Josh Le, CFA, MSc in FE
The recent UEFA Euro 2024 tournament has reminded me that I used to be a hardcore football fan. I grew up watching matches in some of the most popular leagues: the UEFA Champions League, the Italian Series A, and the English Premier League. Subsequently, tennis tournaments caught my attention, and I started following the GOATs (greatest of all time) such as Federer, Nadal or Djokovic. Through the years, although other life pursuits have taken my time away, valuable lessons from football and tennis stay with me and reshape the ways I look at many things in life including investment.
Football and tennis, by nature, strike a stark difference. The former emphasizes teamwork and system while the latter individuality and mentality. Nonetheless, a well-crafted and balanced strategy targeting long-term success arguably forms the foundation for the most established teams or individuals in both sports. Before I delve into the most topical theme in the investment world at this point – “Generative AI”, let me share with you my own “70/30 strategy” inspired by these sports and how I have applied this strategy to investments throughout the years.
Let’s explore the 70/30 strategy through the examples of Novak Djokovic, a living legend in the tennis world, and Real Madrid, the undisputed king of European football:
First of all, these are not hard rules. They are rather a set of guidelines shaped by my own experience, perspectives, the equity market paradigm during my career span, and what has worked best for our clients at Covenant Capital.
I believe in buying good companies at reasonable prices. The 4M framework looking at companies’ market size, market share, margins, and management helps me to identify outstanding companies in the long run. These companies operate in growing markets (size), gain market shares, improve margins, and are led by exceptional management. Nevertheless, great companies might not equate to great stocks in the shorter term as valuations might be expensive or stocks might fall short of catalysts and growth momentum. When evaluating an investment opportunity, placing 70% of the weight on long-term 4M factors and 30% on short-term valuation/catalyst factors achieves a balanced approach for identifying excellent companies and stocks to own, akin to Real Madrid’s strategy in the football world.
Figure 1. Covenant Capital’s 4M framework
In managing my equity portfolio, I apply a strategy similar to that of Djokovic: a 70% allocation in “core” holdings and a 30% allocation in “satellite and tactical” positions. “Core” holdings consist of high-quality, well-established, and stable companies under the “4M” framework. This part forms the foundation of the portfolio. On the other hand, a 30% “satellite and tactical” allocation allows me to capitalize on emerging & innovative companies in less developed segments and “tactical” macro trades. These trades could potentially enhance the portfolio’s return albeit containing higher risks. However, in a time of need to avoid prolonged drawdowns, cash is raised by reducing “satellite” and “tactical” positions while retaining high conviction “core” stocks.
Excessive allocation to “satellite” and “tactical” trades exposes the portfolio to unnecessarily high volatility and drawdowns in market gyrations and reduces the ability to hold other long-term positions, or worse, forces investors to liquidate them at unfavorable prices. On the flip side, allocating 100% to the “core” strategy can also be detrimental. Without the flexibility provided by “satellite” and “tactical” positions, it can result in missing opportunities in emerging areas and sectors that require certain levels of “leap of faith” at times. Moreover, during rapidly changing market conditions and macro environment, the portfolio might be too rigid to adapt and fail to adjust swiftly enough. Therefore, a balanced 70/30 approach ensures long-term stability, growth potential, and timely response in the short term.
As an equity manager and analyst, an important part of my job is to analyze individual companies and pick stocks. Hence, 70% of resources are dedicated to this task. However, I agree with John Maynard Keynes as he aptly noted: “Markets can remain irrational longer than you can remain solvent”, highlighting the undeniable role of a 30% emphasis on macro-overlay.
First of all, do not fall in love with your stocks, regardless of how strong your conviction may be. Emotional attachment can cloud judgment and hinder objective decision-making. Additionally, the thesis-breaking factors, more often than not, are the macro environment underpinning companies’ businesses which can cause critical flaws in your thesis if overlooked. To mitigate this risk, it is important to continually reassess the potential consequences of these macro factors on individual stocks and the overall portfolio. If these factors alter the long-term stock thesis or persist long enough to impact the portfolio’s sustainability, investors should adjust the size or even exit their positions. The 30% allocation to “satellite & tactical” trades does provide room for maneuver in this case.
For example, 2022 was marked by the most aggressive inflation and rate-rising environment since the hyper-inflation period of the 1970s-1980s. As a result, the traditional stock & bond portfolios suffered one of the worst drawdown years alongside the 1931 great depression and the 1969 recession. Such an environment proves inconducive for most stocks regardless of their long-term prospects. Investors might consider reducing tactical allocation to raise cash and trimming exposure to companies most sensitive to inflation and rates. This balancing act creates a cash buffer against market turbulence, hence protecting against further downside. Moreover, the long-term “core” positions are then less likely to be liquidated under pressure, allowing the overall portfolio to participate in the rebound when the market turns.
Let’s shift gears to dive into the most topical debate – Generative AI and how we think about investing in this theme.
Suffice to say that Generative AI has taken the world by storm since late 2022. Many of us have now been using ChatGPT, Gemini, or some Copilot products on a daily basis to improve the efficiency of our work, learn something new quickly, or get creative. Although AI or artificial intelligence is not new, applications have been confined to corporate and industry levels. ChatGPT and Gen AI have brought accessibility at scale to consumers and internet users.
As it has worked in many past technology trends, the “IPA” framework should once again be employed to guide our thinking around Gen AI investment. “IPA” stands for “Infrastructure”, “Platform”, and “Applications”. A technological advancement first starts with infrastructure build-out. Subsequently, the emergence of platforms on top of the infrastructure layer fosters corporates to develop and deploy applications.
For example, in the smartphone evolution, telco companies provided the 3G/4G/5G infrastructure. Following this, various platforms such as iOS and Android emerged. These platforms in turn have enabled the deployment of a myriad of applications that we are using today.
The below table encapsulates the IPA ecosystem of Gen AI:
Figure 2. Generative AI’s IPA framework. Source: Covenant Capital, Goldman Sachs Research.
In the “infrastructure” layer, the hyperscalers (e.g. Microsoft, Amazon, and Alphabet) procure a large number of AI accelerators, memory, networking, and connectivity chips from the semiconductor companies to construct AI servers, data centers, and data campuses. These hyperscalers open up their infrastructure for the likes of OpenAI, Anthropic, Mistral, and Musk’s xAI … to train their LLM (large language models) and create the “platform” layer. Subsequently, software and application developers such as Salesforce, Service Now, OpenAI, and Booking.com … leverage these platforms to develop innovative applications and services, driving further advancements in AI technology and its practical implementations.
Although users have found great utility in applications such as the ones abovementioned, skepticism about Gen AI’s longevity remains high in the absence of “killer apps”. Killer apps, akin to search and e-commerce in the internet era, social media, and the sharing economy in the smartphone & 5G era, arguably will take years to come to fruition. As of now, we have yet to reach that stage with the Gen AI trend.
For instance, the e-commerce penetration rate only reached ~5% and revenue ~$200bn in the US in 2011, more than a decade after the start of the Internet boom:
Figure 3. US e-commerce revenue (left axis – $mn) and penetration rate (right axis). Source: JP Morgan Research.
Smartphone technology took 8 years to exceed a 50% penetration rate in the US:
Figure 4. Smartphone penetration rate in the US from 2005. Source: comScore.
We are only in the second year of Gen AI and early infrastructure ramp. As such, the investment should emphasize 70% infrastructure layer vs 30% platform & application layer. That will also mean 70% allocation to the infrastructure enablers such as semiconductors and hyper-scalers vs 30% allocation to software and non-semi stocks. This balance might shift in the future alongside the ecosystem’s maturity stage.
Nonetheless, we have seen adoption picking up and applications spreading into many industries:
One of the most compelling long-term benefits of Gen AI is its potential for significant productivity enhancement. Goldman Sachs estimates potential labor productivity gain using AI by identifying industries’ labor costs as % of revenues and their exposure to AI automation (figure 5). For instance, the software & services sector is prone to high labor costs and the likelihood of being replaced by AI. Thus, the productivity of companies in this sector will be lifted meaningfully by adopting AI.
Figure 5. Potential productivity gain by adopting AI by sectors. Source: Goldman Sachs Research.
At a 5% adoption rate across industries in the US, we have only touched the tip of the iceberg of what Gen AI can do.
Figure 6. Percentage of US firms are using Gen AI. Source: Goldman Sachs Research.
In every gold rush, our preferred plays have always been the shovel makers who thrive by selling tools and equipment to the gold miners. The Gen AI trend is no exception. As a result, infrastructure enablers such as semiconductor companies are the backbone of our strategy. Within this space, fabless chipmakers (Nvidia, AMD, and Broadcom …), memory (Hynix, Micron, and Samsung), networking & connectivity (Broadcom and Marvell …), and chip IP & design (e.g. ARM) companies that win content and share in AI servers and accelerated computing era are our favored choices. Top-tier foundry (e.g. TSMC) and semi-equipment companies (ASML, Lam Research, and Applied Materials…) will also reap the rewards as well.
The big-3 hyperscalers are expected to invest a large amount of CapEx into ramping up the Gen AI ecosystem which will continue to bode well for our “shovel makers”. While I will take a stab at concern about their CapEx investment later, it is worth noting that the early monetization of Gen AI “platforms” has helped to propel the hyperscalers’ cloud revenue growth back to above +20% yoy level as per figure 7. In other words, the monetization of the platform layer has started.
Figure 7. Big-3’s quarterly cloud revenue (left axis – $mn) and yoy growth (right axis). Source: Visible Alpha, Covenant Capital.
Is this a good or bad thing that infrastructure build-up is highly concentrated among mega-cap companies? One major issue that plagued the 1990s and 2000s cycles was the large amount of under-utilized capacity resulting from suboptimal investments by numerous smaller-scale companies and ventures. In contrast, the giants in this cycle arguably possess deep pockets, access to capital, and extensive distribution networks to essentially bring this new technology to fruition. Nevertheless, the number of participants will broaden in the future when we transition to the “application” phase.
Are areas such as power utilities and data-center operators good proxies of the Gen AI infrastructure build? Admittedly, they fall outside of my core competence and therefore are not included in the 70% “core” holdings. However, from time to time, these sectors can be excellent additions to the 30% “satellite & tactical” bucket.
Invest, grow, repeat.
There is a reason why the hyperscalers – Microsoft, Amazon, and Alphabet are among the largest companies in the world: they invest for future growth. The Gen AI cycle is no exception as this big-3 group is expected to invest ~$166bn/$187bn in CapEx in 2024E/2025E respectively. As a result, the CapEx intensity (CapEx/revenue) will reach ~14.2% in these two years. As mentioned earlier, infrastructure enablers have been the main beneficiaries of this CapEx spending from the big-3.
Figure 8. Big-3’s annual CapEx (right axis – $mn) and CapEx intensity (left axis). Source: Visible Alpha, Covenant Capital.
We observed a similar uplift of CapEx during the previous “Cloud computing” cycle (+30% p.a from 2012 to 2017) as the big-3’s CapEx intensity rose from 5.4% in 2012 to ~12% in 2020. This investment in turn spurred an impressive +51% CAGR growth for their Cloud segment in the 2015-2020 period (figure 9).
Figure 9. Big-3’s Cloud annual revenue ($mn) and annual CapEx ($mn). Source: Visible Alpha, Covenant Capital.
Moreover, following a period of plateaued growth, investments in Gen AI have begun to rejuvenate the Cloud growth rate, as illustrated in figure 5 above. Consequently, the big-3 will continue to invest as long as future growth prospects remain promising. They can sustain this level of spending as they are still projected to generate over $200bn in free cash flow in CY2024E.
Is the 14% CapEx intensity level too high? I think the “asset-light” period, with CapEx intensity below 5% and a significant portion of revenues derived from software, search, and advertising, has long gone. Suffice to say that these corporates have gone “asset heavier”. In my opinion, the 14% level is acceptable at the moment, provided that growth continues. This level is also considerably lower compared to other asset-heavy and investment-intensive areas in technology, such as foundry and memory, which have CapEx intensities of 30-40%.
One important indicator to gauge the longevity of this Gen AI infrastructure phase is to track Gen AI models’ “refresh cycle” – specifically, how frequently the large language models (LLMs) are being updated. The concept of Gen AI models’ refresh cycle is similar to the “replacement cycle” during the smartphone and PC eras. The replacement cycle refers to how often users replace their devices.
The smartphone market experienced a stellar +40% CAGR growth rate in the 2008-2015 period (figure 10). However, as the replacement cycle extended to nearly two years by 2015, users became less motivated to upgrade to new phones. This was due to increased durability and the commoditization of smartphones. Consequently, the smartphone market’s growth cooled down, declining to a -2% CAGR from 2016 onwards.
Figure 10. Smartphone market’s shipments (right axis – mn units) and replacement cycle (left axis – years). Source: Statista, Counterpoint Research, Business Wire, TechInsights, IDC.
A similar finding was observed in the PC market as the growth rate tapered off from +10% CAGR in the 2005-2010 period to -4% CAGR in subsequent years as the replacement cycle exceeded 4 years.
Figure 11. PC market’s shipments (right axis – mn units) and replacement cycle (left axis – years). Source: Wikipedia, Statista, ITCandor, GSMA Intelligence.
At the moment, major models such as OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, and Meta’s Llama are being refreshed at a cadence of shorter than 1 year (figure 12).
Figure 12. Major Generative AI large language models & available dates.
Therefore, I believe that we are still in the early innings of the build-up phase. However, when models are deemed adequate, the “refresh cycle” will lengthen and infrastructure investment slow. This stage will warrant a cautious reassessment of the infrastructure plays.
Cisco had an eye-popping run (+762%) from 1998 to March 2000 before the Dot com bubble burst, resulting in the subsequent collapse of the stock (blue line in figure 13). Will Nvidia meet the same end after a +919% rally since October 2022 (green line in Figure 13)? The short answer is not likely as the underlying drivers of their performances draw striking distinctions: Cisco’s price was propelled largely by an exuberance reflected in valuation expansion while earnings and margins lifted Nvidia’s stock.
Figure 13. Rallies of Cisco (blue) and Nvidia (green). Source: Bloomberg.
I have broken down the drivers of these 2 incredible rallies:
Figure 14. Breakdown comparison between Nvidia and Cisco’s runs. Source: Bloomberg, Covenant Capital
A few takeaways from the above table (figure 14):
Nvidia has run on a solid foundation and not false hope. Therefore, we do not believe that the stock will face the same fate as Cisco.
In summary, the 70/30 strategy offers a robust framework for stock analysis, balancing long-term fundamentals (4M) and short-term dynamics (valuations & catalysts). This strategy effectively combines bottom-up stock selection with top-down macro-overlay, while providing a structure for portfolio construction that blends core holdings with satellite and tactical positions.
When evaluating technological trends, particularly the current Gen AI, the IPA (Infrastructure, Platform, and Application) model proves invaluable. However, it’s crucial to recognize that Gen AI is still in its nascent stages. Given this early phase, our investment focus is primarily on the infrastructure enablers – the shovel makers of this gold rush.
While killer apps will likely emerge in a later stage, this process may take years. In the interim, monitoring the refresh cycles of Gen AI models serves as a key indicator of the infrastructure cycle’s longevity. At the moment, the hyperscalers have experienced a resurgence in growth and will likely continue their investment to build up this ecosystem, mirroring their approach during the Cloud computing cycle.
Featured Picture/Quote: ChatGPT dating gone wrong. Thank God I am married.
https://www.tiktok.com/@thechattinghour/video/7371150343163350305
Josh Le, CFA, MSc in FE
Senior Vice President
Portfolio Manager
joshle@covenant-capital.com
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