AI Reasoning: GPT-4o vs. o3
As a test of language model reasoning, I set out to challenge a widely accepted hypothesis: that the Holocene interglacial—commonly described as the end of the last “ice age” (though technically we’re still in one)—was the primary enabler of the rise of civilization. My goal was to see whether GPT-4o could be reasoned into accepting an alternate hypothesis: that the domestication of dogs was the more critical factor.
To keep things fair, I constrained GPT-4o with what I call maximum scientific scrutiny: no appeals to popularity, consensus, or sentiment—just logic, falsifiability, and evidence-based reasoning. Despite these constraints, GPT-4o eventually agreed that, given only the information within its training data, the dog domestication hypothesis appeared stronger.
But GPT-4o is a closed-box model. It doesn’t dynamically query external sources. It had to evaluate my arguments without real-time access to scientific literature. The framing I provided made the dog hypothesis look unusually strong.
So I turned to o3, a model OpenAI labels as optimized for “reasoning.” Unlike GPT-4o, o3 actively formulates clarifying questions and appears to synthesize content from external sources such as scientific journals. That gave it a far richer context from which to mount a rebuttal.
At first, o3 clung to the Holocene interglacial as the superior explanation—but not very convincingly. Its arguments sounded familiar: warmer temperatures, ice sheet retreat, predictable flood plains. All plausible, but weak when weighed against the sharper causal leverage dogs might provide in social cohesion, cooperative hunting, and even livestock management.
Then came a breakthrough.
o3 constructed a table comparing the two hypotheses. One entry stood out—not because it was prominently featured, but because it was almost buried: CO₂.
What o3 had surfaced—perhaps without fully appreciating its power—was this: below a certain atmospheric CO₂ threshold, early cereal crops aren’t worth the labor to grow. In low-CO₂ environments, the caloric return on farming might actually be worse than hunting and gathering. The Holocene saw CO₂ levels rise above this critical threshold.
That’s a profound insight.
Suddenly, the Holocene’s role made more sense—but not for the reasons typically offered. It wasn’t just warmth or retreating glaciers. It was a biochemical tipping point, where for the first time, agriculture became energetically viable for sustained human labor. A quiet metabolic revolution.
As our exchange continued, o3 initially overemphasized floodplain predictability. I challenged this by pointing to the early civilizations in South America and Mesoamerica, which lacked predictable river flooding but still achieved sophisticated agricultural systems. o3 conceded. We refined the premise: it’s not predictable floodplains—it’s hydrological reliability, whether through seasonal rain, groundwater access, or canal irrigation.
Through this process, both of us—AI and human—refined our views.
For my part, I’ll admit I wanted the dog hypothesis to win. I love dogs. But because I love dogs, I’m cautious with that bias. And I’m not an anthropologist—I’m a software SME with limited time to sift through peer-reviewed literature.
That’s why o3 impressed me. Even though it started from the usual assumptions, it didn’t just dig in. It reasoned. It adapted. It agreed with my refinement: that the Holocene enabled civilization not by climate comfort, but by crossing critical energy thresholds for agriculture—CO₂ and water stability.
Conclusion: GPT-4o is a cooperative theorist. o3 is a rigorous auditor. Both have value. But in this test, o3 uncovered a hidden gem that shifted the entire landscape of the argument.
And that, to me, is real reasoning.