03 Why this time is different: what large models changed
AI used to be "dedicated": one model could do only one task, and one that recognized cats might not recognize dogs. Large models are "general-purpose": the same model can write, code, translate and reason — you just direct it in natural language.
What really changed is not the terminology but three variables. First, the barrier to use has dropped to "just be able to type" — no programming background needed. Second, costs are falling fast, making it affordable for individuals and small teams. Third, the capability boundary has expanded from "recognition" to "generation" — AI now creates new content rather than merely judging existing content.
Because of this, this wave of AI is no longer confined to the tech world. Gartner predicts that by the end of 2026, 40% of enterprise applications will embed task-oriented agents, up from less than 5% in early 2025. Like water and electricity, AI is becoming infrastructure across offices, healthcare, education, manufacturing and beyond.
04 A few common misconceptions to rule out first
Misconception 1: "AI can do everything and will soon replace everyone." More accurately: AI excels at pattern recognition and content generation — tasks that can be covered by large numbers of examples. Work that needs real-world interaction, complex judgment and ultimate accountability is hard to fully replace in the short term. AI is more likely to change how we work than to eliminate jobs outright.
Misconception 2: "AI's answers are always correct." A large model's answer is "predicted", not "verified", and it can sometimes fabricate information in a confident tone (known as "hallucination"). In critical areas such as facts, law and healthcare, human verification is essential.
Misconception 3: "AI is just a chatbot." Chat is only the entry point. Image generation, speech synthesis, coding, data analysis, workflow automation — AI's capabilities go far beyond chatting.
05 How ordinary people should judge and use AI
To judge whether an AI product is worth using, look at three variables.
First, is the need real: does it solve a frequent, concrete pain point, or a manufactured concept? Second, is the cost acceptable: money, learning time and the cost of errors? Third, is the feedback clear: can mistakes be spotted and corrected in time?
For those new to AI, a safer approach is not to chase every new tool but to start with a frequent, low-risk scenario with clear feedback — such as writing assistance, information organization or chart generation — and expand to more complex workflows once comfortable.
Back to the opening question: why did AI suddenly become so powerful? The answer is that it has been on the road for 70 years — with highlights and winters — until computing power, data and algorithms truly converged after 2012 to bring us to today.
The truly important question is not whether AI will replace people, but into which parts of the process human value will be redistributed. Understand this timeline and you hold the first key to understanding the AI era.
Science and Technology Daily | "70 Years of AI" | Verified: 2026-09-15
China Science Communication | "AI Development" feature | Verified: 2026-09-15
People's Daily Online | "China's AI large-model token usage stays No. 1 globally" | Verified: 2026-09-15
Gartner 2026 forecast on enterprise applications and AI spending (as reported by TMTPost and others) | Verified: 2026-09-15