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From the desk of the Mackenzie Bluewater Team Q1, 2024
Markets started 2024 strongly as the rally that began in the fourth quarter of 2023 continued into the new year. Equity markets have been buoyed by optimism that the Central Bank tightening cycle is behind us, and monetary policy easing is beginning. At this point, the degree of monetary easing continues to be highly uncertain. While fixed income markets began the year signalling significant Central Bank interest rate cuts--a view that we did not subscribe to--that expectation continues to be gradually tempered as the year progresses, with milder rate cuts now projected. Although the Covid-driven inflation spike is now behind us with inflation continuing to slow globally, service inflation remains elevated which should continue to pressure Central Banks to delay rate cuts. In addition, on-going global conflicts are once again squeezing energy prices higher, which will gradually flow into core goods inflation. This combination continues to suggest to us that monetary policy will remain tighter for longer than the market anticipates. From a global GDP standpoint, economic growth continues to be uneven, with the United States stronger, while Canada, Europe, and Asia are generally softer. The economic strength seen in the United States also suggests that monetary easing will be slower than markets expect, as the Federal Reserve, in particular, sees no pressing need to cut rates to stimulate growth.
The big economic backdrop question facing markets at this point is whether we are at the dawn of a new economic cycle, with a multi-year period of strong, synchronized global GDP growth ahead of us. We continue to believe that this favourable set-up is highly unlikely, as most major economies have yet to reset from the prior cycle. Unemployment, in particular, has remained at end of cycle levels. In our view, the economic boost from people returning to work post a recession-driven spike in unemployment is the single largest driver of above trend economic growth rates during the expansion phase of the economic cycle. With unemployment in North America at very low levels, that boost to growth will not be forthcoming. Instead, we anticipate that global growth will continue to be fairly anemic. As we have detailed in past quarterlies, this is an environment that tends to be supportive of the Bluewater investment process.
Recent Developments in AI
Since the public launch of ChatGPT in November 2022, Artificial Intelligence (AI) has become a source of considerable investment excitement. As the topic continues to evolve, we wanted to provide an update on recent developments, the beneficiaries, the areas of AI that we are most excited about, and how we have positioned the portfolios to benefit from it.
The most impactful development is the interest in Large Language Models (LLMs), which has kicked off an arms race among Hyperscalers (Alphabet, Meta, Amazon, Microsoft) to build out data factories, in the hope that corporate clients will eventually use AI technology at massive scale to enhance productivity. These companies are best positioned to build these data factories due to their existing infrastructure and the significant capital resources available to them. Much of the spending so far that has been related to AI is from the build-out of these datacenters, with approximately half of that cost being semiconductors.
Despite the excitement, very little of this technology is actually being used by paying corporate customers, while consumer usage is being subsidized by those who run and host the models. Essentially, to date, the AI industry has been operating under the premise that “if you build it, they will come”.
AI computational workload can be broken down into two parts: model training (training current and future models for future use) and inference (using the model for output). Nvidia CEO Jensen Huang estimated that approximately 40% of data center revenue was on inference, but that much of this was the use of "recommender" systems from social media and other platforms, which were already prevalent but now use more advanced processing capabilities. In other words, truly new AI workloads are still in their infancy.
While it seems like almost every company in the world is assessing how they can use AI to increase their productivity, evidence from companies such as Accenture indicates that so far, adoption has been quite limited. At this point, there are few actual corporate use cases making the required computational power highly uncertain. This will continue to evolve over the next few years.
Beneficiaries of AI
Despite the strong stock performance of AI-related companies over the past six months, we see a narrow list of companies that are actually benefitting or will benefit in the near future from a financial perspective.
Semiconductors, with Nvidia being the posterchild, is one obvious area benefitting from AI. Nvidia currently has a dominant technological lead, and their chips, while expensive, are the most sought after by buyers such as Google and Microsoft, with companies commonly boasting about their allocations to Nvidia’s most powerful chips. Nvidia's revenue expectations for last year were initially $30 billion but ended up at $60 billion, highlighting the difficulty in forecasting demand in the space.
A great majority of demand for building out these datacenters is coming from the Hyperscalers (either directly or through ownership interests in other AI companies). The massive scale of these companies gives them the means to make these investments and they already have businesses that make use of high-caliber datacenters. However, their capital investments are currently outweighing the revenues they are bringing in from AI and will likely do so for the foreseeable future. While the Hyperscalers will have the infrastructure to process AI workloads for their customers, it is too early to tell if associated revenues will be merely incremental to their current cloud infrastructure offerings and core offerings or will result in material new businesses.
From a software perspective, rather than creating new disruptive competitors, the early winners seem to be with those who already have an entrenched customer relationship. AI tools appear to be additive to current offerings from companies such as Microsoft and ServiceNow, who are deeply embedded in their customers' IT systems. It is easier to add on an AI offering from this advantageous entrenched position, given that AI tools rely on other data proprietary to the customer. This has the potential to give these incumbents important pricing power and is likely to increase their runway for growth.
Exciting Areas of AI Beyond ‘Large Language Models’
While most of the focus in the media and markets has been around LLM technology provided by ChatGPT (OpenAI), Google Gemini, Perplexity, and others, there are other equally, if not more exciting developments from a societal and investment perspective.
AlphaFold, an AI system developed by DeepMind (an Alphabet subsidiary), predicts protein structures with high accuracy. While it took scientists 50 years of laboratory work to accurately predict the structure of 150,000 proteins, AlphaFold has already accurately predicted over 200 million. This information is now broadly accessible to scientists and has the potential to significantly advance drug discovery and other important treatments for disease and aging.
Microsoft and Pacific Northwest National Laboratory (PNNL) recently undertook a project where an AI engine was trained on material science and the properties of all standard elements to find new battery materials. From an initial pool of 32 million candidates, using various criteria such as cost-effectiveness, stability, and availability, the AI model produced 23 candidates for further study, only 5 of which were known to scientists before the study. This process, which would have taken scientists many years, took merely 80 hours using AI technology.
In both examples above, we believe these are areas that could truly be revolutionary in terms of the positive impact of AI driven technology.
Positioning of Bluewater portfolios
Investment-wise, while AI offers promising prospects, in many cases the businesses attached to it carry inherent risks within rapidly evolving landscapes. Bluewater's investment approach hinges on identifying key change enablers, much like those who supplied 'picks and shovels' during a gold rush, businesses that are software and services focussed with a level of recurring revenue that avoids the cyclicality typically associated with hardware type businesses. This is most evident with companies such as Accenture, Cadence Design Systems, Microsoft Corporation, Thomson Reuters and on the healthcare side with Thermo-Fisher Scientific.
Accenture represents one of the best ways to invest in the roll out of AI and digital transformation, as it is virtually impossible to implement any of these strategies without the assistance of sophisticated technology consultants and implementation teams. With decades of accumulated intellectual property and significant investments in AI --Accenture has widened its moat over consulting peers and is set to benefit as businesses digitize in almost all respects in the coming decade.
Furthermore, while the semiconductor space is notorious for its cyclicality and capital intensity, the Electronic Design and Automation (EDA) companies such as Cadence provide access to these powerful secular trends, but with a subscription based recurring revenue business model that removes many of these risks. In addition, the company is unique in that it operates in a virtual oligopoly where two players (Synopsis and Cadence) essentially control the industry. From a customer standpoint it is impossible to design semiconductors (which are critical to AI) without EDA tools. With decades of accumulated intellectual property embedded in Cadence software tools and the costs of a failed chip design extraordinarily high, the barriers to entry in the EDA industry are truly massive; at this point it seems impossible to create a new EDA supplier. As a result, Cadence occupies a key protected niche in a very important growth industry and with a business model that fits the Bluewater investment philosophy.
The fidelity of generative AI output is a key barrier in its widespread adoption and is demonstrably enhanced when AI models are trained on more reliable datasets. Thomson Reuters addresses this for the legal and tax & accounting industries through their proprietary content. As an example, the company owns and maintains datasets of over 100 years of case law that are carefully curated and also provides value added features such as citations, editorial annotations, case summaries, legal commentaries and opinions by a team of over 1,000 attorneys. This allows for vastly more reliable output and is a formidable moat that is not easily replicable by AI or competitors, including new entrants. Not surprisingly, when Microsoft launched their AI legal drafting tool on co-pilot, they partnered with Thomson Reuters to leverage their high-quality content. While it is certainly early days and the pricing and revenue models are still evolving, the company expects generative AI to accelerate their organic growth and profitability.
While the energy transition and artificial intelligence are important secular growth areas where we continue to find attractive opportunities, much of the portfolio remains in high quality, durable growth companies. These businesses benefit from idiosyncratic tailwinds that make them less reliant on the underlying economy and are able to grow in virtually any environment.
An example is a Brookfield Asset Management, a recent addition in Canada. We have long admired the Brookfield family of companies for their strong growth profile, best in class management and exceptional track record in capital raising. However, the sheer complexity of the organization and how decisions and capital flows across its web of entities, partnerships and operating companies made it extraordinarily difficult to model and kept us on the sidelines. The spin out of the asset management arm in late 2022, established a simplified entity with attributes that we highly value including a capital light model, strong free cash flow, and a clean balance sheet.
With over $450 billion in fee bearing capital, Brookfield Asset Management is a leader and first mover in the fastest growing segments of private markets including infrastructure, renewables, and credit. In fact, they are one of the world's largest investors in renewable power and climate transition, sharing our view that the energy transition is the largest investment opportunity in the coming decade. The company expects to generate 15-20% growth in earnings and free cash flow in the medium term, underpinned by their capital raising efforts, targeting $90 – 100 billion per year, the stickiness of their assets with over 85% in long term or permanent structures that cannot be redeemed, the stability of their fee structures and strong operating efficiency with margins in excess of 50% makes this a very admirable business model.
Conclusion
In summary, while there have been many exciting developments occurring with respect to AI over the past year, there is still much uncertainty. This is why we continue to focus on companies who are enablers of this important secular change. These companies benefit from being global leaders in their respective business areas and are in a strong position to benefit from serving new applications and uses to their clients. We believe that this is a prudent way to invest in these important changes, without subjecting the portfolio to the uncertainty and extreme volatility that often comes with emerging technologies. Bluewater has delivered value to investors by concentrating on a select group of companies that stand as leaders in their respective industries. These companies are spearheaded by best-in-class management teams and exhibit growth rates surpassing those of the overall economy. Their resilient business models enable them to navigate uncertain environments adeptly and enhance their competitive advantages.
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