As artificial intelligence reshapes industries across the board, the value of data is indisputable. Raw data alone, however, does not make machines “intelligent.” The critical process that turns vast, unstructured data into information machines can understand and learn from is data annotation. By adding precise semantic labels to data, annotation builds the framework through which machines perceive the world — and it is fundamental to training, optimizing, and reliably deploying AI models.
From December 4–5, 2025, the 2025 New Energy and Intelligent Connected Vehicles IP Innovation Summit (NIIPS 2025) was successfully held in Shanghai. As a top-100 global language services provider, Glodom made a strong appearance at the summit and engaged in substantive discussions with leading automotive-industry experts, corporate representatives, and intellectual-property professionals.
Recently, a domestically developed open-world title from NetEase launched on Steam's international store and drew unexpectedly strong attention: peak concurrent players rose into the hundreds of thousands, and the game has maintained steady interest among a predominantly English-speaking audience.
AI has already woven itself into every corner of the language services industry—from R&D to market applications—and change is visible everywhere. Yet beneath the rapid technological advances lies a deeper question that receives far less attention: if the AI bubble bursts, how will an industry so tightly bound to it respond? Perhaps now is the moment to step away from the noise and think clearly.
Language is our primary tool for transmitting information, building trust, and expressing emotion. When artificial intelligence can produce fluent sentences and mimic conversational styles, it demonstrates a mastery of grammatical patterns — yet it struggles to carry the genuine concern, hesitation, humor, and vulnerability that characterize real human exchange.
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