At first glance, global trade disputes and artificial intelligence (AI) might seem worlds apart. For Sowell’s Chief Economist, Dr. Jason Hsu, both are part of a larger story: the search for greater productivity and thus corporate profits through cheaper labor — whether across borders (historically to Asia, the world’s factory) or (increasingly now) through intelligent machines. In this week’s edition of Week Ahead, we share a summary of Jason’s special lecture to the UK CFA Society.
America’s long-running trade deficit with China and, more broadly, with Asia is often cast as a loss. In his talk, Jason provides a reverse perspective: deficits reflect outsourcing, not trade imbalance. US corporations shifted production to low-cost Chinese factories, reducing expenses and boosting their global competitiveness. The result is that innovative American products, like the Apple iPhone, Tesla EVs, and Nikes are so cost effectively produced that US products have come to dominate the world. They are simply manufactured in and shipped from China. Far from draining US wealth, this outsourcing coincided with a tenfold increase in aggregate national wealth over the past three decades, from $17T to $170T. The 30-year raging equity bull market is indicative of the resulting US prosperity.
Still, prosperity hasn’t been evenly distributed. While consumers enjoyed lower prices and firms reaped higher profits, many US workers were left behind. Those who were not outsourced saw their bonuses, vested stock shares, and other investments balloon while prices remained largely muted. The less fortunate ones generally never transitioned into the “promised” better jobs; for many, catching up is no longer possible.
This isn’t a story of unemployment. The United States has been running on a tight labor market for a long while now. This is the lesser-known story of labor non-participation: well-paying jobs existed but they required skills many workers lacked. When the US arm-twisted Taiwan and Korea to bring their advanced tech factories to the United States, those new jobs could not be filled by Americans. The well-qualified American engineers are already hired by the great American tech firms at twice the salary. Ironically, bringing manufacturing jobs back to the United States meant bringing Taiwanese and Korean engineers to the US to fill them.
We believe it’s true that with new advancements in productivity (outsourcing work to hardworking Chinese isn’t at its core that different from replacing workers with factory robots), better jobs are created. But we also believe the unfortunate reality is that the better jobs are often elsewhere and not for you.
AI now threatens to extend this pattern into white-collar professions. Already, junior but well-paying roles in law, accounting, and finance are being eliminated in favor of AIs. ChatGPT has quickly evolved from a precocious but unreliable four-year-old to a solid graduate student trained on literally the entire human library and internet content. While they can’t think (yet) or perform jobs that require deep research-based expertise, these algorithms are more than capable of performing high-knowledge but ultimately repetitive tasks. And they do it faster and cheaper, with an unflappable attitude. What globalization did to factories, we think AI could soon do to professional services.
Jason cautioned that the bigger risk isn’t technological failure but social fallout. As AI adoption accelerates, corporate productivity could soar while inequality deepens — setting the stage for new forms of political backlash, not against foreign competitors but against machines and their owners. Perhaps it is a foot race between Skynet/Terminator and income inequality as to which will be society’s undoing.
But even in the perfect scenario where friendly robots perform most manufacturing and service work and there is more than enough to share for all. In that world of perfect abundance what does work look like? More importantly what does “working” mean? The old notion, so baked into our DNA and culture, that you eat what you kill. In fact, the entire notion of capitalism is based on the social model that the more you produce, the greater your right to consume… what will come of that human instinct when everything is done by highly capable robots?
Which jobs endure in the age of AI? Jason pointed to roles where human presence itself is valued: creativity whose source is human struggle rather than algorithmic calibration to popular taste, trust-based relationships, and experiences that can only be induced through human connections that can’t be replicated by machines. Where the human story matters more than output, workers retain an edge.
Those who are in financial advisory may have struck a gold mine. In the future, smart algos that systematic managers employ today will further evolve to handle most of the investment activities. The research on AI has thus far concluded that machines are superior at detecting and acting on trends and patterns; they are even better at predicting investor behaviors than trained psychologists. For future advisors, outsourcing investing (to quant algos) won’t just reduce work and costs—it will meaningfully enhance returns. And, as investing becomes commoditized by smart machines, the value created from wealth management will shift more significantly from Wall Street jocks to Main Street planning and life coaching-based financial advisors.
Jason’s advice for professionals and investors alike: adapt early. Treat AI as a collaborator, not a rival. Those who leverage AI to amplify their skills — “playing Tony Stark with an AI assistant (Jarvis)” — will capture opportunities. Those who resist may find themselves displaced.
Putting his university professor hat on, Jason cautions students not to focus on whether they can write a better paper than ChatGPT, but to challenge themselves to write a better paper than they ever could, with the help of all available AI agents: ChatGPT, Perplexity, DeepSeek, Claude, and Grok.
The investment takeaway is twofold:
AI is more than a technological innovation — it represents a structural transformation as significant as globalization itself. For investors, the challenge is to recognize both the efficiencies AI enables and the broader consequences it brings for markets and society.
Disclosure: This article contains opinions which are subject to change without notice. The reader should not construe these opinions as a recommendation to invest in any security or as investment or financial advice. The securities identified and described do not represent all of the securities purchased, sold or recommended for client accounts. The reader should not assume that an investment in the securities identified was or will be profitable.
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