Sam Altman Draws Parallels Between AI and Human Energy Costs in Training

Sam Altman Draws Parallels Between AI and Human Energy Costs in Training

In a candid observation that has reverberated across tech and ethics circles, OpenAI CEO Sam Altman has challenged the conventional narrative surrounding the environmental cost of training large AI models. During a public Q&A session earlier this month, Altman noted, "People talk about how much energy it takes to train an AI model… But it also takes a lot of energy to train a human. It takes like 20 years of life and all of the food you eat during that time before you get smart." The comment, initially shared on Reddit’s r/singularity forum, has since been cited by major tech publications as a provocative lens through which to evaluate sustainability in artificial intelligence development.

According to TechCrunch, Altman’s remarks were made in the context of a broader discussion on the energy footprint of AI infrastructure, particularly as models like GPT-5 and beyond require exponentially more computational power. While critics have pointed to the staggering electricity consumption of data centers — some estimates suggest training a single large model can emit as much carbon as five cars over their lifetimes — Altman’s analogy shifts the focus toward comparative efficiency. "We’re measuring AI’s energy use in isolation," he reportedly said, "but we rarely quantify the energy invested in the human beings who design, supervise, and deploy these systems."

Yahoo Tech’s coverage of the remarks underscores the cultural resonance of Altman’s statement. In an era where AI ethics often centers on bias, transparency, and job displacement, his framing introduces a novel metric: embodied energy. The human developmental process — from prenatal nutrition and childhood education to years of schooling, socialization, and physical sustenance — represents a complex, multi-decade energy investment. According to estimates from nutritional science and developmental biology, the average human consumes roughly 2,000 to 3,000 kilocalories per day for two decades, translating to approximately 14.6 to 21.9 gigajoules of biological energy over a lifetime before reaching cognitive maturity. When contextualized against the estimated 100–500 megajoules used to train state-of-the-art AI models, the comparison becomes striking: while AI training is intense, it is also remarkably condensed in time.

Altman’s analogy, however, is not an attempt to justify AI’s energy consumption. Rather, it serves as a rhetorical tool to encourage more nuanced discourse. "The goal isn’t to say AI is better," he clarified in follow-up remarks. "It’s to say we’re comparing apples to oranges. We should be asking: What’s the energy cost per unit of intelligence produced?"

This perspective has drawn both praise and skepticism. Environmental scientists caution against using human development as a benchmark for acceptable AI energy use, noting that human intelligence is not a product designed for mass replication. "We don’t mass-produce humans," said Dr. Elena Ruiz, an energy ethicist at Stanford. "AI models are replicated at scale, deployed globally, and updated continuously. The environmental impact compounds exponentially."

Meanwhile, AI engineers and policymakers are beginning to incorporate Altman’s framing into efficiency benchmarks. OpenAI has since announced a new internal metric, "Intelligence per Joule," to evaluate model performance relative to energy expenditure. Competitors like Anthropic and Google DeepMind are reportedly exploring similar frameworks.

While the Zhihu discussion referenced in the sources mistakenly conflates Altman’s remarks with Meta’s SegmentAnythingModels (SAM), the confusion itself highlights a broader public tendency to conflate terminology. Altman’s name, not the model, is the subject of this discourse — a reminder of the importance of precise sourcing in the age of viral tech commentary.

As the global conversation on AI sustainability intensifies, Altman’s provocative comparison may prove pivotal. It compels us to ask not just how much energy AI uses, but what we’re willing to invest — biologically, socially, and environmentally — to create intelligence, whether synthetic or human.