Is AI the Next Great Bubble?
▼ Summary
– Major AI companies like OpenAI and Anthropic are burning through billions without demonstrating sustainable business models, while inference costs remain high and profitability is unproven.
– Uncertainty around AI’s effectiveness and integration challenges is increasing rather than decreasing, with experts warning the market underestimates these difficulties.
– The AI boom shares similarities with the 1920s radio bubble, where revolutionary technology lacked clear business models before crashing spectacularly.
– “Pure-play” AI companies like Nvidia and OpenAI attract massive investment despite uncertainty, mirroring Tesla’s valuation premium over traditional automakers.
– Heavy VC funding in AI pure-plays and interconnected investments between major players like Nvidia, OpenAI, and Microsoft increase bubble concerns.
For nearly three years, artificial intelligence has dominated conversations in Silicon Valley, yet the path to profitability for most leading companies remains uncertain. While Nvidia has solidified its position by supplying the essential hardware, other major AI firms like OpenAI and Anthropic, along with tech giants heavily invested in the technology, continue to operate at a significant loss. The core issue lies in the immense inference costs, which ensure these companies lose money on nearly every user interaction. Critical questions about long-term business models remain unanswered: Will AI replace search engines or social media platforms? Can workplace automation generate sufficient returns? How will soaring energy and computing expenses be managed? Adding to the uncertainty, unresolved copyright lawsuits could force AI developers to license training data, potentially passing those costs to consumers. A recent MIT study intensified these concerns by revealing that a staggering 95 percent of businesses using generative AI have not seen any profit from it.
Economist Avi Goldfarb observes that uncertainty typically decreases over time as industries learn what succeeds and what fails. With AI, however, the opposite seems true. He notes that recent months have exposed a “jagged frontier” where initial promises about AI’s effectiveness have delivered mixed or disappointing results. Goldfarb believes the market consistently underestimates the profound challenges of integrating AI into existing organizational structures. Should this underestimation persist, he warns, the conditions for a speculative bubble become increasingly likely.
Historically, AI’s situation may resemble the early days of radio more than other technological breakthroughs. When RCA began broadcasting in 1919, the transformative potential of the technology was immediately apparent, but viable business models remained elusive. Contemporary observers debated whether radio would serve as promotional tool for retailers, a platform for religious services, or an advertising-supported entertainment medium. This uncertainty fueled speculative narratives that eventually created one of history’s largest bubbles. RCA’s stock peaked in 1929 before collapsing by 97 percent, having been, much like Nvidia today, among the most actively traded securities of its era.
The concept of “pure-play” investments helps explain why valuation disparities emerge between established companies and speculative ventures. Toyota’s market valuation stands at $273 billion despite shipping more vehicles and generating triple the revenue of Tesla, which commands a $1.5 trillion valuation. This discrepancy stems from Tesla’s status as a pure-play investment in electric and autonomous vehicles. During the 2010s, Elon Musk captivated investors with visions of a combustion-engine-free future, enabling his volatile startup to attract massive investment over proven manufacturers. Pure-play companies become vehicles for speculative narratives, their fortunes entirely tied to specific technological innovations materializing as promised. These narrative-driven investments provide the essential fuel for bubble formation.
Recent data from Silicon Valley Bank indicates that 58 percent of all venture capital investment this year has flowed to AI companies. While retail investors have limited access to pure-play AI opportunities, another bubble characteristic, several major examples exist. Nvidia stands as the prime example, having transformed itself into history’s first $4 trillion company by betting everything on AI chip production. According to research by Goldfarb and David Kirsch, sectors with numerous pure-play investments tend toward overheating. SoftBank plans to invest tens of billions into OpenAI, the definitive AI pure play, though public market access remains limited. Analysts speculate that when OpenAI eventually goes public, it could achieve the first trillion-dollar IPO. Other pure-play beneficiaries include Perplexity, now valued at $20 billion, and CoreWeave, with a $61 billion market capitalization. The interdependence among these major players creates additional concern, as demonstrated by Nvidia’s proposed $100 billion investment in OpenAI, a company that relies entirely on Nvidia’s chips. Similarly, OpenAI depends on Microsoft’s computational resources through their $10 billion partnership, while Microsoft requires OpenAI’s AI models, creating a complex web of mutual dependency that could amplify systemic risks.
(Source: Wired)
