Finding Quality Growth in Emerging Markets with Ian Smith

Episode Summary

Executive Summary: The discussion frames emerging markets as a highly differentiated asset class with renewed opportunity, driven by AI capex, a weaker dollar, and long-term structural underrepresentation in global indices. Ian argues EM is best approached through bottom-up selection, focusing on leaders or future leaders with durable moats, improving capital allocation, and attractive valuation gaps, while remaining mindful of country-specific risks like energy imports, governance, and cyclicality.

Main Topics: EM’s renewed opportunity and structural underrepresentation (Priority: 5/5): Ian argues EM equities have recovered after a long post-2010 slump, but remain historically cheap and underrepresented in global benchmarks, suggesting long-term opportunity rather than just a short cyclical bounce. AI capex as the dominant market driver (Priority: 5/5): He says AI spending is shaping EM performance, especially through the semiconductor, foundry, memory, power equipment, and data-center supply chains, with Korea, Taiwan, and China most exposed. Dollar weakness and macro conditions (Priority: 4/5): A benign or weaker U.S. dollar supports EM by easing debt burdens, allowing looser fiscal/monetary policy, and improving the environment for domestic-demand growth and commodities. Country-by-country EM differentiation (Priority: 5/5): Ian stresses EM is not monolithic: Korea/Taiwan benefit from tech and AI, India offers long-term growth but faces oil and valuation pressure, and China combines manufacturing strength with government and competition risks. Quality vs growth dispersion in EM (Priority: 5/5): He explains that quality and growth have decoupled recently, with cyclical and physical-world companies outperforming, creating major dispersion that active managers can exploit. Investment process: leaders, trajectory, and underappreciation (Priority: 4/5): William Blair’s framework emphasizes identifying leaders or future leaders, assessing trajectory and durable quality, and valuing stocks using IRRs and forward-looking underappreciation rather than simple multiples. AI adoption inside the investment process (Priority: 3/5): Ian says AI is already improving research, modeling, coding, and information gathering at the firm, though token usage will likely become more optimized and disciplined over time.

Key Arguments: EM has been weak since 2010, so recent outperformance is partly a mean reversion from a long period of underperformance. Despite the rally, EM remains cheap versus MSCI World and still materially underrepresented in global equity indices. The AI capex cycle is now one of the most important determinants of EM index performance because tech is over 40% of the index. Korea and Taiwan are the clearest AI beneficiaries due to their semiconductor and related supply-chain dominance. A weaker dollar is constructive for EM because it reduces pressure on dollar debt, supports looser policy, and favors domestic growth. EM’s country risks vary widely; energy import dependence and fiscal fragility matter more than broad EM labels. The current environment has produced unusually high dispersion, which is favorable for active managers with strong stock-picking skill. Quality and growth usually align over time, but recent years have seen a split because cyclical/physical-world businesses have benefited from underinvestment and AI demand. Investors should avoid stale notions of quality and instead reassess sustainability using both qualitative and quantitative indicators. China offers world-class manufacturing and globally competitive firms, but investment must account for competition, regulation, and alignment with government policy. India has strong long-term fundamentals but remains expensive and cyclically pressured by oil and limited AI exposure. Using IRRs and forward-looking valuation can reveal value in markets that look expensive on headline P/E ratios. AI is useful in the investment process, but firms will likely shift from broad experimentation to more centralized and efficient usage. Good investing ultimately remains simple: find good companies and buy them at good prices.

Data Points: EM index share of global population: 60% - Used to illustrate how large EM is economically and demographically relative to its index weight. EM share of global GDP: 40% - Cited alongside population to show EM’s importance in the real economy. EM share of MSCI All-Country World Index: 11% - Highlights structural underrepresentation of EM in global equity benchmarks. Technology weight in EM index: over 40% - Used to explain why AI capex is such a powerful driver of EM index returns. Performance since the eve of the war: EM index has performed in line with the S&P 500 at about 11% - Ian notes EM initially lagged but later recovered to match U.S. performance during the period discussed. EM vs World Index since war: EM has outperformed the World Index - Shows the relative resilience of EM outside the U.S. during the conflict period. Relative performance dispersion in EM: 12-month trailing dispersion is as high as ever, equal to post-GFC levels and above COVID levels - Supports the claim that stock-picking opportunities are unusually strong. Hyperscaler/related spend: about $750 billion this year - Combined spending estimate for the big three hyperscalers plus Meta, Oracle, and CoreWeave. Hyperscaler spend growth: 80% this year and 80% last year - Shows how rapidly AI-related capex has scaled. Hyperscaler spend projection: $8 trillion by 2030 - Projected roll-forward from current spend rates, used to argue the current pace is unsustainable. EM growth vs quality divergence: 20% relative performance gap in 2025 - Cited as an example of the recent split between growth and quality sub-indices. Growth/quality co-movement: 7 out of 8 years from 2013 to 2020 - Illustrates how closely growth and quality had historically moved together before the recent divergence. India underperformance vs EM: almost 90% since September 2024 - Explains why India’s relative valuation has moderated after a sharp selloff. Typical investment hurdle: 10% dollarized IRR - Ian says this is a common target threshold in their valuation framework. Opportunity set size: about 250 companies - Approximate number of companies in their quality-focused EM opportunity set. MSCI EM launch year: 1987 - Used when discussing long-term dollar and performance cycles in EM.

Pivotal Quotes: "Technology is over 40% of the index." — Ian: Explaining why AI capex is a major determinant of EM index performance. "The outlook for EM equities ... is going to be heavily influenced by the way that this AI capex cycle plays out." — Ian: Core thesis linking EM returns to AI infrastructure spending. "Good companies and buying them at good prices." — Ian: Closing lesson for retail investors and a concise summary of his investment philosophy.

Implications: For investors, EM is less a broad macro trade than a stock-picking opportunity shaped by AI, currency, and country-specific fundamentals. Active managers who can separate durable winners from cyclical beneficiaries may find significant upside, while passive exposure may miss the real dispersion.