‘Odd Lots’ Cohost Joe Weisenthal Offers Sharp Predictions on the AI Bubble’s Inevitable Burst
The current technological landscape is undeniably dominated by the buzz around Artificial Intelligence. From coding and writing to accounting, AI is heralded as a revolutionary force, with countless conversations and massive investments fueling an economic scenario that many, including WIRED, suspect might be an unsustainable bubble. Amidst this fervent discussion, Joe Weisenthal, cohost of Bloomberg’s acclaimed “Odd Lots” podcast, offers a refreshingly grounded and often contrarian perspective on the AI phenomenon and its broader economic implications. A former colleague known for his passionate economic insights, Weisenthal delves beyond the headlines, questioning not just if, but how, this AI-driven economic bubble might eventually burst, and what that means for the United States and the global economy.
Weisenthal’s expertise lies in dissecting the true forces at play beneath the surface of economic news. Currently, while headlines scream about AI’s build-out and market representation, Weisenthal points to a less discussed yet critical consequence: the "crowding-out effect." He illustrates this with a compelling example from the real estate development sector, where basic electrical equipment, crucial for building drive-through coffee shops or other commercial facilities, is in chronic short supply. The reason? Major data center operators, driven by the AI boom, are willing to pay top dollar for these components, diverting resources that would otherwise support diverse economic activities. This isn’t about AI being wasteful, he clarifies, but about understanding the second-order effects of a market economy where capital goods are allocated based on price signals. The builder of a data center will inevitably outbid a coffee shop developer for the same essential gear, potentially constraining other forms of economic growth.
This phenomenon is not isolated. Weisenthal links it to a broader theme of persistent underinvestment across various sectors. He cites the housing market, which experienced a significant slowdown and business failures post-financial crisis in the 2010s. The current surge in home prices, he argues, is a direct consequence of that decade of under-investment. Similarly, when the economy experiences a boom, whether across the board as in 2021-2022 or in specific sectors like AI data centers today, the economy quickly encounters supply crunches because the capacity to produce necessary components hasn’t kept pace. Building new factories or expanding production is a risky, long-term bet for companies, especially in uncertain times. Many prefer to maintain existing supply, raise prices, and enjoy consistent order books rather than take on the substantial upfront capital outlays required for expansion, which could soften prices later. This leads to a scenario where critical components, like electrical equipment, remain in short supply for years, despite obvious demand.
The conversation then shifts to tariffs, another perplexing economic force. Weisenthal explains that current tariffs aren’t necessarily high enough to induce massive domestic investment in manufacturing, but they are significant enough to prompt companies to seek alternative suppliers outside of China or Vietnam, often turning to countries like India. This "switching" process, while not causing widespread shortages, significantly increases the "cost of doing business" in the United States. Companies incur expenses in finding new suppliers, building new relationships, and reconfiguring supply chains. Furthermore, these new suppliers face uncertainty about future tariff rates, making them reluctant to commit to long-term agreements. Consequently, tariffs contribute to a broader economic strain, even if not directly reflected in dramatic price increases for all goods, by raising the operational costs for the entire economy.
Weisenthal describes the current US economy as a "two-speed" system. One slice, encompassing big tech and AI, is performing phenomenally well, acting as a significant money-maker globally. The other, representing the vast majority of the economy, is "creaking." This broader economy is characterized by stagflationary pressures, mediocre hiring, and a high cost of living. Crucially, Weisenthal remains skeptical that AI, as a tool, is currently having a significant, measurable impact on global productivity or hiring. He challenges the narrative that AI directly leads to massive job elimination, suggesting that many announced layoffs using AI as a justification are often "air cover" for pre-existing plans to reduce headcount. The actual ability of AI tools to perform the same amount of work with fewer people is still largely unproven in many white-collar domains.
This disconnect between the soaring market valuations of AI companies like Nvidia and Google, and the scarcity of tangible, widespread productivity enhancements, raises a critical question: what happens when these two trends diverge? Weisenthal acknowledges two possibilities: either the productivity-enhancing use cases will eventually materialize, validating current investments, or companies will pull back their spending, leading to a stock market crash. He points out that historical technological shifts, like the internet or mobile phones, didn’t immediately translate into massive, measurable productivity booms in aggregate economic statistics, despite fundamentally changing society.
The psychological dimension of an AI bubble is also significant. Weisenthal notes that bubbles are inherently stressful. Those outside feel jealous, those inside regret not investing more, and those with significant allocations are stressed about timing their exit. He muses that the "buy the dip" mentality, ingrained in retail investors over the last 15 years, might prolong the current bullish sentiment, as every market hiccup has historically proven to be a buying opportunity. However, if the AI bubble were to burst, the implications for the United States could be severe. Weisenthal expresses concern about the US economy’s lack of diversified growth drivers, with many industrial sectors struggling against international competition, particularly from China. Industries like automotive and pharmaceuticals face immense pressure, and an aging population places growing strain on the labor force, directing workers towards lower-productivity care roles. This creates a deeply stressed economy with little "slack," making it vulnerable if its primary growth engine – big tech and AI – falters.
Despite these concerns, Weisenthal finds reasons for optimism in America’s immense inherent wealth, abundant natural resources, advanced technology, and robust education system. He believes that, hypothetically, if any country could achieve self-sufficiency, it would be the United States. This underlying strength provides a buffer, even amidst structural economic challenges.
The intersection of AI, global supply chains, and international competition is particularly evident in energy demand. The massive energy requirements of AI data centers have led to a scramble for natural gas turbines and other energy infrastructure, with manufacturers sold out for years. This competition for energy resources affects every country, as national sovereignty in the AI age is increasingly linked to energy security.
Regarding the US-China AI "arms race," Weisenthal dismisses the "super intelligent AI kills us all" hysteria. Instead, he focuses on the very real concerns about manufacturing capacity and national defense. He highlights the troubling decline in the US’s ability to build complex products, citing the struggles of companies like Boeing. This "hollowing out" of manufacturing expertise, starting from lower-value industrial goods, eventually creeps up to affect advanced manufacturers. Similarly, Intel’s challenges in constructing advanced semiconductor fabs point to a deeper issue about the ability of advanced US manufacturers to thrive. For Weisenthal, the core of the China conversation isn’t a sci-fi dystopia, but the very tangible vulnerability created by industrial competition and the erosion of domestic manufacturing muscle.
Looking forward, Weisenthal urges attention to the future of Europe, which is pressured from multiple directions – caught between the US and China, struggling with internal integration versus forming a larger trading bloc with the US, and facing its own nationalist political currents. He also points to emerging markets like Indonesia, Vietnam, and Brazil, whose trade relationships with China are booming but who must balance economic ties with concerns about national sovereignty and protecting their own domestic industries. These nations face significant questions about how they will orient their economies in a rapidly changing global landscape.
In a concluding thought, reflecting a "Control, Alt, Delete" framework, Weisenthal offers practical perspectives on technology. He would delete sports gambling apps, expressing concern over their pervasive and potentially unhealthy influence. He would alter social media, suggesting a national limit of one hour per day for everyone, acknowledging its utility but also its potential for overconsumption. Finally, he would control the cryptocurrency industry, guiding it toward productive economic uses like stablecoins and tokenization, while acknowledging much of it remains "garbage." Weisenthal’s insights underscore a pragmatic, informed approach to navigating the complexities of the current economic environment, one that prioritizes understanding the underlying dynamics over succumbing to hype or alarmism.
