Commentary: Appreciation ESG Strategy

• Although technology and AI remain a dominant part of the market narrative, benchmark performance has broadened significantly in the current rally.
• Although we remain concerned a QT-driven liquidity drain is a matter of when and not if, we expect the Fed to ensure ample liquidity exists through the presidential election.
• ClearBridge is closely watching sustainability-related opportunities presented by artificial intelligence as well as its energy intensity and social dimensions as the phenomenon plays out in our companies across sectors.

The soft landing rally that took hold in November 2023 continued in 2024’s first quarter. The S&P 500 Index advanced in each month of the quarter, bringing the benchmark’s winning streak to five consecutive months and a 27% cumulative return. The S&P 500’s 10.56% quarterly advance was the 11th best start to a year since 1950, with the index making a record high in nearly 40% of the period’s trading days.

Although technology and AI remain a dominant part of the market narrative, benchmark performance has broadened significantly in the current rally. During the first quarter every sector except for real estate posted positive absolute returns. Yes, the technology-heavy communication services sector led performance (on the backs of Meta and Netflix), but energy was the next best performing sector as oil rallied above $80 per barrel. Information technology (IT) outperformed yet again, but returns were essentially in-line with the financials sector. The paths to outperformance have — finally — broadened beyond mega cap tech.

Elsewhere in the S&P 500, real estate, the lone sector to decline, was down just 0.55%. Utilities lagged the benchmark for a fifth consecutive quarter but still rose 4.57% on an absolute basis. Finally, consumer discretionary was a noteworthy laggard as Tesla’s 29% decline in the quarter weighed significantly on the sector’s return.

We noted in our fourth-quarter commentary the market’s powerful year-end advance was broad-based with small cap participation, the type of market internals supportive of a sustainable uptrend (characteristics absent from the highly concentrated market in the first half of 2023). At the close of the first quarter, the average stock continues to perform well with 87% of S&P 500 stocks above their 100-day moving average. Although we harbor some longer-term concerns and believe the market is long overdue for a correction, the current environment is one where pullbacks remain buyable.

We also follow measures of liquidity to assess the environment for risky assets. Although we have been concerned that the Federal Reserve’s withdrawal of liquidity via quantitative tightening (QT) would put pressure on equities and the economy, this has not yet been the case. Although pundits point to COVID stimulus excess savings or alternative liquidity pools such as the NY Fed Reverse Repo facility to explain this phenomenon, the fact is that current financial conditions are expansionary. Measures of liquidity suggest ample liquidity exists for risk assets. Money supply has stabilized, bank deposits are expanding for the first time since 2022, corporate credit spreads are tightening and at benign levels, and debt/equity issuance is expanding (Exhibit 1). Although we remain concerned a QT-driven liquidity drain is a matter of when and not if, we expect the Fed to ensure ample liquidity exists through the presidential election.

Exhibit 1: Tightening Credit Spreads Supportive of Liquidity

Incoming data suggests that not only can the economic expansion persist near-term, but activity is also likely accelerating today. In March, the ISM Manufacturing PMI crossed into expansionary territory for the first time since October 2022 (Exhibit 2). Business inventories are lean, and there are signs that increases are required (Exhibit 3). Railcar loadings improved through the first quarter and turned positive in March. Capital spending expectations have firmed from a 2023 malaise and no longer suggest a decline in capex over the coming year. Finally, the employment market remains tight with unemployment under 4%, job openings plentiful and employers still paying higher wages to attract human capital.

This real-time swing in activity is manifesting itself in a firming of industrial commodities such as oil and copper. While inflation data slowed during the quarter, almost all of the reports were somewhat ahead of expectations — enough to spur higher Treasury yields and reduce the number of rate cuts predicted by the Fed’s dot plot.

Exhibit 2: ISM Manufacturing PMI Crosses into Expansionary Territory

Exhibit 3: Business Inventories Look to Reload

From an equity investor standpoint, the economic news is great, hence the 10.56% jump in the market. But stock market investors are obsessed with interest rate cuts supporting a soft landing. How does the Fed cut rates into an environment of rising demand, higher input costs and full employment? Cut too soon and run the risk that the Fed ushers in an Arthur Burns-esque re-acceleration in inflation, a situation Powell has repeatedly stated he will not tolerate. Hold rates high too long — until labor markets and wage inflation crack — and risk losing too much economic momentum to avoid a recession.

In sum, although near-term risks to the economy have seemingly abated, we continue to monitor the same risks that existed at the outset of the interest rate hiking cycle. In fact, we would argue those risks have heightened as the soft landing narrative is consensus among investors and largely factored into today’s stock market valuation. We are monitoring crosscurrents, including the winding down of excess consumer savings (COVID stimulus payments are largely exhausted) and a housing market still sluggish and unaffordable versus the potential productivity enhancement and deflationary impact of AI. In addition, the recent influx of immigration could prove a boon for businesses desperate for labor at a reasonable cost.

Longer-term, we worry about the sustainability of government debt and the increasing burden of higher interest rates on the budget deficit. At some point the U.S. will need to increase taxes or cut spending to prevent debt costs from spiraling out of control. Neither will happen in an election year, but the next Congress faces stark choices. By no means is the U.S. dollar’s status as reserve currency our birthright.

While we are optimistic about the long-term benefits generative AI will have on workplace productivity, aggressive assumptions need to be made to justify current valuations. We admire the business models of the large technology companies but are mindful of their regulatory risks as well as the concentration risk they create for our portfolio. We are positive on much of the health care sector as market expectations for medical device and life science/tool companies have come in markedly, while we find the non-cyclical nature and modest valuation of pharmaceutical companies appealing. We are positive on select cyclicals such as rails, where expectations are low and sustained economic growth would create upside. We believe stable financial conditions and the potential for rate cuts and a steeper yield curve will benefit select financial companies such as banks. We do not expect the top-heavy market of 2023 to continue and believe a diversified portfolio with investments focused on durable growth at attractive valuations is best positioned in this transitioning interest rate regime.

Although we remain constructive on the near-term outlook, we believe total return expectations should be closer to long-term trend (high single digits) versus the larger swings that have become common in the post-COVID environment. We believe the market is overdue for a correction but that any pullback will be short-lived and buyable.

We are long-term investors. Rather than trying to bet on near-term earnings trends, we believe it is better to lookout two to three years and make investment decisions based upon our assessment of a company’s longer-term, sustainable growth rate relative to what is implied in today’s share price.

The ClearBridge Appreciation ESG Strategy outperformed the benchmark S&P 500 Index in the first quarter. On an absolute basis, the Strategy had positive contributions from 10 of 11 sectors. The IT and financials sectors were the main positive contributors to performance, while the real estate sector was the main detractor.

In relative terms, stock selection contributed positively. Specifically, stock selection in the consumer discretionary, financials, health care and consumer staples sectors and a consumer discretionary underweight contributed the most, while stock selection in the materials sector detracted.

On an individual stock basis, the biggest contributors to absolute performance during the quarter were Nvidia, Microsoft, Costo, Berkshire Hathaway and Meta Platforms. The biggest detractors were Apple, Adobe, American Tower, Crown Holdings and UnitedHealth Group.

During the quarter, we initiated positions in Icon in the health care sector, Synopsys and Intel in the IT sector and Target and Nestle in the consumer staples sector. We exited positions in Pfizer and Becton Dickinson in the health care sector and ArcelorMittal and Air Products and Chemicals in the materials sector.

Artificial intelligence (AI) is transforming the investment landscape, and while its rapid development sparks some valid social and environmental caution, it also brings with it enormous potential for helping sustainability goals, with better data to improve energy efficiency, optimize renewable energy, make agriculture more sustainable and improve human health. ClearBridge is closely watching these opportunities even while we observe AI’s energy intensity and social dimensions as the phenomenon plays out in our portfolio companies across sectors.

On the regulatory front, the world’s first comprehensive AI law, the EU’s AI Act (AIA), will come into force later in 2024. The AIA classifies AI systems according to the risk they pose to users: there is unacceptable risk (such as emotion recognition in schools and workplaces), high risk (such as critical infrastructure and medical devices), limited risk (such as chatbots, which carry the risk of manipulation or deceit) and minimal risk (such as spam filters). Each level of risk is subject to different requirements, and there are heavy fines at the company level for noncompliance. President Biden also issued an executive order on safe, secure and trustworthy AI in October 2023, aimed at establishing standards for AI safety and security, protecting privacy, equity and civil rights, and supporting consumers and workers.

On the labor front, AI can boost productivity, but automation has always threatened labor disruption, potentially deepening global inequalities as AI growth may favor advanced economies with sufficient infrastructure and skilled workforces (Exhibit 4). Hiring algorithms may also rely on and perpetuate race and gender biases. Almost 40% of global employment is exposed to AI.¹

Exhibit 4: Employment Shares by AI Exposure and Complementarity

With looming questions of misinformation and digital safety, cybersecurity, and even human capital management, as hiring for AI ethics jobs is picking up,² it is clear that as AI develops and hits multiple inflection points over the next few years, companies across ClearBridge portfolios will need to negotiate a variety of sustainability-related AI opportunities and risks.

AI and Power Demand Implications

AI is energy intensive, with data centers running the large language models requiring significant electricity and complicating the already complex power supply and demand picture of the energy transition. Overall estimates for data-center-driven U.S. power demand growth vary, but they generally forecast the electric load to roughly double by the end of this decade (from the current 3%–4% to ~8% by 2030).

On the surface, this kind of demand growth should cause generation shortages on the grid, especially in places where the data centers have been expanding rapidly such as Virginia and California. Factors that could mitigate projected power shortages in the future would be: 

• Continued improvement in technologies/efficiency of the data centers
• Expansion of the data center locations toward less congested grids
• An increase in utilization of the existent gas generation capacity and even delays in the scheduled coal plant retirements, which would increase the emissions intensity from AI

Another important mitigating factor over the next five years will be faster development of renewable power sources, as many data center hyperscalers have public commitments to carbon free energy. The renewable projects’ shorter development/construction timeline and locational flexibility to satisfy the data center demand should push demand for renewables higher and improve the renewable projects’ returns. Renewable developers such as NextEra Energy and AES should be beneficiaries of these trends. As highlighted at NextEra’s recent renewable-focused investor day, current forecasts call for renewable capacity to reach between 375 GW and 450 GW over the next seven years (2024-2030). This implies a 13% compound annual growth rate through the end of this decade and suggests a rapid acceleration in renewable development (235 GW of renewables were added over the last 30 years). According to the company, this anticipated power demand acceleration is expected to be driven by consumption growth from data centers (+108%), oil and gas industry (+56%) and chemicals (+14%) between 2025 and 2030.

"Faster development of renewable power sources will be a key mitigating factor in power demand shortages as AI data centers consume more power." 

Over the long term, data center power consumption growth and companies’ green targets should advance the development and utilization of more effective power storage and carbon capture and storage technologies as well as green baseload power solutions, such as green hydrogen and small modular nuclear reactors.

From the regulated utilities’ perspective, the ultimate impact of the data center demand growth will vary by region, but the overall implications for the sector should be positive. Data center additions to regional grids will not only drive incremental investments into the local transmission and distribution systems, but in some cases result in incremental generation needs. In the near-term, utilities located in the territories with planned data center expansions, such as Dominion Energy, Southern Company, Sempra and CenterPoint Energy, should benefit from higher required investments into the grid to accommodate additional demand.

AI Impact on Labor Conditions

Prior waves of technology dating back to the 19th century have changed the fabric of the global workforce. AI is similar but potentially more impactful in that it might affect white collar jobs just as much as it does blue collar labor. The power of generative AI (Gen AI) over prior AI advances is its ability to generate creative output. However, most companies are using Gen AI to augment their employees’ capabilities, rather than seeking to replace them. In ClearBridge engagements with technology companies using newly released code generation tools, we find they generally use them to speed up the first draft of a software engineer’s code output. This frees up the engineer to focus on larger problems such as user experience and system design. The risk of AI causing mass unemployment is therefore overstated, while the need to upskill and reskill today's workforce is likely understated. By 2030, management consulting firm McKinsey estimates that as many as 375 million workers or roughly 14% of the global workforce might need to switch occupational categories and acquire new skills.³ Just as prior waves of innovation did, the AI wave promises to create demand for new skills around model training, prompt engineering and data science.

While AI can often outperform human counterparts on a growing range of tasks, it lacks human intuition, context awareness and ethical judgment. Recognizing these limitations will help companies use AI more effectively and responsibly. When deploying AI to generate content, the primary ethical considerations are around protection of intellectual property rights and avoidance of unintended bias. Google’s missteps with Gemini are a recent lesson on how difficult it is to tune an AI system to account for biases and ambiguity. However, Alphabet, Meta and Amazon are also taking the challenge seriously and stepping up their investment in AI ethics and safety. Meta currently has around 40,000 people working on safety and security, with more than $20 billion invested in teams and technology in this area since 2016. Google’s Vertex AI platform provides a suite of tools that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. By integrating robust security measures, promoting transparency through explainable AI and adhering to stringent ethical guidelines, Vertex AI empowers businesses of all sizes to develop and deploy AI solutions with confidence.

Misinformation and Social Manipulation

AI and Gen AI in particular make it much easier for bad actors to spread misinformation. We have already seen AI being used to impersonate individuals, including the two leading candidates for the U.S. presidential election in 2024. Meta and Google are working to thwart the misuse of AI-generated content on their respective platforms. In August 2023, Google debuted a watermark software for AI content, letting the user know that the content is AI generated. Meta meanwhile ensures its AI-generated content is labeled “imagined with AI” and is expanding this feature to include content created by third-party tools. The company is also focused on election transparency, namely serving over 500 million notifications on its apps since 2020 informing users how and when to vote, and building an industry-leading library of political ads that is publicly available and elucidates the entity funding each ad and who they are targeting. Given how quickly the tools are evolving, including high-quality AI-generated video in the near future, this remains an open area of both risk and opportunity for the world’s leading digital media platforms.

AI’s Potential in Health Care

The growth and increasing complexity of data in health care also makes AI potentially transformative in the sector. In drug discovery and development, for example, some companies are successfully using AI to create and optimize molecules to go into development, largely with applications in chemistry and protein engineering. Some companies are hoping to use AI to pick better targets for dugs, although we are skeptical about the near-term prospects, as the complexity of biology may pose a challenge for current AI models. Other companies are hoping to use AI and advanced computer models to better design clinical trials, although these attempts are in the very early stages.

"Most companies are using Gen AI to augment their employees’ capabilities, rather than seeking to replace them." 

There is also significant potential for AI in the field of diagnostics, both traditional testing and advanced genetic tests. For traditional methods of diagnosis, like blood/serum based tests and images such as X-rays, CTs and MRIs, AI should be useful for prescreening, enhancing or even replacing human reading of test results. AI models have already been used to develop tests looking for patterns of genes that indicate cancers or the prognosis for cancer.

Along these lines, Hologic, a medical technology company focused on women’s health and the leading manufacturer of mammography machines, is incorporating AI in its breast imaging business to assist radiologists in locating possible breast cancer lesions. Siemens Healthineers, one of the leading manufacturers of CT and MRI machines, is also incorporating AI into its imaging platforms, which provide automatic post-processing of imaging datasets through AI-powered algorithms in order to reduce basic repetitive tasks and increase diagnostic precision when interpreting medical images. The company is the global leader in AI patent applications in health care.

Conclusion

The rapid ascension of large-language model AI in 2023 has made the technology relevant to companies’ futures in nearly every sector. It will be important for AI to be firmly tied to sustainable futures, and we will continue to monitor how ClearBridge portfolio companies and the market at large are navigating AI’s sustainability-related opportunities and risks.