The rise of artificial intelligence is the single most important trend of our time. The changes it will wreak on human life eclipse not only all other contemporary technological developments, but also any since the Industrial Revolution. The new pope recognizes its salience, choosing as his regnal name “Leo XIV,” because just as Leo XIII was confronted with the social change from the Industrial Revolution, he seeks to confront the even more dramatic change from AI.
Breakthroughs happen monthly now. OpenAI’s o3 model recently scored higher than 99.8 percent of competitive programmers—while the same lab’s Sora engine, launched in February and now being integrated into ChatGPT, can already generate minute-long, high-definition video from text. In July, frontier models from both Google DeepMind and OpenAI sat the International Mathematical Olympiad under the same four-and-a-half-hour rules given to the world’s brightest teens, solved five of the six problems, and earned gold medals. In April, a University at Buffalo team unveiled Semantic Clinical AI (SCAI), an architecture that grafts formal medical knowledge onto a large language model. SCAI scored as high as 95 percent on Step 3 of the US Medical Licensing Examination—better than most practicing physicians and ahead of every previous AI benchmark—showing that well-structured retrieval can turn AI into a skilled general diagnostician.
AI’s new methodological phase is the emergence of AI agents, systems that autonomously execute sequences of tasks in pursuit of a goal. This phase is called agentic AI, which builds on Large Language Models (LLMs) that have dominated AI in the last few years. Those LLMs are neural networks that understand the relation of words in text and can use that understanding to generate competent, even expert, answers on every subject.
Agentic AI marries the predictive eloquence of LLM to an institutional framework of memory, goal-seeking, and tool use. No longer confined to completing sentences, the system can now formulate a purpose, decompose it into ordered tasks, engage external software, monitor its own performance, and revise its course when it makes mistakes. In short, where the LLM offers fluent speech, the agentic overlay supplies the infrastructure necessary for transforming mere words into coordinated action. One way of measuring progress in agentic AI is the uninterrupted duration of its competent autonomy at human tasks.
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