A.I. Bubble? The Clash Between Scientific Uncertainty and Evolving Business Perspectives

The New Yorker article, “Is A.I. Actually a Bubble?” offered, in our opinion, a compelling and engaging counter-narrative to the typical expectation of an impending A.I. “boom-and-bust” cycle.

We begin outlining the author’s main argument and supporting points. Following this, we examine the author's view that the “A.I. bubble” stems from the convergence of scientific uncertainty and changing business philosophies. The discussion will then present arguments for and against this perspective, concluding with our synthesis.

The Main Argument

Joshua Rothman contends that the common “boom and bust” narrative, which casts artificial intelligence as a speculative bubble, is flawed and fails to capture the technology's true potential. While he recognizes financial volatility and valuation challenges, he argues that calling A.I. a “bubble” ignores its real utility. Rothman views A.I. as a transformative tool that is currently misunderstood by markets and business leaders.

Argument Supporting Points

Rothman supports his argument by highlighting key misconceptions about A.I.’s intended use versus its actual capabilities:

The “Replacement” Fallacy: 

Rothman notes that businesses often mistakenly view A.I. as a means of directly replacing workers and cutting costs. He argues that this view is “profoundly at odds” with reality, as A.I. lacks the autonomy to fully replace human judgment and expertise.

Augmentation Over Automation: 

Rothman asserts that A.I.’s real value lies in enhancing, not replacing, human workers. He suggests A.I. can accelerate skill and knowledge acquisition. Companies make a “grave mistake” by focusing on labor replacement instead of empowerment; successful firms will use A.I. to strengthen employee capabilities.

The Valuation Challenge: 

Rothman acknowledges that businesses are “struggling to figure out what the technology is worth,” contributing to economic instability. He distinguishes this financial uncertainty from technological uselessness, noting that an unsettled market price does not mean the technology lacks “possibilities.”

The Collision

Rothman connects his argument to two forces: scientific uncertainty and business thinking. He explains that their interaction produces market behavior resembling a bubble, but with different underlying dynamics.

Arguments For and Against the Collition View

AI as a Collision, Not Just a Bubble

Unlike classic financial bubbles such as the dot-com crash of 2000 or the Dutch tulip mania, where asset values became disconnected from fundamentals, the AI surge is grounded in real scientific progress. Breakthroughs in deep learning, transformer architectures, large language models, and multimodal systems have expanded what machines can achieve. These advances have measurable, real-world applications in healthcare, logistics, scientific research, and creative industries.

However, this progress coexists with significant scientific uncertainty. Researchers lack consensus on key questions: Can current AI paradigms achieve general intelligence? Are today’s data-intensive models sustainable? How can safety, alignment, and interpretability be ensured? This uncertainty encourages both speculation and innovation, as investors and entrepreneurs bet not only on products but also on which scientific directions will succeed.

At the same time, business thinking is adapting. Traditional valuation metrics such as revenue and margins do not fully capture the strategic value of AI capabilities. Companies are investing in future access to ecosystems, talent, and platform leadership, rather than immediate returns. This approach mirrors early internet investments, which focused on positioning within an emerging networked economy. What may seem like “irrational exuberance” could instead be an adaptive, though imperfect, response to a changing paradigm.

Counterarguments: Why It Still Looks Like a Bubble

Critics highlight several bubble-like features. First, AI valuations often exceed tangible results. Startups with minimal revenue but a “proprietary AI model” can command nine-figure valuations, much like dot-com-era firms with little more than a URL. 

Second, much of the AI economy relies on narrative arbitrage, where existing automation or analytics are rebranded as “AI-powered” to attract investment. This practice inflates perceived innovation without matching technical substance.

Third, the capital intensity of AI development, which requires significant computing power, data, and talent, has created a winner-takes-all environment that may stifle genuine innovation in favor of scale. This raises concerns that the current boom is consolidating power among a few tech giants, rather than democratizing intelligence, and may reduce long-term industry dynamism.

Yet, history shows that most speculative booms end in correction, even when based on real innovation. The dot-com crash eliminated trillions in value but also paved the way for companies like Amazon and Google to succeed. A similar adjustment may be needed for AI to move beyond hype and mature into sustainable industries.

Our Synthesis 

Labeling the AI phenomenon as merely a bubble mistakes surface volatility for the system as a whole. While bubble-like attributes exist and pose real risks of financial correction and wasted investment, they do not define the whole picture.

However, the deeper driver is the unprecedented and messy collision between two forces: the uncertain, evolving trajectory of cognitive and computer science, and the highly capitalized, fast-moving global tech industry. This collision creates friction that manifests as a bubble.

The outcome will likely be a series of boom-bust cycles, such as AI Winters and Summers, as business expectations adjust to scientific realities. Each cycle will consolidate gains, eliminate weaker approaches, and refine how the technology integrates into society. While AI’s transformative potential is real, its path to maturity is shaped and visibly distorted by the volatile environment in which it is developing. The bubble isn't the main story but rather an underlying element of the central story: the world’s effort to capitalize on a science still in its early stages.

Reference:

Rothman, J. (2025, December 12). Is A.I. actually a bubble? The New Yorker. https://www.newyorker.com/culture/open-questions/is-ai-actually-a-bubble

Note:

A group of friends from “Organizational DNA Labs,” a private network of current and former team members from equity firms, entrepreneurs, Disney Research, and universities like NYU, Cornell, MIT, Eastern University, and UPR, gather to share articles and studies based on their experiences, insights, inferences and deductions, often using AI platforms to assist with research and communication flow. While we rely on high-quality sources to shape our views, this conclusion reflects our personal perspectives, not those of our employers or affiliated organizations. It is based on our current understanding, informed by ongoing research and a review of relevant literature. We welcome your insights as we continue to explore this evolving field.