AI queries. Producing 50g of protein in the form of chicken produces an equivalent amount of CO2 to about 95,000 AI queries. And producing 50g of protein in the form of peas produces the equivalent of about 6700 queries.

The point of the analysis, presented at the Fiduciary Investors Symposium by Viktoras Kulionas, an investment manager and senior environmental economist at Pictet Asset Management, was not to spark a dietary rethink but to put AI’s environmental impact into perspective.

Kulionis said a comprehensive life cycle assessment of AI must weigh both its footprint – the impact of building and running AI systems – and its handprint, the potential positive effects of AI in cutting other industries’ emissions.

The power consumption of AI, for example, is routinely described as “obscene” or “shocking”. Some estimates suggest that if every Google search became an AI chatbot interaction, the company would consume as much electricity as Ireland. The capital investment needed to support that volume of AI interactions is estimated at $100 billion, making such a shift unlikely.

Nevertheless, such analyses “usually give an impression that AI will have a substantially negative environmental impact because it uses a lot of energy and water and also emits a lot of greenhouse gas emissions,” Kulionis told FIS.

“However, when you start to look at these numbers in a bit more detail, you start to see a different picture, a picture that is a bit more positive.”

Kulionis said the take-up of AI and AI-enabled processes can be seen as a continuation of a decoupling of GDP growth and energy consumption dating back to around 1860 and the introduction of the steam engine.

“During that period, what happened is that energy use was growing in parallel with GDP. Those two were very, very closely linked together,” he said.

“Then that relationship started to change during the Second Industrial Revolution. What happened then was that GDP continued to grow. Energy use continued to grow, but… GDP grew faster than energy use.

“That relationship changed once again around 1970s with the introduction of semiconductors, and during that time what happened is that there were even weaker links between energy use and GDP – GDP continues to grow, but energy use levels out. It still increased, but it didn’t increase as much as before.”

We are now entering potentially a fourth industrial revolution, Kulionis said, where GDP growth and energy consumption will decouple even further, and “AI is that kind of catalyst that can bring us those big changes”.

“And the question is, what will be the impact of AI? Will it have this positive impact on energy use, where it will lead to increasing energy use; or will it have this negative impact on energy use, where it will help us to reduce energy use, because it helps to achieve certain efficiencies?”

“AI is only part of – is a subset of – that entire data centre energy usage, and it’s relatively difficult to estimate,” Kulionis said.

“Different literature sources suggest that today it’s between 15 to 25 per cent, so here I assume that it is 25 per cent and if we do the numbers, then AI-related energy use would account for about 0.09 per cent of total final energy consumption globally. So it’s not a huge number, it’s not small number, but it’s not also as dramatic as some of the headlines suggest.”

Kulionis said energy demand from data centres will undoubtedly continue to increase but “it will not be the key driver for energy demand growth” – there are other categories such as electrification of industry, air conditioning and electric vehicles that will contribute significantly more to that growth in energy demand.

The handprint of AI can be very significant, Kulionis said. For example, the aviation industry is responsible for about 3 per cent of global climate change through aeroplane emissions and through contrails that can trap heat and prevent it from escaping the atmosphere.

“If you change the flight path of your plane, you can reduce these contrails,” Kulionis said. An analysis of flight paths suggested that “about 54 per cent of these contrails could be reduced, if you slightly modify that route”.

“You would be able to reduce that climate change impact by about 0.57 per cent and… this application alone, if it would be successful, would be more than enough to offset entire data centre emissions,” he said.

“So it’s quite substantial, and that’s only one application.”

The key insight, Kulionis said, is that “AI has a footprint, but in a grand scale of things, that footprint remains modest and not large”.

“It’s also likely to remain small because of grid decarbonisation,” he said.

“But there are also some things that are important to keep in mind and monitor, and one of those things is water usage, especially water usage in water-stressed regions.

“And if we put everything together the key message or insight that we get here is that the downside associated with AI, the downside on the environment, seems to be limited, but the potential upside can be huge because of all these positive impacts.”

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Institutional investing is on the cusp of a structural reset as advances in blockchain-based technologies rewire the market plumbing that underpins asset ownership.

This next generation of infrastructure will operate continuously with near-instant settlement across traditional and newly tokenized assets, according to Franklin Templeton executive vice president and head of innovation Sandy Kaul.

“By 2030, every market and every asset settles instantly,” she told the Top1000Funds Fiduciary Investors Symposium at Stanford University, adding that portfolios will become highly customised “portfolios of one” tailored to each institution’s goals, liabilities and cashflow needs.

Kaul pointed to the recent passing of the US GENIUS Act, which marked the United States’ first major legislative step towards regulating stablecoins, as “the starting gun on a complete transformation” towards a wallet-based system that will replace today’s account-based system.

“This is a huge threat to banks – this stablecoin act – and this is just the beginning. But it has created a parallel economic system now and that parallel economic system is going to recreate everything that we have in the current system.

“There is going to be cash sweeps between stablecoins and tokenised money market funds so that you can manage your cash in the wallet-based system. There are going to be new investment options where I can now create liquidity around illiquid investments that I hold in my portfolio by tokenising interests and shares in them.”

Rest Super head of portfolio construction, external equities, Chris Drew, said the Australian pension fund held about 1 per cent of its global equity portfolio in US dollar denominated cash with a custodian.

“We’re looking at can we replace that custodial-held cash with a tokenised money market fund? And there’s our use case where we can get far superior yields. We get instant settlement and we get the added benefits of the intraday yield that you get… we said, ‘here’s a problem to solve. Let’s go and look at a number of ways we can do that’. That led us to tokenisation.”

Kaul said the technology underpinning its tokenised money market fund allowed investors to receive a proportionate share of interest for the exact time they were invested.

“If you owned a tokenised money market fund for three hours of the five hour or the eight hour trading session, you will get that proportionate share of the day’s interest, and we are paying that interest out every single calendar day of the year.”

But such a radical change to the market’s underlying infrastructure also challenges existing governance approaches.

CFA Institute CEO and president Marg Franklin said the tokenisation of assets will change their characteristics and also drive returns. Many asset owners will need to consider these interconnections, particularly on illiquid assets such as infrastructure.

“You have to determine, is your valuation methodology accurate, what does it look like, and then how does that impact your performance measurement and what you’re reporting to beneficiaries or to your board?

“The responsibility for due diligence, and the sort of accuracy of your due diligence is going to increase, and the complexity of that due diligence is going to increase.”

Drew said tokenising real world assets would create a new operating environment – one that Australian regulation had yet to catch up to.

“You’ve  got to completely re-imagine how you think about your investment structure and that’s what we’re grappling with.”

Separately, Drew said the fund was also now increasingly using AI as part of its investment decision making process including to more accurately and quickly distil information and action items from manager meetings; to perform deep research and identify market thematics; as well as data analytics.

For example, AI can predict index rebalances or produce insights about shifting correlations in near real-time.

“Everyone talks about the Mag Seven. We would argue there is no Mag Seven… at some point in time, there’s a Mag Three, at some point in time, there’s a Mag Five. These correlations move around through time, and so that’s information for us as well – how exposed you want to be to these correlated groups of stocks.”

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The two Thinking Ahead Institute articles I read the most are closely connected.

The 4-3-2-1 PIN code for a more sustainable economy highlights the roles of public policy (four units), corporations (three units), investors (two units) and individuals (one unit) in driving meaningful change. It emphasises the importance of collective effort and shared responsibility to achieve a more sustainable economy. The number assigned to each party represents their power to create an impact, with public policy having the greatest influence.

Written at the same time, Tim Hodgson’s article, If you want to see change, you can stop counting at ‘3’ is by far one of my favourites. It argues that relying on public policy to lead sustainability efforts is misguided, using the historical example of slavery abolition to demonstrate how change often starts with individuals and other sectors.

Corporations, the investment industry, and individuals must take the lead in experimenting and innovating, while public policy should later enforce best practices to ensure widespread adoption.

Despite the increasing evidence of the climate emergency, most of us seem complacent as our planet moves rapidly toward a 2C+ scenario. This widespread inaction can lead to feelings of hopelessness and climate depression among those actively trying to make a difference, creating a negative cascading effect.

What caused the inaction?

Part of this can be explained by the sociopsychological phenomenon known as the diffusion of responsibility, where members of a group feel less responsible when facing a shared problem.

Psychologists John Darley and Bibb Latané conducted research on this phenomenon. They set up an experiment involving a simulated distress call, making it seem as though someone nearby was injured. When participants believed they were the only ones who heard the cry, 85 per cent responded to help.

However, if participants thought one other person had also heard the call, only 62 per cent helped. When they believed four others had heard it, just 31 per cent took action.

The size of the bystander group is a key factor in the diffusion of responsibility. The individual need to intervene decreases as the group size increases.

With a world population over 8.1 billion, it’s clear how the diffusion of responsibility contributes to our inaction on the climate crisis.

However, we can’t not afford to just passively observe the unfolding climate disaster and hope that policymakers can be the superheroes. As Tim pointed out in his blog, more action needs to come from the six units (corporations, investors and individuals).

So, how do we overcome the barriers created by the diffusion of responsibility?

Finding a way through

Darley and Latané suggested there are five steps people go through before helping, which we can connect to addressing inaction in the climate crisis. Failing at any of these steps can lead to inaction.

Step 1 – Noticing the event
In 1950, global CO2 emission were six billion tonnes. The estimated level in 2024 will exceed 40 billion tonnes and is yet to peak. But people can’t taste, see or smell CO2. So these numbers mean little to most of us. Communication about the climate crisis needs to tell a story and build an emotional connection with the receivers, whether they are the general public, investors, or corporations.

Step 2 – Interpreting the event as an emergency
Our ancient human brain isn’t good at dealing with long-term problems. Primarily focused on immediate dangers, our brain fails to recognise the climate crisis as a threat. The urgency of acting now and the consequences of delaying must be emphasised more clearly (read the Thinking Ahead Institute’s paper, Pay now or pay later? discussing the need to address climate change sooner).

Step 3 – Feeling a sense of responsibility to take action
We’ve seen this play out in corporate commitments to net-zero targets. While many companies pledge to reduce emissions, too often the responsibility is perceived as collective, leading to delays in implementation. Accountability mechanisms, like transparent reporting and external validation, are essential to reinforce that responsibility is both shared and individual (such as senior leaders).

Step 4 – Deciding how to help
The path to action is clearer when people know what steps to take. For example, many investors struggle with integrating climate considerations into portfolios in a practical way. Initiatives like the Institutional Investors Group on Climate Change (IIGCC) and its Net Zero Investment Framework provide investors with a structured approach to setting targets and aligning their portfolios with net-zero goals, helping them decide where to prioritise their efforts.

Step 5 – Having the skills to carry out the action
Even with the will and sense of responsibility to act, a lack of skills remains a barrier. Knowledge development and skill-building are essential to address this. Various industry bodies and organisations offer online courses to assist investment professionals to make informed decisions. Such as TAI’s systems curriculum, which provides investors with practical tools to apply systems thinking, helping them address systemic risks.

Any issue that requires behaviour change is difficult to overcome.

In the latest Marvel superhero movie, Deadpool needs the right Wolverine to save his world. However, his world wasn’t saved by Wolverine alone, Deadpool played an equally important role – but had to change behaviour to achieve this.

Similarly, no single group can solve the climate crisis on its own. Progress requires a collective effort across individuals, investors, corporations, and policymakers to move the needle on one of the world’s greatest challenges.

Jessica Gao is a researcher at the Thinking Ahead Institute at WTW, an innovation network of asset owners and asset managers committed to mobilising capital for a sustainable future.