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The demo video of the new Siri at Apple's product launch did look pretty cool—able to understand screen content, operate across apps, and even make calls on your behalf. But the truly interesting part isn't the announcement itself; it's the anxiety it reveals: the company that once defined the smartphone is now scrambling to catch up in an AI race first laid out by OpenAI and Google. Apple must prove it can still lead the next interaction revolution, or the crown of "AI smartphone" might slip fr
Analysis
The demo video of the new Siri at Apple's product launch did look pretty cool—able to understand screen content, operate across apps, and even make calls on your behalf. But the truly interesting part isn't the announcement itself; it's the anxiety it reveals: the company that once defined the smartphone is now scrambling to catch up in an AI race first laid out by OpenAI and Google. Apple must prove it can still lead the next interaction revolution, or the crown of "AI smartphone" might slip from its grasp. The grand vision Siri painted won't be bite-sized until at least next year, and by then, the Android ecosystem and various AI-native hardware will have sprinted several laps ahead.
Meanwhile, across the Pacific, the storyline takes the opposite turn. While American giants are burning billions to train top-tier large models, players like DeepSeek are emerging with narratives of "cutting costs by 99%." This is no longer a simple tech race but a clash of two philosophies: one is an "arms race" pursuing peak performance at any cost; the other is a strategy of "rural encirclement of the cities," focusing on extreme efficiency, open-source, and accessibility. Meta just saw its stock wobble due to massive losses in its AI business, while Elon Musk's SpaceX pitch deck showcased a $1.77 trillion valuation—with AI and autonomous driving stories playing a significant role. The capital market's divergence underscores one thing: no one knows who the ultimate winner will be, but everyone knows the stakes at the table have ballooned to staggering numbers.
Perhaps the most pragmatic move comes from the China Securities Association. It speaks not of disruption or AGI but earnestly solicits real-world AI implementation cases across six major scenarios. From operational efficiency to risk compliance, each addresses tangible pain points in financial institutions. This is somewhat like the Industrial Revolution—less about what steam engines could theoretically do, and more about installing them in textile mills, mines, and trains to see how much labor they save and profits they generate. The securities industry, a conservative and vast system, is systematically embracing AI, marking a shift of AI's main battlefield from labs and papers to the most traditional, rule-bound, and results-driven sectors. This is the true beginning of AI weaving into the fabric of society.
So, on one side are the dazzling demos of consumer AI; on the other, the solid groundwork of industrial AI. On one side, American companies use sky-high funding to bet on a vague future; on the other, Chinese counterparts leverage cost efficiency to unlock real markets. Apple's Siri might redefine human-computer interaction, but DeepSeek's open-source models could spawn a wave of killer applications even sooner. Ultimately, the measure of AI's success may not be whose model is larger or flashier, but whose technology is cheaper, more usable, and faster to integrate into myriad industries. That glamorous product launch will eventually end, but the first risk report an analyst runs through AI in countless offices, the initial diagnosis a community clinic makes with AI assistance—these might be the true milestones of this revolution.
Disclaimer: The above content is generated by AI and is for reference only.