The annual Morningstar Investment Conference occurred June 17 to June 18 at Navy Pier in Chicago. It was my sixth time at the conference, which I first attended in 2014 at the city’s McCormick Place convention center. Although the venue has changed in recent years, I’ve found that the event’s insights remain as relevant as ever.
More than 2,000 people attended the 2026 conference, according to Stephanie Lerdall, Head of Global Communications for Morningstar. Its three overarching themes were how financial advisors and their firms can address AI, private markets and rising complexity.
Among the approximately two dozen companies participating in the event’s sprawling exhibitor hall were Blackstone, Invesco, Nuveen, PIMCO, Principal Asset Management, Harris | Oakmark, Loomis Sayles, Nitrogen and Wealth.com.
An array of sessions featured panelists from multiple firms. Those topics on the agenda included dividend-focused strategies; credit markets; emerging market bonds; passive versus active high-yield ETFs; values-based and faith-based investing; retail investor access to private markets; liquid alternatives; shocks to retirement plans; global equity portfolio management; small-cap and mid-cap stocks; as well as diversifying across gold, commodities and crypto.
However, my two favorite sessions were the ones that I consider the most useful to forward-thinkers in the wealth management industry.
“Geopolitics & Portfolios: What Really Matters for Investors,” featured Walter Russell Mead, a Wall Street Journal columnist, and Daniel Needham, President of Morningstar Wealth. “Moats and Monetization in AI” featured Morningstar experts Eric Compton, Director of Equity Research, Technology, and Malik Ahmed Khan, Senior Equity Analyst.
Geopolitics & Portfolios
Mead’s keynote described four schools of thought on U.S. foreign policy throughout American history, and how they relate to current events affecting asset classes.
The Hamiltonian school, inspired by Alexander Hamilton, supports a strong central bank and believes countries can cooperate for mutual interest. The Wilsonian school, inspired by Woodrow Wilson, holds that America stands for promoting democracy abroad alongside international law instead of kings and despots. The Jeffersonian school, inspired by Thomas Jefferson, wants small government so Americans can live in liberty, and seeks to minimize U.S. global engagement. And the Jacksonian school, inspired by Andrew Jackson, embodies a national populism folk identity of the American people that is suspicious of big government.
According to Mead, who is also a fellow in strategy and statesmanship at Hudson Institute, President Donald Trump’s backers are mostly Jacksonian and Jeffersonian, with some Hamiltonians coming from the technology sector.
Regarding this year’s Iran War, Mead argued that daily headlines have been driving portfolio decisions for many investors. With that in mind, he noted, during war, the side that’s winning tends to up the ante for peace stipulations while the side that’s losing is quicker to compromise. He also labeled Iran, Russia, China and North Korea as a bloc seeking to act against the U.S-led global order.
Mead sees Ukraine as a battle-hardened and battle-tested nation that knows how to innovate in combat, led by an extraordinary president. Yet the war could still go either way, because it’s a war of attrition and both Ukraine and Russia have significant economic problems. The outcome is complicated by China supporting Russia in ways the West has not been supporting Ukraine. Even so, Russian President Vladimir Putin’s own grip on power could weaken as Russia continues to lose influence over neighboring countries and China gains influence in Central Asia.
In both Iran and Ukraine, drone warfare is changing how militaries operate in ways that make it difficult to predict how combat will occur in the near future, Mead said.
Mead doubts President Xi’s warnings of a Thucydides Trap for the U.S. are legitimate.
He does not view China as on an inevitable path to global domination. To be sure, it is the world’s second largest economy and biggest manufacturer, which brings diplomatic weight with other countries. In that sense China is here to stay as a major player on the world stage. But Mead doubts President Xi’s warnings of a Thucydides Trap for the U.S. are legitimate, nor does he see the U.S. as a declining power.
Even China maintaining its status as the hegemon of Asia would require it to stay farther and farther ahead of India, Japan and Australia combined — a goal that is becoming a struggle now that those nations have acknowledged the risk of allowing China to keep that status. (India’s economic growth rate is currently faster than China’s.)
As a result, Mead foresees, the U.S. may not need to exert excessive effort to counter China long-term.
Moats & Monetization In AI
Khan and Compton emphasized that when it comes to the AI sector, the questions of value creation and value capture are key to assessing the prospects for upstart companies such as Anthropic through Claude / Mythos / Fable and OpenAI through ChatGPT, as well as established businesses that have evolved into the space, such as Alphabet through Gemini.
Khan and Compton asked whether AI companies are building durable businesses or whether their tech will become commoditized, then they argued that the major AI companies are building durable excess advantages which justify their lofty valuations.
Their thesis is that as inference scales up exponentially, revenues and margins will inflect upwards at strong rates. Inference is “running the factory”: what it takes for a user query to result in the large language model (LLM) executing to deliver a response. Whereas inference has low marginal costs, “building the factory” by training the LLM beforehand has high upfront costs. Those high upfront costs scale up linearly, at much weaker rates than inference, according to Khan and Compton.
Recognizing the counter argument, they also summarized the bear case. Namely that converging AI capabilities along with declining prices and low switching costs for users will lead to commodification of the technology, while low public opinion about AI hinders data center buildout and thus the scale needed for high ROI.
The presenters offered two main reasons why they doubt that rationale: network effects and cost advantage.
AI companies are gaining enormous amounts of users and revenues at an extremely fast pace, while also increasing their ratios of revenue per employee compared with established non-AI tech companies, thus allowing them to build ever better tools. In addition, they can capture compute volume discounts by securing bulk gigawatt-scale capacity agreements. The marginal returns are exceptionally high on very talented employees who can solve critical problems. Furthermore, major AI companies are investing in custom silicon that enables them to bypass third-party hardware.
It’s also worth noting that Anthropic, OpenAI and Alphabet have different business mixes, meaning their split of consumer and enterprise revenue streams, and also have different distribution platforms, meaning direct and third-party channels.
Ultimately, Khan and Compton insist, the odds of many additional rivals chipping away at the AI pie seem low. This makes the current industry leaders appealing potential investment opportunities.
Chris Latham is a former Managing Editor at Wealth Solutions Report.
