In a recent interview on the Big Technology Podcast, OpenAI CEO Sam Altman addressed the company’s ongoing struggle with profitability amid rising training costs. The discussion, which began to heat up around the 36-minute mark, centered on OpenAI’s financial trajectory and its ambitious spending plans.
Altman acknowledged that while revenue is increasing, the costs associated with training artificial intelligence models are growing even faster. He noted that OpenAI’s projected losses could reach approximately $120 billion by 2028, with profitability seemingly on the horizon only if training expenditures taper off. “If we weren’t continuing to grow our training costs by so much, we would be profitable way, way earlier,” Altman explained. The company currently appears to be betting heavily on future revenue growth stemming from its significant investments in training large models.
The interviewer pressed Altman on the specifics, noting the staggering commitment of $1.4 trillion against projected revenues of about $20 billion. “I think it would be great just to lay it out for everyone once and for all how those numbers are gonna work,” he challenged. Altman initially struggled to articulate a clear answer, referring to the complexities of exponential growth and the difficulty of modeling it mentally. “Modeling exponential growth doesn’t seem to be one of [the things evolution needed us to do well],” he said, illustrating his point with a candid admission of the challenges in quantifying such rapid growth.
However, Altman quickly regained his composure, asserting that OpenAI expects to maintain a steep revenue growth trajectory. He emphasized the critical role of computing power in this equation, stating, “We have always been in a compute deficit. It has always constrained what we’re able to do.” He underscored that the company’s growth is inherently tied to its ability to monetize the compute resources it can bring online. Altman opined that if OpenAI were to find itself with considerable unused computing capability, it would raise legitimate concerns about the sustainability of its current spending levels.
As the conversation continued, the interviewer sought further clarification, asking whether OpenAI’s anticipated revenue growth would stem from enterprise adoption of its products and consumer willingness to pay for services like ChatGPT through the API. Altman affirmed, “Yeah, that is the plan.” This statement crystallizes his belief that revenue growth, derived from both consumer and enterprise channels, will ultimately allow the company to support its ambitious spending commitments.
Central to Altman’s outlook is the idea that OpenAI is leveraging a strategic investment in compute to drive future profitability. He noted that the company has consistently been in a position where it lacked sufficient computing resources to meet demand, a scenario Altman believes will persist. Yet he remains optimistic about the potential for efficiency improvements, stating, “We will of course also get more efficient on like a flops per dollar basis.”
Altman’s comments delineate a clear threshold for assessing whether OpenAI’s spending is problematic. He argues that concern about expenditures would only be warranted if the company reaches a point where it cannot profitably monetize its computing power. The focus, therefore, is not merely on current losses but on the future potential for revenue generation. As OpenAI continues to expand its offerings, including new types of products and services, the company aims to ensure that its investments yield sufficient returns to cover its significant expenditure on training.
Ultimately, Altman’s vision for OpenAI rests on a foundational bet: that the company can continue to find buyers for its computing capacity as quickly as it can scale its infrastructure. The success of this strategy may determine whether OpenAI can sustain its vast investments in AI training and transition to a profitable future. As the AI landscape evolves, the convergence of consumer demand and enterprise adoption will be pivotal in shaping OpenAI’s financial health.
For those interested in the full discussion, the interview can be watched starting at the 36-minute mark.
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