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While US chip designer NVIDIA gets most of the AI press, TSMC manufactures virtually all of the high-performance AI chips designed by NVIDIA. The Taiwan-based company uses some of its most sophisticated manufacturing technologies for these chips, enabling them to be at the forefront of transistor density, speed, and energy efficiency. AI chip customers also increasingly depend on TSMC’s innovative capabilities in integrating and packaging the logic, memory, and input/output components involved. TSMC’s CEO recently projected that in four years about 20% of this company’s total revenue would come from AI-specific chips.

Samsung Electronics has received less attention from investors but is also carving out a niche for itself in AI-related semiconductor manufacturing. Samsung anticipates a rebound in their memory chip prices in 2024, in part due to the expected proliferation of on-device AI, a memory-hungry implementation, and is investing for longer term in the next generation of high-bandwidth memory chips integral to AI applications, promising to leapfrog competitors in performance. Samsung Electronics is also positioning itself to compete with TSMC in manufacturing advanced logic chips, using its capability of designing and manufacturing memory, logic, and chip packaging to unlock new opportunities for its foundry business.

Infineon, widely recognized for its strength in automotive and power semiconductors, is less directly exposed to AI themes but does have products supporting growth data centers and AI computing whose high-power demands is a good match for Infineon’s switched mode power supplies and voltage regulator designs that power high performance systems.

During 2023, Amazon overhauled the sprawling US logistics network that supports its online-retail operations, switching from a national system for filling and routing orders to a regional setup. The core change was to partition the US into eight main regions, assigning each region dedicated fulfillment warehouses, sorting centers, last-mile delivery hubs, and vehicles. By limiting the area each facility serves, the company doesn’t need as many connecting points along its distribution routes, and trucks are filled closer to their capacity and travel shorter distances. This helps Amazon save money on fuel, fleet upkeep, labor, and other overhead, as well as potentially reduce its environmental impact.

Amazon’s latest financial report in February revealed that fulfillment costs, which had soared to 16.4% of net sales in 2022, shrank to 15.8% of net sales in 2023 as the new system was implemented. As the chart below shows, it was only the second time in 14 years that this metric’s long ascension was interrupted. This time, we view it as an inflection point, and so the downward trend should continue.

Given the opaque relationship between the Chinese government and private companies, the exact terms of the exchange are unknown; Baidu’s move could result in the US putting the company on the restricted list that blocks US investments in companies that could be supporting China’s military. However, there is a plausible market-based reason for the donation: the quantum computer was expensive to build and maintain and the effort wasn’t profitable.

At a meeting with analysts in January, Baidu’s chief financial officer, Rong Luo, explained that the kind of “moonshot research” the quantum computer represented was too expensive and didn’t add to the company’s bottom line. Donating it helped cut costs and foster goodwill with the government. It was the kind of pragmatic move that the company had gotten away from in recent years.

Like their global peers, Chinese internet companies enjoyed years of growth and strong cash flows. That allowed them to expand beyond their core operations to invest in new business formats and technologies, such as Alibaba in offline retail and Tencent in fintech. They usually focused on creating scale even if the business itself was initially unprofitable. That goal depressed margins in the sector. But as growth has slowed, Baidu, Alibaba, Tencent, Netease, and other Chinese tech companies have been refocusing their operations on turning a profit.

At Baidu, this included bringing in Luo, who has a PhD in management science from Peking University and had been the CFO at TAL Education Group. During his tenure there, Luo played an important role in TAL’s transformation from operation-driven to data- and technology-driven, and from offline to online.

When Luo arrived at Baidu two years ago, some senior management didn’t recognize the importance of return-focused performance metrics. That has changed. Expenses have been cut. Research and development spending as a percentage of sales, which had been about 20% (and more than its peers), has been reduced. The company was making investments in innovative technologies, such as quantum computing, but there wasn’t likely to be a return on these investments in the foreseeable future. The effectiveness of projects is now measured on the return on investment and efficiency. Thus, the decision to give away the computer.

Now the company is focused on the parts of its business that are or can be profitable: its core ad business, cloud computing, and AI. In the highly competitive business of search, Baidu released an AI-based advertising platform called QingGe. The new platform essentially examines a users’ interactions with a chatbot and generates a new ad specifically tailored to them. The goal is to create a better advertising platform, with the goal of improving the click-through rate by 10x. Hitting that goal would add several billion in incremental RMB in 2024, the company said.

That’s one example of how management is employing AI, a crucial test. In 2017, Baidu was the leader among its peers in AI. It had Andrew Ng, the former head of Google Brain, as its chief scientist and signaled its intentions with its slogan “All in AI.” But Ng left the company in 2017.

Despite his departure, Baidu was the first among Chinese companies to launch a chatbot in 2019, shortly after OpenAI released ChatGPT. That chatbot, called Ernie, is not as advanced as ChatGPT 4.0 but is more advanced than its Chinese peers and many Chinese companies are using it to train their own more specialized models. In December, Baidu reported Ernie had more than 100 million users.

Even with a technological lead over its peers, Baidu hasn’t yet shown it can make AI a profit center the way some US-based companies have. It is testing monetization models, such as a subscription service, and fusing it into its search business, but neither investors nor Baidu itself can be certain how the AI efforts will affect the company’s profits (the company does not provide revenue projections for its AI business). What investors do know is that the company’s criteria for the success of that effort and everything else it does will be measured on the bottom line.

One might expect that a decrease in the number of babies would be a major problem for a company like Unicharm, which dominates the Japanese market for disposable diapers. But by taking advantage of other demographic trends that have opened new markets for the company, Unicharm has seen its overall business grow, even as its core market shrinks.

Unicharm was founded in 1961 as an insulation and building materials supplier and later began manufacturing feminine sanitary pads. In the following decades, Unicharm relied on its expertise in processing non-woven fabrics and absorbent materials made from petroleum-based polymers to expand into other markets beyond feminine care, especially diapers.

Unicharm grew to dominate the market for baby diapers in Japan, with an estimated 41% retail market share in 2023. But the product segment has become a challenging, low-margin business, partly because consumers have not been swayed to pay a premium for baby diapers with special features, and the decreasing number of babies being born has meant that Unicharm is fighting for its portion of a shrinking pie. Meanwhile in neighboring China, once a key market for the company’s baby diapers, stiff competition from local manufacturers has driven down retail prices.

While Unicharm says it will continue to make baby diapers, because of the positive and happy associations consumers attach to baby products, the company has increasingly focused on market segments that offer higher margins (if not the same positive feelings) such as adult incontinence products.

Japan now has the highest proportion of elderly citizens of any country in the world. About 29% of Japanese are over age 65, while more than 10% are 80 or older. As the population has gotten older, Unicharm’s adult products have become a larger part of its business. Adult diapers constitute about 33% of the company’s net sales in Japan today, up from an estimated 25% in 2018. Overall, the company derived one-third of its sales and 40% of its profits from Japan last year.

Adult incontinence products are a high-margin business because consumers are willing to pay a premium for products with special features, such as improved fit to prevent leakage. Furthermore, the company’s marketing campaigns have been successful at creating demand for its light and mini-incontinence pads by associating them with an active lifestyle.

The sort of quality companies that we look to invest in have, among other characteristics, strong management teams who can respond to changing market and industry dynamics. Unicharm’s ability to leverage its core competency in absorbent materials in different markets is an example of how managing change can help a business thrive over time. And Unicharm might even find a way to help address Japan’s aging demographics—it’s recently introduced a new feminine hygiene product that monitors ovulation to help women plan their pregnancies.

It’s an expansive deal, beginning in January 2025 and running for ten years. Netflix will air Raw in the US and internationally, and will have the rights to all WWE shows outside the US. It also may be a harbinger of what’s to come in the world of streaming services and sports. Over the next several years, the contracts for broadcast rights to the NBA, NFL, NHL, and MLB in the US will come up for renewal, as well as the domestic and US rights to top soccer leagues such as the Premier League (UK), La Liga (Spain), Seria A (Italy) and Bundesliga (Germany). The streaming services will be serious contenders in the bidding for all of them.

Netflix has previously declined to jump into sports. “We’re not anti-sports, we’re pro-profits,” co-CEO Ted Sarandos said last year. Making a major foray into sports, even if it’s a scripted one, may be a signal that Netflix is seeing—or at least planning for—a ceiling in terms of how many new subscribers it can attract without sports.

The biggest problem with sports programming is that while it does the things a broadcaster wants it to—steadily bring in dedicated viewers—over time the rights can become addictive for companies. If a sport increases in popularity, that improves profitability and hopefully leads to good returns on the investment for the rights holder. However, those attractive returns also lead to a higher renewal contract, which resets profitability at a lower level even when competitive dynamics are benign.

The problem for Netflix is that the sports-rights competition is not benign. Netflix will be competing with Paramount, Peacock, and Disney, as well as big-tech companies that have completely different strategic goals. For tech companies like Amazon and Apple, for example, “success” in terms of sports programming is more about e-commerce or hardware sales than subscriber profitability.

Netflix has spent roughly US$17 billion a year on content since 2021, a level it plans to maintain for the foreseeable future. The company said that the WWE deal would not add to its content budget, meaning it expects to spend less elsewhere. On one level, then, this is not an expensive bet. WWE already has a fanatical global audience and NBC is paying nearly US$300 million annually just for the US rights. Maybe US$500 million a year for global rights is a bargain.

Given Netflix doesn’t have any legacy networks to protect like some of its competitors do, it sidesteps a lot of the messy problems in the industry. But Netflix has its own set of pressures with which to contend. In 2023, Netflix’s subscriber base rose to 260 million, which was a record. Its growth rate, however, is slowing. In the past two years, the subscriber base is up 17%. In the two years prior to that, it rose 33%. Netflix used to be valued and treated like a tech stock. These days it is valued and treated more like a media company.

But one Japanese company in the Information Technology (IT) sector has taken an unusual approach to reinvigorating the labor market. SHIFT, which provides software-testing services, is poaching and re-training workers from other industries, turning them into IT pros. As Prime Minister Fumio Kishida advocates for the reskilling of workers and for companies to increase wages, SHIFT’s system is an example of how companies could help put a dent in Japan’s entrenched model of lifetime employment, helping the country dig out of a decades-long deflationary environment.

Among its labor-market challenges, Japan faces a major shortage of IT engineers. Of the 75 million people in the country’s working population, only about one million are IT professionals. That’s a lower proportion than in other developed countries such as the US and Germany. But SHIFT estimates that 10% of Japan’s non-IT workers aspire to join the space.

Software testing is also one of the most labor-intensive processes in software development, as it entails the time-consuming work of manually looking for bugs within code and making sure an application works as intended. For firms that struggle to find experienced programmers to begin with, having those expensive programmers do testing doesn’t make sense. Some of SHIFT’s biggest customers are consulting firms that would rather have their own high-paid employees focus on their main business of consulting while outsourcing something like software testing to save money. For example, the average salary at Nomura Research Institute (NRI), an IT-consulting firm, is double the average at SHIFT:

Perhaps most intriguingly, NVIDIA is a leading company in the biggest tech-driven trend to come along in years, artificial intelligence. It makes the chips that power AI-based computer systems, and it sells the software and services companies need to operate their own AI programs. And NVIDIA was a big company before AI took off.

As if to emphasize the point, this week the company posted third-quarter revenue of US$18.1 billion—tripling from a year ago and setting a company record. Earnings were also a record at US$10 billion.

NVIDIA meets all our core criteria for a quality growth company: It has a sustainable business and a competitive advantage, solid management, and financial strength. Over the last six years, its revenue has grown from nearly US$7 billion in fiscal 2017 to just under US$27 billion in fiscal 2022.

But as NVIDIA reaches dizzying valuations—it currently trades at 118 times earnings—we can’t help thinking of another good technology company whose stock once commanded nose-bleed premiums: Cisco Systems.

Cisco makes the networking equipment that enables much of the internet. As the World Wide Web was taking off in the mid-1990s, Cisco Chief Executive John Chambers was telling people a new era of computing was coming, and the company’s hardware played a big part in making that new era real. Between 1995 and 2000, its revenue surged 850%, from about US$2 billion to US$19 billion.

Cisco stock did even better, growing an incredible 3,800%, from US$2 in January 1995 to US$79 in March 2000. The title of its 1999 annual report exhorted investors to “Capture the Momentum.” Many took that advice. In March 2000, at the height of the dot-com bubble, Cisco became the most valuable company in the world, with a market capitalization of more than US$500 billion.

Then the dot-com bubble burst. Cisco shares fell 88%, dropping from US$79 to a low of US$9.50 two years later. That drop wasn’t the result of any large change in the company’s performance. Revenue was nearly US$19 billion in fiscal 2000, US$22 billion in 2001, and about US$19 billion in 2002. But the premium that investors were willing to pay for the company disappeared as the hype around the web faded.

Over the next two decades, Cisco kept growing. Its sales in 2022 were nearly US$52 billion, more than double the US$19 billion it hit in 2000. Yet its stock has never traded as high as it did on March 1, 2000. Today it’s at roughly US$53. On a total return basis, it took 20 years for Cisco stock to recover from the dot-com crash.

So, are there lessons we can learn from Cisco as we look at NVIDIA? Just as Chambers argued 25 years ago that a new era of computing was at hand, Huang has been talking up AI. Just this month he said “In the last 40 years, nothing has been this big. It’s bigger than PC, it’s bigger than mobile, and it’s gonna be bigger than the internet, by far.” The enthusiasm for AI’s future has led to very high valuation multiples for NVIDIA, although not as high as Cisco reached in 2000.

The possibilities are mind-blowing. The DRC, with a population of 112 million and situated almost precisely in the middle of the continent, is the second-largest country in Africa by landmass and the 11th largest in the world. The Congo River, which winds through the country and is the second largest in the world by discharge volume, has the potential to produce 100 gigawatts of clean electricity, of which less than 3% is currently being harnessed.

With the river and its tributaries coursing through the countryside, the DRC has an estimated 80 million hectares of arable land, enough to produce food for two billion people. The DRC also has abundant mineral resources. A recent UN study estimated its untapped mineral reserves were worth US$24 trillion. The DRC holds 46% of the world’s cobalt reserves, 23% of the world’s lithium reserves, and 4% of the world’s copper reserves. These are all commodities essential in the effort to enable the global transition to green energy.

Property Deleveraging

Disney and Charter clashed over a new cable agreement. Charter complained about the high fees it paid for ESPN, FX, and other Disney channels while Disney was funneling premium content to its own Disney+ streaming service. Eventually, Disney’s channels disappeared from Charter’s Spectrum TV service, and Spectrum stood firm: it was prepared to give up Disney’s networks, seemingly forever.

It didn’t come to that, though, and the terms of the agreement don’t sound monumental: Disney+ will now be an option for Charter subscribers for an additional fee. Disney gets new eyeballs, but hands over a chunk of its fees to Charter, which had faced a seemingly inevitable revenue decline from its cable business due to the rise of streaming. The agreement offers a reprieve from that.

However, the terms of the resolution show that Disney as a supplier had far less bargaining power with its customer than it had assumed. It was forced to make material concessions that will affect its transition to a full-streaming model. Make no mistake, Disney backed down, and that loss is going to hurt its bottom line in the long run. Moreover, if the world’s largest entertainment company couldn’t use its muscle to get what it wants, then all the other networks are going to face the same dilemma when their carriage agreements come up for renewal. Disney’s capitulation could signal a major shift in the entertainment industry.

For decades, “content is king” was the mantra of the media industry. While cable companies were the primary distributor of content made by studios, the ultimate pricing power was always in the hands of the studios, especially studios like Disney that owned must-see kids, sports, and movie content. A cable provider could not attract subscribers without these must-have channels. As a result, even though there would be heated negotiations over fees, ultimately cable providers would yield to the studios’ demands.

The studios’ leverage has been weakened by the very thing they are focused on: streaming. Streaming subscribers often cherry pick services, signing up for a short time if there’s a must-see program and then canceling. For Disney and the others, there is a fundamental mismatch between costs and revenue duration. Streaming platforms need a constant flow of new programming to limit subscriber churn, but that pushes operating costs up into the billions. Not all of them can afford it.

It’s possible Charter perceived this, or just felt like it had nothing to lose. It was willing to take a loss of subscribers upfront by terminating its Disney arrangement rather than bleed subscribers over a number of years if it didn’t challenge Disney, given either course would likely lead to the same terminal point, just in different time frames. Charter’s hard stand showed the flaw in the Disney plan.

The content king may not be dead but it definitely seems like he holds less power.

Ozempic being used as a substitute for a different medical treatment goes beyond Novo Nordisk selling the same drug under different names. Ozempic could also serve as a replacement for people contemplating bariatric surgery, since it stands to reason that anybody considering surgery would likely be willing to try a less-invasive treatment. Recently, Intuitive Surgical noted a slowdown in the growth of bariatric surgical procedures because of the increasing use of drugs such as Ozempic. “Some customers have indicated that they are seeing increased patient interest in weight-loss drugs,” Intuitive chief financial officer Jamie Samath said in July.

Given that obesity is the most important reversible factor contributing to sleep apnea, there’s a chance Ozempic and similar drugs could become a treatment for that, too. Eli Lilly, for instance, is running a study of its type 2 diabetes medication Mounjaro, which is in the same drug class as Ozempic/Wegovy, as a possible treatment for sleep apnea. Results are due in March 2024.

That could be a problem for a company like ResMed, which makes sleep-apnea treatments with its CPAP (continuous positive airway pressure) therapy. These are machines that force air through the patient’s nasal passages, enabling better sleep. For patients, the issue may go beyond just which treatment is more effective.

Cost will be a big factor. Medicare and private insurers cover Ozempic for its commercially approved use, diabetes, but not for weight loss or other off-label uses. That could be important because the cost of Ozempic can be considerable (without insurance, it can cost about $1,000 a month). For sleep apnea patients, ResMed products may end up delivering the same or better results at a much lower price.

ResMed CEO Michael Ferrell in fact seems to welcome the new attention, since sleep apnea tends to be an underdiagnosed condition. Ozempic and similar drugs could bring potential patients “into the funnel,” he said on a conference call, while issues like cost and side effects will limit any hit to sales caused by the drugs. “That’s good for us, too,” he said.

The big-picture view suggests that Meta is building a hub for user-generated content (UGC) discovery and distribution that aims to go head-to-head with the likes of YouTube and TikTok. In the world of unlimited content, the power shifts from creators to distributors and curators. In this scenario, the ultimate constraint on demand for content is the totality of leisure man-hours; Meta’s goal is to optimize how to fill those hours with the most compelling content.

Generative AI could also help transform Meta’s main money-maker, its advertising business. The digital-ad market comprises roughly 70% of the overall advertising market, and is dominated by two firms, Meta and Alphabet. Finding a way to expand its market share has been a stumbling block for Meta. However, with these new AI tools, Meta may have found a way around that constraint. It may not be able to take much more share of the advertising market, but it could start taking share of the advertising-creation market. On the call, Zuckerberg specifically mentioned the strides made by its Advantage+ generative AI tools that allows advertisers to supply just an image, from which the AI will then develop an entire ad campaign. For small companies that can’t afford to hire an advertising agency, this could be a powerful alternative and a lucrative market for Meta. Its Advantage+ ad program has the potential to take share from creatives and even advertising agencies by customizing, altering, or outright creating content for advertising, rather than just targeting or measuring it.

ChatGPT, the chatbot that helped spark the market’s AI enthusiasm, is an important innovation because it can digest large amounts of text (hence the term large language model), communicate in natural (human) language, and generate sophisticated responses. Based on the Transformer architecture, a technique first introduced by Google in 2017, ChatGPT demonstrates the advances that have been made in AI that open the door to a wider set of business uses. But while natural language models recently produced epiphanies among lay chief executives and investors regarding AI, some tech companies were already investing in such capabilities and are being rewarded for that foresight. NVIDIA has been the biggest beneficiary this year in terms of its stock run and projected revenue gains; however, our other holdings, such as Adobe, Microsoft, Salesforce, ServiceNow, Synopsys, and TSMC, also appear among the possible beneficiaries. More companies—including, perhaps, some not yet in existence—will certainly join the ranks over time.

While it is still early, it’s evident that these companies see generative AI as transformative to their businesses and something upon which they can build new revenue models. Additionally, they are turning to AI to boost internal productivity, enhance existing customer offerings, and improve the quality and efficiency of customer interactions.

Most notably, Microsoft was able to gain an immediate leadership position in generative AI by making a US$10 billion investment in OpenAI, the company behind ChatGPT, earlier this year. Microsoft’s Bing search engine has since introduced ChatGPT into its web index data—a collection so large that it is rivaled by the dataset of only one other business in the world, Alphabet’s Google. Data are the feedstock of AI models, and an AI-enhanced search engine trained on so much data may attract more users to Bing, allowing Microsoft to sell more ads on the service. Microsoft is also adding generative AI to other products, including the Azure cloud service, enabling business customers who use Azure to easily integrate OpenAI models to glean more insights from their data and automate functions such as certain IT tasks. These added capabilities should motivate more businesses to migrate their data to the cloud and make Azure more competitive with Amazon.com’s AWS and Google Cloud.

The beneficiaries of demand for generative AI aren’t limited to traditional IT-sector companies. Data centers used to train AI models require up to ten times more power than typical data centers, thus requiring more-powerful equipment and backup power. Given the amount of heat they generate, new liquid-cooling solutions will be needed as well. This creates an opportunity for Schneider Electric, which has been developing innovative data-center equipment solutions for many years.

In the meantime, NVIDIA has emerged as the unrivaled global leader in providing the technologies at the center of the AI arms race. Due to an explosion of demand related to generative AI and LLMs from across its customer base, NVIDIA projects that data-center revenue for its fiscal second quarter ending in July will surge to US$11 billion. Not only is that more than double last quarter’s total, but the forecast also shattered the average analyst estimate that called for about US$7 billion.

Our investments in NVIDIA and Schneider are reflective of how we are thinking through the many unknowns and approaching portfolio structure in this environment. Through our fundamental framework, we can appreciate the broad excitement for AI, but we also remain conscious of valuations and thoughtful about diversification, recognizing that it’s unlikely anyone can predict today the biggest long-term winners.

In the first three months of 2023, P&G raised prices a whopping 10% year over year, following a series of large price hikes implemented throughout 2022:

By “not doing macro,” he means that we try not to allow our judgments about macroeconomic variables—GDP growth, inflation, and real interest rates—or geopolitical events to dictate our views on individual companies. By “macro does us,” he means that when the market’s risk tolerance and underlying assumptions change because of unexpected shifts in the macroeconomic environment, the consequential price movements can dominate a portfolio’s periodic absolute and relative returns. Although the injury may be only temporary, it is hard to avoid getting swept up in the general fervor. That’s a problem if it leads to reflexive and hasty reactions. It is precisely to avoid getting whipsawed in this way that we devote much of our efforts to restraining our inherent behavioral biases. But even with the sturdiest of behavioral guardrails designed to curb our responsiveness, the sudden jump in portfolio volatility and tracking error feels no less jarring.

Our investment approach centers on analysis of the prospects for specific companies and the industries in which they operate. As a result, the portfolios we construct are a mosaic of company-centric views, with the final picture coming into focus only after all of the pieces are assembled. Sometimes our bottom-up investment process leads us to sidestep systemic issues. In the years before the global financial crisis, for instance, we became disenchanted with the traditional banking industry. We didn’t like how the increased price transparency that came with the migration of services online diminished banks’ bargaining power over their borrowers and depositors, or how rising levels of consumer debt portended that growth could be weaker, and rivalry and risk-taking fiercer. That was enough to lead us largely to steer clear of banking stocks. Although in hindsight our portfolio positioning appeared to anticipate the subsequent dislocations, in fact we had no overarching view on systemic financial stability.

There is no question it would be nice to have clear foresight on GDP, inflation, and real rates. Like it or not, economic growth is the lifeblood of industrial economies, and, despite its ever-shifting relationship to equity returns, is closely associated with aggregate earnings. Similarly, inflation and real rates are both barometers and agents of economic transformation that always could and frequently do alter the path of economic growth. And there is strong reason to believe that macro-level dislocations are likely to be an order of magnitude greater than the mispricings that occur at the security level. Given the periodic importance of such dislocations, this raises the question: Why don’t we attempt to shape our portfolios more explicitly by directly forecasting economic variables or geopolitical events? The question is particularly vexing given the current importance of the inflation outlook for equities.

Tetlock’s conclusion was that expert predictions about geopolitical crises were no better than guesses. The only contribution that expertise seemed consistently to confer was a perverse boost in confidence regarding one’s (ineffective) forecasts.

The standard response typically trotted out is that forecasting is exceptionally hard, or as the Danish physicist Niels Bohr is alleged to have quipped, “Prediction is very difficult, especially about the future.” Nowhere is this more true than with geopolitical events, which by all accounts appear to defy anyone’s ability to anticipate them with anything approaching consistency. The political scientist Philip Tetlock tackled this issue head-on in a multidecade study described in his 2015 book, Superforecasting: The Art and Science of Prediction. Tetlock’s conclusion was that expert predictions about geopolitical crises were no better than guesses. What’s more, the only contribution that expertise seemed consistently to confer was a perverse boost in confidence regarding one’s (ineffective) forecasts.

The record for macroeconomic forecasting is not quite as wretched; at least there are frameworks and models on which to hang one’s thinking. But it’s still one of those endeavors where you’re doing very well if you’re right a little more often than you’re wrong. Even so, it is not as though the ground-level forecasting of cashflows, business prospects, and competitive forces is easy. So perhaps the real question is why we consider the latter sensible but the former a fool’s errand, at least for fundamental equity investors such as us.

The answer in large part comes down to the size of the opportunity set, or the number of times you get to apply your investing edge. Even the most skilled forecasters, whatever their forecasting game, have but the tiniest of edges and so the surest way to increase their chances of success is to apply that minute edge as many times as possible. In a global investment universe, there are roughly 8,000 equity securities, each operating in its own industry and geography with their own sets of return drivers, compared with a relative handful of forecastable macroeconomic variables. Given equal forecasting skill, you are going to have a far higher likelihood of some overall success by applying that skill across many securities rather than over a few economic statistics. Even allowing for the fact that not every security’s return is entirely idiosyncratic, there are still far more independent and durable drivers of individual security returns than there are of macroeconomic trends, which may allow you to get the micro right without so much as taking a swing at the macro.

Even if you were one of the few hyper-skilled and hyper-accurate macro forecasters, a portfolio of stocks would be a poor way to capitalize on views about inflation or economic growth. Although there’s a relationship between the macroeconomy and stock returns, that relationship is neither simple nor determinate. In practical terms, stocks are a terribly inefficient way to express a view on macroeconomic variables. Better to bet on currencies, yields curves, and commodity prices directly, all of which are far more closely tethered to the outlook for growth, inflation, and real rates.

It’s not just that there are better, more precise, and more levered ways to express macroeconomic views. It’s also that trying to do so with stocks risks erasing the hard-won company-level insights that are the linchpin of our portfolios.

And it’s not just that there are better, more precise, and more levered ways to express such views. It’s also that trying to do so with stocks risks erasing the hard-won company-level insights that are the linchpin of our portfolios. All the companies in which we invest have track records of successfully generating cash and reinvesting it wisely. In many cases these companies have survived wars, recessions, pandemics, inflation, deflation, and geopolitical shocks. Sacrificing those financially valuable fundamental attributes in a most likely vain attempt to time a particular economic cycle not only presupposes a preternatural ability to tie economic outcomes to individual security returns but also risks the long-term health of the portfolio.

We don’t do macro, so by default we allow macro to do us. There are, though, ways in which we can protect against developments that result in sudden changes in risk aversion. One is to diversify—events that damage the outlook in one industry or part of the world may have no impact, or even a beneficial one, on stock prices elsewhere. That said, diversification cannot work during times of systemic crisis, when correlations between geographies, industries, sectors, and individual securities converge. That’s where our reliance on a company’s strength comes in. Two hallmarks of a company’s quality are the ability of its management to prepare for a wide range of outcomes and whether it has the financial strength to survive the worst possible operating conditions.

Although we can’t estimate the probability of market-moving events, we can think about the magnitude and range of potential outcomes so we may more fully understand our exposures and ensure we are sufficiently diversified to protect against them. For example, before Russia’s invasion of Ukraine, many people thought about a range of outcomes that included war versus no war or disruption to energy supplies. But, given prior Western responses, few considered the potential for sanctions that would freeze all Russian assets and render them worthless, at least for the time being. Now, as we think about the financial market implications if China were to invade Taiwan, we must consider the possibility that Chinese assets could be similarly impaired.

So, what do we do about it? We certainly aren’t going to try to parse Chinese troop movements or overturn our investment theses on the dozens of companies, not only in China but also throughout the global supply chain, that could be impacted by what at this point must still be considered a very low-probability event. On the other hand, thinking long and hard about the potential risks to supply lines, revenues, or the corporate structures of portfolio companies and what further levels of diversification might be in order is very much in our wheelhouse.

As policymakers picked up where they had left off, they found themselves facing stakes heightened by the pandemic: stagnating incomes, weak consumer confidence, and a growing demographic crisis as birthrates continue to decline. These challenges may have accentuated their top priorities, ones that have been repeatedly highlighted in official policy statements over the last few years: innovation, rule of law, culture, the environment, and social harmony.

The fact is that ever since Deng Xiaoping initiated the initial series of capitalist overhauls in the 1980s, China has undergone multiple periods of reform. These changes cut a wide swath across economic activity and drastically curtailed certain targeted sectors. They were painful in their time, creating mass unemployment and fueling social discontent. Ultimately, they laid the groundwork and helped sustain several decades of nearly uninterrupted growth.

Previous reforms were far less visible to foreign observers because they barely touched the companies widely held by global investors at the time. For example, the coordinated supply-side reforms of 2015, undertaken in part to reduce chronic pollution, shuttered roughly one-fifth of China’s steel capacity (equivalent to Japan’s entire steel output) in under two years. Air quality improved dramatically, while bankruptcies almost tripled as many marginal producers were killed off. But not many foreign investors owned marginal steel producers, preferring to own faster growing companies such as Alibaba and New Oriental. Likewise, the anti-corruption campaigns that began in 2013 may have ushered in a more sustainable business environment, but they were terrible for liquor makers, whose products had become popular high-priced gifts to lubricate business deals and lobbying efforts. Kweichow Moutai, producer of its fiery namesake liquor, saw its sales growth plummet in 2014 and 2015, but the company was not nearly as widely owned externally as Tencent is today.

Much of the focus of late has been on one policy priority: common prosperity. Redolent of China’s collectivist past (the term was first used by Mao in 1950), the phrase frightens some foreign investors who are unsure which companies’ prosperity will be sacrificed at the altar of the commons. Yet policymakers have been clear: their focus is on growing middle-class disposable income, not “robbing the rich to help the poor,” according to Han Wenxiu, executive deputy director of the General Office of the Central Financial and Economic Affairs Commission. This overt aversion to a European-style welfare model may seem contradictory for a party that still pays lip service to its Marxist roots. But the reality is that China systematically underinvested in education, health care, and other social spending—especially in rural areas—as it sought to catch up economically with more developed economies. Until now, policymakers have done little in the way of redistribution; indirect taxes, which generally serve to widen income inequality, still represent two-thirds of fiscal revenue. With China coming into its own, we should expect its practices to converge with those in more advanced economies, including some form of income and wealth redistribution.

To my mind, these regulations are reminiscent of the US Progressive Era of the late 19th and early 20th centuries, epitomized by Theodore Roosevelt’s Square Deal.

In practice, the government’s targets for common prosperity—judging from recent policies and the detailed roadmap for its first pilot program in Zhejiang, the richest province in China and home to Alibaba—are education, health care, and housing. In these pivotal areas, structural impediments have exacerbated inequalities over time, producing a set of challenges that would be very familiar, for example, to residents of California. One of the more draconian national policy shifts, which recently consigned much of the private after-school tutoring business to the non-profit sector, does not go as far as South Korea’s complete ban of private tutoring in the 1980s.¹ In each country, the reforms were designed to ease the burden on parents who spend up to thousands of dollars each month coaching their children on how to pass exams. (To put this cost in perspective, the Chinese city with the highest average annual per capita disposable income in 2020 was Shanghai at $11,000.) Likewise, China’s recent online regulations covering antitrust, data security, and the safety of minors are similar to the concerns of consumer advocates everywhere.

To my mind, these regulations are reminiscent of the US Progressive Era of the late 19th and early 20th centuries, epitomized by Theodore Roosevelt’s Square Deal. It was not an easy time to invest and was marked by muscular antitrust interventions, the inception of a progressive income tax, and the appearance of the first federal consumer and environmental protections. Certain industries faced a permanently higher level of regulation with which they had been unfamiliar. But many companies thrived, and the reforms arguably laid the foundation for a century of growth that shaped the American economy into the largest in the world today, home to the largest number of globally competitive companies.

Structural changes of this magnitude will inevitably shake up competitive forces, buffeting the outlook for growth and strength of free cash flow generation for many businesses—but not all of them in negative ways. If China’s reforms succeed in improving middle-class disposable income while opening more opportunities for more people and still ensuring that the country remains a meritocracy, the government will have set the stage for more sustainable end demand for many industries. It’s a tall order, but one notable advantage enjoyed by Chinese policymakers today is the benefit of a century of hindsight observing which policies worked—and which did not—in the countries that have tried them.

The fact that filtering information is the long-term investor’s task, of course, doesn’t prevent most from feverishly pursuing every bit of information they can about the companies they follow. Egged on by the 24/7 news cycle, internet chat rooms, and by data providers to know their companies in as much depth as possible, investors often seek to become experts in every aspect of each company’s business. It’s dubious whether this helps them make better estimates of what a company’s shares are worth. Instead, the evidence suggests that, beyond a certain amount, each extra piece of information has zero marginal impact on forecast accuracy. The impact remains positive, however, on the investor’s confidence in their forecast.

The evidence suggests that, beyond a certain amount, each extra piece of information has zero marginal impact on forecast accuracy. The impact remains positive, however, on the investor’s confidence in their forecast.

In one unpublished study, described in Winning Decisions by J. Edward Russo and Paul J. H. Schoemaker, people who handicap horse races were asked to predict, based on increasing amounts of information, which horse would win a race. They found that, beyond a very limited amount of data, forecast accuracy did not improve with more data, but the handicappers’ confidence in their forecasts continued to grow with each extra bit of information.

Knowing more makes you feel better, in other words, but doesn’t help your decision-making. In investing, feelings and actual results arguably are even more disconnected. After all, feeling good about a decision is rarely a help in successful investing—to have a good outcome, an investment view must be different from that of most other investors, and standing apart from the herd almost always leads to discomfort.